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....
DEFF Research Database (Denmark)
Müller, Emmanuel; Assent, Ira; Günnemann, Stephan
2009-01-01
Subspace clustering aims at detecting clusters in any subspace projection of a high dimensional space. As the number of possible subspace projections is exponential in the number of dimensions, the result is often tremendously large. Recent approaches fail to reduce results to relevant subspace...... clusters. Their results are typically highly redundant, i.e. many clusters are detected multiple times in several projections. In this work, we propose a novel model for relevant subspace clustering (RESCU). We present a global optimization which detects the most interesting non-redundant subspace clusters...... achieves top clustering quality while competing approaches show greatly varying performance....
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).
Random matrix improved subspace clustering
Couillet, Romain; Kammoun, Abla
2017-01-01
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
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...
Subspace identification of distributed clusters of homogeneous systems
Yu, C.; Verhaegen, M.H.G.
2017-01-01
This note studies the identification of a network comprised of interconnected clusters of LTI systems. Each cluster consists of homogeneous dynamical systems, and its interconnections with the rest of the network are unmeasurable. A subspace identification method is proposed for identifying a single
Evaluating Clustering in Subspace Projections of High Dimensional Data
DEFF Research Database (Denmark)
Müller, Emmanuel; Günnemann, Stephan; Assent, Ira
2009-01-01
Clustering high dimensional data is an emerging research field. Subspace clustering or projected clustering group similar objects in subspaces, i.e. projections, of the full space. In the past decade, several clustering paradigms have been developed in parallel, without thorough evaluation...... and comparison between these paradigms on a common basis. Conclusive evaluation and comparison is challenged by three major issues. First, there is no ground truth that describes the "true" clusters in real world data. Second, a large variety of evaluation measures have been used that reflect different aspects...... of the clustering result. Finally, in typical publications authors have limited their analysis to their favored paradigm only, while paying other paradigms little or no attention. In this paper, we take a systematic approach to evaluate the major paradigms in a common framework. We study representative clustering...
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.
Enhancing Low-Rank Subspace Clustering by Manifold Regularization.
Liu, Junmin; Chen, Yijun; Zhang, JiangShe; Xu, Zongben
2014-07-25
Recently, low-rank representation (LRR) method has achieved great success in subspace clustering (SC), which aims to cluster the data points that lie in a union of low-dimensional subspace. Given a set of data points, LRR seeks the lowest rank representation among the many possible linear combinations of the bases in a given dictionary or in terms of the data itself. However, LRR only considers the global Euclidean structure, while the local manifold structure, which is often important for many real applications, is ignored. In this paper, to exploit the local manifold structure of the data, a manifold regularization characterized by a Laplacian graph has been incorporated into LRR, leading to our proposed Laplacian regularized LRR (LapLRR). An efficient optimization procedure, which is based on alternating direction method of multipliers (ADMM), is developed for LapLRR. Experimental results on synthetic and real data sets are presented to demonstrate that the performance of LRR has been enhanced by using the manifold regularization.
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.
A Rank-Constrained Matrix Representation for Hypergraph-Based Subspace Clustering
Directory of Open Access Journals (Sweden)
Yubao Sun
2015-01-01
Full Text Available This paper presents a novel, rank-constrained matrix representation combined with hypergraph spectral analysis to enable the recovery of the original subspace structures of corrupted data. Real-world data are frequently corrupted with both sparse error and noise. Our matrix decomposition model separates the low-rank, sparse error, and noise components from the data in order to enhance robustness to the corruption. In order to obtain the desired rank representation of the data within a dictionary, our model directly utilizes rank constraints by restricting the upper bound of the rank range. An alternative projection algorithm is proposed to estimate the low-rank representation and separate the sparse error from the data matrix. To further capture the complex relationship between data distributed in multiple subspaces, we use hypergraph to represent the data by encapsulating multiple related samples into one hyperedge. The final clustering result is obtained by spectral decomposition of the hypergraph Laplacian matrix. Validation experiments on the Extended Yale Face Database B, AR, and Hopkins 155 datasets show that the proposed method is a promising tool for subspace clustering.
DEFF Research Database (Denmark)
Müller, Emmanuel; Assent, Ira; Günnemann, Stephan
2009-01-01
Subspace clustering and projected clustering are recent research areas for clustering in high dimensional spaces. As the field is rather young, there is a lack of comparative studies on the advantages and disadvantages of the different algorithms. Part of the underlying problem is the lack...... of available open source implementations that could be used by researchers to understand, compare, and extend subspace and projected clustering algorithms. In this paper, we discuss the requirements for open source evaluation software. We propose OpenSubspace, an open source framework that meets...... these requirements. OpenSubspace integrates state-of-the-art performance measures and visualization techniques to foster research in subspace and projected clustering....
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.
TreeCluster: Massively scalable transmission clustering using phylogenetic trees
Moshiri, Alexander
2018-01-01
Background: The ability to infer transmission clusters from molecular data is critical to designing and evaluating viral control strategies. Viral sequencing datasets are growing rapidly, but standard methods of transmission cluster inference do not scale well beyond thousands of sequences. Results: I present TreeCluster, a cross-platform tool that performs transmission cluster inference on a given phylogenetic tree orders of magnitude faster than existing inference methods and supports multi...
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 Integrated Region-Based Image Retrieval Using IRM and Statistical Clustering.
Wang, James Z.; Du, Yanping
Statistical clustering is critical in designing scalable image retrieval systems. This paper presents a scalable algorithm for indexing and retrieving images based on region segmentation. The method uses statistical clustering on region features and IRM (Integrated Region Matching), a measure developed to evaluate overall similarity between images…
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,
Directory of Open Access Journals (Sweden)
Thenmozhi Srinivasan
2015-01-01
Full Text Available Clusters of high-dimensional data techniques are emerging, according to data noisy and poor quality challenges. This paper has been developed to cluster data using high-dimensional similarity based PCM (SPCM, with ant colony optimization intelligence which is effective in clustering nonspatial data without getting knowledge about cluster number from the user. The PCM becomes similarity based by using mountain method with it. Though this is efficient clustering, it is checked for optimization using ant colony algorithm with swarm intelligence. Thus the scalable clustering technique is obtained and the evaluation results are checked with synthetic datasets.
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.
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.
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
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.
Scalability analysis of the synchronizability for ring or chain networks with dense clusters
International Nuclear Information System (INIS)
Lu, Jun-An; Zhang, Yong; Chen, Juan; Lü, Jinhu
2014-01-01
It is well known that most real-world complex networks, such as the Internet and the World Wide Web, are evolving networks. An interesting fundamental question is: how do some important functions or dynamical behaviors of complex networks evolve with increasing network scale? This paper aims at investigating the scalability of the synchronizability for ring or chain networks with dense clusters as the network size increases. We discover some interesting phenomena as follows: (i) the synchronizability of ring or chain networks with clusters decreases with increasing network scale regardless of the inner structures of all communities; (ii) for the same network scale, the network synchronizability decreases more quickly with increasing number of cluster blocks than with increasing number of nodes within the cluster block; (iii) the number of rings or chains has a much more significant influence on the network synchronizability than the size of the rings or chains. Our results indicate that network synchronizability can be maintained with increasing network scale by avoiding ring and chain structures. (paper)
A Multi-Hop Clustering Mechanism for Scalable IoT Networks.
Sung, Yoonyoung; Lee, Sookyoung; Lee, Meejeong
2018-03-23
It is expected that up to 26 billion Internet of Things (IoT) equipped with sensors and wireless communication capabilities will be connected to the Internet by 2020 for various purposes. With a large scale IoT network, having each node connected to the Internet with an individual connection may face serious scalability issues. The scalability problem of the IoT network may be alleviated by grouping the nodes of the IoT network into clusters and having a representative node in each cluster connect to the Internet on behalf of the other nodes in the cluster instead of having a per-node Internet connection and communication. In this paper, we propose a multi-hop clustering mechanism for IoT networks to minimize the number of required Internet connections. Specifically, the objective of proposed mechanism is to select the minimum number of coordinators, which take the role of a representative node for the cluster, i.e., having the Internet connection on behalf of the rest of the nodes in the cluster and to map a partition of the IoT nodes onto the selected set of coordinators to minimize the total distance between the nodes and their respective coordinator under a certain constraint in terms of maximum hop count between the IoT nodes and their respective coordinator. Since this problem can be mapped into a set cover problem which is known as NP-hard, we pursue a heuristic approach to solve the problem and analyze the complexity of the proposed solution. Through a set of experiments with varying parameters, the proposed scheme shows 63-87.3% reduction of the Internet connections depending on the number of the IoT nodes while that of the optimal solution is 65.6-89.9% in a small scale network. Moreover, it is shown that the performance characteristics of the proposed mechanism coincide with expected performance characteristics of the optimal solution in a large-scale network.
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.
Scalable Algorithms for Clustering Large Geospatiotemporal Data Sets on Manycore Architectures
Mills, R. T.; Hoffman, F. M.; Kumar, J.; Sreepathi, S.; Sripathi, V.
2016-12-01
The increasing availability of high-resolution geospatiotemporal data sets from sources such as observatory networks, remote sensing platforms, and computational Earth system models has opened new possibilities for knowledge discovery using data sets fused from disparate sources. Traditional algorithms and computing platforms are impractical for the analysis and synthesis of data sets of this size; however, new algorithmic approaches that can effectively utilize the complex memory hierarchies and the extremely high levels of available parallelism in state-of-the-art high-performance computing platforms can enable such analysis. We describe a massively parallel implementation of accelerated k-means clustering and some optimizations to boost computational intensity and utilization of wide SIMD lanes on state-of-the art multi- and manycore processors, including the second-generation Intel Xeon Phi ("Knights Landing") processor based on the Intel Many Integrated Core (MIC) architecture, which includes several new features, including an on-package high-bandwidth memory. We also analyze the code in the context of a few practical applications to the analysis of climatic and remotely-sensed vegetation phenology data sets, and speculate on some of the new applications that such scalable analysis methods may enable.
Kikuchi, Hideaki; Kalia, Rajiv K.; Nakano, Aiichiro; Vashishta, Priya; Shimojo, Fuyuki; Saini, Subhash
2003-01-01
Scalability of a low-cost, Intel Xeon-based, multi-Teraflop Linux cluster is tested for two high-end scientific applications: Classical atomistic simulation based on the molecular dynamics method and quantum mechanical calculation based on the density functional theory. These scalable parallel applications use space-time multiresolution algorithms and feature computational-space decomposition, wavelet-based adaptive load balancing, and spacefilling-curve-based data compression for scalable I/O. Comparative performance tests are performed on a 1,024-processor Linux cluster and a conventional higher-end parallel supercomputer, 1,184-processor IBM SP4. The results show that the performance of the Linux cluster is comparable to that of the SP4. We also study various effects, such as the sharing of memory and L2 cache among processors, on the performance.
On Covering Approximation Subspaces
Directory of Open Access Journals (Sweden)
Xun Ge
2009-06-01
Full Text Available Let (U';C' be a subspace of a covering approximation space (U;C and X⊂U'. In this paper, we show that and B'(X⊂B(X∩U'. Also, iff (U;C has Property Multiplication. Furthermore, some connections between outer (resp. inner definable subsets in (U;C and outer (resp. inner definable subsets in (U';C' are established. These results answer a question on covering approximation subspace posed by J. Li, and are helpful to obtain further applications of Pawlak rough set theory in pattern recognition and artificial intelligence.
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.
Swarm v2: highly-scalable and high-resolution amplicon clustering.
Mahé, Frédéric; Rognes, Torbjørn; Quince, Christopher; de Vargas, Colomban; Dunthorn, Micah
2015-01-01
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.
Semitransitive subspaces of operators
Czech Academy of Sciences Publication Activity Database
Bernik, J.; Drnovšek, R.; Hadwin, D.; Jafarian, A.; Bukovšek, D.K.; Košir, T.; Fijavž, M.K.; Laffey, T.; Livshits, L.; Mastnak, M.; Meshulam, R.; Müller, Vladimír; Nordgren, E.; Okniński, J.; Omladič, M.; Radjavi, H.; Sourour, A.; Timoney, R.
2006-01-01
Roč. 15, č. 1 (2006), s. 225-238 E-ISSN 1081-3810 Institutional research plan: CEZ:AV0Z10190503 Keywords : semitransitive subspaces Subject RIV: BA - General Mathematics Impact factor: 0.322, year: 2006 http://www.math.technion.ac.il/iic/ ela
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.
Design and implementation of a scalable monitor system (IF-monitor) for Linux clusters
International Nuclear Information System (INIS)
Zhang Weiyi; Yu Chuansong; Sun Gongxing; Gu Ming
2003-01-01
PC clusters have become a cost-effective solution for high performance computing, usually only with the abilities of resource management and job scheduling, and unfortunately, with lack of powerful monitoring for built PC Farms. Therefore it is like a 'black box' for administrators who don't know how they run and where the bottlenecks are. In present there are a few of running PC Farms such as BES-Farm, LHC-Farm, YBJ-Farm at IHEP, CAS. As the scale of PC Farms growing and the IHEP campus grid computing environment implemented, it is more difficult to predict how these PC Farms perform. As a result, the SNMP-based tool called IF-Monitor that allows effective monitoring of large clusters have been designed and developed at IHEP. (authors)
PIConGPU - A highly-scalable particle-in-cell implementation for GPU clusters
Energy Technology Data Exchange (ETDEWEB)
Bussmann, Michael; Burau, Heiko; Debus, Alexander; Huebl, Axel; Kluge, Thomas; Pausch, Richard; Schmeisser, Nils; Steiniger, Klaus; Widera, Rene; Wyderka, Nikolai; Schramm, Ulrich; Cowan, Thomas [HZDR, Dresden (Germany); Schneider, Benjamin [HZDR, Dresden (Germany); TU Dresden (Germany); Schmitt, Felix [NVIDIA, Austin, TX (United States); Grottel, Sebastian; Gumhold, Stefan [TU Dresden (Germany); Juckeland, Guido; Angel, Wolfgang [TU Dresden (Germany); ZIH, Dresden (Germany)
2013-07-01
PIConGPU can handle large-scale simulations of laser plasma and astrophysical plasma dynamics on GPU clusters with thousands of GPUs. High data throughput allows to conduct large parameter surveys but makes it necessary to rethink data analysis and look for new ways of analyzing large simulation data sets. The speedup seen on GPUs enables scientists to add physical effects to their code that up until recently have been too computationally demanding. We present recent results obtained with PIConGPU, discuss scaling behaviour, the most important building blocks of the code and new physics modules recently added. In addition we give an outlook on data analysis, resiliance and load balancing with PIConGPU.
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.
Geometric mean for subspace selection.
Tao, Dacheng; Li, Xuelong; Wu, Xindong; Maybank, Stephen J
2009-02-01
Subspace selection approaches are powerful tools in pattern classification and data visualization. One of the most important subspace approaches is the linear dimensionality reduction step in the Fisher's linear discriminant analysis (FLDA), which has been successfully employed in many fields such as biometrics, bioinformatics, and multimedia information management. However, the linear dimensionality reduction step in FLDA has a critical drawback: for a classification task with c classes, if the dimension of the projected subspace is strictly lower than c - 1, the projection to a subspace tends to merge those classes, which are close together in the original feature space. If separate classes are sampled from Gaussian distributions, all with identical covariance matrices, then the linear dimensionality reduction step in FLDA maximizes the mean value of the Kullback-Leibler (KL) divergences between different classes. Based on this viewpoint, the geometric mean for subspace selection is studied in this paper. Three criteria are analyzed: 1) maximization of the geometric mean of the KL divergences, 2) maximization of the geometric mean of the normalized KL divergences, and 3) the combination of 1 and 2. Preliminary experimental results based on synthetic data, UCI Machine Learning Repository, and handwriting digits show that the third criterion is a potential discriminative subspace selection method, which significantly reduces the class separation problem in comparing with the linear dimensionality reduction step in FLDA and its several representative extensions.
2011-01-01
present performance statistics to explain the scalability behavior. Keywords-atmospheric models, time intergrators , MPI, scal- ability, performance; I...across inter-element bound- aries. Basis functions are constructed as tensor products of Lagrange polynomials ψi (x) = hα(ξ) ⊗ hβ(η) ⊗ hγ(ζ)., where hα
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).
Subspace preservation, subspace locality, and gluing of completely positive maps
International Nuclear Information System (INIS)
Aaberg, Johan
2004-01-01
Three concepts concerning completely positive maps (CPMs) and trace preserving CPMs (channels) are introduced and investigated. These are named subspace preserving (SP) CPMs, subspace local (SL) channels, and gluing of CPMs. SP CPMs has, in the case of trace preserving CPMs, a simple interpretation as those which preserve probability weights on a given orthogonal sum decomposition of the Hilbert space of a quantum system. The proposed definition of subspace locality of quantum channels is an attempt to answer the question of what kind of restriction should be put on a channel, if it is to act 'locally' with respect to two 'locations', when these naturally correspond to a separation of the total Hilbert space in an orthogonal sum of subspaces, rather than a tensor product decomposition. As a description of the concept of gluings of quantum channels, consider a pair of 'evolution machines', each with the ability to evolve the internal state of a 'particle' inserted into its input. Each of these machines is characterized by a channel describing the operation the internal state has experienced when the particle is returned at the output. Suppose a particle is put in a superposition between the input of the first and the second machine. Here it is shown that the total evolution caused by a pair of such devices is not uniquely determined by the channels of the two machines. Such 'global' channels describing the machine pair are examples of gluings of the two single machine channels. Various expressions to generate the set of SP and SL channels, as well as expressions to generate the set of gluings of given channels, are deduced. We discuss conceptual aspects of the nature of these channels and the nature of the non-uniqueness of gluings
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
Krü ger, Jens J.; Hadwiger, Markus
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
Consistency Analysis of Nearest Subspace Classifier
Wang, Yi
2015-01-01
The Nearest subspace classifier (NSS) finds an estimation of the underlying subspace within each class and assigns data points to the class that corresponds to its nearest subspace. This paper mainly studies how well NSS can be generalized to new samples. It is proved that NSS is strongly consistent under certain assumptions. For completeness, NSS is evaluated through experiments on various simulated and real data sets, in comparison with some other linear model based classifiers. It is also ...
Controllable Subspaces of Open Quantum Dynamical Systems
International Nuclear Information System (INIS)
Zhang Ming; Gong Erling; Xie Hongwei; Hu Dewen; Dai Hongyi
2008-01-01
This paper discusses the concept of controllable subspace for open quantum dynamical systems. It is constructively demonstrated that combining structural features of decoherence-free subspaces with the ability to perform open-loop coherent control on open quantum systems will allow decoherence-free subspaces to be controllable. This is in contrast to the observation that open quantum dynamical systems are not open-loop controllable. To a certain extent, this paper gives an alternative control theoretical interpretation on why decoherence-free subspaces can be useful for quantum computation.
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.
Scalable cloud without dedicated storage
Batkovich, D. V.; Kompaniets, M. V.; Zarochentsev, A. K.
2015-05-01
We present a prototype of a scalable computing cloud. It is intended to be deployed on the basis of a cluster without the separate dedicated storage. The dedicated storage is replaced by the distributed software storage. In addition, all cluster nodes are used both as computing nodes and as storage nodes. This solution increases utilization of the cluster resources as well as improves fault tolerance and performance of the distributed storage. Another advantage of this solution is high scalability with a relatively low initial and maintenance cost. The solution is built on the basis of the open source components like OpenStack, CEPH, etc.
Subspace Based Blind Sparse Channel Estimation
DEFF Research Database (Denmark)
Hayashi, Kazunori; Matsushima, Hiroki; Sakai, Hideaki
2012-01-01
The paper proposes a subspace based blind sparse channel estimation method using 1–2 optimization by replacing the 2–norm minimization in the conventional subspace based method by the 1–norm minimization problem. Numerical results confirm that the proposed method can significantly improve...
Subspace exclusion zones for damage localization
DEFF Research Database (Denmark)
Bernal, Dionisio; Ulriksen, Martin Dalgaard
2018-01-01
, this is exploited in the context of structural damage localization to cast the Subspace Exclusion Zone (SEZ) scheme, which locates damage by reconstructing the captured field quantity shifts from analytical subspaces indexed by postulated boundaries, the so-called exclusion zones (EZs), in a model of the structure...
Subspace learning from image gradient orientations
Tzimiropoulos, Georgios; Zafeiriou, Stefanos; Pantic, Maja
2012-01-01
We introduce the notion of subspace learning from image gradient orientations for appearance-based object recognition. As image data is typically noisy and noise is substantially different from Gaussian, traditional subspace learning from pixel intensities fails very often to estimate reliably the
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...
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.
van Dam, Hubertus J J; Vishnu, Abhinav; de Jong, Wibe A
2011-01-11
In the past couple of decades, the massive computational power provided by the most modern supercomputers has resulted in simulation of higher-order computational chemistry methods, previously considered intractable. As the system sizes continue to increase, the computational chemistry domain continues to escalate this trend using parallel computing with programming models such as Message Passing Interface (MPI) and Partitioned Global Address Space (PGAS) programming models such as Global Arrays. The ever increasing scale of these supercomputers comes at a cost of reduced Mean Time Between Failures (MTBF), currently on the order of days and projected to be on the order of hours for upcoming extreme scale systems. While traditional disk-based check pointing methods are ubiquitous for storing intermediate solutions, they suffer from high overhead of writing and recovering from checkpoints. In practice, checkpointing itself often brings the system down. Clearly, methods beyond checkpointing are imperative to handling the aggravating issue of reducing MTBF. In this paper, we address this challenge by designing and implementing an efficient fault tolerant version of the Coupled Cluster (CC) method with NWChem, using in-memory data redundancy. We present the challenges associated with our design, including an efficient data storage model, maintenance of at least one consistent data copy, and the recovery process. Our performance evaluation without faults shows that the current design exhibits a small overhead. In the presence of a simulated fault, the proposed design incurs negligible overhead in comparison to the state of the art implementation without faults.
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...
Code subspaces for LLM geometries
Berenstein, David; Miller, Alexandra
2018-03-01
We consider effective field theory around classical background geometries with a gauge theory dual, specifically those in the class of LLM geometries. These are dual to half-BPS states of N= 4 SYM. We find that the language of code subspaces is natural for discussing the set of nearby states, which are built by acting with effective fields on these backgrounds. This work extends our previous work by going beyond the strict infinite N limit. We further discuss how one can extract the topology of the state beyond N→∞ and find that, as before, uncertainty and entanglement entropy calculations provide a useful tool to do so. Finally, we discuss obstructions to writing down a globally defined metric operator. We find that the answer depends on the choice of reference state that one starts with. Therefore, within this setup, there is ambiguity in trying to write an operator that describes the metric globally.
Keshtkaran, Mohammad Reza; Yang, Zhi
2017-06-01
Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.
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.
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...
Sinusoidal Order Estimation Using Angles between Subspaces
Directory of Open Access Journals (Sweden)
Søren Holdt Jensen
2009-01-01
Full Text Available We consider the problem of determining the order of a parametric model from a noisy signal based on the geometry of the space. More specifically, we do this using the nontrivial angles between the candidate signal subspace model and the noise subspace. The proposed principle is closely related to the subspace orthogonality property known from the MUSIC algorithm, and we study its properties and compare it to other related measures. For the problem of estimating the number of complex sinusoids in white noise, a computationally efficient implementation exists, and this problem is therefore considered in detail. In computer simulations, we compare the proposed method to various well-known methods for order estimation. These show that the proposed method outperforms the other previously published subspace methods and that it is more robust to the noise being colored than the previously published methods.
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...... to vectors (subspaces) or elements of vectors; we focus on the latter and use a trimmed average. The resulting Trimmed Grassmann Average (TGA) is particularly appropriate for computer vision because it is robust to pixel outliers. The algorithm has low computational complexity and minimal memory requirements...
Outlier Ranking via Subspace Analysis in Multiple Views of the Data
DEFF Research Database (Denmark)
Muller, Emmanuel; Assent, Ira; Iglesias, Patricia
2012-01-01
, a novel outlier ranking concept. Outrank exploits subspace analysis to determine the degree of outlierness. It considers different subsets of the attributes as individual outlier properties. It compares clustered regions in arbitrary subspaces and derives an outlierness score for each object. Its...... principled integration of multiple views into an outlierness measure uncovers outliers that are not detectable in the full attribute space. Our experimental evaluation demonstrates that Outrank successfully determines a high quality outlier ranking, and outperforms state-of-the-art outlierness measures....
Active Subspaces for Wind Plant Surrogate Modeling
Energy Technology Data Exchange (ETDEWEB)
King, Ryan N [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Quick, Julian [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Dykes, Katherine L [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Adcock, Christiane [Massachusetts Institute of Technology
2018-01-12
Understanding the uncertainty in wind plant performance is crucial to their cost-effective design and operation. However, conventional approaches to uncertainty quantification (UQ), such as Monte Carlo techniques or surrogate modeling, are often computationally intractable for utility-scale wind plants because of poor congergence rates or the curse of dimensionality. In this paper we demonstrate that wind plant power uncertainty can be well represented with a low-dimensional active subspace, thereby achieving a significant reduction in the dimension of the surrogate modeling problem. We apply the active sub-spaces technique to UQ of plant power output with respect to uncertainty in turbine axial induction factors, and find a single active subspace direction dominates the sensitivity in power output. When this single active subspace direction is used to construct a quadratic surrogate model, the number of model unknowns can be reduced by up to 3 orders of magnitude without compromising performance on unseen test data. We conclude that the dimension reduction achieved with active subspaces makes surrogate-based UQ approaches tractable for utility-scale wind plants.
Active Subspaces of Airfoil Shape Parameterizations
Grey, Zachary J.; Constantine, Paul G.
2018-05-01
Design and optimization benefit from understanding the dependence of a quantity of interest (e.g., a design objective or constraint function) on the design variables. A low-dimensional active subspace, when present, identifies important directions in the space of design variables; perturbing a design along the active subspace associated with a particular quantity of interest changes that quantity more, on average, than perturbing the design orthogonally to the active subspace. This low-dimensional structure provides insights that characterize the dependence of quantities of interest on design variables. Airfoil design in a transonic flow field with a parameterized geometry is a popular test problem for design methodologies. We examine two particular airfoil shape parameterizations, PARSEC and CST, and study the active subspaces present in two common design quantities of interest, transonic lift and drag coefficients, under each shape parameterization. We mathematically relate the two parameterizations with a common polynomial series. The active subspaces enable low-dimensional approximations of lift and drag that relate to physical airfoil properties. In particular, we obtain and interpret a two-dimensional approximation of both transonic lift and drag, and we show how these approximation inform a multi-objective design problem.
Subspace confinement: how good is your qubit?
International Nuclear Information System (INIS)
Devitt, Simon J; Schirmer, Sonia G; Oi, Daniel K L; Cole, Jared H; Hollenberg, Lloyd C L
2007-01-01
The basic operating element of standard quantum computation is the qubit, an isolated two-level system that can be accurately controlled, initialized and measured. However, the majority of proposed physical architectures for quantum computation are built from systems that contain much more complicated Hilbert space structures. Hence, defining a qubit requires the identification of an appropriate controllable two-dimensional sub-system. This prompts the obvious question of how well a qubit, thus defined, is confined to this subspace, and whether we can experimentally quantify the potential leakage into states outside the qubit subspace. We demonstrate how subspace leakage can be characterized using minimal theoretical assumptions by examining the Fourier spectrum of the oscillation experiment
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.
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.
Quantum Computing in Decoherence-Free Subspace Constructed by Triangulation
Bi, Qiao; Guo, Liu; Ruda, H. E.
2010-01-01
A formalism for quantum computing in decoherence-free subspaces is presented. The constructed subspaces are partial triangulated to an index related to environment. The quantum states in the subspaces are just projected states which are ruled by a subdynamic kinetic equation. These projected states can be used to perform ideal quantum logical operations without decoherence.
Quantum Computing in Decoherence-Free Subspace Constructed by Triangulation
Directory of Open Access Journals (Sweden)
Qiao Bi
2010-01-01
Full Text Available A formalism for quantum computing in decoherence-free subspaces is presented. The constructed subspaces are partial triangulated to an index related to environment. The quantum states in the subspaces are just projected states which are ruled by a subdynamic kinetic equation. These projected states can be used to perform ideal quantum logical operations without decoherence.
LBAS: Lanczos Bidiagonalization with Subspace Augmentation for Discrete Inverse Problems
DEFF Research Database (Denmark)
Hansen, Per Christian; Abe, Kyniyoshi
The regularizing properties of Lanczos bidiagonalization are powerful when the underlying Krylov subspace captures the dominating components of the solution. In some applications the regularized solution can be further improved by augmenting the Krylov subspace with a low-dimensional subspace tha...
Subspace System Identification of the Kalman Filter
Directory of Open Access Journals (Sweden)
David Di Ruscio
2003-07-01
Full Text Available Some proofs concerning a subspace identification algorithm are presented. It is proved that the Kalman filter gain and the noise innovations process can be identified directly from known input and output data without explicitly solving the Riccati equation. Furthermore, it is in general and for colored inputs, proved that the subspace identification of the states only is possible if the deterministic part of the system is known or identified beforehand. However, if the inputs are white, then, it is proved that the states can be identified directly. Some alternative projection matrices which can be used to compute the extended observability matrix directly from the data are presented. Furthermore, an efficient method for computing the deterministic part of the system is presented. The closed loop subspace identification problem is also addressed and it is shown that this problem is solved and unbiased estimates are obtained by simply including a filter in the feedback. Furthermore, an algorithm for consistent closed loop subspace estimation is presented. This algorithm is using the controller parameters in order to overcome the bias problem.
Quantum Zeno subspaces induced by temperature
Energy Technology Data Exchange (ETDEWEB)
Militello, B.; Scala, M.; Messina, A. [Dipartimento di Fisica dell' Universita di Palermo, Via Archirafi 36, I-90123 Palermo (Italy)
2011-08-15
We discuss the partitioning of the Hilbert space of a quantum system induced by the interaction with another system at thermal equilibrium, showing that the higher the temperature the more effective is the formation of Zeno subspaces. We show that our analysis keeps its validity even in the case of interaction with a bosonic reservoir, provided appropriate limitations of the relevant bandwidth.
Counting Subspaces of a Finite Vector Space
Indian Academy of Sciences (India)
Home; Journals; Resonance – Journal of Science Education; Volume 15; Issue 11. Counting Subspaces of a Finite Vector Space – 1. Amritanshu Prasad. General Article Volume 15 Issue 11 November 2010 pp 977-987. Fulltext. Click here to view fulltext PDF. Permanent link:
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.
INDOOR SUBSPACING TO IMPLEMENT INDOORGML FOR INDOOR NAVIGATION
Directory of Open Access Journals (Sweden)
H. Jung
2015-10-01
Full Text Available According to an increasing demand for indoor navigation, there are great attempts to develop applicable indoor network. Representation for a room as a node is not sufficient to apply complex and large buildings. As OGC established IndoorGML, subspacing to partition the space for constructing logical network is introduced. Concerning subspacing for indoor network, transition space like halls or corridors also have to be considered. This study presents the subspacing process for creating an indoor network in shopping mall. Furthermore, categorization of transition space is performed and subspacing of this space is considered. Hall and squares in mall is especially defined for subspacing. Finally, implementation of subspacing process for indoor network is presented.
Indoor Subspacing to Implement Indoorgml for Indoor Navigation
Jung, H.; Lee, J.
2015-10-01
According to an increasing demand for indoor navigation, there are great attempts to develop applicable indoor network. Representation for a room as a node is not sufficient to apply complex and large buildings. As OGC established IndoorGML, subspacing to partition the space for constructing logical network is introduced. Concerning subspacing for indoor network, transition space like halls or corridors also have to be considered. This study presents the subspacing process for creating an indoor network in shopping mall. Furthermore, categorization of transition space is performed and subspacing of this space is considered. Hall and squares in mall is especially defined for subspacing. Finally, implementation of subspacing process for indoor network is presented.
On the maximal dimension of a completely entangled subspace for ...
Indian Academy of Sciences (India)
R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22
dim S = d1d2 ...dk − (d1 +···+ dk) + k − 1, where E is the collection of all completely entangled subspaces. When H1 = H2 and k = 2 an explicit orthonormal basis of a maximal completely entangled subspace of H1 ⊗ H2 is given. We also introduce a more delicate notion of a perfectly entangled subspace for a multipartite ...
Detecting anomalies in crowded scenes via locality-constrained affine subspace coding
Fan, Yaxiang; Wen, Gongjian; Qiu, Shaohua; Li, Deren
2017-07-01
Video anomaly event detection is the process of finding an abnormal event deviation compared with the majority of normal or usual events. The main challenges are the high structure redundancy and the dynamic changes in the scenes that are in surveillance videos. To address these problems, we present a framework for anomaly detection and localization in videos that is based on locality-constrained affine subspace coding (LASC) and a model updating procedure. In our algorithm, LASC attempts to reconstruct the test sample by its top-k nearest subspaces, which are obtained by segmenting the normal samples space using a clustering method. A sample with a large reconstruction cost is detected as abnormal by setting a threshold. To adapt to the scene changes over time, a model updating strategy is proposed. We experiment on two public datasets: the UCSD dataset and the Avenue dataset. The results demonstrate that our method achieves competitive performance at a 700 fps on a single desktop PC.
Central subspace dimensionality reduction using covariance operators.
Kim, Minyoung; Pavlovic, Vladimir
2011-04-01
We consider the task of dimensionality reduction informed by real-valued multivariate labels. The problem is often treated as Dimensionality Reduction for Regression (DRR), whose goal is to find a low-dimensional representation, the central subspace, of the input data that preserves the statistical correlation with the targets. A class of DRR methods exploits the notion of inverse regression (IR) to discover central subspaces. Whereas most existing IR techniques rely on explicit output space slicing, we propose a novel method called the Covariance Operator Inverse Regression (COIR) that generalizes IR to nonlinear input/output spaces without explicit target slicing. COIR's unique properties make DRR applicable to problem domains with high-dimensional output data corrupted by potentially significant amounts of noise. Unlike recent kernel dimensionality reduction methods that employ iterative nonconvex optimization, COIR yields a closed-form solution. We also establish the link between COIR, other DRR techniques, and popular supervised dimensionality reduction methods, including canonical correlation analysis and linear discriminant analysis. We then extend COIR to semi-supervised settings where many of the input points lack their labels. We demonstrate the benefits of COIR on several important regression problems in both fully supervised and semi-supervised settings.
Seismic noise attenuation using an online subspace tracking algorithm
Zhou, Yatong; Li, Shuhua; Zhang, D.; Chen, Yangkang
2018-01-01
We propose a new low-rank based noise attenuation method using an efficient algorithm for tracking subspaces from highly corrupted seismic observations. The subspace tracking algorithm requires only basic linear algebraic manipulations. The algorithm is derived by analysing incremental gradient
On the dimension of subspaces with bounded Schmidt rank
International Nuclear Information System (INIS)
Cubitt, Toby; Montanaro, Ashley; Winter, Andreas
2008-01-01
We consider the question of how large a subspace of a given bipartite quantum system can be when the subspace contains only highly entangled states. This is motivated in part by results of Hayden et al. [e-print arXiv:quant-ph/0407049; Commun. Math. Phys., 265, 95 (2006)], which show that in large dxd-dimensional systems there exist random subspaces of dimension almost d 2 , all of whose states have entropy of entanglement at least log d-O(1). It is also a generalization of results on the dimension of completely entangled subspaces, which have connections with the construction of unextendible product bases. Here we take as entanglement measure the Schmidt rank, and determine, for every pair of local dimensions d A and d B , and every r, the largest dimension of a subspace consisting only of entangled states of Schmidt rank r or larger. This exact answer is a significant improvement on the best bounds that can be obtained using the random subspace techniques in Hayden et al. We also determine the converse: the largest dimension of a subspace with an upper bound on the Schmidt rank. Finally, we discuss the question of subspaces containing only states with Schmidt equal to r
On the numerical stability analysis of pipelined Krylov subspace methods
Czech Academy of Sciences Publication Activity Database
Carson, E.T.; Rozložník, Miroslav; Strakoš, Z.; Tichý, P.; Tůma, M.
submitted 2017 (2018) R&D Projects: GA ČR GA13-06684S Grant - others:GA MŠk(CZ) LL1202 Institutional support: RVO:67985807 Keywords : Krylov subspace methods * the conjugate gradient method * numerical stability * inexact computations * delay of convergence * maximal attainable accuracy * pipelined Krylov subspace methods * exascale computations
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
Scalable fast multipole accelerated vortex methods
Hu, Qi; Gumerov, Nail A.; Yokota, Rio; Barba, Lorena A.; Duraiswami, Ramani
2014-01-01
-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
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.
Robust adaptive subspace detection in impulsive noise
Ben Atitallah, Ismail; Kammoun, Abla; Alouini, Mohamed-Slim; Al-Naffouri, Tareq Y.
2016-01-01
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.
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
International Nuclear Information System (INIS)
Xu Xiaoliang; Xu, Bobby; He Bin
2004-01-01
In the present study, we investigate a new approach to electroencephalography (EEG) three-dimensional (3D) dipole source localization by using a non-recursive subspace algorithm called FINES. In estimating source dipole locations, the present approach employs projections onto a subspace spanned by a small set of particular vectors (FINES vector set) in the estimated noise-only subspace instead of the entire estimated noise-only subspace in the case of classic MUSIC. The subspace spanned by this vector set is, in the sense of principal angle, closest to the subspace spanned by the array manifold associated with a particular brain region. By incorporating knowledge of the array manifold in identifying FINES vector sets in the estimated noise-only subspace for different brain regions, the present approach is able to estimate sources with enhanced accuracy and spatial resolution, thus enhancing the capability of resolving closely spaced sources and reducing estimation errors. The present computer simulations show, in EEG 3D dipole source localization, that compared to classic MUSIC, FINES has (1) better resolvability of two closely spaced dipolar sources and (2) better estimation accuracy of source locations. In comparison with RAP-MUSIC, FINES' performance is also better for the cases studied when the noise level is high and/or correlations among dipole sources exist
Microscopic theory of dynamical subspace for large amplitude collective motion
International Nuclear Information System (INIS)
Sakata, Fumihiko; Marumori, Toshio; Ogura, Masanori.
1986-01-01
A full quantum theory appropriate for describing large amplitude collective motion is proposed by exploiting the basic idea of the semi-classical theory so far developed within the time-depedent Hartree-Fock theory. A central problem of the quantum theory is how to determine an optimal representation called a dynamical representation specific for the collective subspace where the large amplitude collective motion is replicated as precisely as possible. As an extension of the semi-classical theory where the concept of an approximate integral surface played an important role, the collective subspace is properly characterized by introducing a concept of an approximate invariant subspace of the Hamiltonian. (author)
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)
2017-09-27
Sep 27, 2017 ... Author for correspondence (zh4403701@126.com). MS received 15 ... lic clusters using density functional theory (DFT)-GGA of the DMOL3 package. ... In the process of geometric optimization, con- vergence thresholds ..... and Postgraduate Research & Practice Innovation Program of. Jiangsu Province ...
Indian Academy of Sciences (India)
environmental as well as technical problems during fuel gas utilization. ... adsorption on some alloys of Pd, namely PdAu, PdAg ... ried out on small neutral and charged Au24,26,27, Cu,28 ... study of Zanti et al.29 on Pdn (n = 1–9) clusters.
Subspace Correction Methods for Total Variation and $\\ell_1$-Minimization
Fornasier, Massimo; Schö nlieb, Carola-Bibiane
2009-01-01
This paper is concerned with the numerical minimization of energy functionals in Hilbert spaces involving convex constraints coinciding with a seminorm for a subspace. The optimization is realized by alternating minimizations of the functional on a
Declarative and Scalable Selection for Map Visualizations
DEFF Research Database (Denmark)
Kefaloukos, Pimin Konstantin Balic
and is itself a source and cause of prolific data creation. This calls for scalable map processing techniques that can handle the data volume and which play well with the predominant data models on the Web. (4) Maps are now consumed around the clock by a global audience. While historical maps were singleuser......-defined constraints as well as custom objectives. The purpose of the language is to derive a target multi-scale database from a source database according to holistic specifications. (b) The Glossy SQL compiler allows Glossy SQL to be scalably executed in a spatial analytics system, such as a spatial relational......, 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...
On spectral subspaces and their applications to automorphism groups
International Nuclear Information System (INIS)
Olesen, Dorte
1974-03-01
An attempt is made to give a survey of the theory of spectra and spectral subspaces of group representations in an abstract Banach space setting. The theory is applied to the groups of automorphisms of operator algebras (mostly C*-algebras) and some important results of interest for mathematical physicists are proved (restrictions of the bitransposed action, spectral subspaces for the transposed action on a C*-algebra, and positive states and representations of Rsup(n)) [fr
Banerjee, Amartya S; Lin, Lin; Suryanarayana, Phanish; Yang, Chao; Pask, John E
2018-06-12
We describe a novel iterative strategy for Kohn-Sham density functional theory calculations aimed at large systems (>1,000 electrons), applicable to metals and insulators alike. In lieu of explicit diagonalization of the Kohn-Sham Hamiltonian on every self-consistent field (SCF) iteration, we employ a two-level Chebyshev polynomial filter based complementary subspace strategy to (1) compute a set of vectors that span the occupied subspace of the Hamiltonian; (2) reduce subspace diagonalization to just partially occupied states; and (3) obtain those states in an efficient, scalable manner via an inner Chebyshev filter iteration. By reducing the necessary computation to just partially occupied states and obtaining these through an inner Chebyshev iteration, our approach reduces the cost of large metallic calculations significantly, while eliminating subspace diagonalization for insulating systems altogether. We describe the implementation of the method within the framework of the discontinuous Galerkin (DG) electronic structure method and show that this results in a computational scheme that can effectively tackle bulk and nano systems containing tens of thousands of electrons, with chemical accuracy, within a few minutes or less of wall clock time per SCF iteration on large-scale computing platforms. We anticipate that our method will be instrumental in pushing the envelope of large-scale ab initio molecular dynamics. As a demonstration of this, we simulate a bulk silicon system containing 8,000 atoms at finite temperature, and obtain an average SCF step wall time of 51 s on 34,560 processors; thus allowing us to carry out 1.0 ps of ab initio molecular dynamics in approximately 28 h (of wall time).
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.
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
International Nuclear Information System (INIS)
Alnaes, K.; Kristiansen, E.H.; Gustavson, D.B.; James, D.V.
1990-01-01
The Scalable Coherent Interface (IEEE P1596) is establishing an interface standard for very high performance multiprocessors, supporting a cache-coherent-memory model scalable to systems with up to 64K nodes. This Scalable Coherent Interface (SCI) will supply a peak bandwidth per node of 1 GigaByte/second. The SCI standard should facilitate assembly of processor, memory, I/O and bus bridge cards from multiple vendors into massively parallel systems with throughput far above what is possible today. The SCI standard encompasses two levels of interface, a physical level and a logical level. The physical level specifies electrical, mechanical and thermal characteristics of connectors and cards that meet the standard. The logical level describes the address space, data transfer protocols, cache coherence mechanisms, synchronization primitives and error recovery. In this paper we address logical level issues such as packet formats, packet transmission, transaction handshake, flow control, and cache coherence. 11 refs., 10 figs
Two-qubit quantum computing in a projected subspace
International Nuclear Information System (INIS)
Bi Qiao; Ruda, H.E.; Zhan, M.S.
2002-01-01
A formulation for performing quantum computing in a projected subspace is presented, based on the subdynamical kinetic equation (SKE) for an open quantum system. The eigenvectors of the kinetic equation are shown to remain invariant before and after interaction with the environment. However, the eigenvalues in the projected subspace exhibit a type of phase shift to the evolutionary states. This phase shift does not destroy the decoherence-free (DF) property of the subspace because the associated fidelity is 1. This permits a universal formalism to be presented--the eigenprojectors of the free part of the Hamiltonian for the system and bath may be used to construct a DF projected subspace based on the SKE. To eliminate possible phase or unitary errors induced by the change in the eigenvalues, a cancellation technique is proposed, using the adjustment of the coupling time, and applied to a two-qubit computing system. A general criteria for constructing a DF-projected subspace from the SKE is discussed. Finally, a proposal for using triangulation to realize a decoherence-free subsystem based on SKE is presented. The concrete formulation for a two-qubit model is given exactly. Our approach is general and appears to be applicable to any type of decoherence
Adiabatic evolution of decoherence-free subspaces and its shortcuts
Wu, S. L.; Huang, X. L.; Li, H.; Yi, X. X.
2017-10-01
The adiabatic theorem and shortcuts to adiabaticity for time-dependent open quantum systems are explored in this paper. Starting from the definition of dynamical stable decoherence-free subspace, we show that, under a compact adiabatic condition, the quantum state remains in the time-dependent decoherence-free subspace with an extremely high purity, even though the dynamics of the open quantum system may not be adiabatic. The adiabatic condition mentioned here in the adiabatic theorem for open systems is very similar to that for closed quantum systems, except that the operators required to change slowly are the Lindblad operators. We also show that the adiabatic evolution of decoherence-free subspaces depends on the existence of instantaneous decoherence-free subspaces, which requires that the Hamiltonian of open quantum systems be engineered according to the incoherent control protocol. In addition, shortcuts to adiabaticity for adiabatic decoherence-free subspaces are also presented based on the transitionless quantum driving method. Finally, we provide an example that consists of a two-level system coupled to a broadband squeezed vacuum field to show our theory. Our approach employs Markovian master equations and the theory can apply to finite-dimensional quantum open systems.
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.
Independence and totalness of subspaces in phase space methods
Vourdas, A.
2018-04-01
The concepts of independence and totalness of subspaces are introduced in the context of quasi-probability distributions in phase space, for quantum systems with finite-dimensional Hilbert space. It is shown that due to the non-distributivity of the lattice of subspaces, there are various levels of independence, from pairwise independence up to (full) independence. Pairwise totalness, totalness and other intermediate concepts are also introduced, which roughly express that the subspaces overlap strongly among themselves, and they cover the full Hilbert space. A duality between independence and totalness, that involves orthocomplementation (logical NOT operation), is discussed. Another approach to independence is also studied, using Rota's formalism on independent partitions of the Hilbert space. This is used to define informational independence, which is proved to be equivalent to independence. As an application, the pentagram (used in discussions on contextuality) is analysed using these concepts.
Reduced-Rank Adaptive Filtering Using Krylov Subspace
Directory of Open Access Journals (Sweden)
Sergueï Burykh
2003-01-01
Full Text Available A unified view of several recently introduced reduced-rank adaptive filters is presented. As all considered methods use Krylov subspace for rank reduction, the approach taken in this work is inspired from Krylov subspace methods for iterative solutions of linear systems. The alternative interpretation so obtained is used to study the properties of each considered technique and to relate one reduced-rank method to another as well as to algorithms used in computational linear algebra. Practical issues are discussed and low-complexity versions are also included in our study. It is believed that the insight developed in this paper can be further used to improve existing reduced-rank methods according to known results in the domain of Krylov subspace methods.
A generalized Schwinger boson mapping with a physical subspace
International Nuclear Information System (INIS)
Scholtz, F.G.; Geyer, H.B.
1988-01-01
We investigate the existence of a physical subspace for generalized Schwinger boson mappings of SO(2n+1) contains SO(2n) in view of previous observations by Marshalek and the recent construction of such a mapping and subspace for SO(8) by Kaup. It is shown that Kaup's construction can be attributed to the existence of a unique SO(8) automorphism. We proceed to construct a generalized Schwinger-type mapping for SO(2n+1) contains SO(2n) which, in contrast to a similar attempt by Yamamura and Nishiyama, indeed has a corresponding physical subspace. This new mapping includes in the special case of SO(8) the mapping by Kaup which is equivalent to the one given by Yamamura and Nishiyama for n=4. Nevertheless, we indicate the limitations of the generalized Schwinger mapping regarding its applicability to situations where one seeks to establish a direct link between phenomenological boson models and an underlying fermion microscopy. (orig.)
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.
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.
Krylov subspace methods for solving large unsymmetric linear systems
International Nuclear Information System (INIS)
Saad, Y.
1981-01-01
Some algorithms based upon a projection process onto the Krylov subspace K/sub m/ = Span(r 0 , Ar 0 ,...,A/sup m/-1r 0 ) are developed, generalizing the method of conjugate gradients to unsymmetric systems. These methods are extensions of Arnoldi's algorithm for solving eigenvalue problems. The convergence is analyzed in terms of the distance of the solution to the subspace K/sub m/ and some error bounds are established showing, in particular, a similarity with the conjugate gradient method (for symmetric matrices) when the eigenvalues are real. Several numerical experiments are described and discussed
A subspace preconditioning algorithm for eigenvector/eigenvalue computation
Energy Technology Data Exchange (ETDEWEB)
Bramble, J.H.; Knyazev, A.V.; Pasciak, J.E.
1996-12-31
We consider the problem of computing a modest number of the smallest eigenvalues along with orthogonal bases for the corresponding eigen-spaces of a symmetric positive definite matrix. In our applications, the dimension of a matrix is large and the cost of its inverting is prohibitive. In this paper, we shall develop an effective parallelizable technique for computing these eigenvalues and eigenvectors utilizing subspace iteration and preconditioning. Estimates will be provided which show that the preconditioned method converges linearly and uniformly in the matrix dimension when used with a uniform preconditioner under the assumption that the approximating subspace is close enough to the span of desired eigenvectors.
Directory of Open Access Journals (Sweden)
Alim Samat
2016-03-01
Full Text Available In order to deal with scenarios where the training data, used to deduce a model, and the validation data have different statistical distributions, we study the problem of transformed subspace feature transfer for domain adaptation (DA in the context of hyperspectral image classification via a geodesic Gaussian flow kernel based support vector machine (GFKSVM. To show the superior performance of the proposed approach, conventional support vector machines (SVMs and state-of-the-art DA algorithms, including information-theoretical learning of discriminative cluster for domain adaptation (ITLDC, joint distribution adaptation (JDA, and joint transfer matching (JTM, are also considered. Additionally, unsupervised linear and nonlinear subspace feature transfer techniques including principal component analysis (PCA, randomized nonlinear principal component analysis (rPCA, factor analysis (FA and non-negative matrix factorization (NNMF are investigated and compared. Experiments on two real hyperspectral images show the cross-image classification performances of the GFKSVM, confirming its effectiveness and suitability when applied to hyperspectral images.
Scalable Resolution Display Walls
Leigh, Jason; Johnson, Andrew; Renambot, Luc; Peterka, Tom; Jeong, Byungil; Sandin, Daniel J.; Talandis, Jonas; Jagodic, Ratko; Nam, Sungwon; Hur, Hyejung; Sun, Yiwen
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.
Fast regularizing sequential subspace optimization in Banach spaces
International Nuclear Information System (INIS)
Schöpfer, F; Schuster, T
2009-01-01
We are concerned with fast computations of regularized solutions of linear operator equations in Banach spaces in case only noisy data are available. To this end we modify recently developed sequential subspace optimization methods in such a way that the therein employed Bregman projections onto hyperplanes are replaced by Bregman projections onto stripes whose width is in the order of the noise level
Experimental Comparison of Signal Subspace Based Noise Reduction Methods
DEFF Research Database (Denmark)
Hansen, Peter Søren Kirk; Hansen, Per Christian; Hansen, Steffen Duus
1999-01-01
The signal subspace approach for non-parametric speech enhancement is considered. Several algorithms have been proposed in the literature but only partly analyzed. Here, the different algorithms are compared, and the emphasis is put onto the limiting factors and practical behavior of the estimators...
Recursive subspace identification for in flight modal analysis of airplanes
De Cock , Katrien; Mercère , Guillaume; De Moor , Bart
2006-01-01
International audience; In this paper recursive subspace identification algorithms are applied to track the modal parameters of airplanes on-line during test flights. The ability to track changes in the damping ratios and the influence of the forgetting factor are studied through simulations.
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...
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 ...
Intrinsic Grassmann Averages for Online Linear and Robust Subspace Learning
DEFF Research Database (Denmark)
Chakraborty, Rudrasis; Hauberg, Søren; Vemuri, Baba C.
2017-01-01
Principal Component Analysis (PCA) is a fundamental method for estimating a linear subspace approximation to high-dimensional data. Many algorithms exist in literature to achieve a statistically robust version of PCA called RPCA. In this paper, we present a geometric framework for computing the p...
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.
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.
Scalable optical quantum computer
Energy Technology Data Exchange (ETDEWEB)
Manykin, E A; Mel' nichenko, E V [Institute for Superconductivity and Solid-State Physics, Russian Research Centre ' Kurchatov Institute' , Moscow (Russian Federation)
2014-12-31
A way of designing a scalable optical quantum computer based on the photon echo effect is proposed. Individual rare earth ions Pr{sup 3+}, regularly located in the lattice of the orthosilicate (Y{sub 2}SiO{sub 5}) crystal, are suggested to be used as optical qubits. Operations with qubits are performed using coherent and incoherent laser pulses. The operation protocol includes both the method of measurement-based quantum computations and the technique of optical computations. Modern hybrid photon echo protocols, which provide a sufficient quantum efficiency when reading recorded states, are considered as most promising for quantum computations and communications. (quantum computer)
Scalable optical quantum computer
International Nuclear Information System (INIS)
Manykin, E A; Mel'nichenko, E V
2014-01-01
A way of designing a scalable optical quantum computer based on the photon echo effect is proposed. Individual rare earth ions Pr 3+ , regularly located in the lattice of the orthosilicate (Y 2 SiO 5 ) crystal, are suggested to be used as optical qubits. Operations with qubits are performed using coherent and incoherent laser pulses. The operation protocol includes both the method of measurement-based quantum computations and the technique of optical computations. Modern hybrid photon echo protocols, which provide a sufficient quantum efficiency when reading recorded states, are considered as most promising for quantum computations and communications. (quantum computer)
Subspace Analysis of Indoor UWB Channels
Directory of Open Access Journals (Sweden)
Rachid Saadane
2005-03-01
Full Text Available This work aims at characterizing the second-order statistics of indoor ultra-wideband (UWB channels using channel sounding techniques. We present measurement results for different scenarios conducted in a laboratory setting at Institut EurÃƒÂ©com. These are based on an eigendecomposition of the channel autocovariance matrix, which allows for the analysis of the growth in the number of significant degrees of freedom of the channel process as a function of the signaling bandwidth as well as the statistical correlation between different propagation paths. We show empirical eigenvalue distributions as a function of the signal bandwidth for both line-of-sight and non-line-of-sight situations. Furthermore, we give examples where paths from different propagation clusters (possibly arising from reflection or diffraction show strong statistical dependence.
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.
Integrated Phoneme Subspace Method for Speech Feature Extraction
Directory of Open Access Journals (Sweden)
Park Hyunsin
2009-01-01
Full Text Available Speech feature extraction has been a key focus in robust speech recognition research. In this work, we discuss data-driven linear feature transformations applied to feature vectors in the logarithmic mel-frequency filter bank domain. Transformations are based on principal component analysis (PCA, independent component analysis (ICA, and linear discriminant analysis (LDA. Furthermore, this paper introduces a new feature extraction technique that collects the correlation information among phoneme subspaces and reconstructs feature space for representing phonemic information efficiently. The proposed speech feature vector is generated by projecting an observed vector onto an integrated phoneme subspace (IPS based on PCA or ICA. The performance of the new feature was evaluated for isolated word speech recognition. The proposed method provided higher recognition accuracy than conventional methods in clean and reverberant environments.
A 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.
A scalable distributed RRT for motion planning
Jacobs, Sam Ade; Stradford, Nicholas; Rodriguez, Cesar; Thomas, Shawna; Amato, Nancy M.
2013-01-01
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.
Invariant subspaces in some function spaces on symmetric spaces. II
International Nuclear Information System (INIS)
Platonov, S S
1998-01-01
Let G be a semisimple connected Lie group with finite centre, K a maximal compact subgroup of G, and M=G/K a Riemannian symmetric space of non-compact type. We study the problem of describing the structure of closed linear subspaces in various function spaces on M that are invariant under the quasiregular representation of the group G. We consider the case when M is a symplectic symmetric space of rank 1
Quantum cloning of mixed states in symmetric subspaces
International Nuclear Information System (INIS)
Fan Heng
2003-01-01
Quantum-cloning machine for arbitrary mixed states in symmetric subspaces is proposed. This quantum-cloning machine can be used to copy part of the output state of another quantum-cloning machine and is useful in quantum computation and quantum information. The shrinking factor of this quantum cloning achieves the well-known upper bound. When the input is identical pure states, two different fidelities of this cloning machine are optimal
Bi Sparsity Pursuit: A Paradigm for Robust Subspace Recovery
2016-09-27
Bian, Student Member, IEEE, and Hamid Krim, Fellow, IEEE Abstract The success of sparse models in computer vision and machine learning is due to the...16. SECURITY CLASSIFICATION OF: The success of sparse models in computer vision and machine learning is due to the fact that, high dimensional data...vision and machine learning is due to the fact that, high dimensional data is distributed in a union of low dimensional subspaces in many real-world
Index Formulae for Subspaces of Kreĭn Spaces
Dijksma, Aad; Gheondea, Aurelian
1996-01-01
For a subspace S of a Kreĭn space K and an arbitrary fundamental decomposition K = K-[+]K+ of K, we prove the index formula κ-(S) + dim(S⊥ ∩ K+) = κ+(S⊥) + dim(S ∩ K-), where κ±(S) stands for the positive/negative signature of S. The difference dim(S ∩ K-) - dim(S⊥ ∩ K+), provided it is well
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.
Subspace-based Inverse Uncertainty Quantification for Nuclear Data Assessment
Energy Technology Data Exchange (ETDEWEB)
Khuwaileh, B.A., E-mail: bakhuwai@ncsu.edu; Abdel-Khalik, H.S.
2015-01-15
Safety analysis and design optimization depend on the accurate prediction of various reactor attributes. Predictions can be enhanced by reducing the uncertainty associated with the attributes of interest. An inverse problem can be defined and solved to assess the sources of uncertainty, and experimental effort can be subsequently directed to further improve the uncertainty associated with these sources. In this work a subspace-based algorithm for inverse sensitivity/uncertainty quantification (IS/UQ) has been developed to enable analysts account for all sources of nuclear data uncertainties in support of target accuracy assessment-type analysis. An approximate analytical solution of the optimization problem is used to guide the search for the dominant uncertainty subspace. By limiting the search to a subspace, the degrees of freedom available for the optimization search are significantly reduced. A quarter PWR fuel assembly is modeled and the accuracy of the multiplication factor and the fission reaction rate are used as reactor attributes whose uncertainties are to be reduced. Numerical experiments are used to demonstrate the computational efficiency of the proposed algorithm. Our ongoing work is focusing on extending the proposed algorithm to account for various forms of feedback, e.g., thermal-hydraulics and depletion effects.
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.
Improved Stochastic Subspace System Identification for Structural Health Monitoring
Chang, Chia-Ming; Loh, Chin-Hsiung
2015-07-01
Structural health monitoring acquires structural information through numerous sensor measurements. Vibrational measurement data render the dynamic characteristics of structures to be extracted, in particular of the modal properties such as natural frequencies, damping, and mode shapes. The stochastic subspace system identification has been recognized as a power tool which can present a structure in the modal coordinates. To obtain qualitative identified data, this tool needs to spend computational expense on a large set of measurements. In study, a stochastic system identification framework is proposed to improve the efficiency and quality of the conventional stochastic subspace system identification. This framework includes 1) measured signal processing, 2) efficient space projection, 3) system order selection, and 4) modal property derivation. The measured signal processing employs the singular spectrum analysis algorithm to lower the noise components as well as to present a data set in a reduced dimension. The subspace is subsequently derived from the data set presented in a delayed coordinate. With the proposed order selection criteria, the number of structural modes is determined, resulting in the modal properties. This system identification framework is applied to a real-world bridge for exploring the feasibility in real-time applications. The results show that this improved system identification method significantly decreases computational time, while qualitative modal parameters are still attained.
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.
Scalable photoreactor for hydrogen production
Takanabe, Kazuhiro; Shinagawa, Tatsuya
2017-01-01
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.
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.
Subspace Dimensionality: A Tool for Automated QC in Seismic Array Processing
Rowe, C. A.; Stead, R. J.; Begnaud, M. L.
2013-12-01
Because of the great resolving power of seismic arrays, the application of automated processing to array data is critically important in treaty verification work. A significant problem in array analysis is the inclusion of bad sensor channels in the beamforming process. We are testing an approach to automated, on-the-fly quality control (QC) to aid in the identification of poorly performing sensor channels prior to beam-forming in routine event detection or location processing. The idea stems from methods used for large computer servers, when monitoring traffic at enormous numbers of nodes is impractical on a node-by node basis, so the dimensionality of the node traffic is instead monitoried for anomalies that could represent malware, cyber-attacks or other problems. The technique relies upon the use of subspace dimensionality or principal components of the overall system traffic. The subspace technique is not new to seismology, but its most common application has been limited to comparing waveforms to an a priori collection of templates for detecting highly similar events in a swarm or seismic cluster. In the established template application, a detector functions in a manner analogous to waveform cross-correlation, applying a statistical test to assess the similarity of the incoming data stream to known templates for events of interest. In our approach, we seek not to detect matching signals, but instead, we examine the signal subspace dimensionality in much the same way that the method addresses node traffic anomalies in large computer systems. Signal anomalies recorded on seismic arrays affect the dimensional structure of the array-wide time-series. We have shown previously that this observation is useful in identifying real seismic events, either by looking at the raw signal or derivatives thereof (entropy, kurtosis), but here we explore the effects of malfunctioning channels on the dimension of the data and its derivatives, and how to leverage this effect for
Ancestors protocol for scalable key management
Directory of Open Access Journals (Sweden)
Dieter Gollmann
2010-06-01
Full Text Available Group key management is an important functional building block for secure multicast architecture. Thereby, it has been extensively studied in the literature. The main proposed protocol is Adaptive Clustering for Scalable Group Key Management (ASGK. According to ASGK protocol, the multicast group is divided into clusters, where each cluster consists of areas of members. Each cluster uses its own Traffic Encryption Key (TEK. These clusters are updated periodically depending on the dynamism of the members during the secure session. The modified protocol has been proposed based on ASGK with some modifications to balance the number of affected members and the encryption/decryption overhead with any number of the areas when a member joins or leaves the group. This modified protocol is called Ancestors protocol. According to Ancestors protocol, every area receives the dynamism of the members from its parents. The main objective of the modified protocol is to reduce the number of affected members during the leaving and joining members, then 1 affects n overhead would be reduced. A comparative study has been done between ASGK protocol and the modified protocol. According to the comparative results, it found that the modified protocol is always outperforming the ASGK protocol.
2017-09-27
100 times larger for the minimal Krylov subspace. 0 5 10 15 20 25 Krylov subspace dimension 10-2 10-1 100 101 102 103 104 jjĜ ¡ 1 jj F SVD...approximation Kn (G;u(0) ) 0 5 10 15 20 25 Krylov subspace dimension 10-2 10-1 100 101 102 103 104 jjx jj fo r m in x jjĜ x ¡ bjj SVD approximation Kn (G;u(0
Gamow state vectors as functionals over subspaces of the nuclear space
International Nuclear Information System (INIS)
Bohm, A.
1979-12-01
Exponentially decaying Gamow state vectors are obtained from S-matrix poles in the lower half of the second sheet, and are defined as functionals over a subspace of the nuclear space, PHI. Exponentially growing Gamow state vectors are obtained from S-matrix poles in the upper half of the second sheet, and are defined as functionals over another subspace of PHI. On functionals over these two subspaces the dynamical group of time development splits into two semigroups
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.
Scalable Nonlinear Compact Schemes
Energy Technology Data Exchange (ETDEWEB)
Ghosh, Debojyoti [Argonne National Lab. (ANL), Argonne, IL (United States); Constantinescu, Emil M. [Univ. of Chicago, IL (United States); Brown, Jed [Univ. of Colorado, Boulder, CO (United States)
2014-04-01
In this work, we focus on compact schemes resulting in tridiagonal systems of equations, specifically the fifth-order CRWENO scheme. We propose a scalable implementation of the nonlinear compact schemes by implementing a parallel tridiagonal solver based on the partitioning/substructuring approach. We use an iterative solver for the reduced system of equations; however, we solve this system to machine zero accuracy to ensure that no parallelization errors are introduced. It is possible to achieve machine-zero convergence with few iterations because of the diagonal dominance of the system. The number of iterations is specified a priori instead of a norm-based exit criterion, and collective communications are avoided. The overall algorithm thus involves only point-to-point communication between neighboring processors. Our implementation of the tridiagonal solver differs from and avoids the drawbacks of past efforts in the following ways: it introduces no parallelization-related approximations (multiprocessor solutions are exactly identical to uniprocessor ones), it involves minimal communication, the mathematical complexity is similar to that of the Thomas algorithm on a single processor, and it does not require any communication and computation scheduling.
International Nuclear Information System (INIS)
Barnes, J.; Dekel, A.; Efstathiou, G.; Frenk, C.S.; Yale Univ., New Haven, CT; California Univ., Santa Barbara; Cambridge Univ., England; Sussex Univ., Brighton, England)
1985-01-01
The cluster correlation function xi sub c(r) is compared with the particle correlation function, xi(r) in cosmological N-body simulations with a wide range of initial conditions. The experiments include scale-free initial conditions, pancake models with a coherence length in the initial density field, and hybrid models. Three N-body techniques and two cluster-finding algorithms are used. In scale-free models with white noise initial conditions, xi sub c and xi are essentially identical. In scale-free models with more power on large scales, it is found that the amplitude of xi sub c increases with cluster richness; in this case the clusters give a biased estimate of the particle correlations. In the pancake and hybrid models (with n = 0 or 1), xi sub c is steeper than xi, but the cluster correlation length exceeds that of the points by less than a factor of 2, independent of cluster richness. Thus the high amplitude of xi sub c found in studies of rich clusters of galaxies is inconsistent with white noise and pancake models and may indicate a primordial fluctuation spectrum with substantial power on large scales. 30 references
A subspace approach to high-resolution spectroscopic imaging.
Lam, Fan; Liang, Zhi-Pei
2014-04-01
To accelerate spectroscopic imaging using sparse sampling of (k,t)-space and subspace (or low-rank) modeling to enable high-resolution metabolic imaging with good signal-to-noise ratio. The proposed method, called SPectroscopic Imaging by exploiting spatiospectral CorrElation, exploits a unique property known as partial separability of spectroscopic signals. This property indicates that high-dimensional spectroscopic signals reside in a very low-dimensional subspace and enables special data acquisition and image reconstruction strategies to be used to obtain high-resolution spatiospectral distributions with good signal-to-noise ratio. More specifically, a hybrid chemical shift imaging/echo-planar spectroscopic imaging pulse sequence is proposed for sparse sampling of (k,t)-space, and a low-rank model-based algorithm is proposed for subspace estimation and image reconstruction from sparse data with the capability to incorporate prior information and field inhomogeneity correction. The performance of the proposed method has been evaluated using both computer simulations and phantom studies, which produced very encouraging results. For two-dimensional spectroscopic imaging experiments on a metabolite phantom, a factor of 10 acceleration was achieved with a minimal loss in signal-to-noise ratio compared to the long chemical shift imaging experiments and with a significant gain in signal-to-noise ratio compared to the accelerated echo-planar spectroscopic imaging experiments. The proposed method, SPectroscopic Imaging by exploiting spatiospectral CorrElation, is able to significantly accelerate spectroscopic imaging experiments, making high-resolution metabolic imaging possible. Copyright © 2014 Wiley Periodicals, Inc.
Subspace-based analysis of the ERT inverse problem
Ben Hadj Miled, Mohamed Khames; Miller, Eric L.
2004-05-01
In a previous work, we proposed a source-type formulation to the electrical resistance tomography (ERT) problem. Specifically, we showed that inhomogeneities in the medium can be viewed as secondary sources embedded in the homogeneous background medium and located at positions associated with variation in electrical conductivity. Assuming a piecewise constant conductivity distribution, the support of equivalent sources is equal to the boundary of the inhomogeneity. The estimation of the anomaly shape takes the form of an inverse source-type problem. In this paper, we explore the use of subspace methods to localize the secondary equivalent sources associated with discontinuities in the conductivity distribution. Our first alternative is the multiple signal classification (MUSIC) algorithm which is commonly used in the localization of multiple sources. The idea is to project a finite collection of plausible pole (or dipole) sources onto an estimated signal subspace and select those with largest correlations. In ERT, secondary sources are excited simultaneously but in different ways, i.e. with distinct amplitude patterns, depending on the locations and amplitudes of primary sources. If the number of receivers is "large enough", different source configurations can lead to a set of observation vectors that span the data subspace. However, since sources that are spatially close to each other have highly correlated signatures, seperation of such signals becomes very difficult in the presence of noise. To overcome this problem we consider iterative MUSIC algorithms like R-MUSIC and RAP-MUSIC. These recursive algorithms pose a computational burden as they require multiple large combinatorial searches. Results obtained with these algorithms using simulated data of different conductivity patterns are presented.
Robust and scalable optical one-way quantum computation
International Nuclear Information System (INIS)
Wang Hefeng; Yang Chuiping; Nori, Franco
2010-01-01
We propose an efficient approach for deterministically generating scalable cluster states with photons. This approach involves unitary transformations performed on atoms coupled to optical cavities. Its operation cost scales linearly with the number of qubits in the cluster state, and photon qubits are encoded such that single-qubit operations can be easily implemented by using linear optics. Robust optical one-way quantum computation can be performed since cluster states can be stored in atoms and then transferred to photons that can be easily operated and measured. Therefore, this proposal could help in performing robust large-scale optical one-way quantum computation.
A Krylov Subspace Method for Unstructured Mesh SN Transport Computation
International Nuclear Information System (INIS)
Yoo, Han Jong; Cho, Nam Zin; Kim, Jong Woon; Hong, Ser Gi; Lee, Young Ouk
2010-01-01
Hong, et al., have developed a computer code MUST (Multi-group Unstructured geometry S N Transport) for the neutral particle transport calculations in three-dimensional unstructured geometry. In this code, the discrete ordinates transport equation is solved by using the discontinuous finite element method (DFEM) or the subcell balance methods with linear discontinuous expansion. In this paper, the conventional source iteration in the MUST code is replaced by the Krylov subspace method to reduce computing time and the numerical test results are given
Perturbation for Frames for a Subspace of a Hilbert Space
DEFF Research Database (Denmark)
Christensen, Ole; deFlicht, C.; Lennard, C.
1997-01-01
We extend a classical result stating that a sufficiently small perturbation$\\{ g_i \\}$ of a Riesz sequence $\\{ f_i \\}$ in a Hilbert space $H$ is again a Riesz sequence. It turns out that the analog result for a frame does not holdunless the frame is complete. However, we are able to prove a very...... similarresult for frames in the case where the gap between the subspaces$\\overline{span} \\{f_i \\}$ and $\\overline{span} \\{ g_i \\}$ is small enough. We give a geometric interpretation of the result....
Scalable Automated Model Search
2014-05-20
profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on...minimization. The computer journal, 7(4):308–313, 1965. [31] K. Ousterhout, A. Panda , J. Rosen, S. Venkataraman, R. Xin, S. Ratnasamy, S. Shenker...and I. Stoica. The case for tiny tasks in compute clusters. [32] B. Panda , J. S. Herbach, S. Basu, and R. J. Bayardo. Planet: massively parallel
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...
Morton, E.; Bilek, S. L.; Rowe, C. A.
2017-12-01
Understanding the spatial extent and behavior of the interplate contact in the Cascadia Subduction Zone (CSZ) may prove pivotal to preparation for future great earthquakes, such as the M9 event of 1700. Current and historic seismic catalogs are limited in their integrity by their short duration, given the recurrence rate of great earthquakes, and by their rather high magnitude of completeness for the interplate seismic zone, due to its offshore distance from these land-based networks. This issue is addressed via the 2011-2015 Cascadia Initiative (CI) amphibious seismic array deployment, which combined coastal land seismometers with more than 60 ocean-bottom seismometers (OBS) situated directly above the presumed plate interface. We search the CI dataset for small, previously undetected interplate earthquakes to identify seismic patches on the megathrust. Using the automated subspace detection method, we search for previously undetected events. Our subspace comprises eigenvectors derived from CI OBS and on-land waveforms extracted for existing catalog events that appear to have occurred on the plate interface. Previous work focused on analysis of two repeating event clusters off the coast of Oregon spanning all 4 years of deployment. Here we expand earlier results to include detection and location analysis to the entire CSZ margin during the first year of CI deployment, with more than 200 new events detected for the central portion of the margin. Template events used for subspace scanning primarily occurred beneath the land surface along the coast, at the downdip edge of modeled high slip patches for the 1700 event, with most concentrated at the northwestern edge of the Olympic Peninsula.
View subspaces for indexing and retrieval of 3D models
Dutagaci, Helin; Godil, Afzal; Sankur, Bülent; Yemez, Yücel
2010-02-01
View-based indexing schemes for 3D object retrieval are gaining popularity since they provide good retrieval results. These schemes are coherent with the theory that humans recognize objects based on their 2D appearances. The viewbased techniques also allow users to search with various queries such as binary images, range images and even 2D sketches. The previous view-based techniques use classical 2D shape descriptors such as Fourier invariants, Zernike moments, Scale Invariant Feature Transform-based local features and 2D Digital Fourier Transform coefficients. These methods describe each object independent of others. In this work, we explore data driven subspace models, such as Principal Component Analysis, Independent Component Analysis and Nonnegative Matrix Factorization to describe the shape information of the views. We treat the depth images obtained from various points of the view sphere as 2D intensity images and train a subspace to extract the inherent structure of the views within a database. We also show the benefit of categorizing shapes according to their eigenvalue spread. Both the shape categorization and data-driven feature set conjectures are tested on the PSB database and compared with the competitor view-based 3D shape retrieval algorithms.
Subspace methods for identification of human ankle joint stiffness.
Zhao, Y; Westwick, D T; Kearney, R E
2011-11-01
Joint stiffness, the dynamic relationship between the angular position of a joint and the torque acting about it, describes the dynamic, mechanical behavior of a joint during posture and movement. Joint stiffness arises from both intrinsic and reflex mechanisms, but the torques due to these mechanisms cannot be measured separately experimentally, since they appear and change together. Therefore, the direct estimation of the intrinsic and reflex stiffnesses is difficult. In this paper, we present a new, two-step procedure to estimate the intrinsic and reflex components of ankle stiffness. In the first step, a discrete-time, subspace-based method is used to estimate a state-space model for overall stiffness from the measured overall torque and then predict the intrinsic and reflex torques. In the second step, continuous-time models for the intrinsic and reflex stiffnesses are estimated from the predicted intrinsic and reflex torques. Simulations and experimental results demonstrate that the algorithm estimates the intrinsic and reflex stiffnesses accurately. The new subspace-based algorithm has three advantages over previous algorithms: 1) It does not require iteration, and therefore, will always converge to an optimal solution; 2) it provides better estimates for data with high noise or short sample lengths; and 3) it provides much more accurate results for data acquired under the closed-loop conditions, that prevail when subjects interact with compliant loads.
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.
Structured Kernel Subspace Learning for Autonomous Robot Navigation.
Kim, Eunwoo; Choi, Sungjoon; Oh, Songhwai
2018-02-14
This paper considers two important problems for autonomous robot navigation in a dynamic environment, where the goal is to predict pedestrian motion and control a robot with the prediction for safe navigation. While there are several methods for predicting the motion of a pedestrian and controlling a robot to avoid incoming pedestrians, it is still difficult to safely navigate in a dynamic environment due to challenges, such as the varying quality and complexity of training data with unwanted noises. This paper addresses these challenges simultaneously by proposing a robust kernel subspace learning algorithm based on the recent advances in nuclear-norm and l 1 -norm minimization. We model the motion of a pedestrian and the robot controller using Gaussian processes. The proposed method efficiently approximates a kernel matrix used in Gaussian process regression by learning low-rank structured matrix (with symmetric positive semi-definiteness) to find an orthogonal basis, which eliminates the effects of erroneous and inconsistent data. Based on structured kernel subspace learning, we propose a robust motion model and motion controller for safe navigation in dynamic environments. We evaluate the proposed robust kernel learning in various tasks, including regression, motion prediction, and motion control problems, and demonstrate that the proposed learning-based systems are robust against outliers and outperform existing regression and navigation methods.
Bochev, Mikhail A.; Oseledets, I.V.; Tyrtyshnikov, E.E.
2013-01-01
The aim of this paper is two-fold. First, we propose an efficient implementation of the continuous time waveform relaxation method based on block Krylov subspaces. Second, we compare this new implementation against Krylov subspace methods combined with the shift and invert technique.
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…
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.
Laplacian embedded regression for scalable manifold regularization.
Chen, Lin; Tsang, Ivor W; Xu, Dong
2012-06-01
Semi-supervised learning (SSL), as a powerful tool to learn from a limited number of labeled data and a large number of unlabeled data, has been attracting increasing attention in the machine learning community. In particular, the manifold regularization framework has laid solid theoretical foundations for a large family of SSL algorithms, such as Laplacian support vector machine (LapSVM) and Laplacian regularized least squares (LapRLS). However, most of these algorithms are limited to small scale problems due to the high computational cost of the matrix inversion operation involved in the optimization problem. In this paper, we propose a novel framework called Laplacian embedded regression by introducing an intermediate decision variable into the manifold regularization framework. By using ∈-insensitive loss, we obtain the Laplacian embedded support vector regression (LapESVR) algorithm, which inherits the sparse solution from SVR. Also, we derive Laplacian embedded RLS (LapERLS) corresponding to RLS under the proposed framework. Both LapESVR and LapERLS possess a simpler form of a transformed kernel, which is the summation of the original kernel and a graph kernel that captures the manifold structure. The benefits of the transformed kernel are two-fold: (1) we can deal with the original kernel matrix and the graph Laplacian matrix in the graph kernel separately and (2) if the graph Laplacian matrix is sparse, we only need to perform the inverse operation for a sparse matrix, which is much more efficient when compared with that for a dense one. Inspired by kernel principal component analysis, we further propose to project the introduced decision variable into a subspace spanned by a few eigenvectors of the graph Laplacian matrix in order to better reflect the data manifold, as well as accelerate the calculation of the graph kernel, allowing our methods to efficiently and effectively cope with large scale SSL problems. Extensive experiments on both toy and real
Parallel scalability of Hartree-Fock calculations
Chow, Edmond; Liu, Xing; Smelyanskiy, Mikhail; Hammond, Jeff R.
2015-03-01
Quantum chemistry is increasingly performed using large cluster computers consisting of multiple interconnected nodes. For a fixed molecular problem, the efficiency of a calculation usually decreases as more nodes are used, due to the cost of communication between the nodes. This paper empirically investigates the parallel scalability of Hartree-Fock calculations. The construction of the Fock matrix and the density matrix calculation are analyzed separately. For the former, we use a parallelization of Fock matrix construction based on a static partitioning of work followed by a work stealing phase. For the latter, we use density matrix purification from the linear scaling methods literature, but without using sparsity. When using large numbers of nodes for moderately sized problems, density matrix computations are network-bandwidth bound, making purification methods potentially faster than eigendecomposition methods.
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.
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...
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.
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.
Clustering of Sun Exposure Measurements
DEFF Research Database (Denmark)
Have, Anna Szynkowiak; Larsen, Jan; Hansen, Lars Kai
2002-01-01
In a medically motivated Sun-exposure study, questionnaires concerning Sun-habits were collected from a number of subjects together with UV radiation measurements. This paper focuses on identifying clusters in the heterogeneous set of data for the purpose of understanding possible relations between...... Sun-habits exposure and eventually assessing the risk of skin cancer. A general probabilistic framework originally developed for text and Web mining is demonstrated to be useful for clustering of behavioral data. The framework combines principal component subspace projection with probabilistic...
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.
The influence of different PAST-based subspace trackers on DaPT parameter estimation
Lechtenberg, M.; Götze, J.
2012-09-01
In the context of parameter estimation, subspace-based methods like ESPRIT have become common. They require a subspace separation e.g. based on eigenvalue/-vector decomposition. In time-varying environments, this can be done by subspace trackers. One class of these is based on the PAST algorithm. Our non-linear parameter estimation algorithm DaPT builds on-top of the ESPRIT algorithm. Evaluation of the different variants of the PAST algorithm shows which variant of the PAST algorithm is worthwhile in the context of frequency estimation.
International Nuclear Information System (INIS)
Ikuno, Soichiro; Chen, Gong; Yamamoto, Susumu; Itoh, Taku; Abe, Kuniyoshi; Nakamura, Hiroaki
2016-01-01
Krylov subspace method and the variable preconditioned Krylov subspace method with communication avoiding technique for a linear system obtained from electromagnetic analysis are numerically investigated. In the k−skip Krylov method, the inner product calculations are expanded by Krylov basis, and the inner product calculations are transformed to the scholar operations. k−skip CG method is applied for the inner-loop solver of Variable Preconditioned Krylov subspace methods, and the converged solution of electromagnetic problem is obtained using the method. (author)
Geometric subspace updates with applications to online adaptive nonlinear model reduction
DEFF Research Database (Denmark)
Zimmermann, Ralf; Peherstorfer, Benjamin; Willcox, Karen
2018-01-01
In many scientific applications, including model reduction and image processing, subspaces are used as ansatz spaces for the low-dimensional approximation and reconstruction of the state vectors of interest. We introduce a procedure for adapting an existing subspace based on information from...... Estimation (GROUSE). We establish for GROUSE a closed-form expression for the residual function along the geodesic descent direction. Specific applications of subspace adaptation are discussed in the context of image processing and model reduction of nonlinear partial differential equation systems....
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...
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.
Energy Technology Data Exchange (ETDEWEB)
Starke, G. [Universitaet Karlsruhe (Germany)
1994-12-31
For nonselfadjoint elliptic boundary value problems which are preconditioned by a substructuring method, i.e., nonoverlapping domain decomposition, the author introduces and studies the concept of subspace orthogonalization. In subspace orthogonalization variants of Krylov methods the computation of inner products and vector updates, and the storage of basis elements is restricted to a (presumably small) subspace, in this case the edge and vertex unknowns with respect to the partitioning into subdomains. The author investigates subspace orthogonalization for two specific iterative algorithms, GMRES and the full orthogonalization method (FOM). This is intended to eliminate certain drawbacks of the Arnoldi-based Krylov subspace methods mentioned above. Above all, the length of the Arnoldi recurrences grows linearly with the iteration index which is therefore restricted to the number of basis elements that can be held in memory. Restarts become necessary and this often results in much slower convergence. The subspace orthogonalization methods, in contrast, require the storage of only the edge and vertex unknowns of each basis element which means that one can iterate much longer before restarts become necessary. Moreover, the computation of inner products is also restricted to the edge and vertex points which avoids the disturbance of the computational flow associated with the solution of subdomain problems. The author views subspace orthogonalization as an alternative to restarting or truncating Krylov subspace methods for nonsymmetric linear systems of equations. Instead of shortening the recurrences, one restricts them to a subset of the unknowns which has to be carefully chosen in order to be able to extend this partial solution to the entire space. The author discusses the convergence properties of these iteration schemes and its advantages compared to restarted or truncated versions of Krylov methods applied to the full preconditioned system.
Overview of hybrid subspace methods for uncertainty quantification, sensitivity analysis
International Nuclear Information System (INIS)
Abdel-Khalik, Hany S.; Bang, Youngsuk; Wang, Congjian
2013-01-01
Highlights: ► We overview the state-of-the-art in uncertainty quantification and sensitivity analysis. ► We overview new developments in above areas using hybrid methods. ► We give a tutorial introduction to above areas and the new developments. ► Hybrid methods address the explosion in dimensionality in nonlinear models. ► Representative numerical experiments are given. -- Abstract: The role of modeling and simulation has been heavily promoted in recent years to improve understanding of complex engineering systems. To realize the benefits of modeling and simulation, concerted efforts in the areas of uncertainty quantification and sensitivity analysis are required. The manuscript intends to serve as a pedagogical presentation of the material to young researchers and practitioners with little background on the subjects. We believe this is important as the role of these subjects is expected to be integral to the design, safety, and operation of existing as well as next generation reactors. In addition to covering the basics, an overview of the current state-of-the-art will be given with particular emphasis on the challenges pertaining to nuclear reactor modeling. The second objective will focus on presenting our own development of hybrid subspace methods intended to address the explosion in the computational overhead required when handling real-world complex engineering systems.
Subspace identification of Hammer stein models using support vector machines
International Nuclear Information System (INIS)
Al-Dhaifallah, Mujahed
2011-01-01
System identification is the art of finding mathematical tools and algorithms that build an appropriate mathematical model of a system from measured input and output data. Hammerstein model, consisting of a memoryless nonlinearity followed by a dynamic linear element, is often a good trade-off as it can represent some dynamic nonlinear systems very accurately, but is nonetheless quite simple. Moreover, the extensive knowledge about LTI system representations can be applied to the dynamic linear block. On the other hand, finding an effective representation for the nonlinearity is an active area of research. Recently, support vector machines (SVMs) and least squares support vector machines (LS-SVMs) have demonstrated powerful abilities in approximating linear and nonlinear functions. In contrast with other approximation methods, SVMs do not require a-priori structural information. Furthermore, there are well established methods with guaranteed convergence (ordinary least squares, quadratic programming) for fitting LS-SVMs and SVMs. The general objective of this research is to develop new subspace algorithms for Hammerstein systems based on SVM regression.
Parallelised Krylov subspace method for reactor kinetics by IQS approach
International Nuclear Information System (INIS)
Gupta, Anurag; Modak, R.S.; Gupta, H.P.; Kumar, Vinod; Bhatt, K.
2005-01-01
Nuclear reactor kinetics involves numerical solution of space-time-dependent multi-group neutron diffusion equation. Two distinct approaches exist for this purpose: the direct (implicit time differencing) approach and the improved quasi-static (IQS) approach. Both the approaches need solution of static space-energy-dependent diffusion equations at successive time-steps; the step being relatively smaller for the direct approach. These solutions are usually obtained by Gauss-Seidel type iterative methods. For a faster solution, the Krylov sub-space methods have been tried and also parallelised by many investigators. However, these studies seem to have been done only for the direct approach. In the present paper, parallelised Krylov methods are applied to the IQS approach in addition to the direct approach. It is shown that the speed-up obtained for IQS is higher than that for the direct approach. The reasons for this are also discussed. Thus, the use of IQS approach along with parallelised Krylov solvers seems to be a promising scheme
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.
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.
Optimal Design of Large Dimensional Adaptive Subspace Detectors
Ben Atitallah, Ismail; Kammoun, Abla; Alouini, Mohamed-Slim; Alnaffouri, Tareq Y.
2016-01-01
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.
Reverse time migration by Krylov subspace reduced order modeling
Basir, Hadi Mahdavi; Javaherian, Abdolrahim; Shomali, Zaher Hossein; Firouz-Abadi, Roohollah Dehghani; Gholamy, Shaban Ali
2018-04-01
Imaging is a key step in seismic data processing. To date, a myriad of advanced pre-stack depth migration approaches have been developed; however, reverse time migration (RTM) is still considered as the high-end imaging algorithm. The main limitations associated with the performance cost of reverse time migration are the intensive computation of the forward and backward simulations, time consumption, and memory allocation related to imaging condition. Based on the reduced order modeling, we proposed an algorithm, which can be adapted to all the aforementioned factors. Our proposed method benefit from Krylov subspaces method to compute certain mode shapes of the velocity model computed by as an orthogonal base of reduced order modeling. Reverse time migration by reduced order modeling is helpful concerning the highly parallel computation and strongly reduces the memory requirement of reverse time migration. The synthetic model results showed that suggested method can decrease the computational costs of reverse time migration by several orders of magnitudes, compared with reverse time migration by finite element method.
Supervised orthogonal discriminant subspace projects learning for face recognition.
Chen, Yu; Xu, Xiao-Hong
2014-02-01
In this paper, a new linear dimension reduction method called supervised orthogonal discriminant subspace projection (SODSP) is proposed, which addresses high-dimensionality of data and the small sample size problem. More specifically, given a set of data points in the ambient space, a novel weight matrix that describes the relationship between the data points is first built. And in order to model the manifold structure, the class information is incorporated into the weight matrix. Based on the novel weight matrix, the local scatter matrix as well as non-local scatter matrix is defined such that the neighborhood structure can be preserved. In order to enhance the recognition ability, we impose an orthogonal constraint into a graph-based maximum margin analysis, seeking to find a projection that maximizes the difference, rather than the ratio between the non-local scatter and the local scatter. In this way, SODSP naturally avoids the singularity problem. Further, we develop an efficient and stable algorithm for implementing SODSP, especially, on high-dimensional data set. Moreover, the theoretical analysis shows that LPP is a special instance of SODSP by imposing some constraints. Experiments on the ORL, Yale, Extended Yale face database B and FERET face database are performed to test and evaluate the proposed algorithm. The results demonstrate the effectiveness of SODSP. Copyright © 2013 Elsevier Ltd. All rights reserved.
A Variational Approach to Video Registration with Subspace Constraints.
Garg, Ravi; Roussos, Anastasios; Agapito, Lourdes
2013-01-01
This paper addresses the problem of non-rigid video registration, or the computation of optical flow from a reference frame to each of the subsequent images in a sequence, when the camera views deformable objects. We exploit the high correlation between 2D trajectories of different points on the same non-rigid surface by assuming that the displacement of any point throughout the sequence can be expressed in a compact way as a linear combination of a low-rank motion basis. This subspace constraint effectively acts as a trajectory regularization term leading to temporally consistent optical flow. We formulate it as a robust soft constraint within a variational framework by penalizing flow fields that lie outside the low-rank manifold. The resulting energy functional can be decoupled into the optimization of the brightness constancy and spatial regularization terms, leading to an efficient optimization scheme. Additionally, we propose a novel optimization scheme for the case of vector valued images, based on the dualization of the data term. This allows us to extend our approach to deal with colour images which results in significant improvements on the registration results. Finally, we provide a new benchmark dataset, based on motion capture data of a flag waving in the wind, with dense ground truth optical flow for evaluation of multi-frame optical flow algorithms for non-rigid surfaces. Our experiments show that our proposed approach outperforms state of the art optical flow and dense non-rigid registration algorithms.
Conjunctive patches subspace learning with side information for collaborative image retrieval.
Zhang, Lining; Wang, Lipo; Lin, Weisi
2012-08-01
Content-Based Image Retrieval (CBIR) has attracted substantial attention during the past few years for its potential practical applications to image management. A variety of Relevance Feedback (RF) schemes have been designed to bridge the semantic gap between the low-level visual features and the high-level semantic concepts for an image retrieval task. Various Collaborative Image Retrieval (CIR) schemes aim to utilize the user historical feedback log data with similar and dissimilar pairwise constraints to improve the performance of a CBIR system. However, existing subspace learning approaches with explicit label information cannot be applied for a CIR task, although the subspace learning techniques play a key role in various computer vision tasks, e.g., face recognition and image classification. In this paper, we propose a novel subspace learning framework, i.e., Conjunctive Patches Subspace Learning (CPSL) with side information, for learning an effective semantic subspace by exploiting the user historical feedback log data for a CIR task. The CPSL can effectively integrate the discriminative information of labeled log images, the geometrical information of labeled log images and the weakly similar information of unlabeled images together to learn a reliable subspace. We formally formulate this problem into a constrained optimization problem and then present a new subspace learning technique to exploit the user historical feedback log data. Extensive experiments on both synthetic data sets and a real-world image database demonstrate the effectiveness of the proposed scheme in improving the performance of a CBIR system by exploiting the user historical feedback log data.
Domain decomposed preconditioners with Krylov subspace methods as subdomain solvers
Energy Technology Data Exchange (ETDEWEB)
Pernice, M. [Univ. of Utah, Salt Lake City, UT (United States)
1994-12-31
Domain decomposed preconditioners for nonsymmetric partial differential equations typically require the solution of problems on the subdomains. Most implementations employ exact solvers to obtain these solutions. Consequently work and storage requirements for the subdomain problems grow rapidly with the size of the subdomain problems. Subdomain solves constitute the single largest computational cost of a domain decomposed preconditioner, and improving the efficiency of this phase of the computation will have a significant impact on the performance of the overall method. The small local memory available on the nodes of most message-passing multicomputers motivates consideration of the use of an iterative method for solving subdomain problems. For large-scale systems of equations that are derived from three-dimensional problems, memory considerations alone may dictate the need for using iterative methods for the subdomain problems. In addition to reduced storage requirements, use of an iterative solver on the subdomains allows flexibility in specifying the accuracy of the subdomain solutions. Substantial savings in solution time is possible if the quality of the domain decomposed preconditioner is not degraded too much by relaxing the accuracy of the subdomain solutions. While some work in this direction has been conducted for symmetric problems, similar studies for nonsymmetric problems appear not to have been pursued. This work represents a first step in this direction, and explores the effectiveness of performing subdomain solves using several transpose-free Krylov subspace methods, GMRES, transpose-free QMR, CGS, and a smoothed version of CGS. Depending on the difficulty of the subdomain problem and the convergence tolerance used, a reduction in solution time is possible in addition to the reduced memory requirements. The domain decomposed preconditioner is a Schur complement method in which the interface operators are approximated using interface probing.
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
Developing Scalable Information Security Systems
Directory of Open Access Journals (Sweden)
Valery Konstantinovich Ablekov
2013-06-01
Full Text Available Existing physical security systems has wide range of lacks, including: high cost, a large number of vulnerabilities, problems of modification and support system. This paper covers an actual problem of developing systems without this list of drawbacks. The paper presents the architecture of the information security system, which operates through the network protocol TCP/IP, including the ability to connect different types of devices and integration with existing security systems. The main advantage is a significant increase in system reliability, scalability, both vertically and horizontally, with minimal cost of both financial and time resources.
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.
CODA: A scalable, distributed data acquisition system
International Nuclear Information System (INIS)
Watson, W.A. III; Chen, J.; Heyes, G.; Jastrzembski, E.; Quarrie, D.
1994-01-01
A new data acquisition system has been designed for physics experiments scheduled to run at CEBAF starting in the summer of 1994. This system runs on Unix workstations connected via ethernet, FDDI, or other network hardware to multiple intelligent front end crates -- VME, CAMAC or FASTBUS. CAMAC crates may either contain intelligent processors, or may be interfaced to VME. The system is modular and scalable, from a single front end crate and one workstation linked by ethernet, to as may as 32 clusters of front end crates ultimately connected via a high speed network to a set of analysis workstations. The system includes an extensible, device independent slow controls package with drivers for CAMAC, VME, and high voltage crates, as well as a link to CEBAF accelerator controls. All distributed processes are managed by standard remote procedure calls propagating change-of-state requests, or reading and writing program variables. Custom components may be easily integrated. The system is portable to any front end processor running the VxWorks real-time kernel, and to most workstations supplying a few standard facilities such as rsh and X-windows, and Motif and socket libraries. Sample implementations exist for 2 Unix workstation families connected via ethernet or FDDI to VME (with interfaces to FASTBUS or CAMAC), and via ethernet to FASTBUS or CAMAC
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.
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.
Extending the subspace hybrid method for eigenvalue problems in reactor physics calculation
International Nuclear Information System (INIS)
Zhang, Q.; Abdel-Khalik, H. S.
2013-01-01
This paper presents an innovative hybrid Monte-Carlo-Deterministic method denoted by the SUBSPACE method designed for improving the efficiency of hybrid methods for reactor analysis applications. The SUBSPACE method achieves its high computational efficiency by taking advantage of the existing correlations between desired responses. Recently, significant gains in computational efficiency have been demonstrated using this method for source driven problems. Within this work the mathematical theory behind the SUBSPACE method is introduced and extended to address core wide level k-eigenvalue problems. The method's efficiency is demonstrated based on a three-dimensional quarter-core problem, where responses are sought on the pin cell level. The SUBSPACE method is compared to the FW-CADIS method and is found to be more efficient for the utilized test problem because of the reason that the FW-CADIS method solves a forward eigenvalue problem and an adjoint fixed-source problem while the SUBSPACE method only solves an adjoint fixed-source problem. Based on the favorable results obtained here, we are confident that the applicability of Monte Carlo for large scale reactor analysis could be realized in the near future. (authors)
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.
pcircle - A Suite of Scalable Parallel File System Tools
Energy Technology Data Exchange (ETDEWEB)
2015-10-01
Most of the software related to file system are written for conventional local file system, they are serialized and can't take advantage of the benefit of a large scale parallel file system. "pcircle" software builds on top of ubiquitous MPI in cluster computing environment and "work-stealing" pattern to provide a scalable, high-performance suite of file system tools. In particular - it implemented parallel data copy and parallel data checksumming, with advanced features such as async progress report, checkpoint and restart, as well as integrity checking.
Event metadata records as a testbed for scalable data mining
International Nuclear Information System (INIS)
Gemmeren, P van; Malon, D
2010-01-01
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.
Persymmetric Adaptive Detectors of Subspace Signals in Homogeneous and Partially Homogeneous Clutter
Directory of Open Access Journals (Sweden)
Ding Hao
2015-08-01
Full Text Available In the field of adaptive radar detection, an effective strategy to improve the detection performance is to exploit the structural information of the covariance matrix, especially in the case of insufficient reference cells. Thus, in this study, the problem of detecting multidimensional subspace signals is discussed by considering the persymmetric structure of the clutter covariance matrix, which implies that the covariance matrix is persymmetric about its cross diagonal. Persymmetric adaptive detectors are derived on the basis of the one-step principle as well as the two-step Generalized Likelihood Ratio Test (GLRT in homogeneous and partially homogeneous clutter. The proposed detectors consider the structural information of the covariance matrix at the design stage. Simulation results suggest performance improvement compared with existing detectors when reference cells are insufficient. Moreover, the detection performance is assessed with respect to the effects of the covariance matrix, signal subspace dimension, and mismatched performance of signal subspace as well as signal fluctuations.
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.
Directory of Open Access Journals (Sweden)
Chen Shi
2014-01-01
Full Text Available Subsynchronous oscillation (SSO usually caused by series compensation, power system stabilizer (PSS, high voltage direct current transmission (HVDC and other power electronic equipment, which will affect the safe operation of generator shafting even the system. It is very important to identify the modal parameters of SSO to take effective control strategies as well. Since the identification accuracy of traditional methods are not high enough, the stochastic subspace identification (SSI method is proposed to improve the identification accuracy of subsynchronous oscillation modal. The stochastic subspace identification method was compared with the other two methods on subsynchronous oscillation IEEE benchmark model and Xiang-Shang HVDC system model, the simulation results show that the stochastic subspace identification method has the advantages of high identification precision, high operation efficiency and strong ability of anti-noise.
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.
Boundary regularity of Nevanlinna domains and univalent functions in model subspaces
International Nuclear Information System (INIS)
Baranov, Anton D; Fedorovskiy, Konstantin Yu
2011-01-01
In the paper we study boundary regularity of Nevanlinna domains, which have appeared in problems of uniform approximation by polyanalytic polynomials. A new method for constructing Nevanlinna domains with essentially irregular nonanalytic boundaries is suggested; this method is based on finding appropriate univalent functions in model subspaces, that is, in subspaces of the form K Θ =H 2 ominus ΘH 2 , where Θ is an inner function. To describe the irregularity of the boundaries of the domains obtained, recent results by Dolzhenko about boundary regularity of conformal mappings are used. Bibliography: 18 titles.
Embeddings of model subspaces of the Hardy space: compactness and Schatten-von Neumann ideals
International Nuclear Information System (INIS)
Baranov, Anton D
2009-01-01
We study properties of the embedding operators of model subspaces K p Θ (defined by inner functions) in the Hardy space H p (coinvariant subspaces of the shift operator). We find a criterion for the embedding of K p Θ in L p (μ) to be compact similar to the Volberg-Treil theorem on bounded embeddings, and give a positive answer to a question of Cima and Matheson. The proof is based on Bernstein-type inequalities for functions in K p Θ . We investigate measures μ such that the embedding operator belongs to some Schatten-von Neumann ideal.
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
Subspace-Based Noise Reduction for Speech Signals via Diagonal and Triangular Matrix Decompositions
DEFF Research Database (Denmark)
Hansen, Per Christian; Jensen, Søren Holdt
We survey the definitions and use of rank-revealing matrix decompositions in single-channel noise reduction algorithms for speech signals. Our algorithms are based on the rank-reduction paradigm and, in particular, signal subspace techniques. The focus is on practical working algorithms, using both...... diagonal (eigenvalue and singular value) decompositions and rank-revealing triangular decompositions (ULV, URV, VSV, ULLV and ULLIV). In addition we show how the subspace-based algorithms can be evaluated and compared by means of simple FIR filter interpretations. The algorithms are illustrated...... with working Matlab code and applications in speech processing....
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....
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.
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.
Requirements for Scalable Access Control and Security Management Architectures
National Research Council Canada - National Science Library
Keromytis, Angelos D; Smith, Jonathan M
2005-01-01
Maximizing local autonomy has led to a scalable Internet. Scalability and the capacity for distributed control have unfortunately not extended well to resource access control policies and mechanisms...
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.
Adaptive format conversion for scalable video coding
Wan, Wade K.; Lim, Jae S.
2001-12-01
The enhancement layer in many scalable coding algorithms is composed of residual coding information. There is another type of information that can be transmitted instead of (or in addition to) residual coding. Since the encoder has access to the original sequence, it can utilize adaptive format conversion (AFC) to generate the enhancement layer and transmit the different format conversion methods as enhancement data. This paper investigates the use of adaptive format conversion information as enhancement data in scalable video coding. Experimental results are shown for a wide range of base layer qualities and enhancement bitrates to determine when AFC can improve video scalability. Since the parameters needed for AFC are small compared to residual coding, AFC can provide video scalability at low enhancement layer bitrates that are not possible with residual coding. In addition, AFC can also be used in addition to residual coding to improve video scalability at higher enhancement layer bitrates. Adaptive format conversion has not been studied in detail, but many scalable applications may benefit from it. An example of an application that AFC is well-suited for is the migration path for digital television where AFC can provide immediate video scalability as well as assist future migrations.
Subspace-Based Noise Reduction for Speech Signals via Diagonal and Triangular Matrix Decompositions
DEFF Research Database (Denmark)
Hansen, Per Christian; Jensen, Søren Holdt
2007-01-01
We survey the definitions and use of rank-revealing matrix decompositions in single-channel noise reduction algorithms for speech signals. Our algorithms are based on the rank-reduction paradigm and, in particular, signal subspace techniques. The focus is on practical working algorithms, using both...... with working Matlab code and applications in speech processing....
DEFF Research Database (Denmark)
Macieszczak, Katarzyna; Zhou, Yanli; Hofferberth, Sebastian
2017-01-01
to stationarity this leads to a slow dynamics, which renders the typical assumption of fast relaxation invalid. We derive analytically the effective nonequilibrium dynamics in the decoherence-free subspace, which features coherent and dissipative two-body interactions. We discuss the use of this scenario...
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
Third-order nonlinear differential operators preserving invariant subspaces of maximal dimension
International Nuclear Information System (INIS)
Qu Gai-Zhu; Zhang Shun-Li; Li Yao-Long
2014-01-01
In this paper, third-order nonlinear differential operators are studied. It is shown that they are quadratic forms when they preserve invariant subspaces of maximal dimension. A complete description of third-order quadratic operators with constant coefficients is obtained. One example is given to derive special solutions for evolution equations with third-order quadratic operators. (general)
A frequency domain subspace algorithm for mixed causal, anti-causal LTI systems
Fraanje, Rufus; Verhaegen, Michel; Verdult, Vincent; Pintelon, Rik
2003-01-01
The paper extends the subspacc identification method to estimate state-space models from frequency response function (FRF) samples, proposed by McKelvey et al. (1996) for mixed causal/anti-causal systems, and shows that other frequency domain subspace algorithms can be extended similarly. The method
ALADDIN - enhancing applicability and scalability
International Nuclear Information System (INIS)
Roverso, Davide
2001-02-01
The ALADDIN project aims at the study and development of flexible, accurate, and reliable techniques and principles for computerised event classification and fault diagnosis for complex machinery and industrial processes. The main focus of the project is on advanced numerical techniques, such as wavelets, and empirical modelling with neural networks. This document reports on recent important advancements, which significantly widen the practical applicability of the developed principles, both in terms of flexibility of use, and in terms of scalability to large problem domains. In particular, two novel techniques are here described. The first, which we call Wavelet On- Line Pre-processing (WOLP), is aimed at extracting, on-line, relevant dynamic features from the process data streams. This technique allows a system a greater flexibility in detecting and processing transients at a range of different time scales. The second technique, which we call Autonomous Recursive Task Decomposition (ARTD), is aimed at tackling the problem of constructing a classifier able to discriminate among a large number of different event/fault classes, which is often the case when the application domain is a complex industrial process. ARTD also allows for incremental application development (i.e. the incremental addition of new classes to an existing classifier, without the need of retraining the entire system), and for simplified application maintenance. The description of these novel techniques is complemented by reports of quantitative experiments that show in practice the extent of these improvements. (Author)
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
Brunton, Steven L; Brunton, Bingni W; Proctor, Joshua L; Kutz, J Nathan
2016-01-01
In this wIn this work, we explore finite-dimensional linear representations of nonlinear dynamical systems by restricting the Koopman operator to an invariant subspace spanned by specially chosen observable functions. The Koopman operator is an infinite-dimensional linear operator that evolves functions of the state of a dynamical system. Dominant terms in the Koopman expansion are typically computed using dynamic mode decomposition (DMD). DMD uses linear measurements of the state variables, and it has recently been shown that this may be too restrictive for nonlinear systems. Choosing the right nonlinear observable functions to form an invariant subspace where it is possible to obtain linear reduced-order models, especially those that are useful for control, is an open challenge. Here, we investigate the choice of observable functions for Koopman analysis that enable the use of optimal linear control techniques on nonlinear problems. First, to include a cost on the state of the system, as in linear quadratic regulator (LQR) control, it is helpful to include these states in the observable subspace, as in DMD. However, we find that this is only possible when there is a single isolated fixed point, as systems with multiple fixed points or more complicated attractors are not globally topologically conjugate to a finite-dimensional linear system, and cannot be represented by a finite-dimensional linear Koopman subspace that includes the state. We then present a data-driven strategy to identify relevant observable functions for Koopman analysis by leveraging a new algorithm to determine relevant terms in a dynamical system by ℓ1-regularized regression of the data in a nonlinear function space; we also show how this algorithm is related to DMD. Finally, we demonstrate the usefulness of nonlinear observable subspaces in the design of Koopman operator optimal control laws for fully nonlinear systems using techniques from linear optimal control.ork, we explore finite
Clustering of near clusters versus cluster compactness
International Nuclear Information System (INIS)
Yu Gao; Yipeng Jing
1989-01-01
The clustering properties of near Zwicky clusters are studied by using the two-point angular correlation function. The angular correlation functions for compact and medium compact clusters, for open clusters, and for all near Zwicky clusters are estimated. The results show much stronger clustering for compact and medium compact clusters than for open clusters, and that open clusters have nearly the same clustering strength as galaxies. A detailed study of the compactness-dependence of correlation function strength is worth investigating. (author)
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
Lyapunov vectors and assimilation in the unstable subspace: theory and applications
International Nuclear Information System (INIS)
Palatella, Luigi; Carrassi, Alberto; Trevisan, Anna
2013-01-01
Based on a limited number of noisy observations, estimation algorithms provide a complete description of the state of a system at current time. Estimation algorithms that go under the name of assimilation in the unstable subspace (AUS) exploit the nonlinear stability properties of the forecasting model in their formulation. Errors that grow due to sensitivity to initial conditions are efficiently removed by confining the analysis solution in the unstable and neutral subspace of the system, the subspace spanned by Lyapunov vectors with positive and zero exponents, while the observational noise does not disturb the system along the stable directions. The formulation of the AUS approach in the context of four-dimensional variational assimilation (4DVar-AUS) and the extended Kalman filter (EKF-AUS) and its application to chaotic models is reviewed. In both instances, the AUS algorithms are at least as efficient but simpler to implement and computationally less demanding than their original counterparts. As predicted by the theory when error dynamics is linear, the optimal subspace dimension for 4DVar-AUS is given by the number of positive and null Lyapunov exponents, while the EKF-AUS algorithm, using the same unstable and neutral subspace, recovers the solution of the full EKF algorithm, but dealing with error covariance matrices of a much smaller dimension and significantly reducing the computational burden. Examples of the application to a simplified model of the atmospheric circulation and to the optimal velocity model for traffic dynamics are given. This article is part of a special issue of Journal of Physics A: Mathematical and Theoretical devoted to ‘Lyapunov analysis: from dynamical systems theory to applications’. (paper)
Brunton, Steven L.; Brunton, Bingni W.; Proctor, Joshua L.; Kutz, J. Nathan
2016-01-01
In this work, we explore finite-dimensional linear representations of nonlinear dynamical systems by restricting the Koopman operator to an invariant subspace spanned by specially chosen observable functions. The Koopman operator is an infinite-dimensional linear operator that evolves functions of the state of a dynamical system. Dominant terms in the Koopman expansion are typically computed using dynamic mode decomposition (DMD). DMD uses linear measurements of the state variables, and it has recently been shown that this may be too restrictive for nonlinear systems. Choosing the right nonlinear observable functions to form an invariant subspace where it is possible to obtain linear reduced-order models, especially those that are useful for control, is an open challenge. Here, we investigate the choice of observable functions for Koopman analysis that enable the use of optimal linear control techniques on nonlinear problems. First, to include a cost on the state of the system, as in linear quadratic regulator (LQR) control, it is helpful to include these states in the observable subspace, as in DMD. However, we find that this is only possible when there is a single isolated fixed point, as systems with multiple fixed points or more complicated attractors are not globally topologically conjugate to a finite-dimensional linear system, and cannot be represented by a finite-dimensional linear Koopman subspace that includes the state. We then present a data-driven strategy to identify relevant observable functions for Koopman analysis by leveraging a new algorithm to determine relevant terms in a dynamical system by ℓ1-regularized regression of the data in a nonlinear function space; we also show how this algorithm is related to DMD. Finally, we demonstrate the usefulness of nonlinear observable subspaces in the design of Koopman operator optimal control laws for fully nonlinear systems using techniques from linear optimal control. PMID:26919740
Embedded High Performance Scalable Computing Systems
National Research Council Canada - National Science Library
Ngo, David
2003-01-01
The Embedded High Performance Scalable Computing Systems (EHPSCS) program is a cooperative agreement between Sanders, A Lockheed Martin Company and DARPA that ran for three years, from Apr 1995 - Apr 1998...
Resource-aware complexity scalability for mobile MPEG encoding
Mietens, S.O.; With, de P.H.N.; Hentschel, C.; Panchanatan, S.; Vasudev, B.
2004-01-01
Complexity scalability attempts to scale the required resources of an algorithm with the chose quality settings, in order to broaden the application range. In this paper, we present complexity-scalable MPEG encoding of which the core processing modules are modified for scalability. Scalability is
Evaluating Sparse Linear System Solvers on Scalable Parallel Architectures
National Research Council Canada - National Science Library
Grama, Ananth; Manguoglu, Murat; Koyuturk, Mehmet; Naumov, Maxim; Sameh, Ahmed
2008-01-01
.... The study was motivated primarily by the lack of robustness of Krylov subspace iterative schemes with generic, black-box, pre-conditioners such as approximate (or incomplete) LU-factorizations...
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.
Hankel Matrix Correlation Function-Based Subspace Identification Method for UAV Servo System
Directory of Open Access Journals (Sweden)
Minghong She
2018-01-01
Full Text Available For the identification problem of closed-loop subspace model, we propose a zero space projection method based on the estimation of correlation function to fill the block Hankel matrix of identification model by combining the linear algebra with geometry. By using the same projection of related data in time offset set and LQ decomposition, the multiplication operation of projection is achieved and dynamics estimation of the unknown equipment system model is obtained. Consequently, we have solved the problem of biased estimation caused when the open-loop subspace identification algorithm is applied to the closed-loop identification. A simulation example is given to show the effectiveness of the proposed approach. In final, the practicability of the identification algorithm is verified by hardware test of UAV servo system in real environment.
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.
International Nuclear Information System (INIS)
Chen, Xudong
2010-01-01
This paper proposes a version of the subspace-based optimization method to solve the inverse scattering problem with an inhomogeneous background medium where the known inhomogeneities are bounded in a finite domain. Although the background Green's function at each discrete point in the computational domain is not directly available in an inhomogeneous background scenario, the paper uses the finite element method to simultaneously obtain the Green's function at all discrete points. The essence of the subspace-based optimization method is that part of the contrast source is determined from the spectrum analysis without using any optimization, whereas the orthogonally complementary part is determined by solving a lower dimension optimization problem. This feature significantly speeds up the convergence of the algorithm and at the same time makes it robust against noise. Numerical simulations illustrate the efficacy of the proposed algorithm. The algorithm presented in this paper finds wide applications in nondestructive evaluation, such as through-wall imaging
Pyshkin, P V; Luo, Da-Wei; Jing, Jun; You, J Q; Wu, Lian-Ao
2016-11-25
Holonomic quantum computation (HQC) may not show its full potential in quantum speedup due to the prerequisite of a long coherent runtime imposed by the adiabatic condition. Here we show that the conventional HQC can be dramatically accelerated by using external control fields, of which the effectiveness is exclusively determined by the integral of the control fields in the time domain. This control scheme can be realized with net zero energy cost and it is fault-tolerant against fluctuation and noise, significantly relaxing the experimental constraints. We demonstrate how to realize the scheme via decoherence-free subspaces. In this way we unify quantum robustness merits of this fault-tolerant control scheme, the conventional HQC and decoherence-free subspace, and propose an expedited holonomic quantum computation protocol.
Experimental fault-tolerant quantum cryptography in a decoherence-free subspace
International Nuclear Information System (INIS)
Zhang Qiang; Pan Jianwei; Yin Juan; Chen Tengyun; Lu Shan; Zhang Jun; Li Xiaoqiang; Yang Tao; Wang Xiangbin
2006-01-01
We experimentally implement a fault-tolerant quantum key distribution protocol with two photons in a decoherence-free subspace [Phys. Rev. A 72, 050304(R) (2005)]. It is demonstrated that our protocol can yield a good key rate even with a large bit-flip error rate caused by collective rotation, while the usual realization of the Bennett-Brassard 1984 protocol cannot produce any secure final key given the same channel. Since the experiment is performed in polarization space and does not need the calibration of a reference frame, important applications in free-space quantum communication are expected. Moreover, our method can also be used to robustly transmit an arbitrary two-level quantum state in a type of decoherence-free subspace
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
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.
Kernel based subspace projection of near infrared hyperspectral images of maize kernels
DEFF Research Database (Denmark)
Larsen, Rasmus; Arngren, Morten; Hansen, Per Waaben
2009-01-01
In this paper we present an exploratory analysis of hyper- spectral 900-1700 nm images of maize kernels. The imaging device is a line scanning hyper spectral camera using a broadband NIR illumi- nation. In order to explore the hyperspectral data we compare a series of subspace projection methods ......- tor transform outperform the linear methods as well as kernel principal components in producing interesting projections of the data.......In this paper we present an exploratory analysis of hyper- spectral 900-1700 nm images of maize kernels. The imaging device is a line scanning hyper spectral camera using a broadband NIR illumi- nation. In order to explore the hyperspectral data we compare a series of subspace projection methods...... including principal component analysis and maximum autocorrelation factor analysis. The latter utilizes the fact that interesting phenomena in images exhibit spatial autocorrelation. However, linear projections often fail to grasp the underlying variability on the data. Therefore we propose to use so...
Quantum theory of dynamical collective subspace for large-amplitude collective motion
International Nuclear Information System (INIS)
Sakata, Fumihiko; Marumori, Toshio; Ogura, Masanori.
1986-03-01
By placing emphasis on conceptual correspondence to the ''classical'' theory which has been developed within the framework of the time-dependent Hartree-Fock theory, a full quantum theory appropriate for describing large-amplitude collective motion is proposed. A central problem of the quantum theory is how to determine an optimal representation called a dynamical representation; the representation is specific for the collective subspace where the large-amplitude collective motion is replicated as satisfactorily as possible. As an extension of the classical theory where the concept of an approximate integral surface plays an important role, the dynamical representation is properly characterized by introducing a concept of an approximate invariant subspace of the Hamiltonian. (author)
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...... calculations. Numerical tests within a density functional theory framework are provided to validate the accuracy and robustness of the proposed method, which in most cases is an order of magnitude faster than conventional methods.......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...
Hsu, Wei-Ting; Loh, Chin-Hsiung; Chao, Shu-Hsien
2015-03-01
Stochastic subspace identification method (SSI) has been proven to be an efficient algorithm for the identification of liner-time-invariant system using multivariate measurements. Generally, the estimated modal parameters through SSI may be afflicted with statistical uncertainty, e.g. undefined measurement noises, non-stationary excitation, finite number of data samples etc. Therefore, the identified results are subjected to variance errors. Accordingly, the concept of the stabilization diagram can help users to identify the correct model, i.e. through removing the spurious modes. Modal parameters are estimated at successive model orders where the physical modes of the system are extracted and separated from the spurious modes. Besides, an uncertainty computation scheme was derived for the calculation of uncertainty bounds for modal parameters at some given model order. The uncertainty bounds of damping ratios are particularly interesting, as the estimation of damping ratios are difficult to obtain. In this paper, an automated stochastic subspace identification algorithm is addressed. First, the identification of modal parameters through covariance-driven stochastic subspace identification from the output-only measurements is used for discussion. A systematic way of investigation on the criteria for the stabilization diagram is presented. Secondly, an automated algorithm of post-processing on stabilization diagram is demonstrated. Finally, the computation of uncertainty bounds for each mode with all model order in the stabilization diagram is utilized to determine system natural frequencies and damping ratios. Demonstration of this study on the system identification of a three-span steel bridge under operation condition is presented. It is shown that the proposed new operation procedure for the automated covariance-driven stochastic subspace identification can enhance the robustness and reliability in structural health monitoring.
Projected Gauss-Seidel subspace minimization method for interactive rigid body dynamics
DEFF Research Database (Denmark)
Silcowitz-Hansen, Morten; Abel, Sarah Maria Niebe; Erleben, Kenny
2010-01-01
artifacts such as viscous or damped contact response. In this paper, we present a new approach to contact force determination. We formulate the contact force problem as a nonlinear complementarity problem, and discretize the problem to derive the Projected Gauss–Seidel method. We combine the Projected Gauss......–Seidel method with a subspace minimization method. Our new method shows improved qualities and superior convergence properties for specific configurations....
Residual and Backward Error Bounds in Minimum Residual Krylov Subspace Methods
Czech Academy of Sciences Publication Activity Database
Paige, C. C.; Strakoš, Zdeněk
2002-01-01
Roč. 23, č. 6 (2002), s. 1899-1924 ISSN 1064-8275 R&D Projects: GA AV ČR IAA1030103 Institutional research plan: AV0Z1030915 Keywords : linear equations * eigenproblem * large sparse matrices * iterative solutions * Krylov subspace methods * Arnoldi method * GMRES * modified Gram-Schmidt * least squares * total least squares * singular values Subject RIV: BA - General Mathematics Impact factor: 1.291, year: 2002
Czech Academy of Sciences Publication Activity Database
Liesen, J.; Strakoš, Zdeněk
2008-01-01
Roč. 50, č. 3 (2008), s. 485-503 ISSN 0036-1445 R&D Projects: GA AV ČR 1ET400300415; GA AV ČR IAA100300802 Institutional research plan: CEZ:AV0Z10300504 Keywords : Krylov subspace methods * orthogonal bases * short reccurences * conjugate gradient -like methods Subject RIV: IN - Informatics, Computer Science Impact factor: 2.739, year: 2008
Subspace based adaptive denoising of surface EMG from neurological injury patients
Liu, Jie; Ying, Dongwen; Zev Rymer, William; Zhou, Ping
2014-10-01
Objective: After neurological injuries such as spinal cord injury, voluntary surface electromyogram (EMG) signals recorded from affected muscles are often corrupted by interferences, such as spurious involuntary spikes and background noises produced by physiological and extrinsic/accidental origins, imposing difficulties for signal processing. Conventional methods did not well address the problem caused by interferences. It is difficult to mitigate such interferences using conventional methods. The aim of this study was to develop a subspace-based denoising method to suppress involuntary background spikes contaminating voluntary surface EMG recordings. Approach: The Karhunen-Loeve transform was utilized to decompose a noisy signal into a signal subspace and a noise subspace. An optimal estimate of EMG signal is derived from the signal subspace and the noise power. Specifically, this estimator is capable of making a tradeoff between interference reduction and signal distortion. Since the estimator partially relies on the estimate of noise power, an adaptive method was presented to sequentially track the variation of interference power. The proposed method was evaluated using both semi-synthetic and real surface EMG signals. Main results: The experiments confirmed that the proposed method can effectively suppress interferences while keep the distortion of voluntary EMG signal in a low level. The proposed method can greatly facilitate further signal processing, such as onset detection of voluntary muscle activity. Significance: The proposed method can provide a powerful tool for suppressing background spikes and noise contaminating voluntary surface EMG signals of paretic muscles after neurological injuries, which is of great importance for their multi-purpose applications.
Banach C*-algebras not containing a subspace isomorphic to C0
International Nuclear Information System (INIS)
Basit, B.
1989-09-01
If X is a locally Hausdorff space and C 0 (X) the Banach algebra of continuous functions defined on X vanishing at infinity, we showed that a subalgebra A of C 0 (X) is finite dimensional if it does not contain a subspace isomorphic to the Banach space C 0 of convergent to zero complex sequences. In this paper we extend this result to noncommutative Banach C*-algebras and Banach* algebras. 10 refs
Scalable quantum information processing with atomic ensembles and flying photons
International Nuclear Information System (INIS)
Mei Feng; Yu Yafei; Feng Mang; Zhang Zhiming
2009-01-01
We present a scheme for scalable quantum information processing with atomic ensembles and flying photons. Using the Rydberg blockade, we encode the qubits in the collective atomic states, which could be manipulated fast and easily due to the enhanced interaction in comparison to the single-atom case. We demonstrate that our proposed gating could be applied to generation of two-dimensional cluster states for measurement-based quantum computation. Moreover, the atomic ensembles also function as quantum repeaters useful for long-distance quantum state transfer. We show the possibility of our scheme to work in bad cavity or in weak coupling regime, which could much relax the experimental requirement. The efficient coherent operations on the ensemble qubits enable our scheme to be switchable between quantum computation and quantum communication using atomic ensembles.
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.
Krylov Subspace Methods for Complex Non-Hermitian Linear Systems. Thesis
Freund, Roland W.
1991-01-01
We consider Krylov subspace methods for the solution of large sparse linear systems Ax = b with complex non-Hermitian coefficient matrices. Such linear systems arise in important applications, such as inverse scattering, numerical solution of time-dependent Schrodinger equations, underwater acoustics, eddy current computations, numerical computations in quantum chromodynamics, and numerical conformal mapping. Typically, the resulting coefficient matrices A exhibit special structures, such as complex symmetry, or they are shifted Hermitian matrices. In this paper, we first describe a Krylov subspace approach with iterates defined by a quasi-minimal residual property, the QMR method, for solving general complex non-Hermitian linear systems. Then, we study special Krylov subspace methods designed for the two families of complex symmetric respectively shifted Hermitian linear systems. We also include some results concerning the obvious approach to general complex linear systems by solving equivalent real linear systems for the real and imaginary parts of x. Finally, numerical experiments for linear systems arising from the complex Helmholtz equation are reported.
International Nuclear Information System (INIS)
Yamamoto, Akio; Tatsumi, Masahiro; Sugimura, Naoki
2007-01-01
The Krylov subspace method is applied to solve nuclide burnup equations used for lattice physics calculations. The Krylov method is an efficient approach for solving ordinary differential equations with stiff nature such as the nuclide burnup with short lived nuclides. Some mathematical fundamentals of the Krylov subspace method and its application to burnup equations are discussed. Verification calculations are carried out in a PWR pin-cell geometry with UO 2 fuel. A detailed burnup chain that includes 193 fission products and 28 heavy nuclides is used in the verification calculations. Shortest half life found in the present burnup chain is approximately 30 s ( 106 Rh). Therefore, conventional methods (e.g., the Taylor series expansion with scaling and squaring) tend to require longer computation time due to numerical stiffness. Comparison with other numerical methods (e.g., the 4-th order Runge-Kutta-Gill) reveals that the Krylov subspace method can provide accurate solution for a detailed burnup chain used in the present study with short computation time. (author)
Directory of Open Access Journals (Sweden)
Carlos A. L. Pires
2017-01-01
Full Text Available We propose an expansion of multivariate time-series data into maximally independent source subspaces. The search is made among rotations of prewhitened data which maximize non-Gaussianity of candidate sources. We use a tensorial invariant approximation of the multivariate negentropy in terms of a linear combination of squared coskewness and cokurtosis. By solving a high-order singular value decomposition problem, we extract the axes associated with most non-Gaussianity. Moreover, an estimate of the Gaussian subspace is provided by the trailing singular vectors. The independent subspaces are obtained through the search of “quasi-independent” components within the estimated non-Gaussian subspace, followed by the identification of groups with significant joint negentropies. Sources result essentially from the coherency of extremes of the data components. The method is then applied to the global sea surface temperature anomalies, equatorward of 65°, after being tested with non-Gaussian surrogates consistent with the data anomalies. The main emerging independent components and subspaces, supposedly generated by independent forcing, include different variability modes, namely, The East-Pacific, the Central Pacific, and the Atlantic Niños, the Atlantic Multidecadal Oscillation, along with the subtropical dipoles in the Indian, South Pacific, and South-Atlantic oceans. Benefits and usefulness of independent subspaces are then discussed.
The Concept of Business Model Scalability
DEFF Research Database (Denmark)
Lund, Morten; Nielsen, Christian
2018-01-01
-term pro table business. However, the main message of this article is that while providing a good value proposition may help the rm ‘get by’, the really successful businesses of today are those able to reach the sweet-spot of business model scalability. Design/Methodology/Approach: The article is based...... on a ve-year longitudinal action research project of over 90 companies that participated in the International Center for Innovation project aimed at building 10 global network-based business models. Findings: This article introduces and discusses the term scalability from a company-level perspective......Purpose: The purpose of the article is to de ne what scalable business models are. Central to the contemporary understanding of business models is the value proposition towards the customer and the hypotheses generated about delivering value to the customer which become a good foundation for a long...
Scalable cavity-QED-based scheme of generating entanglement of atoms and of cavity fields
Lee, Jaehak; Park, Jiyong; Lee, Sang Min; Lee, Hai-Woong; Khosa, Ashfaq H.
2008-01-01
We propose a cavity-QED-based scheme of generating entanglement between atoms. The scheme is scalable to an arbitrary number of atoms, and can be used to generate a variety of multipartite entangled states such as the Greenberger-Horne-Zeilinger, W, and cluster states. Furthermore, with a role switching of atoms with photons, the scheme can be used to generate entanglement between cavity fields. We also introduce a scheme that can generate an arbitrary multipartite field graph state.
Enhancing Scalability of Sparse Direct Methods
International Nuclear Information System (INIS)
Li, Xiaoye S.; Demmel, James; Grigori, Laura; Gu, Ming; Xia, Jianlin; Jardin, Steve; Sovinec, Carl; Lee, Lie-Quan
2007-01-01
TOPS is providing high-performance, scalable sparse direct solvers, which have had significant impacts on the SciDAC applications, including fusion simulation (CEMM), accelerator modeling (COMPASS), as well as many other mission-critical applications in DOE and elsewhere. Our recent developments have been focusing on new techniques to overcome scalability bottleneck of direct methods, in both time and memory. These include parallelizing symbolic analysis phase and developing linear-complexity sparse factorization methods. The new techniques will make sparse direct methods more widely usable in large 3D simulations on highly-parallel petascale computers
Software performance and scalability a quantitative approach
Liu, Henry H
2009-01-01
Praise from the Reviewers:"The practicality of the subject in a real-world situation distinguishes this book from othersavailable on the market."—Professor Behrouz Far, University of Calgary"This book could replace the computer organization texts now in use that every CS and CpEstudent must take. . . . It is much needed, well written, and thoughtful."—Professor Larry Bernstein, Stevens Institute of TechnologyA distinctive, educational text onsoftware performance and scalabilityThis is the first book to take a quantitative approach to the subject of software performance and scalability
From Digital Disruption to Business Model Scalability
DEFF Research Database (Denmark)
Nielsen, Christian; Lund, Morten; Thomsen, Peter Poulsen
2017-01-01
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)....... 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...
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.
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.
Using scalable vector graphics to evolve art
den Heijer, E.; Eiben, A. E.
2016-01-01
In this paper, we describe our investigations of the use of scalable vector graphics as a genotype representation in evolutionary art. We describe the technical aspects of using SVG in evolutionary art, and explain our custom, SVG specific operators initialisation, mutation and crossover. We perform
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.
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...
Cooperative Scalable Moving Continuous Query Processing
DEFF Research Database (Denmark)
Li, Xiaohui; Karras, Panagiotis; Jensen, Christian S.
2012-01-01
of the global view and handle the majority of the workload. Meanwhile, moving clients, having basic memory and computation resources, handle small portions of the workload. This model is further enhanced by dynamic region allocation and grid size adjustment mechanisms that reduce the communication...... and computation cost for both servers and clients. An experimental study demonstrates that our approaches offer better scalability than competitors...
Scalable optical switches for computing applications
White, I.H.; Aw, E.T.; Williams, K.A.; Wang, Haibo; Wonfor, A.; Penty, R.V.
2009-01-01
A scalable photonic interconnection network architecture is proposed whereby a Clos network is populated with broadcast-and-select stages. This enables the efficient exploitation of an emerging class of photonic integrated switch fabric. A low distortion space switch technology based on recently
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 scalable method for parallelizing sampling-based motion planning algorithms
Jacobs, Sam Ade; Manavi, Kasra; Burgos, Juan; Denny, Jory; Thomas, Shawna; Amato, Nancy M.
2012-01-01
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.
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)
Sakata, Fumihiko [Tokyo Univ., Tanashi (Japan). Inst. for Nuclear Study; Yamamoto, Yoshifumi; Marumori, Toshio; Iida, Shinji; Tsukuma, Hidehiko
1989-11-01
It is the purpose of the present paper to study 'global structure' of the state space of an N-body interacting fermion system, which exhibits regular, transient and stochastic phases depending on strength of the interaction. An optimum representation called a dynamical representation plays an essential role in this investigation. The concept of the dynamical representation has been introduced in the quantum theory of dynamical subspace in our previous paper, in order to determine self-consistently an optimum collective subspace as well as an optimum collective Hamiltonian. In the theory, furthermore, dynamical conditions called separability and stability conditions have been provided in order to identify the optimum collective subspace as an approximate invariant subspace of the Hamiltonian. Physical meaning of these conditions are clarified from a viewpoint to relate breaking of them with bifurcation of the collectivity and an onset of quantum chaos from the regular collective motion, by illustrating the general idea with numerical results obtained for a simple soluble model. It turns out that the onset of the stochastic phase is associated with dissolution of the quantum numbers to specify the collective subspace and this dissolution is induced by the breaking of the separability condition in the dynamical representation. (author).
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.
International Nuclear Information System (INIS)
Yi-Min, Wang; Yan-Li, Zhou; Lin-Mei, Liang; Cheng-Zu, Li
2009-01-01
We propose a feasible scheme to achieve universal quantum gate operations in decoherence-free subspace with superconducting charge qubits placed in a microwave cavity. Single-logic-qubit gates can be realized with cavity assisted interaction, which possesses the advantages of unconventional geometric gate operation. The two-logic-qubit controlled-phase gate between subsystems can be constructed with the help of a variable electrostatic transformer. The collective decoherence can be successfully avoided in our well-designed system. Moreover, GHZ state for logical qubits can also be easily produced in this system
Consistency analysis of subspace identification methods based on a linear regression approach
DEFF Research Database (Denmark)
Knudsen, Torben
2001-01-01
In the literature results can be found which claim consistency for the subspace method under certain quite weak assumptions. Unfortunately, a new result gives a counter example showing inconsistency under these assumptions and then gives new more strict sufficient assumptions which however does n...... not include important model structures as e.g. Box-Jenkins. Based on a simple least squares approach this paper shows the possible inconsistency under the weak assumptions and develops only slightly stricter assumptions sufficient for consistency and which includes any model structure...
Subspace Barzilai-Borwein Gradient Method for Large-Scale Bound Constrained Optimization
International Nuclear Information System (INIS)
Xiao Yunhai; Hu Qingjie
2008-01-01
An active set subspace Barzilai-Borwein gradient algorithm for large-scale bound constrained optimization is proposed. The active sets are estimated by an identification technique. The search direction consists of two parts: some of the components are simply defined; the other components are determined by the Barzilai-Borwein gradient method. In this work, a nonmonotone line search strategy that guarantees global convergence is used. Preliminary numerical results show that the proposed method is promising, and competitive with the well-known method SPG on a subset of bound constrained problems from CUTEr collection
Experimental Study of Generalized Subspace Filters for the Cocktail Party Situation
DEFF Research Database (Denmark)
Christensen, Knud Bank; Christensen, Mads Græsbøll; Boldt, Jesper B.
2016-01-01
This paper investigates the potential performance of generalized subspace filters for speech enhancement in cocktail party situations with very poor signal/noise ratio, e.g. down to -15 dB. Performance metrics output signal/noise ratio, signal/ distortion ratio, speech quality rating and speech...... intelligibility rating are mapped as functions of two algorithm parameters, revealing clear trade-off options between noise, distortion and subjective performances and a recommended choice of trade-off. Given sufficiently good noise statistics, SNR improvements around 20 dB as well as PESQ quality and STOI...
A General Algorithm for Reusing Krylov Subspace Information. I. Unsteady Navier-Stokes
Carpenter, Mark H.; Vuik, C.; Lucas, Peter; vanGijzen, Martin; Bijl, Hester
2010-01-01
A general algorithm is developed that reuses available information to accelerate the iterative convergence of linear systems with multiple right-hand sides A x = b (sup i), which are commonly encountered in steady or unsteady simulations of nonlinear equations. The algorithm is based on the classical GMRES algorithm with eigenvector enrichment but also includes a Galerkin projection preprocessing step and several novel Krylov subspace reuse strategies. The new approach is applied to a set of test problems, including an unsteady turbulent airfoil, and is shown in some cases to provide significant improvement in computational efficiency relative to baseline approaches.
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.
Practical Low Data-Complexity Subspace-Trail Cryptanalysis of Round-Reduced PRINCE
DEFF Research Database (Denmark)
Grassi, Lorenzo; Rechberger, Christian
2016-01-01
Subspace trail cryptanalysis is a very recent new cryptanalysis technique, and includes differential, truncated differential, impossible differential, and integral attacks as special cases. In this paper, we consider PRINCE, a widely analyzed block cipher proposed in 2012. After the identification......-plaintext category. The attacks have been verified using a C implementation. Of independent interest, we consider a variant of PRINCE in which ShiftRows and MixLayer operations are exchanged in position. In particular, our result shows that the position of ShiftRows and MixLayer operations influences the security...
Random subspaces for encryption based on a private shared Cartesian frame
International Nuclear Information System (INIS)
Bartlett, Stephen D.; Hayden, Patrick; Spekkens, Robert W.
2005-01-01
A private shared Cartesian frame is a novel form of private shared correlation that allows for both private classical and quantum communication. Cryptography using a private shared Cartesian frame has the remarkable property that asymptotically, if perfect privacy is demanded, the private classical capacity is three times the private quantum capacity. We demonstrate that if the requirement for perfect privacy is relaxed, then it is possible to use the properties of random subspaces to nearly triple the private quantum capacity, almost closing the gap between the private classical and quantum capacities
Towards automatic music transcription: note extraction based on independent subspace analysis
Wellhausen, Jens; Hoynck, Michael
2005-01-01
Due to the increasing amount of music available electronically the need of automatic search, retrieval and classification systems for music becomes more and more important. In this paper an algorithm for automatic transcription of polyphonic piano music into MIDI data is presented, which is a very interesting basis for database applications, music analysis and music classification. The first part of the algorithm performs a note accurate temporal audio segmentation. In the second part, the resulting segments are examined using Independent Subspace Analysis to extract sounding notes. Finally, the results are used to build a MIDI file as a new representation of the piece of music which is examined.
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.
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.
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.
Scalable Algorithms for Adaptive Statistical Designs
Directory of Open Access Journals (Sweden)
Robert Oehmke
2000-01-01
Full Text Available We present a scalable, high-performance solution to multidimensional recurrences that arise in adaptive statistical designs. Adaptive designs are an important class of learning algorithms for a stochastic environment, and we focus on the problem of optimally assigning patients to treatments in clinical trials. While adaptive designs have significant ethical and cost advantages, they are rarely utilized because of the complexity of optimizing and analyzing them. Computational challenges include massive memory requirements, few calculations per memory access, and multiply-nested loops with dynamic indices. We analyze the effects of various parallelization options, and while standard approaches do not work well, with effort an efficient, highly scalable program can be developed. This allows us to solve problems thousands of times more complex than those solved previously, which helps make adaptive designs practical. Further, our work applies to many other problems involving neighbor recurrences, such as generalized string matching.
Scalable Packet Classification with Hash Tables
Wang, Pi-Chung
In the last decade, the technique of packet classification has been widely deployed in various network devices, including routers, firewalls and network intrusion detection systems. In this work, we improve the performance of packet classification by using multiple hash tables. The existing hash-based algorithms have superior scalability with respect to the required space; however, their search performance may not be comparable to other algorithms. To improve the search performance, we propose a tuple reordering algorithm to minimize the number of accessed hash tables with the aid of bitmaps. We also use pre-computation to ensure the accuracy of our search procedure. Performance evaluation based on both real and synthetic filter databases shows that our scheme is effective and scalable and the pre-computation cost is moderate.
Scalable fabrication of perovskite solar cells
Energy Technology Data Exchange (ETDEWEB)
Li, Zhen; Klein, Talysa R.; Kim, Dong Hoe; Yang, Mengjin; Berry, Joseph J.; van Hest, Maikel F. A. M.; Zhu, Kai
2018-03-27
Perovskite materials use earth-abundant elements, have low formation energies for deposition and are compatible with roll-to-roll and other high-volume manufacturing techniques. These features make perovskite solar cells (PSCs) suitable for terawatt-scale energy production with low production costs and low capital expenditure. Demonstrations of performance comparable to that of other thin-film photovoltaics (PVs) and improvements in laboratory-scale cell stability have recently made scale up of this PV technology an intense area of research focus. Here, we review recent progress and challenges in scaling up PSCs and related efforts to enable the terawatt-scale manufacturing and deployment of this PV technology. We discuss common device and module architectures, scalable deposition methods and progress in the scalable deposition of perovskite and charge-transport layers. We also provide an overview of device and module stability, module-level characterization techniques and techno-economic analyses of perovskite PV modules.
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.
Scalable manufacturing processes with soft materials
White, Edward; Case, Jennifer; Kramer, Rebecca
2014-01-01
The emerging field of soft robotics will benefit greatly from new scalable manufacturing techniques for responsive materials. Currently, most of soft robotic examples are fabricated one-at-a-time, using techniques borrowed from lithography and 3D printing to fabricate molds. This limits both the maximum and minimum size of robots that can be fabricated, and hinders batch production, which is critical to gain wider acceptance for soft robotic systems. We have identified electrical structures, ...
Architecture Knowledge for Evaluating Scalable Databases
2015-01-16
Architecture Knowledge for Evaluating Scalable Databases 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Nurgaliev... Scala , Erlang, Javascript Cursor-based queries Supported, Not Supported JOIN queries Supported, Not Supported Complex data types Lists, maps, sets...is therefore needed, using technology such as machine learning to extract content from product documentation. The terminology used in the database
Randomized Algorithms for Scalable Machine Learning
Kleiner, Ariel Jacob
2012-01-01
Many existing procedures in machine learning and statistics are computationally intractable in the setting of large-scale data. As a result, the advent of rapidly increasing dataset sizes, which should be a boon yielding improved statistical performance, instead severely blunts the usefulness of a variety of existing inferential methods. In this work, we use randomness to ameliorate this lack of scalability by reducing complex, computationally difficult inferential problems to larger sets o...
Bitcoin-NG: A Scalable Blockchain Protocol
Eyal, Ittay; Gencer, Adem Efe; Sirer, Emin Gun; van 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...
Scuba: scalable kernel-based gene prioritization.
Zampieri, Guido; Tran, Dinh Van; Donini, Michele; Navarin, Nicolò; Aiolli, Fabio; Sperduti, Alessandro; Valle, Giorgio
2018-01-25
The uncovering of genes linked to human diseases is a pressing challenge in molecular biology and precision medicine. This task is often hindered by the large number of candidate genes and by the heterogeneity of the available information. Computational methods for the prioritization of candidate genes can help to cope with these problems. In particular, kernel-based methods are a powerful resource for the integration of heterogeneous biological knowledge, however, their practical implementation is often precluded by their limited scalability. We propose Scuba, a scalable kernel-based method for gene prioritization. It implements a novel multiple kernel learning approach, based on a semi-supervised perspective and on the optimization of the margin distribution. Scuba is optimized to cope with strongly unbalanced settings where known disease genes are few and large scale predictions are required. Importantly, it is able to efficiently deal both with a large amount of candidate genes and with an arbitrary number of data sources. As a direct consequence of scalability, Scuba integrates also a new efficient strategy to select optimal kernel parameters for each data source. We performed cross-validation experiments and simulated a realistic usage setting, showing that Scuba outperforms a wide range of state-of-the-art methods. Scuba achieves state-of-the-art performance and has enhanced scalability compared to existing kernel-based approaches for genomic data. This method can be useful to prioritize candidate genes, particularly when their number is large or when input data is highly heterogeneous. The code is freely available at https://github.com/gzampieri/Scuba .
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%.
Scalable robotic biofabrication of tissue spheroids
International Nuclear Information System (INIS)
Mehesz, A Nagy; Hajdu, Z; Visconti, R P; Markwald, R R; Mironov, V; Brown, J; Beaver, W; Da Silva, J V L
2011-01-01
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.
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.
International Nuclear Information System (INIS)
Zhang, Liangwei; Lin, Jing; Karim, Ramin
2015-01-01
The accuracy of traditional anomaly detection techniques implemented on full-dimensional spaces degrades significantly as dimensionality increases, thereby hampering many real-world applications. This work proposes an approach to selecting meaningful feature subspace and conducting anomaly detection in the corresponding subspace projection. The aim is to maintain the detection accuracy in high-dimensional circumstances. The suggested approach assesses the angle between all pairs of two lines for one specific anomaly candidate: the first line is connected by the relevant data point and the center of its adjacent points; the other line is one of the axis-parallel lines. Those dimensions which have a relatively small angle with the first line are then chosen to constitute the axis-parallel subspace for the candidate. Next, a normalized Mahalanobis distance is introduced to measure the local outlier-ness of an object in the subspace projection. To comprehensively compare the proposed algorithm with several existing anomaly detection techniques, we constructed artificial datasets with various high-dimensional settings and found the algorithm displayed superior accuracy. A further experiment on an industrial dataset demonstrated the applicability of the proposed algorithm in fault detection tasks and highlighted another of its merits, namely, to provide preliminary interpretation of abnormality through feature ordering in relevant subspaces. - Highlights: • An anomaly detection approach for high-dimensional reliability data is proposed. • The approach selects relevant subspaces by assessing vectorial angles. • The novel ABSAD approach displays superior accuracy over other alternatives. • Numerical illustration approves its efficacy in fault detection applications
Simultaneous multislice magnetic resonance fingerprinting with low-rank and subspace modeling.
Bo Zhao; Bilgic, Berkin; Adalsteinsson, Elfar; Griswold, Mark A; Wald, Lawrence L; Setsompop, Kawin
2017-07-01
Magnetic resonance fingerprinting (MRF) is a new quantitative imaging paradigm that enables simultaneous acquisition of multiple magnetic resonance tissue parameters (e.g., T 1 , T 2 , and spin density). Recently, MRF has been integrated with simultaneous multislice (SMS) acquisitions to enable volumetric imaging with faster scan time. In this paper, we present a new image reconstruction method based on low-rank and subspace modeling for improved SMS-MRF. Here the low-rank model exploits strong spatiotemporal correlation among contrast-weighted images, while the subspace model captures the temporal evolution of magnetization dynamics. With the proposed model, the image reconstruction problem is formulated as a convex optimization problem, for which we develop an algorithm based on variable splitting and the alternating direction method of multipliers. The performance of the proposed method has been evaluated by numerical experiments, and the results demonstrate that the proposed method leads to improved accuracy over the conventional approach. Practically, the proposed method has a potential to allow for a 3× speedup with minimal reconstruction error, resulting in less than 5 sec imaging time per slice.
Robust Switching Control and Subspace Identification for Flutter of Flexible Wing
Directory of Open Access Journals (Sweden)
Yizhe Wang
2018-01-01
Full Text Available Active flutter suppression and subspace identification for a flexible wing model using micro fiber composite actuator were experimentally studied in a low speed wind tunnel. NACA0006 thin airfoil model was used for the experimental object to verify the performance of identification algorithm and designed controller. The equation of the fluid, vibration, and piezoelectric coupled motion was theoretically analyzed and experimentally identified under the open-loop and closed-loop condition by subspace method for controller design. A robust pole placement algorithm in terms of linear matrix inequality that accommodates the model uncertainty caused by identification deviation and flow speed variation was utilized to stabilize the divergent aeroelastic system. For further enlarging the flutter envelope, additional controllers were designed subject to the models beyond the flutter speed. Wind speed was measured online as the decision parameter of switching between the controllers. To ensure the stability of arbitrary switching, Common Lyapunov function method was applied to design the robust pole placement controllers for different models to ensure that the closed-loop system shared a common Lyapunov function. Wind tunnel result showed that the designed controllers could stabilize the time varying aeroelastic system over a wide range under arbitrary switching.
An Improved EMD-Based Dissimilarity Metric for Unsupervised Linear Subspace Learning
Directory of Open Access Journals (Sweden)
Xiangchun Yu
2018-01-01
Full Text Available We investigate a novel way of robust face image feature extraction by adopting the methods based on Unsupervised Linear Subspace Learning to extract a small number of good features. Firstly, the face image is divided into blocks with the specified size, and then we propose and extract pooled Histogram of Oriented Gradient (pHOG over each block. Secondly, an improved Earth Mover’s Distance (EMD metric is adopted to measure the dissimilarity between blocks of one face image and the corresponding blocks from the rest of face images. Thirdly, considering the limitations of the original Locality Preserving Projections (LPP, we proposed the Block Structure LPP (BSLPP, which effectively preserves the structural information of face images. Finally, an adjacency graph is constructed and a small number of good features of a face image are obtained by methods based on Unsupervised Linear Subspace Learning. A series of experiments have been conducted on several well-known face databases to evaluate the effectiveness of the proposed algorithm. In addition, we construct the noise, geometric distortion, slight translation, slight rotation AR, and Extended Yale B face databases, and we verify the robustness of the proposed algorithm when faced with a certain degree of these disturbances.
Directory of Open Access Journals (Sweden)
Omar Eldwaik
2018-01-01
Full Text Available Wind induced noise is one of the major concerns of outdoor acoustic signal acquisition. It affects many field measurement and audio recording scenarios. Filtering such noise is known to be difficult due to its broadband and time varying nature. In this paper, a new method to mitigate wind induced noise in microphone signals is developed. Instead of applying filtering techniques, wind induced noise is statistically separated from wanted signals in a singular spectral subspace. The paper is presented in the context of handling microphone signals acquired outdoor for acoustic sensing and environmental noise monitoring or soundscapes sampling. The method includes two complementary stages, namely decomposition and reconstruction. The first stage decomposes mixed signals in eigen-subspaces, selects and groups the principal components according to their contributions to wind noise and wanted signals in the singular spectrum domain. The second stage reconstructs the signals in the time domain, resulting in the separation of wind noise and wanted signals. Results show that microphone wind noise is separable in the singular spectrum domain evidenced by the weighted correlation. The new method might be generalized to other outdoor sound acquisition applications.
International Nuclear Information System (INIS)
Abdel-Khalik, Hany S.; Zhang, Qiong
2014-01-01
The development of hybrid Monte-Carlo-Deterministic (MC-DT) approaches, taking place over the past few decades, have primarily focused on shielding and detection applications where the analysis requires a small number of responses, i.e. at the detector locations(s). This work further develops a recently introduced global variance reduction approach, denoted by the SUBSPACE approach is designed to allow the use of MC simulation, currently limited to benchmarking calculations, for routine engineering calculations. By way of demonstration, the SUBSPACE approach is applied to assembly level calculations used to generate the few-group homogenized cross-sections. These models are typically expensive and need to be executed in the order of 10 3 - 10 5 times to properly characterize the few-group cross-sections for downstream core-wide calculations. Applicability to k-eigenvalue core-wide models is also demonstrated in this work. Given the favorable results obtained in this work, we believe the applicability of the MC method for reactor analysis calculations could be realized in the near future.
Improved magnetic resonance fingerprinting reconstruction with low-rank and subspace modeling.
Zhao, Bo; Setsompop, Kawin; Adalsteinsson, Elfar; Gagoski, Borjan; Ye, Huihui; Ma, Dan; Jiang, Yun; Ellen Grant, P; Griswold, Mark A; Wald, Lawrence L
2018-02-01
This article introduces a constrained imaging method based on low-rank and subspace modeling to improve the accuracy and speed of MR fingerprinting (MRF). A new model-based imaging method is developed for MRF to reconstruct high-quality time-series images and accurate tissue parameter maps (e.g., T 1 , T 2 , and spin density maps). Specifically, the proposed method exploits low-rank approximations of MRF time-series images, and further enforces temporal subspace constraints to capture magnetization dynamics. This allows the time-series image reconstruction problem to be formulated as a simple linear least-squares problem, which enables efficient computation. After image reconstruction, tissue parameter maps are estimated via dictionary-based pattern matching, as in the conventional approach. The effectiveness of the proposed method was evaluated with in vivo experiments. Compared with the conventional MRF reconstruction, the proposed method reconstructs time-series images with significantly reduced aliasing artifacts and noise contamination. Although the conventional approach exhibits some robustness to these corruptions, the improved time-series image reconstruction in turn provides more accurate tissue parameter maps. The improvement is pronounced especially when the acquisition time becomes short. The proposed method significantly improves the accuracy of MRF, and also reduces data acquisition time. Magn Reson Med 79:933-942, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.
An efficient preconditioning technique using Krylov subspace methods for 3D characteristics solvers
International Nuclear Information System (INIS)
Dahmani, M.; Le Tellier, R.; Roy, R.; Hebert, A.
2005-01-01
The Generalized Minimal RESidual (GMRES) method, using a Krylov subspace projection, is adapted and implemented to accelerate a 3D iterative transport solver based on the characteristics method. Another acceleration technique called the self-collision rebalancing technique (SCR) can also be used to accelerate the solution or as a left preconditioner for GMRES. The GMRES method is usually used to solve a linear algebraic system (Ax=b). It uses K(r (o) ,A) as projection subspace and AK(r (o) ,A) for the orthogonalization of the residual. This paper compares the performance of these two combined methods on various problems. To implement the GMRES iterative method, the characteristics equations are derived in linear algebra formalism by using the equivalence between the method of characteristics and the method of collision probability to end up with a linear algebraic system involving fluxes and currents. Numerical results show good performance of the GMRES technique especially for the cases presenting large material heterogeneity with a scattering ratio close to 1. Similarly, the SCR preconditioning slightly increases the GMRES efficiency
Gene selection for microarray data classification via subspace learning and manifold regularization.
Tang, Chang; Cao, Lijuan; Zheng, Xiao; Wang, Minhui
2017-12-19
With the rapid development of DNA microarray technology, large amount of genomic data has been generated. Classification of these microarray data is a challenge task since gene expression data are often with thousands of genes but a small number of samples. In this paper, an effective gene selection method is proposed to select the best subset of genes for microarray data with the irrelevant and redundant genes removed. Compared with original data, the selected gene subset can benefit the classification task. We formulate the gene selection task as a manifold regularized subspace learning problem. In detail, a projection matrix is used to project the original high dimensional microarray data into a lower dimensional subspace, with the constraint that the original genes can be well represented by the selected genes. Meanwhile, the local manifold structure of original data is preserved by a Laplacian graph regularization term on the low-dimensional data space. The projection matrix can serve as an importance indicator of different genes. An iterative update algorithm is developed for solving the problem. Experimental results on six publicly available microarray datasets and one clinical dataset demonstrate that the proposed method performs better when compared with other state-of-the-art methods in terms of microarray data classification. Graphical Abstract The graphical abstract of this work.
Structural damage diagnosis based on on-line recursive stochastic subspace identification
International Nuclear Information System (INIS)
Loh, Chin-Hsiung; Weng, Jian-Huang; Liu, Yi-Cheng; Lin, Pei-Yang; Huang, Shieh-Kung
2011-01-01
This paper presents a recursive stochastic subspace identification (RSSI) technique for on-line and almost real-time structural damage diagnosis using output-only measurements. Through RSSI the time-varying natural frequencies of a system can be identified. To reduce the computation time in conducting LQ decomposition in RSSI, the Givens rotation as well as the matrix operation appending a new data set are derived. The relationship between the size of the Hankel matrix and the data length in each shifting moving window is examined so as to extract the time-varying features of the system without loss of generality and to establish on-line and almost real-time system identification. The result from the RSSI technique can also be applied to structural damage diagnosis. Off-line data-driven stochastic subspace identification was used first to establish the system matrix from the measurements of an undamaged (reference) case. Then the RSSI technique incorporating a Kalman estimator is used to extract the dynamic characteristics of the system through continuous monitoring data. The predicted residual error is defined as a damage feature and through the outlier statistics provides an indicator of damage. Verification of the proposed identification algorithm by using the bridge scouring test data and white noise response data of a reinforced concrete frame structure is conducted
Quantum probabilities as Dempster-Shafer probabilities in the lattice of subspaces
International Nuclear Information System (INIS)
Vourdas, A.
2014-01-01
The orthocomplemented modular lattice of subspaces L[H(d)], of a quantum system with d-dimensional Hilbert space H(d), is considered. A generalized additivity relation which holds for Kolmogorov probabilities is violated by quantum probabilities in the full lattice L[H(d)] (it is only valid within the Boolean subalgebras of L[H(d)]). This suggests the use of more general (than Kolmogorov) probability theories, and here the Dempster-Shafer probability theory is adopted. An operator D(H 1 ,H 2 ), which quantifies deviations from Kolmogorov probability theory is introduced, and it is shown to be intimately related to the commutator of the projectors P(H 1 ),P(H 2 ), to the subspaces H 1 , H 2 . As an application, it is shown that the proof of the inequalities of Clauser, Horne, Shimony, and Holt for a system of two spin 1/2 particles is valid for Kolmogorov probabilities, but it is not valid for Dempster-Shafer probabilities. The violation of these inequalities in experiments supports the interpretation of quantum probabilities as Dempster-Shafer probabilities
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.
On the selection of user-defined parameters in data-driven stochastic subspace identification
Priori, C.; De Angelis, M.; Betti, R.
2018-02-01
The paper focuses on the time domain output-only technique called Data-Driven Stochastic Subspace Identification (DD-SSI); in order to identify modal models (frequencies, damping ratios and mode shapes), the role of its user-defined parameters is studied, and rules to determine their minimum values are proposed. Such investigation is carried out using, first, the time histories of structural responses to stationary excitations, with a large number of samples, satisfying the hypothesis on the input imposed by DD-SSI. Then, the case of non-stationary seismic excitations with a reduced number of samples is considered. In this paper, partitions of the data matrix different from the one proposed in the SSI literature are investigated, together with the influence of different choices of the weighting matrices. The study is carried out considering two different applications: (1) data obtained from vibration tests on a scaled structure and (2) in-situ tests on a reinforced concrete building. Referring to the former, the identification of a steel frame structure tested on a shaking table is performed using its responses in terms of absolute accelerations to a stationary (white noise) base excitation and to non-stationary seismic excitations of low intensity. Black-box and modal models are identified in both cases and the results are compared with those from an input-output subspace technique. With regards to the latter, the identification of a complex hospital building is conducted using data obtained from ambient vibration tests.
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.
Chen, Peng; Yang, Yixin; Wang, Yong; Ma, Yuanliang
2018-05-08
When sensor position errors exist, the performance of recently proposed interference-plus-noise covariance matrix (INCM)-based adaptive beamformers may be severely degraded. In this paper, we propose a weighted subspace fitting-based INCM reconstruction algorithm to overcome sensor displacement for linear arrays. By estimating the rough signal directions, we construct a novel possible mismatched steering vector (SV) set. We analyze the proximity of the signal subspace from the sample covariance matrix (SCM) and the space spanned by the possible mismatched SV set. After solving an iterative optimization problem, we reconstruct the INCM using the estimated sensor position errors. Then we estimate the SV of the desired signal by solving an optimization problem with the reconstructed INCM. The main advantage of the proposed algorithm is its robustness against SV mismatches dominated by unknown sensor position errors. Numerical examples show that even if the position errors are up to half of the assumed sensor spacing, the output signal-to-interference-plus-noise ratio is only reduced by 4 dB. Beam patterns plotted using experiment data show that the interference suppression capability of the proposed beamformer outperforms other tested beamformers.
Yu, Yinan; Diamantaras, Konstantinos I; McKelvey, Tomas; Kung, Sun-Yuan
2018-02-01
In kernel-based classification models, given limited computational power and storage capacity, operations over the full kernel matrix becomes prohibitive. In this paper, we propose a new supervised learning framework using kernel models for sequential data processing. The framework is based on two components that both aim at enhancing the classification capability with a subset selection scheme. The first part is a subspace projection technique in the reproducing kernel Hilbert space using a CLAss-specific Subspace Kernel representation for kernel approximation. In the second part, we propose a novel structural risk minimization algorithm called the adaptive margin slack minimization to iteratively improve the classification accuracy by an adaptive data selection. We motivate each part separately, and then integrate them into learning frameworks for large scale data. We propose two such frameworks: the memory efficient sequential processing for sequential data processing and the parallelized sequential processing for distributed computing with sequential data acquisition. We test our methods on several benchmark data sets and compared with the state-of-the-art techniques to verify the validity of the proposed techniques.
Energy Technology Data Exchange (ETDEWEB)
Renaut, R.; He, Q. [Arizona State Univ., Tempe, AZ (United States)
1994-12-31
In a new parallel iterative algorithm for unconstrained optimization by multisplitting is proposed. In this algorithm the original problem is split into a set of small optimization subproblems which are solved using well known sequential algorithms. These algorithms are iterative in nature, e.g. DFP variable metric method. Here the authors use sequential algorithms based on an inexact subspace search, which is an extension to the usual idea of an inexact fine search. Essentially the idea of the inexact line search for nonlinear minimization is that at each iteration the authors only find an approximate minimum in the line search direction. Hence by inexact subspace search, they mean that, instead of finding the minimum of the subproblem at each interation, they do an incomplete down hill search to give an approximate minimum. Some convergence and numerical results for this algorithm will be presented. Further, the original theory will be generalized to the situation with a singular Hessian. Applications for nonlinear least squares problems will be presented. Experimental results will be presented for implementations on an Intel iPSC/860 Hypercube with 64 nodes as well as on the Intel Paragon.
On the Kalman Filter error covariance collapse into the unstable subspace
Directory of Open Access Journals (Sweden)
A. Trevisan
2011-03-01
Full Text Available When the Extended Kalman Filter is applied to a chaotic system, the rank of the error covariance matrices, after a sufficiently large number of iterations, reduces to N^{+} + N^{0} where N^{+} and N^{0} are the number of positive and null Lyapunov exponents. This is due to the collapse into the unstable and neutral tangent subspace of the solution of the full Extended Kalman Filter. Therefore the solution is the same as the solution obtained by confining the assimilation to the space spanned by the Lyapunov vectors with non-negative Lyapunov exponents. Theoretical arguments and numerical verification are provided to show that the asymptotic state and covariance estimates of the full EKF and of its reduced form, with assimilation in the unstable and neutral subspace (EKF-AUS are the same. The consequences of these findings on applications of Kalman type Filters to chaotic models are discussed.
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.
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
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.
Histamine headache; Headache - histamine; Migrainous neuralgia; Headache - cluster; Horton's headache; Vascular headache - cluster ... Doctors do not know exactly what causes cluster headaches. They ... (chemical in the body released during an allergic response) or ...
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.
Scalable Distributed Architectures for Information Retrieval
National Research Council Canada - National Science Library
Lu, Zhihong
1999-01-01
.... Our distributed architectures exploit parallelism in information retrieval on a cluster of parallel IR servers using symmetric multiprocessors, and use partial collection replication and selection...
Directory of Open Access Journals (Sweden)
Alexander Caicedo
2016-11-01
Full Text Available Clinical data is comprised by a large number of synchronously collected biomedical signals that are measured at different locations. Deciphering the interrelationships of these signals can yield important information about their dependence providing some useful clinical diagnostic data. For instance, by computing the coupling between Near-Infrared Spectroscopy signals (NIRS and systemic variables the status of the hemodynamic regulation mechanisms can be assessed. In this paper we introduce an algorithm for the decomposition of NIRS signals into additive components. The algorithm, SIgnal DEcomposition base on Obliques Subspace Projections (SIDE-ObSP, assumes that the measured NIRS signal is a linear combination of the systemic measurements, following the linear regression model y = Ax + _. SIDE-ObSP decomposes the output such that, each component in the decomposition represents the sole linear influence of one corresponding regressor variable. This decomposition scheme aims at providing a better understanding of the relation between NIRS and systemic variables, and to provide a framework for the clinical interpretation of regression algorithms, thereby, facilitating their introduction into clinical practice. SIDE-ObSP combines oblique subspace projections (ObSP with the structure of a mean average system in order to define adequate signal subspaces. To guarantee smoothness in the estimated regression parameters, as observed in normal physiological processes, we impose a Tikhonov regularization using a matrix differential operator. We evaluate the performance of SIDE-ObSP by using a synthetic dataset, and present two case studies in the field of cerebral hemodynamics monitoring using NIRS. In addition, we compare the performance of this method with other system identification techniques. In the first case study data from 20 neonates during the first three days of life was used, here SIDE-ObSP decoupled the influence of changes in arterial oxygen
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
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
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.
Tip-Based Nanofabrication for Scalable Manufacturing
International Nuclear Information System (INIS)
Hu, Huan; Somnath, Suhas
2017-01-01
Tip-based nanofabrication (TBN) is a family of emerging nanofabrication techniques that use a nanometer scale tip to fabricate nanostructures. Here 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.
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
Scalable Optical-Fiber Communication Networks
Chow, Edward T.; Peterson, John C.
1993-01-01
Scalable arbitrary fiber extension network (SAFEnet) is conceptual fiber-optic communication network passing digital signals among variety of computers and input/output devices at rates from 200 Mb/s to more than 100 Gb/s. Intended for use with very-high-speed computers and other data-processing and communication systems in which message-passing delays must be kept short. Inherent flexibility makes it possible to match performance of network to computers by optimizing configuration of interconnections. In addition, interconnections made redundant to provide tolerance to faults.
Scalable Tensor Factorizations with Missing Data
DEFF Research Database (Denmark)
Acar, Evrim; Dunlavy, Daniel M.; Kolda, Tamara G.
2010-01-01
of missing data, many important data sets will be discarded or improperly analyzed. Therefore, we need a robust and scalable approach for factorizing multi-way arrays (i.e., tensors) in the presence of missing data. We focus on one of the most well-known tensor factorizations, CANDECOMP/PARAFAC (CP...... is shown to successfully factor tensors with noise and up to 70% missing data. Moreover, our approach is significantly faster than the leading alternative and scales to larger problems. To show the real-world usefulness of CP-WOPT, we illustrate its applicability on a novel EEG (electroencephalogram...
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.
International Nuclear Information System (INIS)
Arvieu, R.
The assumptions and principles of the spectral distribution method are reviewed. The object of the method is to deduce information on the nuclear spectra by constructing a frequency function which has the same first few moments, as the exact frequency function, these moments being then exactly calculated. The method is applied to subspaces containing a large number of quasi particles [fr
Hierarchical sets: analyzing pangenome structure through scalable set visualizations
2017-01-01
Abstract Motivation: 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. Results: 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. Availability and Implementation: The described clustering algorithm and visualizations are implemented in the hierarchicalSets R package available from CRAN (https://cran.r-project.org/web/packages/hierarchicalSets) Contact: thomasp85@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28130242
Directory of Open Access Journals (Sweden)
Ulf Hensen
Full Text Available We develop a general minimally coupled subspace approach (MCSA to compute absolute entropies of macromolecules, such as proteins, from computer generated canonical ensembles. Our approach overcomes limitations of current estimates such as the quasi-harmonic approximation which neglects non-linear and higher-order correlations as well as multi-minima characteristics of protein energy landscapes. Here, Full Correlation Analysis, adaptive kernel density estimation, and mutual information expansions are combined and high accuracy is demonstrated for a number of test systems ranging from alkanes to a 14 residue peptide. We further computed the configurational entropy for the full 67-residue cofactor of the TATA box binding protein illustrating that MCSA yields improved results also for large macromolecular systems.
Adaptive Detectors for Two Types of Subspace Targets in an Inverse Gamma Textured Background
Directory of Open Access Journals (Sweden)
Ding Hao
2017-06-01
Full Text Available Considering an inverse Gamma prior distribution model for texture, the adaptive detection problems for both first order Gaussian and second order Gaussian subspace targets are researched in a compound Gaussian sea clutter. Test statistics are derived on the basis of the two-step generalized likelihood ratio test. From these tests, new adaptive detectors are proposed by substituting the covariance matrix with estimation results from the Sample Covariance Matrix (SCM, normalized SCM, and fixed point estimator. The proposed detectors consider the prior distribution model for sea clutter during the design stage, and they model parameters that match the working environment during the detection stage. Analysis and validation results indicate that the detection performance of the proposed detectors out performs existing AMF (Adaptive Matched Filter, AMF and ANMF (Adaptive Normalized Matched Filter, ANMF detectors.
Siddiqui, Bilal A.; El-Ferik, Sami; Abdelkader, Mohamed
2016-01-01
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.
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...... has narrow-band characteristics, or in the case of tonal noise. We extend the concept of prewhitening to include the case when the noise covariance matrix is rank deficient, using a weighted pseudoinverse and the quotient SVD, and we show how to formulate a general rank-reduction algorithm that works...... also for rank deficient noise. We also demonstrate how to formulate this algorithm by means of a quotient ULV decomposition, which allows for faster computation and updating. Finally we apply our algorithm to a problem involving a speech signal contaminated by narrow-band noise....
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.
Acoustic Source Localization via Subspace Based Method Using Small Aperture MEMS Arrays
Directory of Open Access Journals (Sweden)
Xin Zhang
2014-01-01
Full Text Available Small aperture microphone arrays provide many advantages for portable devices and hearing aid equipment. In this paper, a subspace based localization method is proposed for acoustic source using small aperture arrays. The effects of array aperture on localization are analyzed by using array response (array manifold. Besides array aperture, the frequency of acoustic source and the variance of signal power are simulated to demonstrate how to optimize localization performance, which is carried out by introducing frequency error with the proposed method. The proposed method for 5 mm array aperture is validated by simulations and experiments with MEMS microphone arrays. Different types of acoustic sources can be localized with the highest precision of 6 degrees even in the presence of wind noise and other noises. Furthermore, the proposed method reduces the computational complexity compared with other methods.
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.
Rank-defective millimeter-wave channel estimation based on subspace-compressive sensing
Directory of Open Access Journals (Sweden)
Majid Shakhsi Dastgahian
2016-11-01
Full Text Available Millimeter-wave communication (mmWC is considered as one of the pioneer candidates for 5G indoor and outdoor systems in E-band. To subdue the channel propagation characteristics in this band, high dimensional antenna arrays need to be deployed at both the base station (BS and mobile sets (MS. Unlike the conventional MIMO systems, Millimeter-wave (mmW systems lay away to employ the power predatory equipment such as ADC or RF chain in each branch of MIMO system because of hardware constraints. Such systems leverage to the hybrid precoding (combining architecture for downlink deployment. Because there is a large array at the transceiver, it is impossible to estimate the channel by conventional methods. This paper develops a new algorithm to estimate the mmW channel by exploiting the sparse nature of the channel. The main contribution is the representation of a sparse channel model and the exploitation of a modified approach based on Multiple Measurement Vector (MMV greedy sparse framework and subspace method of Multiple Signal Classification (MUSIC which work together to recover the indices of non-zero elements of an unknown channel matrix when the rank of the channel matrix is defected. In practical rank-defective channels, MUSIC fails, and we need to propose new extended MUSIC approaches based on subspace enhancement to compensate the limitation of MUSIC. Simulation results indicate that our proposed extended MUSIC algorithms will have proper performances and moderate computational speeds, and that they are even able to work in channels with an unknown sparsity level.
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.
Highly scalable Ab initio genomic motif identification
Marchand, Benoit; Bajic, Vladimir B.; Kaushik, Dinesh
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.
Algorithmic psychometrics and the scalable subject.
Stark, Luke
2018-04-01
Recent public controversies, ranging from the 2014 Facebook 'emotional contagion' study to psychographic data profiling by Cambridge Analytica in the 2016 American presidential election, Brexit referendum and elsewhere, signal watershed moments in which the intersecting trajectories of psychology and computer science have become matters of public concern. The entangled history of these two fields grounds the application of applied psychological techniques to digital technologies, and an investment in applying calculability to human subjectivity. Today, a quantifiable psychological subject position has been translated, via 'big data' sets and algorithmic analysis, into a model subject amenable to classification through digital media platforms. I term this position the 'scalable subject', arguing it has been shaped and made legible by algorithmic psychometrics - a broad set of affordances in digital platforms shaped by psychology and the behavioral sciences. In describing the contours of this 'scalable subject', this paper highlights the urgent need for renewed attention from STS scholars on the psy sciences, and on a computational politics attentive to psychology, emotional expression, and sociality via digital media.
Scalable Simulation of Electromagnetic Hybrid Codes
International Nuclear Information System (INIS)
Perumalla, Kalyan S.; Fujimoto, Richard; Karimabadi, Dr. Homa
2006-01-01
New discrete-event formulations of physics simulation models are emerging that can outperform models based on traditional time-stepped techniques. Detailed simulation of the Earth's magnetosphere, for example, requires execution of sub-models that are at widely differing timescales. In contrast to time-stepped simulation which requires tightly coupled updates to entire system state at regular time intervals, the new discrete event simulation (DES) approaches help evolve the states of sub-models on relatively independent timescales. However, parallel execution of DES-based models raises challenges with respect to their scalability and performance. One of the key challenges is to improve the computation granularity to offset synchronization and communication overheads within and across processors. Our previous work was limited in scalability and runtime performance due to the parallelization challenges. Here we report on optimizations we performed on DES-based plasma simulation models to improve parallel performance. The net result is the capability to simulate hybrid particle-in-cell (PIC) models with over 2 billion ion particles using 512 processors on supercomputing platforms
Towards Scalable Graph Computation on Mobile Devices.
Chen, Yiqi; Lin, Zhiyuan; Pienta, Robert; Kahng, Minsuk; Chau, Duen Horng
2014-10-01
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.
Computational scalability of large size image dissemination
Kooper, Rob; Bajcsy, Peter
2011-01-01
We have investigated the computational scalability of image pyramid building needed for dissemination of very large image data. The sources of large images include high resolution microscopes and telescopes, remote sensing and airborne imaging, and high resolution scanners. The term 'large' is understood from a user perspective which means either larger than a display size or larger than a memory/disk to hold the image data. The application drivers for our work are digitization projects such as the Lincoln Papers project (each image scan is about 100-150MB or about 5000x8000 pixels with the total number to be around 200,000) and the UIUC library scanning project for historical maps from 17th and 18th century (smaller number but larger images). The goal of our work is understand computational scalability of the web-based dissemination using image pyramids for these large image scans, as well as the preservation aspects of the data. We report our computational benchmarks for (a) building image pyramids to be disseminated using the Microsoft Seadragon library, (b) a computation execution approach using hyper-threading to generate image pyramids and to utilize the underlying hardware, and (c) an image pyramid preservation approach using various hard drive configurations of Redundant Array of Independent Disks (RAID) drives for input/output operations. The benchmarks are obtained with a map (334.61 MB, JPEG format, 17591x15014 pixels). The discussion combines the speed and preservation objectives.
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
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.
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).
Naeem, Raeece; Rashid, Mamoon; Pain, Arnab
2012-01-01
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).
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...
Reduce, reuse, recycle for robust cluster-state generation
International Nuclear Information System (INIS)
Horsman, Clare; Brown, Katherine L.; Kendon, Vivien M.; Munro, William J.
2011-01-01
Efficient generation of cluster states is crucial for engineering large-scale measurement-based quantum computers. Hybrid matter-optical systems offer a robust, scalable path to this goal. Such systems have an ancilla which acts as a bus connecting the qubits. We show that by generating the cluster in smaller sections of interlocking bricks, reusing one ancilla per brick, the cluster can be produced with maximal efficiency, requiring fewer than half the operations compared with no bus reuse. By reducing the time required to prepare sections of the cluster, bus reuse more than doubles the size of the computational workspace that can be used before decoherence effects dominate. A row of buses in parallel provides fully scalable cluster-state generation requiring only 20 controlled-phase gates per bus use.
Domain decomposition method of stochastic PDEs: a two-level scalable preconditioner
International Nuclear Information System (INIS)
Subber, Waad; Sarkar, Abhijit
2012-01-01
For uncertainty quantification in many practical engineering problems, the stochastic finite element method (SFEM) may be computationally challenging. In SFEM, the size of the algebraic linear system grows rapidly with the spatial mesh resolution and the order of the stochastic dimension. In this paper, we describe a non-overlapping domain decomposition method, namely the iterative substructuring method to tackle the large-scale linear system arising in the SFEM. The SFEM is based on domain decomposition in the geometric space and a polynomial chaos expansion in the probabilistic space. In particular, a two-level scalable preconditioner is proposed for the iterative solver of the interface problem for the stochastic systems. The preconditioner is equipped with a coarse problem which globally connects the subdomains both in the geometric and probabilistic spaces via their corner nodes. This coarse problem propagates the information quickly across the subdomains leading to a scalable preconditioner. For numerical illustrations, a two-dimensional stochastic elliptic partial differential equation (SPDE) with spatially varying non-Gaussian random coefficients is considered. The numerical scalability of the the preconditioner is investigated with respect to the mesh size, subdomain size, fixed problem size per subdomain and order of polynomial chaos expansion. The numerical experiments are performed on a Linux cluster using MPI and PETSc parallel libraries.
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.
Quantification of the clustering properties of nuclear states
International Nuclear Information System (INIS)
Beck, R.; Dickmann, F.
1985-05-01
The amount of particular type of clustering in a nuclear state is defined in this paper as the norm square of the projection of the wave function onto the particular cluster model subspace. It is pointed out that, although the clusters can not be localized in space by measurement, the amount of clustering characterizes the cluster formation in close analogy with a quantum mechanical probability. The cluster model component of the wave function is proved to have a variational property. This facilitates the computation of the amount of clustering. The model dependence of the amounts of various clusterings and their relationship to the corresponding spectroscopic factors are studied via numerical examples for two models of sup(6)Li. It is concluded that, in a relative sense, the spectroscopic factor, which is more directly related to experiment, is also characteristic of the clustering contents of different states of the same nucleus, but it can not be used for comparisons between different nuclei or clusterings. (author)
Cluster computing software for GATE simulations
International Nuclear Information System (INIS)
Beenhouwer, Jan de; Staelens, Steven; Kruecker, Dirk; Ferrer, Ludovic; D'Asseler, Yves; Lemahieu, Ignace; Rannou, Fernando R.
2007-01-01
Geometry and tracking (GEANT4) is a Monte Carlo package designed for high energy physics experiments. It is used as the basis layer for Monte Carlo simulations of nuclear medicine acquisition systems in GEANT4 Application for Tomographic Emission (GATE). GATE allows the user to realistically model experiments using accurate physics models and time synchronization for detector movement through a script language contained in a macro file. The downside of this high accuracy is long computation time. This paper describes a platform independent computing approach for running GATE simulations on a cluster of computers in order to reduce the overall simulation time. Our software automatically creates fully resolved, nonparametrized macros accompanied with an on-the-fly generated cluster specific submit file used to launch the simulations. The scalability of GATE simulations on a cluster is investigated for two imaging modalities, positron emission tomography (PET) and single photon emission computed tomography (SPECT). Due to a higher sensitivity, PET simulations are characterized by relatively high data output rates that create rather large output files. SPECT simulations, on the other hand, have lower data output rates but require a long collimator setup time. Both of these characteristics hamper scalability as a function of the number of CPUs. The scalability of PET simulations is improved here by the development of a fast output merger. The scalability of SPECT simulations is improved by greatly reducing the collimator setup time. Accordingly, these two new developments result in higher scalability for both PET and SPECT simulations and reduce the computation time to more practical values
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.
Scalable conditional induction variables (CIV) analysis
DEFF Research Database (Denmark)
Oancea, Cosmin Eugen; Rauchwerger, Lawrence
2015-01-01
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.......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 filter, 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...
Scalable Faceted Ranking in Tagging Systems
Orlicki, José I.; Alvarez-Hamelin, J. Ignacio; Fierens, Pablo I.
Nowadays, web collaborative tagging systems which allow users to upload, comment on and recommend contents, are growing. Such systems can be represented as graphs where nodes correspond to users and tagged-links to recommendations. In this paper we analyze the problem of computing a ranking of users with respect to a facet described as a set of tags. A straightforward solution is to compute a PageRank-like algorithm on a facet-related graph, but it is not feasible for online computation. We propose an alternative: (i) a ranking for each tag is computed offline on the basis of tag-related subgraphs; (ii) a faceted order is generated online by merging rankings corresponding to all the tags in the facet. Based on the graph analysis of YouTube and Flickr, we show that step (i) is scalable. We also present efficient algorithms for step (ii), which are evaluated by comparing their results with two gold standards.
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.
iSIGHT-FD scalability test report.
Energy Technology Data Exchange (ETDEWEB)
Clay, Robert L.; Shneider, Max S.
2008-07-01
The engineering analysis community at Sandia National Laboratories uses a number of internal and commercial software codes and tools, including mesh generators, preprocessors, mesh manipulators, simulation codes, post-processors, and visualization packages. We define an analysis workflow as the execution of an ordered, logical sequence of these tools. Various forms of analysis (and in particular, methodologies that use multiple function evaluations or samples) involve executing parameterized variations of these workflows. As part of the DART project, we are evaluating various commercial workflow management systems, including iSIGHT-FD from Engineous. This report documents the results of a scalability test that was driven by DAKOTA and conducted on a parallel computer (Thunderbird). The purpose of this experiment was to examine the suitability and performance of iSIGHT-FD for large-scale, parameterized analysis workflows. As the results indicate, we found iSIGHT-FD to be suitable for this type of application.
Scalable group level probabilistic sparse factor analysis
DEFF Research Database (Denmark)
Hinrich, Jesper Løve; Nielsen, Søren Føns Vind; Riis, Nicolai Andre Brogaard
2017-01-01
Many data-driven approaches exist to extract neural representations of functional magnetic resonance imaging (fMRI) data, but most of them lack a proper probabilistic formulation. We propose a scalable group level probabilistic sparse factor analysis (psFA) allowing spatially sparse maps, component...... pruning using automatic relevance determination (ARD) and subject specific heteroscedastic spatial noise modeling. For task-based and resting state fMRI, we show that the sparsity constraint gives rise to components similar to those obtained by group independent component analysis. The noise modeling...... shows that noise is reduced in areas typically associated with activation by the experimental design. The psFA model identifies sparse components and the probabilistic setting provides a natural way to handle parameter uncertainties. The variational Bayesian framework easily extends to more complex...
Scalable on-chip quantum state tomography
Titchener, James G.; Gräfe, Markus; Heilmann, René; Solntsev, Alexander S.; Szameit, Alexander; Sukhorukov, Andrey A.
2018-03-01
Quantum information systems are on a path to vastly exceed the complexity of any classical device. The number of entangled qubits in quantum devices is rapidly increasing, and the information required to fully describe these systems scales exponentially with qubit number. This scaling is the key benefit of quantum systems, however it also presents a severe challenge. To characterize such systems typically requires an exponentially long sequence of different measurements, becoming highly resource demanding for large numbers of qubits. Here we propose and demonstrate a novel and scalable method for characterizing quantum systems based on expanding a multi-photon state to larger dimensionality. We establish that the complexity of this new measurement technique only scales linearly with the number of qubits, while providing a tomographically complete set of data without a need for reconfigurability. We experimentally demonstrate an integrated photonic chip capable of measuring two- and three-photon quantum states with statistical reconstruction fidelity of 99.71%.
A versatile scalable PET processing system
International Nuclear Information System (INIS)
Dong, H.; Weisenberger, A.; McKisson, J.; Wenze, Xi; Cuevas, C.; Wilson, J.; Zukerman, L.
2011-01-01
Positron Emission Tomography (PET) historically has major clinical and preclinical applications in cancerous oncology, neurology, and cardiovascular diseases. Recently, in a new direction, an application specific PET system is being developed at Thomas Jefferson National Accelerator Facility (Jefferson Lab) in collaboration with Duke University, University of Maryland at Baltimore (UMAB), and West Virginia University (WVU) targeted for plant eco-physiology research. The new plant imaging PET system is versatile and scalable such that it could adapt to several plant imaging needs - imaging many important plant organs including leaves, roots, and stems. The mechanical arrangement of the detectors is designed to accommodate the unpredictable and random distribution in space of the plant organs without requiring the plant be disturbed. Prototyping such a system requires a new data acquisition system (DAQ) and data processing system which are adaptable to the requirements of these unique and versatile detectors.
The Concept of Business Model Scalability
DEFF Research Database (Denmark)
Nielsen, Christian; Lund, Morten
2015-01-01
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...... 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...... 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...
The scalable coherent interface, IEEE P1596
International Nuclear Information System (INIS)
Gustavson, D.B.
1990-01-01
IEEE P1596, the scalable coherent interface (formerly known as SuperBus) is based on experience gained while developing Fastbus (ANSI/IEEE 960--1986, IEC 935), Futurebus (IEEE P896.x) and other modern 32-bit buses. SCI goals include a minimum bandwidth of 1 GByte/sec per processor in multiprocessor systems with thousands of processors; efficient support of a coherent distributed-cache image of distributed shared memory; support for repeaters which interface to existing or future buses; and support for inexpensive small rings as well as for general switched interconnections like Banyan, Omega, or crossbar networks. This paper presents a summary of current directions, reports the status of the work in progress, and suggests some applications in data acquisition and physics
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.
Scalable quantum search using trapped ions
International Nuclear Information System (INIS)
Ivanov, S. S.; Ivanov, P. A.; Linington, I. E.; Vitanov, N. V.
2010-01-01
We propose a scalable implementation of Grover's quantum search algorithm in a trapped-ion quantum information processor. The system is initialized in an entangled Dicke state by using adiabatic techniques. The inversion-about-average and oracle operators take the form of single off-resonant laser pulses. This is made possible by utilizing the physical symmetries of the trapped-ion linear crystal. The physical realization of the algorithm represents a dramatic simplification: each logical iteration (oracle and inversion about average) requires only two physical interaction steps, in contrast to the large number of concatenated gates required by previous approaches. This not only facilitates the implementation but also increases the overall fidelity of the algorithm.
Scalable graphene aptasensors for drug quantification
Vishnubhotla, Ramya; Ping, Jinglei; Gao, Zhaoli; Lee, Abigail; Saouaf, Olivia; Vrudhula, Amey; Johnson, A. T. Charlie
2017-11-01
Simpler and more rapid approaches for therapeutic drug-level monitoring are highly desirable to enable use at the point-of-care. We have developed an all-electronic approach for detection of the HIV drug tenofovir based on scalable fabrication of arrays of graphene field-effect transistors (GFETs) functionalized with a commercially available DNA aptamer. The shift in the Dirac voltage of the GFETs varied systematically with the concentration of tenofovir in deionized water, with a detection limit less than 1 ng/mL. Tests against a set of negative controls confirmed the specificity of the sensor response. This approach offers the potential for further development into a rapid and convenient point-of-care tool with clinically relevant performance.
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.
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.
Fourier transform based scalable image quality measure.
Narwaria, Manish; Lin, Weisi; McLoughlin, Ian; Emmanuel, Sabu; Chia, Liang-Tien
2012-08-01
We present a new image quality assessment (IQA) algorithm based on the phase and magnitude of the 2D (twodimensional) Discrete Fourier Transform (DFT). The basic idea is to compare the phase and magnitude of the reference and distorted images to compute the quality score. However, it is well known that the Human Visual Systems (HVSs) sensitivity to different frequency components is not the same. We accommodate this fact via a simple yet effective strategy of nonuniform binning of the frequency components. This process also leads to reduced space representation of the image thereby enabling the reduced-reference (RR) prospects of the proposed scheme. We employ linear regression to integrate the effects of the changes in phase and magnitude. In this way, the required weights are determined via proper training and hence more convincing and effective. Lastly, using the fact that phase usually conveys more information than magnitude, we use only the phase for RR quality assessment. This provides the crucial advantage of further reduction in the required amount of reference image information. The proposed method is therefore further scalable for RR scenarios. We report extensive experimental results using a total of 9 publicly available databases: 7 image (with a total of 3832 distorted images with diverse distortions) and 2 video databases (totally 228 distorted videos). These show that the proposed method is overall better than several of the existing fullreference (FR) algorithms and two RR algorithms. Additionally, there is a graceful degradation in prediction performance as the amount of reference image information is reduced thereby confirming its scalability prospects. To enable comparisons and future study, a Matlab implementation of the proposed algorithm is available at http://www.ntu.edu.sg/home/wslin/reduced_phase.rar.
Improving diabetes medication adherence: successful, scalable interventions
Directory of Open Access Journals (Sweden)
Zullig LL
2015-01-01
Full Text Available Leah L Zullig,1,2 Walid F Gellad,3,4 Jivan Moaddeb,2,5 Matthew J Crowley,1,2 William Shrank,6 Bradi B Granger,7 Christopher B Granger,8 Troy Trygstad,9 Larry Z Liu,10 Hayden B Bosworth1,2,7,11 1Center for Health Services Research in Primary Care, Durham Veterans Affairs Medical Center, Durham, NC, USA; 2Department of Medicine, Duke University, Durham, NC, USA; 3Center for Health Equity Research and Promotion, Pittsburgh Veterans Affairs Medical Center, Pittsburgh, PA, USA; 4Division of General Internal Medicine, University of Pittsburgh, Pittsburgh, PA, USA; 5Institute for Genome Sciences and Policy, Duke University, Durham, NC, USA; 6CVS Caremark Corporation; 7School of Nursing, Duke University, Durham, NC, USA; 8Department of Medicine, Division of Cardiology, Duke University School of Medicine, Durham, NC, USA; 9North Carolina Community Care Networks, Raleigh, NC, USA; 10Pfizer, Inc., and Weill Medical College of Cornell University, New York, NY, USA; 11Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA Abstract: Effective medications are a cornerstone of prevention and disease treatment, yet only about half of patients take their medications as prescribed, resulting in a common and costly public health challenge for the US healthcare system. Since poor medication adherence is a complex problem with many contributing causes, there is no one universal solution. This paper describes interventions that were not only effective in improving medication adherence among patients with diabetes, but were also potentially scalable (ie, easy to implement to a large population. We identify key characteristics that make these interventions effective and scalable. This information is intended to inform healthcare systems seeking proven, low resource, cost-effective solutions to improve medication adherence. Keywords: medication adherence, diabetes mellitus, chronic disease, dissemination research
Scalable and Media Aware Adaptive Video Streaming over Wireless Networks
Directory of Open Access Journals (Sweden)
Béatrice Pesquet-Popescu
2008-07-01
Full Text Available 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.
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.
International Nuclear Information System (INIS)
Geraedts, J.M.P.
1983-01-01
Spectra of isotopically mixed clusters (dimers of SF 6 ) are calculated as well as transition frequencies. The result leads to speculations about the suitability of the laser-cluster fragmentation process for isotope separation. (Auth.)
... a role. Unlike migraine and tension headache, cluster headache generally isn't associated with triggers, such as foods, hormonal changes or stress. Once a cluster period begins, however, drinking alcohol ...
Pearce, Iris
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...
Queiroz, Dayane Andrade
2015-01-01
Neste trabalho apresentamos as categorias cluster, que foram introduzidas por Aslak Bakke Buan, Robert Marsh, Markus Reineke, Idun Reiten e Gordana Todorov, com o objetivo de categoriíicar as algebras cluster criadas em 2002 por Sergey Fomin e Andrei Zelevinsky. Os autores acima, em [4], mostraram que existe uma estreita relação entre algebras cluster e categorias cluster para quivers cujo grafo subjacente é um diagrama de Dynkin. Para isto desenvolveram uma teoria tilting na estrutura triang...
Halligan, Brian D; Geiger, Joey F; Vallejos, Andrew K; Greene, Andrew S; Twigger, Simon N
2009-06-01
One of the major difficulties for many laboratories setting up proteomics programs has been obtaining and maintaining the computational infrastructure required for the analysis of the large flow of proteomics data. We describe a system that combines distributed cloud computing and open source software to allow laboratories to set up scalable virtual proteomics analysis clusters without the investment in computational hardware or software licensing fees. Additionally, the pricing structure of distributed computing providers, such as Amazon Web Services, allows laboratories or even individuals to have large-scale computational resources at their disposal at a very low cost per run. We provide detailed step-by-step instructions on how to implement the virtual proteomics analysis clusters as well as a list of current available preconfigured Amazon machine images containing the OMSSA and X!Tandem search algorithms and sequence databases on the Medical College of Wisconsin Proteomics Center Web site ( http://proteomics.mcw.edu/vipdac ).
Swarm: robust and fast clustering method for amplicon-based studies
Rognes, Torbjørn; Quince, Christopher; de Vargas, Colomban; Dunthorn, Micah
2014-01-01
Popular de novo amplicon clustering methods suffer from two fundamental flaws: arbitrary global clustering thresholds, and input-order dependency induced by centroid selection. Swarm was developed to address these issues by first clustering nearly identical amplicons iteratively using a local threshold, and then by using clusters’ internal structure and amplicon abundances to refine its results. This fast, scalable, and input-order independent approach reduces the influence of clustering parameters and produces robust operational taxonomic units. PMID:25276506
Swarm: robust and fast clustering method for amplicon-based studies
Directory of Open Access Journals (Sweden)
Frédéric Mahé
2014-09-01
Full Text Available Popular de novo amplicon clustering methods suffer from two fundamental flaws: arbitrary global clustering thresholds, and input-order dependency induced by centroid selection. Swarm was developed to address these issues by first clustering nearly identical amplicons iteratively using a local threshold, and then by using clusters’ internal structure and amplicon abundances to refine its results. This fast, scalable, and input-order independent approach reduces the influence of clustering parameters and produces robust operational taxonomic units.
Clustering of Mobile Ad Hoc Networks: An Adaptive Broadcast Period Approach
Gavalas, Damianos; Pantziou, Grammati; Konstantopoulos, Charalampos; Mamalis, Basilis
2011-01-01
Organization, scalability and routing have been identified as key problems hindering viability and commercial success of mobile ad hoc networks. Clustering of mobile nodes among separate domains has been proposed as an efficient approach to address those issues. In this work, we introduce an efficient distributed clustering algorithm that uses both location and energy metrics for cluster formation. Our proposed solution mainly addresses cluster stability, manageability and energy efficiency i...
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.
Development of a Burnup Module DECBURN Based on the Krylov Subspace Method
Energy Technology Data Exchange (ETDEWEB)
Cho, J. Y.; Kim, K. S.; Shim, H. J.; Song, J. S
2008-05-15
This report is to develop a burnup module DECBURN that is essential for the reactor analysis and the assembly homogenization codes to trace the fuel composition change during the core burnup. The developed burnup module solves the burnup equation by the matrix exponential method based on the Krylov Subspace method. The final solution of the matrix exponential is obtained by the matrix scaling and squaring method. To develop DECBURN module, this report includes the followings as: (1) Krylov Subspace Method for Burnup Equation, (2) Manufacturing of the DECBURN module, (3) Library Structure Setup and Library Manufacturing, (4) Examination of the DECBURN module, (5) Implementation to the DeCART code and Verification. DECBURN library includes the decay constants, one-group cross section and the fission yields. Examination of the DECBURN module is performed by manufacturing a driver program, and the results of the DECBURN module is compared with those of the ORIGEN program. Also, the implemented DECBURN module to the DeCART code is applied to the LWR depletion benchmark and a OPR-1000 pin cell problem, and the solutions are compared with the HELIOS code to verify the computational soundness and accuracy. In this process, the criticality calculation method and the predictor-corrector scheme are introduced to the DeCART code for a function of the homogenization code. The examination by a driver program shows that the DECBURN module produces exactly the same solution with the ORIGEN program. DeCART code that equips the DECBURN module produces a compatible solution to the other codes for the LWR depletion benchmark. Also the multiplication factors of the DeCART code for the OPR-1000 pin cell problem agree to the HELIOS code within 100 pcm over the whole burnup steps. The multiplication factors with the criticality calculation are also compatible with the HELIOS code. These results mean that the developed DECBURN module works soundly and produces an accurate solution
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.
SHERSTIUK S.V.; POSYLAYEVA K.I.
2013-01-01
In the article there are the theoretical and methodological approaches to the nature and existence of the cluster. The cluster differences from other kinds of cooperative and integration associations. Was develop by scientific-practical recommendations for forming a competitive horticultur cluster.
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...
Xia, Ya-Rong; Zhang, Shun-Li; Xin, Xiang-Peng
2018-03-01
In this paper, we propose the concept of the perturbed invariant subspaces (PISs), and study the approximate generalized functional variable separation solution for the nonlinear diffusion-convection equation with weak source by the approximate generalized conditional symmetries (AGCSs) related to the PISs. Complete classification of the perturbed equations which admit the approximate generalized functional separable solutions (AGFSSs) is obtained. As a consequence, some AGFSSs to the resulting equations are explicitly constructed by way of examples.
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
DEFF Research Database (Denmark)
Gulati, Mukesh; Lund-Thomsen, Peter; Suresh, Sangeetha
2018-01-01
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...... of this handbook, which focuses on the role of CSR in MSMEs. Hence we contribute to the literature on CSR in industrial clusters and specifically CSR in Indian industrial clusters by investigating the drivers of CSR in India’s industrial clusters....
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.
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.
International Nuclear Information System (INIS)
Xu, Y; Li, N
2014-01-01
Biological species have produced many simple but efficient rules in their complex and critical survival activities such as hunting and mating. A common feature observed in several biological motion strategies is that the predator only moves along paths in a carefully selected or iteratively refined subspace (or manifold), which might be able to explain why these motion strategies are effective. In this paper, a unified linear algebraic formulation representing such a predator–prey relationship is developed to simplify the construction and refinement process of the subspace (or manifold). Specifically, the following three motion strategies are studied and modified: motion camouflage, constant absolute target direction and local pursuit. The framework constructed based on this varying subspace concept could significantly reduce the computational cost in solving a class of nonlinear constrained optimal trajectory planning problems, particularly for the case with severe constraints. Two non-trivial examples, a ground robot and a hypersonic aircraft trajectory optimization problem, are used to show the capabilities of the algorithms in this new computational framework. (paper)
Weinberg, Seth H.; Smith, Gregory D.
2012-01-01
Cardiac myocyte calcium signaling is often modeled using deterministic ordinary differential equations (ODEs) and mass-action kinetics. However, spatially restricted “domains” associated with calcium influx are small enough (e.g., 10−17 liters) that local signaling may involve 1–100 calcium ions. Is it appropriate to model the dynamics of subspace calcium using deterministic ODEs or, alternatively, do we require stochastic descriptions that account for the fundamentally discrete nature of these local calcium signals? To address this question, we constructed a minimal Markov model of a calcium-regulated calcium channel and associated subspace. We compared the expected value of fluctuating subspace calcium concentration (a result that accounts for the small subspace volume) with the corresponding deterministic model (an approximation that assumes large system size). When subspace calcium did not regulate calcium influx, the deterministic and stochastic descriptions agreed. However, when calcium binding altered channel activity in the model, the continuous deterministic description often deviated significantly from the discrete stochastic model, unless the subspace volume is unrealistically large and/or the kinetics of the calcium binding are sufficiently fast. This principle was also demonstrated using a physiologically realistic model of calmodulin regulation of L-type calcium channels introduced by Yue and coworkers. PMID:23509597
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.
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.
International Nuclear Information System (INIS)
Marumori, Toshio; Hayashi, Akihisa; Tomoda, Toshiaki; Kuriyama, Atsushi; Maskawa, Toshihide
1980-01-01
The aim of this series of papers is to propose a microscopic theory to go beyond the situations where collective motions are described by the random phase approximation, i.e., by small amplitude harmonic oscillations about equilibrium. The theory is thus appropriate for the microscopic description of the large amplitude collective motion of soft nuclei. The essential idea is to develop a method to determine the collective subspace (or submanifold) in the many-particle Hilbert space in an optimal way, on the basis of a fundamental principle called the invariance principle of the Schroedinger equation. By using the principle within the framework of the Hartree-Fock theory, it is shown that the theory can clarify the structure of the so-called ''phonon-bands'' by self-consistently deriving the collective Hamiltonian where the number of the ''physical phonon'' is conserved. The purpose of this paper is not to go into detailed quantitative discussion, but rather to develop the basic idea. (author)
International Nuclear Information System (INIS)
Platoni, K.; Lefkopoulos, D.; Grandjean, P.; Schlienger, M.
1999-01-01
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) AP x (AA) 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) AP x (AA) RL =180 x 140 and (AA) AP x (AA) 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.)
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.
Ren, W. X.; Lin, Y. Q.; Fang, S. E.
2011-11-01
One of the key issues in vibration-based structural health monitoring is to extract the damage-sensitive but environment-insensitive features from sampled dynamic response measurements and to carry out the statistical analysis of these features for structural damage detection. A new damage feature is proposed in this paper by using the system matrices of the forward innovation model based on the covariance-driven stochastic subspace identification of a vibrating system. To overcome the variations of the system matrices, a non-singularity transposition matrix is introduced so that the system matrices are normalized to their standard forms. For reducing the effects of modeling errors, noise and environmental variations on measured structural responses, a statistical pattern recognition paradigm is incorporated into the proposed method. The Mahalanobis and Euclidean distance decision functions of the damage feature vector are adopted by defining a statistics-based damage index. The proposed structural damage detection method is verified against one numerical signal and two numerical beams. It is demonstrated that the proposed statistics-based damage index is sensitive to damage and shows some robustness to the noise and false estimation of the system ranks. The method is capable of locating damage of the beam structures under different types of excitations. The robustness of the proposed damage detection method to the variations in environmental temperature is further validated in a companion paper by a reinforced concrete beam tested in the laboratory and a full-scale arch bridge tested in the field.
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 < n) is realized in the medium. Both the terminology and the general idea of the mathematical construction are chosen by analogy with the case n = 3 and m = 2, which is well known in the classical plane problem of elasticity theory. The quintuples of elastic constants of the same medium that enter both the n-dimensional relations and the relations 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.
Loizou, Nicolas; Richtarik, Peter
2017-01-01
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.
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.
Energy Landscape of Pentapeptides in a Higher-Order (ϕ,ψ Conformational Subspace
Directory of Open Access Journals (Sweden)
Karim M. ElSawy
2016-01-01
Full Text Available The potential energy landscape of pentapeptides was mapped in a collective coordinate principal conformational subspace derived from principal component analysis of a nonredundant representative set of protein structures from the PDB. Three pentapeptide sequences that are known to be distinct in terms of their secondary structure characteristics, (Ala5, (Gly5, and Val.Asn.Thr.Phe.Val, were considered. Partitioning the landscapes into different energy valleys allowed for calculation of the relative propensities of the peptide secondary structures in a statistical mechanical framework. The distribution of the observed conformations of pentapeptide data showed good correspondence to the topology of the energy landscape of the (Ala5 sequence where, in accord with reported trends, the α-helix showed a predominant propensity at 298 K. The topography of the landscapes indicates that the stabilization of the α-helix in the (Ala5 sequence is enthalpic in nature while entropic factors are important for stabilization of the β-sheet in the Val.Asn.Thr.Phe.Val sequence. The results indicate that local interactions within small pentapeptide segments can lead to conformational preference of one secondary structure over the other where account of conformational entropy is important in order to reveal such preference. The method, therefore, can provide critical structural information for ab initio protein folding methods.
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.
Similarity measurement method of high-dimensional data based on normalized net lattice subspace
Institute of Scientific and Technical Information of China (English)
Li Wenfa; Wang Gongming; Li Ke; Huang Su
2017-01-01
The performance of conventional similarity measurement methods is affected seriously by the curse of dimensionality of high-dimensional data.The reason is that data difference between sparse and noisy dimensionalities occupies a large proportion of the similarity, leading to the dissimilarities between any results.A similarity measurement method of high-dimensional data based on normalized net lattice subspace is proposed.The data range of each dimension is divided into several intervals, and the components in different dimensions are mapped onto the corresponding interval.Only the component in the same or adjacent interval is used to calculate the similarity.To validate this meth-od, three data types are used, and seven common similarity measurement methods are compared. The experimental result indicates that the relative difference of the method is increasing with the di-mensionality and is approximately two or three orders of magnitude higher than the conventional method.In addition, the similarity range of this method in different dimensions is [0, 1], which is fit for similarity analysis after dimensionality reduction.
Cancelli, Alessandro; Micheli, Laura; Laflamme, Simon; Alipour, Alice; Sritharan, Sri; Ubertini, Filippo
2017-04-01
Stochastic subspace identification (SSID) is a first-order linear system identification technique enabling modal analysis through the time domain. Research in the field of structural health monitoring has demonstrated that SSID can be used to successfully retrieve modal properties, including modal damping ratios, using output-only measurements. In this paper, the utilization of SSID for indirectly retrieving structures' stiffness matrix was investigated, through the study of a simply supported reinforced concrete beam subjected to dynamic loads. Hence, by introducing a physical model of the structure, a second-order identification method is achieved. The reconstruction is based on system condensation methods, which enables calculation of reduced order stiffness, damping, and mass matrices for the structural system. The methods compute the reduced order matrices directly from the modal properties, obtained through the use of SSID. Lastly, the reduced properties of the system are used to reconstruct the stiffness matrix of the beam. The proposed approach is first verified through numerical simulations and then validated using experimental data obtained from a full-scale reinforced concrete beam that experienced progressive damage. Results show that the SSID technique can be used to diagnose, locate, and quantify damage through the reconstruction of the stiffness matrix.
A unified classifier for robust face recognition based on combining multiple subspace algorithms
Ijaz Bajwa, Usama; Ahmad Taj, Imtiaz; Waqas Anwar, Muhammad
2012-10-01
Face recognition being the fastest growing biometric technology has expanded manifold in the last few years. Various new algorithms and commercial systems have been proposed and developed. However, none of the proposed or developed algorithm is a complete solution because it may work very well on one set of images with say illumination changes but may not work properly on another set of image variations like expression variations. This study is motivated by the fact that any single classifier cannot claim to show generally better performance against all facial image variations. To overcome this shortcoming and achieve generality, combining several classifiers using various strategies has been studied extensively also incorporating the question of suitability of any classifier for this task. The study is based on the outcome of a comprehensive comparative analysis conducted on a combination of six subspace extraction algorithms and four distance metrics on three facial databases. The analysis leads to the selection of the most suitable classifiers which performs better on one task or the other. These classifiers are then combined together onto an ensemble classifier by two different strategies of weighted sum and re-ranking. The results of the ensemble classifier show that these strategies can be effectively used to construct a single classifier that can successfully handle varying facial image conditions of illumination, aging and facial expressions.
Fault Diagnosis for Hydraulic Servo System Using Compressed Random Subspace Based ReliefF
Directory of Open Access Journals (Sweden)
Yu Ding
2018-01-01
Full Text Available Playing an important role in electromechanical systems, hydraulic servo system is crucial to mechanical systems like engineering machinery, metallurgical machinery, ships, and other equipment. Fault diagnosis based on monitoring and sensory signals plays an important role in avoiding catastrophic accidents and enormous economic losses. This study presents a fault diagnosis scheme for hydraulic servo system using compressed random subspace based ReliefF (CRSR method. From the point of view of feature selection, the scheme utilizes CRSR method to determine the most stable feature combination that contains the most adequate information simultaneously. Based on the feature selection structure of ReliefF, CRSR employs feature integration rules in the compressed domain. Meanwhile, CRSR substitutes information entropy and fuzzy membership for traditional distance measurement index. The proposed CRSR method is able to enhance the robustness of the feature information against interference while selecting the feature combination with balanced information expressing ability. To demonstrate the effectiveness of the proposed CRSR method, a hydraulic servo system joint simulation model is constructed by HyPneu and Simulink, and three fault modes are injected to generate the validation data.
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
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
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 Tran...... 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....
Design issues for numerical libraries on scalable multicore architectures
International Nuclear Information System (INIS)
Heroux, M A
2008-01-01
Future generations of scalable computers will rely on multicore nodes for a significant portion of overall system performance. At present, most applications and libraries cannot exploit multiple cores beyond running addition MPI processes per node. In this paper we discuss important multicore architecture issues, programming models, algorithms requirements and software design related to effective use of scalable multicore computers. In particular, we focus on important issues for library research and development, making recommendations for how to effectively develop libraries for future scalable computer systems
Building Scalable Knowledge Graphs for Earth Science
Ramachandran, R.; Maskey, M.; Gatlin, P. N.; Zhang, J.; Duan, X.; Bugbee, K.; Christopher, S. A.; Miller, J. J.
2017-12-01
Estimates indicate that the world's information will grow by 800% in the next five years. In any given field, a single researcher or a team of researchers cannot keep up with this rate of knowledge expansion without the help of cognitive systems. Cognitive computing, defined as the use of information technology to augment human cognition, can help tackle large systemic problems. Knowledge graphs, one of the foundational components of cognitive systems, link key entities in a specific domain with other entities via relationships. Researchers could mine these graphs to make probabilistic recommendations and to infer new knowledge. At this point, however, there is a dearth of tools to generate scalable Knowledge graphs using existing corpus of scientific literature for Earth science research. Our project is currently developing an end-to-end automated methodology for incrementally constructing Knowledge graphs for Earth Science. Semantic Entity Recognition (SER) is one of the key steps in this methodology. SER for Earth Science uses external resources (including metadata catalogs and controlled vocabulary) as references to guide entity extraction and recognition (i.e., labeling) from unstructured text, in order to build a large training set to seed the subsequent auto-learning component in our algorithm. Results from several SER experiments will be presented as well as lessons learned.
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.
Percolator: Scalable Pattern Discovery in Dynamic Graphs
Energy Technology Data Exchange (ETDEWEB)
Choudhury, Sutanay; Purohit, Sumit; Lin, Peng; Wu, Yinghui; Holder, Lawrence B.; Agarwal, Khushbu
2018-02-06
We demonstrate Percolator, a distributed system for graph pattern discovery in dynamic graphs. In contrast to conventional mining systems, Percolator advocates efficient pattern mining schemes that (1) support pattern detection with keywords; (2) integrate incremental and parallel pattern mining; and (3) support analytical queries such as trend analysis. The core idea of Percolator is to dynamically decide and verify a small fraction of patterns and their in- stances that must be inspected in response to buffered updates in dynamic graphs, with a total mining cost independent of graph size. We demonstrate a) the feasibility of incremental pattern mining by walking through each component of Percolator, b) the efficiency and scalability of Percolator over the sheer size of real-world dynamic graphs, and c) how the user-friendly GUI of Percolator inter- acts with users to support keyword-based queries that detect, browse and inspect trending patterns. We also demonstrate two user cases of Percolator, in social media trend analysis and academic collaboration analysis, respectively.
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.
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.
A Programmable, Scalable-Throughput Interleaver
Directory of Open Access Journals (Sweden)
E. J. C. Rijshouwer
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 mm2 in 65 nm CMOS (including memories and proves functional on silicon.
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.
Large Scale Simulations of the Euler Equations on GPU Clusters
Liebmann, Manfred; Douglas, Craig C.; Haase, Gundolf; Horvá th, Zoltá n
2010-01-01
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
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
A Testbed for Highly-Scalable Mission Critical Information Systems
National Research Council Canada - National Science Library
Birman, Kenneth P
2005-01-01
... systems in a networked environment. Headed by Professor Ken Birman, the project is exploring a novel fusion of classical protocols for reliable multicast communication with a new style of peer-to-peer protocol called scalable "gossip...
Scalable Partitioning Algorithms for FPGAs With Heterogeneous Resources
National Research Council Canada - National Science Library
Selvakkumaran, Navaratnasothie; Ranjan, Abhishek; Raje, Salil; Karypis, George
2004-01-01
As FPGA densities increase, partitioning-based FPGA placement approaches are becoming increasingly important as they can be used to provide high-quality and computationally scalable placement solutions...
Scalable Networked Information Processing Environment (SNIPE)
Energy Technology Data Exchange (ETDEWEB)
Fagg, G.E.; Moore, K. [Univ. of Tennessee, Knoxville, TN (United States). Dept. of Computer Science; Dongarra, J.J. [Univ. of Tennessee, Knoxville, TN (United States). Dept. of Computer Science]|[Oak Ridge National Lab., TN (United States). Computer Science and Mathematics Div.; Geist, A. [Oak Ridge National Lab., TN (United States). Computer Science and Mathematics Div.
1997-11-01
SNIPE is a metacomputing system that aims to provide a reliable, secure, fault tolerant environment for long term distributed computing applications and data stores across the global Internet. This system combines global naming and replication of both processing and data to support large scale information processing applications leading to better availability and reliability than currently available with typical cluster computing and/or distributed computer environments.
International Nuclear Information System (INIS)
Schaeffer, R.
1987-01-01
The galaxy and cluster luminosity functions are constructed from a model of the mass distribution based on hierarchical clustering at an epoch where the matter distribution is non-linear. These luminosity functions are seen to reproduce the present distribution of objects as can be inferred from the observations. They can be used to deduce the redshift dependence of the cluster distribution and to extrapolate the observations towards the past. The predicted evolution of the cluster distribution is quite strong, although somewhat less rapid than predicted by the linear theory
SOL: A Library for Scalable Online Learning Algorithms
Wu, Yue; Hoi, Steven C. H.; Liu, Chenghao; Lu, Jing; Sahoo, Doyen; Yu, Nenghai
2016-01-01
SOL is an open-source library for scalable online learning algorithms, and is particularly suitable for learning with high-dimensional data. The library provides a family of regular and sparse online learning algorithms for large-scale binary and multi-class classification tasks with high efficiency, scalability, portability, and extensibility. SOL was implemented in C++, and provided with a collection of easy-to-use command-line tools, python wrappers and library calls for users and develope...
Modular Universal Scalable Ion-trap Quantum Computer
2016-06-02
SECURITY CLASSIFICATION OF: The main goal of the original MUSIQC proposal was to construct and demonstrate a modular and universally- expandable ion...Distribution Unlimited UU UU UU UU 02-06-2016 1-Aug-2010 31-Jan-2016 Final Report: Modular Universal Scalable Ion-trap Quantum Computer The views...P.O. Box 12211 Research Triangle Park, NC 27709-2211 Ion trap quantum computation, scalable modular architectures REPORT DOCUMENTATION PAGE 11
Architectures and Applications for Scalable Quantum Information Systems
2007-01-01
Gershenfeld and I. Chuang. Quantum computing with molecules. Scientific American, June 1998. [16] A. Globus, D. Bailey, J. Han, R. Jaffe, C. Levit , R...AFRL-IF-RS-TR-2007-12 Final Technical Report January 2007 ARCHITECTURES AND APPLICATIONS FOR SCALABLE QUANTUM INFORMATION SYSTEMS...NUMBER 5b. GRANT NUMBER FA8750-01-2-0521 4. TITLE AND SUBTITLE ARCHITECTURES AND APPLICATIONS FOR SCALABLE QUANTUM INFORMATION SYSTEMS 5c
On the scalability of LISP and advanced overlaid services
Coras, Florin
2015-01-01
In just four decades the Internet has gone from a lab experiment to a worldwide, business critical infrastructure that caters to the communication needs of almost a half of the Earth's population. With these figures on its side, arguing against the Internet's scalability would seem rather unwise. However, the Internet's organic growth is far from finished and, as billions of new devices are expected to be joined in the not so distant future, scalability, or lack thereof, is commonly believed ...
Scalable, full-colour and controllable chromotropic plasmonic printing
Xue, Jiancai; Zhou, Zhang-Kai; Wei, Zhiqiang; Su, Rongbin; Lai, Juan; Li, Juntao; Li, Chao; Zhang, Tengwei; Wang, Xue-Hua
2015-01-01
Plasmonic colour printing has drawn wide attention as a promising candidate for the next-generation colour-printing technology. However, an efficient approach to realize full colour and scalable fabrication is still lacking, which prevents plasmonic colour printing from practical applications. Here we present a scalable and full-colour plasmonic printing approach by combining conjugate twin-phase modulation with a plasmonic broadband absorber. More importantly, our approach also demonstrates ...
Responsive, Flexible and Scalable Broader Impacts (Invited)
Decharon, A.; Companion, C.; Steinman, M.
2010-12-01
In many educator professional development workshops, scientists present content in a slideshow-type format and field questions afterwards. Drawbacks of this approach include: inability to begin the lecture with content that is responsive to audience needs; lack of flexible access to specific material within the linear presentation; and “Q&A” sessions are not easily scalable to broader audiences. Often this type of traditional interaction provides little direct benefit to the scientists. The Centers for Ocean Sciences Education Excellence - Ocean Systems (COSEE-OS) applies the technique of concept mapping with demonstrated effectiveness in helping scientists and educators “get on the same page” (deCharon et al., 2009). A key aspect is scientist professional development geared towards improving face-to-face and online communication with non-scientists. COSEE-OS promotes scientist-educator collaboration, tests the application of scientist-educator maps in new contexts through webinars, and is piloting the expansion of maps as long-lived resources for the broader community. Collaboration - COSEE-OS has developed and tested a workshop model bringing scientists and educators together in a peer-oriented process, often clarifying common misconceptions. Scientist-educator teams develop online concept maps that are hyperlinked to “assets” (i.e., images, videos, news) and are responsive to the needs of non-scientist audiences. In workshop evaluations, 91% of educators said that the process of concept mapping helped them think through science topics and 89% said that concept mapping helped build a bridge of communication with scientists (n=53). Application - After developing a concept map, with COSEE-OS staff assistance, scientists are invited to give webinar presentations that include live “Q&A” sessions. The webinars extend the reach of scientist-created concept maps to new contexts, both geographically and topically (e.g., oil spill), with a relatively small
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.
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.
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 (S 1 ) simulator and 0.21cm mean error for the sources of second heart sound (S 2 ) 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.
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.
An acceleration technique for 2D MOC based on Krylov subspace and domain decomposition methods
International Nuclear Information System (INIS)
Zhang Hongbo; Wu Hongchun; Cao Liangzhi
2011-01-01
Highlights: → We convert MOC into linear system solved by GMRES as an acceleration method. → We use domain decomposition method to overcome the inefficiency on large matrices. → Parallel technology is applied and a matched ray tracing system is developed. → Results show good efficiency even in large-scale and strong scattering problems. → The emphasis is that the technique is geometry-flexible. - Abstract: The method of characteristics (MOC) has great geometrical flexibility but poor computational efficiency in neutron transport calculations. The generalized minimal residual (GMRES) method, a type of Krylov subspace method, is utilized to accelerate a 2D generalized geometry characteristics solver AutoMOC. In this technique, a form of linear algebraic equation system for angular flux moments and boundary fluxes is derived to replace the conventional characteristics sweep (i.e. inner iteration) scheme, and then the GMRES method is implemented as an efficient linear system solver. This acceleration method is proved to be reliable in theory and simple for implementation. Furthermore, as introducing no restriction in geometry treatment, it is suitable for acceleration of an arbitrary geometry MOC solver. However, it is observed that the speedup decreases when the matrix becomes larger. The spatial domain decomposition method and multiprocessing parallel technology are then employed to overcome the problem. The calculation domain is partitioned into several sub-domains. For each of them, a smaller matrix is established and solved by GMRES; and the adjacent sub-domains are coupled by 'inner-edges', where the trajectory mismatches are considered adequately. Moreover, a matched ray tracing system is developed on the basis of AutoCAD, which allows a user to define the sub-domains on demand conveniently. Numerical results demonstrate that the acceleration techniques are efficient without loss of accuracy, even in the case of large-scale and strong scattering
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.
Clustering of 1p-shell nuclei in the framework of the shell model
International Nuclear Information System (INIS)
Kwasniewicz, E.
1991-01-01
The two- and three-fragment clustering of the 1p-shell nuclei has been studied in the framework of the shell model. The absolute probabilities of the required types of clustering in a given nucleus have been obtained by projecting its realistic shell-model wavefunction onto the suitable subspace of the orthonormal, completely antisymmetric two- or three-cluster states. With the aid of these data the selectivity in population of final states produced in multinucleon transfer reactions has been discussed. This problem has also been considered in the approach where the exchange of nucleons between clusters has been neglected. This has enabled to demonstrate the role of the complete antisymmetrization in predicting the intensities of states populated in multinucleon transfer reactions. The compact theory of the multinucleon one- and two-cluster spectroscopic amplitudes has been formulated. The examples of studying the nuclear structure and reactions with the aid of these spectroscopic amplitudes have been presented. (author)
Optimizing Nanoelectrode Arrays for Scalable Intracellular Electrophysiology.
Abbott, Jeffrey; Ye, Tianyang; Ham, Donhee; Park, Hongkun
2018-03-20
, clarifying how the nanoelectrode attains intracellular access. This understanding will be translated into a circuit model for the nanobio interface, which we will then use to lay out the strategies for improving the interface. The intracellular interface of the nanoelectrode is currently inferior to that of the patch clamp electrode; reaching this benchmark will be an exciting challenge that involves optimization of electrode geometries, materials, chemical modifications, electroporation protocols, and recording/stimulation electronics, as we describe in the Account. Another important theme of this Account, beyond the optimization of the individual nanoelectrode-cell interface, is the scalability of the nanoscale electrodes. We will discuss this theme using a recent development from our groups as an example, where an array of ca. 1000 nanoelectrode pixels fabricated on a CMOS integrated circuit chip performs parallel intracellular recording from a few hundreds of cardiomyocytes, which marks a new milestone in electrophysiology.
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.
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.
International Nuclear Information System (INIS)
Romli
1997-01-01
Cluster analysis is the name of group of multivariate techniques whose principal purpose is to distinguish similar entities from the characteristics they process.To study this analysis, there are several algorithms that can be used. Therefore, this topic focuses to discuss the algorithms, such as, similarity measures, and hierarchical clustering which includes single linkage, complete linkage and average linkage method. also, non-hierarchical clustering method, which is popular name K -mean method ' will be discussed. Finally, this paper will be described the advantages and disadvantages of every methods
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
DEFF Research Database (Denmark)
Böcker, S.; Baumbach, Jan
2013-01-01
. 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......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...
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.
Scalable real space pseudopotential density functional codes for materials in the exascale regime
Lena, Charles; Chelikowsky, James; Schofield, Grady; Biller, Ariel; Kronik, Leeor; Saad, Yousef; Deslippe, Jack
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 using this approach have been routinely obtained for systems with thousands of atoms. Yet, there are many systems of notable larger sizes where quantum mechanical accuracy is desired, but scalability proves to be a hindrance. Such systems include large biological molecules, complex nanostructures, or mismatched interfaces. We will present an overview of our new massively parallel algorithms, which offer improved scalability in preparation for exascale supercomputing. We will illustrate these algorithms by considering the electronic structure of a Si nanocrystal exceeding 104 atoms. 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).
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.
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.
Scalable Strategies for Computing with Massive Data
Directory of Open Access Journals (Sweden)
Michael Kane
2013-11-01
Full Text Available This paper presents two complementary statistical computing frameworks that address challenges in parallel processing and the analysis of massive data. First, the foreach package allows users of the R programming environment to define parallel loops that may be run sequentially on a single machine, in parallel on a symmetric multiprocessing (SMP machine, or in cluster environments without platform-specific code. Second, the bigmemory package implements memory- and file-mapped data structures that provide (a access to arbitrarily large data while retaining a look and feel that is familiar to R users and (b data structures that are shared across processor cores in order to support efficient parallel computing techniques. Although these packages may be used independently, this paper shows how they can be used in combination to address challenges that have effectively been beyond the reach of researchers who lack specialized software development skills or expensive hardware.
Scalable Quantum Simulation of Molecular Energies
Directory of Open Access Journals (Sweden)
P. J. J. O’Malley
2016-07-01
Full Text Available We report the first electronic structure calculation performed on a quantum computer without exponentially costly precompilation. We use a programmable array of superconducting qubits to compute the energy surface of molecular hydrogen using two distinct quantum algorithms. First, we experimentally execute the unitary coupled cluster method using the variational quantum eigensolver. Our efficient implementation predicts the correct dissociation energy to within chemical accuracy of the numerically exact result. Second, we experimentally demonstrate the canonical quantum algorithm for chemistry, which consists of Trotterization and quantum phase estimation. We compare the experimental performance of these approaches to show clear evidence that the variational quantum eigensolver is robust to certain errors. This error tolerance inspires hope that variational quantum simulations of classically intractable molecules may be viable in the near future.
Pottawattamie County School System, Council Bluffs, IA.
The 15 occupational clusters (transportation, fine arts and humanities, communications and media, personal service occupations, construction, hospitality and recreation, health occupations, marine science occupations, consumer and homemaking-related occupations, agribusiness and natural resources, environment, public service, business and office…
DEFF Research Database (Denmark)
Berks, G.; Keyserlingk, Diedrich Graf von; Jantzen, Jan
2000-01-01
A symptom is a condition indicating the presence of a disease, especially, when regarded as an aid in diagnosis.Symptoms are the smallest units indicating the existence of a disease. A syndrome on the other hand is an aggregate, set or cluster of concurrent symptoms which together indicate...... 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......-mean clustering is an easy and well improved tool, which has been applied in many medical fields. We used c-mean fuzzy clustering after feature extraction from an aphasia database. Factor analysis was applied on a correlation matrix of 26 symptoms of language disorders and led to five factors. The factors...
Donchev, Todor I [Urbana, IL; Petrov, Ivan G [Champaign, IL
2011-05-31
Described herein is an apparatus and a method for producing atom clusters based on a gas discharge within a hollow cathode. The hollow cathode includes one or more walls. The one or more walls define a sputtering chamber within the hollow cathode and include a material to be sputtered. A hollow anode is positioned at an end of the sputtering chamber, and atom clusters are formed when a gas discharge is generated between the hollow anode and the hollow cathode.
Massey, Richard; Kitching, Thomas; Nagai, Daisuke
2010-01-01
The unique properties of dark matter are revealed during collisions between clusters of galaxies, such as the bullet cluster (1E 0657−56) and baby bullet (MACS J0025−12). These systems provide evidence for an additional, invisible mass in the separation between the distributions of their total mass, measured via gravitational lensing, and their ordinary ‘baryonic’ matter, measured via its X-ray emission. Unfortunately, the information available from these systems is limited by their rarity. C...
Leroux, Elizabeth; Ducros, Anne
2008-01-01
Abstract Cluster headache (CH) is a primary headache disease characterized by recurrent short-lasting attacks (15 to 180 minutes) of excruciating unilateral periorbital pain accompanied by ipsilateral autonomic signs (lacrimation, nasal congestion, ptosis, miosis, lid edema, redness of the eye). It affects young adults, predominantly males. Prevalence is estimated at 0.5–1.0/1,000. CH has a circannual and circadian periodicity, attacks being clustered (hence the name) in bouts that can occur ...
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.
A scalable synthesis of highly stable and water dispersible Ag 44(SR)30 nanoclusters
AbdulHalim, Lina G.; Ashraf, Sumaira; Katsiev, Khabiboulakh; Kirmani, Ahmad R.; Kothalawala, Nuwan; Anjum, Dalaver H.; Abbas, Sikandar Zameer; Amassian, Aram; Stellacci, Francesco; Dass, Amala; Hussain, Irshad; Bakr, Osman
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.
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.
Liu, Y J; Tran, T; Postma, G; Buydens, L M C; Jansen, J
2018-08-22
Principal Component Analysis (PCA) is widely used in analytical chemistry, to reduce the dimensionality of a multivariate data set in a few Principal Components (PCs) that summarize the predominant patterns in the data. An accurate estimate of the number of PCs is indispensable to provide meaningful interpretations and extract useful information. We show how existing estimates for the number of PCs may fall short for datasets with considerable coherence, noise or outlier presence. We present here how Angle Distribution of the Loading Subspaces (ADLS) can be used to estimate the number of PCs based on the variability of loading subspace across bootstrap resamples. Based on comprehensive comparisons with other well-known methods applied on simulated dataset, we show that ADLS (1) may quantify the stability of a PCA model with several numbers of PCs simultaneously; (2) better estimate the appropriate number of PCs when compared with the cross-validation and scree plot methods, specifically for coherent data, and (3) facilitate integrated outlier detection, which we introduce in this manuscript. We, in addition, demonstrate how the analysis of different types of real-life spectroscopic datasets may benefit from these advantages of ADLS. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.
Hu, Weiming; Li, Xi; Luo, Wenhan; Zhang, Xiaoqin; Maybank, Stephen; Zhang, Zhongfei
2012-12-01
Object appearance modeling is crucial for tracking objects, especially in videos captured by nonstationary cameras and for reasoning about occlusions between multiple moving objects. Based on the log-euclidean Riemannian metric on symmetric positive definite matrices, we propose an incremental log-euclidean Riemannian subspace learning algorithm in which covariance matrices of image features are mapped into a vector space with the log-euclidean Riemannian metric. Based on the subspace learning algorithm, we develop a log-euclidean block-division appearance model which captures both the global and local spatial layout information about object appearances. Single object tracking and multi-object tracking with occlusion reasoning are then achieved by particle filtering-based Bayesian state inference. During tracking, incremental updating of the log-euclidean block-division appearance model captures changes in object appearance. For multi-object tracking, the appearance models of the objects can be updated even in the presence of occlusions. Experimental results demonstrate that the proposed tracking algorithm obtains more accurate results than six state-of-the-art tracking algorithms.
A Cluster- Based Secure Active Network Environment
Institute of Scientific and Technical Information of China (English)
CHEN Xiao-lin; ZHOU Jing-yang; DAI Han; LU Sang-lu; CHEN Gui-hai
2005-01-01
We introduce a cluster-based secure active network environment (CSANE) which separates the processing of IP packets from that of active packets in active routers. In this environment, the active code authorized or trusted by privileged users is executed in the secure execution environment (EE) of the active router, while others are executed in the secure EE of the nodes in the distributed shared memory (DSM) cluster. With the supports of a multi-process Java virtual machine and KeyNote, untrusted active packets are controlled to securely consume resource. The DSM consistency management makes that active packets can be parallelly processed in the DSM cluster as if they were processed one by one in ANTS (Active Network Transport System). We demonstrate that CSANE has good security and scalability, but imposing little changes on traditional routers.
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.
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...... in order to save transmissions. To ensure decodability at the end-nodes, a priori information about the content of the combined packets must be available. This is gathered during the initial transmissions to the relay. The trade-off between decodability and number of necessary transmissions is analysed...
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.
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.
Design for scalability in 3D computer graphics architectures
DEFF Research Database (Denmark)
Holten-Lund, Hans Erik
2002-01-01
This thesis describes useful methods and techniques for designing scalable hybrid parallel rendering architectures for 3D computer graphics. Various techniques for utilizing parallelism in a pipelines system are analyzed. During the Ph.D study a prototype 3D graphics architecture named Hybris has...
Scalable storage for a DBMS using transparent distribution
J.S. Karlsson; M.L. Kersten (Martin)
1997-01-01
textabstractScalable Distributed Data Structures (SDDSs) provide a self-managing and self-organizing data storage of potentially unbounded size. This stands in contrast to common distribution schemas deployed in conventional distributed DBMS. SDDSs, however, have mostly been used in synthetic
Scalable force directed graph layout algorithms using fast multipole methods
Yunis, Enas Abdulrahman; Yokota, Rio; Ahmadia, Aron
2012-01-01
We present an extension to ExaFMM, a Fast Multipole Method library, as a generalized approach for fast and scalable execution of the Force-Directed Graph Layout algorithm. The Force-Directed Graph Layout algorithm is a physics-based approach
Cascaded column generation for scalable predictive demand side management
Toersche, Hermen; Molderink, Albert; Hurink, Johann L.; Smit, Gerardus Johannes Maria
2014-01-01
We propose a nested Dantzig-Wolfe decomposition, combined with dynamic programming, for the distributed scheduling of a large heterogeneous fleet of residential appliances with nonlinear behavior. A cascaded column generation approach gives a scalable optimization strategy, provided that the problem
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...
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 Robust Principal Component Analysis Using Grassmann Averages
DEFF Research Database (Denmark)
Hauberg, Søren; Feragen, Aasa; Enficiaud, Raffi
2016-01-01
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. Unfortu...
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 ...
Scalability and efficiency of genetic algorithms for geometrical applications
Dijk, van S.F.; Thierens, D.; Berg, de M.; Schoenauer, M.
2000-01-01
We study the scalability and efficiency of a GA that we developed earlier to solve the practical cartographic problem of labeling a map with point features. We argue that the special characteristics of our GA make that it fits in well with theoretical models predicting the optimal population size
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
A Massively Scalable Architecture for Instant Messaging & Presence
Schippers, Jorrit; Remke, Anne Katharina Ingrid; Punt, Henk; Wegdam, M.; Haverkort, Boudewijn R.H.M.; Thomas, N.; Bradley, J.; Knottenbelt, W.; Dingle, N.; Harder, U.
2010-01-01
This paper analyzes the scalability of Instant Messaging & Presence (IM&P) architectures. We take a queueing-based modelling and analysis approach to ��?nd the bottlenecks of the current IM&P architecture at the Dutch social network Hyves, as well as of alternative architectures. We use the
Adolescent sexuality education: An appraisal of some scalable ...
African Journals Online (AJOL)
Adolescent sexuality education: An appraisal of some scalable interventions for the Nigerian context. VC Pam. Abstract. Most issues around sexual intercourse are highly sensitive topics in Nigeria. Despite the disturbingly high adolescent HIV prevalence and teenage pregnancy rate in Nigeria, sexuality education is ...
Scalable multifunction RF system concepts for joint operations
Otten, M.P.G.; Wit, J.J.M. de; Smits, F.M.A.; Rossum, W.L. van; Huizing, A.
2010-01-01
RF systems based on modular architectures have the potential of better re-use of technology, decreasing development time, and decreasing life cycle cost. Moreover, modular architectures provide scalability, allowing low cost upgrades and adaptability to different platforms. To achieve maximum
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…
Minku, Leandro L.; Hou, Siqing
2017-01-01
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
Kreutzer, Moritz; Hager, Georg; Wellein, Gerhard; Fehske, Holger; Basermann, Achim; Bishop, Alan R.
2011-01-01
Sparse matrix-vector multiplication (spMVM) is the dominant operation in many sparse solvers. We investigate performance properties of spMVM with matrices of various sparsity patterns on the nVidia “Fermi” class of GPGPUs. A new “padded jagged diagonals storage” (pJDS) format is proposed which may substantially reduce the memory overhead intrinsic to the widespread ELLPACK-R scheme while making no assumptions about the matrix structure. In our test scenarios the pJDS format cuts the ...
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 ...
Evaluation of 3D printed anatomically scalable transfemoral prosthetic knee.
Ramakrishnan, Tyagi; Schlafly, Millicent; Reed, Kyle B
2017-07-01
This case study compares a transfemoral amputee's gait while using the existing Ossur Total Knee 2000 and our novel 3D printed anatomically scalable transfemoral prosthetic knee. The anatomically scalable transfemoral prosthetic knee is 3D printed out of a carbon-fiber and nylon composite that has a gear-mesh coupling with a hard-stop weight-actuated locking mechanism aided by a cross-linked four-bar spring mechanism. This design can be scaled using anatomical dimensions of a human femur and tibia to have a unique fit for each user. The transfemoral amputee who was tested is high functioning and walked on the Computer Assisted Rehabilitation Environment (CAREN) at a self-selected pace. The motion capture and force data that was collected showed that there were distinct differences in the gait dynamics. The data was used to perform the Combined Gait Asymmetry Metric (CGAM), where the scores revealed that the overall asymmetry of the gait on the Ossur Total Knee was more asymmetric than the anatomically scalable transfemoral prosthetic knee. The anatomically scalable transfemoral prosthetic knee had higher peak knee flexion that caused a large step time asymmetry. This made walking on the anatomically scalable transfemoral prosthetic knee more strenuous due to the compensatory movements in adapting to the different dynamics. This can be overcome by tuning the cross-linked spring mechanism to emulate the dynamics of the subject better. The subject stated that the knee would be good for daily use and has the potential to be adapted as a running knee.
Blind Cooperative Routing for Scalable and Energy-Efficient Internet of Things
Bader, Ahmed; Alouini, Mohamed-Slim
2016-01-01
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
Colliandre, Lionel; Le Guilloux, Vincent; Bourg, Stephane; Morin-Allory, Luc
2012-02-27
High Throughput Screening (HTS) is a standard technique widely used to find hit compounds in drug discovery projects. The high costs associated with such experiments have highlighted the need to carefully design screening libraries in order to avoid wasting resources. Molecular diversity is an established concept that has been used to this end for many years. In this article, a new approach to quantify the molecular diversity of screening libraries is presented. The approach is based on the Delimited Reference Chemical Subspace (DRCS) methodology, a new method that can be used to delimit the densest subspace spanned by a reference library in a reduced 2D continuous space. A total of 22 diversity indices were implemented or adapted to this methodology, which is used here to remove outliers and obtain a relevant cell-based partition of the subspace. The behavior of these indices was assessed and compared in various extreme situations and with respect to a set of theoretical rules that a diversity function should satisfy when libraries of different sizes have to be compared. Some gold standard indices are found inappropriate in such a context, while none of the tested indices behave perfectly in all cases. Five DRCS-based indices accounting for different aspects of diversity were finally selected, and a simple framework is proposed to use them effectively. Various libraries have been profiled with respect to more specific subspaces, which further illustrate the interest of the method.
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.
Message Passing Framework for Globally Interconnected Clusters
International Nuclear Information System (INIS)
Hafeez, M; Riaz, N; Asghar, S; Malik, U A; Rehman, A
2011-01-01
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.
Estrada, T; Zhang, B; Cicotti, P; Armen, R S; Taufer, M
2012-07-01
We present a scalable and accurate method for classifying protein-ligand binding geometries in molecular docking. Our method is a three-step process: the first step encodes the geometry of a three-dimensional (3D) ligand conformation into a single 3D point in the space; the second step builds an octree by assigning an octant identifier to every single point in the space under consideration; and the third step performs an octree-based clustering on the reduced conformation space and identifies the most dense octant. We adapt our method for MapReduce and implement it in Hadoop. The load-balancing, fault-tolerance, and scalability in MapReduce allow screening of very large conformation spaces not approachable with traditional clustering methods. We analyze results for docking trials for 23 protein-ligand complexes for HIV protease, 21 protein-ligand complexes for Trypsin, and 12 protein-ligand complexes for P38alpha kinase. We also analyze cross docking trials for 24 ligands, each docking into 24 protein conformations of the HIV protease, and receptor ensemble docking trials for 24 ligands, each docking in a pool of HIV protease receptors. Our method demonstrates significant improvement over energy-only scoring for the accurate identification of native ligand geometries in all these docking assessments. The advantages of our clustering approach make it attractive for complex applications in real-world drug design efforts. We demonstrate that our method is particularly useful for clustering docking results using a minimal ensemble of representative protein conformational states (receptor ensemble docking), which is now a common strategy to address protein flexibility in molecular docking. Copyright © 2012 Elsevier Ltd. All rights reserved.
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.
Zarifi, Keyvan; Gershman, Alex B.
2006-12-01
We analyze the performance of two popular blind subspace-based signature waveform estimation techniques proposed by Wang and Poor and Buzzi and Poor for direct-sequence code division multiple-access (DS-CDMA) systems with unknown correlated noise. Using the first-order perturbation theory, analytical expressions for the mean-square error (MSE) of these algorithms are derived. We also obtain simple high SNR approximations of the MSE expressions which explicitly clarify how the performance of these techniques depends on the environmental parameters and how it is related to that of the conventional techniques that are based on the standard white noise assumption. Numerical examples further verify the consistency of the obtained analytical results with simulation results.
International Nuclear Information System (INIS)
Bartels, J.; Wu, T.T.
1988-01-01
This paper contains the first part of a systematic semiclassical analysis of the weak-coupling limit of lattice gauge theories, using the Hamiltonian formulation. The model consists of an N 3 cubic lattice of pure SU(2) Yang-Mills theory, and in this first part we limit ourselves to the subspace of constant field configurations. We investigate the flow of classical trajectories, with a particular emphasis on the existence and location of caustics. There the ground-state wave function is expected to peak. It is found that regions densely filled with caustics are very close to the origin, i.e., in the domain of weak field configurations. This strongly supports the expectation that caustics are essential for quantities of physical interest
Trident: scalable compute archives: workflows, visualization, and analysis
Gopu, Arvind; Hayashi, Soichi; Young, Michael D.; Kotulla, Ralf; Henschel, Robert; Harbeck, Daniel
2016-08-01
The Astronomy scientific community has embraced Big Data processing challenges, e.g. associated with time-domain astronomy, and come up with a variety of novel and efficient data processing solutions. However, data processing is only a small part of the Big Data challenge. Efficient knowledge discovery and scientific advancement in the Big Data era requires new and equally efficient tools: modern user interfaces for searching, identifying and viewing data online without direct access to the data; tracking of data provenance; searching, plotting and analyzing metadata; interactive visual analysis, especially of (time-dependent) image data; and the ability to execute pipelines on supercomputing and cloud resources with minimal user overhead or expertise even to novice computing users. The Trident project at Indiana University offers a comprehensive web and cloud-based microservice software suite that enables the straight forward deployment of highly customized Scalable Compute Archive (SCA) systems; including extensive visualization and analysis capabilities, with minimal amount of additional coding. Trident seamlessly scales up or down in terms of data volumes and computational needs, and allows feature sets within a web user interface to be quickly adapted to meet individual project requirements. Domain experts only have to provide code or business logic about handling/visualizing their domain's data products and about executing their pipelines and application work flows. Trident's microservices architecture is made up of light-weight services connected by a REST API and/or a message bus; a web interface elements are built using NodeJS, AngularJS, and HighCharts JavaScript libraries among others while backend services are written in NodeJS, PHP/Zend, and Python. The software suite currently consists of (1) a simple work flow execution framework to integrate, deploy, and execute pipelines and applications (2) a progress service to monitor work flows and sub
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.
Chiew, Mark; Graedel, Nadine N; Miller, Karla L
2018-07-01
Recent developments in highly accelerated fMRI data acquisition have employed low-rank and/or sparsity constraints for image reconstruction, as an alternative to conventional, time-independent parallel imaging. When under-sampling factors are high or the signals of interest are low-variance, however, functional data recovery can be poor or incomplete. We introduce a method for improving reconstruction fidelity using external constraints, like an experimental design matrix, to partially orient the estimated fMRI temporal subspace. Combining these external constraints with low-rank constraints introduces a new image reconstruction model that is analogous to using a mixture of subspace-decomposition (PCA/ICA) and regression (GLM) models in fMRI analysis. We show that this approach improves fMRI reconstruction quality in simulations and experimental data, focusing on the model problem of detecting subtle 1-s latency shifts between brain regions in a block-design task-fMRI experiment. Successful latency discrimination is shown at acceleration factors up to R = 16 in a radial-Cartesian acquisition. We show that this approach works with approximate, or not perfectly informative constraints, where the derived benefit is commensurate with the information content contained in the constraints. The proposed method extends low-rank approximation methods for under-sampled fMRI data acquisition by leveraging knowledge of expected task-based variance in the data, enabling improvements in the speed and efficiency of fMRI data acquisition without the loss of subtle features. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.
Temporal scalability comparison of the H.264/SVC and distributed video codec
DEFF Research Database (Denmark)
Huang, Xin; Ukhanova, Ann; Belyaev, Evgeny
2009-01-01
The problem of the multimedia scalable video streaming is a current topic of interest. There exist many methods for scalable video coding. This paper is focused on the scalable extension of H.264/AVC (H.264/SVC) and distributed video coding (DVC). The paper presents an efficiency comparison of SV...
Capella, Juan V; Perles, Angel; Bonastre, Alberto; Serrano, Juan J
2011-01-01
We present a set of novel low power wireless sensor nodes designed for monitoring wooden masterpieces and historical buildings, in order to perform an early detection of pests. Although our previous star-based system configuration has been in operation for more than 13 years, it does not scale well for sensorization of large buildings or when deploying hundreds of nodes. In this paper we demonstrate the feasibility of a cluster-based dynamic-tree hierarchical Wireless Sensor Network (WSN) architecture where realistic assumptions of radio frequency data transmission are applied to cluster construction, and a mix of heterogeneous nodes are used to minimize economic cost of the whole system and maximize power saving of the leaf nodes. Simulation results show that the specialization of a fraction of the nodes by providing better antennas and some energy harvesting techniques can dramatically extend the life of the entire WSN and reduce the cost of the whole system. A demonstration of the proposed architecture with a new routing protocol and applied to termite pest detection has been implemented on a set of new nodes and should last for about 10 years, but it provides better scalability, reliability and deployment properties.