High-dimensional covariance estimation with high-dimensional data

CERN Document Server

Pourahmadi, Mohsen

2013-01-01

Methods for estimating sparse and large covariance matrices Covariance and correlation matrices play fundamental roles in every aspect of the analysis of multivariate data collected from a variety of fields including business and economics, health care, engineering, and environmental and physical sciences. High-Dimensional Covariance Estimation provides accessible and comprehensive coverage of the classical and modern approaches for estimating covariance matrices as well as their applications to the rapidly developing areas lying at the intersection of statistics and mac

Violation of self-similarity in the expansion of a one-dimensional Bose gas

International Nuclear Information System (INIS)

Pedri, P.; Santos, L.; Oehberg, P.; Stringari, S.

2003-01-01

The expansion of a one-dimensional Bose gas after releasing its initial harmonic confinement is investigated employing the Lieb-Liniger equation of state within the local-density approximation. We show that during the expansion the density profile of the gas does not follow a self-similar solution, as one would expect from a simple scaling ansatz. We carry out a variational calculation, which recovers the numerical results for the expansion, the equilibrium properties of the density profile, and the frequency of the lowest compressional mode. The variational approach allows for the analysis of the expansion in all interaction regimes between the mean-field and the Tonks-Girardeau limits, and in particular shows the range of parameters for which the expansion violates self-similarity

Highly conducting one-dimensional solids

CERN Document Server

Evrard, Roger; Doren, Victor

1979-01-01

Although the problem of a metal in one dimension has long been known to solid-state physicists, it was not until the synthesis of real one-dimensional or quasi-one-dimensional systems that this subject began to attract considerable attention. This has been due in part to the search for high­ temperature superconductivity and the possibility of reaching this goal with quasi-one-dimensional substances. A period of intense activity began in 1973 with the report of a measurement of an apparently divergent conduc­ tivity peak in TfF-TCNQ. Since then a great deal has been learned about quasi-one-dimensional conductors. The emphasis now has shifted from trying to find materials of very high conductivity to the many interesting problems of physics and chemistry involved. But many questions remain open and are still under active investigation. This book gives a review of the experimental as well as theoretical progress made in this field over the last years. All the chapters have been written by scientists who have ...

High-dimensional change-point estimation: Combining filtering with convex optimization

OpenAIRE

Soh, Yong Sheng; Chandrasekaran, Venkat

2017-01-01

We consider change-point estimation in a sequence of high-dimensional signals given noisy observations. Classical approaches to this problem such as the filtered derivative method are useful for sequences of scalar-valued signals, but they have undesirable scaling behavior in the high-dimensional setting. However, many high-dimensional signals encountered in practice frequently possess latent low-dimensional structure. Motivated by this observation, we propose a technique for high-dimensional...

Multivariate statistics high-dimensional and large-sample approximations

CERN Document Server

Fujikoshi, Yasunori; Shimizu, Ryoichi

2010-01-01

A comprehensive examination of high-dimensional analysis of multivariate methods and their real-world applications Multivariate Statistics: High-Dimensional and Large-Sample Approximations is the first book of its kind to explore how classical multivariate methods can be revised and used in place of conventional statistical tools. Written by prominent researchers in the field, the book focuses on high-dimensional and large-scale approximations and details the many basic multivariate methods used to achieve high levels of accuracy. The authors begin with a fundamental presentation of the basic

High dimensional neurocomputing growth, appraisal and applications

CERN Document Server

Tripathi, Bipin Kumar

2015-01-01

The book presents a coherent understanding of computational intelligence from the perspective of what is known as "intelligent computing" with high-dimensional parameters. It critically discusses the central issue of high-dimensional neurocomputing, such as quantitative representation of signals, extending the dimensionality of neuron, supervised and unsupervised learning and design of higher order neurons. The strong point of the book is its clarity and ability of the underlying theory to unify our understanding of high-dimensional computing where conventional methods fail. The plenty of application oriented problems are presented for evaluating, monitoring and maintaining the stability of adaptive learning machine. Author has taken care to cover the breadth and depth of the subject, both in the qualitative as well as quantitative way. The book is intended to enlighten the scientific community, ranging from advanced undergraduates to engineers, scientists and seasoned researchers in computational intelligenc...

Self-organization phenomena and decaying self-similar state in two-dimensional incompressible viscous fluids

International Nuclear Information System (INIS)

Kondoh, Yoshiomi; Serizawa, Shunsuke; Nakano, Akihiro; Takahashi, Toshiki; Van Dam, James W.

2004-01-01

The final self-similar state of decaying two-dimensional (2D) turbulence in 2D incompressible viscous flow is analytically and numerically investigated for the case with periodic boundaries. It is proved by theoretical analysis and simulations that the sinh-Poisson state cω=-sinh(βψ) is not realized in the dynamical system of interest. It is shown by an eigenfunction spectrum analysis that a sufficient explanation for the self-organization to the decaying self-similar state is the faster energy decay of higher eigenmodes and the energy accumulation to the lowest eigenmode for given boundary conditions due to simultaneous normal and inverse cascading by nonlinear mode couplings. The theoretical prediction is demonstrated to be correct by simulations leading to the lowest eigenmode of {(1,0)+(0,1)} of the dissipative operator for the periodic boundaries. It is also clarified that an important process during nonlinear self-organization is an interchange between the dominant operators, which leads to the final decaying self-similar state

HDclassif : An R Package for Model-Based Clustering and Discriminant Analysis of High-Dimensional Data

Directory of Open Access Journals (Sweden)

Laurent Berge

2012-01-01

Full Text Available This paper presents the R package HDclassif which is devoted to the clustering and the discriminant analysis of high-dimensional data. The classification methods proposed in the package result from a new parametrization of the Gaussian mixture model which combines the idea of dimension reduction and model constraints on the covariance matrices. The supervised classification method using this parametrization is called high dimensional discriminant analysis (HDDA. In a similar manner, the associated clustering method iscalled high dimensional data clustering (HDDC and uses the expectation-maximization algorithm for inference. In order to correctly t the data, both methods estimate the specific subspace and the intrinsic dimension of the groups. Due to the constraints on the covariance matrices, the number of parameters to estimate is significantly lower than other model-based methods and this allows the methods to be stable and efficient in high dimensions. Two introductory examples illustrated with R codes allow the user to discover the hdda and hddc functions. Experiments on simulated and real datasets also compare HDDC and HDDA with existing classification methods on high-dimensional datasets. HDclassif is a free software and distributed under the general public license, as part of the R software project.

Generalized similarity method in unsteady two-dimensional MHD ...

African Journals Online (AJOL)

user

International Journal of Engineering, Science and Technology. Vol. 1, No. 1, 2009 ... temperature two-dimensional MHD laminar boundary layer of incompressible fluid. ...... Φ η is Blasius solution for stationary boundary layer on the plate,. ( ). 0.

Hierarchical low-rank approximation for high dimensional approximation

KAUST Repository

Nouy, Anthony

2016-01-07

Tensor methods are among the most prominent tools for the numerical solution of high-dimensional problems where functions of multiple variables have to be approximated. Such high-dimensional approximation problems naturally arise in stochastic analysis and uncertainty quantification. In many practical situations, the approximation of high-dimensional functions is made computationally tractable by using rank-structured approximations. In this talk, we present algorithms for the approximation in hierarchical tensor format using statistical methods. Sparse representations in a given tensor format are obtained with adaptive or convex relaxation methods, with a selection of parameters using crossvalidation methods.

Hierarchical low-rank approximation for high dimensional approximation

KAUST Repository

Nouy, Anthony

2016-01-01

Tensor methods are among the most prominent tools for the numerical solution of high-dimensional problems where functions of multiple variables have to be approximated. Such high-dimensional approximation problems naturally arise in stochastic analysis and uncertainty quantification. In many practical situations, the approximation of high-dimensional functions is made computationally tractable by using rank-structured approximations. In this talk, we present algorithms for the approximation in hierarchical tensor format using statistical methods. Sparse representations in a given tensor format are obtained with adaptive or convex relaxation methods, with a selection of parameters using crossvalidation methods.

Harnessing high-dimensional hyperentanglement through a biphoton frequency comb

Science.gov (United States)

Xie, Zhenda; Zhong, Tian; Shrestha, Sajan; Xu, Xinan; Liang, Junlin; Gong, Yan-Xiao; Bienfang, Joshua C.; Restelli, Alessandro; Shapiro, Jeffrey H.; Wong, Franco N. C.; Wei Wong, Chee

2015-08-01

Quantum entanglement is a fundamental resource for secure information processing and communications, and hyperentanglement or high-dimensional entanglement has been separately proposed for its high data capacity and error resilience. The continuous-variable nature of the energy-time entanglement makes it an ideal candidate for efficient high-dimensional coding with minimal limitations. Here, we demonstrate the first simultaneous high-dimensional hyperentanglement using a biphoton frequency comb to harness the full potential in both the energy and time domain. Long-postulated Hong-Ou-Mandel quantum revival is exhibited, with up to 19 time-bins and 96.5% visibilities. We further witness the high-dimensional energy-time entanglement through Franson revivals, observed periodically at integer time-bins, with 97.8% visibility. This qudit state is observed to simultaneously violate the generalized Bell inequality by up to 10.95 standard deviations while observing recurrent Clauser-Horne-Shimony-Holt S-parameters up to 2.76. Our biphoton frequency comb provides a platform for photon-efficient quantum communications towards the ultimate channel capacity through energy-time-polarization high-dimensional encoding.

Towards novel organic high-Tc superconductors: Data mining using density of states similarity search

Science.gov (United States)

Geilhufe, R. Matthias; Borysov, Stanislav S.; Kalpakchi, Dmytro; Balatsky, Alexander V.

2018-02-01

Identifying novel functional materials with desired key properties is an important part of bridging the gap between fundamental research and technological advancement. In this context, high-throughput calculations combined with data-mining techniques highly accelerated this process in different areas of research during the past years. The strength of a data-driven approach for materials prediction lies in narrowing down the search space of thousands of materials to a subset of prospective candidates. Recently, the open-access organic materials database OMDB was released providing electronic structure data for thousands of previously synthesized three-dimensional organic crystals. Based on the OMDB, we report about the implementation of a novel density of states similarity search tool which is capable of retrieving materials with similar density of states to a reference material. The tool is based on the approximate nearest neighbor algorithm as implemented in the ANNOY library and can be applied via the OMDB web interface. The approach presented here is wide ranging and can be applied to various problems where the density of states is responsible for certain key properties of a material. As the first application, we report about materials exhibiting electronic structure similarities to the aromatic hydrocarbon p-terphenyl which was recently discussed as a potential organic high-temperature superconductor exhibiting a transition temperature in the order of 120 K under strong potassium doping. Although the mechanism driving the remarkable transition temperature remains under debate, we argue that the density of states, reflecting the electronic structure of a material, might serve as a crucial ingredient for the observed high Tc. To provide candidates which might exhibit comparable properties, we present 15 purely organic materials with similar features to p-terphenyl within the electronic structure, which also tend to have structural similarities with p

A Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data.

Directory of Open Access Journals (Sweden)

Ali Seyed Shirkhorshidi

Full Text Available Similarity or distance measures are core components used by distance-based clustering algorithms to cluster similar data points into the same clusters, while dissimilar or distant data points are placed into different clusters. The performance of similarity measures is mostly addressed in two or three-dimensional spaces, beyond which, to the best of our knowledge, there is no empirical study that has revealed the behavior of similarity measures when dealing with high-dimensional datasets. To fill this gap, a technical framework is proposed in this study to analyze, compare and benchmark the influence of different similarity measures on the results of distance-based clustering algorithms. For reproducibility purposes, fifteen publicly available datasets were used for this study, and consequently, future distance measures can be evaluated and compared with the results of the measures discussed in this work. These datasets were classified as low and high-dimensional categories to study the performance of each measure against each category. This research should help the research community to identify suitable distance measures for datasets and also to facilitate a comparison and evaluation of the newly proposed similarity or distance measures with traditional ones.

Engineering two-photon high-dimensional states through quantum interference

Science.gov (United States)

Zhang, Yingwen; Roux, Filippus S.; Konrad, Thomas; Agnew, Megan; Leach, Jonathan; Forbes, Andrew

2016-01-01

Many protocols in quantum science, for example, linear optical quantum computing, require access to large-scale entangled quantum states. Such systems can be realized through many-particle qubits, but this approach often suffers from scalability problems. An alternative strategy is to consider a lesser number of particles that exist in high-dimensional states. The spatial modes of light are one such candidate that provides access to high-dimensional quantum states, and thus they increase the storage and processing potential of quantum information systems. We demonstrate the controlled engineering of two-photon high-dimensional states entangled in their orbital angular momentum through Hong-Ou-Mandel interference. We prepare a large range of high-dimensional entangled states and implement precise quantum state filtering. We characterize the full quantum state before and after the filter, and are thus able to determine that only the antisymmetric component of the initial state remains. This work paves the way for high-dimensional processing and communication of multiphoton quantum states, for example, in teleportation beyond qubits. PMID:26933685

Similarity and self-similarity in high energy density physics: application to laboratory astrophysics

International Nuclear Information System (INIS)

Falize, E.

2008-10-01

The spectacular recent development of powerful facilities allows the astrophysical community to explore, in laboratory, astrophysical phenomena where radiation and matter are strongly coupled. The titles of the nine chapters of the thesis are: from high energy density physics to laboratory astrophysics; Lie groups, invariance and self-similarity; scaling laws and similarity properties in High-Energy-Density physics; the Burgan-Feix-Munier transformation; dynamics of polytropic gases; stationary radiating shocks and the POLAR project; structure, dynamics and stability of optically thin fluids; from young star jets to laboratory jets; modelling and experiences for laboratory jets

Density-based retrieval from high-similarity image databases

DEFF Research Database (Denmark)

Hansen, Michael Edberg; Carstensen, Jens Michael

2004-01-01

Many image classification problems can fruitfully be thought of as image retrieval in a "high similarity image database" (HSID) characterized by being tuned towards a specific application and having a high degree of visual similarity between entries that should be distinguished. We introduce a me...

High dimensional entanglement

CSIR Research Space (South Africa)

Mc

2012-07-01

Full Text Available stream_source_info McLaren_2012.pdf.txt stream_content_type text/plain stream_size 2190 Content-Encoding ISO-8859-1 stream_name McLaren_2012.pdf.txt Content-Type text/plain; charset=ISO-8859-1 High dimensional... entanglement M. McLAREN1,2, F.S. ROUX1 & A. FORBES1,2,3 1. CSIR National Laser Centre, PO Box 395, Pretoria 0001 2. School of Physics, University of the Stellenbosch, Private Bag X1, 7602, Matieland 3. School of Physics, University of Kwazulu...

Using High-Dimensional Image Models to Perform Highly Undetectable Steganography

Science.gov (United States)

Pevný, Tomáš; Filler, Tomáš; Bas, Patrick

This paper presents a complete methodology for designing practical and highly-undetectable stegosystems for real digital media. The main design principle is to minimize a suitably-defined distortion by means of efficient coding algorithm. The distortion is defined as a weighted difference of extended state-of-the-art feature vectors already used in steganalysis. This allows us to "preserve" the model used by steganalyst and thus be undetectable even for large payloads. This framework can be efficiently implemented even when the dimensionality of the feature set used by the embedder is larger than 107. The high dimensional model is necessary to avoid known security weaknesses. Although high-dimensional models might be problem in steganalysis, we explain, why they are acceptable in steganography. As an example, we introduce HUGO, a new embedding algorithm for spatial-domain digital images and we contrast its performance with LSB matching. On the BOWS2 image database and in contrast with LSB matching, HUGO allows the embedder to hide 7× longer message with the same level of security level.

Efficient estimation for high similarities using odd sketches

DEFF Research Database (Denmark)

Mitzenmacher, Michael; Pagh, Rasmus; Pham, Ninh Dang

2014-01-01

. This means that Odd Sketches provide a highly space-efficient estimator for sets of high similarity, which is relevant in applications such as web duplicate detection, collaborative filtering, and association rule learning. The method extends to weighted Jaccard similarity, relevant e.g. for TF-IDF vector...... and web duplicate detection tasks....

Clustering high dimensional data using RIA

Energy Technology Data Exchange (ETDEWEB)

Aziz, Nazrina [School of Quantitative Sciences, College of Arts and Sciences, Universiti Utara Malaysia, 06010 Sintok, Kedah (Malaysia)

2015-05-15

Clustering may simply represent a convenient method for organizing a large data set so that it can easily be understood and information can efficiently be retrieved. However, identifying cluster in high dimensionality data sets is a difficult task because of the curse of dimensionality. Another challenge in clustering is some traditional functions cannot capture the pattern dissimilarity among objects. In this article, we used an alternative dissimilarity measurement called Robust Influence Angle (RIA) in the partitioning method. RIA is developed using eigenstructure of the covariance matrix and robust principal component score. We notice that, it can obtain cluster easily and hence avoid the curse of dimensionality. It is also manage to cluster large data sets with mixed numeric and categorical value.

Spherical anharmonic oscillator in self-similar approximation

International Nuclear Information System (INIS)

Yukalova, E.P.; Yukalov, V.I.

1992-01-01

The method of self-similar approximation is applied here for calculating the eigenvalues of the three-dimensional spherical anharmonic oscillator. The advantage of this method is in its simplicity and high accuracy. The comparison with other known analytical methods proves that this method is more simple and accurate. 25 refs

Comparative Analysis of Mass Spectral Similarity Measures on Peak Alignment for Comprehensive Two-Dimensional Gas Chromatography Mass Spectrometry

Science.gov (United States)

2013-01-01

Peak alignment is a critical procedure in mass spectrometry-based biomarker discovery in metabolomics. One of peak alignment approaches to comprehensive two-dimensional gas chromatography mass spectrometry (GC×GC-MS) data is peak matching-based alignment. A key to the peak matching-based alignment is the calculation of mass spectral similarity scores. Various mass spectral similarity measures have been developed mainly for compound identification, but the effect of these spectral similarity measures on the performance of peak matching-based alignment still remains unknown. Therefore, we selected five mass spectral similarity measures, cosine correlation, Pearson's correlation, Spearman's correlation, partial correlation, and part correlation, and examined their effects on peak alignment using two sets of experimental GC×GC-MS data. The results show that the spectral similarity measure does not affect the alignment accuracy significantly in analysis of data from less complex samples, while the partial correlation performs much better than other spectral similarity measures when analyzing experimental data acquired from complex biological samples. PMID:24151524

Asymptotically Honest Confidence Regions for High Dimensional

DEFF Research Database (Denmark)

Caner, Mehmet; Kock, Anders Bredahl

While variable selection and oracle inequalities for the estimation and prediction error have received considerable attention in the literature on high-dimensional models, very little work has been done in the area of testing and construction of confidence bands in high-dimensional models. However...... develop an oracle inequality for the conservative Lasso only assuming the existence of a certain number of moments. This is done by means of the Marcinkiewicz-Zygmund inequality which in our context provides sharper bounds than Nemirovski's inequality. As opposed to van de Geer et al. (2014) we allow...

Quasi-two-dimensional metallic hydrogen in diphosphide at a high pressure

International Nuclear Information System (INIS)

Degtyarenko, N. N.; Mazur, E. A.

2016-01-01

The structural, electronic, phonon, and other characteristics of the normal phases of phosphorus hydrides with stoichiometry PH k are analyzed. The properties of the initial substance, namely, diphosphine are calculated. In contrast to phosphorus hydrides with stoichiometry PH 3 , a quasi-two-dimensional phosphorus-stabilized lattice of metallic hydrogen can be formed in this substance during hydrostatic compression at a high pressure. The formed structure with H–P–H elements is shown to be locally stable in phonon spectrum, i.e., to be metastable. The properties of diphosphine are compared with the properties of similar structures of sulfur hydrides.

Quasi-two-dimensional metallic hydrogen in diphosphide at a high pressure

Energy Technology Data Exchange (ETDEWEB)

Degtyarenko, N. N.; Mazur, E. A., E-mail: eugen-mazur@mail.ru [National Research Nuclear University MEPhI (Russian Federation)

2016-08-15

The structural, electronic, phonon, and other characteristics of the normal phases of phosphorus hydrides with stoichiometry PH{sub k} are analyzed. The properties of the initial substance, namely, diphosphine are calculated. In contrast to phosphorus hydrides with stoichiometry PH{sub 3}, a quasi-two-dimensional phosphorus-stabilized lattice of metallic hydrogen can be formed in this substance during hydrostatic compression at a high pressure. The formed structure with H–P–H elements is shown to be locally stable in phonon spectrum, i.e., to be metastable. The properties of diphosphine are compared with the properties of similar structures of sulfur hydrides.

High Dimensional Classification Using Features Annealed Independence Rules.

Science.gov (United States)

Fan, Jianqing; Fan, Yingying

2008-01-01

Classification using high-dimensional features arises frequently in many contemporary statistical studies such as tumor classification using microarray or other high-throughput data. The impact of dimensionality on classifications is largely poorly understood. In a seminal paper, Bickel and Levina (2004) show that the Fisher discriminant performs poorly due to diverging spectra and they propose to use the independence rule to overcome the problem. We first demonstrate that even for the independence classification rule, classification using all the features can be as bad as the random guessing due to noise accumulation in estimating population centroids in high-dimensional feature space. In fact, we demonstrate further that almost all linear discriminants can perform as bad as the random guessing. Thus, it is paramountly important to select a subset of important features for high-dimensional classification, resulting in Features Annealed Independence Rules (FAIR). The conditions under which all the important features can be selected by the two-sample t-statistic are established. The choice of the optimal number of features, or equivalently, the threshold value of the test statistics are proposed based on an upper bound of the classification error. Simulation studies and real data analysis support our theoretical results and demonstrate convincingly the advantage of our new classification procedure.

Modeling High-Dimensional Multichannel Brain Signals

KAUST Repository

Hu, Lechuan; Fortin, Norbert J.; Ombao, Hernando

2017-01-01

aspects: first, there are major statistical and computational challenges for modeling and analyzing high-dimensional multichannel brain signals; second, there is no set of universally agreed measures for characterizing connectivity. To model multichannel

Protein structural similarity search by Ramachandran codes

Directory of Open Access Journals (Sweden)

Chang Chih-Hung

2007-08-01

Full Text Available Abstract Background Protein structural data has increased exponentially, such that fast and accurate tools are necessary to access structure similarity search. To improve the search speed, several methods have been designed to reduce three-dimensional protein structures to one-dimensional text strings that are then analyzed by traditional sequence alignment methods; however, the accuracy is usually sacrificed and the speed is still unable to match sequence similarity search tools. Here, we aimed to improve the linear encoding methodology and develop efficient search tools that can rapidly retrieve structural homologs from large protein databases. Results We propose a new linear encoding method, SARST (Structural similarity search Aided by Ramachandran Sequential Transformation. SARST transforms protein structures into text strings through a Ramachandran map organized by nearest-neighbor clustering and uses a regenerative approach to produce substitution matrices. Then, classical sequence similarity search methods can be applied to the structural similarity search. Its accuracy is similar to Combinatorial Extension (CE and works over 243,000 times faster, searching 34,000 proteins in 0.34 sec with a 3.2-GHz CPU. SARST provides statistically meaningful expectation values to assess the retrieved information. It has been implemented into a web service and a stand-alone Java program that is able to run on many different platforms. Conclusion As a database search method, SARST can rapidly distinguish high from low similarities and efficiently retrieve homologous structures. It demonstrates that the easily accessible linear encoding methodology has the potential to serve as a foundation for efficient protein structural similarity search tools. These search tools are supposed applicable to automated and high-throughput functional annotations or predictions for the ever increasing number of published protein structures in this post-genomic era.

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

Science.gov (United States)

Bohn, Bastian; Garcke, Jochen; Griebel, Michael

2016-03-01

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

High-dimensional model estimation and model selection

CERN Multimedia

CERN. Geneva

2015-01-01

I will review concepts and algorithms from high-dimensional statistics for linear model estimation and model selection. I will particularly focus on the so-called p>>n setting where the number of variables p is much larger than the number of samples n. I will focus mostly on regularized statistical estimators that produce sparse models. Important examples include the LASSO and its matrix extension, the Graphical LASSO, and more recent non-convex methods such as the TREX. I will show the applicability of these estimators in a diverse range of scientific applications, such as sparse interaction graph recovery and high-dimensional classification and regression problems in genomics.

Matrix correlations for high-dimensional data: The modified RV-coefficient

NARCIS (Netherlands)

Smilde, A.K.; Kiers, H.A.L.; Bijlsma, S.; Rubingh, C.M.; Erk, M.J. van

2009-01-01

Motivation: Modern functional genomics generates high-dimensional datasets. It is often convenient to have a single simple number characterizing the relationship between pairs of such high-dimensional datasets in a comprehensive way. Matrix correlations are such numbers and are appealing since they

Approximating high-dimensional dynamics by barycentric coordinates with linear programming

Energy Technology Data Exchange (ETDEWEB)

Hirata, Yoshito, E-mail: yoshito@sat.t.u-tokyo.ac.jp; Aihara, Kazuyuki; Suzuki, Hideyuki [Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505 (Japan); Department of Mathematical Informatics, The University of Tokyo, Bunkyo-ku, Tokyo 113-8656 (Japan); CREST, JST, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012 (Japan); Shiro, Masanori [Department of Mathematical Informatics, The University of Tokyo, Bunkyo-ku, Tokyo 113-8656 (Japan); Mathematical Neuroinformatics Group, Advanced Industrial Science and Technology, Tsukuba, Ibaraki 305-8568 (Japan); Takahashi, Nozomu; Mas, Paloma [Center for Research in Agricultural Genomics (CRAG), Consorci CSIC-IRTA-UAB-UB, Barcelona 08193 (Spain)

2015-01-15

The increasing development of novel methods and techniques facilitates the measurement of high-dimensional time series but challenges our ability for accurate modeling and predictions. The use of a general mathematical model requires the inclusion of many parameters, which are difficult to be fitted for relatively short high-dimensional time series observed. Here, we propose a novel method to accurately model a high-dimensional time series. Our method extends the barycentric coordinates to high-dimensional phase space by employing linear programming, and allowing the approximation errors explicitly. The extension helps to produce free-running time-series predictions that preserve typical topological, dynamical, and/or geometric characteristics of the underlying attractors more accurately than the radial basis function model that is widely used. The method can be broadly applied, from helping to improve weather forecasting, to creating electronic instruments that sound more natural, and to comprehensively understanding complex biological data.

Approximating high-dimensional dynamics by barycentric coordinates with linear programming.

Science.gov (United States)

Hirata, Yoshito; Shiro, Masanori; Takahashi, Nozomu; Aihara, Kazuyuki; Suzuki, Hideyuki; Mas, Paloma

2015-01-01

The increasing development of novel methods and techniques facilitates the measurement of high-dimensional time series but challenges our ability for accurate modeling and predictions. The use of a general mathematical model requires the inclusion of many parameters, which are difficult to be fitted for relatively short high-dimensional time series observed. Here, we propose a novel method to accurately model a high-dimensional time series. Our method extends the barycentric coordinates to high-dimensional phase space by employing linear programming, and allowing the approximation errors explicitly. The extension helps to produce free-running time-series predictions that preserve typical topological, dynamical, and/or geometric characteristics of the underlying attractors more accurately than the radial basis function model that is widely used. The method can be broadly applied, from helping to improve weather forecasting, to creating electronic instruments that sound more natural, and to comprehensively understanding complex biological data.

Approximating high-dimensional dynamics by barycentric coordinates with linear programming

International Nuclear Information System (INIS)

Hirata, Yoshito; Aihara, Kazuyuki; Suzuki, Hideyuki; Shiro, Masanori; Takahashi, Nozomu; Mas, Paloma

2015-01-01

The increasing development of novel methods and techniques facilitates the measurement of high-dimensional time series but challenges our ability for accurate modeling and predictions. The use of a general mathematical model requires the inclusion of many parameters, which are difficult to be fitted for relatively short high-dimensional time series observed. Here, we propose a novel method to accurately model a high-dimensional time series. Our method extends the barycentric coordinates to high-dimensional phase space by employing linear programming, and allowing the approximation errors explicitly. The extension helps to produce free-running time-series predictions that preserve typical topological, dynamical, and/or geometric characteristics of the underlying attractors more accurately than the radial basis function model that is widely used. The method can be broadly applied, from helping to improve weather forecasting, to creating electronic instruments that sound more natural, and to comprehensively understanding complex biological data

High-dimensional quantum cloning and applications to quantum hacking.

Science.gov (United States)

Bouchard, Frédéric; Fickler, Robert; Boyd, Robert W; Karimi, Ebrahim

2017-02-01

Attempts at cloning a quantum system result in the introduction of imperfections in the state of the copies. This is a consequence of the no-cloning theorem, which is a fundamental law of quantum physics and the backbone of security for quantum communications. Although perfect copies are prohibited, a quantum state may be copied with maximal accuracy via various optimal cloning schemes. Optimal quantum cloning, which lies at the border of the physical limit imposed by the no-signaling theorem and the Heisenberg uncertainty principle, has been experimentally realized for low-dimensional photonic states. However, an increase in the dimensionality of quantum systems is greatly beneficial to quantum computation and communication protocols. Nonetheless, no experimental demonstration of optimal cloning machines has hitherto been shown for high-dimensional quantum systems. We perform optimal cloning of high-dimensional photonic states by means of the symmetrization method. We show the universality of our technique by conducting cloning of numerous arbitrary input states and fully characterize our cloning machine by performing quantum state tomography on cloned photons. In addition, a cloning attack on a Bennett and Brassard (BB84) quantum key distribution protocol is experimentally demonstrated to reveal the robustness of high-dimensional states in quantum cryptography.

Hypergraph-based anomaly detection of high-dimensional co-occurrences.

Science.gov (United States)

Silva, Jorge; Willett, Rebecca

2009-03-01

This paper addresses the problem of detecting anomalous multivariate co-occurrences using a limited number of unlabeled training observations. A novel method based on using a hypergraph representation of the data is proposed to deal with this very high-dimensional problem. Hypergraphs constitute an important extension of graphs which allow edges to connect more than two vertices simultaneously. A variational Expectation-Maximization algorithm for detecting anomalies directly on the hypergraph domain without any feature selection or dimensionality reduction is presented. The resulting estimate can be used to calculate a measure of anomalousness based on the False Discovery Rate. The algorithm has O(np) computational complexity, where n is the number of training observations and p is the number of potential participants in each co-occurrence event. This efficiency makes the method ideally suited for very high-dimensional settings, and requires no tuning, bandwidth or regularization parameters. The proposed approach is validated on both high-dimensional synthetic data and the Enron email database, where p > 75,000, and it is shown that it can outperform other state-of-the-art methods.

On spectral distribution of high dimensional covariation matrices

DEFF Research Database (Denmark)

Heinrich, Claudio; Podolskij, Mark

In this paper we present the asymptotic theory for spectral distributions of high dimensional covariation matrices of Brownian diffusions. More specifically, we consider N-dimensional Itô integrals with time varying matrix-valued integrands. We observe n equidistant high frequency data points...... of the underlying Brownian diffusion and we assume that N/n -> c in (0,oo). We show that under a certain mixed spectral moment condition the spectral distribution of the empirical covariation matrix converges in distribution almost surely. Our proof relies on method of moments and applications of graph theory....

HSM: Heterogeneous Subspace Mining in High Dimensional Data

DEFF Research Database (Denmark)

Müller, Emmanuel; Assent, Ira; Seidl, Thomas

2009-01-01

Heterogeneous data, i.e. data with both categorical and continuous values, is common in many databases. However, most data mining algorithms assume either continuous or categorical attributes, but not both. In high dimensional data, phenomena due to the "curse of dimensionality" pose additional...... challenges. Usually, due to locally varying relevance of attributes, patterns do not show across the full set of attributes. In this paper we propose HSM, which defines a new pattern model for heterogeneous high dimensional data. It allows data mining in arbitrary subsets of the attributes that are relevant...... for the respective patterns. Based on this model we propose an efficient algorithm, which is aware of the heterogeneity of the attributes. We extend an indexing structure for continuous attributes such that HSM indexing adapts to different attribute types. In our experiments we show that HSM efficiently mines...

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...

Analysing spatially extended high-dimensional dynamics by recurrence plots

Energy Technology Data Exchange (ETDEWEB)

Marwan, Norbert, E-mail: marwan@pik-potsdam.de [Potsdam Institute for Climate Impact Research, 14412 Potsdam (Germany); Kurths, Jürgen [Potsdam Institute for Climate Impact Research, 14412 Potsdam (Germany); Humboldt Universität zu Berlin, Institut für Physik (Germany); Nizhny Novgorod State University, Department of Control Theory, Nizhny Novgorod (Russian Federation); Foerster, Saskia [GFZ German Research Centre for Geosciences, Section 1.4 Remote Sensing, Telegrafenberg, 14473 Potsdam (Germany)

2015-05-08

Recurrence plot based measures of complexity are capable tools for characterizing complex dynamics. In this letter we show the potential of selected recurrence plot measures for the investigation of even high-dimensional dynamics. We apply this method on spatially extended chaos, such as derived from the Lorenz96 model and show that the recurrence plot based measures can qualitatively characterize typical dynamical properties such as chaotic or periodic dynamics. Moreover, we demonstrate its power by analysing satellite image time series of vegetation cover with contrasting dynamics as a spatially extended and potentially high-dimensional example from the real world. - Highlights: • We use recurrence plots for analysing partially extended dynamics. • We investigate the high-dimensional chaos of the Lorenz96 model. • The approach distinguishes different spatio-temporal dynamics. • We use the method for studying vegetation cover time series.

Color-Based Image Retrieval from High-Similarity Image Databases

DEFF Research Database (Denmark)

Hansen, Michael Adsetts Edberg; Carstensen, Jens Michael

2003-01-01

Many image classification problems can fruitfully be thought of as image retrieval in a "high similarity image database" (HSID) characterized by being tuned towards a specific application and having a high degree of visual similarity between entries that should be distinguished. We introduce...... a method for HSID retrieval using a similarity measure based on a linear combination of Jeffreys-Matusita (JM) distances between distributions of color (and color derivatives) estimated from a set of automatically extracted image regions. The weight coefficients are estimated based on optimal retrieval...... performance. Experimental results on the difficult task of visually identifying clones of fungal colonies grown in a petri dish and categorization of pelts show a high retrieval accuracy of the method when combined with standardized sample preparation and image acquisition....

AN EFFECTIVE MULTI-CLUSTERING ANONYMIZATION APPROACH USING DISCRETE COMPONENT TASK FOR NON-BINARY HIGH DIMENSIONAL DATA SPACES

Directory of Open Access Journals (Sweden)

L.V. Arun Shalin

2016-01-01

Full Text Available Clustering is a process of grouping elements together, designed in such a way that the elements assigned to similar data points in a cluster are more comparable to each other than the remaining data points in a cluster. During clustering certain difficulties related when dealing with high dimensional data are ubiquitous and abundant. Works concentrated using anonymization method for high dimensional data spaces failed to address the problem related to dimensionality reduction during the inclusion of non-binary databases. In this work we study methods for dimensionality reduction for non-binary database. By analyzing the behavior of dimensionality reduction for non-binary database, results in performance improvement with the help of tag based feature. An effective multi-clustering anonymization approach called Discrete Component Task Specific Multi-Clustering (DCTSM is presented for dimensionality reduction on non-binary database. To start with we present the analysis of attribute in the non-binary database and cluster projection identifies the sparseness degree of dimensions. Additionally with the quantum distribution on multi-cluster dimension, the solution for relevancy of attribute and redundancy on non-binary data spaces is provided resulting in performance improvement on the basis of tag based feature. Multi-clustering tag based feature reduction extracts individual features and are correspondingly replaced by the equivalent feature clusters (i.e. tag clusters. During training, the DCTSM approach uses multi-clusters instead of individual tag features and then during decoding individual features is replaced by corresponding multi-clusters. To measure the effectiveness of the method, experiments are conducted on existing anonymization method for high dimensional data spaces and compared with the DCTSM approach using Statlog German Credit Data Set. Improved tag feature extraction and minimum error rate compared to conventional anonymization

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

International Nuclear Information System (INIS)

Tripathy, Rohit; Bilionis, Ilias; Gonzalez, Marcial

2016-01-01

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

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

Science.gov (United States)

Tripathy, Rohit; Bilionis, Ilias; Gonzalez, Marcial

2016-09-01

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

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

Energy Technology Data Exchange (ETDEWEB)

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

2016-09-15

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

Detection of structurally similar adulterants in botanical dietary supplements by thin-layer chromatography and surface enhanced Raman spectroscopy combined with two-dimensional correlation spectroscopy.

Science.gov (United States)

Li, Hao; Zhu, Qing xia; Chwee, Tsz sian; Wu, Lin; Chai, Yi feng; Lu, Feng; Yuan, Yong fang

2015-07-09

Thin-layer chromatography (TLC) coupled with surface enhanced Raman spectroscopy (SERS) has been widely used for the study of various complex systems, especially for the detection of adulterants in botanical dietary supplements (BDS). However, this method is not sufficient to distinguish structurally similar adulterants in BDS since the analogs have highly similar chromatographic and/or spectroscopic behaviors. Taking into account the fact that higher cost and more time will be required for comprehensive chromatographic separation, more efforts with respect to spectroscopy are now focused on analyzing the overlapped SERS peaks. In this paper, the combination of a TLC-SERS method with two-dimensional correlation spectroscopy (2DCOS), with duration of exposure to laser as the perturbation, is applied to solve this problem. Besides the usual advantages of the TLC-SERS method, such as its simplicity, rapidness, and sensitivity, more advantages are presented here, such as enhanced selectivity and good reproducibility, which are obtained by 2DCOS. Two chemicals with similar structures are successfully differentiated from the complex BDS matrices. The study provides a more accurate qualitative screening method for detection of BDS with adulterants, and offers a new universal approach for the analysis of highly overlapped SERS peaks. Copyright © 2015 Elsevier B.V. All rights reserved.

FRESCO: Referential compression of highly similar sequences.

Science.gov (United States)

Wandelt, Sebastian; Leser, Ulf

2013-01-01

In many applications, sets of similar texts or sequences are of high importance. Prominent examples are revision histories of documents or genomic sequences. Modern high-throughput sequencing technologies are able to generate DNA sequences at an ever-increasing rate. In parallel to the decreasing experimental time and cost necessary to produce DNA sequences, computational requirements for analysis and storage of the sequences are steeply increasing. Compression is a key technology to deal with this challenge. Recently, referential compression schemes, storing only the differences between a to-be-compressed input and a known reference sequence, gained a lot of interest in this field. In this paper, we propose a general open-source framework to compress large amounts of biological sequence data called Framework for REferential Sequence COmpression (FRESCO). Our basic compression algorithm is shown to be one to two orders of magnitudes faster than comparable related work, while achieving similar compression ratios. We also propose several techniques to further increase compression ratios, while still retaining the advantage in speed: 1) selecting a good reference sequence; and 2) rewriting a reference sequence to allow for better compression. In addition,we propose a new way of further boosting the compression ratios by applying referential compression to already referentially compressed files (second-order compression). This technique allows for compression ratios way beyond state of the art, for instance,4,000:1 and higher for human genomes. We evaluate our algorithms on a large data set from three different species (more than 1,000 genomes, more than 3 TB) and on a collection of versions of Wikipedia pages. Our results show that real-time compression of highly similar sequences at high compression ratios is possible on modern hardware.

INFORMATIVE ENERGY METRIC FOR SIMILARITY MEASURE IN REPRODUCING KERNEL HILBERT SPACES

Directory of Open Access Journals (Sweden)

Songhua Liu

2012-02-01

Full Text Available In this paper, information energy metric (IEM is obtained by similarity computing for high-dimensional samples in a reproducing kernel Hilbert space (RKHS. Firstly, similar/dissimilar subsets and their corresponding informative energy functions are defined. Secondly, IEM is proposed for similarity measure of those subsets, which converts the non-metric distances into metric ones. Finally, applications of this metric is introduced, such as classification problems. Experimental results validate the effectiveness of the proposed method.

TESTING HIGH-DIMENSIONAL COVARIANCE MATRICES, WITH APPLICATION TO DETECTING SCHIZOPHRENIA RISK GENES.

Science.gov (United States)

Zhu, Lingxue; Lei, Jing; Devlin, Bernie; Roeder, Kathryn

2017-09-01

Scientists routinely compare gene expression levels in cases versus controls in part to determine genes associated with a disease. Similarly, detecting case-control differences in co-expression among genes can be critical to understanding complex human diseases; however statistical methods have been limited by the high dimensional nature of this problem. In this paper, we construct a sparse-Leading-Eigenvalue-Driven (sLED) test for comparing two high-dimensional covariance matrices. By focusing on the spectrum of the differential matrix, sLED provides a novel perspective that accommodates what we assume to be common, namely sparse and weak signals in gene expression data, and it is closely related with Sparse Principal Component Analysis. We prove that sLED achieves full power asymptotically under mild assumptions, and simulation studies verify that it outperforms other existing procedures under many biologically plausible scenarios. Applying sLED to the largest gene-expression dataset obtained from post-mortem brain tissue from Schizophrenia patients and controls, we provide a novel list of genes implicated in Schizophrenia and reveal intriguing patterns in gene co-expression change for Schizophrenia subjects. We also illustrate that sLED can be generalized to compare other gene-gene "relationship" matrices that are of practical interest, such as the weighted adjacency matrices.

dimensional nonlinear evolution equations

Indian Academy of Sciences (India)

in real-life situations, it is important to find their exact solutions. Further, in ... But only little work is done on the high-dimensional equations. .... Similarly, to determine the values of d and q, we balance the linear term of the lowest order in eq.

Model-based Clustering of High-Dimensional Data in Astrophysics

Science.gov (United States)

Bouveyron, C.

2016-05-01

The nature of data in Astrophysics has changed, as in other scientific fields, in the past decades due to the increase of the measurement capabilities. As a consequence, data are nowadays frequently of high dimensionality and available in mass or stream. Model-based techniques for clustering are popular tools which are renowned for their probabilistic foundations and their flexibility. However, classical model-based techniques show a disappointing behavior in high-dimensional spaces which is mainly due to their dramatical over-parametrization. The recent developments in model-based classification overcome these drawbacks and allow to efficiently classify high-dimensional data, even in the "small n / large p" situation. This work presents a comprehensive review of these recent approaches, including regularization-based techniques, parsimonious modeling, subspace classification methods and classification methods based on variable selection. The use of these model-based methods is also illustrated on real-world classification problems in Astrophysics using R packages.

An Unbiased Distance-based Outlier Detection Approach for High-dimensional Data

DEFF Research Database (Denmark)

Nguyen, Hoang Vu; Gopalkrishnan, Vivekanand; Assent, Ira

2011-01-01

than a global property. Different from existing approaches, it is not grid-based and dimensionality unbiased. Thus, its performance is impervious to grid resolution as well as the curse of dimensionality. In addition, our approach ranks the outliers, allowing users to select the number of desired...... outliers, thus mitigating the issue of high false alarm rate. Extensive empirical studies on real datasets show that our approach efficiently and effectively detects outliers, even in high-dimensional spaces....

Analysis of chaos in high-dimensional wind power system.

Science.gov (United States)

Wang, Cong; Zhang, Hongli; Fan, Wenhui; Ma, Ping

2018-01-01

A comprehensive analysis on the chaos of a high-dimensional wind power system is performed in this study. A high-dimensional wind power system is more complex than most power systems. An 11-dimensional wind power system proposed by Huang, which has not been analyzed in previous studies, is investigated. When the systems are affected by external disturbances including single parameter and periodic disturbance, or its parameters changed, chaotic dynamics of the wind power system is analyzed and chaotic parameters ranges are obtained. Chaos existence is confirmed by calculation and analysis of all state variables' Lyapunov exponents and the state variable sequence diagram. Theoretical analysis and numerical simulations show that the wind power system chaos will occur when parameter variations and external disturbances change to a certain degree.

Robust and sparse correlation matrix estimation for the analysis of high-dimensional genomics data.

Science.gov (United States)

Serra, Angela; Coretto, Pietro; Fratello, Michele; Tagliaferri, Roberto; Stegle, Oliver

2018-02-15

Microarray technology can be used to study the expression of thousands of genes across a number of different experimental conditions, usually hundreds. The underlying principle is that genes sharing similar expression patterns, across different samples, can be part of the same co-expression system, or they may share the same biological functions. Groups of genes are usually identified based on cluster analysis. Clustering methods rely on the similarity matrix between genes. A common choice to measure similarity is to compute the sample correlation matrix. Dimensionality reduction is another popular data analysis task which is also based on covariance/correlation matrix estimates. Unfortunately, covariance/correlation matrix estimation suffers from the intrinsic noise present in high-dimensional data. Sources of noise are: sampling variations, presents of outlying sample units, and the fact that in most cases the number of units is much larger than the number of genes. In this paper, we propose a robust correlation matrix estimator that is regularized based on adaptive thresholding. The resulting method jointly tames the effects of the high-dimensionality, and data contamination. Computations are easy to implement and do not require hand tunings. Both simulated and real data are analyzed. A Monte Carlo experiment shows that the proposed method is capable of remarkable performances. Our correlation metric is more robust to outliers compared with the existing alternatives in two gene expression datasets. It is also shown how the regularization allows to automatically detect and filter spurious correlations. The same regularization is also extended to other less robust correlation measures. Finally, we apply the ARACNE algorithm on the SyNTreN gene expression data. Sensitivity and specificity of the reconstructed network is compared with the gold standard. We show that ARACNE performs better when it takes the proposed correlation matrix estimator as input. The R

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

Czech Academy of Sciences Publication Activity Database

Kalina, Jan

2017-01-01

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

Clustering by reordering of similarity and Laplacian matrices: Application to galaxy clusters

Science.gov (United States)

Mahmoud, E.; Shoukry, A.; Takey, A.

2018-04-01

Similarity metrics, kernels and similarity-based algorithms have gained much attention due to their increasing applications in information retrieval, data mining, pattern recognition and machine learning. Similarity Graphs are often adopted as the underlying representation of similarity matrices and are at the origin of known clustering algorithms such as spectral clustering. Similarity matrices offer the advantage of working in object-object (two-dimensional) space where visualization of clusters similarities is available instead of object-features (multi-dimensional) space. In this paper, sparse ɛ-similarity graphs are constructed and decomposed into strong components using appropriate methods such as Dulmage-Mendelsohn permutation (DMperm) and/or Reverse Cuthill-McKee (RCM) algorithms. The obtained strong components correspond to groups (clusters) in the input (feature) space. Parameter ɛi is estimated locally, at each data point i from a corresponding narrow range of the number of nearest neighbors. Although more advanced clustering techniques are available, our method has the advantages of simplicity, better complexity and direct visualization of the clusters similarities in a two-dimensional space. Also, no prior information about the number of clusters is needed. We conducted our experiments on two and three dimensional, low and high-sized synthetic datasets as well as on an astronomical real-dataset. The results are verified graphically and analyzed using gap statistics over a range of neighbors to verify the robustness of the algorithm and the stability of the results. Combining the proposed algorithm with gap statistics provides a promising tool for solving clustering problems. An astronomical application is conducted for confirming the existence of 45 galaxy clusters around the X-ray positions of galaxy clusters in the redshift range [0.1..0.8]. We re-estimate the photometric redshifts of the identified galaxy clusters and obtain acceptable values

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

Czech Academy of Sciences Publication Activity Database

Kalina, Jan

2017-01-01

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

An approach to large scale identification of non-obvious structural similarities between proteins

Science.gov (United States)

Cherkasov, Artem; Jones, Steven JM

2004-01-01

Background A new sequence independent bioinformatics approach allowing genome-wide search for proteins with similar three dimensional structures has been developed. By utilizing the numerical output of the sequence threading it establishes putative non-obvious structural similarities between proteins. When applied to the testing set of proteins with known three dimensional structures the developed approach was able to recognize structurally similar proteins with high accuracy. Results The method has been developed to identify pathogenic proteins with low sequence identity and high structural similarity to host analogues. Such protein structure relationships would be hypothesized to arise through convergent evolution or through ancient horizontal gene transfer events, now undetectable using current sequence alignment techniques. The pathogen proteins, which could mimic or interfere with host activities, would represent candidate virulence factors. The developed approach utilizes the numerical outputs from the sequence-structure threading. It identifies the potential structural similarity between a pair of proteins by correlating the threading scores of the corresponding two primary sequences against the library of the standard folds. This approach allowed up to 64% sensitivity and 99.9% specificity in distinguishing protein pairs with high structural similarity. Conclusion Preliminary results obtained by comparison of the genomes of Homo sapiens and several strains of Chlamydia trachomatis have demonstrated the potential usefulness of the method in the identification of bacterial proteins with known or potential roles in virulence. PMID:15147578

An approach to large scale identification of non-obvious structural similarities between proteins

Directory of Open Access Journals (Sweden)

Cherkasov Artem

2004-05-01

Full Text Available Abstract Background A new sequence independent bioinformatics approach allowing genome-wide search for proteins with similar three dimensional structures has been developed. By utilizing the numerical output of the sequence threading it establishes putative non-obvious structural similarities between proteins. When applied to the testing set of proteins with known three dimensional structures the developed approach was able to recognize structurally similar proteins with high accuracy. Results The method has been developed to identify pathogenic proteins with low sequence identity and high structural similarity to host analogues. Such protein structure relationships would be hypothesized to arise through convergent evolution or through ancient horizontal gene transfer events, now undetectable using current sequence alignment techniques. The pathogen proteins, which could mimic or interfere with host activities, would represent candidate virulence factors. The developed approach utilizes the numerical outputs from the sequence-structure threading. It identifies the potential structural similarity between a pair of proteins by correlating the threading scores of the corresponding two primary sequences against the library of the standard folds. This approach allowed up to 64% sensitivity and 99.9% specificity in distinguishing protein pairs with high structural similarity. Conclusion Preliminary results obtained by comparison of the genomes of Homo sapiens and several strains of Chlamydia trachomatis have demonstrated the potential usefulness of the method in the identification of bacterial proteins with known or potential roles in virulence.

High-Dimensional Intrinsic Interpolation Using Gaussian Process Regression and Diffusion Maps

International Nuclear Information System (INIS)

Thimmisetty, Charanraj A.; Ghanem, Roger G.; White, Joshua A.; Chen, Xiao

2017-01-01

This article considers the challenging task of estimating geologic properties of interest using a suite of proxy measurements. The current work recast this task as a manifold learning problem. In this process, this article introduces a novel regression procedure for intrinsic variables constrained onto a manifold embedded in an ambient space. The procedure is meant to sharpen high-dimensional interpolation by inferring non-linear correlations from the data being interpolated. The proposed approach augments manifold learning procedures with a Gaussian process regression. It first identifies, using diffusion maps, a low-dimensional manifold embedded in an ambient high-dimensional space associated with the data. It relies on the diffusion distance associated with this construction to define a distance function with which the data model is equipped. This distance metric function is then used to compute the correlation structure of a Gaussian process that describes the statistical dependence of quantities of interest in the high-dimensional ambient space. The proposed method is applicable to arbitrarily high-dimensional data sets. Here, it is applied to subsurface characterization using a suite of well log measurements. The predictions obtained in original, principal component, and diffusion space are compared using both qualitative and quantitative metrics. Considerable improvement in the prediction of the geological structural properties is observed with the proposed method.

High-Dimensional Function Approximation With Neural Networks for Large Volumes of Data.

Science.gov (United States)

Andras, Peter

2018-02-01

Approximation of high-dimensional functions is a challenge for neural networks due to the curse of dimensionality. Often the data for which the approximated function is defined resides on a low-dimensional manifold and in principle the approximation of the function over this manifold should improve the approximation performance. It has been show that projecting the data manifold into a lower dimensional space, followed by the neural network approximation of the function over this space, provides a more precise approximation of the function than the approximation of the function with neural networks in the original data space. However, if the data volume is very large, the projection into the low-dimensional space has to be based on a limited sample of the data. Here, we investigate the nature of the approximation error of neural networks trained over the projection space. We show that such neural networks should have better approximation performance than neural networks trained on high-dimensional data even if the projection is based on a relatively sparse sample of the data manifold. We also find that it is preferable to use a uniformly distributed sparse sample of the data for the purpose of the generation of the low-dimensional projection. We illustrate these results considering the practical neural network approximation of a set of functions defined on high-dimensional data including real world data as well.

Detection of Subtle Context-Dependent Model Inaccuracies in High-Dimensional Robot Domains.

Science.gov (United States)

Mendoza, Juan Pablo; Simmons, Reid; Veloso, Manuela

2016-12-01

Autonomous robots often rely on models of their sensing and actions for intelligent decision making. However, when operating in unconstrained environments, the complexity of the world makes it infeasible to create models that are accurate in every situation. This article addresses the problem of using potentially large and high-dimensional sets of robot execution data to detect situations in which a robot model is inaccurate-that is, detecting context-dependent model inaccuracies in a high-dimensional context space. To find inaccuracies tractably, the robot conducts an informed search through low-dimensional projections of execution data to find parametric Regions of Inaccurate Modeling (RIMs). Empirical evidence from two robot domains shows that this approach significantly enhances the detection power of existing RIM-detection algorithms in high-dimensional spaces.

A Comparison of Methods for Estimating the Determinant of High-Dimensional Covariance Matrix

KAUST Repository

Hu, Zongliang; Dong, Kai; Dai, Wenlin; Tong, Tiejun

2017-01-01

The determinant of the covariance matrix for high-dimensional data plays an important role in statistical inference and decision. It has many real applications including statistical tests and information theory. Due to the statistical and computational challenges with high dimensionality, little work has been proposed in the literature for estimating the determinant of high-dimensional covariance matrix. In this paper, we estimate the determinant of the covariance matrix using some recent proposals for estimating high-dimensional covariance matrix. Specifically, we consider a total of eight covariance matrix estimation methods for comparison. Through extensive simulation studies, we explore and summarize some interesting comparison results among all compared methods. We also provide practical guidelines based on the sample size, the dimension, and the correlation of the data set for estimating the determinant of high-dimensional covariance matrix. Finally, from a perspective of the loss function, the comparison study in this paper may also serve as a proxy to assess the performance of the covariance matrix estimation.

A Comparison of Methods for Estimating the Determinant of High-Dimensional Covariance Matrix

KAUST Repository

Hu, Zongliang

2017-09-27

The determinant of the covariance matrix for high-dimensional data plays an important role in statistical inference and decision. It has many real applications including statistical tests and information theory. Due to the statistical and computational challenges with high dimensionality, little work has been proposed in the literature for estimating the determinant of high-dimensional covariance matrix. In this paper, we estimate the determinant of the covariance matrix using some recent proposals for estimating high-dimensional covariance matrix. Specifically, we consider a total of eight covariance matrix estimation methods for comparison. Through extensive simulation studies, we explore and summarize some interesting comparison results among all compared methods. We also provide practical guidelines based on the sample size, the dimension, and the correlation of the data set for estimating the determinant of high-dimensional covariance matrix. Finally, from a perspective of the loss function, the comparison study in this paper may also serve as a proxy to assess the performance of the covariance matrix estimation.

A Comparison of Methods for Estimating the Determinant of High-Dimensional Covariance Matrix.

Science.gov (United States)

Hu, Zongliang; Dong, Kai; Dai, Wenlin; Tong, Tiejun

2017-09-21

The determinant of the covariance matrix for high-dimensional data plays an important role in statistical inference and decision. It has many real applications including statistical tests and information theory. Due to the statistical and computational challenges with high dimensionality, little work has been proposed in the literature for estimating the determinant of high-dimensional covariance matrix. In this paper, we estimate the determinant of the covariance matrix using some recent proposals for estimating high-dimensional covariance matrix. Specifically, we consider a total of eight covariance matrix estimation methods for comparison. Through extensive simulation studies, we explore and summarize some interesting comparison results among all compared methods. We also provide practical guidelines based on the sample size, the dimension, and the correlation of the data set for estimating the determinant of high-dimensional covariance matrix. Finally, from a perspective of the loss function, the comparison study in this paper may also serve as a proxy to assess the performance of the covariance matrix estimation.

Highly accurate analytical energy of a two-dimensional exciton in a constant magnetic field

International Nuclear Information System (INIS)

Hoang, Ngoc-Tram D.; Nguyen, Duy-Anh P.; Hoang, Van-Hung; Le, Van-Hoang

2016-01-01

Explicit expressions are given for analytically describing the dependence of the energy of a two-dimensional exciton on magnetic field intensity. These expressions are highly accurate with the precision of up to three decimal places for the whole range of the magnetic field intensity. The results are shown for the ground state and some excited states; moreover, we have all formulae to obtain similar expressions of any excited state. Analysis of numerical results shows that the precision of three decimal places is maintained for the excited states with the principal quantum number of up to n=100.

Highly accurate analytical energy of a two-dimensional exciton in a constant magnetic field

Energy Technology Data Exchange (ETDEWEB)

Hoang, Ngoc-Tram D. [Department of Physics, Ho Chi Minh City University of Pedagogy 280, An Duong Vuong Street, District 5, Ho Chi Minh City (Viet Nam); Nguyen, Duy-Anh P. [Department of Natural Science, Thu Dau Mot University, 6, Tran Van On Street, Thu Dau Mot City, Binh Duong Province (Viet Nam); Hoang, Van-Hung [Department of Physics, Ho Chi Minh City University of Pedagogy 280, An Duong Vuong Street, District 5, Ho Chi Minh City (Viet Nam); Le, Van-Hoang, E-mail: levanhoang@tdt.edu.vn [Atomic Molecular and Optical Physics Research Group, Ton Duc Thang University, 19 Nguyen Huu Tho Street, Tan Phong Ward, District 7, Ho Chi Minh City (Viet Nam); Faculty of Applied Sciences, Ton Duc Thang University, 19 Nguyen Huu Tho Street, Tan Phong Ward, District 7, Ho Chi Minh City (Viet Nam)

2016-08-15

Explicit expressions are given for analytically describing the dependence of the energy of a two-dimensional exciton on magnetic field intensity. These expressions are highly accurate with the precision of up to three decimal places for the whole range of the magnetic field intensity. The results are shown for the ground state and some excited states; moreover, we have all formulae to obtain similar expressions of any excited state. Analysis of numerical results shows that the precision of three decimal places is maintained for the excited states with the principal quantum number of up to n=100.

Introduction to high-dimensional statistics

CERN Document Server

Giraud, Christophe

2015-01-01

Ever-greater computing technologies have given rise to an exponentially growing volume of data. Today massive data sets (with potentially thousands of variables) play an important role in almost every branch of modern human activity, including networks, finance, and genetics. However, analyzing such data has presented a challenge for statisticians and data analysts and has required the development of new statistical methods capable of separating the signal from the noise.Introduction to High-Dimensional Statistics is a concise guide to state-of-the-art models, techniques, and approaches for ha

High-Dimensional Single-Photon Quantum Gates: Concepts and Experiments.

Science.gov (United States)

Babazadeh, Amin; Erhard, Manuel; Wang, Feiran; Malik, Mehul; Nouroozi, Rahman; Krenn, Mario; Zeilinger, Anton

2017-11-03

Transformations on quantum states form a basic building block of every quantum information system. From photonic polarization to two-level atoms, complete sets of quantum gates for a variety of qubit systems are well known. For multilevel quantum systems beyond qubits, the situation is more challenging. The orbital angular momentum modes of photons comprise one such high-dimensional system for which generation and measurement techniques are well studied. However, arbitrary transformations for such quantum states are not known. Here we experimentally demonstrate a four-dimensional generalization of the Pauli X gate and all of its integer powers on single photons carrying orbital angular momentum. Together with the well-known Z gate, this forms the first complete set of high-dimensional quantum gates implemented experimentally. The concept of the X gate is based on independent access to quantum states with different parities and can thus be generalized to other photonic degrees of freedom and potentially also to other quantum systems.

Geomfinder: a multi-feature identifier of similar three-dimensional protein patterns: a ligand-independent approach.

Science.gov (United States)

Núñez-Vivanco, Gabriel; Valdés-Jiménez, Alejandro; Besoaín, Felipe; Reyes-Parada, Miguel

2016-01-01

Since the structure of proteins is more conserved than the sequence, the identification of conserved three-dimensional (3D) patterns among a set of proteins, can be important for protein function prediction, protein clustering, drug discovery and the establishment of evolutionary relationships. Thus, several computational applications to identify, describe and compare 3D patterns (or motifs) have been developed. Often, these tools consider a 3D pattern as that described by the residues surrounding co-crystallized/docked ligands available from X-ray crystal structures or homology models. Nevertheless, many of the protein structures stored in public databases do not provide information about the location and characteristics of ligand binding sites and/or other important 3D patterns such as allosteric sites, enzyme-cofactor interaction motifs, etc. This makes necessary the development of new ligand-independent methods to search and compare 3D patterns in all available protein structures. Here we introduce Geomfinder, an intuitive, flexible, alignment-free and ligand-independent web server for detailed estimation of similarities between all pairs of 3D patterns detected in any two given protein structures. We used around 1100 protein structures to form pairs of proteins which were assessed with Geomfinder. In these analyses each protein was considered in only one pair (e.g. in a subset of 100 different proteins, 50 pairs of proteins can be defined). Thus: (a) Geomfinder detected identical pairs of 3D patterns in a series of monoamine oxidase-B structures, which corresponded to the effectively similar ligand binding sites at these proteins; (b) we identified structural similarities among pairs of protein structures which are targets of compounds such as acarbose, benzamidine, adenosine triphosphate and pyridoxal phosphate; these similar 3D patterns are not detected using sequence-based methods; (c) the detailed evaluation of three specific cases showed the versatility

Distribution of high-dimensional entanglement via an intra-city free-space link.

Science.gov (United States)

Steinlechner, Fabian; Ecker, Sebastian; Fink, Matthias; Liu, Bo; Bavaresco, Jessica; Huber, Marcus; Scheidl, Thomas; Ursin, Rupert

2017-07-24

Quantum entanglement is a fundamental resource in quantum information processing and its distribution between distant parties is a key challenge in quantum communications. Increasing the dimensionality of entanglement has been shown to improve robustness and channel capacities in secure quantum communications. Here we report on the distribution of genuine high-dimensional entanglement via a 1.2-km-long free-space link across Vienna. We exploit hyperentanglement, that is, simultaneous entanglement in polarization and energy-time bases, to encode quantum information, and observe high-visibility interference for successive correlation measurements in each degree of freedom. These visibilities impose lower bounds on entanglement in each subspace individually and certify four-dimensional entanglement for the hyperentangled system. The high-fidelity transmission of high-dimensional entanglement under real-world atmospheric link conditions represents an important step towards long-distance quantum communications with more complex quantum systems and the implementation of advanced quantum experiments with satellite links.

Statistical Analysis for High-Dimensional Data : The Abel Symposium 2014

CERN Document Server

Bühlmann, Peter; Glad, Ingrid; Langaas, Mette; Richardson, Sylvia; Vannucci, Marina

2016-01-01

This book features research contributions from The Abel Symposium on Statistical Analysis for High Dimensional Data, held in Nyvågar, Lofoten, Norway, in May 2014. The focus of the symposium was on statistical and machine learning methodologies specifically developed for inference in “big data” situations, with particular reference to genomic applications. The contributors, who are among the most prominent researchers on the theory of statistics for high dimensional inference, present new theories and methods, as well as challenging applications and computational solutions. Specific themes include, among others, variable selection and screening, penalised regression, sparsity, thresholding, low dimensional structures, computational challenges, non-convex situations, learning graphical models, sparse covariance and precision matrices, semi- and non-parametric formulations, multiple testing, classification, factor models, clustering, and preselection. Highlighting cutting-edge research and casting light on...

Joint Adaptive Mean-Variance Regularization and Variance Stabilization of High Dimensional Data.

Science.gov (United States)

Dazard, Jean-Eudes; Rao, J Sunil

2012-07-01

The paper addresses a common problem in the analysis of high-dimensional high-throughput "omics" data, which is parameter estimation across multiple variables in a set of data where the number of variables is much larger than the sample size. Among the problems posed by this type of data are that variable-specific estimators of variances are not reliable and variable-wise tests statistics have low power, both due to a lack of degrees of freedom. In addition, it has been observed in this type of data that the variance increases as a function of the mean. We introduce a non-parametric adaptive regularization procedure that is innovative in that : (i) it employs a novel "similarity statistic"-based clustering technique to generate local-pooled or regularized shrinkage estimators of population parameters, (ii) the regularization is done jointly on population moments, benefiting from C. Stein's result on inadmissibility, which implies that usual sample variance estimator is improved by a shrinkage estimator using information contained in the sample mean. From these joint regularized shrinkage estimators, we derived regularized t-like statistics and show in simulation studies that they offer more statistical power in hypothesis testing than their standard sample counterparts, or regular common value-shrinkage estimators, or when the information contained in the sample mean is simply ignored. Finally, we show that these estimators feature interesting properties of variance stabilization and normalization that can be used for preprocessing high-dimensional multivariate data. The method is available as an R package, called 'MVR' ('Mean-Variance Regularization'), downloadable from the CRAN website.

Modeling high dimensional multichannel brain signals

KAUST Repository

Hu, Lechuan

2017-03-27

In this paper, our goal is to model functional and effective (directional) connectivity in network of multichannel brain physiological signals (e.g., electroencephalograms, local field potentials). The primary challenges here are twofold: first, there are major statistical and computational difficulties for modeling and analyzing high dimensional multichannel brain signals; second, there is no set of universally-agreed measures for characterizing connectivity. To model multichannel brain signals, our approach is to fit a vector autoregressive (VAR) model with sufficiently high order so that complex lead-lag temporal dynamics between the channels can be accurately characterized. However, such a model contains a large number of parameters. Thus, we will estimate the high dimensional VAR parameter space by our proposed hybrid LASSLE method (LASSO+LSE) which is imposes regularization on the first step (to control for sparsity) and constrained least squares estimation on the second step (to improve bias and mean-squared error of the estimator). Then to characterize connectivity between channels in a brain network, we will use various measures but put an emphasis on partial directed coherence (PDC) in order to capture directional connectivity between channels. PDC is a directed frequency-specific measure that explains the extent to which the present oscillatory activity in a sender channel influences the future oscillatory activity in a specific receiver channel relative all possible receivers in the network. Using the proposed modeling approach, we have achieved some insights on learning in a rat engaged in a non-spatial memory task.

Modeling high dimensional multichannel brain signals

KAUST Repository

Hu, Lechuan; Fortin, Norbert; Ombao, Hernando

2017-01-01

In this paper, our goal is to model functional and effective (directional) connectivity in network of multichannel brain physiological signals (e.g., electroencephalograms, local field potentials). The primary challenges here are twofold: first, there are major statistical and computational difficulties for modeling and analyzing high dimensional multichannel brain signals; second, there is no set of universally-agreed measures for characterizing connectivity. To model multichannel brain signals, our approach is to fit a vector autoregressive (VAR) model with sufficiently high order so that complex lead-lag temporal dynamics between the channels can be accurately characterized. However, such a model contains a large number of parameters. Thus, we will estimate the high dimensional VAR parameter space by our proposed hybrid LASSLE method (LASSO+LSE) which is imposes regularization on the first step (to control for sparsity) and constrained least squares estimation on the second step (to improve bias and mean-squared error of the estimator). Then to characterize connectivity between channels in a brain network, we will use various measures but put an emphasis on partial directed coherence (PDC) in order to capture directional connectivity between channels. PDC is a directed frequency-specific measure that explains the extent to which the present oscillatory activity in a sender channel influences the future oscillatory activity in a specific receiver channel relative all possible receivers in the network. Using the proposed modeling approach, we have achieved some insights on learning in a rat engaged in a non-spatial memory task.

Genuinely high-dimensional nonlocality optimized by complementary measurements

International Nuclear Information System (INIS)

Lim, James; Ryu, Junghee; Yoo, Seokwon; Lee, Changhyoup; Bang, Jeongho; Lee, Jinhyoung

2010-01-01

Qubits exhibit extreme nonlocality when their state is maximally entangled and this is observed by mutually unbiased local measurements. This criterion does not hold for the Bell inequalities of high-dimensional systems (qudits), recently proposed by Collins-Gisin-Linden-Massar-Popescu and Son-Lee-Kim. Taking an alternative approach, called the quantum-to-classical approach, we derive a series of Bell inequalities for qudits that satisfy the criterion as for the qubits. In the derivation each d-dimensional subsystem is assumed to be measured by one of d possible measurements with d being a prime integer. By applying to two qubits (d=2), we find that a derived inequality is reduced to the Clauser-Horne-Shimony-Holt inequality when the degree of nonlocality is optimized over all the possible states and local observables. Further applying to two and three qutrits (d=3), we find Bell inequalities that are violated for the three-dimensionally entangled states but are not violated by any two-dimensionally entangled states. In other words, the inequalities discriminate three-dimensional (3D) entanglement from two-dimensional (2D) entanglement and in this sense they are genuinely 3D. In addition, for the two qutrits we give a quantitative description of the relations among the three degrees of complementarity, entanglement and nonlocality. It is shown that the degree of complementarity jumps abruptly to very close to its maximum as nonlocality starts appearing. These characteristics imply that complementarity plays a more significant role in the present inequality compared with the previously proposed inequality.

Topology of high-dimensional manifolds

Energy Technology Data Exchange (ETDEWEB)

Farrell, F T [State University of New York, Binghamton (United States); Goettshe, L [Abdus Salam ICTP, Trieste (Italy); Lueck, W [Westfaelische Wilhelms-Universitaet Muenster, Muenster (Germany)

2002-08-15

The School on High-Dimensional Manifold Topology took place at the Abdus Salam ICTP, Trieste from 21 May 2001 to 8 June 2001. The focus of the school was on the classification of manifolds and related aspects of K-theory, geometry, and operator theory. The topics covered included: surgery theory, algebraic K- and L-theory, controlled topology, homology manifolds, exotic aspherical manifolds, homeomorphism and diffeomorphism groups, and scalar curvature. The school consisted of 2 weeks of lecture courses and one week of conference. Thwo-part lecture notes volume contains the notes of most of the lecture courses.

Two-dimensional impurity transport calculations for a high recycling divertor

International Nuclear Information System (INIS)

Brooks, J.N.

1986-04-01

Two dimensional analysis of impurity transport in a high recycling divertor shows asymmetric particle fluxes to the divertor plate, low helium pumping efficiency, and high scrapeoff zone shielding for sputtered impurities

Lagrangian-similarity diffusion-deposition model

International Nuclear Information System (INIS)

Horst, T.W.

1979-01-01

A Lagrangian-similarity diffusion model has been incorporated into the surface-depletion deposition model. This model predicts vertical concentration profiles far downwind of the source that agree with those of a one-dimensional gradient-transfer model

On the Zeeman Effect in highly excited atoms: 2. Three-dimensional case

International Nuclear Information System (INIS)

Baseia, B.; Medeiros e Silva Filho, J.

1984-01-01

A previous result, found in two-dimensional hydrogen-atoms, is extended to the three-dimensional case. A mapping of a four-dimensional space R 4 onto R 3 , that establishes an equivalence between Coulomb and harmonic potentials, is used to show that the exact solution of the Zeeman effect in highly excited atoms, cannot be reached. (Author) [pt

Multi-dimensional analysis of high resolution γ-ray data

International Nuclear Information System (INIS)

Flibotte, S.; Huttmeier, U.J.; France, G. de; Haas, B.; Romain, P.; Theisen, Ch.; Vivien, J.P.; Zen, J.; Bednarczyk, P.

1992-01-01

High resolution γ-ray multi-detectors capable of measuring high-fold coincidences with a large efficiency are presently under construction (EUROGAM, GASP, GAMMASPHERE). The future experimental progress in our understanding of nuclear structure at high spin critically depends on our ability to analyze the data in a multi-dimensional space and to resolve small photopeaks of interest from the generally large background. Development of programs to process such high-fold events is still in its infancy and only the 3-fold case has been treated so far. As a contribution to the software development associated with the EUROGAM spectrometer, we have written and tested the performances of computer codes designed to select multi-dimensional gates from 3-, 4- and 5-fold coincidence databases. The tests were performed on events generated with a Monte Carlo simulation and also on experimental data (triples) recorded with the 8π spectrometer and with a preliminary version of the EUROGAM array. (author). 7 refs., 3 tabs., 1 fig

Multi-dimensional analysis of high resolution {gamma}-ray data

Energy Technology Data Exchange (ETDEWEB)

Flibotte, S; Huttmeier, U J; France, G de; Haas, B; Romain, P; Theisen, Ch; Vivien, J P; Zen, J [Centre National de la Recherche Scientifique (CNRS), 67 - Strasbourg (France); Bednarczyk, P [Institute of Nuclear Physics, Cracow (Poland)

1992-08-01

High resolution {gamma}-ray multi-detectors capable of measuring high-fold coincidences with a large efficiency are presently under construction (EUROGAM, GASP, GAMMASPHERE). The future experimental progress in our understanding of nuclear structure at high spin critically depends on our ability to analyze the data in a multi-dimensional space and to resolve small photopeaks of interest from the generally large background. Development of programs to process such high-fold events is still in its infancy and only the 3-fold case has been treated so far. As a contribution to the software development associated with the EUROGAM spectrometer, we have written and tested the performances of computer codes designed to select multi-dimensional gates from 3-, 4- and 5-fold coincidence databases. The tests were performed on events generated with a Monte Carlo simulation and also on experimental data (triples) recorded with the 8{pi} spectrometer and with a preliminary version of the EUROGAM array. (author). 7 refs., 3 tabs., 1 fig.

On Robust Information Extraction from High-Dimensional Data

Czech Academy of Sciences Publication Activity Database

Kalina, Jan

2014-01-01

Roč. 9, č. 1 (2014), s. 131-144 ISSN 1452-4864 Grant - others:GA ČR(CZ) GA13-01930S Institutional support: RVO:67985807 Keywords : data mining * high-dimensional data * robust econometrics * outliers * machine learning Subject RIV: IN - Informatics, Computer Science

High-resolution coherent three-dimensional spectroscopy of Br2.

Science.gov (United States)

Chen, Peter C; Wells, Thresa A; Strangfeld, Benjamin R

2013-07-25

In the past, high-resolution spectroscopy has been limited to small, simple molecules that yield relatively uncongested spectra. Larger and more complex molecules have a higher density of peaks and are susceptible to complications (e.g., effects from conical intersections) that can obscure the patterns needed to resolve and assign peaks. Recently, high-resolution coherent two-dimensional (2D) spectroscopy has been used to resolve and sort peaks into easily identifiable patterns for molecules where pattern-recognition has been difficult. For very highly congested spectra, however, the ability to resolve peaks using coherent 2D spectroscopy is limited by the bandwidth of instrumentation. In this article, we introduce and investigate high-resolution coherent three-dimensional spectroscopy (HRC3D) as a method for dealing with heavily congested systems. The resulting patterns are unlike those in high-resolution coherent 2D spectra. Analysis of HRC3D spectra could provide a means for exploring the spectroscopy of large and complex molecules that have previously been considered too difficult to study.

Inference in High-dimensional Dynamic Panel Data Models

DEFF Research Database (Denmark)

Kock, Anders Bredahl; Tang, Haihan

We establish oracle inequalities for a version of the Lasso in high-dimensional fixed effects dynamic panel data models. The inequalities are valid for the coefficients of the dynamic and exogenous regressors. Separate oracle inequalities are derived for the fixed effects. Next, we show how one can...

Explorations on High Dimensional Landscapes: Spin Glasses and Deep Learning

Science.gov (United States)

Sagun, Levent

This thesis deals with understanding the structure of high-dimensional and non-convex energy landscapes. In particular, its focus is on the optimization of two classes of functions: homogeneous polynomials and loss functions that arise in machine learning. In the first part, the notion of complexity of a smooth, real-valued function is studied through its critical points. Existing theoretical results predict that certain random functions that are defined on high dimensional domains have a narrow band of values whose pre-image contains the bulk of its critical points. This section provides empirical evidence for convergence of gradient descent to local minima whose energies are near the predicted threshold justifying the existing asymptotic theory. Moreover, it is empirically shown that a similar phenomenon may hold for deep learning loss functions. Furthermore, there is a comparative analysis of gradient descent and its stochastic version showing that in high dimensional regimes the latter is a mere speedup. The next study focuses on the halting time of an algorithm at a given stopping condition. Given an algorithm, the normalized fluctuations of the halting time follow a distribution that remains unchanged even when the input data is sampled from a new distribution. Two qualitative classes are observed: a Gumbel-like distribution that appears in Google searches, human decision times, and spin glasses and a Gaussian-like distribution that appears in conjugate gradient method, deep learning with MNIST and random input data. Following the universality phenomenon, the Hessian of the loss functions of deep learning is studied. The spectrum is seen to be composed of two parts, the bulk which is concentrated around zero, and the edges which are scattered away from zero. Empirical evidence is presented for the bulk indicating how over-parametrized the system is, and for the edges that depend on the input data. Furthermore, an algorithm is proposed such that it would

Manifold learning to interpret JET high-dimensional operational space

International Nuclear Information System (INIS)

Cannas, B; Fanni, A; Pau, A; Sias, G; Murari, A

2013-01-01

In this paper, the problem of visualization and exploration of JET high-dimensional operational space is considered. The data come from plasma discharges selected from JET campaigns from C15 (year 2005) up to C27 (year 2009). The aim is to learn the possible manifold structure embedded in the data and to create some representations of the plasma parameters on low-dimensional maps, which are understandable and which preserve the essential properties owned by the original data. A crucial issue for the design of such mappings is the quality of the dataset. This paper reports the details of the criteria used to properly select suitable signals downloaded from JET databases in order to obtain a dataset of reliable observations. Moreover, a statistical analysis is performed to recognize the presence of outliers. Finally data reduction, based on clustering methods, is performed to select a limited and representative number of samples for the operational space mapping. The high-dimensional operational space of JET is mapped using a widely used manifold learning method, the self-organizing maps. The results are compared with other data visualization methods. The obtained maps can be used to identify characteristic regions of the plasma scenario, allowing to discriminate between regions with high risk of disruption and those with low risk of disruption. (paper)

High-dimensional orbital angular momentum entanglement concentration based on Laguerre–Gaussian mode selection

International Nuclear Information System (INIS)

Zhang, Wuhong; Su, Ming; Wu, Ziwen; Lu, Meng; Huang, Bingwei; Chen, Lixiang

2013-01-01

Twisted photons enable the definition of a Hilbert space beyond two dimensions by orbital angular momentum (OAM) eigenstates. Here we propose a feasible entanglement concentration experiment, to enhance the quality of high-dimensional entanglement shared by twisted photon pairs. Our approach is started from the full characterization of entangled spiral bandwidth, and is then based on the careful selection of the Laguerre–Gaussian (LG) modes with specific radial and azimuthal indices p and ℓ. In particular, we demonstrate the possibility of high-dimensional entanglement concentration residing in the OAM subspace of up to 21 dimensions. By means of LabVIEW simulations with spatial light modulators, we show that the Shannon dimensionality could be employed to quantify the quality of the present concentration. Our scheme holds promise in quantum information applications defined in high-dimensional Hilbert space. (letter)

High Dimensional Modulation and MIMO Techniques for Access Networks

DEFF Research Database (Denmark)

Binti Othman, Maisara

Exploration of advanced modulation formats and multiplexing techniques for next generation optical access networks are of interest as promising solutions for delivering multiple services to end-users. This thesis addresses this from two different angles: high dimensionality carrierless...... the capacity per wavelength of the femto-cell network. Bit rate up to 1.59 Gbps with fiber-wireless transmission over 1 m air distance is demonstrated. The results presented in this thesis demonstrate the feasibility of high dimensionality CAP in increasing the number of dimensions and their potentially......) optical access network. 2 X 2 MIMO RoF employing orthogonal frequency division multiplexing (OFDM) with 5.6 GHz RoF signaling over all-vertical cavity surface emitting lasers (VCSEL) WDM passive optical networks (PONs). We have employed polarization division multiplexing (PDM) to further increase...

High-dimensional single-cell cancer biology.

Science.gov (United States)

Irish, Jonathan M; Doxie, Deon B

2014-01-01

Cancer cells are distinguished from each other and from healthy cells by features that drive clonal evolution and therapy resistance. New advances in high-dimensional flow cytometry make it possible to systematically measure mechanisms of tumor initiation, progression, and therapy resistance on millions of cells from human tumors. Here we describe flow cytometry techniques that enable a "single-cell " view of cancer. High-dimensional techniques like mass cytometry enable multiplexed single-cell analysis of cell identity, clinical biomarkers, signaling network phospho-proteins, transcription factors, and functional readouts of proliferation, cell cycle status, and apoptosis. This capability pairs well with a signaling profiles approach that dissects mechanism by systematically perturbing and measuring many nodes in a signaling network. Single-cell approaches enable study of cellular heterogeneity of primary tissues and turn cell subsets into experimental controls or opportunities for new discovery. Rare populations of stem cells or therapy-resistant cancer cells can be identified and compared to other types of cells within the same sample. In the long term, these techniques will enable tracking of minimal residual disease (MRD) and disease progression. By better understanding biological systems that control development and cell-cell interactions in healthy and diseased contexts, we can learn to program cells to become therapeutic agents or target malignant signaling events to specifically kill cancer cells. Single-cell approaches that provide deep insight into cell signaling and fate decisions will be critical to optimizing the next generation of cancer treatments combining targeted approaches and immunotherapy.

High-dimensional statistical inference: From vector to matrix

Science.gov (United States)

Zhang, Anru

Statistical inference for sparse signals or low-rank matrices in high-dimensional settings is of significant interest in a range of contemporary applications. It has attracted significant recent attention in many fields including statistics, applied mathematics and electrical engineering. In this thesis, we consider several problems in including sparse signal recovery (compressed sensing under restricted isometry) and low-rank matrix recovery (matrix recovery via rank-one projections and structured matrix completion). The first part of the thesis discusses compressed sensing and affine rank minimization in both noiseless and noisy cases and establishes sharp restricted isometry conditions for sparse signal and low-rank matrix recovery. The analysis relies on a key technical tool which represents points in a polytope by convex combinations of sparse vectors. The technique is elementary while leads to sharp results. It is shown that, in compressed sensing, delta kA 0, delta kA < 1/3 + epsilon, deltak A + thetak,kA < 1 + epsilon, or deltatkA< √(t - 1) / t + epsilon are not sufficient to guarantee the exact recovery of all k-sparse signals for large k. Similar result also holds for matrix recovery. In addition, the conditions delta kA<1/3, deltak A+ thetak,kA<1, delta tkA < √(t - 1)/t and deltarM<1/3, delta rM+ thetar,rM<1, delta trM< √(t - 1)/ t are also shown to be sufficient respectively for stable recovery of approximately sparse signals and low-rank matrices in the noisy case. For the second part of the thesis, we introduce a rank-one projection model for low-rank matrix recovery and propose a constrained nuclear norm minimization method for stable recovery of low-rank matrices in the noisy case. The procedure is adaptive to the rank and robust against small perturbations. Both upper and lower bounds for the estimation accuracy under the Frobenius norm loss are obtained. The proposed estimator is shown to be rate-optimal under certain conditions. The

Estimating High-Dimensional Time Series Models

DEFF Research Database (Denmark)

Medeiros, Marcelo C.; Mendes, Eduardo F.

We study the asymptotic properties of the Adaptive LASSO (adaLASSO) in sparse, high-dimensional, linear time-series models. We assume both the number of covariates in the model and candidate variables can increase with the number of observations and the number of candidate variables is, possibly......, larger than the number of observations. We show the adaLASSO consistently chooses the relevant variables as the number of observations increases (model selection consistency), and has the oracle property, even when the errors are non-Gaussian and conditionally heteroskedastic. A simulation study shows...

High-Dimensional Quantum Information Processing with Linear Optics

Science.gov (United States)

Fitzpatrick, Casey A.

Quantum information processing (QIP) is an interdisciplinary field concerned with the development of computers and information processing systems that utilize quantum mechanical properties of nature to carry out their function. QIP systems have become vastly more practical since the turn of the century. Today, QIP applications span imaging, cryptographic security, computation, and simulation (quantum systems that mimic other quantum systems). Many important strategies improve quantum versions of classical information system hardware, such as single photon detectors and quantum repeaters. Another more abstract strategy engineers high-dimensional quantum state spaces, so that each successful event carries more information than traditional two-level systems allow. Photonic states in particular bring the added advantages of weak environmental coupling and data transmission near the speed of light, allowing for simpler control and lower system design complexity. In this dissertation, numerous novel, scalable designs for practical high-dimensional linear-optical QIP systems are presented. First, a correlated photon imaging scheme using orbital angular momentum (OAM) states to detect rotational symmetries in objects using measurements, as well as building images out of those interactions is reported. Then, a statistical detection method using chains of OAM superpositions distributed according to the Fibonacci sequence is established and expanded upon. It is shown that the approach gives rise to schemes for sorting, detecting, and generating the recursively defined high-dimensional states on which some quantum cryptographic protocols depend. Finally, an ongoing study based on a generalization of the standard optical multiport for applications in quantum computation and simulation is reported upon. The architecture allows photons to reverse momentum inside the device. This in turn enables realistic implementation of controllable linear-optical scattering vertices for

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

Directory of Open Access Journals (Sweden)

Shen Lu

2013-04-01

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

Non-intrusive low-rank separated approximation of high-dimensional stochastic models

KAUST Repository

Doostan, Alireza; Validi, AbdoulAhad; Iaccarino, Gianluca

2013-01-01

This work proposes a sampling-based (non-intrusive) approach within the context of low-. rank separated representations to tackle the issue of curse-of-dimensionality associated with the solution of models, e.g., PDEs/ODEs, with high-dimensional random inputs. Under some conditions discussed in details, the number of random realizations of the solution, required for a successful approximation, grows linearly with respect to the number of random inputs. The construction of the separated representation is achieved via a regularized alternating least-squares regression, together with an error indicator to estimate model parameters. The computational complexity of such a construction is quadratic in the number of random inputs. The performance of the method is investigated through its application to three numerical examples including two ODE problems with high-dimensional random inputs. © 2013 Elsevier B.V.

Non-intrusive low-rank separated approximation of high-dimensional stochastic models

KAUST Repository

Doostan, Alireza

2013-08-01

This work proposes a sampling-based (non-intrusive) approach within the context of low-. rank separated representations to tackle the issue of curse-of-dimensionality associated with the solution of models, e.g., PDEs/ODEs, with high-dimensional random inputs. Under some conditions discussed in details, the number of random realizations of the solution, required for a successful approximation, grows linearly with respect to the number of random inputs. The construction of the separated representation is achieved via a regularized alternating least-squares regression, together with an error indicator to estimate model parameters. The computational complexity of such a construction is quadratic in the number of random inputs. The performance of the method is investigated through its application to three numerical examples including two ODE problems with high-dimensional random inputs. © 2013 Elsevier B.V.

High dimensional model representation method for fuzzy structural dynamics

Science.gov (United States)

Adhikari, S.; Chowdhury, R.; Friswell, M. I.

2011-03-01

Uncertainty propagation in multi-parameter complex structures possess significant computational challenges. This paper investigates the possibility of using the High Dimensional Model Representation (HDMR) approach when uncertain system parameters are modeled using fuzzy variables. In particular, the application of HDMR is proposed for fuzzy finite element analysis of linear dynamical systems. The HDMR expansion is an efficient formulation for high-dimensional mapping in complex systems if the higher order variable correlations are weak, thereby permitting the input-output relationship behavior to be captured by the terms of low-order. The computational effort to determine the expansion functions using the α-cut method scales polynomically with the number of variables rather than exponentially. This logic is based on the fundamental assumption underlying the HDMR representation that only low-order correlations among the input variables are likely to have significant impacts upon the outputs for most high-dimensional complex systems. The proposed method is first illustrated for multi-parameter nonlinear mathematical test functions with fuzzy variables. The method is then integrated with a commercial finite element software (ADINA). Modal analysis of a simplified aircraft wing with fuzzy parameters has been used to illustrate the generality of the proposed approach. In the numerical examples, triangular membership functions have been used and the results have been validated against direct Monte Carlo simulations. It is shown that using the proposed HDMR approach, the number of finite element function calls can be reduced without significantly compromising the accuracy.

Multigrid for high dimensional elliptic partial differential equations on non-equidistant grids

NARCIS (Netherlands)

bin Zubair, H.; Oosterlee, C.E.; Wienands, R.

2006-01-01

This work presents techniques, theory and numbers for multigrid in a general d-dimensional setting. The main focus is the multigrid convergence for high-dimensional partial differential equations (PDEs). As a model problem we have chosen the anisotropic diffusion equation, on a unit hypercube. We

Interface between path and orbital angular momentum entanglement for high-dimensional photonic quantum information.

Science.gov (United States)

Fickler, Robert; Lapkiewicz, Radek; Huber, Marcus; Lavery, Martin P J; Padgett, Miles J; Zeilinger, Anton

2014-07-30

Photonics has become a mature field of quantum information science, where integrated optical circuits offer a way to scale the complexity of the set-up as well as the dimensionality of the quantum state. On photonic chips, paths are the natural way to encode information. To distribute those high-dimensional quantum states over large distances, transverse spatial modes, like orbital angular momentum possessing Laguerre Gauss modes, are favourable as flying information carriers. Here we demonstrate a quantum interface between these two vibrant photonic fields. We create three-dimensional path entanglement between two photons in a nonlinear crystal and use a mode sorter as the quantum interface to transfer the entanglement to the orbital angular momentum degree of freedom. Thus our results show a flexible way to create high-dimensional spatial mode entanglement. Moreover, they pave the way to implement broad complex quantum networks where high-dimensionally entangled states could be distributed over distant photonic chips.

HIGH DIMENSIONAL COVARIANCE MATRIX ESTIMATION IN APPROXIMATE FACTOR MODELS.

Science.gov (United States)

Fan, Jianqing; Liao, Yuan; Mincheva, Martina

2011-01-01

The variance covariance matrix plays a central role in the inferential theories of high dimensional factor models in finance and economics. Popular regularization methods of directly exploiting sparsity are not directly applicable to many financial problems. Classical methods of estimating the covariance matrices are based on the strict factor models, assuming independent idiosyncratic components. This assumption, however, is restrictive in practical applications. By assuming sparse error covariance matrix, we allow the presence of the cross-sectional correlation even after taking out common factors, and it enables us to combine the merits of both methods. We estimate the sparse covariance using the adaptive thresholding technique as in Cai and Liu (2011), taking into account the fact that direct observations of the idiosyncratic components are unavailable. The impact of high dimensionality on the covariance matrix estimation based on the factor structure is then studied.

Improving Classification of Protein Interaction Articles Using Context Similarity-Based Feature Selection.

Science.gov (United States)

Chen, Yifei; Sun, Yuxing; Han, Bing-Qing

2015-01-01

Protein interaction article classification is a text classification task in the biological domain to determine which articles describe protein-protein interactions. Since the feature space in text classification is high-dimensional, feature selection is widely used for reducing the dimensionality of features to speed up computation without sacrificing classification performance. Many existing feature selection methods are based on the statistical measure of document frequency and term frequency. One potential drawback of these methods is that they treat features separately. Hence, first we design a similarity measure between the context information to take word cooccurrences and phrase chunks around the features into account. Then we introduce the similarity of context information to the importance measure of the features to substitute the document and term frequency. Hence we propose new context similarity-based feature selection methods. Their performance is evaluated on two protein interaction article collections and compared against the frequency-based methods. The experimental results reveal that the context similarity-based methods perform better in terms of the F1 measure and the dimension reduction rate. Benefiting from the context information surrounding the features, the proposed methods can select distinctive features effectively for protein interaction article classification.

Elucidating high-dimensional cancer hallmark annotation via enriched ontology.

Science.gov (United States)

Yan, Shankai; Wong, Ka-Chun

2017-09-01

Cancer hallmark annotation is a promising technique that could discover novel knowledge about cancer from the biomedical literature. The automated annotation of cancer hallmarks could reveal relevant cancer transformation processes in the literature or extract the articles that correspond to the cancer hallmark of interest. It acts as a complementary approach that can retrieve knowledge from massive text information, advancing numerous focused studies in cancer research. Nonetheless, the high-dimensional nature of cancer hallmark annotation imposes a unique challenge. To address the curse of dimensionality, we compared multiple cancer hallmark annotation methods on 1580 PubMed abstracts. Based on the insights, a novel approach, UDT-RF, which makes use of ontological features is proposed. It expands the feature space via the Medical Subject Headings (MeSH) ontology graph and utilizes novel feature selections for elucidating the high-dimensional cancer hallmark annotation space. To demonstrate its effectiveness, state-of-the-art methods are compared and evaluated by a multitude of performance metrics, revealing the full performance spectrum on the full set of cancer hallmarks. Several case studies are conducted, demonstrating how the proposed approach could reveal novel insights into cancers. https://github.com/cskyan/chmannot. Copyright © 2017 Elsevier Inc. All rights reserved.

Multi-Scale Factor Analysis of High-Dimensional Brain Signals

KAUST Repository

Ting, Chee-Ming; Ombao, Hernando; Salleh, Sh-Hussain

2017-01-01

In this paper, we develop an approach to modeling high-dimensional networks with a large number of nodes arranged in a hierarchical and modular structure. We propose a novel multi-scale factor analysis (MSFA) model which partitions the massive

Highly ordered three-dimensional macroporous carbon spheres for determination of heavy metal ions

International Nuclear Information System (INIS)

Zhang, Yuxiao; Zhang, Jianming; Liu, Yang; Huang, Hui; Kang, Zhenhui

2012-01-01

Highlights: ► Highly ordered three dimensional macroporous carbon spheres (MPCSs) were prepared. ► MPCS was covalently modified by cysteine (MPCS–CO–Cys). ► MPCS–CO–Cys was first time used in electrochemical detection of heavy metal ions. ► Heavy metal ions such as Pb 2+ and Cd 2+ can be simultaneously determined. -- Abstract: An effective voltammetric method for detection of trace heavy metal ions using chemically modified highly ordered three dimensional macroporous carbon spheres electrode surfaces is described. The highly ordered three dimensional macroporous carbon spheres were prepared by carbonization of glucose in silica crystal bead template, followed by removal of the template. The highly ordered three dimensional macroporous carbon spheres were covalently modified by cysteine, an amino acid with high affinities towards some heavy metals. The materials were characterized by physical adsorption of nitrogen, scanning electron microscopy, and transmission electron microscopy techniques. While the Fourier-transform infrared spectroscopy was used to characterize the functional groups on the surface of carbon spheres. High sensitivity was exhibited when this material was used in electrochemical detection (square wave anodic stripping voltammetry) of heavy metal ions due to the porous structure. And the potential application for simultaneous detection of heavy metal ions was also investigated.

Mining Diagnostic Assessment Data for Concept Similarity

Science.gov (United States)

Madhyastha, Tara; Hunt, Earl

2009-01-01

This paper introduces a method for mining multiple-choice assessment data for similarity of the concepts represented by the multiple choice responses. The resulting similarity matrix can be used to visualize the distance between concepts in a lower-dimensional space. This gives an instructor a visualization of the relative difficulty of concepts…

High-intensity discharge lamp and Duffing oscillator—Similarities and differences

Science.gov (United States)

Baumann, Bernd; Schwieger, Joerg; Stein, Ulrich; Hallerberg, Sarah; Wolff, Marcus

2017-12-01

The processes inside the arc tube of high-intensity discharge lamps are investigated using finite element simulations. The behavior of the gas mixture inside the arc tube is governed by differential equations describing mass, energy, and charge conservation, as well as the Helmholtz equation for the acoustic pressure and the Reynolds equations for the flow driven by buoyancy and Reynolds stresses. The model is highly nonlinear and requires a recursion procedure to account for the impact of acoustic streaming on the temperature and other fields. The investigations reveal the presence of a hysteresis and the corresponding jump phenomenon, quite similar to a Duffing oscillator. The similarities and, in particular, the differences of the nonlinear behavior of the high-intensity discharge lamp to that of a Duffing oscillator are discussed. For large amplitudes, the high-intensity discharge lamp exhibits a stiffening effect in contrast to the Duffing oscillator. It is speculated on how the stiffening might affect hysteresis suppression.

Renewing the Respect for Similarity

Directory of Open Access Journals (Sweden)

Shimon eEdelman

2012-07-01

Full Text Available In psychology, the concept of similarity has traditionally evoked a mixture of respect, stemmingfrom its ubiquity and intuitive appeal, and concern, due to its dependence on the framing of the problemat hand and on its context. We argue for a renewed focus on similarity as an explanatory concept, bysurveying established results and new developments in the theory and methods of similarity-preservingassociative lookup and dimensionality reduction — critical components of many cognitive functions, aswell as of intelligent data management in computer vision. We focus in particular on the growing familyof algorithms that support associative memory by performing hashing that respects local similarity, andon the uses of similarity in representing structured objects and scenes. Insofar as these similarity-basedideas and methods are useful in cognitive modeling and in AI applications, they should be included inthe core conceptual toolkit of computational neuroscience.

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

Energy Technology Data Exchange (ETDEWEB)

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

2016-07-15

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

Sparse multivariate measures of similarity between intra-modal neuroimaging datasets

Directory of Open Access Journals (Sweden)

Maria J. Rosa

2015-10-01

Full Text Available An increasing number of neuroimaging studies are now based on either combining more than one data modality (inter-modal or combining more than one measurement from the same modality (intra-modal. To date, most intra-modal studies using multivariate statistics have focused on differences between datasets, for instance relying on classifiers to differentiate between effects in the data. However, to fully characterize these effects, multivariate methods able to measure similarities between datasets are needed. One classical technique for estimating the relationship between two datasets is canonical correlation analysis (CCA. However, in the context of high-dimensional data the application of CCA is extremely challenging. A recent extension of CCA, sparse CCA (SCCA, overcomes this limitation, by regularizing the model parameters while yielding a sparse solution. In this work, we modify SCCA with the aim of facilitating its application to high-dimensional neuroimaging data and finding meaningful multivariate image-to-image correspondences in intra-modal studies. In particular, we show how the optimal subset of variables can be estimated independently and we look at the information encoded in more than one set of SCCA transformations. We illustrate our framework using Arterial Spin Labelling data to investigate multivariate similarities between the effects of two antipsychotic drugs on cerebral blood flow.

Dimensional cosmological principles

International Nuclear Information System (INIS)

Chi, L.K.

1985-01-01

The dimensional cosmological principles proposed by Wesson require that the density, pressure, and mass of cosmological models be functions of the dimensionless variables which are themselves combinations of the gravitational constant, the speed of light, and the spacetime coordinates. The space coordinate is not the comoving coordinate. In this paper, the dimensional cosmological principle and the dimensional perfect cosmological principle are reformulated by using the comoving coordinate. The dimensional perfect cosmological principle is further modified to allow the possibility that mass creation may occur. Self-similar spacetimes are found to be models obeying the new dimensional cosmological principle

Three-Dimensional Electromagnetic High Frequency Axisymmetric Cavity Scars.

Energy Technology Data Exchange (ETDEWEB)

Warne, Larry Kevin; Jorgenson, Roy Eberhardt

2014-10-01

This report examines the localization of high frequency electromagnetic fi elds in three-dimensional axisymmetric cavities along periodic paths between opposing sides of the cavity. The cases where these orbits lead to unstable localized modes are known as scars. This report treats both the case where the opposing sides, or mirrors, are convex, where there are no interior foci, and the case where they are concave, leading to interior foci. The scalar problem is treated fi rst but the approximations required to treat the vector fi eld components are also examined. Particular att ention is focused on the normalization through the electromagnetic energy theorem. Both projections of the fi eld along the scarred orbit as well as point statistics are examined. Statistical comparisons are m ade with a numerical calculation of the scars run with an axisymmetric simulation. This axisymmetric cas eformstheoppositeextreme(wherethetwomirror radii at each end of the ray orbit are equal) from the two -dimensional solution examined previously (where one mirror radius is vastly di ff erent from the other). The enhancement of the fi eldontheorbitaxiscanbe larger here than in the two-dimensional case. Intentionally Left Blank

High-Dimensional Adaptive Particle Swarm Optimization on Heterogeneous Systems

International Nuclear Information System (INIS)

Wachowiak, M P; Sarlo, B B; Foster, A E Lambe

2014-01-01

Much work has recently been reported in parallel GPU-based particle swarm optimization (PSO). Motivated by the encouraging results of these investigations, while also recognizing the limitations of GPU-based methods for big problems using a large amount of data, this paper explores the efficacy of employing other types of parallel hardware for PSO. Most commodity systems feature a variety of architectures whose high-performance capabilities can be exploited. In this paper, high-dimensional problems and those that employ a large amount of external data are explored within the context of heterogeneous systems. Large problems are decomposed into constituent components, and analyses are undertaken of which components would benefit from multi-core or GPU parallelism. The current study therefore provides another demonstration that ''supercomputing on a budget'' is possible when subtasks of large problems are run on hardware most suited to these tasks. Experimental results show that large speedups can be achieved on high dimensional, data-intensive problems. Cost functions must first be analysed for parallelization opportunities, and assigned hardware based on the particular task

A hybridized K-means clustering approach for high dimensional ...

African Journals Online (AJOL)

International Journal of Engineering, Science and Technology ... Due to incredible growth of high dimensional dataset, conventional data base querying methods are inadequate to extract useful information, so researchers nowadays ... Recently cluster analysis is a popularly used data analysis method in number of areas.

Asymptotics of empirical eigenstructure for high dimensional spiked covariance.

Science.gov (United States)

Wang, Weichen; Fan, Jianqing

2017-06-01

We derive the asymptotic distributions of the spiked eigenvalues and eigenvectors under a generalized and unified asymptotic regime, which takes into account the magnitude of spiked eigenvalues, sample size, and dimensionality. This regime allows high dimensionality and diverging eigenvalues and provides new insights into the roles that the leading eigenvalues, sample size, and dimensionality play in principal component analysis. Our results are a natural extension of those in Paul (2007) to a more general setting and solve the rates of convergence problems in Shen et al. (2013). They also reveal the biases of estimating leading eigenvalues and eigenvectors by using principal component analysis, and lead to a new covariance estimator for the approximate factor model, called shrinkage principal orthogonal complement thresholding (S-POET), that corrects the biases. Our results are successfully applied to outstanding problems in estimation of risks of large portfolios and false discovery proportions for dependent test statistics and are illustrated by simulation studies.

Can We Train Machine Learning Methods to Outperform the High-dimensional Propensity Score Algorithm?

Science.gov (United States)

Karim, Mohammad Ehsanul; Pang, Menglan; Platt, Robert W

2018-03-01

The use of retrospective health care claims datasets is frequently criticized for the lack of complete information on potential confounders. Utilizing patient's health status-related information from claims datasets as surrogates or proxies for mismeasured and unobserved confounders, the high-dimensional propensity score algorithm enables us to reduce bias. Using a previously published cohort study of postmyocardial infarction statin use (1998-2012), we compare the performance of the algorithm with a number of popular machine learning approaches for confounder selection in high-dimensional covariate spaces: random forest, least absolute shrinkage and selection operator, and elastic net. Our results suggest that, when the data analysis is done with epidemiologic principles in mind, machine learning methods perform as well as the high-dimensional propensity score algorithm. Using a plasmode framework that mimicked the empirical data, we also showed that a hybrid of machine learning and high-dimensional propensity score algorithms generally perform slightly better than both in terms of mean squared error, when a bias-based analysis is used.

Innovation Rather than Improvement: A Solvable High-Dimensional Model Highlights the Limitations of Scalar Fitness

Science.gov (United States)

Tikhonov, Mikhail; Monasson, Remi

2018-01-01

Much of our understanding of ecological and evolutionary mechanisms derives from analysis of low-dimensional models: with few interacting species, or few axes defining "fitness". It is not always clear to what extent the intuition derived from low-dimensional models applies to the complex, high-dimensional reality. For instance, most naturally occurring microbial communities are strikingly diverse, harboring a large number of coexisting species, each of which contributes to shaping the environment of others. Understanding the eco-evolutionary interplay in these systems is an important challenge, and an exciting new domain for statistical physics. Recent work identified a promising new platform for investigating highly diverse ecosystems, based on the classic resource competition model of MacArthur. Here, we describe how the same analytical framework can be used to study evolutionary questions. Our analysis illustrates how, at high dimension, the intuition promoted by a one-dimensional (scalar) notion of fitness can become misleading. Specifically, while the low-dimensional picture emphasizes organism cost or efficiency, we exhibit a regime where cost becomes irrelevant for survival, and link this observation to generic properties of high-dimensional geometry.

Highly ordered three-dimensional macroporous carbon spheres for determination of heavy metal ions

Energy Technology Data Exchange (ETDEWEB)

Zhang, Yuxiao; Zhang, Jianming [Institute of Functional Nano and Soft Materials (FUNSOM) and Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou 215123 (China); Liu, Yang, E-mail: yangl@suda.edu.cn [Institute of Functional Nano and Soft Materials (FUNSOM) and Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou 215123 (China); Huang, Hui [Institute of Functional Nano and Soft Materials (FUNSOM) and Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou 215123 (China); Kang, Zhenhui, E-mail: zhkang@suda.edu.cn [Institute of Functional Nano and Soft Materials (FUNSOM) and Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou 215123 (China)

2012-04-15

Highlights: Black-Right-Pointing-Pointer Highly ordered three dimensional macroporous carbon spheres (MPCSs) were prepared. Black-Right-Pointing-Pointer MPCS was covalently modified by cysteine (MPCS-CO-Cys). Black-Right-Pointing-Pointer MPCS-CO-Cys was first time used in electrochemical detection of heavy metal ions. Black-Right-Pointing-Pointer Heavy metal ions such as Pb{sup 2+} and Cd{sup 2+} can be simultaneously determined. -- Abstract: An effective voltammetric method for detection of trace heavy metal ions using chemically modified highly ordered three dimensional macroporous carbon spheres electrode surfaces is described. The highly ordered three dimensional macroporous carbon spheres were prepared by carbonization of glucose in silica crystal bead template, followed by removal of the template. The highly ordered three dimensional macroporous carbon spheres were covalently modified by cysteine, an amino acid with high affinities towards some heavy metals. The materials were characterized by physical adsorption of nitrogen, scanning electron microscopy, and transmission electron microscopy techniques. While the Fourier-transform infrared spectroscopy was used to characterize the functional groups on the surface of carbon spheres. High sensitivity was exhibited when this material was used in electrochemical detection (square wave anodic stripping voltammetry) of heavy metal ions due to the porous structure. And the potential application for simultaneous detection of heavy metal ions was also investigated.

Irregular grid methods for pricing high-dimensional American options

NARCIS (Netherlands)

Berridge, S.J.

2004-01-01

This thesis proposes and studies numerical methods for pricing high-dimensional American options; important examples being basket options, Bermudan swaptions and real options. Four new methods are presented and analysed, both in terms of their application to various test problems, and in terms of

High-dimensional atom localization via spontaneously generated coherence in a microwave-driven atomic system.

Science.gov (United States)

Wang, Zhiping; Chen, Jinyu; Yu, Benli

2017-02-20

We investigate the two-dimensional (2D) and three-dimensional (3D) atom localization behaviors via spontaneously generated coherence in a microwave-driven four-level atomic system. Owing to the space-dependent atom-field interaction, it is found that the detecting probability and precision of 2D and 3D atom localization behaviors can be significantly improved via adjusting the system parameters, the phase, amplitude, and initial population distribution. Interestingly, the atom can be localized in volumes that are substantially smaller than a cubic optical wavelength. Our scheme opens a promising way to achieve high-precision and high-efficiency atom localization, which provides some potential applications in high-dimensional atom nanolithography.

Mitigating the Insider Threat Using High-Dimensional Search and Modeling

National Research Council Canada - National Science Library

Van Den Berg, Eric; Uphadyaya, Shambhu; Ngo, Phi H; Muthukrishnan, Muthu; Palan, Rajago

2006-01-01

In this project a system was built aimed at mitigating insider attacks centered around a high-dimensional search engine for correlating the large number of monitoring streams necessary for detecting insider attacks...

Dimensional analysis beyond the Pi theorem

CERN Document Server

Zohuri, Bahman

2017-01-01

Dimensional Analysis and Physical Similarity are well understood subjects, and the general concepts of dynamical similarity are explained in this book. Our exposition is essentially different from those available in the literature, although it follows the general ideas known as Pi Theorem. There are many excellent books that one can refer to; however, dimensional analysis goes beyond Pi theorem, which is also known as Buckingham’s Pi Theorem. Many techniques via self-similar solutions can bound solutions to problems that seem intractable. A time-developing phenomenon is called self-similar if the spatial distributions of its properties at different points in time can be obtained from one another by a similarity transformation, and identifying one of the independent variables as time. However, this is where Dimensional Analysis goes beyond Pi Theorem into self-similarity, which has represented progress for researchers. In recent years there has been a surge of interest in self-similar solutions of the First ...

Characterization of discontinuities in high-dimensional stochastic problems on adaptive sparse grids

International Nuclear Information System (INIS)

Jakeman, John D.; Archibald, Richard; Xiu Dongbin

2011-01-01

In this paper we present a set of efficient algorithms for detection and identification of discontinuities in high dimensional space. The method is based on extension of polynomial annihilation for discontinuity detection in low dimensions. Compared to the earlier work, the present method poses significant improvements for high dimensional problems. The core of the algorithms relies on adaptive refinement of sparse grids. It is demonstrated that in the commonly encountered cases where a discontinuity resides on a small subset of the dimensions, the present method becomes 'optimal', in the sense that the total number of points required for function evaluations depends linearly on the dimensionality of the space. The details of the algorithms will be presented and various numerical examples are utilized to demonstrate the efficacy of the method.

One dimensional beam. Asymptotic and self similar solutions

International Nuclear Information System (INIS)

Feix, M.R.; Duranceau, J.L.; Besnard, D.

1982-06-01

Rescaling transformations provide a useful tool to solve nonlinear problems described by partial derivative equations. A brief review of this method is presented together with the connection with the self similar solutions obtained by compacting the independent variable with one of them (the time). The general theory is reported through examples found in Plasma Physics with a careful distinction between systems described by Hamiltonian and others where irreversible phenomena, like diffusion, are taken into account

Prediction of inorganic superconductors with quasi-one-dimensional crystal structure

International Nuclear Information System (INIS)

Volkova, L M; Marinin, D V

2013-01-01

Models of superconductors having a quasi-one-dimensional crystal structure based on the convoluted into a tube Ginzburg sandwich, which comprises a layered dielectric–metal–dielectric structure, have been suggested. The critical crystal chemistry parameters of the Ginzburg sandwich determining the possibility of the emergence of superconductivity and the T c value in layered high-T c cuprates, which could have the same functions in quasi-one-dimensional fragments (sandwich-type tubes), have been examined. The crystal structures of known low-temperature superconductors, in which one can mark out similar quasi-one-dimensional fragments, have been analyzed. Five compounds with quasi-one-dimensional structures, which can be considered as potential parents of new superconductor families, possibly with high transition temperatures, have been suggested. The methods of doping and modification of these compounds are provided. (paper)

Pricing High-Dimensional American Options Using Local Consistency Conditions

NARCIS (Netherlands)

Berridge, S.J.; Schumacher, J.M.

2004-01-01

We investigate a new method for pricing high-dimensional American options. The method is of finite difference type but is also related to Monte Carlo techniques in that it involves a representative sampling of the underlying variables.An approximating Markov chain is built using this sampling and

Scanning three-dimensional x-ray diffraction microscopy using a high-energy microbeam

International Nuclear Information System (INIS)

Hayashi, Y.; Hirose, Y.; Seno, Y.

2016-01-01

A scanning three-dimensional X-ray diffraction (3DXRD) microscope apparatus with a high-energy microbeam was installed at the BL33XU Toyota beamline at SPring-8. The size of the 50 keV beam focused using Kirkpatrick-Baez mirrors was 1.3 μm wide and 1.6 μm high in full width at half maximum. The scanning 3DXRD method was tested for a cold-rolled carbon steel sheet sample. A three-dimensional orientation map with 37 "3 voxels was obtained.

Scanning three-dimensional x-ray diffraction microscopy using a high-energy microbeam

Energy Technology Data Exchange (ETDEWEB)

Hayashi, Y., E-mail: y-hayashi@mosk.tytlabs.co.jp; Hirose, Y.; Seno, Y. [Toyota Central R& D Toyota Central R& D Labs., Inc., 41-1 Nagakute Aichi 480-1192 Japan (Japan)

2016-07-27

A scanning three-dimensional X-ray diffraction (3DXRD) microscope apparatus with a high-energy microbeam was installed at the BL33XU Toyota beamline at SPring-8. The size of the 50 keV beam focused using Kirkpatrick-Baez mirrors was 1.3 μm wide and 1.6 μm high in full width at half maximum. The scanning 3DXRD method was tested for a cold-rolled carbon steel sheet sample. A three-dimensional orientation map with 37 {sup 3} voxels was obtained.

Data analysis in high-dimensional sparse spaces

DEFF Research Database (Denmark)

Clemmensen, Line Katrine Harder

classification techniques for high-dimensional problems are presented: Sparse discriminant analysis, sparse mixture discriminant analysis and orthogonality constrained support vector machines. The first two introduces sparseness to the well known linear and mixture discriminant analysis and thereby provide low...... are applied to classifications of fish species, ear canal impressions used in the hearing aid industry, microbiological fungi species, and various cancerous tissues and healthy tissues. In addition, novel applications of sparse regressions (also called the elastic net) to the medical, concrete, and food...

Preface [HD3-2015: International meeting on high-dimensional data-driven science

International Nuclear Information System (INIS)

2016-01-01

A never-ending series of innovations in measurement technology and evolutions in information and communication technologies have led to the ongoing generation and accumulation of large quantities of high-dimensional data every day. While detailed data-centric approaches have been pursued in respective research fields, situations have been encountered where the same mathematical framework of high-dimensional data analysis can be found in a wide variety of seemingly unrelated research fields, such as estimation on the basis of undersampled Fourier transform in nuclear magnetic resonance spectroscopy in chemistry, in magnetic resonance imaging in medicine, and in astronomical interferometry in astronomy. In such situations, bringing diverse viewpoints together therefore becomes a driving force for the creation of innovative developments in various different research fields. This meeting focuses on “Sparse Modeling” (SpM) as a methodology for creation of innovative developments through the incorporation of a wide variety of viewpoints in various research fields. The objective of this meeting is to offer a forum where researchers with interest in SpM can assemble and exchange information on the latest results and newly established methodologies, and discuss future directions of the interdisciplinary studies for High-Dimensional Data-Driven science (HD 3 ). The meeting was held in Kyoto from 14-17 December 2015. We are pleased to publish 22 papers contributed by invited speakers in this volume of Journal of Physics: Conference Series. We hope that this volume will promote further development of High-Dimensional Data-Driven science. (paper)

Two-dimensional Simulations of Correlation Reflectometry in Fusion Plasmas

International Nuclear Information System (INIS)

Valeo, E.J.; Kramer, G.J.; Nazikian, R.

2001-01-01

A two-dimensional wave propagation code, developed specifically to simulate correlation reflectometry in large-scale fusion plasmas is described. The code makes use of separate computational methods in the vacuum, underdense and reflection regions of the plasma in order to obtain the high computational efficiency necessary for correlation analysis. Simulations of Tokamak Fusion Test Reactor (TFTR) plasma with internal transport barriers are presented and compared with one-dimensional full-wave simulations. It is shown that the two-dimensional simulations are remarkably similar to the results of the one-dimensional full-wave analysis for a wide range of turbulent correlation lengths. Implications for the interpretation of correlation reflectometer measurements in fusion plasma are discussed

On two-dimensionalization of three-dimensional turbulence in shell models

DEFF Research Database (Denmark)

Chakraborty, Sagar; Jensen, Mogens Høgh; Sarkar, A.

2010-01-01

Applying a modified version of the Gledzer-Ohkitani-Yamada (GOY) shell model, the signatures of so-called two-dimensionalization effect of three-dimensional incompressible, homogeneous, isotropic fully developed unforced turbulence have been studied and reproduced. Within the framework of shell m......-similar PDFs for longitudinal velocity differences are also presented for the rotating 3D turbulence case....

Class prediction for high-dimensional class-imbalanced data

Directory of Open Access Journals (Sweden)

Lusa Lara

2010-10-01

Full Text Available Abstract Background The goal of class prediction studies is to develop rules to accurately predict the class membership of new samples. The rules are derived using the values of the variables available for each subject: the main characteristic of high-dimensional data is that the number of variables greatly exceeds the number of samples. Frequently the classifiers are developed using class-imbalanced data, i.e., data sets where the number of samples in each class is not equal. Standard classification methods used on class-imbalanced data often produce classifiers that do not accurately predict the minority class; the prediction is biased towards the majority class. In this paper we investigate if the high-dimensionality poses additional challenges when dealing with class-imbalanced prediction. We evaluate the performance of six types of classifiers on class-imbalanced data, using simulated data and a publicly available data set from a breast cancer gene-expression microarray study. We also investigate the effectiveness of some strategies that are available to overcome the effect of class imbalance. Results Our results show that the evaluated classifiers are highly sensitive to class imbalance and that variable selection introduces an additional bias towards classification into the majority class. Most new samples are assigned to the majority class from the training set, unless the difference between the classes is very large. As a consequence, the class-specific predictive accuracies differ considerably. When the class imbalance is not too severe, down-sizing and asymmetric bagging embedding variable selection work well, while over-sampling does not. Variable normalization can further worsen the performance of the classifiers. Conclusions Our results show that matching the prevalence of the classes in training and test set does not guarantee good performance of classifiers and that the problems related to classification with class

Dimensionally constrained energy confinement analysis of W7-AS data

International Nuclear Information System (INIS)

Dose, V.; Preuss, R.; Linden, W. von der

1998-01-01

A recently assembled W7-AS stellarator database has been subject to dimensionally constrained confinement analysis. The analysis employs Bayesian inference. Dimensional information is taken from the Connor-Taylor (CT) similarity transformation theory, which provides six possible physical scenarios with associated dimensional conditions. Bayesian theory allows the calculations of the probability for each model and it is found that the present W7-AS data are most probably described by the collisionless high-β case. Probabilities for all models and the associated exponents of a power law scaling function are presented. (author)

Three-dimensional true FISP for high-resolution imaging of the whole brain

International Nuclear Information System (INIS)

Schmitz, B.; Hagen, T.; Reith, W.

2003-01-01

While high-resolution T1-weighted sequences, such as three-dimensional magnetization-prepared rapid gradient-echo imaging, are widely available, there is a lack of an equivalent fast high-resolution sequence providing T2 contrast. Using fast high-performance gradient systems we show the feasibility of three-dimensional true fast imaging with steady-state precession (FISP) to fill this gap. We applied a three-dimensional true-FISP protocol with voxel sizes down to 0.5 x 0.5 x 0.5 mm and acquisition times of approximately 8 min on a 1.5-T Sonata (Siemens, Erlangen, Germany) magnetic resonance scanner. The sequence was included into routine brain imaging protocols for patients with cerebrospinal-fluid-related intracranial pathology. Images from 20 patients and 20 healthy volunteers were evaluated by two neuroradiologists with respect to diagnostic image quality and artifacts. All true-FISP scans showed excellent imaging quality free of artifacts in patients and volunteers. They were valuable for the assessment of anatomical and pathologic aspects of the included patients. High-resolution true-FISP imaging is a valuable adjunct for the exploration and neuronavigation of intracranial pathologies especially if cerebrospinal fluid is involved. (orig.)

Similarity between the superconductivity in the graphene with the spin transport in the two-dimensional antiferromagnet in the honeycomb lattice

Science.gov (United States)

Lima, L. S.

2017-02-01

We have used the Dirac's massless quasi-particles together with the Kubo's formula to study the spin transport by electrons in the graphene monolayer. We have calculated the electric conductivity and verified the behavior of the AC and DC currents of this system, that is a relativistic electron plasma. Our results show that the AC conductivity tends to infinity in the limit ω → 0 , similar to the behavior obtained for the spin transport in the two-dimensional frustrated antiferromagnet in the honeycomb lattice. We have made a diagrammatic expansion for the Green's function and we have not gotten significative change in the results.

Global communication schemes for the numerical solution of high-dimensional PDEs

DEFF Research Database (Denmark)

Hupp, Philipp; Heene, Mario; Jacob, Riko

2016-01-01

The numerical treatment of high-dimensional partial differential equations is among the most compute-hungry problems and in urgent need for current and future high-performance computing (HPC) systems. It is thus also facing the grand challenges of exascale computing such as the requirement...

Construction of high-dimensional universal quantum logic gates using a Λ system coupled with a whispering-gallery-mode microresonator.

Science.gov (United States)

He, Ling Yan; Wang, Tie-Jun; Wang, Chuan

2016-07-11

High-dimensional quantum system provides a higher capacity of quantum channel, which exhibits potential applications in quantum information processing. However, high-dimensional universal quantum logic gates is difficult to achieve directly with only high-dimensional interaction between two quantum systems and requires a large number of two-dimensional gates to build even a small high-dimensional quantum circuits. In this paper, we propose a scheme to implement a general controlled-flip (CF) gate where the high-dimensional single photon serve as the target qudit and stationary qubits work as the control logic qudit, by employing a three-level Λ-type system coupled with a whispering-gallery-mode microresonator. In our scheme, the required number of interaction times between the photon and solid state system reduce greatly compared with the traditional method which decomposes the high-dimensional Hilbert space into 2-dimensional quantum space, and it is on a shorter temporal scale for the experimental realization. Moreover, we discuss the performance and feasibility of our hybrid CF gate, concluding that it can be easily extended to a 2n-dimensional case and it is feasible with current technology.

High-resolution two-dimensional and three-dimensional modeling of wire grid polarizers and micropolarizer arrays

Science.gov (United States)

Vorobiev, Dmitry; Ninkov, Zoran

2017-11-01

Recent advances in photolithography allowed the fabrication of high-quality wire grid polarizers for the visible and near-infrared regimes. In turn, micropolarizer arrays (MPAs) based on wire grid polarizers have been developed and used to construct compact, versatile imaging polarimeters. However, the contrast and throughput of these polarimeters are significantly worse than one might expect based on the performance of large area wire grid polarizers or MPAs, alone. We investigate the parameters that affect the performance of wire grid polarizers and MPAs, using high-resolution two-dimensional and three-dimensional (3-D) finite-difference time-domain simulations. We pay special attention to numerical errors and other challenges that arise in models of these and other subwavelength optical devices. Our tests show that simulations of these structures in the visible and near-IR begin to converge numerically when the mesh size is smaller than ˜4 nm. The performance of wire grid polarizers is very sensitive to the shape, spacing, and conductivity of the metal wires. Using 3-D simulations of micropolarizer "superpixels," we directly study the cross talk due to diffraction at the edges of each micropolarizer, which decreases the contrast of MPAs to ˜200∶1.

Deja vu: a database of highly similar citations in the scientific literature.

Science.gov (United States)

Errami, Mounir; Sun, Zhaohui; Long, Tara C; George, Angela C; Garner, Harold R

2009-01-01

In the scientific research community, plagiarism and covert multiple publications of the same data are considered unacceptable because they undermine the public confidence in the scientific integrity. Yet, little has been done to help authors and editors to identify highly similar citations, which sometimes may represent cases of unethical duplication. For this reason, we have made available Déjà vu, a publicly available database of highly similar Medline citations identified by the text similarity search engine eTBLAST. Following manual verification, highly similar citation pairs are classified into various categories ranging from duplicates with different authors to sanctioned duplicates. Déjà vu records also contain user-provided commentary and supporting information to substantiate each document's categorization. Déjà vu and eTBLAST are available to authors, editors, reviewers, ethicists and sociologists to study, intercept, annotate and deter questionable publication practices. These tools are part of a sustained effort to enhance the quality of Medline as 'the' biomedical corpus. The Déjà vu database is freely accessible at http://spore.swmed.edu/dejavu. The tool eTBLAST is also freely available at http://etblast.org.

Accuracy Assessment for the Three-Dimensional Coordinates by High-Speed Videogrammetric Measurement

Directory of Open Access Journals (Sweden)

Xianglei Liu

2018-01-01

Full Text Available High-speed CMOS camera is a new kind of transducer to make the videogrammetric measurement for monitoring the displacement of high-speed shaking table structure. The purpose of this paper is to validate the three-dimensional coordinate accuracy of the shaking table structure acquired from the presented high-speed videogrammetric measuring system. In the paper, all of the key intermediate links are discussed, including the high-speed CMOS videogrammetric measurement system, the layout of the control network, the elliptical target detection, and the accuracy validation of final 3D spatial results. Through the accuracy analysis, the submillimeter accuracy can be made for the final the three-dimensional spatial coordinates which certify that the proposed high-speed videogrammetric technique is a better alternative technique which can replace the traditional transducer technique for monitoring the dynamic response for the shaking table structure.

Secure data storage by three-dimensional absorbers in highly scattering volume medium

International Nuclear Information System (INIS)

Matoba, Osamu; Matsuki, Shinichiro; Nitta, Kouichi

2008-01-01

A novel data storage in a volume medium with highly scattering coefficient is proposed for data security application. Three-dimensional absorbers are used as data. These absorbers can not be measured by interferometer when the scattering in a volume medium is strong enough. We present a method to reconstruct three-dimensional absorbers and present numerical results to show the effectiveness of the proposed data storage.

Quality evaluation of Hypericum ascyron extract by two-dimensional high-performance liquid chromatography coupled with the colorimetric 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide method.

Science.gov (United States)

Li, Xiu-Mei; Luo, Xue-Gang; Zhang, Chao-Zheng; Wang, Nan; Zhang, Tong-Cun

2015-02-01

In this paper, a heart-cutting two-dimensional high-performance liquid chromatography coupled with the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) method was established for controlling the quality of different batches of Hypericum ascyron extract for the first time. In comparison with the common one-dimensional fingerprint, the second-dimensional fingerprint compiled additional spectral data and was hence more informative. The quality of H. ascyron extract was further evaluated by similarity measures and the same results were achieved, the correlation coefficients of the similarity of ten batches of H. ascyron extract were ＞0.99. Furthermore, we also evaluated the quality of the ten batches of H. ascyron extract by antibacterial activity. The result demonstrated that the quality of the ten batches of H. ascyron extract was not significantly different by MTT. Finally, we demonstrated that the second-dimensional fingerprint coupled with the MTT method was a more powerful tool to characterize the quality of samples of batch to batch. Therefore the proposed method could be used to comprehensively conduct the quality control of traditional Chinese medicines. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

High-dimensional quantum cryptography with twisted light

International Nuclear Information System (INIS)

Mirhosseini, Mohammad; Magaña-Loaiza, Omar S; O’Sullivan, Malcolm N; Rodenburg, Brandon; Malik, Mehul; Boyd, Robert W; Lavery, Martin P J; Padgett, Miles J; Gauthier, Daniel J

2015-01-01

Quantum key distribution (QKD) systems often rely on polarization of light for encoding, thus limiting the amount of information that can be sent per photon and placing tight bounds on the error rates that such a system can tolerate. Here we describe a proof-of-principle experiment that indicates the feasibility of high-dimensional QKD based on the transverse structure of the light field allowing for the transfer of more than 1 bit per photon. Our implementation uses the orbital angular momentum (OAM) of photons and the corresponding mutually unbiased basis of angular position (ANG). Our experiment uses a digital micro-mirror device for the rapid generation of OAM and ANG modes at 4 kHz, and a mode sorter capable of sorting single photons based on their OAM and ANG content with a separation efficiency of 93%. Through the use of a seven-dimensional alphabet encoded in the OAM and ANG bases, we achieve a channel capacity of 2.05 bits per sifted photon. Our experiment demonstrates that, in addition to having an increased information capacity, multilevel QKD systems based on spatial-mode encoding can be more resilient against intercept-resend eavesdropping attacks. (paper)

A Near-linear Time Approximation Algorithm for Angle-based Outlier Detection in High-dimensional Data

DEFF Research Database (Denmark)

Pham, Ninh Dang; Pagh, Rasmus

2012-01-01

projection-based technique that is able to estimate the angle-based outlier factor for all data points in time near-linear in the size of the data. Also, our approach is suitable to be performed in parallel environment to achieve a parallel speedup. We introduce a theoretical analysis of the quality...... neighbor are deteriorated in high-dimensional data. Following up on the work of Kriegel et al. (KDD '08), we investigate the use of angle-based outlier factor in mining high-dimensional outliers. While their algorithm runs in cubic time (with a quadratic time heuristic), we propose a novel random......Outlier mining in d-dimensional point sets is a fundamental and well studied data mining task due to its variety of applications. Most such applications arise in high-dimensional domains. A bottleneck of existing approaches is that implicit or explicit assessments on concepts of distance or nearest...

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

OpenAIRE

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

2014-01-01

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

Music Retrieval based on Melodic Similarity

NARCIS (Netherlands)

Typke, R.

2007-01-01

This thesis introduces a method for measuring melodic similarity for notated music such as MIDI files. This music search algorithm views music as sets of notes that are represented as weighted points in the two-dimensional space of time and pitch. Two point sets can be compared by calculating how

Framework to model neutral particle flux in convex high aspect ratio structures using one-dimensional radiosity

Science.gov (United States)

Manstetten, Paul; Filipovic, Lado; Hössinger, Andreas; Weinbub, Josef; Selberherr, Siegfried

2017-02-01

We present a computationally efficient framework to compute the neutral flux in high aspect ratio structures during three-dimensional plasma etching simulations. The framework is based on a one-dimensional radiosity approach and is applicable to simulations of convex rotationally symmetric holes and convex symmetric trenches with a constant cross-section. The framework is intended to replace the full three-dimensional simulation step required to calculate the neutral flux during plasma etching simulations. Especially for high aspect ratio structures, the computational effort, required to perform the full three-dimensional simulation of the neutral flux at the desired spatial resolution, conflicts with practical simulation time constraints. Our results are in agreement with those obtained by three-dimensional Monte Carlo based ray tracing simulations for various aspect ratios and convex geometries. With this framework we present a comprehensive analysis of the influence of the geometrical properties of high aspect ratio structures as well as of the particle sticking probability on the neutral particle flux.

Spectrally-Corrected Estimation for High-Dimensional Markowitz Mean-Variance Optimization

NARCIS (Netherlands)

Z. Bai (Zhidong); H. Li (Hua); M.J. McAleer (Michael); W.-K. Wong (Wing-Keung)

2016-01-01

textabstractThis paper considers the portfolio problem for high dimensional data when the dimension and size are both large. We analyze the traditional Markowitz mean-variance (MV) portfolio by large dimension matrix theory, and find the spectral distribution of the sample covariance is the main

Linear stability theory as an early warning sign for transitions in high dimensional complex systems

International Nuclear Information System (INIS)

Piovani, Duccio; Grujić, Jelena; Jensen, Henrik Jeldtoft

2016-01-01

We analyse in detail a new approach to the monitoring and forecasting of the onset of transitions in high dimensional complex systems by application to the Tangled Nature model of evolutionary ecology and high dimensional replicator systems with a stochastic element. A high dimensional stability matrix is derived in the mean field approximation to the stochastic dynamics. This allows us to determine the stability spectrum about the observed quasi-stable configurations. From overlap of the instantaneous configuration vector of the full stochastic system with the eigenvectors of the unstable directions of the deterministic mean field approximation, we are able to construct a good early-warning indicator of the transitions occurring intermittently. (paper)

High-dimensional quantum channel estimation using classical light

CSIR Research Space (South Africa)

Mabena, Chemist M

2017-11-01

Full Text Available stream_source_info Mabena_20007_2017.pdf.txt stream_content_type text/plain stream_size 960 Content-Encoding UTF-8 stream_name Mabena_20007_2017.pdf.txt Content-Type text/plain; charset=UTF-8 PHYSICAL REVIEW A 96, 053860... (2017) High-dimensional quantum channel estimation using classical light Chemist M. Mabena CSIR National Laser Centre, P.O. Box 395, Pretoria 0001, South Africa and School of Physics, University of the Witwatersrand, Johannesburg 2000, South...

High dimensional classifiers in the imbalanced case

DEFF Research Database (Denmark)

Bak, Britta Anker; Jensen, Jens Ledet

We consider the binary classification problem in the imbalanced case where the number of samples from the two groups differ. The classification problem is considered in the high dimensional case where the number of variables is much larger than the number of samples, and where the imbalance leads...... to a bias in the classification. A theoretical analysis of the independence classifier reveals the origin of the bias and based on this we suggest two new classifiers that can handle any imbalance ratio. The analytical results are supplemented by a simulation study, where the suggested classifiers in some...

An irregular grid approach for pricing high-dimensional American options

NARCIS (Netherlands)

Berridge, S.J.; Schumacher, J.M.

2008-01-01

We propose and test a new method for pricing American options in a high-dimensional setting. The method is centered around the approximation of the associated complementarity problem on an irregular grid. We approximate the partial differential operator on this grid by appealing to the SDE

An Irregular Grid Approach for Pricing High-Dimensional American Options

NARCIS (Netherlands)

Berridge, S.J.; Schumacher, J.M.

2004-01-01

We propose and test a new method for pricing American options in a high-dimensional setting.The method is centred around the approximation of the associated complementarity problem on an irregular grid.We approximate the partial differential operator on this grid by appealing to the SDE

High dimensional ICA analysis detects within-network functional connectivity damage of default mode and sensory motor networks in Alzheimer's disease

Directory of Open Access Journals (Sweden)

Ottavia eDipasquale

2015-02-01

Full Text Available High dimensional independent component analysis (ICA, compared to low dimensional ICA, allows performing a detailed parcellation of the resting state networks. The purpose of this study was to give further insight into functional connectivity (FC in Alzheimer’s disease (AD using high dimensional ICA. For this reason, we performed both low and high dimensional ICA analyses of resting state fMRI (rfMRI data of 20 healthy controls and 21 AD patients, focusing on the primarily altered default mode network (DMN and exploring the sensory motor network (SMN. As expected, results obtained at low dimensionality were in line with previous literature. Moreover, high dimensional results allowed us to observe either the presence of within-network disconnections and FC damage confined to some of the resting state sub-networks. Due to the higher sensitivity of the high dimensional ICA analysis, our results suggest that high-dimensional decomposition in sub-networks is very promising to better localize FC alterations in AD and that FC damage is not confined to the default mode network.

Applying recursive numerical integration techniques for solving high dimensional integrals

International Nuclear Information System (INIS)

Ammon, Andreas; Genz, Alan; Hartung, Tobias; Jansen, Karl; Volmer, Julia; Leoevey, Hernan

2016-11-01

The error scaling for Markov-Chain Monte Carlo techniques (MCMC) with N samples behaves like 1/√(N). This scaling makes it often very time intensive to reduce the error of computed observables, in particular for applications in lattice QCD. It is therefore highly desirable to have alternative methods at hand which show an improved error scaling. One candidate for such an alternative integration technique is the method of recursive numerical integration (RNI). The basic idea of this method is to use an efficient low-dimensional quadrature rule (usually of Gaussian type) and apply it iteratively to integrate over high-dimensional observables and Boltzmann weights. We present the application of such an algorithm to the topological rotor and the anharmonic oscillator and compare the error scaling to MCMC results. In particular, we demonstrate that the RNI technique shows an error scaling in the number of integration points m that is at least exponential.

Applying recursive numerical integration techniques for solving high dimensional integrals

Energy Technology Data Exchange (ETDEWEB)

Ammon, Andreas [IVU Traffic Technologies AG, Berlin (Germany); Genz, Alan [Washington State Univ., Pullman, WA (United States). Dept. of Mathematics; Hartung, Tobias [King' s College, London (United Kingdom). Dept. of Mathematics; Jansen, Karl; Volmer, Julia [Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany). John von Neumann-Inst. fuer Computing NIC; Leoevey, Hernan [Humboldt Univ. Berlin (Germany). Inst. fuer Mathematik

2016-11-15

The error scaling for Markov-Chain Monte Carlo techniques (MCMC) with N samples behaves like 1/√(N). This scaling makes it often very time intensive to reduce the error of computed observables, in particular for applications in lattice QCD. It is therefore highly desirable to have alternative methods at hand which show an improved error scaling. One candidate for such an alternative integration technique is the method of recursive numerical integration (RNI). The basic idea of this method is to use an efficient low-dimensional quadrature rule (usually of Gaussian type) and apply it iteratively to integrate over high-dimensional observables and Boltzmann weights. We present the application of such an algorithm to the topological rotor and the anharmonic oscillator and compare the error scaling to MCMC results. In particular, we demonstrate that the RNI technique shows an error scaling in the number of integration points m that is at least exponential.

Similarity of fluctuations in correlated systems: The case of seismicity

International Nuclear Information System (INIS)

Varotsos, P.A.; Sarlis, N.V.; Tanaka, H.K.; Skordas, E.S.

2005-01-01

We report a similarity of fluctuations in equilibrium critical phenomena and nonequilibrium systems, which is based on the concept of natural time. The worldwide seismicity as well as that of the San Andreas fault system and Japan are analyzed. An order parameter is chosen and its fluctuations relative to the standard deviation of the distribution are studied. We find that the scaled distributions fall on the same curve, which interestingly exhibits, over four orders of magnitude, features similar to those in several equilibrium critical phenomena (e.g., two-dimensional Ising model) as well as in nonequilibrium systems (e.g., three-dimensional turbulent flow)

Four-dimensional (4D) tracking of high-temperature microparticles

International Nuclear Information System (INIS)

Wang, Zhehui; Liu, Q.; Waganaar, W.; Fontanese, J.; James, D.; Munsat, T.

2016-01-01

High-speed tracking of hot and molten microparticles in motion provides rich information about burning plasmas in magnetic fusion. An exploding-wire apparatus is used to produce moving high-temperature metallic microparticles and to develop four-dimensional (4D) or time-resolved 3D particle tracking techniques. The pinhole camera model and algorithms developed for computer vision are used for scene calibration and 4D reconstructions. 3D positions and velocities are then derived for different microparticles. Velocity resolution approaches 0.1 m/s by using the local constant velocity approximation.

Bayesian Multiresolution Variable Selection for Ultra-High Dimensional Neuroimaging Data.

Science.gov (United States)

Zhao, Yize; Kang, Jian; Long, Qi

2018-01-01

Ultra-high dimensional variable selection has become increasingly important in analysis of neuroimaging data. For example, in the Autism Brain Imaging Data Exchange (ABIDE) study, neuroscientists are interested in identifying important biomarkers for early detection of the autism spectrum disorder (ASD) using high resolution brain images that include hundreds of thousands voxels. However, most existing methods are not feasible for solving this problem due to their extensive computational costs. In this work, we propose a novel multiresolution variable selection procedure under a Bayesian probit regression framework. It recursively uses posterior samples for coarser-scale variable selection to guide the posterior inference on finer-scale variable selection, leading to very efficient Markov chain Monte Carlo (MCMC) algorithms. The proposed algorithms are computationally feasible for ultra-high dimensional data. Also, our model incorporates two levels of structural information into variable selection using Ising priors: the spatial dependence between voxels and the functional connectivity between anatomical brain regions. Applied to the resting state functional magnetic resonance imaging (R-fMRI) data in the ABIDE study, our methods identify voxel-level imaging biomarkers highly predictive of the ASD, which are biologically meaningful and interpretable. Extensive simulations also show that our methods achieve better performance in variable selection compared to existing methods.

Multi-dimensional analysis of high resolution {gamma}-ray data

Energy Technology Data Exchange (ETDEWEB)

Flibotte, S.; Huettmeier, U.J.; France, G. de; Haas, B.; Romain, P.; Theisen, Ch.; Vivien, J.P.; Zen, J. [Strasbourg-1 Univ., 67 (France). Centre de Recherches Nucleaires

1992-12-31

A new generation of high resolution {gamma}-ray spectrometers capable of recording high-fold coincidence events with a large efficiency will soon be available. Algorithms are developed to analyze high-fold {gamma}-ray coincidences. As a contribution to the software development associated with the EUROGAM spectrometer, the performances of computer codes designed to select multi-dimensional gates from 3-, 4- and 5-fold coincidence databases were tested. The tests were performed on events generated with a Monte Carlo simulation and also on real experimental triple data recorded with the 8{pi} spectrometer and with a preliminary version of the EUROGAM array. (R.P.) 14 refs.; 3 figs.; 3 tabs.

Multi-dimensional analysis of high resolution γ-ray data

International Nuclear Information System (INIS)

Flibotte, S.; Huettmeier, U.J.; France, G. de; Haas, B.; Romain, P.; Theisen, Ch.; Vivien, J.P.; Zen, J.

1992-01-01

A new generation of high resolution γ-ray spectrometers capable of recording high-fold coincidence events with a large efficiency will soon be available. Algorithms are developed to analyze high-fold γ-ray coincidences. As a contribution to the software development associated with the EUROGAM spectrometer, the performances of computer codes designed to select multi-dimensional gates from 3-, 4- and 5-fold coincidence databases were tested. The tests were performed on events generated with a Monte Carlo simulation and also on real experimental triple data recorded with the 8π spectrometer and with a preliminary version of the EUROGAM array. (R.P.) 14 refs.; 3 figs.; 3 tabs

Network Reconstruction From High-Dimensional Ordinary Differential Equations.

Science.gov (United States)

Chen, Shizhe; Shojaie, Ali; Witten, Daniela M

2017-01-01

We consider the task of learning a dynamical system from high-dimensional time-course data. For instance, we might wish to estimate a gene regulatory network from gene expression data measured at discrete time points. We model the dynamical system nonparametrically as a system of additive ordinary differential equations. Most existing methods for parameter estimation in ordinary differential equations estimate the derivatives from noisy observations. This is known to be challenging and inefficient. We propose a novel approach that does not involve derivative estimation. We show that the proposed method can consistently recover the true network structure even in high dimensions, and we demonstrate empirical improvement over competing approaches. Supplementary materials for this article are available online.

Application of the principle of similarity fluid mechanics

International Nuclear Information System (INIS)

Hendricks, R.C.; Sengers, J.V.

1979-01-01

Possible applications of the principle of similarity to fluid mechanics is described and illustrated. In correlating thermophysical properties of fluids, the similarity principle transcends the traditional corresponding states principle. In fluid mechanics the similarity principle is useful in correlating flow processes that can be modeled adequately with one independent variable (i.e., one-dimensional flows). In this paper we explore the concept of transforming the conservation equations by combining similarity principles for thermophysical properties with those for fluid flow. We illustrate the usefulness of the procedure by applying such a transformation to calculate two phase critical mass flow through a nozzle

One- and two-dimensional sublattices as preconditions for high-Tc superconductivity

International Nuclear Information System (INIS)

Krueger, E.

1989-01-01

In an earlier paper it was proposed describing superconductivity in the framework of a nonadiabatic Heisenberg model in order to interprete the outstanding symmetry proper ties of the (spin-dependent) Wannier functions in the conduction bands of superconductors. This new group-theoretical model suggests that Cooper pair formation can only be mediated by boson excitations carrying crystal-spin-angular momentum. While in the three-dimensionally isotropic lattices of the standard superconductors phonons are able to transport crystal-spin-angular momentum, this is not true for phonons propagating through the one- or two-dimensional Cu-O sublattices of the high-T c compounds. Therefore, if such an anisotropic material is superconducting, it is necessarily higher-energetic excitations (of well-defined symmetry) which mediate pair formation. This fact is proposed being responsible for the high transition temperatures of these compounds. (author)

High-dimensional quantum key distribution based on multicore fiber using silicon photonic integrated circuits

DEFF Research Database (Denmark)

Ding, Yunhong; Bacco, Davide; Dalgaard, Kjeld

2017-01-01

is intrinsically limited to 1 bit/photon. Here we propose and experimentally demonstrate, for the first time, a high-dimensional quantum key distribution protocol based on space division multiplexing in multicore fiber using silicon photonic integrated lightwave circuits. We successfully realized three mutually......-dimensional quantum states, and enables breaking the information efficiency limit of traditional quantum key distribution protocols. In addition, the silicon photonic circuits used in our work integrate variable optical attenuators, highly efficient multicore fiber couplers, and Mach-Zehnder interferometers, enabling...

Two-Dimensional High Definition Versus Three-Dimensional Endoscopy in Endonasal Skull Base Surgery: A Comparative Preclinical Study.

Science.gov (United States)

Rampinelli, Vittorio; Doglietto, Francesco; Mattavelli, Davide; Qiu, Jimmy; Raffetti, Elena; Schreiber, Alberto; Villaret, Andrea Bolzoni; Kucharczyk, Walter; Donato, Francesco; Fontanella, Marco Maria; Nicolai, Piero

2017-09-01

Three-dimensional (3D) endoscopy has been recently introduced in endonasal skull base surgery. Only a relatively limited number of studies have compared it to 2-dimensional, high definition technology. The objective was to compare, in a preclinical setting for endonasal endoscopic surgery, the surgical maneuverability of 2-dimensional, high definition and 3D endoscopy. A group of 68 volunteers, novice and experienced surgeons, were asked to perform 2 tasks, namely simulating grasping and dissection surgical maneuvers, in a model of the nasal cavities. Time to complete the tasks was recorded. A questionnaire to investigate subjective feelings during tasks was filled by each participant. In 25 subjects, the surgeons' movements were continuously tracked by a magnetic-based neuronavigator coupled with dedicated software (ApproachViewer, part of GTx-UHN) and the recorded trajectories were analyzed by comparing jitter, sum of square differences, and funnel index. Total execution time was significantly lower with 3D technology (P < 0.05) in beginners and experts. Questionnaires showed that beginners preferred 3D endoscopy more frequently than experts. A minority (14%) of beginners experienced discomfort with 3D endoscopy. Analysis of jitter showed a trend toward increased effectiveness of surgical maneuvers with 3D endoscopy. Sum of square differences and funnel index analyses documented better values with 3D endoscopy in experts. In a preclinical setting for endonasal skull base surgery, 3D technology appears to confer an advantage in terms of time of execution and precision of surgical maneuvers. Copyright © 2017 Elsevier Inc. All rights reserved.

Method of synthesis of abstract images with high self-similarity

Science.gov (United States)

Matveev, Nikolay V.; Shcheglov, Sergey A.; Romanova, Galina E.; Koneva, Ð.¢atiana A.

2017-06-01

Abstract images with high self-similarity could be used for drug-free stress therapy. This based on the fact that a complex visual environment has a high affective appraisal. To create such an image we can use the setup based on the three laser sources of small power and different colors (Red, Green, Blue), the image is the pattern resulting from the reflecting and refracting by the complicated form object placed into the laser ray paths. The images were obtained experimentally which showed the good therapy effect. However, to find and to choose the object which gives needed image structure is very difficult and requires many trials. The goal of the work is to develop a method and a procedure of finding the object form which if placed into the ray paths can provide the necessary structure of the image In fact the task means obtaining the necessary irradiance distribution on the given surface. Traditionally such problems are solved using the non-imaging optics methods. In the given case this task is very complicated because of the complicated structure of the illuminance distribution and its high non-linearity. Alternative way is to use the projected image of a mask with a given structure. We consider both ways and discuss how they can help to speed up the synthesis procedure for the given abstract image of the high self-similarity for the setups of drug-free therapy.

Quantifying high dimensional entanglement with two mutually unbiased bases

Directory of Open Access Journals (Sweden)

Paul Erker

2017-07-01

Full Text Available We derive a framework for quantifying entanglement in multipartite and high dimensional systems using only correlations in two unbiased bases. We furthermore develop such bounds in cases where the second basis is not characterized beyond being unbiased, thus enabling entanglement quantification with minimal assumptions. Furthermore, we show that it is feasible to experimentally implement our method with readily available equipment and even conservative estimates of physical parameters.

Scalable Nearest Neighbor Algorithms for High Dimensional Data.

Science.gov (United States)

Muja, Marius; Lowe, David G

2014-11-01

For many computer vision and machine learning problems, large training sets are key for good performance. However, the most computationally expensive part of many computer vision and machine learning algorithms consists of finding nearest neighbor matches to high dimensional vectors that represent the training data. We propose new algorithms for approximate nearest neighbor matching and evaluate and compare them with previous algorithms. For matching high dimensional features, we find two algorithms to be the most efficient: the randomized k-d forest and a new algorithm proposed in this paper, the priority search k-means tree. We also propose a new algorithm for matching binary features by searching multiple hierarchical clustering trees and show it outperforms methods typically used in the literature. We show that the optimal nearest neighbor algorithm and its parameters depend on the data set characteristics and describe an automated configuration procedure for finding the best algorithm to search a particular data set. In order to scale to very large data sets that would otherwise not fit in the memory of a single machine, we propose a distributed nearest neighbor matching framework that can be used with any of the algorithms described in the paper. All this research has been released as an open source library called fast library for approximate nearest neighbors (FLANN), which has been incorporated into OpenCV and is now one of the most popular libraries for nearest neighbor matching.

The role of three-dimensional high-definition laparoscopic surgery for gynaecology.

Science.gov (United States)

Usta, Taner A; Gundogdu, Elif C

2015-08-01

This article reviews the potential benefits and disadvantages of new three-dimensional (3D) high-definition laparoscopic surgery for gynaecology. With the new-generation 3D high-definition laparoscopic vision systems (LVSs), operation time and learning period are reduced and procedural error margin is decreased. New-generation 3D high-definition LVSs enable to reduce operation time both for novice and experienced surgeons. Headache, eye fatigue or nausea reported with first-generation systems are not different than two-dimensional (2D) LVSs. The system's being more expensive, having the obligation to wear glasses, big and heavy camera probe in some of the devices are accounted for negative aspects of the system that need to be improved. Depth loss in tissues in 2D LVSs and associated adverse events can be eliminated with 3D high-definition LVSs. By virtue of faster learning curve, shorter operation time, reduced error margin and lack of side-effects reported by surgeons with first-generation systems, 3D LVSs seem to be a strong competition to classical laparoscopic imaging systems. Thanks to technological advancements, using lighter and smaller cameras and monitors without glasses is in the near future.

A New Ensemble Method with Feature Space Partitioning for High-Dimensional Data Classification

Directory of Open Access Journals (Sweden)

Yongjun Piao

2015-01-01

Full Text Available Ensemble data mining methods, also known as classifier combination, are often used to improve the performance of classification. Various classifier combination methods such as bagging, boosting, and random forest have been devised and have received considerable attention in the past. However, data dimensionality increases rapidly day by day. Such a trend poses various challenges as these methods are not suitable to directly apply to high-dimensional datasets. In this paper, we propose an ensemble method for classification of high-dimensional data, with each classifier constructed from a different set of features determined by partitioning of redundant features. In our method, the redundancy of features is considered to divide the original feature space. Then, each generated feature subset is trained by a support vector machine, and the results of each classifier are combined by majority voting. The efficiency and effectiveness of our method are demonstrated through comparisons with other ensemble techniques, and the results show that our method outperforms other methods.

High dimensional biological data retrieval optimization with NoSQL technology

Science.gov (United States)

2014-01-01

Background High-throughput transcriptomic data generated by microarray experiments is the most abundant and frequently stored kind of data currently used in translational medicine studies. Although microarray data is supported in data warehouses such as tranSMART, when querying relational databases for hundreds of different patient gene expression records queries are slow due to poor performance. Non-relational data models, such as the key-value model implemented in NoSQL databases, hold promise to be more performant solutions. Our motivation is to improve the performance of the tranSMART data warehouse with a view to supporting Next Generation Sequencing data. Results In this paper we introduce a new data model better suited for high-dimensional data storage and querying, optimized for database scalability and performance. We have designed a key-value pair data model to support faster queries over large-scale microarray data and implemented the model using HBase, an implementation of Google's BigTable storage system. An experimental performance comparison was carried out against the traditional relational data model implemented in both MySQL Cluster and MongoDB, using a large publicly available transcriptomic data set taken from NCBI GEO concerning Multiple Myeloma. Our new key-value data model implemented on HBase exhibits an average 5.24-fold increase in high-dimensional biological data query performance compared to the relational model implemented on MySQL Cluster, and an average 6.47-fold increase on query performance on MongoDB. Conclusions The performance evaluation found that the new key-value data model, in particular its implementation in HBase, outperforms the relational model currently implemented in tranSMART. We propose that NoSQL technology holds great promise for large-scale data management, in particular for high-dimensional biological data such as that demonstrated in the performance evaluation described in this paper. We aim to use this new data

High dimensional biological data retrieval optimization with NoSQL technology.

Science.gov (United States)

Wang, Shicai; Pandis, Ioannis; Wu, Chao; He, Sijin; Johnson, David; Emam, Ibrahim; Guitton, Florian; Guo, Yike

2014-01-01

High-throughput transcriptomic data generated by microarray experiments is the most abundant and frequently stored kind of data currently used in translational medicine studies. Although microarray data is supported in data warehouses such as tranSMART, when querying relational databases for hundreds of different patient gene expression records queries are slow due to poor performance. Non-relational data models, such as the key-value model implemented in NoSQL databases, hold promise to be more performant solutions. Our motivation is to improve the performance of the tranSMART data warehouse with a view to supporting Next Generation Sequencing data. In this paper we introduce a new data model better suited for high-dimensional data storage and querying, optimized for database scalability and performance. We have designed a key-value pair data model to support faster queries over large-scale microarray data and implemented the model using HBase, an implementation of Google's BigTable storage system. An experimental performance comparison was carried out against the traditional relational data model implemented in both MySQL Cluster and MongoDB, using a large publicly available transcriptomic data set taken from NCBI GEO concerning Multiple Myeloma. Our new key-value data model implemented on HBase exhibits an average 5.24-fold increase in high-dimensional biological data query performance compared to the relational model implemented on MySQL Cluster, and an average 6.47-fold increase on query performance on MongoDB. The performance evaluation found that the new key-value data model, in particular its implementation in HBase, outperforms the relational model currently implemented in tranSMART. We propose that NoSQL technology holds great promise for large-scale data management, in particular for high-dimensional biological data such as that demonstrated in the performance evaluation described in this paper. We aim to use this new data model as a basis for migrating

Three-dimensional CT imaging of soft-tissue anatomy

International Nuclear Information System (INIS)

Fishman, E.K.; Ney, D.R.; Magid, D.; Kuhlman, J.E.

1988-01-01

Three-dimensional display of computed tomographic data has been limited to skeletal structures. This was in part related to the reconstruction algorithm used, which relied on a binary classification scheme. A new algorithm, volumetric rendering with percentage classification, provides the ability to display three-dimensional images of muscle and soft tissue. A review was conducted of images in 35 cases in which muscle and/or soft tissue were part of the clinical problem. In all cases, individual muscle groups could be clearly identified and discriminated. Branching vessels in the range of 2.3 mm could be identified. Similarly, lymph nodes could be clearly defined. High-resolution three-dimensional images were found to be useful both in providing an increased understanding of complex muscle and soft tissue anatomy and in surgical planning

The additive hazards model with high-dimensional regressors

DEFF Research Database (Denmark)

Martinussen, Torben; Scheike, Thomas

2009-01-01

This paper considers estimation and prediction in the Aalen additive hazards model in the case where the covariate vector is high-dimensional such as gene expression measurements. Some form of dimension reduction of the covariate space is needed to obtain useful statistical analyses. We study...... model. A standard PLS algorithm can also be constructed, but it turns out that the resulting predictor can only be related to the original covariates via time-dependent coefficients. The methods are applied to a breast cancer data set with gene expression recordings and to the well known primary biliary...

Reliable Exfoliation of Large-Area High-Quality Flakes of Graphene and Other Two-Dimensional Materials.

Science.gov (United States)

Huang, Yuan; Sutter, Eli; Shi, Norman N; Zheng, Jiabao; Yang, Tianzhong; Englund, Dirk; Gao, Hong-Jun; Sutter, Peter

2015-11-24

Mechanical exfoliation has been a key enabler of the exploration of the properties of two-dimensional materials, such as graphene, by providing routine access to high-quality material. The original exfoliation method, which remained largely unchanged during the past decade, provides relatively small flakes with moderate yield. Here, we report a modified approach for exfoliating thin monolayer and few-layer flakes from layered crystals. Our method introduces two process steps that enhance and homogenize the adhesion force between the outermost sheet in contact with a substrate: Prior to exfoliation, ambient adsorbates are effectively removed from the substrate by oxygen plasma cleaning, and an additional heat treatment maximizes the uniform contact area at the interface between the source crystal and the substrate. For graphene exfoliation, these simple process steps increased the yield and the area of the transferred flakes by more than 50 times compared to the established exfoliation methods. Raman and AFM characterization shows that the graphene flakes are of similar high quality as those obtained in previous reports. Graphene field-effect devices were fabricated and measured with back-gating and solution top-gating, yielding mobilities of ∼4000 and 12,000 cm(2)/(V s), respectively, and thus demonstrating excellent electrical properties. Experiments with other layered crystals, e.g., a bismuth strontium calcium copper oxide (BSCCO) superconductor, show enhancements in exfoliation yield and flake area similar to those for graphene, suggesting that our modified exfoliation method provides an effective way for producing large area, high-quality flakes of a wide range of 2D materials.

EPS-LASSO: Test for High-Dimensional Regression Under Extreme Phenotype Sampling of Continuous Traits.

Science.gov (United States)

Xu, Chao; Fang, Jian; Shen, Hui; Wang, Yu-Ping; Deng, Hong-Wen

2018-01-25

Extreme phenotype sampling (EPS) is a broadly-used design to identify candidate genetic factors contributing to the variation of quantitative traits. By enriching the signals in extreme phenotypic samples, EPS can boost the association power compared to random sampling. Most existing statistical methods for EPS examine the genetic factors individually, despite many quantitative traits have multiple genetic factors underlying their variation. It is desirable to model the joint effects of genetic factors, which may increase the power and identify novel quantitative trait loci under EPS. The joint analysis of genetic data in high-dimensional situations requires specialized techniques, e.g., the least absolute shrinkage and selection operator (LASSO). Although there are extensive research and application related to LASSO, the statistical inference and testing for the sparse model under EPS remain unknown. We propose a novel sparse model (EPS-LASSO) with hypothesis test for high-dimensional regression under EPS based on a decorrelated score function. The comprehensive simulation shows EPS-LASSO outperforms existing methods with stable type I error and FDR control. EPS-LASSO can provide a consistent power for both low- and high-dimensional situations compared with the other methods dealing with high-dimensional situations. The power of EPS-LASSO is close to other low-dimensional methods when the causal effect sizes are small and is superior when the effects are large. Applying EPS-LASSO to a transcriptome-wide gene expression study for obesity reveals 10 significant body mass index associated genes. Our results indicate that EPS-LASSO is an effective method for EPS data analysis, which can account for correlated predictors. The source code is available at https://github.com/xu1912/EPSLASSO. hdeng2@tulane.edu. Supplementary data are available at Bioinformatics online. © The Author (2018). Published by Oxford University Press. All rights reserved. For Permissions, please

[Extraction of buildings three-dimensional information from high-resolution satellite imagery based on Barista software].

Science.gov (United States)

Zhang, Pei-feng; Hu, Yuan-man; He, Hong-shi

2010-05-01

The demand for accurate and up-to-date spatial information of urban buildings is becoming more and more important for urban planning, environmental protection, and other vocations. Today's commercial high-resolution satellite imagery offers the potential to extract the three-dimensional information of urban buildings. This paper extracted the three-dimensional information of urban buildings from QuickBird imagery, and validated the precision of the extraction based on Barista software. It was shown that the extraction of three-dimensional information of the buildings from high-resolution satellite imagery based on Barista software had the advantages of low professional level demand, powerful universality, simple operation, and high precision. One pixel level of point positioning and height determination accuracy could be achieved if the digital elevation model (DEM) and sensor orientation model had higher precision and the off-Nadir View Angle was relatively perfect.

High-Efficiency Dye-Sensitized Solar Cell with Three-Dimensional Photoanode

KAUST Repository

Tétreault, Nicolas

2011-11-09

Herein, we present a straightforward bottom-up synthesis of a high electron mobility and highly light scattering macroporous photoanode for dye-sensitized solar cells. The dense three-dimensional Al/ZnO, SnO2, or TiO 2 host integrates a conformal passivation thin film to reduce recombination and a large surface-area mesoporous anatase guest for high dye loading. This novel photoanode is designed to improve the charge extraction resulting in higher fill factor and photovoltage for DSCs. An increase in photovoltage of up to 110 mV over state-of-the-art DSC is demonstrated. © 2011 American Chemical Society.

High-Efficiency Dye-Sensitized Solar Cell with Three-Dimensional Photoanode

KAUST Repository

Té treault, Nicolas; Arsenault, É ric; Heiniger, Leo-Philipp; Soheilnia, Navid; Brillet, Jé ré mie; Moehl, Thomas; Zakeeruddin, Shaik; Ozin, Geoffrey A.; Grä tzel, Michael

2011-01-01

Herein, we present a straightforward bottom-up synthesis of a high electron mobility and highly light scattering macroporous photoanode for dye-sensitized solar cells. The dense three-dimensional Al/ZnO, SnO2, or TiO 2 host integrates a conformal passivation thin film to reduce recombination and a large surface-area mesoporous anatase guest for high dye loading. This novel photoanode is designed to improve the charge extraction resulting in higher fill factor and photovoltage for DSCs. An increase in photovoltage of up to 110 mV over state-of-the-art DSC is demonstrated. © 2011 American Chemical Society.

Direct numerical simulation of a compressible boundary-layer flow past an isolated three-dimensional hump in a high-speed subsonic regime

Science.gov (United States)

De Grazia, D.; Moxey, D.; Sherwin, S. J.; Kravtsova, M. A.; Ruban, A. I.

2018-02-01

In this paper we study the boundary-layer separation produced in a high-speed subsonic boundary layer by a small wall roughness. Specifically, we present a direct numerical simulation (DNS) of a two-dimensional boundary-layer flow over a flat plate encountering a three-dimensional Gaussian-shaped hump. This work was motivated by the lack of DNS data of boundary-layer flows past roughness elements in a similar regime which is typical of civil aviation. The Mach and Reynolds numbers are chosen to be relevant for aeronautical applications when considering small imperfections at the leading edge of wings. We analyze different heights of the hump: The smaller heights result in a weakly nonlinear regime, while the larger result in a fully nonlinear regime with an increasing laminar separation bubble arising downstream of the roughness element and the formation of a pair of streamwise counterrotating vortices which appear to support themselves.

High-resolution three-dimensional mapping of semiconductor dopant potentials

DEFF Research Database (Denmark)

Twitchett, AC; Yates, TJV; Newcomb, SB

2007-01-01

Semiconductor device structures are becoming increasingly three-dimensional at the nanometer scale. A key issue that must be addressed to enable future device development is the three-dimensional mapping of dopant distributions, ideally under "working conditions". Here we demonstrate how a combin......Semiconductor device structures are becoming increasingly three-dimensional at the nanometer scale. A key issue that must be addressed to enable future device development is the three-dimensional mapping of dopant distributions, ideally under "working conditions". Here we demonstrate how...... a combination of electron holography and electron tomography can be used to determine quantitatively the three-dimensional electrostatic potential in an electrically biased semiconductor device with nanometer spatial resolution....

[Advances in the research of application of hydrogels in three-dimensional bioprinting].

Science.gov (United States)

Yang, J; Zhao, Y; Li, H H; Zhu, S H

2016-08-20

Hydrogels are three-dimensional networks made of hydrophilic polymer crosslinked through covalent bonds or physical intermolecular attractions, which can contain growth media and growth factors to support cell growth. In bioprinting, hydrogels are used to provide accurate control over cellular microenvironment and to dramatically reduce experimental repetition times, meanwhile we can obtain three-dimensional cell images of high quality. Hydrogels in three-dimensional bioprinting have received a considerable interest due to their structural similarities to the natural extracellular matrix and polyporous frameworks which can support the cellular proliferation and survival. Meanwhile, they are accompanied by many challenges.

Three-dimensionality of field-induced magnetism in a high-temperature superconductor

DEFF Research Database (Denmark)

Lake, B.; Lefmann, K.; Christensen, N.B.

2005-01-01

Many physical properties of high-temperature superconductors are two-dimensional phenomena derived from their square-planar CuO(2) building blocks. This is especially true of the magnetism from the copper ions. As mobile charge carriers enter the CuO(2) layers, the antiferromagnetism of the parent...

Pricing and hedging high-dimensional American options : an irregular grid approach

NARCIS (Netherlands)

Berridge, S.; Schumacher, H.

2002-01-01

We propose and test a new method for pricing American options in a high dimensional setting. The method is centred around the approximation of the associated variational inequality on an irregular grid. We approximate the partial differential operator on this grid by appealing to the SDE

A Feature Subset Selection Method Based On High-Dimensional Mutual Information

Directory of Open Access Journals (Sweden)

Chee Keong Kwoh

2011-04-01

Full Text Available Feature selection is an important step in building accurate classifiers and provides better understanding of the data sets. In this paper, we propose a feature subset selection method based on high-dimensional mutual information. We also propose to use the entropy of the class attribute as a criterion to determine the appropriate subset of features when building classifiers. We prove that if the mutual information between a feature set X and the class attribute Y equals to the entropy of Y , then X is a Markov Blanket of Y . We show that in some cases, it is infeasible to approximate the high-dimensional mutual information with algebraic combinations of pairwise mutual information in any forms. In addition, the exhaustive searches of all combinations of features are prerequisite for finding the optimal feature subsets for classifying these kinds of data sets. We show that our approach outperforms existing filter feature subset selection methods for most of the 24 selected benchmark data sets.

Metallic and highly conducting two-dimensional atomic arrays of sulfur enabled by molybdenum disulfide nanotemplate

Science.gov (United States)

Zhu, Shuze; Geng, Xiumei; Han, Yang; Benamara, Mourad; Chen, Liao; Li, Jingxiao; Bilgin, Ismail; Zhu, Hongli

2017-10-01

Element sulfur in nature is an insulating solid. While it has been tested that one-dimensional sulfur chain is metallic and conducting, the investigation on two-dimensional sulfur remains elusive. We report that molybdenum disulfide layers are able to serve as the nanotemplate to facilitate the formation of two-dimensional sulfur. Density functional theory calculations suggest that confined in-between layers of molybdenum disulfide, sulfur atoms are able to form two-dimensional triangular arrays that are highly metallic. As a result, these arrays contribute to the high conductivity and metallic phase of the hybrid structures of molybdenum disulfide layers and two-dimensional sulfur arrays. The experimentally measured conductivity of such hybrid structures reaches up to 223 S/m. Multiple experimental results, including X-ray photoelectron spectroscopy (XPS), transition electron microscope (TEM), selected area electron diffraction (SAED), agree with the computational insights. Due to the excellent conductivity, the current density is linearly proportional to the scan rate until 30,000 mV s-1 without the attendance of conductive additives. Using such hybrid structures as electrode, the two-electrode supercapacitor cells yield a power density of 106 Wh kg-1 and energy density 47.5 Wh kg-1 in ionic liquid electrolytes. Our findings offer new insights into using two-dimensional materials and their Van der Waals heterostructures as nanotemplates to pattern foreign atoms for unprecedented material properties.

Universal self-similarity of propagating populations.

Science.gov (United States)

Eliazar, Iddo; Klafter, Joseph

2010-07-01

This paper explores the universal self-similarity of propagating populations. The following general propagation model is considered: particles are randomly emitted from the origin of a d-dimensional Euclidean space and propagate randomly and independently of each other in space; all particles share a statistically common--yet arbitrary--motion pattern; each particle has its own random propagation parameters--emission epoch, motion frequency, and motion amplitude. The universally self-similar statistics of the particles' displacements and first passage times (FPTs) are analyzed: statistics which are invariant with respect to the details of the displacement and FPT measurements and with respect to the particles' underlying motion pattern. Analysis concludes that the universally self-similar statistics are governed by Poisson processes with power-law intensities and by the Fréchet and Weibull extreme-value laws.

Universal self-similarity of propagating populations

Science.gov (United States)

Eliazar, Iddo; Klafter, Joseph

2010-07-01

This paper explores the universal self-similarity of propagating populations. The following general propagation model is considered: particles are randomly emitted from the origin of a d -dimensional Euclidean space and propagate randomly and independently of each other in space; all particles share a statistically common—yet arbitrary—motion pattern; each particle has its own random propagation parameters—emission epoch, motion frequency, and motion amplitude. The universally self-similar statistics of the particles’ displacements and first passage times (FPTs) are analyzed: statistics which are invariant with respect to the details of the displacement and FPT measurements and with respect to the particles’ underlying motion pattern. Analysis concludes that the universally self-similar statistics are governed by Poisson processes with power-law intensities and by the Fréchet and Weibull extreme-value laws.

An adaptive ANOVA-based PCKF for high-dimensional nonlinear inverse modeling

Science.gov (United States)

Li, Weixuan; Lin, Guang; Zhang, Dongxiao

2014-02-01

The probabilistic collocation-based Kalman filter (PCKF) is a recently developed approach for solving inverse problems. It resembles the ensemble Kalman filter (EnKF) in every aspect-except that it represents and propagates model uncertainty by polynomial chaos expansion (PCE) instead of an ensemble of model realizations. Previous studies have shown PCKF is a more efficient alternative to EnKF for many data assimilation problems. However, the accuracy and efficiency of PCKF depends on an appropriate truncation of the PCE series. Having more polynomial chaos basis functions in the expansion helps to capture uncertainty more accurately but increases computational cost. Selection of basis functions is particularly important for high-dimensional stochastic problems because the number of polynomial chaos basis functions required to represent model uncertainty grows dramatically as the number of input parameters (random dimensions) increases. In classic PCKF algorithms, the PCE basis functions are pre-set based on users' experience. Also, for sequential data assimilation problems, the basis functions kept in PCE expression remain unchanged in different Kalman filter loops, which could limit the accuracy and computational efficiency of classic PCKF algorithms. To address this issue, we present a new algorithm that adaptively selects PCE basis functions for different problems and automatically adjusts the number of basis functions in different Kalman filter loops. The algorithm is based on adaptive functional ANOVA (analysis of variance) decomposition, which approximates a high-dimensional function with the summation of a set of low-dimensional functions. Thus, instead of expanding the original model into PCE, we implement the PCE expansion on these low-dimensional functions, which is much less costly. We also propose a new adaptive criterion for ANOVA that is more suited for solving inverse problems. The new algorithm was tested with different examples and demonstrated

CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets [version 1; referees: 2 approved

Directory of Open Access Journals (Sweden)

Malgorzata Nowicka

2017-05-01

Full Text Available High dimensional mass and flow cytometry (HDCyto experiments have become a method of choice for high throughput interrogation and characterization of cell populations.Here, we present an R-based pipeline for differential analyses of HDCyto data, largely based on Bioconductor packages. We computationally define cell populations using FlowSOM clustering, and facilitate an optional but reproducible strategy for manual merging of algorithm-generated clusters. Our workflow offers different analysis paths, including association of cell type abundance with a phenotype or changes in signaling markers within specific subpopulations, or differential analyses of aggregated signals. Importantly, the differential analyses we show are based on regression frameworks where the HDCyto data is the response; thus, we are able to model arbitrary experimental designs, such as those with batch effects, paired designs and so on. In particular, we apply generalized linear mixed models to analyses of cell population abundance or cell-population-specific analyses of signaling markers, allowing overdispersion in cell count or aggregated signals across samples to be appropriately modeled. To support the formal statistical analyses, we encourage exploratory data analysis at every step, including quality control (e.g. multi-dimensional scaling plots, reporting of clustering results (dimensionality reduction, heatmaps with dendrograms and differential analyses (e.g. plots of aggregated signals.

Reduced order surrogate modelling (ROSM) of high dimensional deterministic simulations

Science.gov (United States)

Mitry, Mina

Often, computationally expensive engineering simulations can prohibit the engineering design process. As a result, designers may turn to a less computationally demanding approximate, or surrogate, model to facilitate their design process. However, owing to the the curse of dimensionality, classical surrogate models become too computationally expensive for high dimensional data. To address this limitation of classical methods, we develop linear and non-linear Reduced Order Surrogate Modelling (ROSM) techniques. Two algorithms are presented, which are based on a combination of linear/kernel principal component analysis and radial basis functions. These algorithms are applied to subsonic and transonic aerodynamic data, as well as a model for a chemical spill in a channel. The results of this thesis show that ROSM can provide a significant computational benefit over classical surrogate modelling, sometimes at the expense of a minor loss in accuracy.

Efficient and accurate nearest neighbor and closest pair search in high-dimensional space

KAUST Repository

Tao, Yufei

2010-07-01

Nearest Neighbor (NN) search in high-dimensional space is an important problem in many applications. From the database perspective, a good solution needs to have two properties: (i) it can be easily incorporated in a relational database, and (ii) its query cost should increase sublinearly with the dataset size, regardless of the data and query distributions. Locality-Sensitive Hashing (LSH) is a well-known methodology fulfilling both requirements, but its current implementations either incur expensive space and query cost, or abandon its theoretical guarantee on the quality of query results. Motivated by this, we improve LSH by proposing an access method called the Locality-Sensitive B-tree (LSB-tree) to enable fast, accurate, high-dimensional NN search in relational databases. The combination of several LSB-trees forms a LSB-forest that has strong quality guarantees, but improves dramatically the efficiency of the previous LSH implementation having the same guarantees. In practice, the LSB-tree itself is also an effective index which consumes linear space, supports efficient updates, and provides accurate query results. In our experiments, the LSB-tree was faster than: (i) iDistance (a famous technique for exact NN search) by two orders ofmagnitude, and (ii) MedRank (a recent approximate method with nontrivial quality guarantees) by one order of magnitude, and meanwhile returned much better results. As a second step, we extend our LSB technique to solve another classic problem, called Closest Pair (CP) search, in high-dimensional space. The long-term challenge for this problem has been to achieve subquadratic running time at very high dimensionalities, which fails most of the existing solutions. We show that, using a LSB-forest, CP search can be accomplished in (worst-case) time significantly lower than the quadratic complexity, yet still ensuring very good quality. In practice, accurate answers can be found using just two LSB-trees, thus giving a substantial

A Multi-layer Hybrid Framework for Dimensional Emotion Classification

NARCIS (Netherlands)

Nicolaou, Mihalis A.; Gunes, Hatice; Pantic, Maja

2011-01-01

This paper investigates dimensional emotion prediction and classification from naturalistic facial expressions. Similarly to many pattern recognition problems, dimensional emotion classification requires generating multi-dimensional outputs. To date, classification for valence and arousal dimensions

An overview of techniques for linking high-dimensional molecular data to time-to-event endpoints by risk prediction models.

Science.gov (United States)

Binder, Harald; Porzelius, Christine; Schumacher, Martin

2011-03-01

Analysis of molecular data promises identification of biomarkers for improving prognostic models, thus potentially enabling better patient management. For identifying such biomarkers, risk prediction models can be employed that link high-dimensional molecular covariate data to a clinical endpoint. In low-dimensional settings, a multitude of statistical techniques already exists for building such models, e.g. allowing for variable selection or for quantifying the added value of a new biomarker. We provide an overview of techniques for regularized estimation that transfer this toward high-dimensional settings, with a focus on models for time-to-event endpoints. Techniques for incorporating specific covariate structure are discussed, as well as techniques for dealing with more complex endpoints. Employing gene expression data from patients with diffuse large B-cell lymphoma, some typical modeling issues from low-dimensional settings are illustrated in a high-dimensional application. First, the performance of classical stepwise regression is compared to stage-wise regression, as implemented by a component-wise likelihood-based boosting approach. A second issues arises, when artificially transforming the response into a binary variable. The effects of the resulting loss of efficiency and potential bias in a high-dimensional setting are illustrated, and a link to competing risks models is provided. Finally, we discuss conditions for adequately quantifying the added value of high-dimensional gene expression measurements, both at the stage of model fitting and when performing evaluation. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Spectral Classification of Similar Materials using the Tetracorder Algorithm: The Calcite-Epidote-Chlorite Problem

Science.gov (United States)

Dalton, J. Brad; Bove, Dana; Mladinich, Carol; Clark, Roger; Rockwell, Barnaby; Swayze, Gregg; King, Trude; Church, Stanley

2001-01-01

Recent work on automated spectral classification algorithms has sought to distinguish ever-more similar materials. From modest beginnings separating shade, soil, rock and vegetation to ambitious attempts to discriminate mineral types and specific plant species, the trend seems to be toward using increasingly subtle spectral differences to perform the classification. Rule-based expert systems exploiting the underlying physics of spectroscopy such as the US Geological Society Tetracorder system are now taking advantage of the high spectral resolution and dimensionality of current imaging spectrometer designs to discriminate spectrally similar materials. The current paper details recent efforts to discriminate three minerals having absorptions centered at the same wavelength, with encouraging results.

DataHigh: Graphical user interface for visualizing and interacting with high-dimensional neural activity

OpenAIRE

Cowley, Benjamin R.; Kaufman, Matthew T.; Churchland, Mark M.; Ryu, Stephen I.; Shenoy, Krishna V.; Yu, Byron M.

2012-01-01

The activity of tens to hundreds of neurons can be succinctly summarized by a smaller number of latent variables extracted using dimensionality reduction methods. These latent variables define a reduced-dimensional space in which we can study how population activity varies over time, across trials, and across experimental conditions. Ideally, we would like to visualize the population activity directly in the reduced-dimensional space, whose optimal dimensionality (as determined from the data)...

Similarity of eigenstates in generalized labyrinth tilings

International Nuclear Information System (INIS)

Thiem, Stefanie; Schreiber, Michael

2010-01-01

The eigenstates of d-dimensional quasicrystalline models with a separable Hamiltonian are studied within the tight-binding model. The approach is based on mathematical sequences, constructed by an inflation rule P = {w → s,s → sws b-1 } describing the weak/strong couplings of atoms in a quasiperiodic chain. Higher-dimensional quasiperiodic tilings are constructed as a direct product of these chains and their eigenstates can be directly calculated by multiplying the energies E or wave functions ψ of the chain, respectively. Applying this construction rule, the grid in d dimensions splits into 2 d-1 different tilings, for which we investigated the characteristics of the wave functions. For the standard two-dimensional labyrinth tiling constructed from the octonacci sequence (b = 2) the lattice breaks up into two identical lattices, which consequently yield the same eigenstates. While this is not the case for b ≠ 2, our numerical results show that the wave functions of the different grids become increasingly similar for large system sizes. This can be explained by the fact that the structure of the 2 d-1 grids mainly differs at the boundaries and thus for large systems the eigenstates approach each other. This property allows us to analytically derive properties of the higher-dimensional generalized labyrinth tilings from the one-dimensional results. In particular participation numbers and corresponding scaling exponents have been determined.

High-velocity two-phase flow two-dimensional modeling

International Nuclear Information System (INIS)

Mathes, R.; Alemany, A.; Thilbault, J.P.

1995-01-01

The two-phase flow in the nozzle of a LMMHD (liquid metal magnetohydrodynamic) converter has been studied numerically and experimentally. A two-dimensional model for two-phase flow has been developed including the viscous terms (dragging and turbulence) and the interfacial mass, momentum and energy transfer between the phases. The numerical results were obtained by a finite volume method based on the SIMPLE algorithm. They have been verified by an experimental facility using air-water as a simulation pair and a phase Doppler particle analyzer for velocity and droplet size measurement. The numerical simulation of a lithium-cesium high-temperature pair showed that a nearly homogeneous and isothermal expansion of the two phases is possible with small pressure losses and high kinetic efficiencies. In the throat region a careful profiling is necessary to reduce the inertial effects on the liquid velocity field

Self-similarity in incompressible Navier-Stokes equations.

Science.gov (United States)

Ercan, Ali; Kavvas, M Levent

2015-12-01

The self-similarity conditions of the 3-dimensional (3D) incompressible Navier-Stokes equations are obtained by utilizing one-parameter Lie group of point scaling transformations. It is found that the scaling exponents of length dimensions in i = 1, 2, 3 coordinates in 3-dimensions are not arbitrary but equal for the self-similarity of 3D incompressible Navier-Stokes equations. It is also shown that the self-similarity in this particular flow process can be achieved in different time and space scales when the viscosity of the fluid is also scaled in addition to other flow variables. In other words, the self-similarity of Navier-Stokes equations is achievable under different fluid environments in the same or different gravity conditions. Self-similarity criteria due to initial and boundary conditions are also presented. Utilizing the proposed self-similarity conditions of the 3D hydrodynamic flow process, the value of a flow variable at a specified time and space can be scaled to a corresponding value in a self-similar domain at the corresponding time and space.

High-dimensional cluster analysis with the Masked EM Algorithm

Science.gov (United States)

Kadir, Shabnam N.; Goodman, Dan F. M.; Harris, Kenneth D.

2014-01-01

Cluster analysis faces two problems in high dimensions: first, the “curse of dimensionality” that can lead to overfitting and poor generalization performance; and second, the sheer time taken for conventional algorithms to process large amounts of high-dimensional data. We describe a solution to these problems, designed for the application of “spike sorting” for next-generation high channel-count neural probes. In this problem, only a small subset of features provide information about the cluster member-ship of any one data vector, but this informative feature subset is not the same for all data points, rendering classical feature selection ineffective. We introduce a “Masked EM” algorithm that allows accurate and time-efficient clustering of up to millions of points in thousands of dimensions. We demonstrate its applicability to synthetic data, and to real-world high-channel-count spike sorting data. PMID:25149694

Dissecting high-dimensional phenotypes with bayesian sparse factor analysis of genetic covariance matrices.

Science.gov (United States)

Runcie, Daniel E; Mukherjee, Sayan

2013-07-01

Quantitative genetic studies that model complex, multivariate phenotypes are important for both evolutionary prediction and artificial selection. For example, changes in gene expression can provide insight into developmental and physiological mechanisms that link genotype and phenotype. However, classical analytical techniques are poorly suited to quantitative genetic studies of gene expression where the number of traits assayed per individual can reach many thousand. Here, we derive a Bayesian genetic sparse factor model for estimating the genetic covariance matrix (G-matrix) of high-dimensional traits, such as gene expression, in a mixed-effects model. The key idea of our model is that we need consider only G-matrices that are biologically plausible. An organism's entire phenotype is the result of processes that are modular and have limited complexity. This implies that the G-matrix will be highly structured. In particular, we assume that a limited number of intermediate traits (or factors, e.g., variations in development or physiology) control the variation in the high-dimensional phenotype, and that each of these intermediate traits is sparse - affecting only a few observed traits. The advantages of this approach are twofold. First, sparse factors are interpretable and provide biological insight into mechanisms underlying the genetic architecture. Second, enforcing sparsity helps prevent sampling errors from swamping out the true signal in high-dimensional data. We demonstrate the advantages of our model on simulated data and in an analysis of a published Drosophila melanogaster gene expression data set.

On-chip generation of high-dimensional entangled quantum states and their coherent control.

Science.gov (United States)

Kues, Michael; Reimer, Christian; Roztocki, Piotr; Cortés, Luis Romero; Sciara, Stefania; Wetzel, Benjamin; Zhang, Yanbing; Cino, Alfonso; Chu, Sai T; Little, Brent E; Moss, David J; Caspani, Lucia; Azaña, José; Morandotti, Roberto

2017-06-28

Optical quantum states based on entangled photons are essential for solving questions in fundamental physics and are at the heart of quantum information science. Specifically, the realization of high-dimensional states (D-level quantum systems, that is, qudits, with D > 2) and their control are necessary for fundamental investigations of quantum mechanics, for increasing the sensitivity of quantum imaging schemes, for improving the robustness and key rate of quantum communication protocols, for enabling a richer variety of quantum simulations, and for achieving more efficient and error-tolerant quantum computation. Integrated photonics has recently become a leading platform for the compact, cost-efficient, and stable generation and processing of non-classical optical states. However, so far, integrated entangled quantum sources have been limited to qubits (D = 2). Here we demonstrate on-chip generation of entangled qudit states, where the photons are created in a coherent superposition of multiple high-purity frequency modes. In particular, we confirm the realization of a quantum system with at least one hundred dimensions, formed by two entangled qudits with D = 10. Furthermore, using state-of-the-art, yet off-the-shelf telecommunications components, we introduce a coherent manipulation platform with which to control frequency-entangled states, capable of performing deterministic high-dimensional gate operations. We validate this platform by measuring Bell inequality violations and performing quantum state tomography. Our work enables the generation and processing of high-dimensional quantum states in a single spatial mode.

HSTLBO: A hybrid algorithm based on Harmony Search and Teaching-Learning-Based Optimization for complex high-dimensional optimization problems.

Directory of Open Access Journals (Sweden)

Shouheng Tuo

Full Text Available Harmony Search (HS and Teaching-Learning-Based Optimization (TLBO as new swarm intelligent optimization algorithms have received much attention in recent years. Both of them have shown outstanding performance for solving NP-Hard optimization problems. However, they also suffer dramatic performance degradation for some complex high-dimensional optimization problems. Through a lot of experiments, we find that the HS and TLBO have strong complementarity each other. The HS has strong global exploration power but low convergence speed. Reversely, the TLBO has much fast convergence speed but it is easily trapped into local search. In this work, we propose a hybrid search algorithm named HSTLBO that merges the two algorithms together for synergistically solving complex optimization problems using a self-adaptive selection strategy. In the HSTLBO, both HS and TLBO are modified with the aim of balancing the global exploration and exploitation abilities, where the HS aims mainly to explore the unknown regions and the TLBO aims to rapidly exploit high-precision solutions in the known regions. Our experimental results demonstrate better performance and faster speed than five state-of-the-art HS variants and show better exploration power than five good TLBO variants with similar run time, which illustrates that our method is promising in solving complex high-dimensional optimization problems. The experiment on portfolio optimization problems also demonstrate that the HSTLBO is effective in solving complex read-world application.

TSAR: a program for automatic resonance assignment using 2D cross-sections of high dimensionality, high-resolution spectra

Energy Technology Data Exchange (ETDEWEB)

Zawadzka-Kazimierczuk, Anna; Kozminski, Wiktor [University of Warsaw, Faculty of Chemistry (Poland); Billeter, Martin, E-mail: martin.billeter@chem.gu.se [University of Gothenburg, Biophysics Group, Department of Chemistry and Molecular Biology (Sweden)

2012-09-15

While NMR studies of proteins typically aim at structure, dynamics or interactions, resonance assignments represent in almost all cases the initial step of the analysis. With increasing complexity of the NMR spectra, for example due to decreasing extent of ordered structure, this task often becomes both difficult and time-consuming, and the recording of high-dimensional data with high-resolution may be essential. Random sampling of the evolution time space, combined with sparse multidimensional Fourier transform (SMFT), allows for efficient recording of very high dimensional spectra ({>=}4 dimensions) while maintaining high resolution. However, the nature of this data demands for automation of the assignment process. Here we present the program TSAR (Tool for SMFT-based Assignment of Resonances), which exploits all advantages of SMFT input. Moreover, its flexibility allows to process data from any type of experiments that provide sequential connectivities. The algorithm was tested on several protein samples, including a disordered 81-residue fragment of the {delta} subunit of RNA polymerase from Bacillus subtilis containing various repetitive sequences. For our test examples, TSAR achieves a high percentage of assigned residues without any erroneous assignments.

Assessing the detectability of antioxidants in two-dimensional high-performance liquid chromatography.

Science.gov (United States)

Bassanese, Danielle N; Conlan, Xavier A; Barnett, Neil W; Stevenson, Paul G

2015-05-01

This paper explores the analytical figures of merit of two-dimensional high-performance liquid chromatography for the separation of antioxidant standards. The cumulative two-dimensional high-performance liquid chromatography peak area was calculated for 11 antioxidants by two different methods--the areas reported by the control software and by fitting the data with a Gaussian model; these methods were evaluated for precision and sensitivity. Both methods demonstrated excellent precision in regards to retention time in the second dimension (%RSD below 1.16%) and cumulative second dimension peak area (%RSD below 3.73% from the instrument software and 5.87% for the Gaussian method). Combining areas reported by the high-performance liquid chromatographic control software displayed superior limits of detection, in the order of 1 × 10(-6) M, almost an order of magnitude lower than the Gaussian method for some analytes. The introduction of the countergradient eliminated the strong solvent mismatch between dimensions, leading to a much improved peak shape and better detection limits for quantification. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Reducing the Complexity of Genetic Fuzzy Classifiers in Highly-Dimensional Classification Problems

Directory of Open Access Journals (Sweden)

DimitrisG. Stavrakoudis

2012-04-01

Full Text Available This paper introduces the Fast Iterative Rule-based Linguistic Classifier (FaIRLiC, a Genetic Fuzzy Rule-Based Classification System (GFRBCS which targets at reducing the structural complexity of the resulting rule base, as well as its learning algorithm's computational requirements, especially when dealing with high-dimensional feature spaces. The proposed methodology follows the principles of the iterative rule learning (IRL approach, whereby a rule extraction algorithm (REA is invoked in an iterative fashion, producing one fuzzy rule at a time. The REA is performed in two successive steps: the first one selects the relevant features of the currently extracted rule, whereas the second one decides the antecedent part of the fuzzy rule, using the previously selected subset of features. The performance of the classifier is finally optimized through a genetic tuning post-processing stage. Comparative results in a hyperspectral remote sensing classification as well as in 12 real-world classification datasets indicate the effectiveness of the proposed methodology in generating high-performing and compact fuzzy rule-based classifiers, even for very high-dimensional feature spaces.

The validation and assessment of machine learning: a game of prediction from high-dimensional data

DEFF Research Database (Denmark)

Pers, Tune Hannes; Albrechtsen, A; Holst, C

2009-01-01

In applied statistics, tools from machine learning are popular for analyzing complex and high-dimensional data. However, few theoretical results are available that could guide to the appropriate machine learning tool in a new application. Initial development of an overall strategy thus often...... the ideas, the game is applied to data from the Nugenob Study where the aim is to predict the fat oxidation capacity based on conventional factors and high-dimensional metabolomics data. Three players have chosen to use support vector machines, LASSO, and random forests, respectively....

Quantum secret sharing based on modulated high-dimensional time-bin entanglement

International Nuclear Information System (INIS)

Takesue, Hiroki; Inoue, Kyo

2006-01-01

We propose a scheme for quantum secret sharing (QSS) that uses a modulated high-dimensional time-bin entanglement. By modulating the relative phase randomly by {0,π}, a sender with the entanglement source can randomly change the sign of the correlation of the measurement outcomes obtained by two distant recipients. The two recipients must cooperate if they are to obtain the sign of the correlation, which is used as a secret key. We show that our scheme is secure against intercept-and-resend (IR) and beam splitting attacks by an outside eavesdropper thanks to the nonorthogonality of high-dimensional time-bin entangled states. We also show that a cheating attempt based on an IR attack by one of the recipients can be detected by changing the dimension of the time-bin entanglement randomly and inserting two 'vacant' slots between the packets. Then, cheating attempts can be detected by monitoring the count rate in the vacant slots. The proposed scheme has better experimental feasibility than previously proposed entanglement-based QSS schemes

Minimax Rate-optimal Estimation of High-dimensional Covariance Matrices with Incomplete Data.

Science.gov (United States)

Cai, T Tony; Zhang, Anru

2016-09-01

Missing data occur frequently in a wide range of applications. In this paper, we consider estimation of high-dimensional covariance matrices in the presence of missing observations under a general missing completely at random model in the sense that the missingness is not dependent on the values of the data. Based on incomplete data, estimators for bandable and sparse covariance matrices are proposed and their theoretical and numerical properties are investigated. Minimax rates of convergence are established under the spectral norm loss and the proposed estimators are shown to be rate-optimal under mild regularity conditions. Simulation studies demonstrate that the estimators perform well numerically. The methods are also illustrated through an application to data from four ovarian cancer studies. The key technical tools developed in this paper are of independent interest and potentially useful for a range of related problems in high-dimensional statistical inference with missing data.

Minimax Rate-optimal Estimation of High-dimensional Covariance Matrices with Incomplete Data*

Science.gov (United States)

Cai, T. Tony; Zhang, Anru

2016-01-01

Missing data occur frequently in a wide range of applications. In this paper, we consider estimation of high-dimensional covariance matrices in the presence of missing observations under a general missing completely at random model in the sense that the missingness is not dependent on the values of the data. Based on incomplete data, estimators for bandable and sparse covariance matrices are proposed and their theoretical and numerical properties are investigated. Minimax rates of convergence are established under the spectral norm loss and the proposed estimators are shown to be rate-optimal under mild regularity conditions. Simulation studies demonstrate that the estimators perform well numerically. The methods are also illustrated through an application to data from four ovarian cancer studies. The key technical tools developed in this paper are of independent interest and potentially useful for a range of related problems in high-dimensional statistical inference with missing data. PMID:27777471

AucPR: an AUC-based approach using penalized regression for disease prediction with high-dimensional omics data.

Science.gov (United States)

Yu, Wenbao; Park, Taesung

2014-01-01

It is common to get an optimal combination of markers for disease classification and prediction when multiple markers are available. Many approaches based on the area under the receiver operating characteristic curve (AUC) have been proposed. Existing works based on AUC in a high-dimensional context depend mainly on a non-parametric, smooth approximation of AUC, with no work using a parametric AUC-based approach, for high-dimensional data. We propose an AUC-based approach using penalized regression (AucPR), which is a parametric method used for obtaining a linear combination for maximizing the AUC. To obtain the AUC maximizer in a high-dimensional context, we transform a classical parametric AUC maximizer, which is used in a low-dimensional context, into a regression framework and thus, apply the penalization regression approach directly. Two kinds of penalization, lasso and elastic net, are considered. The parametric approach can avoid some of the difficulties of a conventional non-parametric AUC-based approach, such as the lack of an appropriate concave objective function and a prudent choice of the smoothing parameter. We apply the proposed AucPR for gene selection and classification using four real microarray and synthetic data. Through numerical studies, AucPR is shown to perform better than the penalized logistic regression and the nonparametric AUC-based method, in the sense of AUC and sensitivity for a given specificity, particularly when there are many correlated genes. We propose a powerful parametric and easily-implementable linear classifier AucPR, for gene selection and disease prediction for high-dimensional data. AucPR is recommended for its good prediction performance. Beside gene expression microarray data, AucPR can be applied to other types of high-dimensional omics data, such as miRNA and protein data.

Three-dimensional graphene/polyaniline composite material for high-performance supercapacitor applications

International Nuclear Information System (INIS)

Liu, Huili; Wang, Yi; Gou, Xinglong; Qi, Tao; Yang, Jun; Ding, Yulong

2013-01-01

Highlights: ► A novel 3D graphene showed high specific surface area and large mesopore volume. ► Aniline monomer was polymerized in the presence of 3D graphene at room temperature. ► The supercapacitive properties were studied by CV and charge–discharge tests. ► The composite show a high gravimetric capacitance and good cyclic stability. ► The 3D graphene/polyaniline has never been report before our work. -- Abstract: A novel three-dimensional (3D) graphene/polyaniline nanocomposite material which is synthesized using in situ polymerization of aniline monomer on the graphene surface is reported as an electrode for supercapacitors. The morphology and structure of the material are characterized by scanning electron microscopy (SEM), transmission electron microscope (TEM), Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD). The electrochemical properties of the resulting materials are systematically studied using cyclic voltammetry (CV) and constant current charge–discharge tests. A high gravimetric capacitance of 463 F g −1 at a scan rate of 1 mV s −1 is obtained by means of CVs with 3 mol L −1 KOH as the electrolyte. In addition, the composite material shows only 9.4% capacity loss after 500 cycles, indicating better cyclic stability for supercapacitor applications. The high specific surface area, large mesopore volume and three-dimensional nanoporous structure of 3D graphene could contribute to the high specific capacitance and good cyclic life

Simulating three-dimensional nonthermal high-energy photon emission in colliding-wind binaries

Energy Technology Data Exchange (ETDEWEB)

Reitberger, K.; Kissmann, R.; Reimer, A.; Reimer, O., E-mail: klaus.reitberger@uibk.ac.at [Institut für Astro- und Teilchenphysik and Institut für Theoretische Physik, Leopold-Franzens-Universität Innsbruck, A-6020 Innsbruck (Austria)

2014-07-01

Massive stars in binary systems have long been regarded as potential sources of high-energy γ rays. The emission is principally thought to arise in the region where the stellar winds collide and accelerate relativistic particles which subsequently emit γ rays. On the basis of a three-dimensional distribution function of high-energy particles in the wind collision region—as obtained by a numerical hydrodynamics and particle transport model—we present the computation of the three-dimensional nonthermal photon emission for a given line of sight. Anisotropic inverse Compton emission is modeled using the target radiation field of both stars. Photons from relativistic bremsstrahlung and neutral pion decay are computed on the basis of local wind plasma densities. We also consider photon-photon opacity effects due to the dense radiation fields of the stars. Results are shown for different stellar separations of a given binary system comprising of a B star and a Wolf-Rayet star. The influence of orbital orientation with respect to the line of sight is also studied by using different orbital viewing angles. For the chosen electron-proton injection ratio of 10{sup –2}, we present the ensuing photon emission in terms of two-dimensional projections maps, spectral energy distributions, and integrated photon flux values in various energy bands. Here, we find a transition from hadron-dominated to lepton-dominated high-energy emission with increasing stellar separations. In addition, we confirm findings from previous analytic modeling that the spectral energy distribution varies significantly with orbital orientation.

Approximation of High-Dimensional Rank One Tensors

KAUST Repository

Bachmayr, Markus

2013-11-12

Many real world problems are high-dimensional in that their solution is a function which depends on many variables or parameters. This presents a computational challenge since traditional numerical techniques are built on model classes for functions based solely on smoothness. It is known that the approximation of smoothness classes of functions suffers from the so-called \\'curse of dimensionality\\'. Avoiding this curse requires new model classes for real world functions that match applications. This has led to the introduction of notions such as sparsity, variable reduction, and reduced modeling. One theme that is particularly common is to assume a tensor structure for the target function. This paper investigates how well a rank one function f(x 1,...,x d)=f 1(x 1)⋯f d(x d), defined on Ω=[0,1]d can be captured through point queries. It is shown that such a rank one function with component functions f j in W∞ r([0,1]) can be captured (in L ∞) to accuracy O(C(d,r)N -r) from N well-chosen point evaluations. The constant C(d,r) scales like d dr. The queries in our algorithms have two ingredients, a set of points built on the results from discrepancy theory and a second adaptive set of queries dependent on the information drawn from the first set. Under the assumption that a point z∈Ω with nonvanishing f(z) is known, the accuracy improves to O(dN -r). © 2013 Springer Science+Business Media New York.

Approximation of High-Dimensional Rank One Tensors

KAUST Repository

Bachmayr, Markus; Dahmen, Wolfgang; DeVore, Ronald; Grasedyck, Lars

2013-01-01

Many real world problems are high-dimensional in that their solution is a function which depends on many variables or parameters. This presents a computational challenge since traditional numerical techniques are built on model classes for functions based solely on smoothness. It is known that the approximation of smoothness classes of functions suffers from the so-called 'curse of dimensionality'. Avoiding this curse requires new model classes for real world functions that match applications. This has led to the introduction of notions such as sparsity, variable reduction, and reduced modeling. One theme that is particularly common is to assume a tensor structure for the target function. This paper investigates how well a rank one function f(x 1,...,x d)=f 1(x 1)⋯f d(x d), defined on Ω=[0,1]d can be captured through point queries. It is shown that such a rank one function with component functions f j in W∞ r([0,1]) can be captured (in L ∞) to accuracy O(C(d,r)N -r) from N well-chosen point evaluations. The constant C(d,r) scales like d dr. The queries in our algorithms have two ingredients, a set of points built on the results from discrepancy theory and a second adaptive set of queries dependent on the information drawn from the first set. Under the assumption that a point z∈Ω with nonvanishing f(z) is known, the accuracy improves to O(dN -r). © 2013 Springer Science+Business Media New York.

Zero- and two-dimensional hybrid carbon phosphors for high colorimetric purity white light-emission.

Science.gov (United States)

Ding, Yamei; Chang, Qing; Xiu, Fei; Chen, Yingying; Liu, Zhengdong; Ban, Chaoyi; Cheng, Shuai; Liu, Juqing; Huang, Wei

2018-03-01

Carbon nanomaterials are promising phosphors for white light emission. A facile single-step synthesis method has been developed to prepare zero- and two-dimensional hybrid carbon phosphors for the first time. Zero-dimensional carbon dots (C-dots) emit bright blue luminescence under 365 nm UV light and two-dimensional nanoplates improve the dispersity and film forming ability of C-dots. As a proof-of-concept application, the as-prepared hybrid carbon phosphors emit bright white luminescence in the solid state, and the phosphor-coated blue LEDs exhibit high colorimetric purity white light-emission with a color coordinate of (0.3308, 0.3312), potentially enabling the successful application of white emitting phosphors in the LED field.

Stable high efficiency two-dimensional perovskite solar cells via cesium doping

KAUST Repository

Zhang, Xu

2017-08-15

Two-dimensional (2D) organic-inorganic perovskites have recently emerged as one of the most important thin-film solar cell materials owing to their excellent environmental stability. The remaining major pitfall is their relatively poor photovoltaic performance in contrast to 3D perovskites. In this work we demonstrate cesium cation (Cs) doped 2D (BA)(MA)PbI perovskite solar cells giving a power conversion efficiency (PCE) as high as 13.7%, the highest among the reported 2D devices, with excellent humidity resistance. The enhanced efficiency from 12.3% (without Cs) to 13.7% (with 5% Cs) is attributed to perfectly controlled crystal orientation, an increased grain size of the 2D planes, superior surface quality, reduced trap-state density, enhanced charge-carrier mobility and charge-transfer kinetics. Surprisingly, it is found that the Cs doping yields superior stability for the 2D perovskite solar cells when subjected to a high humidity environment without encapsulation. The device doped using 5% Cs degrades only ca. 10% after 1400 hours of exposure in 30% relative humidity (RH), and exhibits significantly improved stability under heating and high moisture environments. Our results provide an important step toward air-stable and fully printable low dimensional perovskites as a next-generation renewable energy source.

Estimating the effect of a variable in a high-dimensional regression model

DEFF Research Database (Denmark)

Jensen, Peter Sandholt; Wurtz, Allan

assume that the effect is identified in a high-dimensional linear model specified by unconditional moment restrictions. We consider  properties of the following methods, which rely on lowdimensional models to infer the effect: Extreme bounds analysis, the minimum t-statistic over models, Sala...

High-power Yb-fiber comb based on pre-chirped-management self-similar amplification

Science.gov (United States)

Luo, Daping; Liu, Yang; Gu, Chenglin; Wang, Chao; Zhu, Zhiwei; Zhang, Wenchao; Deng, Zejiang; Zhou, Lian; Li, Wenxue; Zeng, Heping

2018-02-01

We report a fiber self-similar-amplification (SSA) comb system that delivers a 250-MHz, 109-W, 42-fs pulse train with a 10-dB spectral width of 85 nm at 1056 nm. A pair of grisms is employed to compensate the group velocity dispersion and third-order dispersion of pre-amplified pulses for facilitating a self-similar evolution and a self-phase modulation (SPM). Moreover, we analyze the stabilities and noise characteristics of both the locked carrier envelope phase and the repetition rate, verifying the stability of the generated high-power comb. The demonstration of the SSA comb at such high power proves the feasibility of the SPM-based low-noise ultrashort comb.

High-dimensional chaos from self-sustained collisions of solitons

Energy Technology Data Exchange (ETDEWEB)

Yildirim, O. Ozgur, E-mail: donhee@seas.harvard.edu, E-mail: oozgury@gmail.com [Cavium, Inc., 600 Nickerson Rd., Marlborough, Massachusetts 01752 (United States); Ham, Donhee, E-mail: donhee@seas.harvard.edu, E-mail: oozgury@gmail.com [Harvard University, 33 Oxford St., Cambridge, Massachusetts 02138 (United States)

2014-06-16

We experimentally demonstrate chaos generation based on collisions of electrical solitons on a nonlinear transmission line. The nonlinear line creates solitons, and an amplifier connected to it provides gain to these solitons for their self-excitation and self-sustenance. Critically, the amplifier also provides a mechanism to enable and intensify collisions among solitons. These collisional interactions are of intrinsically nonlinear nature, modulating the phase and amplitude of solitons, thus causing chaos. This chaos generated by the exploitation of the nonlinear wave phenomena is inherently high-dimensional, which we also demonstrate.

Preparation of three-dimensional graphene foam for high performance supercapacitors

Directory of Open Access Journals (Sweden)

Yunjie Ping

2017-04-01

Full Text Available Supercapacitor is a new type of energy-storage device, and has been attracted widely attentions. As a two dimensional (2D nanomaterials, graphene is considered to be a promising material of supercapacitor because of its excellent properties involving high electrical conductivity and large surface area. In this paper, the large-scale graphene is successfully fabricated via environmental-friendly electrochemical exfoliation of graphite, and then, the three dimensional (3D graphene foam is prepared by using nickel foam as template and FeCl3/HCl solution as etchant. Compared with the regular 2D graphene paper, the 3D graphene foam electrode shows better electrochemical performance, and exhibits the largest specific capacitance of approximately 128 F/g at the current density of 1 A/g in 6 M KOH electrolyte. It is expected that the 3D graphene foam will have a potential application in the supercapacitors.

High-dimensional free-space optical communications based on orbital angular momentum coding

Science.gov (United States)

Zou, Li; Gu, Xiaofan; Wang, Le

2018-03-01

In this paper, we propose a high-dimensional free-space optical communication scheme using orbital angular momentum (OAM) coding. In the scheme, the transmitter encodes N-bits information by using a spatial light modulator to convert a Gaussian beam to a superposition mode of N OAM modes and a Gaussian mode; The receiver decodes the information through an OAM mode analyser which consists of a MZ interferometer with a rotating Dove prism, a photoelectric detector and a computer carrying out the fast Fourier transform. The scheme could realize a high-dimensional free-space optical communication, and decodes the information much fast and accurately. We have verified the feasibility of the scheme by exploiting 8 (4) OAM modes and a Gaussian mode to implement a 256-ary (16-ary) coding free-space optical communication to transmit a 256-gray-scale (16-gray-scale) picture. The results show that a zero bit error rate performance has been achieved.

Single cell proteomics in biomedicine: High-dimensional data acquisition, visualization, and analysis.

Science.gov (United States)

Su, Yapeng; Shi, Qihui; Wei, Wei

2017-02-01

New insights on cellular heterogeneity in the last decade provoke the development of a variety of single cell omics tools at a lightning pace. The resultant high-dimensional single cell data generated by these tools require new theoretical approaches and analytical algorithms for effective visualization and interpretation. In this review, we briefly survey the state-of-the-art single cell proteomic tools with a particular focus on data acquisition and quantification, followed by an elaboration of a number of statistical and computational approaches developed to date for dissecting the high-dimensional single cell data. The underlying assumptions, unique features, and limitations of the analytical methods with the designated biological questions they seek to answer will be discussed. Particular attention will be given to those information theoretical approaches that are anchored in a set of first principles of physics and can yield detailed (and often surprising) predictions. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Three-Dimensional Porous Nitrogen-Doped NiO Nanostructures as Highly Sensitive NO2 Sensors

Directory of Open Access Journals (Sweden)

Van Hoang Luan

2017-10-01

Full Text Available Nickel oxide has been widely used in chemical sensing applications, because it has an excellent p-type semiconducting property with high chemical stability. Here, we present a novel technique of fabricating three-dimensional porous nitrogen-doped nickel oxide nanosheets as a highly sensitive NO2 sensor. The elaborate nanostructure was prepared by a simple and effective hydrothermal synthesis method. Subsequently, nitrogen doping was achieved by thermal treatment with ammonia gas. When the p-type dopant, i.e., nitrogen atoms, was introduced in the three-dimensional nanostructures, the nickel-oxide-nanosheet-based sensor showed considerable NO2 sensing ability with two-fold higher responsivity and sensitivity compared to non-doped nickel-oxide-based sensors.

Equilibrium: two-dimensional configurations

International Nuclear Information System (INIS)

Anon.

1987-01-01

In Chapter 6, the problem of toroidal force balance is addressed in the simplest, nontrivial two-dimensional geometry, that of an axisymmetric torus. A derivation is presented of the Grad-Shafranov equation, the basic equation describing axisymmetric toroidal equilibrium. The solutions to equations provide a complete description of ideal MHD equilibria: radial pressure balance, toroidal force balance, equilibrium Beta limits, rotational transform, shear, magnetic wall, etc. A wide number of configurations are accurately modeled by the Grad-Shafranov equation. Among them are all types of tokamaks, the spheromak, the reversed field pinch, and toroidal multipoles. An important aspect of the analysis is the use of asymptotic expansions, with an inverse aspect ratio serving as the expansion parameter. In addition, an equation similar to the Grad-Shafranov equation, but for helically symmetric equilibria, is presented. This equation represents the leading-order description low-Beta and high-Beta stellarators, heliacs, and the Elmo bumpy torus. The solutions all correspond to infinitely long straight helices. Bending such a configuration into a torus requires a full three-dimensional calculation and is discussed in Chapter 7

Differences and similarities in double special educational needs: high abilities/giftedness x Asperger’s Syndrome

Directory of Open Access Journals (Sweden)

Nara Joyce Wellausen Vieira

2012-08-01

Full Text Available The study was developed from a literature search in books, articles and theses that have been published since the year 2000 on the theme High Abilities / Giftedness and Asperger’s Syndrome. The objectives of this research were to conduct a search on publications from 2000 to 2011, about the common and different features to the person with Asperger syndrome and high ability gifted, and also relate the number of publications found in Education and Special Education. At theoretical we present the conception of High Abilities / Giftedness of Renzulli (2004 and Gardner (2000 and in the conception of Asperger Syndrome, Mello (2007 and Klin (2006. When analyzing the data, were perceived similarities and differences between the behavioral characteristics of individuals with High Abilities / Giftedness and those with Asperger’s Syndrome. It’s possible point out that there is much evidence that separate these two special educational needs and few similarities between them. But do not neglect that there may be a dual disability between these two particular special educational needs, because there are still few studies that verify theoretically the differences and similarities of these subjects, much less those that investigate these similarities and distinctions in the subjects themselves.

Progress in high-dimensional percolation and random graphs

CERN Document Server

Heydenreich, Markus

2017-01-01

This text presents an engaging exposition of the active field of high-dimensional percolation that will likely provide an impetus for future work. With over 90 exercises designed to enhance the reader’s understanding of the material, as well as many open problems, the book is aimed at graduate students and researchers who wish to enter the world of this rich topic.  The text may also be useful in advanced courses and seminars, as well as for reference and individual study. Part I, consisting of 3 chapters, presents a general introduction to percolation, stating the main results, defining the central objects, and proving its main properties. No prior knowledge of percolation is assumed. Part II, consisting of Chapters 4–9, discusses mean-field critical behavior by describing the two main techniques used, namely, differential inequalities and the lace expansion. In Parts I and II, all results are proved, making this the first self-contained text discussing high-dimensiona l percolation.  Part III, consist...

Synthesis of borophenes: Anisotropic, two-dimensional boron polymorphs

Energy Technology Data Exchange (ETDEWEB)

Mannix, A. J.; Zhou, X. -F.; Kiraly, B.; Wood, J. D.; Alducin, D.; Myers, B. D.; Liu, X.; Fisher, B. L.; Santiago, U.; Guest, J. R.; Yacaman, M. J.; Ponce, A.; Oganov, A. R.; Hersam, M. C.; Guisinger, N. P.

2015-12-17

At the atomic-cluster scale, pure boron is markedly similar to carbon, forming simple planar molecules and cage-like fullerenes. Theoretical studies predict that two-dimensional (2D) boron sheets will adopt an atomic configuration similar to that of boron atomic clusters. We synthesized atomically thin, crystalline 2D boron sheets (i.e., borophene) on silver surfaces under ultrahigh-vacuum conditions. Atomic-scale characterization, supported by theoretical calculations, revealed structures reminiscent of fused boron clusters with multiple scales of anisotropic, out-of-plane buckling. Unlike bulk boron allotropes, borophene shows metallic characteristics that are consistent with predictions of a highly anisotropic, 2D metal.

Three Dimensional Dirac Semimetals

Science.gov (United States)

Zaheer, Saad

2014-03-01

Dirac points on the Fermi surface of two dimensional graphene are responsible for its unique electronic behavior. One can ask whether any three dimensional materials support similar pseudorelativistic physics in their bulk electronic spectra. This possibility has been investigated theoretically and is now supported by two successful experimental demonstrations reported during the last year. In this talk, I will summarize the various ways in which Dirac semimetals can be realized in three dimensions with primary focus on a specific theory developed on the basis of representations of crystal spacegroups. A three dimensional Dirac (Weyl) semimetal can appear in the presence (absence) of inversion symmetry by tuning parameters to the phase boundary separating a bulk insulating and a topological insulating phase. More generally, we find that specific rules governing crystal symmetry representations of electrons with spin lead to robust Dirac points at high symmetry points in the Brillouin zone. Combining these rules with microscopic considerations identifies six candidate Dirac semimetals. Another method towards engineering Dirac semimetals involves combining crystal symmetry and band inversion. Several candidate materials have been proposed utilizing this mechanism and one of the candidates has been successfully demonstrated as a Dirac semimetal in two independent experiments. Work carried out in collaboration with: Julia A. Steinberg, Steve M. Young, J.C.Y. Teo, C.L. Kane, E.J. Mele and Andrew M. Rappe.

Two-dimensional and three-dimensional models used for teaching Human Evolution in Secondary Schools. Learning proficiency assessment. A Case Study

Directory of Open Access Journals (Sweden)

Ulisses Dardon

2016-06-01

Full Text Available The evolution of the human species is a topic of extreme importance reported in the “Parâmetros Curriculares Nacionais do Ensino Médio – PCNEM” (National Curriculum Standards of Secondary Education, although it is not often taught as part of basic education. This work presents the results of an experimental work performed with 31 students of a religious high school of State of Rio de Janeiro. Learning proficiency was assessed by using two-dimensional (2D and three-dimensional (3D illustration techniques of hominids skulls and a Pongidae for teaching Human Evolution. The teaching-learning process using these methodologies was more effective with the application of three-dimensional (3D illustration techniques. The group of students that used 3D illustrations were able to observe similarities and differences between the presented taxonomic models, and formulate hypotheses about their palaeobiology more consistently than the students that used 2D models. Results of this work indicate that the use of three-dimensional techniques (3D provides an excellent support to teaching-learning process in basic education, captivating and stimulating new interests of students during the educational process.

Three-dimensional porous graphene-Co{sub 3}O{sub 4} nanocomposites for high performance photocatalysts

Energy Technology Data Exchange (ETDEWEB)

Bin, Zeng, E-mail: 21467855@qq.com [College of Mechanical Engineering, Hunan University of Arts and Science, Changde 415000 (China); Hui, Long [Department of Applied Physics and Materials Research Center, The Hong Kong Polytechnic University, Hung Hom, Kowloon (Hong Kong)

2015-12-01

Highlights: • The three-dimensional porous graphene-Co{sub 3}O{sub 4} nanocomposites were synthesized. • Excellent photocatalytic performance. • Separated from the reaction medium by magnetic decantation. - Abstract: Novel three-dimensional porous graphene-Co{sub 3}O{sub 4} nanocomposites were synthesized by freeze-drying methods. Scanning and transmission electron microscopy revealed that the graphene formed a three-dimensional porous structure with Co{sub 3}O{sub 4} nanoparticles decorated surfaces. The as-obtained product showed high photocatalytic efficiency and could be easily separated from the reaction medium by magnetic decantation. This nanocomposite may be expected to have potential in water purification applications.

Efficient and accurate nearest neighbor and closest pair search in high-dimensional space

KAUST Repository

Tao, Yufei; Yi, Ke; Sheng, Cheng; Kalnis, Panos

2010-01-01

Nearest Neighbor (NN) search in high-dimensional space is an important problem in many applications. From the database perspective, a good solution needs to have two properties: (i) it can be easily incorporated in a relational database, and (ii

High-speed fan-beam reconstruction using direct two-dimensional Fourier transform method

International Nuclear Information System (INIS)

Niki, Noboru; Mizutani, Toshio; Takahashi, Yoshizo; Inouye, Tamon.

1984-01-01

Since the first development of X-ray computer tomography (CT), various efforts have been made to obtain high quality of high-speed image. However, the development of high resolution CT and the ultra-high speed CT to be applied to hearts is still desired. The X-ray beam scanning method was already changed from the parallel beam system to the fan-beam system in order to greatly shorten the scanning time. Also, the filtered back projection (DFBP) method has been employed to directly processing fan-beam projection data as reconstruction method. Although the two-dimensional Fourier transform (TFT) method significantly faster than FBP method was proposed, it has not been sufficiently examined for fan-beam projection data. Thus, the ITFT method was investigated, which first executes rebinning algorithm to convert the fan-beam projection data to the parallel beam projection data, thereafter, uses two-dimensional Fourier transform. By this method, although high speed is expected, the reconstructed images might be degraded due to the adoption of rebinning algorithm. Therefore, the effect of the interpolation error of rebinning algorithm on the reconstructed images has been analyzed theoretically, and finally, the result of the employment of spline interpolation which allows the acquisition of high quality images with less errors has been shown by the numerical and visual evaluation based on simulation and actual data. Computation time was reduced to 1/15 for the image matrix of 512 and to 1/30 for doubled matrix. (Wakatsuki, Y.)

Compound Structure-Independent Activity Prediction in High-Dimensional Target Space.

Science.gov (United States)

Balfer, Jenny; Hu, Ye; Bajorath, Jürgen

2014-08-01

Profiling of compound libraries against arrays of targets has become an important approach in pharmaceutical research. The prediction of multi-target compound activities also represents an attractive task for machine learning with potential for drug discovery applications. Herein, we have explored activity prediction in high-dimensional target space. Different types of models were derived to predict multi-target activities. The models included naïve Bayesian (NB) and support vector machine (SVM) classifiers based upon compound structure information and NB models derived on the basis of activity profiles, without considering compound structure. Because the latter approach can be applied to incomplete training data and principally depends on the feature independence assumption, SVM modeling was not applicable in this case. Furthermore, iterative hybrid NB models making use of both activity profiles and compound structure information were built. In high-dimensional target space, NB models utilizing activity profile data were found to yield more accurate activity predictions than structure-based NB and SVM models or hybrid models. An in-depth analysis of activity profile-based models revealed the presence of correlation effects across different targets and rationalized prediction accuracy. Taken together, the results indicate that activity profile information can be effectively used to predict the activity of test compounds against novel targets. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Dimensional measurement of micro parts with high aspect ratio in HIT-UOI

Science.gov (United States)

Dang, Hong; Cui, Jiwen; Feng, Kunpeng; Li, Junying; Zhao, Shiyuan; Zhang, Haoran; Tan, Jiubin

2016-11-01

Micro parts with high aspect ratios have been widely used in different fields including aerospace and defense industries, while the dimensional measurement of these micro parts becomes a challenge in the field of precision measurement and instrument. To deal with this contradiction, several probes for the micro parts precision measurement have been proposed by researchers in Center of Ultra-precision Optoelectronic Instrument (UOI), Harbin Institute of Technology (HIT). In this paper, optical fiber probes with structures of spherical coupling(SC) with double optical fibers, micro focal-length collimation (MFL-collimation) and fiber Bragg grating (FBG) are described in detail. After introducing the sensing principles, both advantages and disadvantages of these probes are analyzed respectively. In order to improve the performances of these probes, several approaches are proposed. A two-dimensional orthogonal path arrangement is propounded to enhance the dimensional measurement ability of MFL-collimation probes, while a high resolution and response speed interrogation method based on differential method is used to improve the accuracy and dynamic characteristics of the FBG probes. The experiments for these special structural fiber probes are given with a focus on the characteristics of these probes, and engineering applications will also be presented to prove the availability of them. In order to improve the accuracy and the instantaneity of the engineering applications, several techniques are used in probe integration. The effectiveness of these fiber probes were therefore verified through both the analysis and experiments.

Kernel based methods for accelerated failure time model with ultra-high dimensional data

Directory of Open Access Journals (Sweden)

Jiang Feng

2010-12-01

Full Text Available Abstract Background Most genomic data have ultra-high dimensions with more than 10,000 genes (probes. Regularization methods with L1 and Lp penalty have been extensively studied in survival analysis with high-dimensional genomic data. However, when the sample size n ≪ m (the number of genes, directly identifying a small subset of genes from ultra-high (m > 10, 000 dimensional data is time-consuming and not computationally efficient. In current microarray analysis, what people really do is select a couple of thousands (or hundreds of genes using univariate analysis or statistical tests, and then apply the LASSO-type penalty to further reduce the number of disease associated genes. This two-step procedure may introduce bias and inaccuracy and lead us to miss biologically important genes. Results The accelerated failure time (AFT model is a linear regression model and a useful alternative to the Cox model for survival analysis. In this paper, we propose a nonlinear kernel based AFT model and an efficient variable selection method with adaptive kernel ridge regression. Our proposed variable selection method is based on the kernel matrix and dual problem with a much smaller n × n matrix. It is very efficient when the number of unknown variables (genes is much larger than the number of samples. Moreover, the primal variables are explicitly updated and the sparsity in the solution is exploited. Conclusions Our proposed methods can simultaneously identify survival associated prognostic factors and predict survival outcomes with ultra-high dimensional genomic data. We have demonstrated the performance of our methods with both simulation and real data. The proposed method performs superbly with limited computational studies.

Hawking radiation of a high-dimensional rotating black hole

Energy Technology Data Exchange (ETDEWEB)

Zhao, Ren; Zhang, Lichun; Li, Huaifan; Wu, Yueqin [Shanxi Datong University, Institute of Theoretical Physics, Department of Physics, Datong (China)

2010-01-15

We extend the classical Damour-Ruffini method and discuss Hawking radiation spectrum of high-dimensional rotating black hole using Tortoise coordinate transformation defined by taking the reaction of the radiation to the spacetime into consideration. Under the condition that the energy and angular momentum are conservative, taking self-gravitation action into account, we derive Hawking radiation spectrums which satisfy unitary principle in quantum mechanics. It is shown that the process that the black hole radiates particles with energy {omega} is a continuous tunneling process. We provide a theoretical basis for further studying the physical mechanism of black-hole radiation. (orig.)

The Figured Worlds of High School Science Teachers: Uncovering Three-Dimensional Assessment Decisions

Science.gov (United States)

Ewald, Megan

As a result of recent mandates of the Next Generation Science Standards, assessments are a "system of meaning" amidst a paradigm shift toward three-dimensional assessments. This study is motivated by two research questions: 1) how do high school science teachers describe their processes of decision-making in the development and use of three-dimensional assessments and 2) how do high school science teachers negotiate their identities as assessors in designing three-dimensional assessments. An important factor in teachers' assessment decision making is how they identify themselves as assessors. Therefore, this study investigated the teachers' roles as assessors through the Sociocultural Identity Theory. The most important contribution from this study is the emergent teacher assessment sub-identities: the modifier-recycler , the feeler-finder, and the creator. Using a qualitative phenomenological research design, focus groups, three-series interviews, think-alouds, and document analysis were utilized in this study. These qualitative methods were chosen to elicit rich conversations among teachers, make meaning of the teachers' experiences through in-depth interviews, amplify the thought processes of individual teachers while making assessment decisions, and analyze assessment documents in relation to teachers' perspectives. The findings from this study suggest that--of the 19 participants--only two teachers could consistently be identified as creators and aligned their assessment practices with NGSS. However, assessment sub-identities are not static and teachers may negotiate their identities from one moment to the next within socially constructed realms of interpretation known as figured worlds. Because teachers are positioned in less powerful figured worlds within the dominant discourse of standardization, this study raises awareness as to how the external pressures from more powerful figured worlds socially construct teachers' identities as assessors. For teachers

A self-similar magnetohydrodynamic model for ball lightnings

International Nuclear Information System (INIS)

Tsui, K. H.

2006-01-01

Ball lightning is modeled by magnetohydrodynamic (MHD) equations in two-dimensional spherical geometry with azimuthal symmetry. Dynamic evolutions in the radial direction are described by the self-similar evolution function y(t). The plasma pressure, mass density, and magnetic fields are solved in terms of the radial label η. This model gives spherical MHD plasmoids with axisymmetric force-free magnetic field, and spherically symmetric plasma pressure and mass density, which self-consistently determine the polytropic index γ. The spatially oscillating nature of the radial and meridional field structures indicate embedded regions of closed field lines. These regions are named secondary plasmoids, whereas the overall self-similar spherical structure is named the primary plasmoid. According to this model, the time evolution function allows the primary plasmoid expand outward in two modes. The corresponding ejection of the embedded secondary plasmoids results in ball lightning offering an answer as how they come into being. The first is an accelerated expanding mode. This mode appears to fit plasmoids ejected from thundercloud tops with acceleration to ionosphere seen in high altitude atmospheric observations of sprites and blue jets. It also appears to account for midair high-speed ball lightning overtaking airplanes, and ground level high-speed energetic ball lightning. The second is a decelerated expanding mode, and it appears to be compatible to slowly moving ball lightning seen near ground level. The inverse of this second mode corresponds to an accelerated inward collapse, which could bring ball lightning to an end sometimes with a cracking sound

Similarity of the leading contributions to the self-energy and the thermodynamics in two- and three-dimensional Fermi Liquids

International Nuclear Information System (INIS)

Coffey, D.; Bedell, K.S.

1993-01-01

We compare the self-energy and entropy of a two- and three-dimensional Fermi Liquids (FLs) using a model with a contact interaction between fermions. For a two-dimensional (2D) FL we find that there are T 2 contributions to the entropy from interactions separate from those due to the collective modes. These T 2 contributions arise from nonanalytic corrections to the real part of the self-energy and areanalogous to T 3 lnT contributions present in the entropy of a three-dimensional (3D) FL. The difference between the 2D and 3D results arises solely from the different phase space factors

AucPR: An AUC-based approach using penalized regression for disease prediction with high-dimensional omics data

OpenAIRE

Yu, Wenbao; Park, Taesung

2014-01-01

Motivation It is common to get an optimal combination of markers for disease classification and prediction when multiple markers are available. Many approaches based on the area under the receiver operating characteristic curve (AUC) have been proposed. Existing works based on AUC in a high-dimensional context depend mainly on a non-parametric, smooth approximation of AUC, with no work using a parametric AUC-based approach, for high-dimensional data. Results We propose an AUC-based approach u...

Modeling High-Dimensional Multichannel Brain Signals

KAUST Repository

Hu, Lechuan

2017-12-12

Our goal is to model and measure functional and effective (directional) connectivity in multichannel brain physiological signals (e.g., electroencephalograms, local field potentials). The difficulties from analyzing these data mainly come from two aspects: first, there are major statistical and computational challenges for modeling and analyzing high-dimensional multichannel brain signals; second, there is no set of universally agreed measures for characterizing connectivity. To model multichannel brain signals, our approach is to fit a vector autoregressive (VAR) model with potentially high lag order so that complex lead-lag temporal dynamics between the channels can be captured. Estimates of the VAR model will be obtained by our proposed hybrid LASSLE (LASSO + LSE) method which combines regularization (to control for sparsity) and least squares estimation (to improve bias and mean-squared error). Then we employ some measures of connectivity but put an emphasis on partial directed coherence (PDC) which can capture the directional connectivity between channels. PDC is a frequency-specific measure that explains the extent to which the present oscillatory activity in a sender channel influences the future oscillatory activity in a specific receiver channel relative to all possible receivers in the network. The proposed modeling approach provided key insights into potential functional relationships among simultaneously recorded sites during performance of a complex memory task. Specifically, this novel method was successful in quantifying patterns of effective connectivity across electrode locations, and in capturing how these patterns varied across trial epochs and trial types.

Variance inflation in high dimensional Support Vector Machines

DEFF Research Database (Denmark)

Abrahamsen, Trine Julie; Hansen, Lars Kai

2013-01-01

Many important machine learning models, supervised and unsupervised, are based on simple Euclidean distance or orthogonal projection in a high dimensional feature space. When estimating such models from small training sets we face the problem that the span of the training data set input vectors...... the case of Support Vector Machines (SVMS) and we propose a non-parametric scheme to restore proper generalizability. We illustrate the algorithm and its ability to restore performance on a wide range of benchmark data sets....... follow a different probability law with less variance. While the problem and basic means to reconstruct and deflate are well understood in unsupervised learning, the case of supervised learning is less well understood. We here investigate the effect of variance inflation in supervised learning including...

Technical Report: Toward a Scalable Algorithm to Compute High-Dimensional Integrals of Arbitrary Functions

International Nuclear Information System (INIS)

Snyder, Abigail C.; Jiao, Yu

2010-01-01

Neutron experiments at the Spallation Neutron Source (SNS) at Oak Ridge National Laboratory (ORNL) frequently generate large amounts of data (on the order of 106-1012 data points). Hence, traditional data analysis tools run on a single CPU take too long to be practical and scientists are unable to efficiently analyze all data generated by experiments. Our goal is to develop a scalable algorithm to efficiently compute high-dimensional integrals of arbitrary functions. This algorithm can then be used to integrate the four-dimensional integrals that arise as part of modeling intensity from the experiments at the SNS. Here, three different one-dimensional numerical integration solvers from the GNU Scientific Library were modified and implemented to solve four-dimensional integrals. The results of these solvers on a final integrand provided by scientists at the SNS can be compared to the results of other methods, such as quasi-Monte Carlo methods, computing the same integral. A parallelized version of the most efficient method can allow scientists the opportunity to more effectively analyze all experimental data.

Higher dimensional loop quantum cosmology

International Nuclear Information System (INIS)

Zhang, Xiangdong

2016-01-01

Loop quantum cosmology (LQC) is the symmetric sector of loop quantum gravity. In this paper, we generalize the structure of loop quantum cosmology to the theories with arbitrary spacetime dimensions. The isotropic and homogeneous cosmological model in n + 1 dimensions is quantized by the loop quantization method. Interestingly, we find that the underlying quantum theories are divided into two qualitatively different sectors according to spacetime dimensions. The effective Hamiltonian and modified dynamical equations of n + 1 dimensional LQC are obtained. Moreover, our results indicate that the classical big bang singularity is resolved in arbitrary spacetime dimensions by a quantum bounce. We also briefly discuss the similarities and differences between the n + 1 dimensional model and the 3 + 1 dimensional one. Our model serves as a first example of higher dimensional loop quantum cosmology and offers the possibility to investigate quantum gravity effects in higher dimensional cosmology. (orig.)

Challenges and Approaches to Statistical Design and Inference in High Dimensional Investigations

Science.gov (United States)

Garrett, Karen A.; Allison, David B.

2015-01-01

Summary Advances in modern technologies have facilitated high-dimensional experiments (HDEs) that generate tremendous amounts of genomic, proteomic, and other “omic” data. HDEs involving whole-genome sequences and polymorphisms, expression levels of genes, protein abundance measurements, and combinations thereof have become a vanguard for new analytic approaches to the analysis of HDE data. Such situations demand creative approaches to the processes of statistical inference, estimation, prediction, classification, and study design. The novel and challenging biological questions asked from HDE data have resulted in many specialized analytic techniques being developed. This chapter discusses some of the unique statistical challenges facing investigators studying high-dimensional biology, and describes some approaches being developed by statistical scientists. We have included some focus on the increasing interest in questions involving testing multiple propositions simultaneously, appropriate inferential indicators for the types of questions biologists are interested in, and the need for replication of results across independent studies, investigators, and settings. A key consideration inherent throughout is the challenge in providing methods that a statistician judges to be sound and a biologist finds informative. PMID:19588106

Challenges and approaches to statistical design and inference in high-dimensional investigations.

Science.gov (United States)

Gadbury, Gary L; Garrett, Karen A; Allison, David B

2009-01-01

Advances in modern technologies have facilitated high-dimensional experiments (HDEs) that generate tremendous amounts of genomic, proteomic, and other "omic" data. HDEs involving whole-genome sequences and polymorphisms, expression levels of genes, protein abundance measurements, and combinations thereof have become a vanguard for new analytic approaches to the analysis of HDE data. Such situations demand creative approaches to the processes of statistical inference, estimation, prediction, classification, and study design. The novel and challenging biological questions asked from HDE data have resulted in many specialized analytic techniques being developed. This chapter discusses some of the unique statistical challenges facing investigators studying high-dimensional biology and describes some approaches being developed by statistical scientists. We have included some focus on the increasing interest in questions involving testing multiple propositions simultaneously, appropriate inferential indicators for the types of questions biologists are interested in, and the need for replication of results across independent studies, investigators, and settings. A key consideration inherent throughout is the challenge in providing methods that a statistician judges to be sound and a biologist finds informative.

Multi-Scale Factor Analysis of High-Dimensional Brain Signals

KAUST Repository

Ting, Chee-Ming

2017-05-18

In this paper, we develop an approach to modeling high-dimensional networks with a large number of nodes arranged in a hierarchical and modular structure. We propose a novel multi-scale factor analysis (MSFA) model which partitions the massive spatio-temporal data defined over the complex networks into a finite set of regional clusters. To achieve further dimension reduction, we represent the signals in each cluster by a small number of latent factors. The correlation matrix for all nodes in the network are approximated by lower-dimensional sub-structures derived from the cluster-specific factors. To estimate regional connectivity between numerous nodes (within each cluster), we apply principal components analysis (PCA) to produce factors which are derived as the optimal reconstruction of the observed signals under the squared loss. Then, we estimate global connectivity (between clusters or sub-networks) based on the factors across regions using the RV-coefficient as the cross-dependence measure. This gives a reliable and computationally efficient multi-scale analysis of both regional and global dependencies of the large networks. The proposed novel approach is applied to estimate brain connectivity networks using functional magnetic resonance imaging (fMRI) data. Results on resting-state fMRI reveal interesting modular and hierarchical organization of human brain networks during rest.

High-dimensional data: p >> n in mathematical statistics and bio-medical applications

OpenAIRE

Van De Geer, Sara A.; Van Houwelingen, Hans C.

2004-01-01

The workshop 'High-dimensional data: p >> n in mathematical statistics and bio-medical applications' was held at the Lorentz Center in Leiden from 9 to 20 September 2002. This special issue of Bernoulli contains a selection of papers presented at that workshop. ¶ The introduction of high-throughput micro-array technology to measure gene-expression levels and the publication of the pioneering paper by Golub et al. (1999) has brought to life a whole new branch of data analysis under the name of...

TripAdvisor^{N-D}: A Tourism-Inspired High-Dimensional Space Exploration Framework with Overview and Detail.

Science.gov (United States)

Nam, Julia EunJu; Mueller, Klaus

2013-02-01

Gaining a true appreciation of high-dimensional space remains difficult since all of the existing high-dimensional space exploration techniques serialize the space travel in some way. This is not so foreign to us since we, when traveling, also experience the world in a serial fashion. But we typically have access to a map to help with positioning, orientation, navigation, and trip planning. Here, we propose a multivariate data exploration tool that compares high-dimensional space navigation with a sightseeing trip. It decomposes this activity into five major tasks: 1) Identify the sights: use a map to identify the sights of interest and their location; 2) Plan the trip: connect the sights of interest along a specifyable path; 3) Go on the trip: travel along the route; 4) Hop off the bus: experience the location, look around, zoom into detail; and 5) Orient and localize: regain bearings in the map. We describe intuitive and interactive tools for all of these tasks, both global navigation within the map and local exploration of the data distributions. For the latter, we describe a polygonal touchpad interface which enables users to smoothly tilt the projection plane in high-dimensional space to produce multivariate scatterplots that best convey the data relationships under investigation. Motion parallax and illustrative motion trails aid in the perception of these transient patterns. We describe the use of our system within two applications: 1) the exploratory discovery of data configurations that best fit a personal preference in the presence of tradeoffs and 2) interactive cluster analysis via cluster sculpting in N-D.

Bit-Table Based Biclustering and Frequent Closed Itemset Mining in High-Dimensional Binary Data

Directory of Open Access Journals (Sweden)

András Király

2014-01-01

Full Text Available During the last decade various algorithms have been developed and proposed for discovering overlapping clusters in high-dimensional data. The two most prominent application fields in this research, proposed independently, are frequent itemset mining (developed for market basket data and biclustering (applied to gene expression data analysis. The common limitation of both methodologies is the limited applicability for very large binary data sets. In this paper we propose a novel and efficient method to find both frequent closed itemsets and biclusters in high-dimensional binary data. The method is based on simple but very powerful matrix and vector multiplication approaches that ensure that all patterns can be discovered in a fast manner. The proposed algorithm has been implemented in the commonly used MATLAB environment and freely available for researchers.

Analysis of Operating Performance and Three Dimensional Magnetic Field of High Voltage Induction Motors with Stator Chute

Directory of Open Access Journals (Sweden)

WANG Qing-shan

2017-06-01

Full Text Available In view of the difficulties on technology of rotor chute in high voltage induction motor，the desig method adopted stator chute structure is put forward． The mathematical model of three dimensional nonlinear transient field for solving stator chute in high voltage induction motor is set up． Through the three dimensional entity model of motor，three dimensional finite element method based on T，ψ － ψ electromagnetic potential is adopted for the analysis and calculation of stator chute in high voltage induction motor under rated condition． The distributions long axial of fundamental wave magnetic field and tooth harmonic wave magnetic field are analyzed after stator chute，and the weakening effects on main tooth harmonic magnetic field are researched． Further more，the comparison analysis of main performance parameters of chute and straight slot is carried out under rated condition． The results show that the electrical performance of stator chute is better than that of straight slot in high voltage induction motor，and the tooth harmonic has been sharply decreased

Ghosts in high dimensional non-linear dynamical systems: The example of the hypercycle

International Nuclear Information System (INIS)

Sardanyes, Josep

2009-01-01

Ghost-induced delayed transitions are analyzed in high dimensional non-linear dynamical systems by means of the hypercycle model. The hypercycle is a network of catalytically-coupled self-replicating RNA-like macromolecules, and has been suggested to be involved in the transition from non-living to living matter in the context of earlier prebiotic evolution. It is demonstrated that, in the vicinity of the saddle-node bifurcation for symmetric hypercycles, the persistence time before extinction, T ε , tends to infinity as n→∞ (being n the number of units of the hypercycle), thus suggesting that the increase in the number of hypercycle units involves a longer resilient time before extinction because of the ghost. Furthermore, by means of numerical analysis the dynamics of three large hypercycle networks is also studied, focusing in their extinction dynamics associated to the ghosts. Such networks allow to explore the properties of the ghosts living in high dimensional phase space with n = 5, n = 10 and n = 15 dimensions. These hypercyclic networks, in agreement with other works, are shown to exhibit self-maintained oscillations governed by stable limit cycles. The bifurcation scenarios for these hypercycles are analyzed, as well as the effect of the phase space dimensionality in the delayed transition phenomena and in the scaling properties of the ghosts near bifurcation threshold

Pure Cs4PbBr6: Highly Luminescent Zero-Dimensional Perovskite Solids

KAUST Repository

Saidaminov, Makhsud I.

2016-09-26

So-called zero-dimensional perovskites, such as Cs4PbBr6, promise outstanding emissive properties. However, Cs4PbBr6 is mostly prepared by melting of precursors that usually leads to a coformation of undesired phases. Here, we report a simple low-temperature solution-processed synthesis of pure Cs4PbBr6 with remarkable emission properties. We found that pure Cs4PbBr6 in solid form exhibits a 45% photoluminescence quantum yield (PLQY), in contrast to its three-dimensional counterpart, CsPbBr3, which exhibits more than 2 orders of magnitude lower PLQY. Such a PLQY of Cs4PbBr6 is significantly higher than that of other solid forms of lower-dimensional metal halide perovskite derivatives and perovskite nanocrystals. We attribute this dramatic increase in PL to the high exciton binding energy, which we estimate to be ∼353 meV, likely induced by the unique Bergerhoff–Schmitz–Dumont-type crystal structure of Cs4PbBr6, in which metal-halide-comprised octahedra are spatially confined. Our findings bring this class of perovskite derivatives to the forefront of color-converting and light-emitting applications.

A comprehensive analysis of earthquake damage patterns using high dimensional model representation feature selection

Science.gov (United States)

Taşkin Kaya, Gülşen

2013-10-01

Recently, earthquake damage assessment using satellite images has been a very popular ongoing research direction. Especially with the availability of very high resolution (VHR) satellite images, a quite detailed damage map based on building scale has been produced, and various studies have also been conducted in the literature. As the spatial resolution of satellite images increases, distinguishability of damage patterns becomes more cruel especially in case of using only the spectral information during classification. In order to overcome this difficulty, textural information needs to be involved to the classification to improve the visual quality and reliability of damage map. There are many kinds of textural information which can be derived from VHR satellite images depending on the algorithm used. However, extraction of textural information and evaluation of them have been generally a time consuming process especially for the large areas affected from the earthquake due to the size of VHR image. Therefore, in order to provide a quick damage map, the most useful features describing damage patterns needs to be known in advance as well as the redundant features. In this study, a very high resolution satellite image after Iran, Bam earthquake was used to identify the earthquake damage. Not only the spectral information, textural information was also used during the classification. For textural information, second order Haralick features were extracted from the panchromatic image for the area of interest using gray level co-occurrence matrix with different size of windows and directions. In addition to using spatial features in classification, the most useful features representing the damage characteristic were selected with a novel feature selection method based on high dimensional model representation (HDMR) giving sensitivity of each feature during classification. The method called HDMR was recently proposed as an efficient tool to capture the input

Selecting Optimal Feature Set in High-Dimensional Data by Swarm Search

Directory of Open Access Journals (Sweden)

Simon Fong

2013-01-01

Full Text Available Selecting the right set of features from data of high dimensionality for inducing an accurate classification model is a tough computational challenge. It is almost a NP-hard problem as the combinations of features escalate exponentially as the number of features increases. Unfortunately in data mining, as well as other engineering applications and bioinformatics, some data are described by a long array of features. Many feature subset selection algorithms have been proposed in the past, but not all of them are effective. Since it takes seemingly forever to use brute force in exhaustively trying every possible combination of features, stochastic optimization may be a solution. In this paper, we propose a new feature selection scheme called Swarm Search to find an optimal feature set by using metaheuristics. The advantage of Swarm Search is its flexibility in integrating any classifier into its fitness function and plugging in any metaheuristic algorithm to facilitate heuristic search. Simulation experiments are carried out by testing the Swarm Search over some high-dimensional datasets, with different classification algorithms and various metaheuristic algorithms. The comparative experiment results show that Swarm Search is able to attain relatively low error rates in classification without shrinking the size of the feature subset to its minimum.

Stable Graphene-Two-Dimensional Multiphase Perovskite Heterostructure Phototransistors with High Gain.

Science.gov (United States)

Shao, Yuchuan; Liu, Ye; Chen, Xiaolong; Chen, Chen; Sarpkaya, Ibrahim; Chen, Zhaolai; Fang, Yanjun; Kong, Jaemin; Watanabe, Kenji; Taniguchi, Takashi; Taylor, André; Huang, Jinsong; Xia, Fengnian

2017-12-13

Recently, two-dimensional (2D) organic-inorganic perovskites emerged as an alternative material for their three-dimensional (3D) counterparts in photovoltaic applications with improved moisture resistance. Here, we report a stable, high-gain phototransistor consisting of a monolayer graphene on hexagonal boron nitride (hBN) covered by a 2D multiphase perovskite heterostructure, which was realized using a newly developed two-step ligand exchange method. In this phototransistor, the multiple phases with varying bandgap in 2D perovskite thin films are aligned for the efficient electron-hole pair separation, leading to a high responsivity of ∼10 5 A W -1 at 532 nm. Moreover, the designed phase alignment method aggregates more hydrophobic butylammonium cations close to the upper surface of the 2D perovskite thin film, preventing the permeation of moisture and enhancing the device stability dramatically. In addition, faster photoresponse and smaller 1/f noise observed in the 2D perovskite phototransistors indicate a smaller density of deep hole traps in the 2D perovskite thin film compared with their 3D counterparts. These desirable properties not only improve the performance of the phototransistor, but also provide a new direction for the future enhancement of the efficiency of 2D perovskite photovoltaics.

Characterization of differentially expressed genes using high-dimensional co-expression networks

DEFF Research Database (Denmark)

Coelho Goncalves de Abreu, Gabriel; Labouriau, Rodrigo S.

2010-01-01

We present a technique to characterize differentially expressed genes in terms of their position in a high-dimensional co-expression network. The set-up of Gaussian graphical models is used to construct representations of the co-expression network in such a way that redundancy and the propagation...... that allow to make effective inference in problems with high degree of complexity (e.g. several thousands of genes) and small number of observations (e.g. 10-100) as typically occurs in high throughput gene expression studies. Taking advantage of the internal structure of decomposable graphical models, we...... construct a compact representation of the co-expression network that allows to identify the regions with high concentration of differentially expressed genes. It is argued that differentially expressed genes located in highly interconnected regions of the co-expression network are less informative than...

Three-dimensional printing of stem cell-laden hydrogels submerged in a hydrophobic high-density fluid

International Nuclear Information System (INIS)

Duarte Campos, Daniela F; Blaeser, Andreas; Weber, Michael; Fischer, Horst; Jäkel, Jörg; Neuss, Sabine; Jahnen-Dechent, Wilhelm

2013-01-01

Over the last decade, bioprinting technologies have begun providing important tissue engineering strategies for regenerative medicine and organ transplantation. The major drawback of past approaches has been poor or inadequate material-printing device and substrate combinations, as well as the relatively small size of the printed construct. Here, we hypothesise that cell-laden hydrogels can be printed when submerged in perfluorotributylamine (C 12 F 27 N), a hydrophobic high-density fluid, and that these cells placed within three-dimensional constructs remain viable allowing for cell proliferation and production of extracellular matrix. Human mesenchymal stem cells and MG-63 cells were encapsulated into agarose hydrogels, and subsequently printed in high aspect ratio in three dimensional structures that were supported in high density fluorocarbon. Three-dimensional structures with various shapes and sizes were manufactured and remained stable for more than six months. Live/dead and DAPI stainings showed viable cells 24 h after the printing process, as well as after 21 days in culture. Histological and immunohistochemical analyses after 14 and 21 days revealed viable cells with marked matrix production and signs of proliferation. The compressive strength values of the printed gels consequently increased during the two weeks in culture, revealing encouraging results for future applications in regenerative medicine. (paper)

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

Science.gov (United States)

Yu, Hualong; Ni, Jun

2014-01-01

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

A qualitative numerical study of high dimensional dynamical systems

Science.gov (United States)

Albers, David James

Since Poincare, the father of modern mathematical dynamical systems, much effort has been exerted to achieve a qualitative understanding of the physical world via a qualitative understanding of the functions we use to model the physical world. In this thesis, we construct a numerical framework suitable for a qualitative, statistical study of dynamical systems using the space of artificial neural networks. We analyze the dynamics along intervals in parameter space, separating the set of neural networks into roughly four regions: the fixed point to the first bifurcation; the route to chaos; the chaotic region; and a transition region between chaos and finite-state neural networks. The study is primarily with respect to high-dimensional dynamical systems. We make the following general conclusions as the dimension of the dynamical system is increased: the probability of the first bifurcation being of type Neimark-Sacker is greater than ninety-percent; the most probable route to chaos is via a cascade of bifurcations of high-period periodic orbits, quasi-periodic orbits, and 2-tori; there exists an interval of parameter space such that hyperbolicity is violated on a countable, Lebesgue measure 0, "increasingly dense" subset; chaos is much more likely to persist with respect to parameter perturbation in the chaotic region of parameter space as the dimension is increased; moreover, as the number of positive Lyapunov exponents is increased, the likelihood that any significant portion of these positive exponents can be perturbed away decreases with increasing dimension. The maximum Kaplan-Yorke dimension and the maximum number of positive Lyapunov exponents increases linearly with dimension. The probability of a dynamical system being chaotic increases exponentially with dimension. The results with respect to the first bifurcation and the route to chaos comment on previous results of Newhouse, Ruelle, Takens, Broer, Chenciner, and Iooss. Moreover, results regarding the high-dimensional

1D to 3D dimensional crossover in the superconducting transition of the quasi-one-dimensional carbide superconductor Sc3CoC4.

Science.gov (United States)

He, Mingquan; Wong, Chi Ho; Shi, Dian; Tse, Pok Lam; Scheidt, Ernst-Wilhelm; Eickerling, Georg; Scherer, Wolfgang; Sheng, Ping; Lortz, Rolf

2015-02-25

The transition metal carbide superconductor Sc(3)CoC(4) may represent a new benchmark system of quasi-one-dimensional (quasi-1D) superconducting behavior. We investigate the superconducting transition of a high-quality single crystalline sample by electrical transport experiments. Our data show that the superconductor goes through a complex dimensional crossover below the onset T(c) of 4.5 K. First, a quasi-1D fluctuating superconducting state with finite resistance forms in the [CoC(4)](∞) ribbons which are embedded in a Sc matrix in this material. At lower temperature, the transversal Josephson or proximity coupling of neighboring ribbons establishes a 3D bulk superconducting state. This dimensional crossover is very similar to Tl(2)Mo(6)Se(6), which for a long time has been regarded as the most appropriate model system of a quasi-1D superconductor. Sc(3)CoC(4) appears to be even more in the 1D limit than Tl(2)Mo(6)Se(6).

Inferring biological tasks using Pareto analysis of high-dimensional data.

Science.gov (United States)

Hart, Yuval; Sheftel, Hila; Hausser, Jean; Szekely, Pablo; Ben-Moshe, Noa Bossel; Korem, Yael; Tendler, Avichai; Mayo, Avraham E; Alon, Uri

2015-03-01

We present the Pareto task inference method (ParTI; http://www.weizmann.ac.il/mcb/UriAlon/download/ParTI) for inferring biological tasks from high-dimensional biological data. Data are described as a polytope, and features maximally enriched closest to the vertices (or archetypes) allow identification of the tasks the vertices represent. We demonstrate that human breast tumors and mouse tissues are well described by tetrahedrons in gene expression space, with specific tumor types and biological functions enriched at each of the vertices, suggesting four key tasks.

Polyacene and a new class of quasi-one-dimensional conductors

International Nuclear Information System (INIS)

Kivelson, S.; Chapman, O.L.

1983-01-01

Most one-dimensional conductors are quite similar since the Fermi surface is a point and the electron energy dispersion relation near the Fermi surface is linear. It is pointed out that in polyacene the Fermi surface lies at the edge of the Brillouin zone, but that an accidental degeneracy between the valence and conduction bands makes it metallic nonetheless. The dispersion relation is therefore quadratic, and the density of states diverges at the Fermi surface. Thus, polyacene [(C 4 H 2 )/sub n/] and its possible derivatives represent a conceptually new class of quasi-one-dimensional conductors. Moreover, we find that this class of materials has the possibility of possessing interesting condensed phases including high-temperature superconductivity and ferromagnetism

High-Dimensional Exploratory Item Factor Analysis by a Metropolis-Hastings Robbins-Monro Algorithm

Science.gov (United States)

Cai, Li

2010-01-01

A Metropolis-Hastings Robbins-Monro (MH-RM) algorithm for high-dimensional maximum marginal likelihood exploratory item factor analysis is proposed. The sequence of estimates from the MH-RM algorithm converges with probability one to the maximum likelihood solution. Details on the computer implementation of this algorithm are provided. The…

High-resolution and high-throughput multichannel Fourier transform spectrometer with two-dimensional interferogram warping compensation

Science.gov (United States)

Watanabe, A.; Furukawa, H.

2018-04-01

The resolution of multichannel Fourier transform (McFT) spectroscopy is insufficient for many applications despite its extreme advantage of high throughput. We propose an improved configuration to realise both performance using a two-dimensional area sensor. For the spectral resolution, we obtained the interferogram of a larger optical path difference by shifting the area sensor without altering any optical components. The non-linear phase error of the interferometer was successfully corrected using a phase-compensation calculation. Warping compensation was also applied to realise a higher throughput to accumulate the signal between vertical pixels. Our approach significantly improved the resolution and signal-to-noise ratio by factors of 1.7 and 34, respectively. This high-resolution and high-sensitivity McFT spectrometer will be useful for detecting weak light signals such as those in non-invasive diagnosis.

Two-dimensional turbulent flows on a bounded domain

NARCIS (Netherlands)

Kramer, W.

2006-01-01

Large-scale flows in the oceans and the atmosphere reveal strong similarities with purely two-dimensional flows. One of the most typical features is the cascade of energy from smaller flow scales towards larger scales. This is opposed to three-dimensional turbulence where larger flow structures

High-speed three-dimensional plasma temperature determination of axially symmetric free-burning arcs

International Nuclear Information System (INIS)

Bachmann, B; Ekkert, K; Bachmann, J-P; Marques, J-L; Schein, J; Kozakov, R; Gött, G; Schöpp, H; Uhrlandt, D

2013-01-01

In this paper we introduce an experimental technique that allows for high-speed, three-dimensional determination of electron density and temperature in axially symmetric free-burning arcs. Optical filters with narrow spectral bands of 487.5–488.5 nm and 689–699 nm are utilized to gain two-dimensional spectral information of a free-burning argon tungsten inert gas arc. A setup of mirrors allows one to image identical arc sections of the two spectral bands onto a single camera chip. Two-different Abel inversion algorithms have been developed to reconstruct the original radial distribution of emission coefficients detected with each spectral window and to confirm the results. With the assumption of local thermodynamic equilibrium we calculate emission coefficients as a function of temperature by application of the Saha equation, the ideal gas law, the quasineutral gas condition and the NIST compilation of spectral lines. Ratios of calculated emission coefficients are compared with measured ones yielding local plasma temperatures. In the case of axial symmetry the three-dimensional plasma temperature distributions have been determined at dc currents of 100, 125, 150 and 200 A yielding temperatures up to 20000 K in the hot cathode region. These measurements have been validated by four different techniques utilizing a high-resolution spectrometer at different positions in the plasma. Plasma temperatures show good agreement throughout the different methods. Additionally spatially resolved transient plasma temperatures have been measured of a dc pulsed process employing a high-speed frame rate of 33000 frames per second showing the modulation of the arc isothermals with time and providing information about the sensitivity of the experimental approach. (paper)

Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks.

Science.gov (United States)

Vlachas, Pantelis R; Byeon, Wonmin; Wan, Zhong Y; Sapsis, Themistoklis P; Koumoutsakos, Petros

2018-05-01

We introduce a data-driven forecasting method for high-dimensional chaotic systems using long short-term memory (LSTM) recurrent neural networks. The proposed LSTM neural networks perform inference of high-dimensional dynamical systems in their reduced order space and are shown to be an effective set of nonlinear approximators of their attractor. We demonstrate the forecasting performance of the LSTM and compare it with Gaussian processes (GPs) in time series obtained from the Lorenz 96 system, the Kuramoto-Sivashinsky equation and a prototype climate model. The LSTM networks outperform the GPs in short-term forecasting accuracy in all applications considered. A hybrid architecture, extending the LSTM with a mean stochastic model (MSM-LSTM), is proposed to ensure convergence to the invariant measure. This novel hybrid method is fully data-driven and extends the forecasting capabilities of LSTM networks.

High-dimensional quantum key distribution with the entangled single-photon-added coherent state

Energy Technology Data Exchange (ETDEWEB)

Wang, Yang [Zhengzhou Information Science and Technology Institute, Zhengzhou, 450001 (China); Synergetic Innovation Center of Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei, Anhui 230026 (China); Bao, Wan-Su, E-mail: 2010thzz@sina.com [Zhengzhou Information Science and Technology Institute, Zhengzhou, 450001 (China); Synergetic Innovation Center of Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei, Anhui 230026 (China); Bao, Hai-Ze; Zhou, Chun; Jiang, Mu-Sheng; Li, Hong-Wei [Zhengzhou Information Science and Technology Institute, Zhengzhou, 450001 (China); Synergetic Innovation Center of Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei, Anhui 230026 (China)

2017-04-25

High-dimensional quantum key distribution (HD-QKD) can generate more secure bits for one detection event so that it can achieve long distance key distribution with a high secret key capacity. In this Letter, we present a decoy state HD-QKD scheme with the entangled single-photon-added coherent state (ESPACS) source. We present two tight formulas to estimate the single-photon fraction of postselected events and Eve's Holevo information and derive lower bounds on the secret key capacity and the secret key rate of our protocol. We also present finite-key analysis for our protocol by using the Chernoff bound. Our numerical results show that our protocol using one decoy state can perform better than that of previous HD-QKD protocol with the spontaneous parametric down conversion (SPDC) using two decoy states. Moreover, when considering finite resources, the advantage is more obvious. - Highlights: • Implement the single-photon-added coherent state source into the high-dimensional quantum key distribution. • Enhance both the secret key capacity and the secret key rate compared with previous schemes. • Show an excellent performance in view of statistical fluctuations.

High-dimensional quantum key distribution with the entangled single-photon-added coherent state

International Nuclear Information System (INIS)

Wang, Yang; Bao, Wan-Su; Bao, Hai-Ze; Zhou, Chun; Jiang, Mu-Sheng; Li, Hong-Wei

2017-01-01

High-dimensional quantum key distribution (HD-QKD) can generate more secure bits for one detection event so that it can achieve long distance key distribution with a high secret key capacity. In this Letter, we present a decoy state HD-QKD scheme with the entangled single-photon-added coherent state (ESPACS) source. We present two tight formulas to estimate the single-photon fraction of postselected events and Eve's Holevo information and derive lower bounds on the secret key capacity and the secret key rate of our protocol. We also present finite-key analysis for our protocol by using the Chernoff bound. Our numerical results show that our protocol using one decoy state can perform better than that of previous HD-QKD protocol with the spontaneous parametric down conversion (SPDC) using two decoy states. Moreover, when considering finite resources, the advantage is more obvious. - Highlights: • Implement the single-photon-added coherent state source into the high-dimensional quantum key distribution. • Enhance both the secret key capacity and the secret key rate compared with previous schemes. • Show an excellent performance in view of statistical fluctuations.

Metallic few-layered VS2 ultrathin nanosheets: high two-dimensional conductivity for in-plane supercapacitors.

Science.gov (United States)

Feng, Jun; Sun, Xu; Wu, Changzheng; Peng, Lele; Lin, Chenwen; Hu, Shuanglin; Yang, Jinlong; Xie, Yi

2011-11-09

With the rapid development of portable electronics, such as e-paper and other flexible devices, practical power sources with ultrathin geometries become an important prerequisite, in which supercapacitors with in-plane configurations are recently emerging as a favorable and competitive candidate. As is known, electrode materials with two-dimensional (2D) permeable channels, high-conductivity structural scaffolds, and high specific surface areas are the indispensible requirements for the development of in-plane supercapacitors with superior performance, while it is difficult for the presently available inorganic materials to make the best in all aspects. In this sense, vanadium disulfide (VS(2)) presents an ideal material platform due to its synergic properties of metallic nature and exfoliative characteristic brought by the conducting S-V-S layers stacked up by weak van der Waals interlayer interactions, offering great potential as high-performance in-plane supercapacitor electrodes. Herein, we developed a unique ammonia-assisted strategy to exfoliate bulk VS(2) flakes into ultrathin VS(2) nanosheets stacked with less than five S-V-S single layers, representing a brand new two-dimensional material having metallic behavior aside from graphene. Moreover, highly conductive VS(2) thin films were successfully assembled for constructing the electrodes of in-plane supercapacitors. As is expected, a specific capacitance of 4760 μF/cm(2) was realized here in a 150 nm in-plane configuration, of which no obvious degradation was observed even after 1000 charge/discharge cycles, offering as a new in-plane supercapacitor with high performance based on quasi-two-dimensional materials.

Addressing Curse of Dimensionality in Sensitivity Analysis: How Can We Handle High-Dimensional Problems?

Science.gov (United States)

Safaei, S.; Haghnegahdar, A.; Razavi, S.

2016-12-01

Complex environmental models are now the primary tool to inform decision makers for the current or future management of environmental resources under the climate and environmental changes. These complex models often contain a large number of parameters that need to be determined by a computationally intensive calibration procedure. Sensitivity analysis (SA) is a very useful tool that not only allows for understanding the model behavior, but also helps in reducing the number of calibration parameters by identifying unimportant ones. The issue is that most global sensitivity techniques are highly computationally demanding themselves for generating robust and stable sensitivity metrics over the entire model response surface. Recently, a novel global sensitivity analysis method, Variogram Analysis of Response Surfaces (VARS), is introduced that can efficiently provide a comprehensive assessment of global sensitivity using the Variogram concept. In this work, we aim to evaluate the effectiveness of this highly efficient GSA method in saving computational burden, when applied to systems with extra-large number of input factors ( 100). We use a test function and a hydrological modelling case study to demonstrate the capability of VARS method in reducing problem dimensionality by identifying important vs unimportant input factors.

MOSRA-Light; high speed three-dimensional nodal diffusion code for vector computers

Energy Technology Data Exchange (ETDEWEB)

Okumura, Keisuke [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment

1998-10-01

MOSRA-Light is a three-dimensional neutron diffusion calculation code for X-Y-Z geometry. It is based on the 4th order polynomial nodal expansion method (NEM). As the 4th order NEM is not sensitive to mesh sizes, accurate calculation is possible by the use of coarse meshes of about 20 cm. The drastic decrease of number of unknowns in a 3-dimensional problem results in very fast computation. Furthermore, it employs newly developed computation algorithm `boundary separated checkerboard sweep method` appropriate to vector computers. This method is very efficient because the speedup factor by vectorization increases, as a scale of problem becomes larger. Speed-up factor compared to the scalar calculation is from 20 to 40 in the case of PWR core calculation. Considering the both effects by the vectorization and the coarse mesh method, total speedup factor is more than 1000 as compared with conventional scalar code with the finite difference method. MOSRA-Light can be available on most of vector or scalar computers with the UNIX or it`s similar operating systems (e.g. freeware like Linux). Users can easily install it by the help of the conversation style installer. This report contains the general theory of NEM, the fast computation algorithm, benchmark calculation results and detailed information for usage of this code including input data instructions and sample input data. (author)

MOSRA-Light; high speed three-dimensional nodal diffusion code for vector computers

International Nuclear Information System (INIS)

Okumura, Keisuke

1998-10-01

MOSRA-Light is a three-dimensional neutron diffusion calculation code for X-Y-Z geometry. It is based on the 4th order polynomial nodal expansion method (NEM). As the 4th order NEM is not sensitive to mesh sizes, accurate calculation is possible by the use of coarse meshes of about 20 cm. The drastic decrease of number of unknowns in a 3-dimensional problem results in very fast computation. Furthermore, it employs newly developed computation algorithm 'boundary separated checkerboard sweep method' appropriate to vector computers. This method is very efficient because the speedup factor by vectorization increases, as a scale of problem becomes larger. Speed-up factor compared to the scalar calculation is from 20 to 40 in the case of PWR core calculation. Considering the both effects by the vectorization and the coarse mesh method, total speedup factor is more than 1000 as compared with conventional scalar code with the finite difference method. MOSRA-Light can be available on most of vector or scalar computers with the UNIX or it's similar operating systems (e.g. freeware like Linux). Users can easily install it by the help of the conversation style installer. This report contains the general theory of NEM, the fast computation algorithm, benchmark calculation results and detailed information for usage of this code including input data instructions and sample input data. (author)

Compact Representation of High-Dimensional Feature Vectors for Large-Scale Image Recognition and Retrieval.

Science.gov (United States)

Zhang, Yu; Wu, Jianxin; Cai, Jianfei

2016-05-01

In large-scale visual recognition and image retrieval tasks, feature vectors, such as Fisher vector (FV) or the vector of locally aggregated descriptors (VLAD), have achieved state-of-the-art results. However, the combination of the large numbers of examples and high-dimensional vectors necessitates dimensionality reduction, in order to reduce its storage and CPU costs to a reasonable range. In spite of the popularity of various feature compression methods, this paper shows that the feature (dimension) selection is a better choice for high-dimensional FV/VLAD than the feature (dimension) compression methods, e.g., product quantization. We show that strong correlation among the feature dimensions in the FV and the VLAD may not exist, which renders feature selection a natural choice. We also show that, many dimensions in FV/VLAD are noise. Throwing them away using feature selection is better than compressing them and useful dimensions altogether using feature compression methods. To choose features, we propose an efficient importance sorting algorithm considering both the supervised and unsupervised cases, for visual recognition and image retrieval, respectively. Combining with the 1-bit quantization, feature selection has achieved both higher accuracy and less computational cost than feature compression methods, such as product quantization, on the FV and the VLAD image representations.

Hierarchical one-dimensional ammonium nickel phosphate microrods for high-performance pseudocapacitors

CSIR Research Space (South Africa)

Raju, K

2015-12-01

Full Text Available :17629 | DOI: 10.1038/srep17629 www.nature.com/scientificreports Hierarchical One-Dimensional Ammonium Nickel Phosphate Microrods for High-Performance Pseudocapacitors Kumar Raju1 & Kenneth I. Ozoemena1,2 High-performance electrochemical capacitors... OPEN w w w . n a t u r e . c o m / s c i e n t i f i c r e p o r t s / 2S C I E N T I F I C REPORTS | 5:17629 | DOI: 10.1038/srep17629 Hierarchical 1-D and 2-D materials maximize the supercapacitive properties due to their unique ability to permit ion...

Collective oscillations of twin boundaries in high temperature superconductors as an acoustic analogue of two-dimensional plasmons

International Nuclear Information System (INIS)

Kosevich, Yu.A.; Syrkin, E.S.

1990-06-01

Low frequency collective oscillations in a superlattice consisting of alternating highly anisotropic layers are considered. Such superstructure may be formed in the ferroelastic near the structural phase transition by alternation of twins. For the surface waves, propagating along the layers, the conditions and the range of existence of those with the dispersion law ω∼K 1/2 , characteristics for two-dimensional plasmons, have been analyzed for a solid-state system with consideration for elastic anisotropy and retardation of acoustic waves. Such excitations ('dyadons') were used in an attempt to explain the anomalies of low temperature thermodynamic and kinetic characteristics of high-T c superconductors. We have shown that the similarity of the densities of the matching phases and the retardation of elastic waves in the crystal narrow the range of existence of dyadons, but high elastic anisotropy of the solid phases enlarges the range of existence of such excitations in solid-state systems. The example of possible crystalline geometry of the phase matching, for which there arise collective excitations of the type under consideration, is found. For transverse and longitudinal waves propagating across the layers, the existence is proved of low frequency acoustic branches separated by a wide gap from the nearest optical branches. (author). 18 refs

A COMPARISON OF SEMANTIC SIMILARITY MODELS IN EVALUATING CONCEPT SIMILARITY

Directory of Open Access Journals (Sweden)

Q. X. Xu

2012-08-01

Full Text Available The semantic similarities are important in concept definition, recognition, categorization, interpretation, and integration. Many semantic similarity models have been established to evaluate semantic similarities of objects or/and concepts. To find out the suitability and performance of different models in evaluating concept similarities, we make a comparison of four main types of models in this paper: the geometric model, the feature model, the network model, and the transformational model. Fundamental principles and main characteristics of these models are introduced and compared firstly. Land use and land cover concepts of NLCD92 are employed as examples in the case study. The results demonstrate that correlations between these models are very high for a possible reason that all these models are designed to simulate the similarity judgement of human mind.

High-resolution non-destructive three-dimensional imaging of integrated circuits.

Science.gov (United States)

Holler, Mirko; Guizar-Sicairos, Manuel; Tsai, Esther H R; Dinapoli, Roberto; Müller, Elisabeth; Bunk, Oliver; Raabe, Jörg; Aeppli, Gabriel

2017-03-15

Modern nanoelectronics has advanced to a point at which it is impossible to image entire devices and their interconnections non-destructively because of their small feature sizes and the complex three-dimensional structures resulting from their integration on a chip. This metrology gap implies a lack of direct feedback between design and manufacturing processes, and hampers quality control during production, shipment and use. Here we demonstrate that X-ray ptychography-a high-resolution coherent diffractive imaging technique-can create three-dimensional images of integrated circuits of known and unknown designs with a lateral resolution in all directions down to 14.6 nanometres. We obtained detailed device geometries and corresponding elemental maps, and show how the devices are integrated with each other to form the chip. Our experiments represent a major advance in chip inspection and reverse engineering over the traditional destructive electron microscopy and ion milling techniques. Foreseeable developments in X-ray sources, optics and detectors, as well as adoption of an instrument geometry optimized for planar rather than cylindrical samples, could lead to a thousand-fold increase in efficiency, with concomitant reductions in scan times and voxel sizes.

High-resolution non-destructive three-dimensional imaging of integrated circuits

Science.gov (United States)

Holler, Mirko; Guizar-Sicairos, Manuel; Tsai, Esther H. R.; Dinapoli, Roberto; Müller, Elisabeth; Bunk, Oliver; Raabe, Jörg; Aeppli, Gabriel

2017-03-01

Modern nanoelectronics has advanced to a point at which it is impossible to image entire devices and their interconnections non-destructively because of their small feature sizes and the complex three-dimensional structures resulting from their integration on a chip. This metrology gap implies a lack of direct feedback between design and manufacturing processes, and hampers quality control during production, shipment and use. Here we demonstrate that X-ray ptychography—a high-resolution coherent diffractive imaging technique—can create three-dimensional images of integrated circuits of known and unknown designs with a lateral resolution in all directions down to 14.6 nanometres. We obtained detailed device geometries and corresponding elemental maps, and show how the devices are integrated with each other to form the chip. Our experiments represent a major advance in chip inspection and reverse engineering over the traditional destructive electron microscopy and ion milling techniques. Foreseeable developments in X-ray sources, optics and detectors, as well as adoption of an instrument geometry optimized for planar rather than cylindrical samples, could lead to a thousand-fold increase in efficiency, with concomitant reductions in scan times and voxel sizes.

Three-Dimensional Numerical Analysis of an Operating Helical Rotor Pump at High Speeds and High Pressures including Cavitation

Directory of Open Access Journals (Sweden)

Zhou Yang

2017-01-01

Full Text Available High pressures, high speeds, low noise and miniaturization is the direction of development in hydraulic pump. According to the development trend, an operating helical rotor pump (HRP at high speeds and high pressures has been designed and produced, which rotational speed can reach 12000r/min and outlet pressure is as high as 25MPa. Three-dimensional simulation with and without cavitation inside the HRP is completed by the means of the computational fluid dynamics (CFD in this paper, which contributes to understand the complex fluid flow inside it. Moreover, the influences of the rotational speeds of the HRP with and without cavitation has been simulated at 25MPa.

High-precision two-dimensional atom localization via quantum interference in a tripod-type system

International Nuclear Information System (INIS)

Wang, Zhiping; Yu, Benli

2014-01-01

A scheme is proposed for high-precision two-dimensional atom localization in a four-level tripod-type atomic system via measurement of the excited state population. It is found that because of the position-dependent atom–field interaction, the precision of 2D atom localization can be significantly improved by appropriately adjusting the system parameters. Our scheme may be helpful in laser cooling or atom nanolithography via high-precision and high-resolution atom localization. (letter)

Low-resistance gateless high electron mobility transistors using three-dimensional inverted pyramidal AlGaN/GaN surfaces

International Nuclear Information System (INIS)

So, Hongyun; Senesky, Debbie G.

2016-01-01

In this letter, three-dimensional gateless AlGaN/GaN high electron mobility transistors (HEMTs) were demonstrated with 54% reduction in electrical resistance and 73% increase in surface area compared with conventional gateless HEMTs on planar substrates. Inverted pyramidal AlGaN/GaN surfaces were microfabricated using potassium hydroxide etched silicon with exposed (111) surfaces and metal-organic chemical vapor deposition of coherent AlGaN/GaN thin films. In addition, electrical characterization of the devices showed that a combination of series and parallel connections of the highly conductive two-dimensional electron gas along the pyramidal geometry resulted in a significant reduction in electrical resistance at both room and high temperatures (up to 300 °C). This three-dimensional HEMT architecture can be leveraged to realize low-power and reliable power electronics, as well as harsh environment sensors with increased surface area

Low-resistance gateless high electron mobility transistors using three-dimensional inverted pyramidal AlGaN/GaN surfaces

Energy Technology Data Exchange (ETDEWEB)

So, Hongyun, E-mail: hyso@stanford.edu [Department of Aeronautics and Astronautics, Stanford University, Stanford, California 94305 (United States); Senesky, Debbie G. [Department of Aeronautics and Astronautics, Stanford University, Stanford, California 94305 (United States); Department of Electrical Engineering, Stanford University, Stanford, California 94305 (United States)

2016-01-04

In this letter, three-dimensional gateless AlGaN/GaN high electron mobility transistors (HEMTs) were demonstrated with 54% reduction in electrical resistance and 73% increase in surface area compared with conventional gateless HEMTs on planar substrates. Inverted pyramidal AlGaN/GaN surfaces were microfabricated using potassium hydroxide etched silicon with exposed (111) surfaces and metal-organic chemical vapor deposition of coherent AlGaN/GaN thin films. In addition, electrical characterization of the devices showed that a combination of series and parallel connections of the highly conductive two-dimensional electron gas along the pyramidal geometry resulted in a significant reduction in electrical resistance at both room and high temperatures (up to 300 °C). This three-dimensional HEMT architecture can be leveraged to realize low-power and reliable power electronics, as well as harsh environment sensors with increased surface area.

Reconstruction of high-dimensional states entangled in orbital angular momentum using mutually unbiased measurements

CSIR Research Space (South Africa)

Giovannini, D

2013-06-01

Full Text Available : QELS_Fundamental Science, San Jose, California United States, 9-14 June 2013 Reconstruction of High-Dimensional States Entangled in Orbital Angular Momentum Using Mutually Unbiased Measurements D. Giovannini1, ⇤, J. Romero1, 2, J. Leach3, A...

High-efficiency one-dimensional atom localization via two parallel standing-wave fields

International Nuclear Information System (INIS)

Wang, Zhiping; Wu, Xuqiang; Lu, Liang; Yu, Benli

2014-01-01

We present a new scheme of high-efficiency one-dimensional (1D) atom localization via measurement of upper state population or the probe absorption in a four-level N-type atomic system. By applying two classical standing-wave fields, the localization peak position and number, as well as the conditional position probability, can be easily controlled by the system parameters, and the sub-half-wavelength atom localization is also observed. More importantly, there is 100% detecting probability of the atom in the subwavelength domain when the corresponding conditions are satisfied. The proposed scheme may open up a promising way to achieve high-precision and high-efficiency 1D atom localization. (paper)

Three-dimensional magnetospheric equilibrium with isotropic pressure

International Nuclear Information System (INIS)

Cheng, C.Z.

1995-05-01

In the absence of the toroidal flux, two coupled quasi two-dimensional elliptic equilibrium equations have been derived to describe self-consistent three-dimensional static magnetospheric equilibria with isotropic pressure in an optimal (Ψ,α,χ) flux coordinate system, where Ψ is the magnetic flux function, χ is a generalized poloidal angle, α is the toroidal angle, α = φ - δ(Ψ,φ,χ) is the toroidal angle, δ(Ψ,φ,χ) is periodic in φ, and the magnetic field is represented as rvec B = ∇Ψ x ∇α. A three-dimensional magnetospheric equilibrium code, the MAG-3D code, has been developed by employing an iterative metric method. The main difference between the three-dimensional and the two-dimensional axisymmetric solutions is that the field-aligned current and the toroidal magnetic field are finite for the three-dimensional case, but vanish for the two-dimensional axisymmetric case. With the same boundary flux surface shape, the two-dimensional axisymmetric results are similar to the three-dimensional magnetosphere at each local time cross section

Generalized reduced rank latent factor regression for high dimensional tensor fields, and neuroimaging-genetic applications.

Science.gov (United States)

Tao, Chenyang; Nichols, Thomas E; Hua, Xue; Ching, Christopher R K; Rolls, Edmund T; Thompson, Paul M; Feng, Jianfeng

2017-01-01

We propose a generalized reduced rank latent factor regression model (GRRLF) for the analysis of tensor field responses and high dimensional covariates. The model is motivated by the need from imaging-genetic studies to identify genetic variants that are associated with brain imaging phenotypes, often in the form of high dimensional tensor fields. GRRLF identifies from the structure in the data the effective dimensionality of the data, and then jointly performs dimension reduction of the covariates, dynamic identification of latent factors, and nonparametric estimation of both covariate and latent response fields. After accounting for the latent and covariate effects, GRLLF performs a nonparametric test on the remaining factor of interest. GRRLF provides a better factorization of the signals compared with common solutions, and is less susceptible to overfitting because it exploits the effective dimensionality. The generality and the flexibility of GRRLF also allow various statistical models to be handled in a unified framework and solutions can be efficiently computed. Within the field of neuroimaging, it improves the sensitivity for weak signals and is a promising alternative to existing approaches. The operation of the framework is demonstrated with both synthetic datasets and a real-world neuroimaging example in which the effects of a set of genes on the structure of the brain at the voxel level were measured, and the results compared favorably with those from existing approaches. Copyright © 2016. Published by Elsevier Inc.

High-Level Heteroatom Doped Two-Dimensional Carbon Architectures for Highly Efficient Lithium-Ion Storage

Directory of Open Access Journals (Sweden)

Zhijie Wang

2018-04-01

Full Text Available In this work, high-level heteroatom doped two-dimensional hierarchical carbon architectures (H-2D-HCA are developed for highly efficient Li-ion storage applications. The achieved H-2D-HCA possesses a hierarchical 2D morphology consisting of tiny carbon nanosheets vertically grown on carbon nanoplates and containing a hierarchical porosity with multiscale pore size. More importantly, the H-2D-HCA shows abundant heteroatom functionality, with sulfur (S doping of 0.9% and nitrogen (N doping of as high as 15.5%, in which the electrochemically active N accounts for 84% of total N heteroatoms. In addition, the H-2D-HCA also has an expanded interlayer distance of 0.368 nm. When used as lithium-ion battery anodes, it shows excellent Li-ion storage performance. Even at a high current density of 5 A g−1, it still delivers a high discharge capacity of 329 mA h g−1 after 1,000 cycles. First principle calculations verifies that such unique microstructure characteristics and high-level heteroatom doping nature can enhance Li adsorption stability, electronic conductivity and Li diffusion mobility of carbon nanomaterials. Therefore, the H-2D-HCA could be promising candidates for next-generation LIB anodes.

Reduced, three-dimensional, nonlinear equations for high-β plasmas including toroidal effects

International Nuclear Information System (INIS)

Schmalz, R.

1980-11-01

The resistive MHD equations for toroidal plasma configurations are reduced by expanding to the second order in epsilon, the inverse aspect ratio, allowing for high β = μsub(o)p/B 2 of order epsilon. The result is a closed system of nonlinear, three-dimensional equations where the fast magnetohydrodynamic time scale is eliminated. In particular, the equation for the toroidal velocity remains decoupled. (orig.)

Dimensionality analysis of multiparticle production at high energies

International Nuclear Information System (INIS)

Chilingaryan, A.A.

1989-01-01

An algorithm of analysis of multiparticle final states is offered. By the Renyi dimensionalities, which were calculated according to experimental data, though it were hadron distribution over the rapidity intervals or particle distribution in an N-dimensional momentum space, we can judge about the degree of correlation of particles, separate the momentum space projections and areas where the probability measure singularities are observed. The method is tested in a series of calculations with samples of fractal object points and with samples obtained by means of different generators of pseudo- and quasi-random numbers. 27 refs.; 11 figs

Joint High-Dimensional Bayesian Variable and Covariance Selection with an Application to eQTL Analysis

KAUST Repository

Bhadra, Anindya

2013-04-22

We describe a Bayesian technique to (a) perform a sparse joint selection of significant predictor variables and significant inverse covariance matrix elements of the response variables in a high-dimensional linear Gaussian sparse seemingly unrelated regression (SSUR) setting and (b) perform an association analysis between the high-dimensional sets of predictors and responses in such a setting. To search the high-dimensional model space, where both the number of predictors and the number of possibly correlated responses can be larger than the sample size, we demonstrate that a marginalization-based collapsed Gibbs sampler, in combination with spike and slab type of priors, offers a computationally feasible and efficient solution. As an example, we apply our method to an expression quantitative trait loci (eQTL) analysis on publicly available single nucleotide polymorphism (SNP) and gene expression data for humans where the primary interest lies in finding the significant associations between the sets of SNPs and possibly correlated genetic transcripts. Our method also allows for inference on the sparse interaction network of the transcripts (response variables) after accounting for the effect of the SNPs (predictor variables). We exploit properties of Gaussian graphical models to make statements concerning conditional independence of the responses. Our method compares favorably to existing Bayesian approaches developed for this purpose. © 2013, The International Biometric Society.

Three-dimensional interconnected porous graphitic carbon derived from rice straw for high performance supercapacitors

Science.gov (United States)

Jin, Hong; Hu, Jingpeng; Wu, Shichao; Wang, Xiaolan; Zhang, Hui; Xu, Hui; Lian, Kun

2018-04-01

Three-dimensional interconnected porous graphitic carbon materials are synthesized via a combination of graphitization and activation process with rice straw as the carbon source. The physicochemical properties of the three-dimensional interconnected porous graphitic carbon materials are characterized by Nitrogen adsorption/desorption, Fourier-transform infrared spectroscopy, X-ray diffraction, Raman spectroscopy, Scanning electron microscopy and Transmission electron microscopy. The results demonstrate that the as-prepared carbon is a high surface area carbon material (a specific surface area of 3333 m2 g-1 with abundant mesoporous and microporous structures). And it exhibits superb performance in symmetric double layer capacitors with a high specific capacitance of 400 F g-1 at a current density of 0.1 A g-1, good rate performance with 312 F g-1 under a current density of 5 A g-1 and favorable cycle stability with 6.4% loss after 10000 cycles at a current density of 5 A g-1 in the aqueous electrolyte of 6M KOH. Thus, rice straw is a promising carbon source for fabricating inexpensive, sustainable and high performance supercapacitors' electrode materials.

Three-dimensional skeleton networks of graphene wrapped polyaniline nanofibers: an excellent structure for high-performance flexible solid-state supercapacitors

Science.gov (United States)

Hu, Nantao; Zhang, Liling; Yang, Chao; Zhao, Jian; Yang, Zhi; Wei, Hao; Liao, Hanbin; Feng, Zhenxing; Fisher, Adrian; Zhang, Yafei; Xu, Zhichuan J.

2016-01-01

Thin, robust, lightweight, and flexible supercapacitors (SCs) have aroused growing attentions nowadays due to the rapid development of flexible electronics. Graphene-polyaniline (PANI) hybrids are attractive candidates for high performance SCs. In order to utilize them in real devices, it is necessary to improve the capacitance and the structure stability of PANI. Here we report a hierarchical three-dimensional structure, in which all of PANI nanofibers (NFs) are tightly wrapped inside reduced graphene oxide (rGO) nanosheet skeletons, for high-performance flexible SCs. The as-fabricated film electrodes with this unique structure showed a highest gravimetric specific capacitance of 921 F/g and volumetric capacitance of 391 F/cm3. The assembled solid-state SCs gave a high specific capacitance of 211 F/g (1 A/g), a high area capacitance of 0.9 F/cm2, and a competitive volumetric capacitance of 25.6 F/cm3. The SCs also exhibited outstanding rate capability (~75% retention at 20 A/g) as well as excellent cycling stability (100% retention at 10 A/g for 2000 cycles). Additionally, no structural failure and loss of performance were observed under the bending state. This structure design paves a new avenue for engineering rGO/PANI or other similar hybrids for high performance flexible energy storage devices. PMID:26795067

Similarity from multi-dimensional scaling: solving the accuracy and diversity dilemma in information filtering.

Directory of Open Access Journals (Sweden)

Wei Zeng

Full Text Available Recommender systems are designed to assist individual users to navigate through the rapidly growing amount of information. One of the most successful recommendation techniques is the collaborative filtering, which has been extensively investigated and has already found wide applications in e-commerce. One of challenges in this algorithm is how to accurately quantify the similarities of user pairs and item pairs. In this paper, we employ the multidimensional scaling (MDS method to measure the similarities between nodes in user-item bipartite networks. The MDS method can extract the essential similarity information from the networks by smoothing out noise, which provides a graphical display of the structure of the networks. With the similarity measured from MDS, we find that the item-based collaborative filtering algorithm can outperform the diffusion-based recommendation algorithms. Moreover, we show that this method tends to recommend unpopular items and increase the global diversification of the networks in long term.

Similarity from multi-dimensional scaling: solving the accuracy and diversity dilemma in information filtering.

Science.gov (United States)

Zeng, Wei; Zeng, An; Liu, Hao; Shang, Ming-Sheng; Zhang, Yi-Cheng

2014-01-01

Recommender systems are designed to assist individual users to navigate through the rapidly growing amount of information. One of the most successful recommendation techniques is the collaborative filtering, which has been extensively investigated and has already found wide applications in e-commerce. One of challenges in this algorithm is how to accurately quantify the similarities of user pairs and item pairs. In this paper, we employ the multidimensional scaling (MDS) method to measure the similarities between nodes in user-item bipartite networks. The MDS method can extract the essential similarity information from the networks by smoothing out noise, which provides a graphical display of the structure of the networks. With the similarity measured from MDS, we find that the item-based collaborative filtering algorithm can outperform the diffusion-based recommendation algorithms. Moreover, we show that this method tends to recommend unpopular items and increase the global diversification of the networks in long term.

Simulations of dimensionally reduced effective theories of high temperature QCD

CERN Document Server

Hietanen, Ari

Quantum chromodynamics (QCD) is the theory describing interaction between quarks and gluons. At low temperatures, quarks are confined forming hadrons, e.g. protons and neutrons. However, at extremely high temperatures the hadrons break apart and the matter transforms into plasma of individual quarks and gluons. In this theses the quark gluon plasma (QGP) phase of QCD is studied using lattice techniques in the framework of dimensionally reduced effective theories EQCD and MQCD. Two quantities are in particular interest: the pressure (or grand potential) and the quark number susceptibility. At high temperatures the pressure admits a generalised coupling constant expansion, where some coefficients are non-perturbative. We determine the first such contribution of order g^6 by performing lattice simulations in MQCD. This requires high precision lattice calculations, which we perform with different number of colors N_c to obtain N_c-dependence on the coefficient. The quark number susceptibility is studied by perf...

Inference-Based Similarity Search in Randomized Montgomery Domains for Privacy-Preserving Biometric Identification.

Science.gov (United States)

Wang, Yi; Wan, Jianwu; Guo, Jun; Cheung, Yiu-Ming; C Yuen, Pong

2017-07-14

Similarity search is essential to many important applications and often involves searching at scale on high-dimensional data based on their similarity to a query. In biometric applications, recent vulnerability studies have shown that adversarial machine learning can compromise biometric recognition systems by exploiting the biometric similarity information. Existing methods for biometric privacy protection are in general based on pairwise matching of secured biometric templates and have inherent limitations in search efficiency and scalability. In this paper, we propose an inference-based framework for privacy-preserving similarity search in Hamming space. Our approach builds on an obfuscated distance measure that can conceal Hamming distance in a dynamic interval. Such a mechanism enables us to systematically design statistically reliable methods for retrieving most likely candidates without knowing the exact distance values. We further propose to apply Montgomery multiplication for generating search indexes that can withstand adversarial similarity analysis, and show that information leakage in randomized Montgomery domains can be made negligibly small. Our experiments on public biometric datasets demonstrate that the inference-based approach can achieve a search accuracy close to the best performance possible with secure computation methods, but the associated cost is reduced by orders of magnitude compared to cryptographic primitives.

Inference for High-dimensional Differential Correlation Matrices.

Science.gov (United States)

Cai, T Tony; Zhang, Anru

2016-01-01

Motivated by differential co-expression analysis in genomics, we consider in this paper estimation and testing of high-dimensional differential correlation matrices. An adaptive thresholding procedure is introduced and theoretical guarantees are given. Minimax rate of convergence is established and the proposed estimator is shown to be adaptively rate-optimal over collections of paired correlation matrices with approximately sparse differences. Simulation results show that the procedure significantly outperforms two other natural methods that are based on separate estimation of the individual correlation matrices. The procedure is also illustrated through an analysis of a breast cancer dataset, which provides evidence at the gene co-expression level that several genes, of which a subset has been previously verified, are associated with the breast cancer. Hypothesis testing on the differential correlation matrices is also considered. A test, which is particularly well suited for testing against sparse alternatives, is introduced. In addition, other related problems, including estimation of a single sparse correlation matrix, estimation of the differential covariance matrices, and estimation of the differential cross-correlation matrices, are also discussed.

Highly Efficient Broadband Yellow Phosphor Based on Zero-Dimensional Tin Mixed-Halide Perovskite.

Science.gov (United States)

Zhou, Chenkun; Tian, Yu; Yuan, Zhao; Lin, Haoran; Chen, Banghao; Clark, Ronald; Dilbeck, Tristan; Zhou, Yan; Hurley, Joseph; Neu, Jennifer; Besara, Tiglet; Siegrist, Theo; Djurovich, Peter; Ma, Biwu

2017-12-27

Organic-inorganic hybrid metal halide perovskites have emerged as a highly promising class of light emitters, which can be used as phosphors for optically pumped white light-emitting diodes (WLEDs). By controlling the structural dimensionality, metal halide perovskites can exhibit tunable narrow and broadband emissions from the free-exciton and self-trapped excited states, respectively. Here, we report a highly efficient broadband yellow light emitter based on zero-dimensional tin mixed-halide perovskite (C 4 N 2 H 14 Br) 4 SnBr x I 6-x (x = 3). This rare-earth-free ionically bonded crystalline material possesses a perfect host-dopant structure, in which the light-emitting metal halide species (SnBr x I 6-x 4- , x = 3) are completely isolated from each other and embedded in the wide band gap organic matrix composed of C 4 N 2 H 14 Br - . The strongly Stokes-shifted broadband yellow emission that peaked at 582 nm from this phosphor, which is a result of excited state structural reorganization, has an extremely large full width at half-maximum of 126 nm and a high photoluminescence quantum efficiency of ∼85% at room temperature. UV-pumped WLEDs fabricated using this yellow emitter together with a commercial europium-doped barium magnesium aluminate blue phosphor (BaMgAl 10 O 17 :Eu 2+ ) can exhibit high color rendering indexes of up to 85.

On the sensitivity of dimensional stability of high density polyethylene on heating rate

Directory of Open Access Journals (Sweden)

2007-02-01

Full Text Available Although high density polyethylene (HDPE is one of the most widely used industrial polymers, its application compared to its potential has been limited because of its low dimensional stability particularly at high temperature. Dilatometry test is considered as a method for examining thermal dimensional stability (TDS of the material. In spite of the importance of simulation of TDS of HDPE during dilatometry test it has not been paid attention by other investigators. Thus the main goal of this research is concentrated on simulation of TDS of HDPE. Also it has been tried to validate the simulation results and practical experiments. For this purpose the standard dilatometry test was done on the HDPE speci­mens. Secant coefficient of linear thermal expansion was computed from the test. Then by considering boundary conditions and material properties, dilatometry test has been simulated at different heating rates and the thermal strain versus temper­ature was calculated. The results showed that the simulation results and practical experiments were very close together.

A Cross-Lingual Similarity Measure for Detecting Biomedical Term Translations

Science.gov (United States)

Bollegala, Danushka; Kontonatsios, Georgios; Ananiadou, Sophia

2015-01-01

Bilingual dictionaries for technical terms such as biomedical terms are an important resource for machine translation systems as well as for humans who would like to understand a concept described in a foreign language. Often a biomedical term is first proposed in English and later it is manually translated to other languages. Despite the fact that there are large monolingual lexicons of biomedical terms, only a fraction of those term lexicons are translated to other languages. Manually compiling large-scale bilingual dictionaries for technical domains is a challenging task because it is difficult to find a sufficiently large number of bilingual experts. We propose a cross-lingual similarity measure for detecting most similar translation candidates for a biomedical term specified in one language (source) from another language (target). Specifically, a biomedical term in a language is represented using two types of features: (a) intrinsic features that consist of character n-grams extracted from the term under consideration, and (b) extrinsic features that consist of unigrams and bigrams extracted from the contextual windows surrounding the term under consideration. We propose a cross-lingual similarity measure using each of those feature types. First, to reduce the dimensionality of the feature space in each language, we propose prototype vector projection (PVP)—a non-negative lower-dimensional vector projection method. Second, we propose a method to learn a mapping between the feature spaces in the source and target language using partial least squares regression (PLSR). The proposed method requires only a small number of training instances to learn a cross-lingual similarity measure. The proposed PVP method outperforms popular dimensionality reduction methods such as the singular value decomposition (SVD) and non-negative matrix factorization (NMF) in a nearest neighbor prediction task. Moreover, our experimental results covering several language pairs such as

A cross-lingual similarity measure for detecting biomedical term translations.

Directory of Open Access Journals (Sweden)

Danushka Bollegala

Full Text Available Bilingual dictionaries for technical terms such as biomedical terms are an important resource for machine translation systems as well as for humans who would like to understand a concept described in a foreign language. Often a biomedical term is first proposed in English and later it is manually translated to other languages. Despite the fact that there are large monolingual lexicons of biomedical terms, only a fraction of those term lexicons are translated to other languages. Manually compiling large-scale bilingual dictionaries for technical domains is a challenging task because it is difficult to find a sufficiently large number of bilingual experts. We propose a cross-lingual similarity measure for detecting most similar translation candidates for a biomedical term specified in one language (source from another language (target. Specifically, a biomedical term in a language is represented using two types of features: (a intrinsic features that consist of character n-grams extracted from the term under consideration, and (b extrinsic features that consist of unigrams and bigrams extracted from the contextual windows surrounding the term under consideration. We propose a cross-lingual similarity measure using each of those feature types. First, to reduce the dimensionality of the feature space in each language, we propose prototype vector projection (PVP--a non-negative lower-dimensional vector projection method. Second, we propose a method to learn a mapping between the feature spaces in the source and target language using partial least squares regression (PLSR. The proposed method requires only a small number of training instances to learn a cross-lingual similarity measure. The proposed PVP method outperforms popular dimensionality reduction methods such as the singular value decomposition (SVD and non-negative matrix factorization (NMF in a nearest neighbor prediction task. Moreover, our experimental results covering several language

Carbon doped GaAs/AlGaAs heterostructures with high mobility two dimensional hole gas

Energy Technology Data Exchange (ETDEWEB)

Hirmer, Marika; Bougeard, Dominique; Schuh, Dieter [Institut fuer Experimentelle und Angewandte Physik, Universitaet Regensburg, D 93040 Regensburg (Germany); Wegscheider, Werner [Laboratorium fuer Festkoerperphysik, ETH Zuerich, 8093 Zuerich (Switzerland)

2011-07-01

Two dimensional hole gases (2DHG) with high carrier mobilities are required for both fundamental research and possible future ultrafast spintronic devices. Here, two different types of GaAs/AlGaAs heterostructures hosting a 2DHG were investigated. The first structure is a GaAs QW embedded in AlGaAs barrier grown by molecular beam epitaxy with carbon-doping only at one side of the quantum well (QW) (single side doped, ssd), while the second structure is similar but with symmetrically arranged doping layers on both sides of the QW (double side doped, dsd). The ssd-structure shows hole mobilities up to 1.2*10{sup 6} cm{sup 2}/Vs which are achieved after illumination. In contrast, the dsd-structure hosts a 2DHG with mobility up to 2.05*10{sup 6} cm{sup 2}/Vs. Here, carrier mobility and carrier density is not affected by illuminating the sample. Both samples showed distinct Shubnikov-de-Haas oscillations and fractional quantum-Hall-plateaus in magnetotransport experiments done at 20mK, indicating the high quality of the material. In addition, the influence of different temperature profiles during growth and the influence of the Al content of the barrier Al{sub x}Ga{sub 1-x}As on carrier concentration and mobility were investigated and are presented here.

Three-dimensional carbon nanotube networks with a supported nickel oxide nanonet for high-performance supercapacitors.

Science.gov (United States)

Wu, Mao-Sung; Zheng, Yo-Ru; Lin, Guan-Wei

2014-08-04

A three-dimensional porous carbon nanotube film with a supported NiO nanonet was prepared by simple electrophoretic deposition and hydrothermal synthesis, which could deliver a high specific capacitance of 1511 F g(-1) at a high discharge current of 50 A g(-1) due to the significantly improved transport of the electrolyte and electrons.

Three-dimensional iron sulfide-carbon interlocked graphene composites for high-performance sodium-ion storage

DEFF Research Database (Denmark)

Huang, Wei; Sun, Hongyu; Shangguan, Huihui

2018-01-01

Three-dimensional (3D) carbon-wrapped iron sulfide interlocked graphene (Fe7S8@C-G) composites for high-performance sodium-ion storage are designed and produced through electrostatic interactions and subsequent sulfurization. The iron-based metal–organic frameworks (MOFs, MIL-88-Fe) interact with...

Multistack integration of three-dimensional hyperbranched anatase titania architectures for high-efficiency dye-sensitized solar cells.

Science.gov (United States)

Wu, Wu-Qiang; Xu, Yang-Fan; Rao, Hua-Shang; Su, Cheng-Yong; Kuang, Dai-Bin

2014-04-30

An unprecedented attempt was conducted on suitably functionalized integration of three-dimensional hyperbranched titania architectures for efficient multistack photoanode, constructed via layer-by-layer assembly of hyperbranched hierarchical tree-like titania nanowires (underlayer), branched hierarchical rambutan-like titania hollow submicrometer-sized spheres (intermediate layer), and hyperbranched hierarchical urchin-like titania micrometer-sized spheres (top layer). Owing to favorable charge-collection, superior light harvesting efficiency and extended electron lifetime, the multilayered TiO2-based devices showed greater J(sc) and V(oc) than those of a conventional TiO2 nanoparticle (TNP), and an overall power conversion efficiency of 11.01% (J(sc) = 18.53 mA cm(-2); V(oc) = 827 mV and FF = 0.72) was attained, which remarkably outperformed that of a TNP-based reference cell (η = 7.62%) with a similar film thickness. Meanwhile, the facile and operable film-fabricating technique (hydrothermal and drop-casting) provides a promising scheme and great simplicity for high performance/cost ratio photovoltaic device processability in a sustainable way.

Low-storage implicit/explicit Runge-Kutta schemes for the simulation of stiff high-dimensional ODE systems

Science.gov (United States)

Cavaglieri, Daniele; Bewley, Thomas

2015-04-01

Implicit/explicit (IMEX) Runge-Kutta (RK) schemes are effective for time-marching ODE systems with both stiff and nonstiff terms on the RHS; such schemes implement an (often A-stable or better) implicit RK scheme for the stiff part of the ODE, which is often linear, and, simultaneously, a (more convenient) explicit RK scheme for the nonstiff part of the ODE, which is often nonlinear. Low-storage RK schemes are especially effective for time-marching high-dimensional ODE discretizations of PDE systems on modern (cache-based) computational hardware, in which memory management is often the most significant computational bottleneck. In this paper, we develop and characterize eight new low-storage implicit/explicit RK schemes which have higher accuracy and better stability properties than the only low-storage implicit/explicit RK scheme available previously, the venerable second-order Crank-Nicolson/Runge-Kutta-Wray (CN/RKW3) algorithm that has dominated the DNS/LES literature for the last 25 years, while requiring similar storage (two, three, or four registers of length N) and comparable floating-point operations per timestep.

Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification.

Science.gov (United States)

Fan, Jianqing; Feng, Yang; Jiang, Jiancheng; Tong, Xin

We propose a high dimensional classification method that involves nonparametric feature augmentation. Knowing that marginal density ratios are the most powerful univariate classifiers, we use the ratio estimates to transform the original feature measurements. Subsequently, penalized logistic regression is invoked, taking as input the newly transformed or augmented features. This procedure trains models equipped with local complexity and global simplicity, thereby avoiding the curse of dimensionality while creating a flexible nonlinear decision boundary. The resulting method is called Feature Augmentation via Nonparametrics and Selection (FANS). We motivate FANS by generalizing the Naive Bayes model, writing the log ratio of joint densities as a linear combination of those of marginal densities. It is related to generalized additive models, but has better interpretability and computability. Risk bounds are developed for FANS. In numerical analysis, FANS is compared with competing methods, so as to provide a guideline on its best application domain. Real data analysis demonstrates that FANS performs very competitively on benchmark email spam and gene expression data sets. Moreover, FANS is implemented by an extremely fast algorithm through parallel computing.

Dimensional consistency achieved in high-performance synchronizing hubs

International Nuclear Information System (INIS)

Garcia, P.; Campos, M.; Torralba, M.

2013-01-01

The tolerances of parts produced for the automotive industry are so tight that any small process variation may mean that the product does not fulfill them. As dimensional tolerances decrease, the material properties of parts are expected to be improved. Depending on the dimensional and material requirements of a part, different production routes are available to find robust processes, minimizing cost and maximizing process capability. Dimensional tolerances have been reduced in recent years, and as a result, the double pressing-double sintering production via ( 2 P2S ) has again become an accurate way to meet these increasingly narrow tolerances. In this paper, it is shown that the process parameters of the first sintering have great influence on the following production steps and the dimensions of the final parts. The roles of factors other than density and the second sintering process in defining the final dimensions of product are probed. All trials were done in a production line that produces synchronizer hubs for manual transmissions, allowing the maintenance of stable conditions and control of those parameters that are relevant for the product and process. (Author) 21 refs.

Thermophysical and Mechanical Properties of Granite and Its Effects on Borehole Stability in High Temperature and Three-Dimensional Stress

Directory of Open Access Journals (Sweden)

Wang Yu

2014-01-01

Full Text Available When exploiting the deep resources, the surrounding rock readily undergoes the hole shrinkage, borehole collapse, and loss of circulation under high temperature and high pressure. A series of experiments were conducted to discuss the compressional wave velocity, triaxial strength, and permeability of granite cored from 3500 meters borehole under high temperature and three-dimensional stress. In light of the coupling of temperature, fluid, and stress, we get the thermo-fluid-solid model and governing equation. ANSYS-APDL was also used to stimulate the temperature influence on elastic modulus, Poisson ratio, uniaxial compressive strength, and permeability. In light of the results, we establish a temperature-fluid-stress model to illustrate the granite’s stability. The compressional wave velocity and elastic modulus, decrease as the temperature rises, while poisson ratio and permeability of granite increase. The threshold pressure and temperature are 15 MPa and 200°C, respectively. The temperature affects the fracture pressure more than the collapse pressure, but both parameters rise with the increase of temperature. The coupling of thermo-fluid-solid, greatly impacting the borehole stability, proves to be a good method to analyze similar problems of other formations.

Thermophysical and mechanical properties of granite and its effects on borehole stability in high temperature and three-dimensional stress.

Science.gov (United States)

Wang, Yu; Liu, Bao-lin; Zhu, Hai-yan; Yan, Chuan-liang; Li, Zhi-jun; Wang, Zhi-qiao

2014-01-01

When exploiting the deep resources, the surrounding rock readily undergoes the hole shrinkage, borehole collapse, and loss of circulation under high temperature and high pressure. A series of experiments were conducted to discuss the compressional wave velocity, triaxial strength, and permeability of granite cored from 3500 meters borehole under high temperature and three-dimensional stress. In light of the coupling of temperature, fluid, and stress, we get the thermo-fluid-solid model and governing equation. ANSYS-APDL was also used to stimulate the temperature influence on elastic modulus, Poisson ratio, uniaxial compressive strength, and permeability. In light of the results, we establish a temperature-fluid-stress model to illustrate the granite's stability. The compressional wave velocity and elastic modulus, decrease as the temperature rises, while poisson ratio and permeability of granite increase. The threshold pressure and temperature are 15 MPa and 200 °C, respectively. The temperature affects the fracture pressure more than the collapse pressure, but both parameters rise with the increase of temperature. The coupling of thermo-fluid-solid, greatly impacting the borehole stability, proves to be a good method to analyze similar problems of other formations.

Functional enrichment analyses and construction of functional similarity networks with high confidence function prediction by PFP

Directory of Open Access Journals (Sweden)

Kihara Daisuke

2010-05-01

Full Text Available Abstract Background A new paradigm of biological investigation takes advantage of technologies that produce large high throughput datasets, including genome sequences, interactions of proteins, and gene expression. The ability of biologists to analyze and interpret such data relies on functional annotation of the included proteins, but even in highly characterized organisms many proteins can lack the functional evidence necessary to infer their biological relevance. Results Here we have applied high confidence function predictions from our automated prediction system, PFP, to three genome sequences, Escherichia coli, Saccharomyces cerevisiae, and Plasmodium falciparum (malaria. The number of annotated genes is increased by PFP to over 90% for all of the genomes. Using the large coverage of the function annotation, we introduced the functional similarity networks which represent the functional space of the proteomes. Four different functional similarity networks are constructed for each proteome, one each by considering similarity in a single Gene Ontology (GO category, i.e. Biological Process, Cellular Component, and Molecular Function, and another one by considering overall similarity with the funSim score. The functional similarity networks are shown to have higher modularity than the protein-protein interaction network. Moreover, the funSim score network is distinct from the single GO-score networks by showing a higher clustering degree exponent value and thus has a higher tendency to be hierarchical. In addition, examining function assignments to the protein-protein interaction network and local regions of genomes has identified numerous cases where subnetworks or local regions have functionally coherent proteins. These results will help interpreting interactions of proteins and gene orders in a genome. Several examples of both analyses are highlighted. Conclusion The analyses demonstrate that applying high confidence predictions from PFP

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

International Nuclear Information System (INIS)

Zhang, Liangwei; Lin, Jing; Karim, Ramin

2015-01-01

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

Two-dimensional quantum repeaters

Science.gov (United States)

Wallnöfer, J.; Zwerger, M.; Muschik, C.; Sangouard, N.; Dür, W.

2016-11-01

The endeavor to develop quantum networks gave rise to a rapidly developing field with far-reaching applications such as secure communication and the realization of distributed computing tasks. This ultimately calls for the creation of flexible multiuser structures that allow for quantum communication between arbitrary pairs of parties in the network and facilitate also multiuser applications. To address this challenge, we propose a two-dimensional quantum repeater architecture to establish long-distance entanglement shared between multiple communication partners in the presence of channel noise and imperfect local control operations. The scheme is based on the creation of self-similar multiqubit entanglement structures at growing scale, where variants of entanglement swapping and multiparty entanglement purification are combined to create high-fidelity entangled states. We show how such networks can be implemented using trapped ions in cavities.

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

Directory of Open Access Journals (Sweden)

Zekić-Sušac Marijana

2014-09-01

Full Text Available Background: Large-dimensional data modelling often relies on variable reduction methods in the pre-processing and in the post-processing stage. However, such a reduction usually provides less information and yields a lower accuracy of the model. Objectives: The aim of this paper is to assess the high-dimensional classification problem of recognizing entrepreneurial intentions of students by machine learning methods. Methods/Approach: Four methods were tested: artificial neural networks, CART classification trees, support vector machines, and k-nearest neighbour on the same dataset in order to compare their efficiency in the sense of classification accuracy. The performance of each method was compared on ten subsamples in a 10-fold cross-validation procedure in order to assess computing sensitivity and specificity of each model. Results: The artificial neural network model based on multilayer perceptron yielded a higher classification rate than the models produced by other methods. The pairwise t-test showed a statistical significance between the artificial neural network and the k-nearest neighbour model, while the difference among other methods was not statistically significant. Conclusions: Tested machine learning methods are able to learn fast and achieve high classification accuracy. However, further advancement can be assured by testing a few additional methodological refinements in machine learning methods.

The cross-validated AUC for MCP-logistic regression with high-dimensional data.

Science.gov (United States)

Jiang, Dingfeng; Huang, Jian; Zhang, Ying

2013-10-01

We propose a cross-validated area under the receiving operator characteristic (ROC) curve (CV-AUC) criterion for tuning parameter selection for penalized methods in sparse, high-dimensional logistic regression models. We use this criterion in combination with the minimax concave penalty (MCP) method for variable selection. The CV-AUC criterion is specifically designed for optimizing the classification performance for binary outcome data. To implement the proposed approach, we derive an efficient coordinate descent algorithm to compute the MCP-logistic regression solution surface. Simulation studies are conducted to evaluate the finite sample performance of the proposed method and its comparison with the existing methods including the Akaike information criterion (AIC), Bayesian information criterion (BIC) or Extended BIC (EBIC). The model selected based on the CV-AUC criterion tends to have a larger predictive AUC and smaller classification error than those with tuning parameters selected using the AIC, BIC or EBIC. We illustrate the application of the MCP-logistic regression with the CV-AUC criterion on three microarray datasets from the studies that attempt to identify genes related to cancers. Our simulation studies and data examples demonstrate that the CV-AUC is an attractive method for tuning parameter selection for penalized methods in high-dimensional logistic regression models.

High copy number of highly similar mariner-like transposons in planarian (Platyhelminthe): evidence for a trans-phyla horizontal transfer.

Science.gov (United States)

Garcia-Fernàndez, J; Bayascas-Ramírez, J R; Marfany, G; Muñoz-Mármol, A M; Casali, A; Baguñà, J; Saló, E

1995-05-01

Several DNA sequences similar to the mariner element were isolated and characterized in the platyhelminthe Dugesia (Girardia) tigrina. They were 1,288 bp long, flanked by two 32 bp-inverted repeats, and contained a single 339 amino acid open-reading frame (ORF) encoding the transposase. The number of copies of this element is approximately 8,000 per haploid genome, constituting a member of the middle-repetitive DNA of Dugesia tigrina. Sequence analysis of several elements showed a high percentage of conservation between the different copies. Most of them presented an intact ORF and the standard signals of actively expressed genes, which suggests that some of them are or have recently been functional transposons. The high degree of similarity shared with other mariner elements from some arthropods, together with the fact that this element is undetectable in other planarian species, strongly suggests a case of horizontal transfer between these two distant phyla.

Statistical mechanics of complex neural systems and high dimensional data

International Nuclear Information System (INIS)

Advani, Madhu; Lahiri, Subhaneil; Ganguli, Surya

2013-01-01

Recent experimental advances in neuroscience have opened new vistas into the immense complexity of neuronal networks. This proliferation of data challenges us on two parallel fronts. First, how can we form adequate theoretical frameworks for understanding how dynamical network processes cooperate across widely disparate spatiotemporal scales to solve important computational problems? Second, how can we extract meaningful models of neuronal systems from high dimensional datasets? To aid in these challenges, we give a pedagogical review of a collection of ideas and theoretical methods arising at the intersection of statistical physics, computer science and neurobiology. We introduce the interrelated replica and cavity methods, which originated in statistical physics as powerful ways to quantitatively analyze large highly heterogeneous systems of many interacting degrees of freedom. We also introduce the closely related notion of message passing in graphical models, which originated in computer science as a distributed algorithm capable of solving large inference and optimization problems involving many coupled variables. We then show how both the statistical physics and computer science perspectives can be applied in a wide diversity of contexts to problems arising in theoretical neuroscience and data analysis. Along the way we discuss spin glasses, learning theory, illusions of structure in noise, random matrices, dimensionality reduction and compressed sensing, all within the unified formalism of the replica method. Moreover, we review recent conceptual connections between message passing in graphical models, and neural computation and learning. Overall, these ideas illustrate how statistical physics and computer science might provide a lens through which we can uncover emergent computational functions buried deep within the dynamical complexities of neuronal networks. (paper)

Similarity of High-Resolution Tandem Mass Spectrometry Spectra of Structurally Related Micropollutants and Transformation Products

Science.gov (United States)

Schollée, Jennifer E.; Schymanski, Emma L.; Stravs, Michael A.; Gulde, Rebekka; Thomaidis, Nikolaos S.; Hollender, Juliane

2017-12-01

High-resolution tandem mass spectrometry (HRMS2) with electrospray ionization is frequently applied to study polar organic molecules such as micropollutants. Fragmentation provides structural information to confirm structures of known compounds or propose structures of unknown compounds. Similarity of HRMS2 spectra between structurally related compounds has been suggested to facilitate identification of unknown compounds. To test this hypothesis, the similarity of reference standard HRMS2 spectra was calculated for 243 pairs of micropollutants and their structurally related transformation products (TPs); for comparison, spectral similarity was also calculated for 219 pairs of unrelated compounds. Spectra were measured on Orbitrap and QTOF mass spectrometers and similarity was calculated with the dot product. The influence of different factors on spectral similarity [e.g., normalized collision energy (NCE), merging fragments from all NCEs, and shifting fragments by the mass difference of the pair] was considered. Spectral similarity increased at higher NCEs and highest similarity scores for related pairs were obtained with merged spectra including measured fragments and shifted fragments. Removal of the monoisotopic peak was critical to reduce false positives. Using a spectral similarity score threshold of 0.52, 40% of related pairs and 0% of unrelated pairs were above this value. Structural similarity was estimated with the Tanimoto coefficient and pairs with higher structural similarity generally had higher spectral similarity. Pairs where one or both compounds contained heteroatoms such as sulfur often resulted in dissimilar spectra. This work demonstrates that HRMS2 spectral similarity may indicate structural similarity and that spectral similarity can be used in the future to screen complex samples for related compounds such as micropollutants and TPs, assisting in the prioritization of non-target compounds. [Figure not available: see fulltext.

A Penalized Likelihood Framework For High-Dimensional Phylogenetic Comparative Methods And An Application To New-World Monkeys Brain Evolution.

Science.gov (United States)

Julien, Clavel; Leandro, Aristide; Hélène, Morlon

2018-06-19

Working with high-dimensional phylogenetic comparative datasets is challenging because likelihood-based multivariate methods suffer from low statistical performances as the number of traits p approaches the number of species n and because some computational complications occur when p exceeds n. Alternative phylogenetic comparative methods have recently been proposed to deal with the large p small n scenario but their use and performances are limited. Here we develop a penalized likelihood framework to deal with high-dimensional comparative datasets. We propose various penalizations and methods for selecting the intensity of the penalties. We apply this general framework to the estimation of parameters (the evolutionary trait covariance matrix and parameters of the evolutionary model) and model comparison for the high-dimensional multivariate Brownian (BM), Early-burst (EB), Ornstein-Uhlenbeck (OU) and Pagel's lambda models. We show using simulations that our penalized likelihood approach dramatically improves the estimation of evolutionary trait covariance matrices and model parameters when p approaches n, and allows for their accurate estimation when p equals or exceeds n. In addition, we show that penalized likelihood models can be efficiently compared using Generalized Information Criterion (GIC). We implement these methods, as well as the related estimation of ancestral states and the computation of phylogenetic PCA in the R package RPANDA and mvMORPH. Finally, we illustrate the utility of the new proposed framework by evaluating evolutionary models fit, analyzing integration patterns, and reconstructing evolutionary trajectories for a high-dimensional 3-D dataset of brain shape in the New World monkeys. We find a clear support for an Early-burst model suggesting an early diversification of brain morphology during the ecological radiation of the clade. Penalized likelihood offers an efficient way to deal with high-dimensional multivariate comparative data.

Using Localised Quadratic Functions on an Irregular Grid for Pricing High-Dimensional American Options

NARCIS (Netherlands)

Berridge, S.J.; Schumacher, J.M.

2004-01-01

We propose a method for pricing high-dimensional American options on an irregular grid; the method involves using quadratic functions to approximate the local effect of the Black-Scholes operator.Once such an approximation is known, one can solve the pricing problem by time stepping in an explicit

A Methodology to Determine Self-Similarity, Illustrated by Example: Transient Heat Transfer with Constant Flux

Science.gov (United States)

Monroe, Charles; Newman, John

2005-01-01

This simple example demonstrates the physical significance of similarity solutions and the utility of dimensional and asymptotic analysis of partial differential equations. A procedure to determine the existence of similarity solutions is proposed and subsequently applied to transient constant-flux heat transfer. Short-time expressions follow from…

GAMLSS for high-dimensional data – a flexible approach based on boosting

OpenAIRE

Mayr, Andreas; Fenske, Nora; Hofner, Benjamin; Kneib, Thomas; Schmid, Matthias

2010-01-01

Generalized additive models for location, scale and shape (GAMLSS) are a popular semi-parametric modelling approach that, in contrast to conventional GAMs, regress not only the expected mean but every distribution parameter (e.g. location, scale and shape) to a set of covariates. Current fitting procedures for GAMLSS are infeasible for high-dimensional data setups and require variable selection based on (potentially problematic) information criteria. The present work describes a boosting algo...

Turbulence, dynamic similarity and scale effects in high-velocity free-surface flows above a stepped chute

Science.gov (United States)

Felder, Stefan; Chanson, Hubert

2009-07-01

In high-velocity free-surface flows, air entrainment is common through the interface, and intense interactions take place between turbulent structures and entrained bubbles. Two-phase flow properties were measured herein in high-velocity open channel flows above a stepped chute. Detailed turbulence measurements were conducted in a large-size facility, and a comparative analysis was applied to test the validity of the Froude and Reynolds similarities. The results showed consistently that the Froude similitude was not satisfied using a 2:1 geometric scaling ratio. Lesser number of entrained bubbles and comparatively greater bubble sizes were observed at the smaller Reynolds numbers, as well as lower turbulence levels and larger turbulent length and time scales. The results implied that small-size models did underestimate the rate of energy dissipation and the aeration efficiency of prototype stepped spillways for similar flow conditions. Similarly a Reynolds similitude was tested. The results showed also some significant scale effects. However a number of self-similar relationships remained invariant under changes of scale and confirmed the analysis of Chanson and Carosi (Exp Fluids 42:385-401, 2007). The finding is significant because self-similarity may provide a picture general enough to be used to characterise the air-water flow field in large prototype channels.

Three-dimensional object recognition using similar triangles and decision trees

Science.gov (United States)

Spirkovska, Lilly

1993-01-01

A system, TRIDEC, that is capable of distinguishing between a set of objects despite changes in the objects' positions in the input field, their size, or their rotational orientation in 3D space is described. TRIDEC combines very simple yet effective features with the classification capabilities of inductive decision tree methods. The feature vector is a list of all similar triangles defined by connecting all combinations of three pixels in a coarse coded 127 x 127 pixel input field. The classification is accomplished by building a decision tree using the information provided from a limited number of translated, scaled, and rotated samples. Simulation results are presented which show that TRIDEC achieves 94 percent recognition accuracy in the 2D invariant object recognition domain and 98 percent recognition accuracy in the 3D invariant object recognition domain after training on only a small sample of transformed views of the objects.

Fast Markov chain Monte Carlo sampling for sparse Bayesian inference in high-dimensional inverse problems using L1-type priors

International Nuclear Information System (INIS)

Lucka, Felix

2012-01-01

Sparsity has become a key concept for solving of high-dimensional inverse problems using variational regularization techniques. Recently, using similar sparsity-constraints in the Bayesian framework for inverse problems by encoding them in the prior distribution has attracted attention. Important questions about the relation between regularization theory and Bayesian inference still need to be addressed when using sparsity promoting inversion. A practical obstacle for these examinations is the lack of fast posterior sampling algorithms for sparse, high-dimensional Bayesian inversion. Accessing the full range of Bayesian inference methods requires being able to draw samples from the posterior probability distribution in a fast and efficient way. This is usually done using Markov chain Monte Carlo (MCMC) sampling algorithms. In this paper, we develop and examine a new implementation of a single component Gibbs MCMC sampler for sparse priors relying on L1-norms. We demonstrate that the efficiency of our Gibbs sampler increases when the level of sparsity or the dimension of the unknowns is increased. This property is contrary to the properties of the most commonly applied Metropolis–Hastings (MH) sampling schemes. We demonstrate that the efficiency of MH schemes for L1-type priors dramatically decreases when the level of sparsity or the dimension of the unknowns is increased. Practically, Bayesian inversion for L1-type priors using MH samplers is not feasible at all. As this is commonly believed to be an intrinsic feature of MCMC sampling, the performance of our Gibbs sampler also challenges common beliefs about the applicability of sample based Bayesian inference. (paper)

Exact Solutions to (2+1)-Dimensional Kaup-Kupershmidt Equation

International Nuclear Information System (INIS)

Lu Hailing; Liu Xiqiang

2009-01-01

In this paper, by using the symmetry method, the relationships between new explicit solutions and old ones of the (2+1)-dimensional Kaup-Kupershmidt (KK) equation are presented. We successfully obtain more general exact travelling wave solutions for (2+1)-dimensional KK equation by the symmetry method and the (G'/G)-expansion method. Consequently, we find some new solutions of (2+1)-dimensional KK equation, including similarity solutions, solitary wave solutions, and periodic solutions. (general)

Dimensional consistency achieved in high-performance synchronizing hubs

Directory of Open Access Journals (Sweden)

García, P.

2013-02-01

Full Text Available The tolerances of parts produced for the automotive industry are so tight that any small process variation may mean that the product does not fulfill them. As dimensional tolerances decrease, the material properties of parts are expected to be improved. Depending on the dimensional and material requirements of a part, different production routes are available to find robust processes, minimizing cost and maximizing process capability. Dimensional tolerances have been reduced in recent years, and as a result, the double pressing-double sintering production via (“2P2S” has again become an accurate way to meet these increasingly narrow tolerances. In this paper, it is shown that the process parameters of the first sintering have great influence on the following production steps and the dimensions of the final parts. The roles of factors other than density and the second sintering process in defining the final dimensions of product are probed. All trials were done in a production line that produces synchronizer hubs for manual transmissions, allowing the maintenance of stable conditions and control of those parameters that are relevant for the product and process.

Las tolerancias en componentes fabricados para la industria del automóvil son tan estrechas que cualquier modificación en las variables del proceso puede provocar que no se cumplan. Una disminución de las tolerancias dimensionales, puede significar una mejora en las propiedades de las piezas. Dependiendo de los requerimientos dimensionales y del material, distintas rutas de procesado pueden seguirse para encontrar un método de procesado robusto, que minimice costes y maximice la capacidad del proceso. En los últimos años, la tolerancia dimensional se ha ajustado gracias a métodos de procesado como el doble prensado/doble sinterizado (“2P2S”, método de gran precisión para conseguir estrechas tolerancias. En este trabajo, se muestra que los parámetros de procesado

THREE-DIMENSIONAL ATMOSPHERIC CIRCULATION OF HOT JUPITERS ON HIGHLY ECCENTRIC ORBITS

International Nuclear Information System (INIS)

Kataria, T.; Showman, A. P.; Lewis, N. K.; Fortney, J. J.; Marley, M. S.; Freedman, R. S.

2013-01-01

Of the over 800 exoplanets detected to date, over half are on non-circular orbits, with eccentricities as high as 0.93. Such orbits lead to time-variable stellar heating, which has major implications for the planet's atmospheric dynamical regime. However, little is known about the fundamental dynamical regime of such planetary atmospheres, and how it may influence the observations of these planets. Therefore, we present a systematic study of hot Jupiters on highly eccentric orbits using the SPARC/MITgcm, a model which couples a three-dimensional general circulation model (the MITgcm) with a plane-parallel, two-stream, non-gray radiative transfer model. In our study, we vary the eccentricity and orbit-average stellar flux over a wide range. We demonstrate that the eccentric hot Jupiter regime is qualitatively similar to that of planets on circular orbits; the planets possess a superrotating equatorial jet and exhibit large day-night temperature variations. As in Showman and Polvani, we show that the day-night heating variations induce momentum fluxes equatorward to maintain the superrotating jet throughout its orbit. We find that as the eccentricity and/or stellar flux is increased (corresponding to shorter orbital periods), the superrotating jet strengthens and narrows, due to a smaller Rossby deformation radius. For a select number of model integrations, we generate full-orbit light curves and find that the timing of transit and secondary eclipse viewed from Earth with respect to periapse and apoapse can greatly affect what we see in infrared (IR) light curves; the peak in IR flux can lead or lag secondary eclipse depending on the geometry. For those planets that have large temperature differences from dayside to nightside and rapid rotation rates, we find that the light curves can exhibit 'ringing' as the planet's hottest region rotates in and out of view from Earth. These results can be used to explain future observations of eccentric transiting exoplanets.

Three-dimensional laparoscopy vs 2-dimensional laparoscopy with high-definition technology for abdominal surgery

DEFF Research Database (Denmark)

Fergo, Charlotte; Burcharth, Jakob; Pommergaard, Hans-Christian

2017-01-01

BACKGROUND: This systematic review investigates newer generation 3-dimensional (3D) laparoscopy vs 2-dimensional (2D) laparoscopy in terms of error rating, performance time, and subjective assessment as early comparisons have shown contradictory results due to technological shortcomings. DATA...... Central Register of Controlled Trials database. CONCLUSIONS: Of 643 articles, 13 RCTs were included, of which 2 were clinical trials. Nine of 13 trials (69%) and 10 of 13 trials (77%) found a significant reduction in performance time and error, respectively, with the use of 3D-laparoscopy. Overall, 3D......-laparoscopy was found to be superior or equal to 2D-laparoscopy. All trials featuring subjective evaluation found a superiority of 3D-laparoscopy. More clinical RCTs are still awaited for the convincing results to be reproduced....

High-definition resolution three-dimensional imaging systems in laparoscopic radical prostatectomy: randomized comparative study with high-definition resolution two-dimensional systems.

Science.gov (United States)

Kinoshita, Hidefumi; Nakagawa, Ken; Usui, Yukio; Iwamura, Masatsugu; Ito, Akihiro; Miyajima, Akira; Hoshi, Akio; Arai, Yoichi; Baba, Shiro; Matsuda, Tadashi

2015-08-01

Three-dimensional (3D) imaging systems have been introduced worldwide for surgical instrumentation. A difficulty of laparoscopic surgery involves converting two-dimensional (2D) images into 3D images and depth perception rearrangement. 3D imaging may remove the need for depth perception rearrangement and therefore have clinical benefits. We conducted a multicenter, open-label, randomized trial to compare the surgical outcome of 3D-high-definition (HD) resolution and 2D-HD imaging in laparoscopic radical prostatectomy (LRP), in order to determine whether an LRP under HD resolution 3D imaging is superior to that under HD resolution 2D imaging in perioperative outcome, feasibility, and fatigue. One-hundred twenty-two patients were randomly assigned to a 2D or 3D group. The primary outcome was time to perform vesicourethral anastomosis (VUA), which is technically demanding and may include a number of technical difficulties considered in laparoscopic surgeries. VUA time was not significantly shorter in the 3D group (26.7 min, mean) compared with the 2D group (30.1 min, mean) (p = 0.11, Student's t test). However, experienced surgeons and 3D-HD imaging were independent predictors for shorter VUA times (p = 0.000, p = 0.014, multivariate logistic regression analysis). Total pneumoperitoneum time was not different. No conversion case from 3D to 2D or LRP to open RP was observed. Fatigue was evaluated by a simulation sickness questionnaire and critical flicker frequency. Results were not different between the two groups. Subjective feasibility and satisfaction scores were significantly higher in the 3D group. Using a 3D imaging system in LRP may have only limited advantages in decreasing operation times over 2D imaging systems. However, the 3D system increased surgical feasibility and decreased surgeons' effort levels without inducing significant fatigue.

TH-CD-207A-07: Prediction of High Dimensional State Subject to Respiratory Motion: A Manifold Learning Approach

International Nuclear Information System (INIS)

Liu, W; Sawant, A; Ruan, D

2016-01-01

Purpose: The development of high dimensional imaging systems (e.g. volumetric MRI, CBCT, photogrammetry systems) in image-guided radiotherapy provides important pathways to the ultimate goal of real-time volumetric/surface motion monitoring. This study aims to develop a prediction method for the high dimensional state subject to respiratory motion. Compared to conventional linear dimension reduction based approaches, our method utilizes manifold learning to construct a descriptive feature submanifold, where more efficient and accurate prediction can be performed. Methods: We developed a prediction framework for high-dimensional state subject to respiratory motion. The proposed method performs dimension reduction in a nonlinear setting to permit more descriptive features compared to its linear counterparts (e.g., classic PCA). Specifically, a kernel PCA is used to construct a proper low-dimensional feature manifold, where low-dimensional prediction is performed. A fixed-point iterative pre-image estimation method is applied subsequently to recover the predicted value in the original state space. We evaluated and compared the proposed method with PCA-based method on 200 level-set surfaces reconstructed from surface point clouds captured by the VisionRT system. The prediction accuracy was evaluated with respect to root-mean-squared-error (RMSE) for both 200ms and 600ms lookahead lengths. Results: The proposed method outperformed PCA-based approach with statistically higher prediction accuracy. In one-dimensional feature subspace, our method achieved mean prediction accuracy of 0.86mm and 0.89mm for 200ms and 600ms lookahead lengths respectively, compared to 0.95mm and 1.04mm from PCA-based method. The paired t-tests further demonstrated the statistical significance of the superiority of our method, with p-values of 6.33e-3 and 5.78e-5, respectively. Conclusion: The proposed approach benefits from the descriptiveness of a nonlinear manifold and the prediction

The literary uses of high-dimensional space

Directory of Open Access Journals (Sweden)

Ted Underwood

2015-12-01

Full Text Available Debates over “Big Data” shed more heat than light in the humanities, because the term ascribes new importance to statistical methods without explaining how those methods have changed. What we badly need instead is a conversation about the substantive innovations that have made statistical modeling useful for disciplines where, in the past, it truly wasn’t. These innovations are partly technical, but more fundamentally expressed in what Leo Breiman calls a new “culture” of statistical modeling. Where 20th-century methods often required humanists to squeeze our unstructured texts, sounds, or images into some special-purpose data model, new methods can handle unstructured evidence more directly by modeling it in a high-dimensional space. This opens a range of research opportunities that humanists have barely begun to discuss. To date, topic modeling has received most attention, but in the long run, supervised predictive models may be even more important. I sketch their potential by describing how Jordan Sellers and I have begun to model poetic distinction in the long 19th century—revealing an arc of gradual change much longer than received literary histories would lead us to expect.

Weighted similarity-based clustering of chemical structures and bioactivity data in early drug discovery.

Science.gov (United States)

Perualila-Tan, Nolen Joy; Shkedy, Ziv; Talloen, Willem; Göhlmann, Hinrich W H; Moerbeke, Marijke Van; Kasim, Adetayo

2016-08-01

The modern process of discovering candidate molecules in early drug discovery phase includes a wide range of approaches to extract vital information from the intersection of biology and chemistry. A typical strategy in compound selection involves compound clustering based on chemical similarity to obtain representative chemically diverse compounds (not incorporating potency information). In this paper, we propose an integrative clustering approach that makes use of both biological (compound efficacy) and chemical (structural features) data sources for the purpose of discovering a subset of compounds with aligned structural and biological properties. The datasets are integrated at the similarity level by assigning complementary weights to produce a weighted similarity matrix, serving as a generic input in any clustering algorithm. This new analysis work flow is semi-supervised method since, after the determination of clusters, a secondary analysis is performed wherein it finds differentially expressed genes associated to the derived integrated cluster(s) to further explain the compound-induced biological effects inside the cell. In this paper, datasets from two drug development oncology projects are used to illustrate the usefulness of the weighted similarity-based clustering approach to integrate multi-source high-dimensional information to aid drug discovery. Compounds that are structurally and biologically similar to the reference compounds are discovered using this proposed integrative approach.

Two and dimensional heat analysis inside a high pressure electrical discharge tube

International Nuclear Information System (INIS)

Aghanajafi, C.; Dehghani, A. R.; Fallah Abbasi, M.

2005-01-01

This article represents the heat transfer analysis for a horizontal high pressure mercury steam tube. To get a more realistic numerical simulation, heat radiation at different wavelength width bands, has been used besides convection and conduction heat transfer. The analysis for different gases with different pressure in two and three dimensional cases has been investigated and the results compared with empirical and semi empirical values. The effect of the environmental temperature on the arc tube temperature is also studied

Controlling chaos in low and high dimensional systems with periodic parametric perturbations

International Nuclear Information System (INIS)

Mirus, K.A.; Sprott, J.C.

1998-06-01

The effect of applying a periodic perturbation to an accessible parameter of various chaotic systems is examined. Numerical results indicate that perturbation frequencies near the natural frequencies of the unstable periodic orbits of the chaotic systems can result in limit cycles for relatively small perturbations. Such perturbations can also control or significantly reduce the dimension of high-dimensional systems. Initial application to the control of fluctuations in a prototypical magnetic fusion plasma device will be reviewed

Dimensionality reduction of collective motion by principal manifolds

Science.gov (United States)

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

2015-01-01

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

Thermal Investigation of Three-Dimensional GaN-on-SiC High Electron Mobility Transistors

Science.gov (United States)

2017-07-01

University of L’Aquila, (2011). 23 Rao, H. & Bosman, G. Hot-electron induced defect generation in AlGaN/GaN high electron mobility transistors. Solid...AFRL-RY-WP-TR-2017-0143 THERMAL INVESTIGATION OF THREE- DIMENSIONAL GaN-on-SiC HIGH ELECTRON MOBILITY TRANSISTORS Qing Hao The University of Arizona...clarification memorandum dated 16 Jan 09. This report is available to the general public, including foreign nationals. Copies may be obtained from the

Self-similarities of periodic structures for a discrete model of a two-gene system

International Nuclear Information System (INIS)

Souza, S.L.T. de; Lima, A.A.; Caldas, I.L.; Medrano-T, R.O.; Guimarães-Filho, Z.O.

2012-01-01

We report self-similar properties of periodic structures remarkably organized in the two-parameter space for a two-gene system, described by two-dimensional symmetric map. The map consists of difference equations derived from the chemical reactions for gene expression and regulation. We characterize the system by using Lyapunov exponents and isoperiodic diagrams identifying periodic windows, denominated Arnold tongues and shrimp-shaped structures. Period-adding sequences are observed for both periodic windows. We also identify Fibonacci-type series and Golden ratio for Arnold tongues, and period multiple-of-three windows for shrimps. -- Highlights: ► The existence of noticeable periodic windows has been reported recently for several nonlinear systems. ► The periodic window distributions appear highly organized in two-parameter space. ► We characterize self-similar properties of Arnold tongues and shrimps for a two-gene model. ► We determine the period of the Arnold tongues recognizing a Fibonacci-type sequence. ► We explore self-similar features of the shrimps identifying multiple period-three structures.

Self-similarities of periodic structures for a discrete model of a two-gene system

Energy Technology Data Exchange (ETDEWEB)

Souza, S.L.T. de, E-mail: thomaz@ufsj.edu.br [Departamento de Física e Matemática, Universidade Federal de São João del-Rei, Ouro Branco, MG (Brazil); Lima, A.A. [Escola de Farmácia, Universidade Federal de Ouro Preto, Ouro Preto, MG (Brazil); Caldas, I.L. [Instituto de Física, Universidade de São Paulo, São Paulo, SP (Brazil); Medrano-T, R.O. [Departamento de Ciências Exatas e da Terra, Universidade Federal de São Paulo, Diadema, SP (Brazil); Guimarães-Filho, Z.O. [Aix-Marseille Univ., CNRS PIIM UMR6633, International Institute for Fusion Science, Marseille (France)

2012-03-12

We report self-similar properties of periodic structures remarkably organized in the two-parameter space for a two-gene system, described by two-dimensional symmetric map. The map consists of difference equations derived from the chemical reactions for gene expression and regulation. We characterize the system by using Lyapunov exponents and isoperiodic diagrams identifying periodic windows, denominated Arnold tongues and shrimp-shaped structures. Period-adding sequences are observed for both periodic windows. We also identify Fibonacci-type series and Golden ratio for Arnold tongues, and period multiple-of-three windows for shrimps. -- Highlights: ► The existence of noticeable periodic windows has been reported recently for several nonlinear systems. ► The periodic window distributions appear highly organized in two-parameter space. ► We characterize self-similar properties of Arnold tongues and shrimps for a two-gene model. ► We determine the period of the Arnold tongues recognizing a Fibonacci-type sequence. ► We explore self-similar features of the shrimps identifying multiple period-three structures.

Evidence for deep regulatory similarities in early developmental programs across highly diverged insects.

Science.gov (United States)

Kazemian, Majid; Suryamohan, Kushal; Chen, Jia-Yu; Zhang, Yinan; Samee, Md Abul Hassan; Halfon, Marc S; Sinha, Saurabh

2014-09-01

Many genes familiar from Drosophila development, such as the so-called gap, pair-rule, and segment polarity genes, play important roles in the development of other insects and in many cases appear to be deployed in a similar fashion, despite the fact that Drosophila-like "long germband" development is highly derived and confined to a subset of insect families. Whether or not these similarities extend to the regulatory level is unknown. Identification of regulatory regions beyond the well-studied Drosophila has been challenging as even within the Diptera (flies, including mosquitoes) regulatory sequences have diverged past the point of recognition by standard alignment methods. Here, we demonstrate that methods we previously developed for computational cis-regulatory module (CRM) discovery in Drosophila can be used effectively in highly diverged (250-350 Myr) insect species including Anopheles gambiae, Tribolium castaneum, Apis mellifera, and Nasonia vitripennis. In Drosophila, we have successfully used small sets of known CRMs as "training data" to guide the search for other CRMs with related function. We show here that although species-specific CRM training data do not exist, training sets from Drosophila can facilitate CRM discovery in diverged insects. We validate in vivo over a dozen new CRMs, roughly doubling the number of known CRMs in the four non-Drosophila species. Given the growing wealth of Drosophila CRM annotation, these results suggest that extensive regulatory sequence annotation will be possible in newly sequenced insects without recourse to costly and labor-intensive genome-scale experiments. We develop a new method, Regulus, which computes a probabilistic score of similarity based on binding site composition (despite the absence of nucleotide-level sequence alignment), and demonstrate similarity between functionally related CRMs from orthologous loci. Our work represents an important step toward being able to trace the evolutionary history of gene

Evidence for Deep Regulatory Similarities in Early Developmental Programs across Highly Diverged Insects

Science.gov (United States)

Zhang, Yinan; Samee, Md. Abul Hassan; Halfon, Marc S.; Sinha, Saurabh

2014-01-01

Many genes familiar from Drosophila development, such as the so-called gap, pair-rule, and segment polarity genes, play important roles in the development of other insects and in many cases appear to be deployed in a similar fashion, despite the fact that Drosophila-like “long germband” development is highly derived and confined to a subset of insect families. Whether or not these similarities extend to the regulatory level is unknown. Identification of regulatory regions beyond the well-studied Drosophila has been challenging as even within the Diptera (flies, including mosquitoes) regulatory sequences have diverged past the point of recognition by standard alignment methods. Here, we demonstrate that methods we previously developed for computational cis-regulatory module (CRM) discovery in Drosophila can be used effectively in highly diverged (250–350 Myr) insect species including Anopheles gambiae, Tribolium castaneum, Apis mellifera, and Nasonia vitripennis. In Drosophila, we have successfully used small sets of known CRMs as “training data” to guide the search for other CRMs with related function. We show here that although species-specific CRM training data do not exist, training sets from Drosophila can facilitate CRM discovery in diverged insects. We validate in vivo over a dozen new CRMs, roughly doubling the number of known CRMs in the four non-Drosophila species. Given the growing wealth of Drosophila CRM annotation, these results suggest that extensive regulatory sequence annotation will be possible in newly sequenced insects without recourse to costly and labor-intensive genome-scale experiments. We develop a new method, Regulus, which computes a probabilistic score of similarity based on binding site composition (despite the absence of nucleotide-level sequence alignment), and demonstrate similarity between functionally related CRMs from orthologous loci. Our work represents an important step toward being able to trace the evolutionary

Visualizing Article Similarities via Sparsified Article Network and Map Projection for Systematic Reviews.

Science.gov (United States)

Ji, Xiaonan; Machiraju, Raghu; Ritter, Alan; Yen, Po-Yin

2017-01-01

Systematic Reviews (SRs) of biomedical literature summarize evidence from high-quality studies to inform clinical decisions, but are time and labor intensive due to the large number of article collections. Article similarities established from textual features have been shown to assist in the identification of relevant articles, thus facilitating the article screening process efficiently. In this study, we visualized article similarities to extend its utilization in practical settings for SR researchers, aiming to promote human comprehension of article distributions and hidden patterns. To prompt an effective visualization in an interpretable, intuitive, and scalable way, we implemented a graph-based network visualization with three network sparsification approaches and a distance-based map projection via dimensionality reduction. We evaluated and compared three network sparsification approaches and the visualization types (article network vs. article map). We demonstrated the effectiveness in revealing article distribution and exhibiting clustering patterns of relevant articles with practical meanings for SRs.

Thermodynamics of noncommutative high-dimensional AdS black holes with non-Gaussian smeared matter distributions

CERN Document Server

Miao, Yan-Gang

2016-01-01

Considering non-Gaussian smeared matter distributions, we investigate thermodynamic behaviors of the noncommutative high-dimensional Schwarzschild-Tangherlini anti-de Sitter black hole, and obtain the condition for the existence of extreme black holes. We indicate that the Gaussian smeared matter distribution, which is a special case of non-Gaussian smeared matter distributions, is not applicable for the 6- and higher-dimensional black holes due to the hoop conjecture. In particular, the phase transition is analyzed in detail. Moreover, we point out that the Maxwell equal area law maintains for the noncommutative black hole with the Hawking temperature within a specific range, but fails with the Hawking temperature beyond this range.

Problems of high temperature superconductivity in three-dimensional systems

Energy Technology Data Exchange (ETDEWEB)

Geilikman, B T

1973-01-01

A review is given of more recent papers on this subject. These papers have dealt mainly with two-dimensional systems. The present paper extends the treatment to three-dimensional systems, under the following headings: systems with collective electrons of one group and localized electrons of another group (compounds of metals with non-metals-dielectrics, organic substances, undoped semiconductors, molecular crystals); experimental investigations of superconducting compounds of metals with organic compounds, dielectrics, semiconductors, and semi-metals; and systems with two or more groups of collective electrons. Mechanics are considered and models are derived. 86 references.

Development of three-dimensional neoclassical transport simulation code with high performance Fortran on a vector-parallel computer

International Nuclear Information System (INIS)

Satake, Shinsuke; Okamoto, Masao; Nakajima, Noriyoshi; Takamaru, Hisanori

2005-11-01

A neoclassical transport simulation code (FORTEC-3D) applicable to three-dimensional configurations has been developed using High Performance Fortran (HPF). Adoption of computing techniques for parallelization and a hybrid simulation model to the δf Monte-Carlo method transport simulation, including non-local transport effects in three-dimensional configurations, makes it possible to simulate the dynamism of global, non-local transport phenomena with a self-consistent radial electric field within a reasonable computation time. In this paper, development of the transport code using HPF is reported. Optimization techniques in order to achieve both high vectorization and parallelization efficiency, adoption of a parallel random number generator, and also benchmark results, are shown. (author)

Investigating Correlation between Protein Sequence Similarity and Semantic Similarity Using Gene Ontology Annotations.

Science.gov (United States)

Ikram, Najmul; Qadir, Muhammad Abdul; Afzal, Muhammad Tanvir

2018-01-01

Sequence similarity is a commonly used measure to compare proteins. With the increasing use of ontologies, semantic (function) similarity is getting importance. The correlation between these measures has been applied in the evaluation of new semantic similarity methods, and in protein function prediction. In this research, we investigate the relationship between the two similarity methods. The results suggest absence of a strong correlation between sequence and semantic similarities. There is a large number of proteins with low sequence similarity and high semantic similarity. We observe that Pearson's correlation coefficient is not sufficient to explain the nature of this relationship. Interestingly, the term semantic similarity values above 0 and below 1 do not seem to play a role in improving the correlation. That is, the correlation coefficient depends only on the number of common GO terms in proteins under comparison, and the semantic similarity measurement method does not influence it. Semantic similarity and sequence similarity have a distinct behavior. These findings are of significant effect for future works on protein comparison, and will help understand the semantic similarity between proteins in a better way.

CAN AGN FEEDBACK BREAK THE SELF-SIMILARITY OF GALAXIES, GROUPS, AND CLUSTERS?

International Nuclear Information System (INIS)

Gaspari, M.; Brighenti, F.; Temi, P.; Ettori, S.

2014-01-01

It is commonly thought that active galactic nucleus (AGN) feedback can break the self-similar scaling relations of galaxies, groups, and clusters. Using high-resolution three-dimensional hydrodynamic simulations, we isolate the impact of AGN feedback on the L x -T x relation, testing the two archetypal and common regimes, self-regulated mechanical feedback and a quasar thermal blast. We find that AGN feedback has severe difficulty in breaking the relation in a consistent way. The similarity breaking is directly linked to the gas evacuation within R 500 , while the central cooling times are inversely proportional to the core density. Breaking self-similarity thus implies breaking the cool core, morphing all systems to non-cool-core objects, which is in clear contradiction with the observed data populated by several cool-core systems. Self-regulated feedback, which quenches cooling flows and preserves cool cores, prevents dramatic evacuation and similarity breaking at any scale; the relation scatter is also limited. The impulsive thermal blast can break the core-included L x -T x at T 500 ≲ 1 keV, but substantially empties and overheats the halo, generating a perennial non-cool-core group, as experienced by cosmological simulations. Even with partial evacuation, massive systems remain overheated. We show that the action of purely AGN feedback is to lower the luminosity and heat the gas, perpendicular to the fit

Three-dimensional reconstruction of highly complex microscopic samples using scanning electron microscopy and optical flow estimation.

Directory of Open Access Journals (Sweden)

Ahmadreza Baghaie

Full Text Available Scanning Electron Microscope (SEM as one of the major research and industrial equipment for imaging of micro-scale samples and surfaces has gained extensive attention from its emerge. However, the acquired micrographs still remain two-dimensional (2D. In the current work a novel and highly accurate approach is proposed to recover the hidden third-dimension by use of multi-view image acquisition of the microscopic samples combined with pre/post-processing steps including sparse feature-based stereo rectification, nonlocal-based optical flow estimation for dense matching and finally depth estimation. Employing the proposed approach, three-dimensional (3D reconstructions of highly complex microscopic samples were achieved to facilitate the interpretation of topology and geometry of surface/shape attributes of the samples. As a byproduct of the proposed approach, high-definition 3D printed models of the samples can be generated as a tangible means of physical understanding. Extensive comparisons with the state-of-the-art reveal the strength and superiority of the proposed method in uncovering the details of the highly complex microscopic samples.

Benfotiamine is similar to thiamine in correcting endothelial cell defects induced by high glucose.

Science.gov (United States)

Pomero, F; Molinar Min, A; La Selva, M; Allione, A; Molinatti, G M; Porta, M

2001-01-01

We investigated the hypothesis that benfotiamine, a lipophilic derivative of thiamine, affects replication delay and generation of advanced glycosylation end-products (AGE) in human umbilical vein endothelial cells cultured in the presence of high glucose. Cells were grown in physiological (5.6 mM) and high (28.0 mM) concentrations of D-glucose, with and without 150 microM thiamine or benfotiamine. Cell proliferation was measured by mitochondrial dehydrogenase activity. AGE generation after 20 days was assessed fluorimetrically. Cell replication was impaired by high glucose (72.3%+/-5.1% of that in physiological glucose, p=0.001). This was corrected by the addition of either thiamine (80.6%+/-2.4%, p=0.005) or benfotiamine (87.5%+/-8.9%, p=0.006), although it not was completely normalized (p=0.001 and p=0.008, respectively) to that in physiological glucose. Increased AGE production in high glucose (159.7%+/-38.9% of fluorescence in physiological glucose, p=0.003) was reduced by thiamine (113.2%+/-16.3%, p=0.008 vs. high glucose alone) or benfotiamine (135.6%+/-49.8%, p=0.03 vs. high glucose alone) to levels similar to those observed in physiological glucose. Benfotiamine, a derivative of thiamine with better bioavailability, corrects defective replication and increased AGE generation in endothelial cells cultured in high glucose, to a similar extent as thiamine. These effects may result from normalization of accelerated glycolysis and the consequent decrease in metabolites that are extremely active in generating nonenzymatic protein glycation. The potential role of thiamine administration in the prevention or treatment of vascular complications of diabetes deserves further investigation.

Finding and Visualizing Relevant Subspaces for Clustering High-Dimensional Astronomical Data Using Connected Morphological Operators

NARCIS (Netherlands)

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.

Graphene quantum dots-three-dimensional graphene composites for high-performance supercapacitors.

Science.gov (United States)

Chen, Qing; Hu, Yue; Hu, Chuangang; Cheng, Huhu; Zhang, Zhipan; Shao, Huibo; Qu, Liangti

2014-09-28

Graphene quantum dots (GQDs) have been successfully deposited onto the three-dimensional graphene (3DG) by a benign electrochemical method and the ordered 3DG structure remains intact after the uniform deposition of GQDs. In addition, the capacitive properties of the as-formed GQD-3DG composites are evaluated in symmetrical supercapacitors. It is found that the supercapacitor fabricated from the GQD-3DG composite is highly stable and exhibits a high specific capacitance of 268 F g(-1), representing a more than 90% improvement over that of the supercapacitor made from pure 3DG electrodes (136 F g(-1)). Owing to the convenience of the current method, it can be further used in other well-defined electrode materials, such as carbon nanotubes, carbon aerogels and conjugated polymers to improve the performance of the supercapacitors.

Self-similarity of high-pT hadron production in π-p and π- A collisions

International Nuclear Information System (INIS)

Tokarev, M.V.; Panebrattsev, Yu.A.; Skoro, G.P.; Zborovsky, I.

2002-01-01

Self-similar properties of hadron production in π - p and π - A collisions over a high-p T region are studied. The analysis if experimental data is performed in the framework of z-scaling. The scaling variable depends on the anomalous fractal dimension of the incoming pion. Its value is found to be δ π ≅ 0.1. Independence of the scaling function Ψ(z) on the collision energy is shown. A-dependence of data z-presentation confirms self-similarity of particle formation in πA collisions

Dynamics of a two-dimensional order-disorder transition

International Nuclear Information System (INIS)

Sahni, P.S.; Dee, G.; Gunton, J.D.; Phani, M.; Lebowitz, J.L.; Kalos, M.

1981-01-01

We present results of a Monte Carlo study of the time development of a two-dimensional order-disorder model binary alloy following a quench to low temperature from a disordered, high-temperature state. The behavior is qualitatively quite similar to that seen in a recent study of a three-dimensional system. The structure function exhibits a scaling of the form K 2 (t)S(k,t) = G(k/K(t)) where the moment K(t) decreases with time approximately like t/sup -1/2/. If one interprets this moment as being inversely proportional to the domain size, the characteristic domain growth rate is proportional to t/sup -1/2/. Additional insight into this time evolution is obtained from studying the development of the short-range order, as well as from monitoring the growth of a compact ordered domain embedded in a region of opposite order. All these results are consistent with the picture of domain growth as proposed by Lifshitz and by Cahn and Allen

Evaluation of aqueductal patency in patients with hydrocephalus: Three-dimensional high-sampling efficiency technique(SPACE) versus two-dimensional turbo spin echo at 3 Tesla

International Nuclear Information System (INIS)

Ucar, Murat; Guryildirim, Melike; Tokgoz, Nil; Kilic, Koray; Borcek, Alp; Oner, Yusuf; Akkan, Koray; Tali, Turgut

2014-01-01

To compare the accuracy of diagnosing aqueductal patency and image quality between high spatial resolution three-dimensional (3D) high-sampling-efficiency technique (sampling perfection with application optimized contrast using different flip angle evolutions [SPACE]) and T2-weighted (T2W) two-dimensional (2D) turbo spin echo (TSE) at 3-T in patients with hydrocephalus. This retrospective study included 99 patients diagnosed with hydrocephalus. T2W 3D-SPACE was added to the routine sequences which consisted of T2W 2D-TSE, 3D-constructive interference steady state (CISS), and cine phase-contrast MRI (PC-MRI). Two radiologists evaluated independently the patency of cerebral aqueduct and image quality on the T2W 2D-TSE and T2W 3D-SPACE. PC-MRI and 3D-CISS were used as the reference for aqueductal patency and image quality, respectively. Inter-observer agreement was calculated using kappa statistics. The evaluation of the aqueductal patency by T2W 3D-SPACE and T2W 2D-TSE were in agreement with PC-MRI in 100% (99/99; sensitivity, 100% [83/83]; specificity, 100% [16/16]) and 83.8% (83/99; sensitivity, 100% [67/83]; specificity, 100% [16/16]), respectively (p < 0.001). No significant difference in image quality between T2W 2D-TSE and T2W 3D-SPACE (p = 0.056) occurred. The kappa values for inter-observer agreement were 0.714 for T2W 2D-TSE and 0.899 for T2W 3D-SPACE. Three-dimensional-SPACE is superior to 2D-TSE for the evaluation of aqueductal patency in hydrocephalus. T2W 3D-SPACE may hold promise as a highly accurate alternative treatment to PC-MRI for the physiological and morphological evaluation of aqueductal patency.

Evaluation of aqueductal patency in patients with hydrocephalus: Three-dimensional high-sampling efficiency technique(SPACE) versus two-dimensional turbo spin echo at 3 Tesla

Energy Technology Data Exchange (ETDEWEB)

Ucar, Murat; Guryildirim, Melike; Tokgoz, Nil; Kilic, Koray; Borcek, Alp; Oner, Yusuf; Akkan, Koray; Tali, Turgut [School of Medicine, Gazi University, Ankara (Turkey)

2014-12-15

To compare the accuracy of diagnosing aqueductal patency and image quality between high spatial resolution three-dimensional (3D) high-sampling-efficiency technique (sampling perfection with application optimized contrast using different flip angle evolutions [SPACE]) and T2-weighted (T2W) two-dimensional (2D) turbo spin echo (TSE) at 3-T in patients with hydrocephalus. This retrospective study included 99 patients diagnosed with hydrocephalus. T2W 3D-SPACE was added to the routine sequences which consisted of T2W 2D-TSE, 3D-constructive interference steady state (CISS), and cine phase-contrast MRI (PC-MRI). Two radiologists evaluated independently the patency of cerebral aqueduct and image quality on the T2W 2D-TSE and T2W 3D-SPACE. PC-MRI and 3D-CISS were used as the reference for aqueductal patency and image quality, respectively. Inter-observer agreement was calculated using kappa statistics. The evaluation of the aqueductal patency by T2W 3D-SPACE and T2W 2D-TSE were in agreement with PC-MRI in 100% (99/99; sensitivity, 100% [83/83]; specificity, 100% [16/16]) and 83.8% (83/99; sensitivity, 100% [67/83]; specificity, 100% [16/16]), respectively (p < 0.001). No significant difference in image quality between T2W 2D-TSE and T2W 3D-SPACE (p = 0.056) occurred. The kappa values for inter-observer agreement were 0.714 for T2W 2D-TSE and 0.899 for T2W 3D-SPACE. Three-dimensional-SPACE is superior to 2D-TSE for the evaluation of aqueductal patency in hydrocephalus. T2W 3D-SPACE may hold promise as a highly accurate alternative treatment to PC-MRI for the physiological and morphological evaluation of aqueductal patency.

High-resolution liquid patterns via three-dimensional droplet shape control.

Science.gov (United States)

Raj, Rishi; Adera, Solomon; Enright, Ryan; Wang, Evelyn N

2014-09-25

Understanding liquid dynamics on surfaces can provide insight into nature's design and enable fine manipulation capability in biological, manufacturing, microfluidic and thermal management applications. Of particular interest is the ability to control the shape of the droplet contact area on the surface, which is typically circular on a smooth homogeneous surface. Here, we show the ability to tailor various droplet contact area shapes ranging from squares, rectangles, hexagons, octagons, to dodecagons via the design of the structure or chemical heterogeneity on the surface. We simultaneously obtain the necessary physical insights to develop a universal model for the three-dimensional droplet shape by characterizing the droplet side and top profiles. Furthermore, arrays of droplets with controlled shapes and high spatial resolution can be achieved using this approach. This liquid-based patterning strategy promises low-cost fabrication of integrated circuits, conductive patterns and bio-microarrays for high-density information storage and miniaturized biochips and biosensors, among others.

Volume scanning three-dimensional display with an inclined two-dimensional display and a mirror scanner

Science.gov (United States)

Miyazaki, Daisuke; Kawanishi, Tsuyoshi; Nishimura, Yasuhiro; Matsushita, Kenji

2001-11-01

A new three-dimensional display system based on a volume-scanning method is demonstrated. To form a three-dimensional real image, an inclined two-dimensional image is rapidly moved with a mirror scanner while the cross-section patterns of a three-dimensional object are displayed sequentially. A vector-scan CRT display unit is used to obtain a high-resolution image. An optical scanning system is constructed with concave mirrors and a galvanometer mirror. It is confirmed that three-dimensional images, formed by the experimental system, satisfy all the criteria for human stereoscopic vision.

Computing and visualizing time-varying merge trees for high-dimensional data

Energy Technology Data Exchange (ETDEWEB)

Oesterling, Patrick [Univ. of Leipzig (Germany); Heine, Christian [Univ. of Kaiserslautern (Germany); Weber, Gunther H. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Morozov, Dmitry [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Scheuermann, Gerik [Univ. of Leipzig (Germany)

2017-06-03

We introduce a new method that identifies and tracks features in arbitrary dimensions using the merge tree -- a structure for identifying topological features based on thresholding in scalar fields. This method analyzes the evolution of features of the function by tracking changes in the merge tree and relates features by matching subtrees between consecutive time steps. Using the time-varying merge tree, we present a structural visualization of the changing function that illustrates both features and their temporal evolution. We demonstrate the utility of our approach by applying it to temporal cluster analysis of high-dimensional point clouds.

When high similarity copycats lose and moderate similarity copycats gain: The impact of comparative evaluation

NARCIS (Netherlands)

Van Horen, F.; Pieters, R.

2012-01-01

Copycats imitate features of leading brands to free ride on their equity. The prevailing belief is that the more similar copycats are to the leader brand, the more positive their evaluation is, and thus the more they free ride. Three studies demonstrate when the reverse holds true:

When high similarity copycats lose and moderate similarity copycats gain : The impact of comparative evaluation

NARCIS (Netherlands)

van Horen, F.; Pieters, R.

2012-01-01

Copycats imitate features of leading brands to free ride on their equity. The prevailing belief is that the more similar copycats are to the leader brand, the more positive their evaluation is, and thus the more they free ride. Three studies demonstrate when the reverse holds true:

Cooperative simulation of lithography and topography for three-dimensional high-aspect-ratio etching

Science.gov (United States)

Ichikawa, Takashi; Yagisawa, Takashi; Furukawa, Shinichi; Taguchi, Takafumi; Nojima, Shigeki; Murakami, Sadatoshi; Tamaoki, Naoki

2018-06-01

A topography simulation of high-aspect-ratio etching considering transports of ions and neutrals is performed, and the mechanism of reactive ion etching (RIE) residues in three-dimensional corner patterns is revealed. Limited ion flux and CF2 diffusion from the wide space of the corner is found to have an effect on the RIE residues. Cooperative simulation of lithography and topography is used to solve the RIE residue problem.

Five-dimensional visualization of phase transition in BiNiO3 under high pressure

International Nuclear Information System (INIS)

Liu, Yijin; Wang, Junyue; Yang, Wenge; Azuma, Masaki; Mao, Wendy L.

2014-01-01

Colossal negative thermal expansion was recently discovered in BiNiO 3 associated with a low density to high density phase transition under high pressure. The varying proportion of co-existing phases plays a key role in the macroscopic behavior of this material. Here, we utilize a recently developed X-ray Absorption Near Edge Spectroscopy Tomography method and resolve the mixture of high/low pressure phases as a function of pressure at tens of nanometer resolution taking advantage of the charge transfer during the transition. This five-dimensional (X, Y, Z, energy, and pressure) visualization of the phase boundary provides a high resolution method to study the interface dynamics of high/low pressure phase

Growing three-dimensional biomorphic graphene powders using naturally abundant diatomite templates towards high solution processability

Science.gov (United States)

Chen, Ke; Li, Cong; Shi, Liurong; Gao, Teng; Song, Xiuju; Bachmatiuk, Alicja; Zou, Zhiyu; Deng, Bing; Ji, Qingqing; Ma, Donglin; Peng, Hailin; Du, Zuliang; Rümmeli, Mark Hermann; Zhang, Yanfeng; Liu, Zhongfan

2016-11-01

Mass production of high-quality graphene with low cost is the footstone for its widespread practical applications. We present herein a self-limited growth approach for producing graphene powders by a small-methane-flow chemical vapour deposition process on naturally abundant and industrially widely used diatomite (biosilica) substrates. Distinct from the chemically exfoliated graphene, thus-produced biomorphic graphene is highly crystallized with atomic layer-thickness controllability, structural designability and less noncarbon impurities. In particular, the individual graphene microarchitectures preserve a three-dimensional naturally curved surface morphology of original diatom frustules, effectively overcoming the interlayer stacking and hence giving excellent dispersion performance in fabricating solution-processible electrodes. The graphene films derived from as-made graphene powders, compatible with either rod-coating, or inkjet and roll-to-roll printing techniques, exhibit much higher electrical conductivity (~110,700 S m-1 at 80% transmittance) than previously reported solution-based counterparts. This work thus puts forward a practical route for low-cost mass production of various powdery two-dimensional materials.

Multidimensional high harmonic spectroscopy

International Nuclear Information System (INIS)

Bruner, Barry D; Soifer, Hadas; Shafir, Dror; Dudovich, Nirit; Serbinenko, Valeria; Smirnova, Olga

2015-01-01

High harmonic generation (HHG) has opened up a new frontier in ultrafast science where attosecond time resolution and Angstrom spatial resolution are accessible in a single measurement. However, reconstructing the dynamics under study is limited by the multiple degrees of freedom involved in strong field interactions. In this paper we describe a new class of measurement schemes for resolving attosecond dynamics, integrating perturbative nonlinear optics with strong-field physics. These approaches serve as a basis for multidimensional high harmonic spectroscopy. Specifically, we show that multidimensional high harmonic spectroscopy can measure tunnel ionization dynamics with high precision, and resolves the interference between multiple ionization channels. In addition, we show how multidimensional HHG can function as a type of lock-in amplifier measurement. Similar to multi-dimensional approaches in nonlinear optical spectroscopy that have resolved correlated femtosecond dynamics, multi-dimensional high harmonic spectroscopy reveals the underlying complex dynamics behind attosecond scale phenomena. (paper)

Constructing Two-, Zero-, and One-Dimensional Integrated Nanostructures: an Effective Strategy for High Microwave Absorption Performance.

Science.gov (United States)

Sun, Yuan; Xu, Jianle; Qiao, Wen; Xu, Xiaobing; Zhang, Weili; Zhang, Kaiyu; Zhang, Xing; Chen, Xing; Zhong, Wei; Du, Youwei

2016-11-23

A novel "201" nanostructure composite consisting of two-dimensional MoS 2 nanosheets, zero-dimensional Ni nanoparticles and one-dimensional carbon nanotubes (CNTs) was prepared successfully by a two-step method: Ni nanopaticles were deposited onto the surface of few-layer MoS 2 nanosheets by a wet chemical method, followed by chemical vapor deposition growth of CNTs through the catalysis of Ni nanoparticles. The as-prepared 201-MoS 2 -Ni-CNTs composites exhibit remarkably enhanced microwave absorption performance compared to Ni-MoS 2 or Ni-CNTs. The minimum reflection loss (RL) value of 201-MoS 2 -Ni-CNTs/wax composites with filler loading ratio of 30 wt % reached -50.08 dB at the thickness of 2.4 mm. The maximum effective microwave absorption bandwidth (RL< -10 dB) of 6.04 GHz was obtained at the thickness of 2.1 mm. The excellent absorption ability originates from appropriate impedance matching ratio, strong dielectric loss and large surface area, which are attributed to the "201" nanostructure. In addition, this method could be extended to other low-dimensional materials, proving to be an efficient and promising strategy for high microwave absorption performance.

Physics of low-dimensional systems

International Nuclear Information System (INIS)

Anon.

1989-01-01

The physics of low-dimensional systems has developed in a remarkable way over the last decade and has accelerated over the last few years, in particular because of the discovery of the new high temperature superconductors. The new developments started more than fifteen years ago with the discovery of the unexpected quasi-one-dimensional character of the TTF-TCNQ. Since then the field of conducting quasi-one-dimensional organic system have been rapidly growing. Parallel to the experimental work there has been an important theoretical development of great conceptual importance, such as charge density waves, soliton-like excitations, fractional charges, new symmetry properties etc. A new field of fundamental importance was the discovery of the Quantum Hall Effect in 1980. This field is still expanding with new experimental and theoretical discoveries. In 1986, then, came the totally unexpected discovery of high temperature superconductivity which started an explosive development. The three areas just mentioned formed the main themes of the Symposium. They do not in any way exhaust the progress in low-dimensional physics. We should mention the recent important development with both two-dimensional and one-dimensional and even zero-dimensional structures (quantum dots). The physics of mesoscopic systems is another important area where the low dimensionality is a key feature. Because of the small format of this Symposium we could unfortunately not cover these areas

Thermodynamics of noncommutative high-dimensional AdS black holes with non-Gaussian smeared matter distributions

Energy Technology Data Exchange (ETDEWEB)

Miao, Yan-Gang [Nankai University, School of Physics, Tianjin (China); Chinese Academy of Sciences, State Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, P.O. Box 2735, Beijing (China); CERN, PH-TH Division, Geneva 23 (Switzerland); Xu, Zhen-Ming [Nankai University, School of Physics, Tianjin (China)

2016-04-15

Considering non-Gaussian smeared matter distributions, we investigate the thermodynamic behaviors of the noncommutative high-dimensional Schwarzschild-Tangherlini anti-de Sitter black hole, and we obtain the condition for the existence of extreme black holes. We indicate that the Gaussian smeared matter distribution, which is a special case of non-Gaussian smeared matter distributions, is not applicable for the six- and higher-dimensional black holes due to the hoop conjecture. In particular, the phase transition is analyzed in detail. Moreover, we point out that the Maxwell equal area law holds for the noncommutative black hole whose Hawking temperature is within a specific range, but fails for one whose the Hawking temperature is beyond this range. (orig.)

Energy Efficient MAC Scheme for Wireless Sensor Networks with High-Dimensional Data Aggregate

Directory of Open Access Journals (Sweden)

Seokhoon Kim

2015-01-01

Full Text Available This paper presents a novel and sustainable medium access control (MAC scheme for wireless sensor network (WSN systems that process high-dimensional aggregated data. Based on a preamble signal and buffer threshold analysis, it maximizes the energy efficiency of the wireless sensor devices which have limited energy resources. The proposed group management MAC (GM-MAC approach not only sets the buffer threshold value of a sensor device to be reciprocal to the preamble signal but also sets a transmittable group value to each sensor device by using the preamble signal of the sink node. The primary difference between the previous and the proposed approach is that existing state-of-the-art schemes use duty cycle and sleep mode to save energy consumption of individual sensor devices, whereas the proposed scheme employs the group management MAC scheme for sensor devices to maximize the overall energy efficiency of the whole WSN systems by minimizing the energy consumption of sensor devices located near the sink node. Performance evaluations show that the proposed scheme outperforms the previous schemes in terms of active time of sensor devices, transmission delay, control overhead, and energy consumption. Therefore, the proposed scheme is suitable for sensor devices in a variety of wireless sensor networking environments with high-dimensional data aggregate.

Electric Field Guided Assembly of One-Dimensional Nanostructures for High Performance Sensors

Directory of Open Access Journals (Sweden)

Wing Kam Liu

2012-05-01

Full Text Available Various nanowire or nanotube-based devices have been demonstrated to fulfill the anticipated future demands on sensors. To fabricate such devices, electric field-based methods have demonstrated a great potential to integrate one-dimensional nanostructures into various forms. This review paper discusses theoretical and experimental aspects of the working principles, the assembled structures, and the unique functions associated with electric field-based assembly. The challenges and opportunities of the assembly methods are addressed in conjunction with future directions toward high performance sensors.

FPGA Implementation of one-dimensional and two-dimensional cellular automata

International Nuclear Information System (INIS)

D'Antone, I.

1999-01-01

This report describes the hardware implementation of one-dimensional and two-dimensional cellular automata (CAs). After a general introduction to the cellular automata, we consider a one-dimensional CA used to implement pseudo-random techniques in built-in self test for VLSI. Due to the increase in digital ASIC complexity, testing is becoming one of the major costs in the VLSI production. The high electronics complexity, used in particle physics experiments, demands higher reliability than in the past time. General criterions are given to evaluate the feasibility of the circuit used for testing and some quantitative parameters are underlined to optimize the architecture of the cellular automaton. Furthermore, we propose a two-dimensional CA that performs a peak finding algorithm in a matrix of cells mapping a sub-region of a calorimeter. As in a two-dimensional filtering process, the peaks of the energy clusters are found in one evolution step. This CA belongs to Wolfram class II cellular automata. Some quantitative parameters are given to optimize the architecture of the cellular automaton implemented in a commercial field programmable gate array (FPGA)

MULTI-DIMENSIONAL PATTERN DISCOVERY OF TRAJECTORIES USING CONTEXTUAL INFORMATION

Directory of Open Access Journals (Sweden)

M. Sharif

2017-10-01

Full Text Available Movement of point objects are highly sensitive to the underlying situations and conditions during the movement, which are known as contexts. Analyzing movement patterns, while accounting the contextual information, helps to better understand how point objects behave in various contexts and how contexts affect their trajectories. One potential solution for discovering moving objects patterns is analyzing the similarities of their trajectories. This article, therefore, contextualizes the similarity measure of trajectories by not only their spatial footprints but also a notion of internal and external contexts. The dynamic time warping (DTW method is employed to assess the multi-dimensional similarities of trajectories. Then, the results of similarity searches are utilized in discovering the relative movement patterns of the moving point objects. Several experiments are conducted on real datasets that were obtained from commercial airplanes and the weather information during the flights. The results yielded the robustness of DTW method in quantifying the commonalities of trajectories and discovering movement patterns with 80 % accuracy. Moreover, the results revealed the importance of exploiting contextual information because it can enhance and restrict movements.

Multi-Dimensional Pattern Discovery of Trajectories Using Contextual Information

Science.gov (United States)

Sharif, M.; Alesheikh, A. A.

2017-10-01

Movement of point objects are highly sensitive to the underlying situations and conditions during the movement, which are known as contexts. Analyzing movement patterns, while accounting the contextual information, helps to better understand how point objects behave in various contexts and how contexts affect their trajectories. One potential solution for discovering moving objects patterns is analyzing the similarities of their trajectories. This article, therefore, contextualizes the similarity measure of trajectories by not only their spatial footprints but also a notion of internal and external contexts. The dynamic time warping (DTW) method is employed to assess the multi-dimensional similarities of trajectories. Then, the results of similarity searches are utilized in discovering the relative movement patterns of the moving point objects. Several experiments are conducted on real datasets that were obtained from commercial airplanes and the weather information during the flights. The results yielded the robustness of DTW method in quantifying the commonalities of trajectories and discovering movement patterns with 80 % accuracy. Moreover, the results revealed the importance of exploiting contextual information because it can enhance and restrict movements.

Experimental investigation of an actively controlled three-dimensional turret wake

Science.gov (United States)

Shea, Patrick R.

Hemispherical turrets are bluff bodies commonly used to house optical systems on airborne platforms. These bluff bodies develop complex, three-dimensional flow fields that introduce high mean and fluctuating loads to the turret as well as the airframe support structure which reduce the performance of both the optical systems and the aircraft. An experimental investigation of the wake of a three-dimensional, non-conformal turret was performed in a low-speed wind tunnel at Syracuse University to develop a better understanding of the fundamental flow physics associated with the turret wake. The flow field was studied at a diameter based Reynolds number of 550,000 using stereoscopic particle image velocimetry and dynamic pressure measurements both with and without active flow control. Pressure measurements were simultaneously sampled with the PIV measurements and taken on the surrounding boundary layer plate and at several locations on the turret geometry. Active flow control of the turret wake was performed around the leading edge of the turret aperture using dynamic suction in steady open-loop, unsteady open-loop, and simple closed-loop configurations. Analysis of the uncontrolled wake provided insight into the complex three-dimensional wake when evaluated spatially using PIV measurements and temporally using spectral analysis of the pressure measurements. Steady open-loop suction was found to significantly alter the spatial and temporal nature of the turret wake despite the control being applied locally to the aperture region of the turret. Unsteady open-loop and simple closed-loop control were found to provide similar levels of control to the steady open-loop forcing with a 45% reduction in the control input as calculated using the jet momentum coefficient. The data set collected provides unique information regarding the development of the baseline three-dimensional wake and the wake with three different active flow control configurations. These data can be used to

Preparation of wholemount mouse intestine for high-resolution three-dimensional imaging using two-photon microscopy.

Science.gov (United States)

Appleton, P L; Quyn, A J; Swift, S; Näthke, I

2009-05-01

Visualizing overall tissue architecture in three dimensions is fundamental for validating and integrating biochemical, cell biological and visual data from less complex systems such as cultured cells. Here, we describe a method to generate high-resolution three-dimensional image data of intact mouse gut tissue. Regions of highest interest lie between 50 and 200 mum within this tissue. The quality and usefulness of three-dimensional image data of tissue with such depth is limited owing to problems associated with scattered light, photobleaching and spherical aberration. Furthermore, the highest-quality oil-immersion lenses are designed to work at a maximum distance of image at high-resolution deep within tissue. We show that manipulating the refractive index of the mounting media and decreasing sample opacity greatly improves image quality such that the limiting factor for a standard, inverted multi-photon microscope is determined by the working distance of the objective as opposed to detectable fluorescence. This method negates the need for mechanical sectioning of tissue and enables the routine generation of high-quality, quantitative image data that can significantly advance our understanding of tissue architecture and physiology.

High performance top-gated ferroelectric field effect transistors based on two-dimensional ZnO nanosheets

Science.gov (United States)

Tian, Hongzheng; Wang, Xudong; Zhu, Yuankun; Liao, Lei; Wang, Xianying; Wang, Jianlu; Hu, Weida

2017-01-01

High quality ultrathin two-dimensional zinc oxide (ZnO) nanosheets (NSs) are synthesized, and the ZnO NS ferroelectric field effect transistors (FeFETs) are demonstrated based on the P(VDF-TrFE) polymer film used as the top gate insulating layer. The ZnO NSs exhibit a maximum field effect mobility of 588.9 cm2/Vs and a large transconductance of 2.5 μS due to their high crystalline quality and ultrathin two-dimensional structure. The polarization property of the P(VDF-TrFE) film is studied, and a remnant polarization of >100 μC/cm2 is achieved with a P(VDF-TrFE) thickness of 300 nm. Because of the ultrahigh remnant polarization field generated in the P(VDF-TrFE) film, the FeFETs show a large memory window of 16.9 V and a high source-drain on/off current ratio of more than 107 at zero gate voltage and a source-drain bias of 0.1 V. Furthermore, a retention time of >3000 s of the polarization state is obtained, inspiring a promising candidate for applications in data storage with non-volatile features.

Latent class models for joint analysis of disease prevalence and high-dimensional semicontinuous biomarker data.

Science.gov (United States)

Zhang, Bo; Chen, Zhen; Albert, Paul S

2012-01-01

High-dimensional biomarker data are often collected in epidemiological studies when assessing the association between biomarkers and human disease is of interest. We develop a latent class modeling approach for joint analysis of high-dimensional semicontinuous biomarker data and a binary disease outcome. To model the relationship between complex biomarker expression patterns and disease risk, we use latent risk classes to link the 2 modeling components. We characterize complex biomarker-specific differences through biomarker-specific random effects, so that different biomarkers can have different baseline (low-risk) values as well as different between-class differences. The proposed approach also accommodates data features that are common in environmental toxicology and other biomarker exposure data, including a large number of biomarkers, numerous zero values, and complex mean-variance relationship in the biomarkers levels. A Monte Carlo EM (MCEM) algorithm is proposed for parameter estimation. Both the MCEM algorithm and model selection procedures are shown to work well in simulations and applications. In applying the proposed approach to an epidemiological study that examined the relationship between environmental polychlorinated biphenyl (PCB) exposure and the risk of endometriosis, we identified a highly significant overall effect of PCB concentrations on the risk of endometriosis.

Quantum correlation of high dimensional system in a dephasing environment

Science.gov (United States)

Ji, Yinghua; Ke, Qiang; Hu, Juju

2018-05-01

For a high dimensional spin-S system embedded in a dephasing environment, we theoretically analyze the time evolutions of quantum correlation and entanglement via Frobenius norm and negativity. The quantum correlation dynamics can be considered as a function of the decoherence parameters, including the ratio between the system oscillator frequency ω0 and the reservoir cutoff frequency ωc , and the different environment temperature. It is shown that the quantum correlation can not only measure nonclassical correlation of the considered system, but also perform a better robustness against the dissipation. In addition, the decoherence presents the non-Markovian features and the quantum correlation freeze phenomenon. The former is much weaker than that in the sub-Ohmic or Ohmic thermal reservoir environment.

High-Dimensional Modeling for Cytometry: Building Rock Solid Models Using GemStone™ and Verity Cen-se'™ High-Definition t-SNE Mapping.

Science.gov (United States)

Bruce Bagwell, C

2018-01-01

This chapter outlines how to approach the complex tasks associated with designing models for high-dimensional cytometry data. Unlike gating approaches, modeling lends itself to automation and accounts for measurement overlap among cellular populations. Designing these models is now easier because of a new technique called high-definition t-SNE mapping. Nontrivial examples are provided that serve as a guide to create models that are consistent with data.

Molecular similarity measures.

Science.gov (United States)

Maggiora, Gerald M; Shanmugasundaram, Veerabahu

2011-01-01

Molecular similarity is a pervasive concept in chemistry. It is essential to many aspects of chemical reasoning and analysis and is perhaps the fundamental assumption underlying medicinal chemistry. Dissimilarity, the complement of similarity, also plays a major role in a growing number of applications of molecular diversity in combinatorial chemistry, high-throughput screening, and related fields. How molecular information is represented, called the representation problem, is important to the type of molecular similarity analysis (MSA) that can be carried out in any given situation. In this work, four types of mathematical structure are used to represent molecular information: sets, graphs, vectors, and functions. Molecular similarity is a pairwise relationship that induces structure into sets of molecules, giving rise to the concept of chemical space. Although all three concepts - molecular similarity, molecular representation, and chemical space - are treated in this chapter, the emphasis is on molecular similarity measures. Similarity measures, also called similarity coefficients or indices, are functions that map pairs of compatible molecular representations that are of the same mathematical form into real numbers usually, but not always, lying on the unit interval. This chapter presents a somewhat pedagogical discussion of many types of molecular similarity measures, their strengths and limitations, and their relationship to one another. An expanded account of the material on chemical spaces presented in the first edition of this book is also provided. It includes a discussion of the topography of activity landscapes and the role that activity cliffs in these landscapes play in structure-activity studies.

Collective excitations and superconductivity in reduced dimensional systems - Possible mechanism for high Tc

International Nuclear Information System (INIS)

Santoyo, B.M.

1989-01-01

The author studies in full detail a possible mechanism of superconductivity in slender electronic systems of finite cross section. This mechanism is based on the pairing interaction mediated by the multiple modes of acoustic plasmons in these structures. First, he shows that multiple non-Landau-damped acoustic plasmon modes exist for electrons in a quasi-one dimensional wire at finite temperatures. These plasmons are of two basic types. The first one is made up by the collective longitudinal oscillations of the electrons essentially of a given transverse energy level oscillating against the electrons in the neighboring transverse energy level. The modes are called Slender Acoustic Plasmons or SAP's. The other mode is the quasi-one dimensional acoustic plasmon mode in which all the electrons oscillate together in phase among themselves but out of phase against the positive ion background. He shows numerically and argues physically that even for a temperature comparable to the mode separation Δω the SAP's and the quasi-one dimensional plasmon persist. Then, based on a clear physical picture, he develops in terms of the dielectric function a theory of superconductivity capable of treating the simultaneous participation of multiple bosonic modes that mediate the pairing interaction. The effect of mode damping is then incorporated in a simple manner that is free of the encumbrance of the strong-coupling, Green's function formalism usually required for the retardation effect. Explicit formulae including such damping are derived for the critical temperature T c and the energy gap Δ 0 . With those modes and armed with such a formalism, he proceeds to investigate a possible superconducting mechanism for high T c in quasi-one dimensional single-wire and multi-wire systems

A high-speed computerized tomography image reconstruction using direct two-dimensional Fourier transform method

International Nuclear Information System (INIS)

Niki, Noboru; Mizutani, Toshio; Takahashi, Yoshizo; Inouye, Tamon.

1983-01-01

The nescessity for developing real-time computerized tomography (CT) aiming at the dynamic observation of organs such as hearts has lately been advocated. It is necessary for its realization to reconstruct the images which are markedly faster than present CTs. Although various reconstructing methods have been proposed so far, the method practically employed at present is the filtered backprojection (FBP) method only, which can give high quality image reconstruction, but takes much computing time. In the past, the two-dimensional Fourier transform (TFT) method was regarded as unsuitable to practical use because the quality of images obtained was not good, in spite of the promising method for high speed reconstruction because of its less computing time. However, since it was revealed that the image quality by TFT method depended greatly on interpolation accuracy in two-dimensional Fourier space, the authors have developed a high-speed calculation algorithm that can obtain high quality images by pursuing the relationship between the image quality and the interpolation method. In this case, radial data sampling points in Fourier space are increased to β-th power of 2 times, and the linear or spline interpolation is used. Comparison of this method with the present FBP method resulted in the conclusion that the image quality is almost the same in practical image matrix, the computational time by TFT method becomes about 1/10 of FBP method, and the memory capacity also reduces by about 20 %. (Wakatsuki, Y.)

Similarity and Modeling in Science and Engineering

CERN Document Server

Kuneš, Josef

2012-01-01

The present text sets itself in relief to other titles on the subject in that it addresses the means and methodologies versus a narrow specific-task oriented approach. Concepts and their developments which evolved to meet the changing needs of applications are addressed. This approach provides the reader with a general tool-box to apply to their specific needs. Two important tools are presented: dimensional analysis and the similarity analysis methods. The fundamental point of view, enabling one to sort all models, is that of information flux between a model and an original expressed by the similarity and abstraction. Each chapter includes original examples and ap-plications. In this respect, the models can be divided into several groups. The following models are dealt with separately by chapter; mathematical and physical models, physical analogues, deterministic, stochastic, and cybernetic computer models. The mathematical models are divided into asymptotic and phenomenological models. The phenomenological m...

Three-dimensional bicontinuous nanoporous Au/polyaniline hybrid films for high-performance electrochemical supercapacitors

Science.gov (United States)

Lang, Xingyou; Zhang, Ling; Fujita, Takeshi; Ding, Yi; Chen, Mingwei

2012-01-01

We report three-dimensional bicontinuous nanoporous Au/polyaniline (PANI) composite films made by one-step electrochemical polymerization of PANI shell onto dealloyed nanoporous gold (NPG) skeletons for the applications in electrochemical supercapacitors. The NPG/PANI based supercapacitors exhibit ultrahigh volumetric capacitance (∼1500 F cm-3) and energy density (∼0.078 Wh cm-3), which are seven and four orders of magnitude higher than these of electrolytic capacitors, with the same power density up to ∼190 W cm-3. The outstanding capacitive performances result from a novel nanoarchitecture in which pseudocapacitive PANI shells are incorporated into pore channels of highly conductive NPG, making them promising candidates as electrode materials in supercapacitor devices combing high-energy storage densities with high-power delivery.

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

NARCIS (Netherlands)

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

2016-01-01

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

Reducing 4D CT artifacts using optimized sorting based on anatomic similarity.

Science.gov (United States)

Johnston, Eric; Diehn, Maximilian; Murphy, James D; Loo, Billy W; Maxim, Peter G

2011-05-01

Four-dimensional (4D) computed tomography (CT) has been widely used as a tool to characterize respiratory motion in radiotherapy. The two most commonly used 4D CT algorithms sort images by the associated respiratory phase or displacement into a predefined number of bins, and are prone to image artifacts at transitions between bed positions. The purpose of this work is to demonstrate a method of reducing motion artifacts in 4D CT by incorporating anatomic similarity into phase or displacement based sorting protocols. Ten patient datasets were retrospectively sorted using both the displacement and phase based sorting algorithms. Conventional sorting methods allow selection of only the nearest-neighbor image in time or displacement within each bin. In our method, for each bed position either the displacement or the phase defines the center of a bin range about which several candidate images are selected. The two dimensional correlation coefficients between slices bordering the interface between adjacent couch positions are then calculated for all candidate pairings. Two slices have a high correlation if they are anatomically similar. Candidates from each bin are then selected to maximize the slice correlation over the entire data set using the Dijkstra's shortest path algorithm. To assess the reduction of artifacts, two thoracic radiation oncologists independently compared the resorted 4D datasets pairwise with conventionally sorted datasets, blinded to the sorting method, to choose which had the least motion artifacts. Agreement between reviewers was evaluated using the weighted kappa score. Anatomically based image selection resulted in 4D CT datasets with significantly reduced motion artifacts with both displacement (P = 0.0063) and phase sorting (P = 0.00022). There was good agreement between the two reviewers, with complete agreement 34 times and complete disagreement 6 times. Optimized sorting using anatomic similarity significantly reduces 4D CT motion

Covariance Method of the Tunneling Radiation from High Dimensional Rotating Black Holes

Science.gov (United States)

Li, Hui-Ling; Han, Yi-Wen; Chen, Shuai-Ru; Ding, Cong

2018-04-01

In this paper, Angheben-Nadalini-Vanzo-Zerbini (ANVZ) covariance method is used to study the tunneling radiation from the Kerr-Gödel black hole and Myers-Perry black hole with two independent angular momentum. By solving the Hamilton-Jacobi equation and separating the variables, the radial motion equation of a tunneling particle is obtained. Using near horizon approximation and the distance of the proper pure space, we calculate the tunneling rate and the temperature of Hawking radiation. Thus, the method of ANVZ covariance is extended to the research of high dimensional black hole tunneling radiation.

Three-dimensional viscous fingering of miscible fluids in porous media

Science.gov (United States)

Suekane, Tetsuya; Ono, Jei; Hyodo, Akimitsu; Nagatsu, Yuichiro

2017-10-01

Viscous fingering is a flow instability that is induced at the displacement front when a less-viscous fluid (LVF) displaces a more-viscous fluid (MVF). Because of the opaque nature of porous media, most experimental investigations of the structure of viscous fingering and its development in time have been limited to two-dimensional porous media or Hele-Shaw cells. In this study, we investigate the three-dimensional characteristics of viscous fingering in porous media using a microfocused x-ray computer tomography (CT) scanner. Similar to two-dimensional experiments, characteristic events such as tip-splitting, shielding, and coalescence were observed in three-dimensional viscous fingering as well. With an increase in the Péclet number at a fixed viscosity ratio, M , the fingers appearing on the interface tend to be fine; however, the locations of the tips of the fingers remain the same for the same injected volume of the LVF. The finger extensions increase in proportion to ln M , and the number of fingers emerging at the initial interface increases with M . This fact agrees qualitatively with linear stability analyses. Within the fingers, the local concentration of NaI, which is needed for the x-ray CT scanner, linearly decreases, whereas it sharply decreases at the tips of the fingers. A locally high Péclet number as well as unsteady motions in lateral directions may enhance the dispersion at the tips of the fingers. As the viscosity ratio increases, the efficiency of each sweep monotonically decreases and reaches an asymptotic state; in addition, the degree of mixing increases with the viscosity ratio. For high flow rates, the asymptotic value of the sweep efficiency is low for high viscosity ratios, while there is no clear dependence of the asymptotic value on the Péclet number.

Assessing semantic similarity of texts - Methods and algorithms

Science.gov (United States)

Rozeva, Anna; Zerkova, Silvia

2017-12-01

Assessing the semantic similarity of texts is an important part of different text-related applications like educational systems, information retrieval, text summarization, etc. This task is performed by sophisticated analysis, which implements text-mining techniques. Text mining involves several pre-processing steps, which provide for obtaining structured representative model of the documents in a corpus by means of extracting and selecting the features, characterizing their content. Generally the model is vector-based and enables further analysis with knowledge discovery approaches. Algorithms and measures are used for assessing texts at syntactical and semantic level. An important text-mining method and similarity measure is latent semantic analysis (LSA). It provides for reducing the dimensionality of the document vector space and better capturing the text semantics. The mathematical background of LSA for deriving the meaning of the words in a given text by exploring their co-occurrence is examined. The algorithm for obtaining the vector representation of words and their corresponding latent concepts in a reduced multidimensional space as well as similarity calculation are presented.

Enhanced, targeted sampling of high-dimensional free-energy landscapes using variationally enhanced sampling, with an application to chignolin.

Science.gov (United States)

Shaffer, Patrick; Valsson, Omar; Parrinello, Michele

2016-02-02

The capabilities of molecular simulations have been greatly extended by a number of widely used enhanced sampling methods that facilitate escaping from metastable states and crossing large barriers. Despite these developments there are still many problems which remain out of reach for these methods which has led to a vigorous effort in this area. One of the most important problems that remains unsolved is sampling high-dimensional free-energy landscapes and systems that are not easily described by a small number of collective variables. In this work we demonstrate a new way to compute free-energy landscapes of high dimensionality based on the previously introduced variationally enhanced sampling, and we apply it to the miniprotein chignolin.

Enhanced, targeted sampling of high-dimensional free-energy landscapes using variationally enhanced sampling, with an application to chignolin

Science.gov (United States)

Shaffer, Patrick; Valsson, Omar; Parrinello, Michele

2016-01-01

The capabilities of molecular simulations have been greatly extended by a number of widely used enhanced sampling methods that facilitate escaping from metastable states and crossing large barriers. Despite these developments there are still many problems which remain out of reach for these methods which has led to a vigorous effort in this area. One of the most important problems that remains unsolved is sampling high-dimensional free-energy landscapes and systems that are not easily described by a small number of collective variables. In this work we demonstrate a new way to compute free-energy landscapes of high dimensionality based on the previously introduced variationally enhanced sampling, and we apply it to the miniprotein chignolin. PMID:26787868

Investigation on the effect of nonlinear processes on similarity law in high-pressure argon discharges

Science.gov (United States)

Fu, Yangyang; Parsey, Guy M.; Verboncoeur, John P.; Christlieb, Andrew J.

2017-11-01

In this paper, the effect of nonlinear processes (such as three-body collisions and stepwise ionizations) on the similarity law in high-pressure argon discharges has been studied by the use of the Kinetic Global Model framework. In the discharge model, the ground state argon atoms (Ar), electrons (e), atom ions (Ar+), molecular ions (Ar2+), and fourteen argon excited levels Ar*(4s and 4p) are considered. The steady-state electron and ion densities are obtained with nonlinear processes included and excluded in the designed models, respectively. It is found that in similar gas gaps, keeping the product of gas pressure and linear dimension unchanged, with the nonlinear processes included, the normalized density relations deviate from the similarity relations gradually as the scale-up factor decreases. Without the nonlinear processes, the parameter relations are in good agreement with the similarity law predictions. Furthermore, the pressure and the dimension effects are also investigated separately with and without the nonlinear processes. It is shown that the gas pressure effect on the results is less obvious than the dimension effect. Without the nonlinear processes, the pressure and the dimension effects could be estimated from one to the other based on the similarity relations.

Analytic self-similar solutions of the Oberbeck–Boussinesq equations

International Nuclear Information System (INIS)

Barna, I.F.; Mátyás, L.

2015-01-01

In this article we will present pure two-dimensional analytic solutions for the coupled non-compressible Newtonian–Navier–Stokes — with Boussinesq approximation — and the heat conduction equation. The system was investigated from E.N. Lorenz half a century ago with Fourier series and pioneered the way to the paradigm of chaos. We present a novel analysis of the same system where the key idea is the two-dimensional generalization of the well-known self-similar Ansatz of Barenblatt which will be interpreted in a geometrical way. The results, the pressure, temperature and velocity fields are all analytic and can be expressed with the help of the error functions. The temperature field shows a strongly damped single periodic oscillation which can mimic the appearance of Rayleigh–Bénard convection cells. Finally, it is discussed how our result may be related to nonlinear or chaotic dynamical regimes

Classifying and assembling two-dimensional X-ray laser diffraction patterns of a single particle to reconstruct the three-dimensional diffraction intensity function: resolution limit due to the quantum noise

International Nuclear Information System (INIS)

Tokuhisa, Atsushi; Taka, Junichiro; Kono, Hidetoshi; Go, Nobuhiro

2012-01-01

A new algorithm is developed for reconstructing the high-resolution three-dimensional diffraction intensity function of a globular biological macromolecule from many quantum-noise-limited two-dimensional X-ray laser diffraction patterns, each for an unknown orientation. The structural resolution is expressed as a function of the incident X-ray intensity and quantities characterizing the target molecule. A new two-step algorithm is developed for reconstructing the three-dimensional diffraction intensity of a globular biological macromolecule from many experimentally measured quantum-noise-limited two-dimensional X-ray laser diffraction patterns, each for an unknown orientation. The first step is classification of the two-dimensional patterns into groups according to the similarity of direction of the incident X-rays with respect to the molecule and an averaging within each group to reduce the noise. The second step is detection of common intersecting circles between the signal-enhanced two-dimensional patterns to identify their mutual location in the three-dimensional wavenumber space. The newly developed algorithm enables one to detect a signal for classification in noisy experimental photon-count data with as low as ∼0.1 photons per effective pixel. The wavenumber of such a limiting pixel determines the attainable structural resolution. From this fact, the resolution limit due to the quantum noise attainable by this new method of analysis as well as two important experimental parameters, the number of two-dimensional patterns to be measured (the load for the detector) and the number of pairs of two-dimensional patterns to be analysed (the load for the computer), are derived as a function of the incident X-ray intensity and quantities characterizing the target molecule

CAN AGN FEEDBACK BREAK THE SELF-SIMILARITY OF GALAXIES, GROUPS, AND CLUSTERS?

Energy Technology Data Exchange (ETDEWEB)

Gaspari, M. [Max Planck Institute for Astrophysics, Karl-Schwarzschild-Strasse 1, D-85741 Garching (Germany); Brighenti, F. [Astronomy Department, University of Bologna, Via Ranzani 1, I-40127 Bologna (Italy); Temi, P. [Astrophysics Branch, NASA/Ames Research Center, MS 245-6, Moffett Field, CA 94035 (United States); Ettori, S., E-mail: mgaspari@mpa-garching.mpg.de [INAF, Osservatorio Astronomico di Bologna, Via Ranzani 1, I-40127 Bologna (Italy)

2014-03-01

It is commonly thought that active galactic nucleus (AGN) feedback can break the self-similar scaling relations of galaxies, groups, and clusters. Using high-resolution three-dimensional hydrodynamic simulations, we isolate the impact of AGN feedback on the L {sub x}-T {sub x} relation, testing the two archetypal and common regimes, self-regulated mechanical feedback and a quasar thermal blast. We find that AGN feedback has severe difficulty in breaking the relation in a consistent way. The similarity breaking is directly linked to the gas evacuation within R {sub 500}, while the central cooling times are inversely proportional to the core density. Breaking self-similarity thus implies breaking the cool core, morphing all systems to non-cool-core objects, which is in clear contradiction with the observed data populated by several cool-core systems. Self-regulated feedback, which quenches cooling flows and preserves cool cores, prevents dramatic evacuation and similarity breaking at any scale; the relation scatter is also limited. The impulsive thermal blast can break the core-included L {sub x}-T {sub x} at T {sub 500} ≲ 1 keV, but substantially empties and overheats the halo, generating a perennial non-cool-core group, as experienced by cosmological simulations. Even with partial evacuation, massive systems remain overheated. We show that the action of purely AGN feedback is to lower the luminosity and heat the gas, perpendicular to the fit.

Athermal mechanisms of size-dependent crystal flow gleaned from three-dimensional discrete dislocation simulations

International Nuclear Information System (INIS)

Rao, S.I.; Dimiduk, D.M.; Parthasarathy, T.A.; Uchic, M.D.; Tang, M.; Woodward, C.

2008-01-01

Recent experimental studies have revealed that micrometer-scale face-centered cubic (fcc) crystals show strong strengthening effects, even at high initial dislocation densities. We use large-scale three-dimensional discrete dislocation simulations (DDS) to explicitly model the deformation behavior of fcc Ni microcrystals in the size range of 0.5-20 μm. This study shows that two size-sensitive athermal hardening processes, beyond forest hardening, are sufficient to develop the dimensional scaling of the flow stress, stochastic stress variation, flow intermittency and high initial strain-hardening rates, similar to experimental observations for various materials. One mechanism, source-truncation hardening, is especially potent in micrometer-scale volumes. A second mechanism, termed exhaustion hardening, results from a breakdown of the mean-field conditions for forest hardening in small volumes, thus biasing the statistics of ordinary dislocation processes

High-resolution computer-generated reflection holograms with three-dimensional effects written directly on a silicon surface by a femtosecond laser.

Science.gov (United States)

Wædegaard, Kristian J; Balling, Peter

2011-02-14

An infrared femtosecond laser has been used to write computer-generated holograms directly on a silicon surface. The high resolution offered by short-pulse laser ablation is employed to write highly detailed holograms with resolution up to 111 kpixels/mm2. It is demonstrated how three-dimensional effects can be realized in computer-generated holograms. Three-dimensional effects are visualized as a relative motion between different parts of the holographic reconstruction, when the hologram is moved relative to the reconstructing laser beam. Potential security applications are briefly discussed.

Nano-engineering of three-dimensional core/shell nanotube arrays for high performance supercapacitors

Science.gov (United States)

Grote, Fabian; Wen, Liaoyong; Lei, Yong

2014-06-01

Large-scale arrays of core/shell nanostructures are highly desirable to enhance the performance of supercapacitors. Here we demonstrate an innovative template-based fabrication technique with high structural controllability, which is capable of synthesizing well-ordered three-dimensional arrays of SnO2/MnO2 core/shell nanotubes for electrochemical energy storage in supercapacitor applications. The SnO2 core is fabricated by atomic layer deposition and provides a highly electrical conductive matrix. Subsequently a thin MnO2 shell is coated by electrochemical deposition onto the SnO2 core, which guarantees a short ion diffusion length within the shell. The core/shell structure shows an excellent electrochemical performance with a high specific capacitance of 910 F g-1 at 1 A g-1 and a good rate capability of remaining 217 F g-1 at 50 A g-1. These results shall pave the way to realize aqueous based asymmetric supercapacitors with high specific power and high specific energy.

COBRA/TRAC analysis of two-dimensional thermal-hydraulic behavior in SCTF reflood tests

International Nuclear Information System (INIS)

Iwamura, Takamichi; Ohnuki, Akira; Sobajima, Makoto; Adachi, Hiromichi

1987-01-01

The effects of radial power distribution and non-uniform upper plenum water accumulation on thermal-hydraulic behavior in the core were observed in the reflood tests with Slab Core Test Facility (SCTF). In order to examine the predictability of these two effects by a multi-dimensional analysis code, the COBRA/TRAC calculations were made. The calculated results indicated that the heat transfer enhancement in high power bundles above quench front was caused by high vapor flow rate in those bundles due to the radial power distribution. On the other hand, the heat transfer degradation in the peripheral bundles under the condition of non-uniform upper plenum water accumulation was caused by the lower flow rates of vapor and entrained liquid above the quench front in those bundles by the reason that vapor concentrated in the center bundles due to the cross flow induced by the horizontal pressure gradient in the core. The above-mentioned two-dimensional heat transfer behaviors calculated with the COBRA/TRAC code is similar to those observed in SCTF tests and therefore those calculations are useful to investigate the mechanism of the two-dimensional effects in SCTF reflood tests. (author)

The self-similar field and its application to a diffusion problem

International Nuclear Information System (INIS)

Michelitsch, Thomas M

2011-01-01

We introduce a continuum approach which accounts for self-similarity as a symmetry property of an infinite medium. A self-similar Laplacian operator is introduced which is the source of self-similar continuous fields. In this way ‘self-similar symmetry’ appears in an analogous manner as transverse isotropy or cubic symmetry of a medium. As a consequence of the self-similarity the Laplacian is a non-local fractional operator obtained as the continuum limit of the discrete self-similar Laplacian introduced recently by Michelitsch et al (2009 Phys. Rev. E 80 011135). The dispersion relation of the Laplacian and its Green’s function is deduced in closed forms. As a physical application of the approach we analyze a self-similar diffusion problem. The statistical distributions, which constitute the solutions of this problem, turn out to be Lévi-stable distributions with infinite variances characterizing the statistics of one-dimensional Lévi flights. The self-similar continuum approach introduced in this paper has the potential to be applied on a variety of scale invariant and fractal problems in physics such as in continuum mechanics, electrodynamics and in other fields. (paper)

Impact of high-frequency pumping on anomalous finite-size effects in three-dimensional topological insulators

Science.gov (United States)

Pervishko, Anastasiia A.; Yudin, Dmitry; Shelykh, Ivan A.

2018-02-01

Lowering of the thickness of a thin-film three-dimensional topological insulator down to a few nanometers results in the gap opening in the spectrum of topologically protected two-dimensional surface states. This phenomenon, which is referred to as the anomalous finite-size effect, originates from hybridization between the states propagating along the opposite boundaries. In this work, we consider a bismuth-based topological insulator and show how the coupling to an intense high-frequency linearly polarized pumping can further be used to manipulate the value of a gap. We address this effect within recently proposed Brillouin-Wigner perturbation theory that allows us to map a time-dependent problem into a stationary one. Our analysis reveals that both the gap and the components of the group velocity of the surface states can be tuned in a controllable fashion by adjusting the intensity of the driving field within an experimentally accessible range and demonstrate the effect of light-induced band inversion in the spectrum of the surface states for high enough values of the pump.

Growing three-dimensional biomorphic graphene powders using naturally abundant diatomite templates towards high solution processability.

Science.gov (United States)

Chen, Ke; Li, Cong; Shi, Liurong; Gao, Teng; Song, Xiuju; Bachmatiuk, Alicja; Zou, Zhiyu; Deng, Bing; Ji, Qingqing; Ma, Donglin; Peng, Hailin; Du, Zuliang; Rümmeli, Mark Hermann; Zhang, Yanfeng; Liu, Zhongfan

2016-11-07

Mass production of high-quality graphene with low cost is the footstone for its widespread practical applications. We present herein a self-limited growth approach for producing graphene powders by a small-methane-flow chemical vapour deposition process on naturally abundant and industrially widely used diatomite (biosilica) substrates. Distinct from the chemically exfoliated graphene, thus-produced biomorphic graphene is highly crystallized with atomic layer-thickness controllability, structural designability and less noncarbon impurities. In particular, the individual graphene microarchitectures preserve a three-dimensional naturally curved surface morphology of original diatom frustules, effectively overcoming the interlayer stacking and hence giving excellent dispersion performance in fabricating solution-processible electrodes. The graphene films derived from as-made graphene powders, compatible with either rod-coating, or inkjet and roll-to-roll printing techniques, exhibit much higher electrical conductivity (∼110,700 S m -1 at 80% transmittance) than previously reported solution-based counterparts. This work thus puts forward a practical route for low-cost mass production of various powdery two-dimensional materials.

Self-similarity in high Atwood number Rayleigh-Taylor experiments

Science.gov (United States)

Mikhaeil, Mark; Suchandra, Prasoon; Pathikonda, Gokul; Ranjan, Devesh

2017-11-01

Self-similarity is a critical concept in turbulent and mixing flows. In the Rayleigh-Taylor instability, theory and simulations have shown that the flow exhibits properties of self-similarity as the mixing Reynolds number exceeds 20000 and the flow enters the turbulent regime. Here, we present results from the first large Atwood number (0.7) Rayleigh-Taylor experimental campaign for mixing Reynolds number beyond 20000 in an effort to characterize the self-similar nature of the instability. Experiments are performed in a statistically steady gas tunnel facility, allowing for the evaluation of turbulence statistics. A visualization diagnostic is used to study the evolution of the mixing width as the instability grows. This allows for computation of the instability growth rate. For the first time in such a facility, stereoscopic particle image velocimetry is used to resolve three-component velocity information in a plane. Velocity means, fluctuations, and correlations are considered as well as their appropriate scaling. Probability density functions of velocity fields, energy spectra, and higher-order statistics are also presented. The energy budget of the flow is described, including the ratio of the kinetic energy to the released potential energy. This work was supported by the DOE-NNSA SSAA Grant DE-NA0002922.

High performance supercapacitors based on three-dimensional ultralight flexible manganese oxide nanosheets/carbon foam composites

Science.gov (United States)

He, Shuijian; Chen, Wei

2014-09-01

The syntheses and capacitance performances of ultralight and flexible MnO2/carbon foam (MnO2/CF) hybrids are systematically studied. Flexible carbon foam with a low mass density of 6.2 mg cm-3 and high porosity of 99.66% is simply obtained by carbonization of commercially available and low-cost melamine resin foam. With the high porous carbon foam as framework, ultrathin MnO2 nanosheets are grown through in situ redox reaction between KMnO4 and carbon foam. The three-dimensional (3D) MnO2/CF networks exhibit highly ordered hierarchical pore structure. Attributed to the good flexibility and ultralight weight, the MnO2/CF nanomaterials can be directly fabricated into supercapacitor electrodes without any binder and conductive agents. Moreover, the pseudocapacitance of the MnO2 nanosheets is enhanced by the fast ion diffusion in the three-dimensional porous architecture and by the conductive carbon foam skeleton as well as good contact of carbon/oxide interfaces. Supercapacitor based on the MnO2/CF composite with 3.4% weight percent of MnO2 shows a high specific capacitance of 1270.5 F g-1 (92.7% of the theoretical specific capacitance of MnO2) and high energy density of 86.2 Wh kg-1. The excellent capacitance performance of the present 3D ultralight and flexible nanomaterials make them promising candidates as electrode materials for supercapacitors.

A Quantitative Comparison of the Similarity between Genes and Geography in Worldwide Human Populations

Science.gov (United States)

Wang, Chaolong; Zöllner, Sebastian; Rosenberg, Noah A.

2012-01-01

Multivariate statistical techniques such as principal components analysis (PCA) and multidimensional scaling (MDS) have been widely used to summarize the structure of human genetic variation, often in easily visualized two-dimensional maps. Many recent studies have reported similarity between geographic maps of population locations and MDS or PCA maps of genetic variation inferred from single-nucleotide polymorphisms (SNPs). However, this similarity has been evident primarily in a qualitative sense; and, because different multivariate techniques and marker sets have been used in different studies, it has not been possible to formally compare genetic variation datasets in terms of their levels of similarity with geography. In this study, using genome-wide SNP data from 128 populations worldwide, we perform a systematic analysis to quantitatively evaluate the similarity of genes and geography in different geographic regions. For each of a series of regions, we apply a Procrustes analysis approach to find an optimal transformation that maximizes the similarity between PCA maps of genetic variation and geographic maps of population locations. We consider examples in Europe, Sub-Saharan Africa, Asia, East Asia, and Central/South Asia, as well as in a worldwide sample, finding that significant similarity between genes and geography exists in general at different geographic levels. The similarity is highest in our examples for Asia and, once highly distinctive populations have been removed, Sub-Saharan Africa. Our results provide a quantitative assessment of the geographic structure of human genetic variation worldwide, supporting the view that geography plays a strong role in giving rise to human population structure. PMID:22927824

A quantitative comparison of the similarity between genes and geography in worldwide human populations.

Science.gov (United States)

Wang, Chaolong; Zöllner, Sebastian; Rosenberg, Noah A

2012-08-01

Multivariate statistical techniques such as principal components analysis (PCA) and multidimensional scaling (MDS) have been widely used to summarize the structure of human genetic variation, often in easily visualized two-dimensional maps. Many recent studies have reported similarity between geographic maps of population locations and MDS or PCA maps of genetic variation inferred from single-nucleotide polymorphisms (SNPs). However, this similarity has been evident primarily in a qualitative sense; and, because different multivariate techniques and marker sets have been used in different studies, it has not been possible to formally compare genetic variation datasets in terms of their levels of similarity with geography. In this study, using genome-wide SNP data from 128 populations worldwide, we perform a systematic analysis to quantitatively evaluate the similarity of genes and geography in different geographic regions. For each of a series of regions, we apply a Procrustes analysis approach to find an optimal transformation that maximizes the similarity between PCA maps of genetic variation and geographic maps of population locations. We consider examples in Europe, Sub-Saharan Africa, Asia, East Asia, and Central/South Asia, as well as in a worldwide sample, finding that significant similarity between genes and geography exists in general at different geographic levels. The similarity is highest in our examples for Asia and, once highly distinctive populations have been removed, Sub-Saharan Africa. Our results provide a quantitative assessment of the geographic structure of human genetic variation worldwide, supporting the view that geography plays a strong role in giving rise to human population structure.

One-dimensional versus two-dimensional electronic states in vicinal surfaces

International Nuclear Information System (INIS)

Ortega, J E; Ruiz-Oses, M; Cordon, J; Mugarza, A; Kuntze, J; Schiller, F

2005-01-01

Vicinal surfaces with periodic arrays of steps are among the simplest lateral nanostructures. In particular, noble metal surfaces vicinal to the (1 1 1) plane are excellent test systems to explore the basic electronic properties in one-dimensional superlattices by means of angular photoemission. These surfaces are characterized by strong emissions from free-electron-like surface states that scatter at step edges. Thereby, the two-dimensional surface state displays superlattice band folding and, depending on the step lattice constant d, it splits into one-dimensional quantum well levels. Here we use high-resolution, angle-resolved photoemission to analyse surface states in a variety of samples, in trying to illustrate the changes in surface state bands as a function of d

Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regression

Science.gov (United States)

Ndiaye, Eugene; Fercoq, Olivier; Gramfort, Alexandre; Leclère, Vincent; Salmon, Joseph

2017-10-01

In high dimensional settings, sparse structures are crucial for efficiency, both in term of memory, computation and performance. It is customary to consider ℓ 1 penalty to enforce sparsity in such scenarios. Sparsity enforcing methods, the Lasso being a canonical example, are popular candidates to address high dimension. For efficiency, they rely on tuning a parameter trading data fitting versus sparsity. For the Lasso theory to hold this tuning parameter should be proportional to the noise level, yet the latter is often unknown in practice. A possible remedy is to jointly optimize over the regression parameter as well as over the noise level. This has been considered under several names in the literature: Scaled-Lasso, Square-root Lasso, Concomitant Lasso estimation for instance, and could be of interest for uncertainty quantification. In this work, after illustrating numerical difficulties for the Concomitant Lasso formulation, we propose a modification we coined Smoothed Concomitant Lasso, aimed at increasing numerical stability. We propose an efficient and accurate solver leading to a computational cost no more expensive than the one for the Lasso. We leverage on standard ingredients behind the success of fast Lasso solvers: a coordinate descent algorithm, combined with safe screening rules to achieve speed efficiency, by eliminating early irrelevant features.

Stable biexcitons in two-dimensional metal-halide perovskites with strong dynamic lattice disorder

Science.gov (United States)

Thouin, Félix; Neutzner, Stefanie; Cortecchia, Daniele; Dragomir, Vlad Alexandru; Soci, Cesare; Salim, Teddy; Lam, Yeng Ming; Leonelli, Richard; Petrozza, Annamaria; Kandada, Ajay Ram Srimath; Silva, Carlos

2018-03-01

With strongly bound and stable excitons at room temperature, single-layer, two-dimensional organic-inorganic hybrid perovskites are viable semiconductors for light-emitting quantum optoelectronics applications. In such a technological context, it is imperative to comprehensively explore all the factors—chemical, electronic, and structural—that govern strong multiexciton correlations. Here, by means of two-dimensional coherent spectroscopy, we examine excitonic many-body effects in pure, single-layer (PEA) 2PbI4 (PEA = phenylethylammonium). We determine the binding energy of biexcitons—correlated two-electron, two-hole quasiparticles—to be 44 ±5 meV at room temperature. The extraordinarily high values are similar to those reported in other strongly excitonic two-dimensional materials such as transition-metal dichalcogenides. Importantly, we show that this binding energy increases by ˜25 % upon cooling to 5 K. Our work highlights the importance of multiexciton correlations in this class of technologically promising, solution-processable materials, in spite of the strong effects of lattice fluctuations and dynamic disorder.

Factors Affecting the Dimensional Stability of Decorative Papers under Moistening

Directory of Open Access Journals (Sweden)

Andreia B. Figueiredo

2016-01-01

Full Text Available A crucial problem for laminate producers is the dimensional instability of decorative papers during soaking in aqueous solutions, but the source of this dilemma is not completely understood yet. In this study, eight commercial decorative papers of similar fiber composition and sizing were analyzed for their structural, physical, and mechanical properties. These properties were examined for their correlations to the dimensional stability of papers when moistened, as assessed by the wet stretch dynamics. Structure-to-property relationships were evaluated by principal component analysis (PCA. Within the set of parameters examined, PCA revealed that fiber orientation and the content of fillers/pigments influenced the wet expansion of paper web and affected its margins and dimensions in longitudinal and transverse directions of the paper machine. These variables are discussed within the context of decorative paper engineering in order to produce high performance papers with regular wet expansion properties.

Penalized estimation for competing risks regression with applications to high-dimensional covariates

DEFF Research Database (Denmark)

Ambrogi, Federico; Scheike, Thomas H.

2016-01-01

of competing events. The direct binomial regression model of Scheike and others (2008. Predicting cumulative incidence probability by direct binomial regression. Biometrika 95: (1), 205-220) is reformulated in a penalized framework to possibly fit a sparse regression model. The developed approach is easily...... Research 19: (1), 29-51), the research regarding competing risks is less developed (Binder and others, 2009. Boosting for high-dimensional time-to-event data with competing risks. Bioinformatics 25: (7), 890-896). The aim of this work is to consider how to do penalized regression in the presence...... implementable using existing high-performance software to do penalized regression. Results from simulation studies are presented together with an application to genomic data when the endpoint is progression-free survival. An R function is provided to perform regularized competing risks regression according...

COSMO-PAFOG: Three-dimensional fog forecasting with the high-resolution COSMO-model

Science.gov (United States)

Hacker, Maike; Bott, Andreas

2017-04-01

The presence of fog can have critical impact on shipping, aviation and road traffic increasing the risk of serious accidents. Besides these negative impacts of fog, in arid regions fog is explored as a supplementary source of water for human settlements. Thus the improvement of fog forecasts holds immense operational value. The aim of this study is the development of an efficient three-dimensional numerical fog forecast model based on a mesoscale weather prediction model for the application in the Namib region. The microphysical parametrization of the one-dimensional fog forecast model PAFOG (PArameterized FOG) is implemented in the three-dimensional nonhydrostatic mesoscale weather prediction model COSMO (COnsortium for Small-scale MOdeling) developed and maintained by the German Meteorological Service. Cloud water droplets are introduced in COSMO as prognostic variables, thus allowing a detailed description of droplet sedimentation. Furthermore, a visibility parametrization depending on the liquid water content and the droplet number concentration is implemented. The resulting fog forecast model COSMO-PAFOG is run with kilometer-scale horizontal resolution. In vertical direction, we use logarithmically equidistant layers with 45 of 80 layers in total located below 2000 m. Model results are compared to satellite observations and synoptic observations of the German Meteorological Service for a domain in the west of Germany, before the model is adapted to the geographical and climatological conditions in the Namib desert. COSMO-PAFOG is able to represent the horizontal structure of fog patches reasonably well. Especially small fog patches typical of radiation fog can be simulated in agreement with observations. Ground observations of temperature are also reproduced. Simulations without the PAFOG microphysics yield unrealistically high liquid water contents. This in turn reduces the radiative cooling of the ground, thus inhibiting nocturnal temperature decrease. The

Peripheral Vasculature: High-Temporal- and High-Spatial-Resolution Three-dimensional Contrast-enhanced MR Angiography1

Science.gov (United States)

Haider, Clifton R.; Glockner, James F.; Stanson, Anthony W.; Riederer, Stephen J.

2009-01-01

Purpose: To prospectively evaluate the feasibility of performing high-spatial-resolution (1-mm isotropic) time-resolved three-dimensional (3D) contrast material–enhanced magnetic resonance (MR) angiography of the peripheral vasculature with Cartesian acquisition with projection-reconstruction–like sampling (CAPR) and eightfold accelerated two-dimensional (2D) sensitivity encoding (SENSE). Materials and Methods: All studies were approved by the institutional review board and were HIPAA compliant; written informed consent was obtained from all participants. There were 13 volunteers (mean age, 41.9; range, 27–53 years). The CAPR sequence was adapted to provide 1-mm isotropic spatial resolution and a 5-second frame time. Use of different receiver coil element sizes for those placed on the anterior-to-posterior versus left-to-right sides of the field of view reduced signal-to-noise ratio loss due to acceleration. Results from eight volunteers were rated independently by two radiologists according to prominence of artifact, arterial to venous separation, vessel sharpness, continuity of arterial signal intensity in major arteries (anterior and posterior tibial, peroneal), demarcation of origin of major arteries, and overall diagnostic image quality. MR angiographic results in two patients with peripheral vascular disease were compared with their results at computed tomographic angiography. Results: The sequence exhibited no image artifact adversely affecting diagnostic image quality. Temporal resolution was evaluated to be sufficient in all cases, even with known rapid arterial to venous transit. The vessels were graded to have excellent sharpness, continuity, and demarcation of the origins of the major arteries. Distal muscular branches and the communicating and perforating arteries were routinely seen. Excellent diagnostic quality rating was given for 15 (94%) of 16 evaluations. Conclusion: The feasibility of performing high-diagnostic-quality time-resolved 3D

THREE-DIMENSIONAL OBSERVATIONS ON THICK BIOLOGICAL SPECIMENS BY HIGH VOLTAGE ELECTRON MICROSCOPY

Directory of Open Access Journals (Sweden)

Tetsuji Nagata

2011-05-01

Full Text Available Thick biological specimens prepared as whole mount cultured cells or thick sections from embedded tissues were stained with histochemical reactions, such as thiamine pyrophosphatase, glucose-6-phosphatase, cytochrome oxidase, acid phosphatase, DAB reactions and radioautography, to observe 3-D ultrastructures of cell organelles producing stereo-pairs by high voltage electron microscopy at accerelating voltages of 400-1000 kV. The organelles demonstrated were Golgi apparatus, endoplasmic reticulum, mitochondria, lysosomes, peroxisomes, pinocytotic vesicles and incorporations of radioactive compounds. As the results, those cell organelles were observed 3- dimensionally and the relative relationships between these organelles were demonstrated.

Quantification of plant cell coupling with three-dimensional photoactivation microscopy.

Science.gov (United States)

Liesche, J; Schulz, A

2012-07-01

Plant cells are directly connected by plasmodesmata that form channels through the cell wall and enable the intercellular movement of cytosolic solutes, membrane lipids and signalling molecules. Transport through plasmodesmata is regulated not only by a fixed size-exclusion limit, but also by physiological and pathological adaptation. To understand plant cell communication, carbon allocation and pathogen attack, the capacities for a specific molecule to pass a specific cell-wall interface is an essential parameter. So far, the degree of cell coupling was derived from frequency and diameter of plasmodesmata in relevant tissues as assessed by electron microscopy of fixed material. However, plasmodesmata functionality and capacity can only be determined in live material, not from electron microscopy, which is static and prone to fixation artefacts. Plasmodesmata functionality was a few times assessed using fluorescent tracers with diffusion properties similar to cytosolic solutes. Here, we used three-dimensional photoactivation microscopy to quantify plasmodesmata-mediated cell-wall permeability between living Cucurbita maxima leaf mesophyll cells with caged fluorescein as tracer. For the first time, all necessary functional and anatomical data were gathered for each individual cell from three-dimensional time series. This approach utilized a confocal microscope equipped with resonant scanner, which provides the high acquisition speed necessary to record optical sections of whole cells and offers time resolution high enough to follow the kinetics of photoactivation. The results were compared to two-dimensional measurements, which are shown to give a good estimate of cell coupling adequate for homogenous tissues. The two-dimensional approach is limited whenever tissues interfaces are studied that couple different cell types with diverse cell geometries. © 2011 The Authors Journal of Microscopy © 2011 Royal Microscopical Society.

Merger transitions in brane-black-hole systems: Criticality, scaling, and self-similarity

International Nuclear Information System (INIS)

Frolov, Valeri P.

2006-01-01

We propose a toy model for studying merger transitions in a curved spacetime with an arbitrary number of dimensions. This model includes a bulk N-dimensional static spherically symmetric black hole and a test D-dimensional brane (D≤N-1) interacting with the black hole. The brane is asymptotically flat and allows a O(D-1) group of symmetry. Such a brane-black-hole (BBH) system has two different phases. The first one is formed by solutions describing a brane crossing the horizon of the bulk black hole. In this case the internal induced geometry of the brane describes a D-dimensional black hole. The other phase consists of solutions for branes which do not intersect the horizon, and the induced geometry does not have a horizon. We study a critical solution at the threshold of the brane-black-hole formation, and the solutions which are close to it. In particular, we demonstrate that there exists a striking similarity of the merger transition, during which the phase of the BBH system is changed, both with the Choptuik critical collapse and with the merger transitions in the higher dimensional caged black-hole-black-string system

Miniature robust five-dimensional fingertip force/torque sensor with high performance

International Nuclear Information System (INIS)

Liang, Qiaokang; Huang, Xiuxiang; Li, Zhongyang; Zhang, Dan; Ge, Yunjian

2011-01-01

This paper proposes an innovative design and investigation for a five-dimensional fingertip force/torque sensor with a dual annular diaphragm. This sensor can be applied to a robot hand to measure forces along the X-, Y- and Z-axes (F x , F y and F z ) and moments about the X- and Y-axes (M x and M y ) simultaneously. Particularly, the details of the sensing principle, the structural design and the overload protection mechanism are presented. Afterward, based on the design of experiments approach provided by the software ANSYS®, a finite element analysis and an optimization design are performed. These are performed with the objective of achieving both high sensitivity and stiffness of the sensor. Furthermore, static and dynamic calibrations based on the neural network method are carried out. Finally, an application of the developed sensor on a dexterous robot hand is demonstrated. The results of calibration experiments and the application show that the developed sensor possesses high performance and robustness

Three-Dimensional Triplet Tracking for LHC and Future High Rate Experiments

CERN Document Server

Schöning, Andre

2014-10-20

The hit combinatorial problem is a main challenge for track reconstruction and triggering at high rate experiments. At hadron colliders the dominant fraction of hits is due to low momentum tracks for which multiple scattering (MS) effects dominate the hit resolution. MS is also the dominating source for hit confusion and track uncertainties in low energy precision experiments. In all such environments, where MS dominates, track reconstruction and fitting can be largely simplified by using three-dimensional (3D) hit-triplets as provided by pixel detectors. This simplification is possible since track uncertainties are solely determined by MS if high precision spatial information is provided. Fitting of hit-triplets is especially simple for tracking detectors in solenoidal magnetic fields. The over-constrained 3D-triplet method provides a complete set of track parameters and is robust against fake hit combinations. The triplet method is ideally suited for pixel detectors where hits can be treated as 3D-space poi...

Dimensional Stability of Two Polyvinyl Siloxane Impression Materials in Different Time Intervals

Directory of Open Access Journals (Sweden)

Aalaei Sh

2015-12-01

Full Text Available Statement of the Problem: Dental prosthesis is usually made indirectly; there- fore dimensional stability of the impression material is very important. Every few years, new impression materials with different manufacturers’ claims regarding their better properties are introduced to the dental markets which require more research to evaluate their true dimensional changes. Objectives: The aim of this study was to evaluate dimensional stability of additional silicone impression material (Panasil® and Affinis® in different time intervals. Materials and Methods: In this experimental study, using two additional silicones (Panasil® and Affinis®, we made sixty impressions of standard die in similar conditions of 23 °C and 59% relative humidity by a special tray. The die included three horizontal and two vertical lines that were parallel. The vertical line crossed the horizontal ones at a point that served as reference for measurement. All impressions were poured with high strength dental stone. The dimensions were measured by stereo-microscope by two examiners in three interval storage times (1, 24 and 168 hours.The data were statistically analyzed using t-test and ANOVA. Results: All of the stone casts were larger than the standard die. Dimensional changes of Panasil and Affinis were 0.07%, 0.24%, 0.27% and 0.02%, 0.07%, 0.16% after 1, 24 and 168 hours, respectively. Dimensional change for two impression materials wasn’t significant in the interval time, expect for Panasil after one week (p = 0.004. Conclusions: According to the limitations of this study, Affinis impressions were dimensionally more stable than Panasil ones, but it was not significant. Dimensional change of Panasil impression showed a statistically significant difference after one week. Dimensional changes of both impression materials were based on ADA standard limitation in all time intervals (< 0.5%; therefore, dimensional stability of this impression was accepted at least

Dairy Attenuates Weight Gain to a Similar Extent as Exercise in Rats Fed a High-Fat, High-Sugar Diet.

Science.gov (United States)

Trottier, Sarah K; MacPherson, Rebecca E K; Knuth, Carly M; Townsend, Logan K; Peppler, Willem T; Mikhaeil, John S; Leveille, Cam F; LeBlanc, Paul J; Shearer, Jane; Reimer, Raylene A; Wright, David C

2017-10-01

To compare the individual and combined effects of dairy and endurance exercise training in reducing weight gain and adiposity in a rodent model of diet-induced obesity. An 8-week feeding intervention of a high-fat, high-sugar diet was used to induce obesity in male Sprague-Dawley rats. Rats were then assigned to one of four groups for 6 weeks: (1) casein sedentary (casein-S), (2) casein exercise (casein-E), (3) dairy sedentary (dairy-S), and (4) dairy exercise (dairy-E). Rats were exercise trained by treadmill running 5 d/wk. Dairy-E prevented weight gain to a greater extent than either dairy or exercise alone. Adipose tissue and liver mass were reduced to a similar extent in dairy-S, casein-E, and dairy-E groups. Differences in weight gain were not explained by food intake or total energy expenditure. The total amount of lipid excreted was greater in the dairy-S compared to casein-S and dairy-E groups. This study provides evidence that dairy limits weight gain to a similar extent as exercise training and the combined effects are greater than either intervention alone. While exercise training reduces weight gain through increases in energy expenditure, dairy appears to increase lipid excretion in the feces. © 2017 The Obesity Society.

Similarity analysis for the high-pressure inductively coupled plasma source

International Nuclear Information System (INIS)

Vanden-Abeele, D; Degrez, G

2004-01-01

It is well known that the optimal operating parameters of an inductively coupled plasma (ICP) torch strongly depend upon its dimensions. To understand this relationship better, we derive a dimensionless form of the equations governing the behaviour of high-pressure ICPs. The requirement of similarity then naturally leads to expressions for the operating parameters as a function of the plasma radius. In addition to the well-known scaling law for frequency, surprising results appear for the dependence of the mass flow rate, dissipated power and operating pressure upon the plasma radius. While the obtained laws do not appear to be in good agreement with empirical results in the literature, their correctness is supported by detailed numerical calculations of ICP sources of varying diameters. The approximations of local thermodynamic equilibrium and negligible radiative losses restrict the validity of our results and can be responsible for the disagreement with empirical data. The derived scaling laws are useful for the design of new plasma torches and may provide explanations for the unsteadiness observed in certain existing ICP sources

Masturbation Experiences of Swedish Senior High School Students: Gender Differences and Similarities.

Science.gov (United States)

Driemeyer, Wiebke; Janssen, Erick; Wiltfang, Jens; Elmerstig, Eva

Research about masturbation tends to be limited to the assessment of masturbation incidence and frequency. Consequently, little is known about what people experience connected to masturbation. This might be one reason why theoretical approaches that specifically address the persistent gender gap in masturbation frequency are lacking. The aim of the current study was to explore several aspects of masturbation in young men and women, and to examine possible associations with their social backgrounds and sexual histories. Data from 1,566 women and 1,452 men (ages 18 to 22) from 52 Swedish senior high schools were analyzed. Comparisons between men and women were made regarding incidence of and age at first masturbation, the use of objects (e.g., sex toys), fantasies, and sexual functioning during masturbation, as well as about their attitudes toward masturbation and sexual fantasies. Cluster analysis was carried out to identify similarities between and differences within the gender groups. While overall more men than women reported experience with several of the investigated aspects, cluster analyses revealed that a large proportion of men and women reported similar experiences and that fewer experiences are not necessarily associated with negative attitudes toward masturbation. Implications of these findings are discussed in consideration of particular social backgrounds.

Applications of Asymptotic Sampling on High Dimensional Structural Dynamic Problems

DEFF Research Database (Denmark)

Sichani, Mahdi Teimouri; Nielsen, Søren R.K.; Bucher, Christian

2011-01-01

The paper represents application of the asymptotic sampling on various structural models subjected to random excitations. A detailed study on the effect of different distributions of the so-called support points is performed. This study shows that the distribution of the support points has consid...... dimensional reliability problems in structural dynamics.......The paper represents application of the asymptotic sampling on various structural models subjected to random excitations. A detailed study on the effect of different distributions of the so-called support points is performed. This study shows that the distribution of the support points has...... is minimized. Next, the method is applied on different cases of linear and nonlinear systems with a large number of random variables representing the dynamic excitation. The results show that asymptotic sampling is capable of providing good approximations of low failure probability events for very high...

ELECTRON ACCELERATIONS AT HIGH MACH NUMBER SHOCKS: TWO-DIMENSIONAL PARTICLE-IN-CELL SIMULATIONS IN VARIOUS PARAMETER REGIMES

Energy Technology Data Exchange (ETDEWEB)

Matsumoto, Yosuke [Department of Physics, Chiba University, Yayoi-cho 1-33, Inage-ku, Chiba 263-8522 (Japan); Amano, Takanobu; Hoshino, Masahiro, E-mail: ymatumot@astro.s.chiba-u.ac.jp [Department of Earth and Planetary Science, University of Tokyo, Hongo 1-33, Bunkyo-ku, Tokyo 113-0033 (Japan)

2012-08-20

Electron accelerations at high Mach number collisionless shocks are investigated by means of two-dimensional electromagnetic particle-in-cell simulations with various Alfven Mach numbers, ion-to-electron mass ratios, and the upstream electron {beta}{sub e} (the ratio of the thermal pressure to the magnetic pressure). We find electrons are effectively accelerated at a super-high Mach number shock (M{sub A} {approx} 30) with a mass ratio of M/m = 100 and {beta}{sub e} = 0.5. The electron shock surfing acceleration is an effective mechanism for accelerating the particles toward the relativistic regime even in two dimensions with a large mass ratio. Buneman instability excited at the leading edge of the foot in the super-high Mach number shock results in a coherent electrostatic potential structure. While multi-dimensionality allows the electrons to escape from the trapping region, they can interact with the strong electrostatic field several times. Simulation runs in various parameter regimes indicate that the electron shock surfing acceleration is an effective mechanism for producing relativistic particles in extremely high Mach number shocks in supernova remnants, provided that the upstream electron temperature is reasonably low.

Study of two-dimensional interchange turbulence

International Nuclear Information System (INIS)

Sugama, Hideo; Wakatani, Masahiro.

1990-04-01

An eddy viscosity model describing enstrophy transfer in two-dimensional turbulence is presented. This model is similar to that of Canuto et al. and provides an equation for the energy spectral function F(k) as a function of the energy input rate to the system per unit wavenumber, γ s (k). In the enstrophy-transfer inertial range, F(k)∝ k -3 is predicted by the model. The eddy viscosity model is applied to the interchange turbulence of a plasma in shearless magnetic field. Numerical simulation of the two-dimensional interchange turbulence demonstrates that the energy spectrum in the high wavenumber region is well described by this model. The turbulent transport driven by the interchange turbulence is expressed in terms of the Nusselt number Nu, the Rayleigh number Ra and Prantl number Pr in the same manner as that of thermal convection problem. When we use the linear growth rate for γ s (k), our theoretical model predicts that Nu ∝ (Ra·Pr) 1/2 for a constant background pressure gradient and Nu ∝ (Ra·Pr) 1/3 for a self-consistent background pressure profile with the stress-free slip boundary conditions. The latter agrees with our numerical result showing Nu ∝ Ra 1/3 . (author)

Three-dimensional optical reconstruction of vocal fold kinematics using high-speed video with a laser projection system

Science.gov (United States)

Luegmair, Georg; Mehta, Daryush D.; Kobler, James B.; Döllinger, Michael

2015-01-01

Vocal fold kinematics and its interaction with aerodynamic characteristics play a primary role in acoustic sound production of the human voice. Investigating the temporal details of these kinematics using high-speed videoendoscopic imaging techniques has proven challenging in part due to the limitations of quantifying complex vocal fold vibratory behavior using only two spatial dimensions. Thus, we propose an optical method of reconstructing the superior vocal fold surface in three spatial dimensions using a high-speed video camera and laser projection system. Using stereo-triangulation principles, we extend the camera-laser projector method and present an efficient image processing workflow to generate the three-dimensional vocal fold surfaces during phonation captured at 4000 frames per second. Initial results are provided for airflow-driven vibration of an ex vivo vocal fold model in which at least 75% of visible laser points contributed to the reconstructed surface. The method captures the vertical motion of the vocal folds at a high accuracy to allow for the computation of three-dimensional mucosal wave features such as vibratory amplitude, velocity, and asymmetry. PMID:26087485

Development and validation of a comprehensive two-dimensional gas chromatography-mass spectrometry method for the analysis of phytosterol oxidation products in human plasma

NARCIS (Netherlands)

Menéndez-Carreño, M.; Steenbergen, H.; Janssen, H.-G.

2012-01-01

Phytosterol oxidation products (POPs) have been suggested to exert adverse biological effects similar to, although less severe than, their cholesterol counterparts. For that reason, their analysis in human plasma is highly relevant. Comprehensive two-dimensional gas chromatography (GC×GC) coupled

Possible High Thermoelectric Power in Semiconducting Carbon Nanotubes ˜A Case Study of Doped One-Dimensional Semiconductors˜

Science.gov (United States)

Yamamoto, Takahiro; Fukuyama, Hidetoshi

2018-02-01

We have theoretically investigated the thermoelectric properties of impurity-doped one-dimensional semiconductors, focusing on nitrogen-substituted (N-substituted) carbon nanotubes (CNTs), using the Kubo formula combined with a self-consistent t-matrix approximation. N-substituted CNTs exhibit extremely high thermoelectric power factor (PF) values originating from a characteristic of one-dimensional materials where decrease in the carrier density increase both the electrical conductivity and the Seebeck coefficient in the low-N regime. The chemical potential dependence of the PF values of semiconducting CNTs has also been studied as a field-effect transistor and it turns out that the PF values show a noticeable maximum in the vicinity of the band edges. This result demonstrates that "band-edge engineering" will be crucial for solid development of high-performance thermoelectric materials.

Uncovering highly obfuscated plagiarism cases using fuzzy semantic-based similarity model

Directory of Open Access Journals (Sweden)

Salha M. Alzahrani

2015-07-01

Full Text Available Highly obfuscated plagiarism cases contain unseen and obfuscated texts, which pose difficulties when using existing plagiarism detection methods. A fuzzy semantic-based similarity model for uncovering obfuscated plagiarism is presented and compared with five state-of-the-art baselines. Semantic relatedness between words is studied based on the part-of-speech (POS tags and WordNet-based similarity measures. Fuzzy-based rules are introduced to assess the semantic distance between source and suspicious texts of short lengths, which implement the semantic relatedness between words as a membership function to a fuzzy set. In order to minimize the number of false positives and false negatives, a learning method that combines a permission threshold and a variation threshold is used to decide true plagiarism cases. The proposed model and the baselines are evaluated on 99,033 ground-truth annotated cases extracted from different datasets, including 11,621 (11.7% handmade paraphrases, 54,815 (55.4% artificial plagiarism cases, and 32,578 (32.9% plagiarism-free cases. We conduct extensive experimental verifications, including the study of the effects of different segmentations schemes and parameter settings. Results are assessed using precision, recall, F-measure and granularity on stratified 10-fold cross-validation data. The statistical analysis using paired t-tests shows that the proposed approach is statistically significant in comparison with the baselines, which demonstrates the competence of fuzzy semantic-based model to detect plagiarism cases beyond the literal plagiarism. Additionally, the analysis of variance (ANOVA statistical test shows the effectiveness of different segmentation schemes used with the proposed approach.

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

Directory of Open Access Journals (Sweden)

Ernestina Martel

2018-06-01

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

Conjugate-Gradient Neural Networks in Classification of Multisource and Very-High-Dimensional Remote Sensing Data

Science.gov (United States)

Benediktsson, J. A.; Swain, P. H.; Ersoy, O. K.

1993-01-01

Application of neural networks to classification of remote sensing data is discussed. Conventional two-layer backpropagation is found to give good results in classification of remote sensing data but is not efficient in training. A more efficient variant, based on conjugate-gradient optimization, is used for classification of multisource remote sensing and geographic data and very-high-dimensional data. The conjugate-gradient neural networks give excellent performance in classification of multisource data, but do not compare as well with statistical methods in classification of very-high-dimentional data.

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

International Nuclear Information System (INIS)

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

2009-01-01

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

Design guidelines for high dimensional stability of CFRP optical bench

Science.gov (United States)

Desnoyers, Nichola; Boucher, Marc-André; Goyette, Philippe

2013-09-01

In carbon fiber reinforced plastic (CFRP) optomechanical structures, particularly when embodying reflective optics, angular stability is critical. Angular stability or warping stability is greatly affected by moisture absorption and thermal gradients. Unfortunately, it is impossible to achieve the perfect laminate and there will always be manufacturing errors in trying to reach a quasi-iso laminate. Some errors, such as those related to the angular position of each ply and the facesheet parallelism (for a bench) can be easily monitored in order to control the stability more adequately. This paper presents warping experiments and finite-element analyses (FEA) obtained from typical optomechanical sandwich structures. Experiments were done using a thermal vacuum chamber to cycle the structures from -40°C to 50°C. Moisture desorption tests were also performed for a number of specific configurations. The selected composite material for the study is the unidirectional prepreg from Tencate M55J/TC410. M55J is a high modulus fiber and TC410 is a new-generation cyanate ester designed for dimensionally stable optical benches. In the studied cases, the main contributors were found to be: the ply angular errors, laminate in-plane parallelism (between 0° ply direction of both facesheets), fiber volume fraction tolerance and joints. Final results show that some tested configurations demonstrated good warping stability. FEA and measurements are in good agreement despite the fact that some defects or fabrication errors remain unpredictable. Design guidelines to maximize the warping stability by taking into account the main dimensional stability contributors, the bench geometry and the optical mount interface are then proposed.

A novel three-dimensional and high-definition flexible scope.

Science.gov (United States)

Nishiyama, Kenichi; Natori, Yoshihiro; Oka, Kazunari

2014-06-01

Recent high-tech innovations in digital surgical technology have led to advances in three-dimensional (3D) and high-definition (HD) operating scopes. We introduce a novel 3D-HD flexible surgical scope called "3D-Eye-Flex" and evaluate its utility as an alternative to the operating microscope. The 3D-Eye-Flex has a 15 mm long 3D-HD scope-head with a 15 mm outer diameter, a focus distance of 18-100 mm and 80° angle of view. Attached to a 615-mm-long flexible bellows, 3D-Eye-Flex can be easily fixed to the operating table. Microsurgical dissection of wet brain tissue and drilling a skull base model were performed under the scope while using the 3D-HD video monitor. This scope system provided excellent illumination and image quality during the procedures. A large depth of field with stereoscopic vision had a greater advantage over using an operating microscope. 3D-Eye-Flex was easy to manipulate and provided an abundance of space above the operative field. Surgeons felt comfortable while working and could easily shift the position of the scope. This novel 3D-HD flexible scope is an effective alternative to the operating microscope as a new surgeon's eye and will be suitable for digital image-based surgery with further refinement.

The validation and assessment of machine learning: a game of prediction from high-dimensional data.

Directory of Open Access Journals (Sweden)

Tune H Pers

Full Text Available In applied statistics, tools from machine learning are popular for analyzing complex and high-dimensional data. However, few theoretical results are available that could guide to the appropriate machine learning tool in a new application. Initial development of an overall strategy thus often implies that multiple methods are tested and compared on the same set of data. This is particularly difficult in situations that are prone to over-fitting where the number of subjects is low compared to the number of potential predictors. The article presents a game which provides some grounds for conducting a fair model comparison. Each player selects a modeling strategy for predicting individual response from potential predictors. A strictly proper scoring rule, bootstrap cross-validation, and a set of rules are used to make the results obtained with different strategies comparable. To illustrate the ideas, the game is applied to data from the Nugenob Study where the aim is to predict the fat oxidation capacity based on conventional factors and high-dimensional metabolomics data. Three players have chosen to use support vector machines, LASSO, and random forests, respectively.

Efficient computation of k-Nearest Neighbour Graphs for large high-dimensional data sets on GPU clusters.

Directory of Open Access Journals (Sweden)

Ali Dashti

Full Text Available This paper presents an implementation of the brute-force exact k-Nearest Neighbor Graph (k-NNG construction for ultra-large high-dimensional data cloud. The proposed method uses Graphics Processing Units (GPUs and is scalable with multi-levels of parallelism (between nodes of a cluster, between different GPUs on a single node, and within a GPU. The method is applicable to homogeneous computing clusters with a varying number of nodes and GPUs per node. We achieve a 6-fold speedup in data processing as compared with an optimized method running on a cluster of CPUs and bring a hitherto impossible [Formula: see text]-NNG generation for a dataset of twenty million images with 15 k dimensionality into the realm of practical possibility.

Solution of the two-dimensional space-time reactor kinetics equation by a locally one-dimensional method

International Nuclear Information System (INIS)

Chen, G.S.; Christenson, J.M.

1985-01-01

In this paper, the authors present some initial results from an investigation of the application of a locally one-dimensional (LOD) finite difference method to the solution of the two-dimensional, two-group reactor kinetics equations. Although the LOD method is relatively well known, it apparently has not been previously applied to the space-time kinetics equations. In this investigation, the LOD results were benchmarked against similar computational results (using the same computing environment, the same programming structure, and the same sample problems) obtained by the TWIGL program. For all of the problems considered, the LOD method provided accurate results in one-half to one-eight of the time required by the TWIGL program

A novel algorithm of artificial immune system for high-dimensional function numerical optimization

Institute of Scientific and Technical Information of China (English)

DU Haifeng; GONG Maoguo; JIAO Licheng; LIU Ruochen

2005-01-01

Based on the clonal selection theory and immune memory theory, a novel artificial immune system algorithm, immune memory clonal programming algorithm (IMCPA), is put forward. Using the theorem of Markov chain, it is proved that IMCPA is convergent. Compared with some other evolutionary programming algorithms (like Breeder genetic algorithm), IMCPA is shown to be an evolutionary strategy capable of solving complex machine learning tasks, like high-dimensional function optimization, which maintains the diversity of the population and avoids prematurity to some extent, and has a higher convergence speed.

A Cure for Variance Inflation in High Dimensional Kernel Principal Component Analysis

DEFF Research Database (Denmark)

Abrahamsen, Trine Julie; Hansen, Lars Kai

2011-01-01

Small sample high-dimensional principal component analysis (PCA) suffers from variance inflation and lack of generalizability. It has earlier been pointed out that a simple leave-one-out variance renormalization scheme can cure the problem. In this paper we generalize the cure in two directions......: First, we propose a computationally less intensive approximate leave-one-out estimator, secondly, we show that variance inflation is also present in kernel principal component analysis (kPCA) and we provide a non-parametric renormalization scheme which can quite efficiently restore generalizability in kPCA....... As for PCA our analysis also suggests a simplified approximate expression. © 2011 Trine J. Abrahamsen and Lars K. Hansen....

Non-Asymptotic Oracle Inequalities for the High-Dimensional Cox Regression via Lasso.

Science.gov (United States)

Kong, Shengchun; Nan, Bin

2014-01-01

We consider finite sample properties of the regularized high-dimensional Cox regression via lasso. Existing literature focuses on linear models or generalized linear models with Lipschitz loss functions, where the empirical risk functions are the summations of independent and identically distributed (iid) losses. The summands in the negative log partial likelihood function for censored survival data, however, are neither iid nor Lipschitz.We first approximate the negative log partial likelihood function by a sum of iid non-Lipschitz terms, then derive the non-asymptotic oracle inequalities for the lasso penalized Cox regression using pointwise arguments to tackle the difficulties caused by lacking iid Lipschitz losses.

Scaling and interaction of self-similar modes in models of high Reynolds number wall turbulence.

Science.gov (United States)

Sharma, A S; Moarref, R; McKeon, B J

2017-03-13

Previous work has established the usefulness of the resolvent operator that maps the terms nonlinear in the turbulent fluctuations to the fluctuations themselves. Further work has described the self-similarity of the resolvent arising from that of the mean velocity profile. The orthogonal modes provided by the resolvent analysis describe the wall-normal coherence of the motions and inherit that self-similarity. In this contribution, we present the implications of this similarity for the nonlinear interaction between modes with different scales and wall-normal locations. By considering the nonlinear interactions between modes, it is shown that much of the turbulence scaling behaviour in the logarithmic region can be determined from a single arbitrarily chosen reference plane. Thus, the geometric scaling of the modes is impressed upon the nonlinear interaction between modes. Implications of these observations on the self-sustaining mechanisms of wall turbulence, modelling and simulation are outlined.This article is part of the themed issue 'Toward the development of high-fidelity models of wall turbulence at large Reynolds number'. © 2017 The Author(s).

High-functioning autism patients share similar but more severe impairments in verbal theory of mind than schizophrenia patients.

Science.gov (United States)

Tin, L N W; Lui, S S Y; Ho, K K Y; Hung, K S Y; Wang, Y; Yeung, H K H; Wong, T Y; Lam, S M; Chan, R C K; Cheung, E F C

2018-06-01

Evidence suggests that autism and schizophrenia share similarities in genetic, neuropsychological and behavioural aspects. Although both disorders are associated with theory of mind (ToM) impairments, a few studies have directly compared ToM between autism patients and schizophrenia patients. This study aimed to investigate to what extent high-functioning autism patients and schizophrenia patients share and differ in ToM performance. Thirty high-functioning autism patients, 30 schizophrenia patients and 30 healthy individuals were recruited. Participants were matched in age, gender and estimated intelligence quotient. The verbal-based Faux Pas Task and the visual-based Yoni Task were utilised to examine first- and higher-order, affective and cognitive ToM. The task/item difficulty of two paradigms was examined using mixed model analyses of variance (ANOVAs). Multiple ANOVAs and mixed model ANOVAs were used to examine group differences in ToM. The Faux Pas Task was more difficult than the Yoni Task. High-functioning autism patients showed more severely impaired verbal-based ToM in the Faux Pas Task, but shared similar visual-based ToM impairments in the Yoni Task with schizophrenia patients. The findings that individuals with high-functioning autism shared similar but more severe impairments in verbal ToM than individuals with schizophrenia support the autism-schizophrenia continuum. The finding that verbal-based but not visual-based ToM was more impaired in high-functioning autism patients than schizophrenia patients could be attributable to the varied task/item difficulty between the two paradigms.

A selective overview of feature screening for ultrahigh-dimensional data.

Science.gov (United States)

JingYuan, Liu; Wei, Zhong; RunZe, L I

2015-10-01

High-dimensional data have frequently been collected in many scientific areas including genomewide association study, biomedical imaging, tomography, tumor classifications, and finance. Analysis of high-dimensional data poses many challenges for statisticians. Feature selection and variable selection are fundamental for high-dimensional data analysis. The sparsity principle, which assumes that only a small number of predictors contribute to the response, is frequently adopted and deemed useful in the analysis of high-dimensional data. Following this general principle, a large number of variable selection approaches via penalized least squares or likelihood have been developed in the recent literature to estimate a sparse model and select significant variables simultaneously. While the penalized variable selection methods have been successfully applied in many high-dimensional analyses, modern applications in areas such as genomics and proteomics push the dimensionality of data to an even larger scale, where the dimension of data may grow exponentially with the sample size. This has been called ultrahigh-dimensional data in the literature. This work aims to present a selective overview of feature screening procedures for ultrahigh-dimensional data. We focus on insights into how to construct marginal utilities for feature screening on specific models and motivation for the need of model-free feature screening procedures.

Travelling-wave similarity solutions for a steadily translating slender dry patch in a thin fluid film

KAUST Repository

Yatim, Y. M.; Duffy, B. R.; Wilson, S. K.

2013-01-01

A novel family of three-dimensional travelling-wave similarity solutions describing a steadily translating slender dry patch in an infinitely wide thin fluid film on an inclined planar substrate when surface-tension effects are negligible

Dimensional analysis for engineers

CERN Document Server

Simon, Volker; Gomaa, Hassan

2017-01-01

This monograph provides the fundamentals of dimensional analysis and illustrates the method by numerous examples for a wide spectrum of applications in engineering. The book covers thoroughly the fundamental definitions and the Buckingham theorem, as well as the choice of the system of basic units. The authors also include a presentation of model theory and similarity solutions. The target audience primarily comprises researchers and practitioners but the book may also be suitable as a textbook at university level.

Data mining technique for fast retrieval of similar waveforms in Fusion massive databases

International Nuclear Information System (INIS)

Vega, J.; Pereira, A.; Portas, A.; Dormido-Canto, S.; Farias, G.; Dormido, R.; Sanchez, J.; Duro, N.; Santos, M.; Sanchez, E.; Pajares, G.

2008-01-01

Fusion measurement systems generate similar waveforms for reproducible behavior. A major difficulty related to data analysis is the identification, in a rapid and automated way, of a set of discharges with comparable behaviour, i.e. discharges with 'similar' waveforms. Here we introduce a new technique for rapid searching and retrieval of 'similar' signals. The approach consists of building a classification system that avoids traversing the whole database looking for similarities. The classification system diminishes the problem dimensionality (by means of waveform feature extraction) and reduces the searching space to just the most probable 'similar' waveforms (clustering techniques). In the searching procedure, the input waveform is classified in any of the existing clusters. Then, a similarity measure is computed between the input signal and all cluster elements in order to identify the most similar waveforms. The inner product of normalized vectors is used as the similarity measure as it allows the searching process to be independent of signal gain and polarity. This development has been applied recently to TJ-II stellarator databases and has been integrated into its remote participation system

Gender Similarities in Math Performance from Middle School through High School

Science.gov (United States)

Scafidi, Tony; Bui, Khanh

2010-01-01

Using data from 10 states, Hyde, Lindberg, Linn, Ellis, and Williams (2008) found gender similarities in performance on standardized math tests. The present study attempted to replicate this finding with national data and to extend it by examining whether gender similarities in math performance are moderated by race, socioeconomic status, or math…

Method of dimensionality reduction in contact mechanics and friction

CERN Document Server

Popov, Valentin L

2015-01-01

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

Gravitational anomalies and one-dimensional behavior of black holes

Energy Technology Data Exchange (ETDEWEB)

Majhi, Bibhas Ranjan [Indian Institute of Technology Guwahati, Department of Physics, Guwahati, Assam (India)

2015-12-15

It has been pointed out by Bekenstein and Mayo that the behavior of the black hole's entropy or information flow is similar to information flow through one-dimensional channel. Here I analyze the same issue with the use of gravitational anomalies. The rate of the entropy change (S) and the power (P) of the Hawking emission are calculated from the relevant components of the anomalous stress tensor under the Unruh vacuum condition. I show that the dependence of S on the power is S ∝ P{sup 1/2}, which is identical to that for the information flow in a one-dimensional system. This is established by using the (1+1)-dimensional gravitational anomalies first. Then the fact is further bolstered by considering the (1+3)-dimensional gravitational anomalies. It is found that, in the former case, the proportionality constant is exactly identical to the one-dimensional situation, known as Pendry's formula, while in the latter situation its value decreases. (orig.)

Dynamics of the vortex state in high temperature superconductors

International Nuclear Information System (INIS)

Kapitulnik, A.

1991-01-01

The large thermal energy available, the strong anisotropy, and short coherence lengths of high temperature superconductors give rise to new phenomena in the mixed state. The author discusses transport and thermodynamic measurements of high-Tc materials and of model systems. In particular, he uses experiments on two dimensional films to compare and isolate two dimensional effects in the cuprates. By using multilayer systems with similar parameters, he identifies decoupling of the superconducting planes in magnetic fields at temperatures much above the irreversibility line. He shows that if the irreversibility line is to be considered a melting transition line, it implies melting of the solid state into a liquid of three dimensional flux lines. He further uses Monte Carlo simulations to study the structure of the vortex state as well as melting

High-Current Gain Two-Dimensional MoS 2 -Base Hot-Electron Transistors

KAUST Repository

Torres, Carlos M.

2015-12-09

The vertical transport of nonequilibrium charge carriers through semiconductor heterostructures has led to milestones in electronics with the development of the hot-electron transistor. Recently, significant advances have been made with atomically sharp heterostructures implementing various two-dimensional materials. Although graphene-base hot-electron transistors show great promise for electronic switching at high frequencies, they are limited by their low current gain. Here we show that, by choosing MoS2 and HfO2 for the filter barrier interface and using a noncrystalline semiconductor such as ITO for the collector, we can achieve an unprecedentedly high-current gain (α ∼ 0.95) in our hot-electron transistors operating at room temperature. Furthermore, the current gain can be tuned over 2 orders of magnitude with the collector-base voltage albeit this feature currently presents a drawback in the transistor performance metrics such as poor output resistance and poor intrinsic voltage gain. We anticipate our transistors will pave the way toward the realization of novel flexible 2D material-based high-density, low-energy, and high-frequency hot-carrier electronic applications. © 2015 American Chemical Society.

High-Current Gain Two-Dimensional MoS 2 -Base Hot-Electron Transistors