Cowley, Benjamin R.; Kaufman, Matthew T.; Butler, Zachary S.; Churchland, Mark M.; Ryu, Stephen I.; Shenoy, Krishna V.; Yu, Byron M.
2013-12-01
Objective. Analyzing and interpreting the activity of a heterogeneous population of neurons can be challenging, especially as the number of neurons, experimental trials, and experimental conditions increases. One approach is to extract a set of latent variables that succinctly captures the prominent co-fluctuation patterns across the neural population. A key problem is that the number of latent variables needed to adequately describe the population activity is often greater than 3, thereby preventing direct visualization of the latent space. By visualizing a small number of 2-d projections of the latent space or each latent variable individually, it is easy to miss salient features of the population activity. Approach. To address this limitation, we developed a Matlab graphical user interface (called DataHigh) that allows the user to quickly and smoothly navigate through a continuum of different 2-d projections of the latent space. We also implemented a suite of additional visualization tools (including playing out population activity timecourses as a movie and displaying summary statistics, such as covariance ellipses and average timecourses) and an optional tool for performing dimensionality reduction. Main results. To demonstrate the utility and versatility of DataHigh, we used it to analyze single-trial spike count and single-trial timecourse population activity recorded using a multi-electrode array, as well as trial-averaged population activity recorded using single electrodes. Significance. DataHigh was developed to fulfil a need for visualization in exploratory neural data analysis, which can provide intuition that is critical for building scientific hypotheses and models of population activity.
Cowley, Benjamin R; Kaufman, Matthew T; Butler, Zachary S; Churchland, Mark M; Ryu, Stephen I; Shenoy, Krishna V; Yu, Byron M
2013-12-01
Analyzing and interpreting the activity of a heterogeneous population of neurons can be challenging, especially as the number of neurons, experimental trials, and experimental conditions increases. One approach is to extract a set of latent variables that succinctly captures the prominent co-fluctuation patterns across the neural population. A key problem is that the number of latent variables needed to adequately describe the population activity is often greater than 3, thereby preventing direct visualization of the latent space. By visualizing a small number of 2-d projections of the latent space or each latent variable individually, it is easy to miss salient features of the population activity. To address this limitation, we developed a Matlab graphical user interface (called DataHigh) that allows the user to quickly and smoothly navigate through a continuum of different 2-d projections of the latent space. We also implemented a suite of additional visualization tools (including playing out population activity timecourses as a movie and displaying summary statistics, such as covariance ellipses and average timecourses) and an optional tool for performing dimensionality reduction. To demonstrate the utility and versatility of DataHigh, we used it to analyze single-trial spike count and single-trial timecourse population activity recorded using a multi-electrode array, as well as trial-averaged population activity recorded using single electrodes. DataHigh was developed to fulfil a need for visualization in exploratory neural data analysis, which can provide intuition that is critical for building scientific hypotheses and models of population activity.
Cowley, Benjamin R.; Kaufman, Matthew T.; Butler, Zachary S.; Churchland, Mark M.; Ryu, Stephen I.; Shenoy, Krishna V.; Yu, Byron M.
2014-01-01
Objective Analyzing and interpreting the activity of a heterogeneous population of neurons can be challenging, especially as the number of neurons, experimental trials, and experimental conditions increases. One approach is to extract a set of latent variables that succinctly captures the prominent co-fluctuation patterns across the neural population. A key problem is that the number of latent variables needed to adequately describe the population activity is often greater than three, thereby preventing direct visualization of the latent space. By visualizing a small number of 2-d projections of the latent space or each latent variable individually, it is easy to miss salient features of the population activity. Approach To address this limitation, we developed a Matlab graphical user interface (called DataHigh) that allows the user to quickly and smoothly navigate through a continuum of different 2-d projections of the latent space. We also implemented a suite of additional visualization tools (including playing out population activity timecourses as a movie and displaying summary statistics, such as covariance ellipses and average timecourses) and an optional tool for performing dimensionality reduction. Main results To demonstrate the utility and versatility of DataHigh, we used it to analyze single-trial spike count and single-trial timecourse population activity recorded using a multi-electrode array, as well as trial-averaged population activity recorded using single electrodes. Significance DataHigh was developed to fulfill a need for visualization in exploratory neural data analysis, which can provide intuition that is critical for building scientific hypotheses and models of population activity. PMID:24216250
Talib, Imran; Belgacem, Fethi Bin Muhammad; Asif, Naseer Ahmad; Khalil, Hammad
2017-01-01
In this research article, we derive and analyze an efficient spectral method based on the operational matrices of three dimensional orthogonal Jacobi polynomials to solve numerically the mixed partial derivatives type multi-terms high dimensions generalized class of fractional order partial differential equations. We transform the considered fractional order problem to an easily solvable algebraic equations with the aid of the operational matrices. Being easily solvable, the associated algebraic system leads to finding the solution of the problem. Some test problems are considered to confirm the accuracy and validity of the proposed numerical method. The convergence of the method is ensured by comparing our Matlab software simulations based obtained results with the exact solutions in the literature, yielding negligible errors. Moreover, comparative results discussed in the literature are extended and improved in this study.
McParland, D; Phillips, C M; Brennan, L; Roche, H M; Gormley, I C
2017-12-10
The LIPGENE-SU.VI.MAX study, like many others, recorded high-dimensional continuous phenotypic data and categorical genotypic data. LIPGENE-SU.VI.MAX focuses on the need to account for both phenotypic and genetic factors when studying the metabolic syndrome (MetS), a complex disorder that can lead to higher risk of type 2 diabetes and cardiovascular disease. Interest lies in clustering the LIPGENE-SU.VI.MAX participants into homogeneous groups or sub-phenotypes, by jointly considering their phenotypic and genotypic data, and in determining which variables are discriminatory. A novel latent variable model that elegantly accommodates high dimensional, mixed data is developed to cluster LIPGENE-SU.VI.MAX participants using a Bayesian finite mixture model. A computationally efficient variable selection algorithm is incorporated, estimation is via a Gibbs sampling algorithm and an approximate BIC-MCMC criterion is developed to select the optimal model. Two clusters or sub-phenotypes ('healthy' and 'at risk') are uncovered. A small subset of variables is deemed discriminatory, which notably includes phenotypic and genotypic variables, highlighting the need to jointly consider both factors. Further, 7 years after the LIPGENE-SU.VI.MAX data were collected, participants underwent further analysis to diagnose presence or absence of the MetS. The two uncovered sub-phenotypes strongly correspond to the 7-year follow-up disease classification, highlighting the role of phenotypic and genotypic factors in the MetS and emphasising the potential utility of the clustering approach in early screening. Additionally, the ability of the proposed approach to define the uncertainty in sub-phenotype membership at the participant level is synonymous with the concepts of precision medicine and nutrition. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.
DEFF Research Database (Denmark)
Højsgaard, Søren; Edwards, David; Lauritzen, Steffen
Graphical models in their modern form have been around since the late 1970s and appear today in many areas of the sciences. Along with the ongoing developments of graphical models, a number of different graphical modeling software programs have been written over the years. In recent years many...... of these software developments have taken place within the R community, either in the form of new packages or by providing an R ingerface to existing software. This book attempts to give the reader a gentle introduction to graphical modeling using R and the main features of some of these packages. In addition......, the book provides examples of how more advanced aspects of graphical modeling can be represented and handled within R. Topics covered in the seven chapters include graphical models for contingency tables, Gaussian and mixed graphical models, Bayesian networks and modeling high dimensional data...
On the graphical extraction of multipole mixing ratios of nuclear transitions
International Nuclear Information System (INIS)
Rezynkina, K.; Lopez-Martens, A.; Hauschild, K.
2017-01-01
We propose a novel graphical method for determining the mixing ratios δ and their associated uncertainties for mixed nuclear transitions. It incorporates the uncertainties on both the measured and the theoretical conversion coefficients. The accuracy of the method has been studied by deriving the corresponding probability density function. The domains of applicability of the method are carefully defined.
On the graphical extraction of multipole mixing ratios of nuclear transitions
Energy Technology Data Exchange (ETDEWEB)
Rezynkina, K., E-mail: kseniia.rezynkina@csnsm.in2p3.fr; Lopez-Martens, A.; Hauschild, K.
2017-02-01
We propose a novel graphical method for determining the mixing ratios δ and their associated uncertainties for mixed nuclear transitions. It incorporates the uncertainties on both the measured and the theoretical conversion coefficients. The accuracy of the method has been studied by deriving the corresponding probability density function. The domains of applicability of the method are carefully defined.
Stable Graphical Model Estimation with Random Forests for Discrete, Continuous, and Mixed Variables
Fellinghauer, Bernd; Bühlmann, Peter; Ryffel, Martin; von Rhein, Michael; Reinhardt, Jan D.
2011-01-01
A conditional independence graph is a concise representation of pairwise conditional independence among many variables. Graphical Random Forests (GRaFo) are a novel method for estimating pairwise conditional independence relationships among mixed-type, i.e. continuous and discrete, variables. The number of edges is a tuning parameter in any graphical model estimator and there is no obvious number that constitutes a good choice. Stability Selection helps choosing this parameter with respect to...
Yue, Chen; Chen, Shaojie; Sair, Haris I; Airan, Raag; Caffo, Brian S
2015-09-01
Data reproducibility is a critical issue in all scientific experiments. In this manuscript, the problem of quantifying the reproducibility of graphical measurements is considered. The image intra-class correlation coefficient (I2C2) is generalized and the graphical intra-class correlation coefficient (GICC) is proposed for such purpose. The concept for GICC is based on multivariate probit-linear mixed effect models. A Markov Chain Monte Carlo EM (mcm-cEM) algorithm is used for estimating the GICC. Simulation results with varied settings are demonstrated and our method is applied to the KIRBY21 test-retest dataset.
Clustering high dimensional data
DEFF Research Database (Denmark)
Assent, Ira
2012-01-01
High-dimensional data, i.e., data described by a large number of attributes, pose specific challenges to clustering. The so-called ‘curse of dimensionality’, coined originally to describe the general increase in complexity of various computational problems as dimensionality increases, is known...... to render traditional clustering algorithms ineffective. The curse of dimensionality, among other effects, means that with increasing number of dimensions, a loss of meaningful differentiation between similar and dissimilar objects is observed. As high-dimensional objects appear almost alike, new approaches...... for clustering are required. Consequently, recent research has focused on developing techniques and clustering algorithms specifically for high-dimensional data. Still, open research issues remain. Clustering is a data mining task devoted to the automatic grouping of data based on mutual similarity. Each cluster...
GRAPHICS-IMAGE MIXED METHOD FOR LARGE-SCALE BUILDINGS RENDERING
Directory of Open Access Journals (Sweden)
Y. Zhou
2018-05-01
Full Text Available Urban 3D model data is huge and unstructured, LOD and Out-of-core algorithm are usually used to reduce the amount of data that drawn in each frame to improve the rendering efficiency. When the scene is large enough, even the complex optimization algorithm is difficult to achieve better results. Based on the traditional study, a novel idea was developed. We propose a graphics and image mixed method for large-scale buildings rendering. Firstly, the view field is divided into several regions, the graphics-image mixed method used to render the scene on both screen and FBO, then blending the FBO with scree. The algorithm is tested on the huge CityGML model data in the urban areas of New York which contained 188195 public building models, and compared with the Cesium platform. The experiment result shows the system was running smoothly. The experimental results confirm that the algorithm can achieve more massive building scene roaming under the same hardware conditions, and can rendering the scene without vision loss.
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...
Chernozhukov, Victor; Hansen, Christian; Spindler, Martin
2016-01-01
In this article the package High-dimensional Metrics (\\texttt{hdm}) is introduced. It is a collection of statistical methods for estimation and quantification of uncertainty in high-dimensional approximately sparse models. It focuses on providing confidence intervals and significance testing for (possibly many) low-dimensional subcomponents of the high-dimensional parameter vector. Efficient estimators and uniformly valid confidence intervals for regression coefficients on target variables (e...
Wang, Wei; Yang, Jiong
With the rapid growth of computational biology and e-commerce applications, high-dimensional data becomes very common. Thus, mining high-dimensional data is an urgent problem of great practical importance. However, there are some unique challenges for mining data of high dimensions, including (1) the curse of dimensionality and more crucial (2) the meaningfulness of the similarity measure in the high dimension space. In this chapter, we present several state-of-art techniques for analyzing high-dimensional data, e.g., frequent pattern mining, clustering, and classification. We will discuss how these methods deal with the challenges of high dimensionality.
Dewey, Christopher; Wakefield, Jonathan P.
2017-01-01
The two-dimensional stage paradigm (2DSP) has been suggested as an alternative audio mixing interface (AMI). This study seeks to refine the 2DSP by formally evaluating graphical track visualisation styles. Track visualisations considered were text only, circles containing text, individually coloured circles containing text, circles colour coded by instrument type with text, icons with text superimposed, circles with RMS related dynamic opacity and a traditional AMI. The usability evaluation f...
Magezi, David A
2015-01-01
Linear mixed-effects models (LMMs) are increasingly being used for data analysis in cognitive neuroscience and experimental psychology, where within-participant designs are common. The current article provides an introductory review of the use of LMMs for within-participant data analysis and describes a free, simple, graphical user interface (LMMgui). LMMgui uses the package lme4 (Bates et al., 2014a,b) in the statistical environment R (R Core Team).
High-dimensional covariance estimation with high-dimensional data
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
High-dimensional model estimation and model selection
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.
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.
Heckbert, Paul S
1994-01-01
Graphics Gems IV contains practical techniques for 2D and 3D modeling, animation, rendering, and image processing. The book presents articles on polygons and polyhedral; a mix of formulas, optimized algorithms, and tutorial information on the geometry of 2D, 3D, and n-D space; transformations; and parametric curves and surfaces. The text also includes articles on ray tracing; shading 3D models; and frame buffer techniques. Articles on image processing; algorithms for graphical layout; basic interpolation methods; and subroutine libraries for vector and matrix algebra are also demonstrated. Com
Brundage, Michael D; Smith, Katherine C; Little, Emily A; Bantug, Elissa T; Snyder, Claire F
2015-10-01
Patient-reported outcomes (PROs) promote patient-centered care by using PRO research results ("group-level data") to inform decision making and by monitoring individual patient's PROs ("individual-level data") to inform care. We investigated the interpretability of current PRO data presentation formats. This cross-sectional mixed-methods study randomized purposively sampled cancer patients and clinicians to evaluate six group-data or four individual-data formats. A self-directed exercise assessed participants' interpretation accuracy and ratings of ease-of-understanding and usefulness (0 = least to 10 = most) of each format. Semi-structured qualitative interviews explored helpful and confusing format attributes. We reached thematic saturation with 50 patients (44 % < college graduate) and 20 clinicians. For group-level data, patients rated simple line graphs highest for ease-of-understanding and usefulness (median 8.0; 33 % selected for easiest to understand/most useful) and clinicians rated simple line graphs highest for ease-of-understanding and usefulness (median 9.0, 8.5) but most often selected line graphs with confidence limits or norms (30 % for each format for easiest to understand/most useful). Qualitative results support that clinicians value confidence intervals, norms, and p values, but patients find them confusing. For individual-level data, both patients and clinicians rated line graphs highest for ease-of-understanding (median 8.0 patients, 8.5 clinicians) and usefulness (median 8.0, 9.0) and selected them as easiest to understand (50, 70 %) and most useful (62, 80 %). The qualitative interviews supported highlighting scores requiring clinical attention and providing reference values. This study has identified preferences and opportunities for improving on current formats for PRO presentation and will inform development of best practices for PRO presentation. Both patients and clinicians prefer line graphs across group-level data and individual
Chernozhukov, Victor; Hansen, Chris; Spindler, Martin
2016-01-01
The package High-dimensional Metrics (\\Rpackage{hdm}) is an evolving collection of statistical methods for estimation and quantification of uncertainty in high-dimensional approximately sparse models. It focuses on providing confidence intervals and significance testing for (possibly many) low-dimensional subcomponents of the high-dimensional parameter vector. Efficient estimators and uniformly valid confidence intervals for regression coefficients on target variables (e.g., treatment or poli...
Modeling High-Dimensional Multichannel Brain Signals
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
High dimensional neurocomputing growth, appraisal and applications
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...
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...
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....
Murrell, Paul
2005-01-01
R is revolutionizing the world of statistical computing. Powerful, flexible, and best of all free, R is now the program of choice for tens of thousands of statisticians. Destined to become an instant classic, R Graphics presents the first complete, authoritative exposition on the R graphical system. Paul Murrell, widely known as the leading expert on R graphics, has developed an in-depth resource that takes nothing for granted and helps both neophyte and seasoned users master the intricacies of R graphics. After an introductory overview of R graphics facilities, the presentation first focuses
Introduction to high-dimensional statistics
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
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 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...
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.
High-Dimensional Quantum Information Processing with Linear Optics
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
Modeling high dimensional multichannel brain signals
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
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.
Thompson, John
2009-01-01
Graphic storytelling is a medium that allows students to make and share stories, while developing their art communication skills. American comics today are more varied in genre, approach, and audience than ever before. When considering the impact of Japanese manga on the youth, graphic storytelling emerges as a powerful player in pop culture. In…
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)
Glassner, Andrew S
1993-01-01
""The GRAPHICS GEMS Series"" was started in 1990 by Andrew Glassner. The vision and purpose of the Series was - and still is - to provide tips, techniques, and algorithms for graphics programmers. All of the gems are written by programmers who work in the field and are motivated by a common desire to share interesting ideas and tools with their colleagues. Each volume provides a new set of innovative solutions to a variety of programming problems.
DEFF Research Database (Denmark)
Bergstrøm-Nielsen, Carl
1992-01-01
Texbook to be used along with training the practise of graphic notation. Describes method; exercises; bibliography; collection of examples. If you can read Danish, please refer to that edition which is by far much more updated.......Texbook to be used along with training the practise of graphic notation. Describes method; exercises; bibliography; collection of examples. If you can read Danish, please refer to that edition which is by far much more updated....
Modeling High-Dimensional Multichannel Brain Signals
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.
Markov chain Monte Carlo methods in directed graphical models
DEFF Research Database (Denmark)
Højbjerre, Malene
Directed graphical models present data possessing a complex dependence structure, and MCMC methods are computer-intensive simulation techniques to approximate high-dimensional intractable integrals, which emerge in such models with incomplete data. MCMC computations in directed graphical models h...
Secretan, Y.
A discussion of the modular program Mikado is presented. Mikado was developed with the goal of creating a flexible graphic tool to display and help analyze the results of finite element fluid flow computations. Mikado works on unstructured meshes, with elements of mixed geometric type, but also offers the possibility of using structured meshes. The program can be operated by both menu and mouse (interactive), or by command file (batch). Mikado is written in FORTRAN, except for a few system dependent subroutines which are in C. It runs presently on Silicon Graphics' workstations and could be easily ported to the IBM-RISC System/6000 family of workstations.
Statistical Analysis for High-Dimensional Data : The Abel Symposium 2014
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...
Directory of Open Access Journals (Sweden)
Brook Weld Muller
2014-12-01
Full Text Available This essay describes strategic approaches to graphic representation associated with critical environmental engagement and that build from the idea of works of architecture as stitches in the ecological fabric of the city. It focuses on the building up of partial or fragmented graphics in order to describe inclusive, open-ended possibilities for making architecture that marry rich experience and responsive performance. An aphoristic approach to crafting drawings involves complex layering, conscious absence and the embracing of tension. A self-critical attitude toward the generation of imagery characterized by the notion of ‘loose precision’ may lead to more transformative and environmentally responsive architectures.
Learning Graphical Models With Hubs.
Tan, Kean Ming; London, Palma; Mohan, Karthik; Lee, Su-In; Fazel, Maryam; Witten, Daniela
2014-10-01
We consider the problem of learning a high-dimensional graphical model in which there are a few hub nodes that are densely-connected to many other nodes. Many authors have studied the use of an ℓ 1 penalty in order to learn a sparse graph in the high-dimensional setting. However, the ℓ 1 penalty implicitly assumes that each edge is equally likely and independent of all other edges. We propose a general framework to accommodate more realistic networks with hub nodes, using a convex formulation that involves a row-column overlap norm penalty. We apply this general framework to three widely-used probabilistic graphical models: the Gaussian graphical model, the covariance graph model, and the binary Ising model. An alternating direction method of multipliers algorithm is used to solve the corresponding convex optimization problems. On synthetic data, we demonstrate that our proposed framework outperforms competitors that do not explicitly model hub nodes. We illustrate our proposal on a webpage data set and a gene expression data set.
DEFF Research Database (Denmark)
Bergstrøm-Nielsen, Carl
2010-01-01
Graphic notation is taught to music therapy students at Aalborg University in both simple and elaborate forms. This is a method of depicting music visually, and notations may serve as memory aids, as aids for analysis and reflection, and for communication purposes such as supervision or within...
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)...
Directory of Open Access Journals (Sweden)
Prof. Patty K. Wongpakdee
2013-06-01
Full Text Available “Resurfacing Graphics” deals with the subject of unconventional design, with the purpose of engaging the viewer to experience the graphics beyond paper’s passive surface. Unconventional designs serve to reinvigorate people, whose senses are dulled by the typical, printed graphics, which bombard them each day. Today’s cutting-edge designers, illustrators and artists utilize graphics in a unique manner that allows for tactile interaction. Such works serve as valuable teaching models and encourage students to do the following: 1 investigate the trans-disciplines of art and technology; 2 appreciate that this approach can have a positive effect on the environment; 3 examine and research other approaches of design communications and 4 utilize new mediums to stretch the boundaries of artistic endeavor. This paper examines how visuals communicators are “Resurfacing Graphics” by using atypical surfaces and materials such as textile, wood, ceramics and even water. Such non-traditional transmissions of visual language serve to demonstrate student’s overreliance on paper as an outdated medium. With this exposure, students can become forward-thinking, eco-friendly, creative leaders by expanding their creative breadth and continuing the perpetual exploration for new ways to make their mark.
Directory of Open Access Journals (Sweden)
Prof. Patty K. Wongpakdee
2013-06-01
Full Text Available “Resurfacing Graphics” deals with the subject of unconventional design, with the purpose of engaging the viewer to experience the graphics beyond paper’s passive surface. Unconventional designs serve to reinvigorate people, whose senses are dulled by the typical, printed graphics, which bombard them each day. Today’s cutting-edge designers, illustrators and artists utilize graphics in a unique manner that allows for tactile interaction. Such works serve as valuable teaching models and encourage students to do the following: 1 investigate the trans-disciplines of art and technology; 2 appreciate that this approach can have a positive effect on the environment; 3 examine and research other approaches of design communications and 4 utilize new mediums to stretch the boundaries of artistic endeavor. This paper examines how visuals communicators are “Resurfacing Graphics” by using atypical surfaces and materials such as textile, wood, ceramics and even water. Such non-traditional transmissions of visual language serve to demonstrate student’s overreliance on paper as an outdated medium. With this exposure, students can become forward-thinking, eco-friendly, creative leaders by expanding their creative breadth and continuing the perpetual exploration for new ways to make their mark.
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...
Multivariate statistics high-dimensional and large-sample approximations
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
Hierarchical low-rank approximation for high dimensional approximation
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.
Hierarchical low-rank approximation for high dimensional approximation
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.
1990-01-01
A mathematician, David R. Hedgley, Jr. developed a computer program that considers whether a line in a graphic model of a three-dimensional object should or should not be visible. Known as the Hidden Line Computer Code, the program automatically removes superfluous lines and displays an object from a specific viewpoint, just as the human eye would see it. An example of how one company uses the program is the experience of Birdair which specializes in production of fabric skylights and stadium covers. The fabric called SHEERFILL is a Teflon coated fiberglass material developed in cooperation with DuPont Company. SHEERFILL glazed structures are either tension structures or air-supported tension structures. Both are formed by patterned fabric sheets supported by a steel or aluminum frame or cable network. Birdair uses the Hidden Line Computer Code, to illustrate a prospective structure to an architect or owner. The program generates a three- dimensional perspective with the hidden lines removed. This program is still used by Birdair and continues to be commercially available to the public.
Harnessing high-dimensional hyperentanglement through a biphoton frequency comb
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.
Directory of Open Access Journals (Sweden)
Pau Baya
2011-05-01
Full Text Available Remenat (Catalan (Mixed, "revoltillo" (Scrambled in Spanish, is a dish which, in Catalunya, consists of a beaten egg cooked with vegetables or other ingredients, normally prawns or asparagus. It is delicious. Scrambled refers to the action of mixing the beaten egg with other ingredients in a pan, normally using a wooden spoon Thought is frequently an amalgam of past ideas put through a spinner and rhythmically shaken around like a cocktail until a uniform and dense paste is made. This malleable product, rather like a cake mixture can be deformed pulling it out, rolling it around, adapting its shape to the commands of one’s hands or the tool which is being used on it. In the piece Mixed, the contortion of the wood seeks to reproduce the plasticity of this slow heavy movement. Each piece lays itself on the next piece consecutively like a tongue of incandescent lava slowly advancing but with unstoppable inertia.
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.
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.
Printing--Graphic Arts--Graphic Communications
Hauenstein, A. Dean
1975-01-01
Recently, "graphic arts" has shifted from printing skills to a conceptual approach of production processes. "Graphic communications" must embrace the total system of communication through graphic media, to serve broad career education purposes; students taught concepts and principles can be flexible and adaptive. The author…
Nuclear reactors; graphical symbols
International Nuclear Information System (INIS)
1987-11-01
This standard contains graphical symbols that reveal the type of nuclear reactor and is used to design graphical and technical presentations. Distinguishing features for nuclear reactors are laid down in graphical symbols. (orig.) [de
Supporting Dynamic Quantization for High-Dimensional Data Analytics.
Guzun, Gheorghi; Canahuate, Guadalupe
2017-05-01
Similarity searches are at the heart of exploratory data analysis tasks. Distance metrics are typically used to characterize the similarity between data objects represented as feature vectors. However, when the dimensionality of the data increases and the number of features is large, traditional distance metrics fail to distinguish between the closest and furthest data points. Localized distance functions have been proposed as an alternative to traditional distance metrics. These functions only consider dimensions close to query to compute the distance/similarity. Furthermore, in order to enable interactive explorations of high-dimensional data, indexing support for ad-hoc queries is needed. In this work we set up to investigate whether bit-sliced indices can be used for exploratory analytics such as similarity searches and data clustering for high-dimensional big-data. We also propose a novel dynamic quantization called Query dependent Equi-Depth (QED) quantization and show its effectiveness on characterizing high-dimensional similarity. When applying QED we observe improvements in kNN classification accuracy over traditional distance functions. Gheorghi Guzun and Guadalupe Canahuate. 2017. Supporting Dynamic Quantization for High-Dimensional Data Analytics. In Proceedings of Ex-ploreDB'17, Chicago, IL, USA, May 14-19, 2017, 6 pages. https://doi.org/http://dx.doi.org/10.1145/3077331.3077336.
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.
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
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...
Pricing High-Dimensional American Options Using Local Consistency Conditions
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
Irregular grid methods for pricing high-dimensional American options
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
Advanced computer graphics techniques as applied to the nuclear industry
International Nuclear Information System (INIS)
Thomas, J.J.; Koontz, A.S.
1985-08-01
Computer graphics is a rapidly advancing technological area in computer science. This is being motivated by increased hardware capability coupled with reduced hardware costs. This paper will cover six topics in computer graphics, with examples forecasting how each of these capabilities could be used in the nuclear industry. These topics are: (1) Image Realism with Surfaces and Transparency; (2) Computer Graphics Motion; (3) Graphics Resolution Issues and Examples; (4) Iconic Interaction; (5) Graphic Workstations; and (6) Data Fusion - illustrating data coming from numerous sources, for display through high dimensional, greater than 3-D, graphics. All topics will be discussed using extensive examples with slides, video tapes, and movies. Illustrations have been omitted from the paper due to the complexity of color reproduction. 11 refs., 2 figs., 3 tabs
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...
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...
Analysis of chaos in high-dimensional wind power system.
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.
HIGH DIMENSIONAL COVARIANCE MATRIX ESTIMATION IN APPROXIMATE FACTOR MODELS.
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.
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
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
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.
High dimensional model representation method for fuzzy structural dynamics
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.
High-dimensional quantum cloning and applications to quantum hacking.
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.
High Dimensional Classification Using Features Annealed Independence Rules.
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.
International Nuclear Information System (INIS)
Yang, Jin Seok
1993-04-01
This book gives description of basic of graphic knowledge and understanding and realization of graphic file form. The first part deals with graphic with graphic data, store of graphic data and compress of data, programing language such as assembling, stack, compile and link of program and practice and debugging. The next part mentions graphic file form such as Mac paint file, GEM/IMG file, PCX file, GIF file, and TIFF file, consideration of hardware like mono screen driver and color screen driver in high speed, basic conception of dithering and conversion of formality.
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.
Kronholm, Scott C.; Capel, Paul D.
2015-01-01
Quantifying the relative contributions of different sources of water to a stream hydrograph is important for understanding the hydrology and water quality dynamics of a given watershed. To compare the performance of two methods of hydrograph separation, a graphical program [baseflow index (BFI)] and an end-member mixing analysis that used high-resolution specific conductance measurements (SC-EMMA) were used to estimate daily and average long-term slowflow additions of water to four small, primarily agricultural streams with different dominant sources of water (natural groundwater, overland flow, subsurface drain outflow, and groundwater from irrigation). Because the result of hydrograph separation by SC-EMMA is strongly related to the choice of slowflow and fastflow end-member values, a sensitivity analysis was conducted based on the various approaches reported in the literature to inform the selection of end-members. There were substantial discrepancies among the BFI and SC-EMMA, and neither method produced reasonable results for all four streams. Streams that had a small difference in the SC of slowflow compared with fastflow or did not have a monotonic relationship between streamflow and stream SC posed a challenge to the SC-EMMA method. The utility of the graphical BFI program was limited in the stream that had only gradual changes in streamflow. The results of this comparison suggest that the two methods may be quantifying different sources of water. Even though both methods are easy to apply, they should be applied with consideration of the streamflow and/or SC characteristics of a stream, especially where anthropogenic water sources (irrigation and subsurface drainage) are present.
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 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...
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...
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...
National Oceanic and Atmospheric Administration, Department of Commerce — Forecast turbulence hazards identified by the Graphical Turbulence Guidance algorithm. The Graphical Turbulence Guidance product depicts mid-level and upper-level...
Graphical Turbulence Guidance - Composite
National Oceanic and Atmospheric Administration, Department of Commerce — Forecast turbulence hazards identified by the Graphical Turbulence Guidance algorithm. The Graphical Turbulence Guidance product depicts mid-level and upper-level...
DEFF Research Database (Denmark)
Kreiner, Svend; Christensen, Karl Bang
Rasch models; Partial Credit models; Rating Scale models; Item bias; Differential item functioning; Local independence; Graphical models......Rasch models; Partial Credit models; Rating Scale models; Item bias; Differential item functioning; Local independence; Graphical models...
International Nuclear Information System (INIS)
Wang, C.C.; Booth, A.W.; Chen, Y.M.; Botlo, M.
1993-06-01
At the Superconducting Super Collider Laboratory (SSCL) a tool called DAQSIM has been developed to study the behavior of Data Acquisition (DAQ) systems. This paper reports and discusses the graphics used in DAQSIM. DAQSIM graphics includes graphical user interface (GUI), animation, debugging, and control facilities. DAQSIM graphics not only provides a convenient DAQ simulation environment, it also serves as an efficient manager in simulation development and verification
DEFF Research Database (Denmark)
Jensen, Finn Verner; Nielsen, Thomas Dyhre
2016-01-01
Mathematically, a Bayesian graphical model is a compact representation of the joint probability distribution for a set of variables. The most frequently used type of Bayesian graphical models are Bayesian networks. The structural part of a Bayesian graphical model is a graph consisting of nodes...
Scalable Nearest Neighbor Algorithms for High Dimensional Data.
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.
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)
Elucidating high-dimensional cancer hallmark annotation via enriched ontology.
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.
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
High-dimensional single-cell cancer biology.
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.
Animated GIFs as vernacular graphic design
DEFF Research Database (Denmark)
Gürsimsek, Ödül Akyapi
2016-01-01
and often a mix of some of these modes, seem to enable participatory conversations by the audience communities that continue over a period of time. One example of such multimodal digital content is the graphic format called the animated GIF (graphics interchange format). This article focuses on content......Online television audiences create a variety of digital content on the internet. Fans of television production design produce and share such content to express themselves and engage with the objects of their interest. These digital expressions, which exist in the form of graphics, text, videos...... as design, both in the sense that multimodal meaning making is an act of design and in the sense that web-based graphics are designed graphics that are created through a design process. She specifically focuses on the transmedia television production entitled Lost and analyzes the design of animated GIFs...
Sanchez, Julio
2003-01-01
Part I - Graphics Fundamentals PC GRAPHICS OVERVIEW History and Evolution Short History of PC Video PS/2 Video Systems SuperVGA Graphics Coprocessors and Accelerators Graphics Applications State-of-the-Art in PC Graphics 3D Application Programming Interfaces POLYGONAL MODELING Vector and Raster Data Coordinate Systems Modeling with Polygons IMAGE TRANSFORMATIONS Matrix-based Representations Matrix Arithmetic 3D Transformations PROGRAMMING MATRIX TRANSFORMATIONS Numeric Data in Matrix Form Array Processing PROJECTIONS AND RENDERING Perspective The Rendering Pipeline LIGHTING AND SHADING Lightin
Arvo, James
1991-01-01
Graphics Gems II is a collection of articles shared by a diverse group of people that reflect ideas and approaches in graphics programming which can benefit other computer graphics programmers.This volume presents techniques for doing well-known graphics operations faster or easier. The book contains chapters devoted to topics on two-dimensional and three-dimensional geometry and algorithms, image processing, frame buffer techniques, and ray tracing techniques. The radiosity approach, matrix techniques, and numerical and programming techniques are likewise discussed.Graphics artists and comput
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
High-dimensional change-point estimation: Combining filtering with convex optimization
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...
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...
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.
High-dimensional cluster analysis with the Masked EM Algorithm
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
Network Reconstruction From High-Dimensional Ordinary Differential Equations.
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.
Quantum correlation of high dimensional system in a dephasing environment
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.
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...
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.
Reduced order surrogate modelling (ROSM) of high dimensional deterministic simulations
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.
Asymptotics of empirical eigenstructure for high dimensional spiked covariance.
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.
Deterministic Graphical Games Revisited
DEFF Research Database (Denmark)
Andersson, Daniel; Hansen, Kristoffer Arnsfelt; Miltersen, Peter Bro
2008-01-01
We revisit the deterministic graphical games of Washburn. A deterministic graphical game can be described as a simple stochastic game (a notion due to Anne Condon), except that we allow arbitrary real payoffs but disallow moves of chance. We study the complexity of solving deterministic graphical...... games and obtain an almost-linear time comparison-based algorithm for computing an equilibrium of such a game. The existence of a linear time comparison-based algorithm remains an open problem....
International Nuclear Information System (INIS)
Allensworth, J.A.
1984-04-01
EASI (Estimate of Adversary Sequence Interruption) is an analytical technique for measuring the effectiveness of physical protection systems. EASI Graphics is a computer graphics extension of EASI which provides a capability for performing sensitivity and trade-off analyses of the parameters of a physical protection system. This document reports on the implementation of the Version II of EASI Graphics and illustrates its application with some examples. 5 references, 15 figures, 6 tables
Højsgaard, Søren; Lauritzen, Steffen
2012-01-01
Graphical models in their modern form have been around since the late 1970s and appear today in many areas of the sciences. Along with the ongoing developments of graphical models, a number of different graphical modeling software programs have been written over the years. In recent years many of these software developments have taken place within the R community, either in the form of new packages or by providing an R interface to existing software. This book attempts to give the reader a gentle introduction to graphical modeling using R and the main features of some of these packages. In add
The computer graphics metafile
Henderson, LR; Shepherd, B; Arnold, D B
1990-01-01
The Computer Graphics Metafile deals with the Computer Graphics Metafile (CGM) standard and covers topics ranging from the structure and contents of a metafile to CGM functionality, metafile elements, and real-world applications of CGM. Binary Encoding, Character Encoding, application profiles, and implementations are also discussed. This book is comprised of 18 chapters divided into five sections and begins with an overview of the CGM standard and how it can meet some of the requirements for storage of graphical data within a graphics system or application environment. The reader is then intr
The computer graphics interface
Steinbrugge Chauveau, Karla; Niles Reed, Theodore; Shepherd, B
2014-01-01
The Computer Graphics Interface provides a concise discussion of computer graphics interface (CGI) standards. The title is comprised of seven chapters that cover the concepts of the CGI standard. Figures and examples are also included. The first chapter provides a general overview of CGI; this chapter covers graphics standards, functional specifications, and syntactic interfaces. Next, the book discusses the basic concepts of CGI, such as inquiry, profiles, and registration. The third chapter covers the CGI concepts and functions, while the fourth chapter deals with the concept of graphic obje
Explorations on High Dimensional Landscapes: Spin Glasses and Deep Learning
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
A qualitative numerical study of high dimensional dynamical systems
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
Interactive Graphic Journalism
Schlichting, Laura
2016-01-01
textabstractThis paper examines graphic journalism (GJ) in a transmedial context, and argues that transmedial graphic journalism (TMGJ) is an important and fruitful new form of visual storytelling, that will re-invigorate the field of journalism, as it steadily tests out and plays with new media,
Mathematics for computer graphics
Vince, John
2006-01-01
Helps you understand the mathematical ideas used in computer animation, virtual reality, CAD, and other areas of computer graphics. This work also helps you to rediscover the mathematical techniques required to solve problems and design computer programs for computer graphic applications
Graphic Communications. Curriculum Guide.
North Dakota State Board for Vocational Education, Bismarck.
This guide provides the basic foundation to develop a one-semester course based on the cluster concept, graphic communications. One of a set of six guides for an industrial arts curriculum at the junior high school level, it suggests exploratory experiences designed to (1) develop an awareness and understanding of the drafting and graphic arts…
Progress in high-dimensional percolation and random graphs
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...
Effects of dependence in high-dimensional multiple testing problems
Directory of Open Access Journals (Sweden)
van de Wiel Mark A
2008-02-01
Full Text Available Abstract Background We consider effects of dependence among variables of high-dimensional data in multiple hypothesis testing problems, in particular the False Discovery Rate (FDR control procedures. Recent simulation studies consider only simple correlation structures among variables, which is hardly inspired by real data features. Our aim is to systematically study effects of several network features like sparsity and correlation strength by imposing dependence structures among variables using random correlation matrices. Results We study the robustness against dependence of several FDR procedures that are popular in microarray studies, such as Benjamin-Hochberg FDR, Storey's q-value, SAM and resampling based FDR procedures. False Non-discovery Rates and estimates of the number of null hypotheses are computed from those methods and compared. Our simulation study shows that methods such as SAM and the q-value do not adequately control the FDR to the level claimed under dependence conditions. On the other hand, the adaptive Benjamini-Hochberg procedure seems to be most robust while remaining conservative. Finally, the estimates of the number of true null hypotheses under various dependence conditions are variable. Conclusion We discuss a new method for efficient guided simulation of dependent data, which satisfy imposed network constraints as conditional independence structures. Our simulation set-up allows for a structural study of the effect of dependencies on multiple testing criterions and is useful for testing a potentially new method on π0 or FDR estimation in a dependency context.
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)
Inference for High-dimensional Differential Correlation Matrices.
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.
Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regression
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.
Bayesian Subset Modeling for High-Dimensional Generalized Linear Models
Liang, Faming
2013-06-01
This article presents a new prior setting for high-dimensional generalized linear models, which leads to a Bayesian subset regression (BSR) with the maximum a posteriori model approximately equivalent to the minimum extended Bayesian information criterion model. The consistency of the resulting posterior is established under mild conditions. Further, a variable screening procedure is proposed based on the marginal inclusion probability, which shares the same properties of sure screening and consistency with the existing sure independence screening (SIS) and iterative sure independence screening (ISIS) procedures. However, since the proposed procedure makes use of joint information from all predictors, it generally outperforms SIS and ISIS in real applications. This article also makes extensive comparisons of BSR with the popular penalized likelihood methods, including Lasso, elastic net, SIS, and ISIS. The numerical results indicate that BSR can generally outperform the penalized likelihood methods. The models selected by BSR tend to be sparser and, more importantly, of higher prediction ability. In addition, the performance of the penalized likelihood methods tends to deteriorate as the number of predictors increases, while this is not significant for BSR. Supplementary materials for this article are available online. © 2013 American Statistical Association.
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.
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.
High-dimensional statistical inference: From vector to matrix
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
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.
Approximation of High-Dimensional Rank One Tensors
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.
Quality and efficiency in high dimensional Nearest neighbor search
Tao, Yufei; Yi, Ke; Sheng, Cheng; Kalnis, Panos
2009-01-01
Nearest neighbor (NN) search in high dimensional space is an important problem in many applications. Ideally, a practical solution (i) should be implementable in a relational database, and (ii) its query cost should grow sub-linearly with the dataset size, regardless of the data and query distributions. Despite the bulk of NN literature, no solution fulfills both requirements, except locality sensitive hashing (LSH). The existing LSH implementations are either rigorous or adhoc. Rigorous-LSH ensures good quality of query results, but requires expensive space and query cost. Although adhoc-LSH is more efficient, it abandons quality control, i.e., the neighbor it outputs can be arbitrarily bad. As a result, currently no method is able to ensure both quality and efficiency simultaneously in practice. Motivated by this, we propose a new access method called the locality sensitive B-tree (LSB-tree) that enables fast highdimensional NN search with excellent quality. The combination of several LSB-trees leads to a structure called the LSB-forest that ensures the same result quality as rigorous-LSH, but reduces its space and query cost dramatically. The LSB-forest also outperforms adhoc-LSH, even though the latter has no quality guarantee. Besides its appealing theoretical properties, the LSB-tree itself also serves as an effective index that consumes linear space, and supports efficient updates. Our extensive experiments confirm that the LSB-tree is faster than (i) the state of the art of exact NN search by two orders of magnitude, and (ii) the best (linear-space) method of approximate retrieval by an order of magnitude, and at the same time, returns neighbors with much better quality. © 2009 ACM.
Approximation of High-Dimensional Rank One Tensors
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.
Perception in statistical graphics
VanderPlas, Susan Ruth
There has been quite a bit of research on statistical graphics and visualization, generally focused on new types of graphics, new software to create graphics, interactivity, and usability studies. Our ability to interpret and use statistical graphics hinges on the interface between the graph itself and the brain that perceives and interprets it, and there is substantially less research on the interplay between graph, eye, brain, and mind than is sufficient to understand the nature of these relationships. The goal of the work presented here is to further explore the interplay between a static graph, the translation of that graph from paper to mental representation (the journey from eye to brain), and the mental processes that operate on that graph once it is transferred into memory (mind). Understanding the perception of statistical graphics should allow researchers to create more effective graphs which produce fewer distortions and viewer errors while reducing the cognitive load necessary to understand the information presented in the graph. Taken together, these experiments should lay a foundation for exploring the perception of statistical graphics. There has been considerable research into the accuracy of numerical judgments viewers make from graphs, and these studies are useful, but it is more effective to understand how errors in these judgments occur so that the root cause of the error can be addressed directly. Understanding how visual reasoning relates to the ability to make judgments from graphs allows us to tailor graphics to particular target audiences. In addition, understanding the hierarchy of salient features in statistical graphics allows us to clearly communicate the important message from data or statistical models by constructing graphics which are designed specifically for the perceptual system.
International Nuclear Information System (INIS)
Balashov, V.K.
1991-01-01
The structure of the software for computer graphics at VAX JINR is described. It consists of graphical packages GKS, WAND and a set graphicals packages for High Energy Physics application designed at CERN. 17 refs.; 1 tab
K.C. , Santosh; Wendling , Laurent
2015-01-01
International audience; The chapter focuses on one of the key issues in document image processing i.e., graphical symbol recognition. Graphical symbol recognition is a sub-field of a larger research domain: pattern recognition. The chapter covers several approaches (i.e., statistical, structural and syntactic) and specially designed symbol recognition techniques inspired by real-world industrial problems. It, in general, contains research problems, state-of-the-art methods that convey basic s...
Desautel, Richard
1993-01-01
The objectives of this research include supporting the Aerothermodynamics Branch's research by developing graphical visualization tools for both the branch's adaptive grid code and flow field ray tracing code. The completed research for the reporting period includes development of a graphical user interface (GUI) and its implementation into the NAS Flowfield Analysis Software Tool kit (FAST), for both the adaptive grid code (SAGE) and the flow field ray tracing code (CISS).
Matrix correlations for high-dimensional data: The modified RV-coefficient
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
The joint graphical lasso for inverse covariance estimation across multiple classes.
Danaher, Patrick; Wang, Pei; Witten, Daniela M
2014-03-01
We consider the problem of estimating multiple related Gaussian graphical models from a high-dimensional data set with observations belonging to distinct classes. We propose the joint graphical lasso , which borrows strength across the classes in order to estimate multiple graphical models that share certain characteristics, such as the locations or weights of nonzero edges. Our approach is based upon maximizing a penalized log likelihood. We employ generalized fused lasso or group lasso penalties, and implement a fast ADMM algorithm to solve the corresponding convex optimization problems. The performance of the proposed method is illustrated through simulated and real data examples.
Introduction to regression graphics
Cook, R Dennis
2009-01-01
Covers the use of dynamic and interactive computer graphics in linear regression analysis, focusing on analytical graphics. Features new techniques like plot rotation. The authors have composed their own regression code, using Xlisp-Stat language called R-code, which is a nearly complete system for linear regression analysis and can be utilized as the main computer program in a linear regression course. The accompanying disks, for both Macintosh and Windows computers, contain the R-code and Xlisp-Stat. An Instructor's Manual presenting detailed solutions to all the problems in the book is ava
General-Purpose Software For Computer Graphics
Rogers, Joseph E.
1992-01-01
NASA Device Independent Graphics Library (NASADIG) is general-purpose computer-graphics package for computer-based engineering and management applications which gives opportunity to translate data into effective graphical displays for presentation. Features include two- and three-dimensional plotting, spline and polynomial interpolation, control of blanking of areas, multiple log and/or linear axes, control of legends and text, control of thicknesses of curves, and multiple text fonts. Included are subroutines for definition of areas and axes of plots; setup and display of text; blanking of areas; setup of style, interpolation, and plotting of lines; control of patterns and of shading of colors; control of legends, blocks of text, and characters; initialization of devices; and setting of mixed alphabets. Written in FORTRAN 77.
Interactive computer graphics applications for compressible aerodynamics
Benson, Thomas J.
1994-01-01
Three computer applications have been developed to solve inviscid compressible fluids problems using interactive computer graphics. The first application is a compressible flow calculator which solves for isentropic flow, normal shocks, and oblique shocks or centered expansions produced by two dimensional ramps. The second application couples the solutions generated by the first application to a more graphical presentation of the results to produce a desk top simulator of three compressible flow problems: 1) flow past a single compression ramp; 2) flow past two ramps in series; and 3) flow past two opposed ramps. The third application extends the results of the second to produce a design tool which solves for the flow through supersonic external or mixed compression inlets. The applications were originally developed to run on SGI or IBM workstations running GL graphics. They are currently being extended to solve additional types of flow problems and modified to operate on any X-based workstation.
Mathematical structures for computer graphics
Janke, Steven J
2014-01-01
A comprehensive exploration of the mathematics behind the modeling and rendering of computer graphics scenes Mathematical Structures for Computer Graphics presents an accessible and intuitive approach to the mathematical ideas and techniques necessary for two- and three-dimensional computer graphics. Focusing on the significant mathematical results, the book establishes key algorithms used to build complex graphics scenes. Written for readers with various levels of mathematical background, the book develops a solid foundation for graphics techniques and fills in relevant grap
International Nuclear Information System (INIS)
Bass, L.; Wynholds, H.W.; Porterfield, W.R.
1975-01-01
Described is an operational system that enables the user, through an intelligent graphics terminal, to construct, modify, analyze, and store fault trees. With this system, complex engineering designs can be analyzed. This paper discusses the system and its capabilities. Included is a brief discussion of fault tree analysis, which represents an aspect of reliability and safety modeling
Mathematical Graphic Organizers
Zollman, Alan
2009-01-01
As part of a math-science partnership, a university mathematics educator and ten elementary school teachers developed a novel approach to mathematical problem solving derived from research on reading and writing pedagogy. Specifically, research indicates that students who use graphic organizers to arrange their ideas improve their comprehension…
Graphical Interfaces for Simulation.
Hollan, J. D.; And Others
This document presents a discussion of the development of a set of software tools to assist in the construction of interfaces to simulations and real-time systems. Presuppositions to the approach to interface design that was used are surveyed, the tools are described, and the conclusions drawn from these experiences in graphical interface design…
Blanchard, D. C.
1986-01-01
Printer Graphics Package (PGP) is tool for making two-dimensional symbolic plots on line printer. PGP created to support development of Heads-Up Display (HUD) simulation. Standard symbols defined with HUD in mind. Available symbols include circle, triangle, quadrangle, window, line, numbers, and text. Additional symbols easily added or built up from available symbols.
Cleaver, Samantha
2008-01-01
Not so many years ago, comic books in school were considered the enemy. Students caught sneaking comics between the pages of bulky--and less engaging--textbooks were likely sent to the principal. Today, however, comics, including classics such as "Superman" but also their generally more complex, nuanced cousins, graphic novels, are not only…
Graphics gems V (Macintosh version)
Paeth, Alan W
1995-01-01
Graphics Gems V is the newest volume in The Graphics Gems Series. It is intended to provide the graphics community with a set of practical tools for implementing new ideas and techniques, and to offer working solutions to real programming problems. These tools are written by a wide variety of graphics programmers from industry, academia, and research. The books in the series have become essential, time-saving tools for many programmers.Latest collection of graphics tips in The Graphics Gems Series written by the leading programmers in the field.Contains over 50 new gems displaying some of t
Topographic Digital Raster Graphics - USGS DIGITAL RASTER GRAPHICS
NSGIC Local Govt | GIS Inventory — USGS Topographic Digital Raster Graphics downloaded from LABINS (http://data.labins.org/2003/MappingData/drg/drg_stpl83.cfm). A digital raster graphic (DRG) is a...
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...
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
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
Approximating high-dimensional dynamics by barycentric coordinates with linear programming.
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.
Efficient and accurate nearest neighbor and closest pair search in high-dimensional space
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
Interactive Graphic Journalism
Directory of Open Access Journals (Sweden)
Laura Schlichting
2016-12-01
Full Text Available This paper examines graphic journalism (GJ in a transmedial context, and argues that transmedial graphic journalism (TMGJ is an important and fruitful new form of visual storytelling, that will re-invigorate the field of journalism, as it steadily tests out and plays with new media, ultimately leading to new challenges in both the production and reception process. With TMGJ, linear narratives may be broken up and ethical issues concerning the emotional and entertainment value are raised when it comes to ‘playing the news’. The aesthetic characteristics of TMGJ will be described and interactivity’s influence on non-fiction storytelling will be explored in an analysis of The Nisoor Square Shooting (2011 and Ferguson Firsthand (2015.
International Nuclear Information System (INIS)
Bray, M.A.; Petersen, R.J.; Clark, M.T.; Gertman, D.I.
1981-01-01
This paper reports US NRC-sponsored research at the Idaho National Engineering Laboratory (INEL) involving evaluation of computer-based diagnostic graphics. The specific targets of current evaluations are multivariate data display formats which may be used in Safety Parameter Display Systems (SPDS) being developed for nuclear power plant control rooms. The purpose of the work is to provide a basis for NRC action in regulating licensee SPDSs or later computer/cathode ray tube (CRT) applications in nuclear control rooms
Kim, Ann
2009-01-01
It's no secret that children and YAs are clued in to graphic novels (GNs) and that comics-loving adults are positively giddy that this format is getting the recognition it deserves. Still, there is a whole swath of library card-carrying grown-up readers out there with no idea where to start. Splashy movies such as "300" and "Spider-Man" and their…
Career Opportunities in Computer Graphics.
Langer, Victor
1983-01-01
Reviews the impact of computer graphics on industrial productivity. Details the computer graphics technician curriculum at Milwaukee Area Technical College and the cooperative efforts of business and industry to fund and equip the program. (SK)
Critical frameworks for graphic design: graphic design and visual culture
Dauppe, Michele-Anne
2011-01-01
The paper considers an approach to the study of graphic design which addresses the expanding nature of graphic design in the 21st century and the purposeful application of theory to the subject of graphic design. In recent years graphic design has expanded its domain from the world of print culture (e.g. books, posters) into what is sometimes called screen culture. Everything from a mobile phone to a display in an airport lounge to the A.T.M. carries graphic design. It has become ever more ub...
Directory of Open Access Journals (Sweden)
Steven Hoover
2012-04-01
Full Text Available Many libraries and librarians have embraced graphic novels. A number of books, articles, and presentations have focused on the history of the medium and offered advice on building and maintaining collections, but very little attention has been given the question of how integrate graphic novels into a library’s instructional efforts. This paper will explore the characteristics of graphic novels that make them a valuable resource for librarians who focus on research and information literacy instruction, identify skills and competencies that can be taught by the study of graphic novels, and will provide specific examples of how to incorporate graphic novels into instruction.
Kirk, David
1994-01-01
This sequel to Graphics Gems (Academic Press, 1990), and Graphics Gems II (Academic Press, 1991) is a practical collection of computer graphics programming tools and techniques. Graphics Gems III contains a larger percentage of gems related to modeling and rendering, particularly lighting and shading. This new edition also covers image processing, numerical and programming techniques, modeling and transformations, 2D and 3D geometry and algorithms,ray tracing and radiosity, rendering, and more clever new tools and tricks for graphics programming. Volume III also includes a
Deterministic Graphical Games Revisited
DEFF Research Database (Denmark)
Andersson, Klas Olof Daniel; Hansen, Kristoffer Arnsfelt; Miltersen, Peter Bro
2012-01-01
Starting from Zermelo’s classical formal treatment of chess, we trace through history the analysis of two-player win/lose/draw games with perfect information and potentially infinite play. Such chess-like games have appeared in many different research communities, and methods for solving them......, such as retrograde analysis, have been rediscovered independently. We then revisit Washburn’s deterministic graphical games (DGGs), a natural generalization of chess-like games to arbitrary zero-sum payoffs. We study the complexity of solving DGGs and obtain an almost-linear time comparison-based algorithm...
Hybrid compression of video with graphics in DTV communication systems
Schaar, van der, M.; With, de, P.H.N.
2000-01-01
Advanced broadcast manipulation of TV sequences and enhanced user interfaces for TV systems have resulted in an increased amount of pre- and post-editing of video sequences, where graphical information is inserted. However, in the current broadcasting chain, there are no provisions for enabling an efficient transmission/storage of these mixed video and graphics signals and, at this emerging stage of DTV systems, introducing new standards is not desired. Nevertheless, in the professional video...
Engineering two-photon high-dimensional states through quantum interference
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
A Comparison of Methods for Estimating the Determinant of High-Dimensional Covariance Matrix
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.
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.
A Comparison of Methods for Estimating the Determinant of High-Dimensional Covariance Matrix
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 Hybrid Semi-Supervised Anomaly Detection Model for High-Dimensional Data
Directory of Open Access Journals (Sweden)
Hongchao Song
2017-01-01
Full Text Available Anomaly detection, which aims to identify observations that deviate from a nominal sample, is a challenging task for high-dimensional data. Traditional distance-based anomaly detection methods compute the neighborhood distance between each observation and suffer from the curse of dimensionality in high-dimensional space; for example, the distances between any pair of samples are similar and each sample may perform like an outlier. In this paper, we propose a hybrid semi-supervised anomaly detection model for high-dimensional data that consists of two parts: a deep autoencoder (DAE and an ensemble k-nearest neighbor graphs- (K-NNG- based anomaly detector. Benefiting from the ability of nonlinear mapping, the DAE is first trained to learn the intrinsic features of a high-dimensional dataset to represent the high-dimensional data in a more compact subspace. Several nonparametric KNN-based anomaly detectors are then built from different subsets that are randomly sampled from the whole dataset. The final prediction is made by all the anomaly detectors. The performance of the proposed method is evaluated on several real-life datasets, and the results confirm that the proposed hybrid model improves the detection accuracy and reduces the computational complexity.
Model-based Clustering of High-Dimensional Data in Astrophysics
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.
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)
Detection of Subtle Context-Dependent Model Inaccuracies in High-Dimensional Robot Domains.
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.
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)
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.
Can we be more Graphic about Graphic Design?
Vienne, Véronique
2012-01-01
Can you objectify a subjective notion? This is the question graphic designers must face when they talk about their work. Even though graphic design artifacts are omnipresent in our culture, graphic design is still an exceptionally ill-defined profession. This is one of the reasons design criticism is still a rudimentary discipline. No one knows for sure what is this thing we sometimes call “graphic communication” for lack of a better word–a technique my Webster’s dictionary describes as “the ...
[Hardware for graphics systems].
Goetz, C
1991-02-01
In all personal computer applications, be it for private or professional use, the decision of which "brand" of computer to buy is of central importance. In the USA Apple computers are mainly used in universities, while in Europe computers of the so-called "industry standard" by IBM (or clones thereof) have been increasingly used for many years. Independently of any brand name considerations, the computer components purchased must meet the current (and projected) needs of the user. Graphic capabilities and standards, processor speed, the use of co-processors, as well as input and output devices such as "mouse", printers and scanners are discussed. This overview is meant to serve as a decision aid. Potential users are given a short but detailed summary of current technical features.
Safety Parameters Graphical Interface
International Nuclear Information System (INIS)
Canamero, B.
1998-01-01
Nuclear power plant data are received at the Operations Center of the Consejo de Seguridad Nuclear in emergency situations. In order to achieve the required interface and to prepare those data to perform simulation and forecasting with already existing computer codes a Safety Parameters Graphical Interface (IGPS) has been developed. The system runs in a UNIX environment and use the Xwindows capabilities. The received data are stored in such a way that it can be easily used for further analysis and training activities. The system consists of task-oriented modules (processes) which communicate each other using well known UNIX mechanisms (signals, sockets and shared memory segments). IGPS conceptually have two different parts: Data collection and preparation, and Data monitorization. (Author)
Directory of Open Access Journals (Sweden)
Thenmozhi Srinivasan
2015-01-01
Full Text Available Clusters of high-dimensional data techniques are emerging, according to data noisy and poor quality challenges. This paper has been developed to cluster data using high-dimensional similarity based PCM (SPCM, with ant colony optimization intelligence which is effective in clustering nonspatial data without getting knowledge about cluster number from the user. The PCM becomes similarity based by using mountain method with it. Though this is efficient clustering, it is checked for optimization using ant colony algorithm with swarm intelligence. Thus the scalable clustering technique is obtained and the evaluation results are checked with synthetic datasets.
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.
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....
Graphics and visualization principles & algorithms
Theoharis, T; Platis, Nikolaos; Patrikalakis, Nicholas M
2008-01-01
Computer and engineering collections strong in applied graphics and analysis of visual data via computer will find Graphics & Visualization: Principles and Algorithms makes an excellent classroom text as well as supplemental reading. It integrates coverage of computer graphics and other visualization topics, from shadow geneeration and particle tracing to spatial subdivision and vector data visualization, and it provides a thorough review of literature from multiple experts, making for a comprehensive review essential to any advanced computer study.-California Bookw
Steven Hoover
2012-01-01
Many libraries and librarians have embraced graphic novels. A number of books, articles, and presentations have focused on the history of the medium and offered advice on building and maintaining collections, but very little attention has been given the question of how integrate graphic novels into a library’s instructional efforts. This paper will explore the characteristics of graphic novels that make them a valuable resource for librarians who focus on research and information literacy i...
Graphical models for genetic analyses
DEFF Research Database (Denmark)
Lauritzen, Steffen Lilholt; Sheehan, Nuala A.
2003-01-01
This paper introduces graphical models as a natural environment in which to formulate and solve problems in genetics and related areas. Particular emphasis is given to the relationships among various local computation algorithms which have been developed within the hitherto mostly separate areas...... of graphical models and genetics. The potential of graphical models is explored and illustrated through a number of example applications where the genetic element is substantial or dominating....
Connell, Ellery
2011-01-01
Helping graphic designers expand their 2D skills into the 3D space The trend in graphic design is towards 3D, with the demand for motion graphics, animation, photorealism, and interactivity rapidly increasing. And with the meteoric rise of iPads, smartphones, and other interactive devices, the design landscape is changing faster than ever.2D digital artists who need a quick and efficient way to join this brave new world will want 3D for Graphic Designers. Readers get hands-on basic training in working in the 3D space, including product design, industrial design and visualization, modeling, ani
Computer graphics and research projects
International Nuclear Information System (INIS)
Ingtrakul, P.
1994-01-01
This report was prepared as an account of scientific visualization tools and application tools for scientists and engineers. It is provided a set of tools to create pictures and to interact with them in natural ways. It applied many techniques of computer graphics and computer animation through a number of full-color presentations as computer animated commercials, 3D computer graphics, dynamic and environmental simulations, scientific modeling and visualization, physically based modelling, and beavioral, skelatal, dynamics, and particle animation. It took in depth at original hardware and limitations of existing PC graphics adapters contain syste m performance, especially with graphics intensive application programs and user interfaces
Computer graphics in engineering education
Rogers, David F
2013-01-01
Computer Graphics in Engineering Education discusses the use of Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) as an instructional material in engineering education. Each of the nine chapters of this book covers topics and cites examples that are relevant to the relationship of CAD-CAM with engineering education. The first chapter discusses the use of computer graphics in the U.S. Naval Academy, while Chapter 2 covers key issues in instructional computer graphics. This book then discusses low-cost computer graphics in engineering education. Chapter 4 discusses the uniform b
Evaluating Texts for Graphical Literacy Instruction: The Graphic Rating Tool
Roberts, Kathryn L.; Brugar, Kristy A.; Norman, Rebecca R.
2015-01-01
In this article, we present the Graphical Rating Tool (GRT), which is designed to evaluate the graphical devices that are commonly found in content-area, non-fiction texts, in order to identify books that are well suited for teaching about those devices. We also present a "best of" list of science and social studies books, which includes…
An irregular grid approach for pricing high-dimensional American options
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
Can We Train Machine Learning Methods to Outperform the High-dimensional Propensity Score Algorithm?
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.
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...
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...
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.
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.
High-Dimensional Exploratory Item Factor Analysis by a Metropolis-Hastings Robbins-Monro Algorithm
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…
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...
Multi-Scale Factor Analysis of High-Dimensional Brain Signals
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
Spectrally-Corrected Estimation for High-Dimensional Markowitz Mean-Variance Optimization
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
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
Multigrid for high dimensional elliptic partial differential equations on non-equidistant grids
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
An Irregular Grid Approach for Pricing High-Dimensional American Options
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
Pricing and hedging high-dimensional American options : an irregular grid approach
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
Practical algorithms for 3D computer graphics
Ferguson, R Stuart
2013-01-01
""A valuable book to accompany any course that mixes the theory and practice of 3D graphics. The book's web site has many useful programs and code samples.""-Karen Rafferty, Queen's University, Belfast""The topics covered by this book are backed by the OpenFX modeling and animation software. This is a big plus in that it provides a practical perspective and encourages experimentation. … [This] will offer students a more interesting and hands-on learning experience, especially for those wishing to pursue a career in computer game development.""-Naganand Madhavapeddy, GameDeveloper>
THE GRAPHIC LANGUAGE OF A LOGO
Directory of Open Access Journals (Sweden)
ADIR Victor
2013-11-01
Full Text Available The paper analyses the support elements of a logo graphic structure and their role to design an intelligent and translatable one. The colour management is an important work to characterize the colour palette of a logo and it’s reproducible. Also it is necessary to pay attention to the thickness or thinness of the line concerning the possibility to multiply in different sizes a logo and to have a good and clear image of it. The typology of logos is presented relative to the 3 axes of design: logotype, iconic and mixed.
Oklahoma's Mobile Computer Graphics Laboratory.
McClain, Gerald R.
This Computer Graphics Laboratory houses an IBM 1130 computer, U.C.C. plotter, printer, card reader, two key punch machines, and seminar-type classroom furniture. A "General Drafting Graphics System" (GDGS) is used, based on repetitive use of basic coordinate and plot generating commands. The system is used by 12 institutions of higher education…
Software for graphic display systems
International Nuclear Information System (INIS)
Karlov, A.A.
1978-01-01
In this paper some aspects of graphic display systems are discussed. The design of a display subroutine library is described, with an example, and graphic dialogue software is considered primarily from the point of view of the programmer who uses a high-level language. (Auth.)
Graphics Education Survey. Part II.
Ernst, Sandra B.
After a 1977 survey reflected the importance of graphics education for news students, a study was developed to investigate the state of graphics education in the whole field of journalism. A questionnaire was sent to professors and administrators in four print-oriented professional fields of education: magazine, advertising, public relations, and…
ElectroEncephaloGraphics: Making waves in computer graphics research.
Mustafa, Maryam; Magnor, Marcus
2014-01-01
Electroencephalography (EEG) is a novel modality for investigating perceptual graphics problems. Until recently, EEG has predominantly been used for clinical diagnosis, in psychology, and by the brain-computer-interface community. Researchers are extending it to help understand the perception of visual output from graphics applications and to create approaches based on direct neural feedback. Researchers have applied EEG to graphics to determine perceived image and video quality by detecting typical rendering artifacts, to evaluate visualization effectiveness by calculating the cognitive load, and to automatically optimize rendering parameters for images and videos on the basis of implicit neural feedback.
Directory of Open Access Journals (Sweden)
Nils Ternès
2017-05-01
Full Text Available Abstract Background Thanks to the advances in genomics and targeted treatments, more and more prediction models based on biomarkers are being developed to predict potential benefit from treatments in a randomized clinical trial. Despite the methodological framework for the development and validation of prediction models in a high-dimensional setting is getting more and more established, no clear guidance exists yet on how to estimate expected survival probabilities in a penalized model with biomarker-by-treatment interactions. Methods Based on a parsimonious biomarker selection in a penalized high-dimensional Cox model (lasso or adaptive lasso, we propose a unified framework to: estimate internally the predictive accuracy metrics of the developed model (using double cross-validation; estimate the individual survival probabilities at a given timepoint; construct confidence intervals thereof (analytical or bootstrap; and visualize them graphically (pointwise or smoothed with spline. We compared these strategies through a simulation study covering scenarios with or without biomarker effects. We applied the strategies to a large randomized phase III clinical trial that evaluated the effect of adding trastuzumab to chemotherapy in 1574 early breast cancer patients, for which the expression of 462 genes was measured. Results In our simulations, penalized regression models using the adaptive lasso estimated the survival probability of new patients with low bias and standard error; bootstrapped confidence intervals had empirical coverage probability close to the nominal level across very different scenarios. The double cross-validation performed on the training data set closely mimicked the predictive accuracy of the selected models in external validation data. We also propose a useful visual representation of the expected survival probabilities using splines. In the breast cancer trial, the adaptive lasso penalty selected a prediction model with 4
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.
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.
Non-intrusive low-rank separated approximation of high-dimensional stochastic models
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
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.
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.
A Shell Multi-dimensional Hierarchical Cubing Approach for High-Dimensional Cube
Zou, Shuzhi; Zhao, Li; Hu, Kongfa
The pre-computation of data cubes is critical for improving the response time of OLAP systems and accelerating data mining tasks in large data warehouses. However, as the sizes of data warehouses grow, the time it takes to perform this pre-computation becomes a significant performance bottleneck. In a high dimensional data warehouse, it might not be practical to build all these cuboids and their indices. In this paper, we propose a shell multi-dimensional hierarchical cubing algorithm, based on an extension of the previous minimal cubing approach. This method partitions the high dimensional data cube into low multi-dimensional hierarchical cube. Experimental results show that the proposed method is significantly more efficient than other existing cubing methods.
Minimax Rate-optimal Estimation of High-dimensional Covariance Matrices with Incomplete Data.
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*
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
Distribution of high-dimensional entanglement via an intra-city free-space link.
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.
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
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....
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
A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem
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 ...
GAMLSS for high-dimensional data – a flexible approach based on boosting
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...
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)
Reinforcement learning on slow features of high-dimensional input streams.
Directory of Open Access Journals (Sweden)
Robert Legenstein
Full Text Available Humans and animals are able to learn complex behaviors based on a massive stream of sensory information from different modalities. Early animal studies have identified learning mechanisms that are based on reward and punishment such that animals tend to avoid actions that lead to punishment whereas rewarded actions are reinforced. However, most algorithms for reward-based learning are only applicable if the dimensionality of the state-space is sufficiently small or its structure is sufficiently simple. Therefore, the question arises how the problem of learning on high-dimensional data is solved in the brain. In this article, we propose a biologically plausible generic two-stage learning system that can directly be applied to raw high-dimensional input streams. The system is composed of a hierarchical slow feature analysis (SFA network for preprocessing and a simple neural network on top that is trained based on rewards. We demonstrate by computer simulations that this generic architecture is able to learn quite demanding reinforcement learning tasks on high-dimensional visual input streams in a time that is comparable to the time needed when an explicit highly informative low-dimensional state-space representation is given instead of the high-dimensional visual input. The learning speed of the proposed architecture in a task similar to the Morris water maze task is comparable to that found in experimental studies with rats. This study thus supports the hypothesis that slowness learning is one important unsupervised learning principle utilized in the brain to form efficient state representations for behavioral learning.
Hypergraph-based anomaly detection of high-dimensional co-occurrences.
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.
High-Dimensional Function Approximation With Neural Networks for Large Volumes of Data.
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.
Stereoscopic 3D graphics generation
Li, Zhi; Liu, Jianping; Zan, Y.
1997-05-01
Stereoscopic display technology is one of the key techniques of areas such as simulation, multimedia, entertainment, virtual reality, and so on. Moreover, stereoscopic 3D graphics generation is an important part of stereoscopic 3D display system. In this paper, at first, we describe the principle of stereoscopic display and summarize some methods to generate stereoscopic 3D graphics. Secondly, to overcome the problems which came from the methods of user defined models (such as inconvenience, long modifying period and so on), we put forward the vector graphics files defined method. Thus we can design more directly; modify the model simply and easily; generate more conveniently; furthermore, we can make full use of graphics accelerator card and so on. Finally, we discuss the problem of how to speed up the generation.
EPA Communications Stylebook: Graphics Guide
Includes standards and guidance for graphics typography, layout, composition, color scheme, appropriate use of charts and graphs, logos and related symbols, and consistency with the message of accompanied content.
IAU Graphics Extension - Gnu C
DEFF Research Database (Denmark)
Denver, Troelz; Jørgensen, John Leif; Riis, Troels
1999-01-01
This document contains a description of the library GrxIau (former UtilVesa and UTILLE). The library acts as shell to the graphics commands used at the exercises at the course 50240 Image Analysis with Microcomputers....
Digital Raster Graphics (DRG) Lambert
Kansas Data Access and Support Center — The Digital Raster Graphic-Lambert (DRG-Lam) is a raster image of a scanned USGS topographic map with the collar information clipped out, georeferenced to the...
Storyboard dalam Pembuatan Motion Graphic
Satrya Mahardhika; A.F. Choiril Anam Fathoni
2013-01-01
Motion graphics is one category in the animation that makes animation with lots of design elements in each component. Motion graphics needs long process including preproduction, production, and postproduction. Preproduction has an important role so that the next stage may provide guidance or instructions for the production process or the animation process. Preproduction includes research, making the story, script, screenplay, character, environment design and storyboards. The storyboard will ...
International Nuclear Information System (INIS)
Antcheva, Ilka; Brun, Rene; Hof, Carsten; Rademakers, Fons
2006-01-01
A well-designed Graphical User Interface (GUI) has critical importance in any computer application. The user interface is where the end users and the complex system intersect. An effective interface design can make a powerful and complex system, such as ROOT, easy and intuitive to learn and operate. This paper describes the main goals we defined and the design solution we found developing the graphics editor in ROOT
Randomness in Contemporary Graphic Art
Zavřelová, Veronika
2016-01-01
Veronika Zavřelová Bachelor thesis Charles University in Prague, Faculty of Education, Department of Art Education Randomness in contemporary graphic art imaginative picture card game ANNOTATION This (bachelor) thesis concerns itself with a connection between verbal and visual character system within the topic of Randomness in contemporary graphic art - imaginative picture card game. The thesis is mainly based on the practical part - exclusively created card game Piktim. The card game uses as...
Graphic design of pinhole cameras
Edwards, H. B.; Chu, W. P.
1979-01-01
The paper describes a graphic technique for the analysis and optimization of pinhole size and focal length. The technique is based on the use of the transfer function of optical elements described by Scott (1959) to construct the transfer function of a circular pinhole camera. This transfer function is the response of a component or system to a pattern of lines having a sinusoidally varying radiance at varying spatial frequencies. Some specific examples of graphic design are presented.
Statistical mechanics of sparse generalization and graphical model selection
International Nuclear Information System (INIS)
Lage-Castellanos, Alejandro; Pagnani, Andrea; Weigt, Martin
2009-01-01
One of the crucial tasks in many inference problems is the extraction of an underlying sparse graphical model from a given number of high-dimensional measurements. In machine learning, this is frequently achieved using, as a penalty term, the L p norm of the model parameters, with p≤1 for efficient dilution. Here we propose a statistical mechanics analysis of the problem in the setting of perceptron memorization and generalization. Using a replica approach, we are able to evaluate the relative performance of naive dilution (obtained by learning without dilution, following by applying a threshold to the model parameters), L 1 dilution (which is frequently used in convex optimization) and L 0 dilution (which is optimal but computationally hard to implement). Whereas both L p diluted approaches clearly outperform the naive approach, we find a small region where L 0 works almost perfectly and strongly outperforms the simpler to implement L 1 dilution
Interplay of Computer and Paper-Based Sketching in Graphic Design
Pan, Rui; Kuo, Shih-Ping; Strobel, Johannes
2013-01-01
The purpose of this study is to investigate student designers' attitude and choices towards the use of computers and paper sketches when involved in a graphic design process. 65 computer graphic technology undergraduates participated in this research. A mixed method study with survey and in-depth interviews was applied to answer the research…
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.
Structural Graphical Lasso for Learning Mouse Brain Connectivity
Yang, Sen
2015-08-07
Investigations into brain connectivity aim to recover networks of brain regions connected by anatomical tracts or by functional associations. The inference of brain networks has recently attracted much interest due to the increasing availability of high-resolution brain imaging data. Sparse inverse covariance estimation with lasso and group lasso penalty has been demonstrated to be a powerful approach to discover brain networks. Motivated by the hierarchical structure of the brain networks, we consider the problem of estimating a graphical model with tree-structural regularization in this paper. The regularization encourages the graphical model to exhibit a brain-like structure. Specifically, in this hierarchical structure, hundreds of thousands of voxels serve as the leaf nodes of the tree. A node in the intermediate layer represents a region formed by voxels in the subtree rooted at that node. The whole brain is considered as the root of the tree. We propose to apply the tree-structural regularized graphical model to estimate the mouse brain network. However, the dimensionality of whole-brain data, usually on the order of hundreds of thousands, poses significant computational challenges. Efficient algorithms that are capable of estimating networks from high-dimensional data are highly desired. To address the computational challenge, we develop a screening rule which can quickly identify many zero blocks in the estimated graphical model, thereby dramatically reducing the computational cost of solving the proposed model. It is based on a novel insight on the relationship between screening and the so-called proximal operator that we first establish in this paper. We perform experiments on both synthetic data and real data from the Allen Developing Mouse Brain Atlas; results demonstrate the effectiveness and efficiency of the proposed approach.
Design of Graphic Aggregation Model for Evaluation of Energy Systems
International Nuclear Information System (INIS)
An, Sang Ha; Jeong, Yong Hoon; Chang, Won Joon; Chang, Soon Heung; Kim, Sung Ho; Kim, Tae Woon
2006-01-01
Korea is meeting the growing electric power needs by mix of nuclear, fossil, hydro energy and so on. But we can not depend on fossil energy forever, and the people's concern about environment has been changed. So it is time to plan future energy mix considering multiple parameters such as economics, environment, social, energy security, etc. A multiple aggregation model has been used for decision making process in which multiple variables should be considered like energy mix. In this context, we designed Graphic Aggregation Model for Evaluation of energy systems (GAME) for the dynamic analysis of decision on the energy systems. It can support Analytic Hierarchy Process (AHP) analysis based on Graphic User Interface
Transforming Graphical System Models to Graphical Attack Models
DEFF Research Database (Denmark)
Ivanova, Marieta Georgieva; Probst, Christian W.; Hansen, Rene Rydhof
2016-01-01
Manually identifying possible attacks on an organisation is a complex undertaking; many different factors must be considered, and the resulting attack scenarios can be complex and hard to maintain as the organisation changes. System models provide a systematic representation of organisations...... approach to transforming graphical system models to graphical attack models in the form of attack trees. Based on an asset in the model, our transformations result in an attack tree that represents attacks by all possible actors in the model, after which the actor in question has obtained the asset....
Configurable software for satellite graphics
Energy Technology Data Exchange (ETDEWEB)
Hartzman, P D
1977-12-01
An important goal in interactive computer graphics is to provide users with both quick system responses for basic graphics functions and enough computing power for complex calculations. One solution is to have a distributed graphics system in which a minicomputer and a powerful large computer share the work. The most versatile type of distributed system is an intelligent satellite system in which the minicomputer is programmable by the application user and can do most of the work while the large remote machine is used for difficult computations. At New York University, the hardware was configured from available equipment. The level of system intelligence resulted almost completely from software development. Unlike previous work with intelligent satellites, the resulting system had system control centered in the satellite. It also had the ability to reconfigure software during realtime operation. The design of the system was done at a very high level using set theoretic language. The specification clearly illustrated processor boundaries and interfaces. The high-level specification also produced a compact, machine-independent virtual graphics data structure for picture representation. The software was written in a systems implementation language; thus, only one set of programs was needed for both machines. A user can program both machines in a single language. Tests of the system with an application program indicate that is has very high potential. A major result of this work is the demonstration that a gigantic investment in new hardware is not necessary for computing facilities interested in graphics.
On-chip generation of high-dimensional entangled quantum states and their coherent control.
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.
Covariance Method of the Tunneling Radiation from High Dimensional Rotating Black Holes
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.
Efficient and accurate nearest neighbor and closest pair search in high-dimensional space
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
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...
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.
Inferring biological tasks using Pareto analysis of high-dimensional data.
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.
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.
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.
Non-Asymptotic Oracle Inequalities for the High-Dimensional Cox Regression via Lasso.
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.
High-dimensional data: p >> n in mathematical statistics and bio-medical applications
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...
VAX Professional Workstation goes graphic
International Nuclear Information System (INIS)
Downward, J.G.
1984-01-01
The VAX Professional Workstation (VPW) is a collection of programs and procedures designed to provide an integrated work-station environment for the staff at KMS Fusion's research laboratories. During the past year numerous capabilities have been added to VPW, including support for VT125/VT240/4014 graphic workstations, editing windows, and additional desk utilities. Graphics workstation support allows users to create, edit, and modify graph data files, enter the data via a graphic tablet, create simple plots with DATATRIEVE or DECgraph on ReGIS terminals, or elaborate plots with TEKGRAPH on ReGIS or Tektronix terminals. Users may assign display error bars to the data and interactively plot it in a variety of ways. Users also can create and display viewgraphs. Hard copy output for a large network of office terminals is obtained by multiplexing each terminal's video output into a recently developed video multiplexer front ending a single channel video hard copy unit
Fractal geometry and computer graphics
Sakas, Georgios; Peitgen, Heinz-Otto; Englert, Gabriele
1992-01-01
Fractal geometry has become popular in the last 15 years, its applications can be found in technology, science, or even arts. Fractal methods and formalism are seen today as a general, abstract, but nevertheless practical instrument for the description of nature in a wide sense. But it was Computer Graphics which made possible the increasing popularity of fractals several years ago, and long after their mathematical formulation. The two disciplines are tightly linked. The book contains the scientificcontributions presented in an international workshop in the "Computer Graphics Center" in Darmstadt, Germany. The target of the workshop was to present the wide spectrum of interrelationships and interactions between Fractal Geometry and Computer Graphics. The topics vary from fundamentals and new theoretical results to various applications and systems development. All contributions are original, unpublished papers.The presentations have been discussed in two working groups; the discussion results, together with a...
Graphical programming of telerobotic tasks
International Nuclear Information System (INIS)
Small, D.E.; McDonald, M.J.
1997-01-01
With a goal of producing faster, safer, and cheaper technologies for nuclear waste cleanup, Sandia is actively developing and extending intelligent systems technologies. Graphical Programming is a key technology for robotic waste cleanup that Sandia is developing for this goal. This paper describes Sancho, Sandia most advanced Graphical Programming supervisory software. Sancho, now operational on several robot systems, incorporates all of Sandia's recent advances in supervisory control. Sancho, developed to rapidly apply Graphical Programming on a diverse set of robot systems, uses a general set of tools to implement task and operational behavior. Sancho can be rapidly reconfigured for new tasks and operations without modifying the supervisory code. Other innovations include task-based interfaces, event-based sequencing, and sophisticated GUI design. These innovations have resulted in robot control programs and approaches that are easier and safer to use than teleoperation, off-line programming, or full automation
Cartooning History: Canada's Stories in Graphic Novels
King, Alyson E.
2012-01-01
In recent years, historical events, issues, and characters have been portrayed in an increasing number of non-fiction graphic texts. Similar to comics and graphic novels, graphic texts are defined as fully developed, non-fiction narratives told through panels of sequential art. Such non-fiction graphic texts are being used to teach history in…
An Opening: Graphic Design's Discursive Spaces.
Blauvelt, Andrew
1994-01-01
Introduces a special issue on critical histories of graphic design with a review of the particular problems identified with the history of graphic design as a field of study and the emerging discipline of graphic design history. Makes a case for the examination of graphic design through its relationships with larger discourses. (SR)
Antinomies of Semiotics in Graphic Design
Storkerson, Peter
2010-01-01
The following paper assesses the roles played by semiotics in graphic design and in graphic design education, which both reflects and shapes practice. It identifies a series of factors; graphic design education methods and culture; semiotic theories themselves and their application to graphic design; the two wings of Peircian semiotics and…
Graphic Design Career Guide 2. Revised Edition.
Craig, James
The graphic design field is diverse and includes many areas of specialization. This guide introduces students to career opportunities in graphic design. The guide is organized in four parts. "Part One: Careers in Graphic Design" identifies and discusses the various segments of the graphic design industry, including: Advertising, Audio-Visual, Book…
Graphic Journeys: Graphic Novels' Representations of Immigrant Experiences
Boatright, Michael D.
2010-01-01
This article explores how immigrant experiences are represented in the narratives of three graphic novels published in the last decade: Tan's (2007) "The Arrival," Kiyama's (1931/1999) "The Four Immigrants Manga: A Japanese Experience in San Francisco, 1904-1924," and Yang's (2006) "American Born Chinese." Through a theoretical lens informed by…
Graphics Technology Study. Volume 1. State of Graphics Technology
1986-12-01
reaction of special heat sensitive paper when exposed to the heated elements of a thermal print head. Copy quality was poor due to characteristics...Vendors are now attempting to offer smaller units aimed at applications such as typography , graphic arts, CAD, and office automation. The key element in
Trend Monitoring System (TMS) graphics software
Brown, J. S.
1979-01-01
A prototype bus communications systems, which is being used to support the Trend Monitoring System (TMS) and to evaluate the bus concept is considered. A set of FORTRAN-callable graphics subroutines for the host MODCOMP comuter, and an approach to splitting graphics work between the host and the system's intelligent graphics terminals are described. The graphics software in the MODCOMP and the operating software package written for the graphics terminals are included.
Collection Of Software For Computer Graphics
Hibbard, Eric A.; Makatura, George
1990-01-01
Ames Research Graphics System (ARCGRAPH) collection of software libraries and software utilities assisting researchers in generating, manipulating, and visualizing graphical data. Defines metafile format containing device-independent graphical data. File format used with various computer-graphics-manipulation and -animation software packages at Ames, including SURF (COSMIC Program ARC-12381) and GAS (COSMIC Program ARC-12379). Consists of two-stage "pipeline" used to put out graphical primitives. ARCGRAPH libraries developed on VAX computer running VMS.
A probabilistic graphical model based stochastic input model construction
International Nuclear Information System (INIS)
Wan, Jiang; Zabaras, Nicholas
2014-01-01
Model reduction techniques have been widely used in modeling of high-dimensional stochastic input in uncertainty quantification tasks. However, the probabilistic modeling of random variables projected into reduced-order spaces presents a number of computational challenges. Due to the curse of dimensionality, the underlying dependence relationships between these random variables are difficult to capture. In this work, a probabilistic graphical model based approach is employed to learn the dependence by running a number of conditional independence tests using observation data. Thus a probabilistic model of the joint PDF is obtained and the PDF is factorized into a set of conditional distributions based on the dependence structure of the variables. The estimation of the joint PDF from data is then transformed to estimating conditional distributions under reduced dimensions. To improve the computational efficiency, a polynomial chaos expansion is further applied to represent the random field in terms of a set of standard random variables. This technique is combined with both linear and nonlinear model reduction methods. Numerical examples are presented to demonstrate the accuracy and efficiency of the probabilistic graphical model based stochastic input models. - Highlights: • Data-driven stochastic input models without the assumption of independence of the reduced random variables. • The problem is transformed to a Bayesian network structure learning problem. • Examples are given in flows in random media
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
High-dimensional free-space optical communications based on orbital angular momentum coding
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.
Bayesian Multiresolution Variable Selection for Ultra-High Dimensional Neuroimaging Data.
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.
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.
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.
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.
Arif, Muhammad
2012-06-01
In pattern classification problems, feature extraction is an important step. Quality of features in discriminating different classes plays an important role in pattern classification problems. In real life, pattern classification may require high dimensional feature space and it is impossible to visualize the feature space if the dimension of feature space is greater than four. In this paper, we have proposed a Similarity-Dissimilarity plot which can project high dimensional space to a two dimensional space while retaining important characteristics required to assess the discrimination quality of the features. Similarity-dissimilarity plot can reveal information about the amount of overlap of features of different classes. Separable data points of different classes will also be visible on the plot which can be classified correctly using appropriate classifier. Hence, approximate classification accuracy can be predicted. Moreover, it is possible to know about whom class the misclassified data points will be confused by the classifier. Outlier data points can also be located on the similarity-dissimilarity plot. Various examples of synthetic data are used to highlight important characteristics of the proposed plot. Some real life examples from biomedical data are also used for the analysis. The proposed plot is independent of number of dimensions of the feature space.
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.
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.
Using High-Dimensional Image Models to Perform Highly Undetectable Steganography
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.
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.
Compound Structure-Independent Activity Prediction in High-Dimensional Target Space.
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.
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
Similarity measurement method of high-dimensional data based on normalized net lattice subspace
Institute of Scientific and Technical Information of China (English)
Li Wenfa; Wang Gongming; Li Ke; Huang Su
2017-01-01
The performance of conventional similarity measurement methods is affected seriously by the curse of dimensionality of high-dimensional data.The reason is that data difference between sparse and noisy dimensionalities occupies a large proportion of the similarity, leading to the dissimilarities between any results.A similarity measurement method of high-dimensional data based on normalized net lattice subspace is proposed.The data range of each dimension is divided into several intervals, and the components in different dimensions are mapped onto the corresponding interval.Only the component in the same or adjacent interval is used to calculate the similarity.To validate this meth-od, three data types are used, and seven common similarity measurement methods are compared. The experimental result indicates that the relative difference of the method is increasing with the di-mensionality and is approximately two or three orders of magnitude higher than the conventional method.In addition, the similarity range of this method in different dimensions is [0, 1], which is fit for similarity analysis after dimensionality reduction.
The cross-validated AUC for MCP-logistic regression with high-dimensional data.
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.
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.
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.
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.
High-Dimensional Single-Photon Quantum Gates: Concepts and Experiments.
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.
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.
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.
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.
Challenges and Approaches to Statistical Design and Inference in High Dimensional Investigations
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.
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.
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.
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.
Fluid simulation for computer graphics
Bridson, Robert
2008-01-01
Animating fluids like water, smoke, and fire using physics-based simulation is increasingly important in visual effects, in particular in movies, like The Day After Tomorrow, and in computer games. This book provides a practical introduction to fluid simulation for graphics. The focus is on animating fully three-dimensional incompressible flow, from understanding the math and the algorithms to the actual implementation.
Storyboard dalam Pembuatan Motion Graphic
Directory of Open Access Journals (Sweden)
Satrya Mahardhika
2013-10-01
Full Text Available Motion graphics is one category in the animation that makes animation with lots of design elements in each component. Motion graphics needs long process including preproduction, production, and postproduction. Preproduction has an important role so that the next stage may provide guidance or instructions for the production process or the animation process. Preproduction includes research, making the story, script, screenplay, character, environment design and storyboards. The storyboard will be determined through camera angles, blocking, sets, and many supporting roles involved in a scene. Storyboard is also useful as a production reference in recording or taping each scene in sequence or as an efficient priority. The example used is an ad creation using motion graphic animation storyboard which has an important role as a blueprint for every scene and giving instructions to make the transition movement, layout, blocking, and defining camera movement that everything should be done periodically in animation production. Planning before making the animation or motion graphic will make the job more organized, presentable, and more efficient in the process.
Baker, Steve
1994-01-01
Proposes that the work of the French feminist writers Helene Cixous and Luce Irigaray could serve as the basis for devising a more imaginative form of critical writing that might help to draw the history and practice of graphic design into a closer and more purposeful relation. (SR)
Recorded Music and Graphic Design.
Osterer, Irv
1998-01-01
Reviews the history of art as an element of music-recording packaging. Describes a project in which students design a jacket for either cassette or CD using a combination of computerized and traditional rendering techniques. Reports that students have been inspired to look into careers in graphic design. (DSK)
GRAPHIC INTERFACES FOR ENGINEERING APPLICATIONS
Directory of Open Access Journals (Sweden)
Ion PANA,
2012-05-01
Full Text Available Using effective the method of calculating Fitness for Service requires the achievement of graphical interfaces. This paper presents an example of such interfaces, made with Visual Basic program and used in the evaluation of pipelines in a research contract [4
Overview of Graphical User Interfaces.
Hulser, Richard P.
1993-01-01
Discussion of graphical user interfaces for online public access catalogs (OPACs) covers the history of OPACs; OPAC front-end design, including examples from Indiana University and the University of Illinois; and planning and implementation of a user interface. (10 references) (EA)
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...
TEKLIB, Tektronix Graphics Subroutine Library
International Nuclear Information System (INIS)
Wolf, M.L.
1987-01-01
1 - Description of program or function: TEKLIB is a library of subroutines that produces graphical output on all Tektronix terminals with model numbers from 4010 through 4115. The application program identifies the model number in the initialization subroutine, GRSTRT, and then uses this number as a flag throughout the run to tailor the graphical output to that specific device. Output includes lines, markers, text, polygons, and panels. Graphic input is supported on all terminals. 2 - Method of solution: TEKLIB uses two coordinate systems, viewport and world, to generate graphic images on the screen. Viewport coordinates refer to an imaginary rectangular grid on the display screen surface. The world coordinate system is a rectangular grid on any x-y plane, defined by the application program. The rectangular portion of the x-y plane specified, termed a w indow , is projected onto a rectangular region of the screen, called a v iewport . In addition to 2-D graphics, TEKLIB also provides subroutines for drawing projections of 3-D objects. The application program first defines a point in space to look at the v iew point , and the direction and distance from that point to the observer's position. A viewing transformation is performed on each x,y,z point to project it onto a v iew plane , a plane perpendicular to the line of sight and passing through the view point. This view plane then becomes the x-y plane of the world window which is mapped into the viewport. 3 - Restrictions on the complexity of the problem: TEKLIB does not attempt to emulate attributes such as line style and marker type in software if the terminal does not support those attributes in hardware
Reverse-engineering graphical innovation: an introduction to graphical regimes
Directory of Open Access Journals (Sweden)
Dominic Arsenault
2013-03-01
Full Text Available Technological innovation in the video games industry is a rich area of research that has barely been explored as of yet. Gamers are always clamoring for novelty and a remedy to the oft-decried “sequelitis” that “plagues” the industry, while game publishers and platform holders secretly plan a next-gen platform to capture the ever-shifting market. In this light, the importance of graphics cannot be understated, as it is usually taken for granted in game historiography that “[g]ame graphics were, and to a large extent still are, the main criteria by which advancing video game technology is benchmarked” (Wolf, 2003, p.53.
High dimensional biological data retrieval optimization with NoSQL technology
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
Geraci, Joseph; Dharsee, Moyez; Nuin, Paulo; Haslehurst, Alexandria; Koti, Madhuri; Feilotter, Harriet E; Evans, Ken
2014-03-01
We introduce a novel method for visualizing high dimensional data via a discrete dynamical system. This method provides a 2D representation of the relationship between subjects according to a set of variables without geometric projections, transformed axes or principal components. The algorithm exploits a memory-type mechanism inherent in a certain class of discrete dynamical systems collectively referred to as the chaos game that are closely related to iterative function systems. The goal of the algorithm was to create a human readable representation of high dimensional patient data that was capable of detecting unrevealed subclusters of patients from within anticipated classifications. This provides a mechanism to further pursue a more personalized exploration of pathology when used with medical data. For clustering and classification protocols, the dynamical system portion of the algorithm is designed to come after some feature selection filter and before some model evaluation (e.g. clustering accuracy) protocol. In the version given here, a univariate features selection step is performed (in practice more complex feature selection methods are used), a discrete dynamical system is driven by this reduced set of variables (which results in a set of 2D cluster models), these models are evaluated for their accuracy (according to a user-defined binary classification) and finally a visual representation of the top classification models are returned. Thus, in addition to the visualization component, this methodology can be used for both supervised and unsupervised machine learning as the top performing models are returned in the protocol we describe here. Butterfly, the algorithm we introduce and provide working code for, uses a discrete dynamical system to classify high dimensional data and provide a 2D representation of the relationship between subjects. We report results on three datasets (two in the article; one in the appendix) including a public lung cancer
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
High dimensional biological data retrieval optimization with NoSQL technology.
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
Turtle Graphics implementation using a graphical dataflow programming approach
Lovejoy, Robert Steven
1992-01-01
Approved for public release; distribution is unlimited This thesis expands the concepts of object-oriented programming to implement a visual dataflow programming language. The main thrust of this research is to develop a functional prototype language, based upon the Turtle Graphics tool provided by LOGO programming language, for children to develop both their problem solving skills as well as their general programming skills. The language developed for this thesis was implemented in the...
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...
Saini, Harsh; Lal, Sunil Pranit; Naidu, Vimal Vikash; Pickering, Vincel Wince; Singh, Gurmeet; Tsunoda, Tatsuhiko; Sharma, Alok
2016-12-05
High dimensional feature space generally degrades classification in several applications. In this paper, we propose a strategy called gene masking, in which non-contributing dimensions are heuristically removed from the data to improve classification accuracy. Gene masking is implemented via a binary encoded genetic algorithm that can be integrated seamlessly with classifiers during the training phase of classification to perform feature selection. It can also be used to discriminate between features that contribute most to the classification, thereby, allowing researchers to isolate features that may have special significance. This technique was applied on publicly available datasets whereby it substantially reduced the number of features used for classification while maintaining high accuracies. The proposed technique can be extremely useful in feature selection as it heuristically removes non-contributing features to improve the performance of classifiers.
Energy Technology Data Exchange (ETDEWEB)
Tahira, Rabia; Ikram, Manzoor; Zubairy, M Suhail [Centre for Quantum Physics, COMSATS Institute of Information Technology, Islamabad (Pakistan); Bougouffa, Smail [Department of Physics, Faculty of Science, Taibah University, PO Box 30002, Madinah (Saudi Arabia)
2010-02-14
We investigate the phenomenon of sudden death of entanglement in a high-dimensional bipartite system subjected to dissipative environments with an arbitrary initial pure entangled state between two fields in the cavities. We find that in a vacuum reservoir, the presence of the state where one or more than one (two) photons in each cavity are present is a necessary condition for the sudden death of entanglement. Otherwise entanglement remains for infinite time and decays asymptotically with the decay of individual qubits. For pure two-qubit entangled states in a thermal environment, we observe that sudden death of entanglement always occurs. The sudden death time of the entangled states is related to the number of photons in the cavities, the temperature of the reservoir and the initial preparation of the entangled states.
International Nuclear Information System (INIS)
Tahira, Rabia; Ikram, Manzoor; Zubairy, M Suhail; Bougouffa, Smail
2010-01-01
We investigate the phenomenon of sudden death of entanglement in a high-dimensional bipartite system subjected to dissipative environments with an arbitrary initial pure entangled state between two fields in the cavities. We find that in a vacuum reservoir, the presence of the state where one or more than one (two) photons in each cavity are present is a necessary condition for the sudden death of entanglement. Otherwise entanglement remains for infinite time and decays asymptotically with the decay of individual qubits. For pure two-qubit entangled states in a thermal environment, we observe that sudden death of entanglement always occurs. The sudden death time of the entangled states is related to the number of photons in the cavities, the temperature of the reservoir and the initial preparation of the entangled states.
Time–energy high-dimensional one-side device-independent quantum key distribution
International Nuclear Information System (INIS)
Bao Hai-Ze; Bao Wan-Su; Wang Yang; Chen Rui-Ke; Ma Hong-Xin; Zhou Chun; Li Hong-Wei
2017-01-01
Compared with full device-independent quantum key distribution (DI-QKD), one-side device-independent QKD (1sDI-QKD) needs fewer requirements, which is much easier to meet. In this paper, by applying recently developed novel time–energy entropic uncertainty relations, we present a time–energy high-dimensional one-side device-independent quantum key distribution (HD-QKD) and provide the security proof against coherent attacks. Besides, we connect the security with the quantum steering. By numerical simulation, we obtain the secret key rate for Alice’s different detection efficiencies. The results show that our protocol can performance much better than the original 1sDI-QKD. Furthermore, we clarify the relation among the secret key rate, Alice’s detection efficiency, and the dispersion coefficient. Finally, we simply analyze its performance in the optical fiber channel. (paper)
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....
Inference for feature selection using the Lasso with high-dimensional data
DEFF Research Database (Denmark)
Brink-Jensen, Kasper; Ekstrøm, Claus Thorn
2014-01-01
Penalized regression models such as the Lasso have proved useful for variable selection in many fields - especially for situations with high-dimensional data where the numbers of predictors far exceeds the number of observations. These methods identify and rank variables of importance but do...... not generally provide any inference of the selected variables. Thus, the variables selected might be the "most important" but need not be significant. We propose a significance test for the selection found by the Lasso. We introduce a procedure that computes inference and p-values for features chosen...... by the Lasso. This method rephrases the null hypothesis and uses a randomization approach which ensures that the error rate is controlled even for small samples. We demonstrate the ability of the algorithm to compute $p$-values of the expected magnitude with simulated data using a multitude of scenarios...
Diagonal Likelihood Ratio Test for Equality of Mean Vectors in High-Dimensional Data
Hu, Zongliang; Tong, Tiejun; Genton, Marc G.
2017-01-01
We propose a likelihood ratio test framework for testing normal mean vectors in high-dimensional data under two common scenarios: the one-sample test and the two-sample test with equal covariance matrices. We derive the test statistics under the assumption that the covariance matrices follow a diagonal matrix structure. In comparison with the diagonal Hotelling's tests, our proposed test statistics display some interesting characteristics. In particular, they are a summation of the log-transformed squared t-statistics rather than a direct summation of those components. More importantly, to derive the asymptotic normality of our test statistics under the null and local alternative hypotheses, we do not require the assumption that the covariance matrix follows a diagonal matrix structure. As a consequence, our proposed test methods are very flexible and can be widely applied in practice. Finally, simulation studies and a real data analysis are also conducted to demonstrate the advantages of our likelihood ratio test method.
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.
An adaptive ANOVA-based PCKF for high-dimensional nonlinear inverse modeling
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
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.
Joint Adaptive Mean-Variance Regularization and Variance Stabilization of High Dimensional Data.
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.
Intuitive Music and Graphic Notation
DEFF Research Database (Denmark)
Bergstrøm-Nielsen, Carl
Describes subjects existing at Aalborg University since the middle eighties. "Intuitive Music" trains free improvisation through exercises including group-dynamic exercises, awareness exercises and parameter exercises. Students also create open compositions. "Graphic notation"concerns aural scores....... Students' works are quoted. The writer discusses the theoretical context and advocates for giving more attention to music as the medium in which music therapy takes place, referring to language theory and Jakobson. NB: the description of the two subjects are, at the present moment (2011) no longer up...... to date. Intuitive music stresses less making compositions and more using the main instrument intuitively. Graphic notation has been integrated into a larger subject (also taught by the present author) which also comprises other methods of description and interpretation of music....
Graphical Classification of Entangled Qutrits
Directory of Open Access Journals (Sweden)
Kentaro Honda
2012-10-01
Full Text Available A multipartite quantum state is entangled if it is not separable. Quantum entanglement plays a fundamental role in many applications of quantum information theory, such as quantum teleportation. Stochastic local quantum operations and classical communication (SLOCC cannot essentially change quantum entanglement without destroying it. Therefore, entanglement can be classified by dividing quantum states into equivalence classes, where two states are equivalent if each can be converted into the other by SLOCC. Properties of this classification, especially in the case of non two-dimensional quantum systems, have not been well studied. Graphical representation is sometimes used to clarify the nature and structural features of entangled states. SLOCC equivalence of quantum bits (qubits has been described graphically via a connection between tripartite entangled qubit states and commutative Frobenius algebras (CFAs in monoidal categories. In this paper, we extend this method to qutrits, i.e., systems that have three basis states. We examine the correspondence between CFAs and tripartite entangled qutrits. Using the symmetry property, which is required by the definition of a CFA, we find that there are only three equivalence classes that correspond to CFAs. We represent qutrits graphically, using the connection to CFAs. We derive equations that characterize the three equivalence classes. Moreover, we show that any qutrit can be represented as a composite of three graphs that correspond to the three classes.
International Nuclear Information System (INIS)
Stewart, C.R. Jr.; Joubert, W.D.; Overbey, D.R.; Stewart, K.A.
1978-10-01
The TEK11 graphics library was written for use on PDP-11 minicomputers running the RT-11 operating system to drive Tektronix 4010 graphics display terminals. Library subroutines are coded in FORTRAN and assembly language. The library includes routines to draw axes, either linear or semilog, to plot data in terms of logical values without first scaling to screen coordinates, to label graphs, and to plot in a maximum of four regions on the screen. Modes of plotting may be point plot with any character at the point, vector plot, or bar plot. Two features, automatic scaling and windowing, permit the researcher to use computer graphics without spending time first to learn about scaling or ''Tek points'' and preparing long parameter lists for subroutines. Regions on the screen are defined by specifying minima and maxima logical coordinates, i.e., 0 K or milliseconds, and a region number. After definition, a region may be activated for plotting by calling REGN with the region number as an argument
Graphical Language for Data Processing
Alphonso, Keith
2011-01-01
A graphical language for processing data allows processing elements to be connected with virtual wires that represent data flows between processing modules. The processing of complex data, such as lidar data, requires many different algorithms to be applied. The purpose of this innovation is to automate the processing of complex data, such as LIDAR, without the need for complex scripting and programming languages. The system consists of a set of user-interface components that allow the user to drag and drop various algorithmic and processing components onto a process graph. By working graphically, the user can completely visualize the process flow and create complex diagrams. This innovation supports the nesting of graphs, such that a graph can be included in another graph as a single step for processing. In addition to the user interface components, the system includes a set of .NET classes that represent the graph internally. These classes provide the internal system representation of the graphical user interface. The system includes a graph execution component that reads the internal representation of the graph (as described above) and executes that graph. The execution of the graph follows the interpreted model of execution in that each node is traversed and executed from the original internal representation. In addition, there are components that allow external code elements, such as algorithms, to be easily integrated into the system, thus making the system infinitely expandable.
Directory of Open Access Journals (Sweden)
Jan Benacka
2009-06-01
Full Text Available In the article, the formulas for orthographic parallel projection of 3D bodies on computer screen are derived using secondary school vector algebra. The spreadsheet implementation is demonstrated in six applications that project bodies with increasing intricacy – a convex body (cube with non-solved visibility, convex bodies (cube, chapel with solved visibility, a coloured convex body (chapel with solved visibility, and a coloured non-convex body (church with solved visibility. The projections are revolvable in horizontal and vertical plane, and they are changeable in size. The examples show an unusual way of using spreadsheets as a 3D computer graphics tool. The applications can serve as a simple introduction to the general principles of computer graphics, to the graphics with spreadsheets, and as a tool for exercising stereoscopic vision. The presented approach is usable at visualising 3D scenes within some topics of secondary school curricula as solid geometry (angles and distances of lines and planes within simple bodies or analytic geometry in space (angles and distances of lines and planes in E3, and even at university level within calculus at visualising graphs of z = f(x,y functions. Examples are pictured.
Graphics-oriented application language for LASNEX
International Nuclear Information System (INIS)
Stringer, L.M.
1985-01-01
GOAL, a graphics-oriented application language, was developed to help physicists understand the large amounts of data produced by LASNEX. GOAL combines many aspects of the old LASNEX language, computer graphics, and standard computer languages
The influence of annotation in graphical organizers
Bezdan, Eniko; Kester, Liesbeth; Kirschner, Paul A.
2013-01-01
Bezdan, E., Kester, L., & Kirschner, P. A. (2012, 29-31 August). The influence of annotation in graphical organizers. Poster presented at the biannual meeting of the EARLI Special Interest Group Comprehension of Text and Graphics, Grenoble, France.
Microcomputer Simulated CAD for Engineering Graphics.
Huggins, David L.; Myers, Roy E.
1983-01-01
Describes a simulated computer-aided-graphics (CAD) program at The Pennsylvania State University. Rationale for the program, facilities, microcomputer equipment (Apple) used, and development of a software package for simulating applied engineering graphics are considered. (JN)
Computer Graphics and Administrative Decision-Making.
Yost, Michael
1984-01-01
Reduction in prices now makes it possible for almost any institution to use computer graphics for administrative decision making and research. Current and potential uses of computer graphics in these two areas are discussed. (JN)
Computer Graphics for Multimedia and Hypermedia Development.
Mohler, James L.
1998-01-01
Discusses several theoretical and technical aspects of computer-graphics development that are useful for creating hypermedia and multimedia materials. Topics addressed include primary bitmap attributes in computer graphics, the jigsaw principle, and raster layering. (MSE)
Computer graphics from basic to application
International Nuclear Information System (INIS)
Kim, Do Hyeong; Mun, Sung Min
1998-04-01
This book mentions conception of computer graphics, background history, necessity and applied field like construction design, image processing, auto mobile design, fashion design and TV broadcast, basic principle of computer, computer graphics hardware, computer graphics software such as adobe illustrator tool box and adobe photo shop, quarkXpress like introduction, application and operating circumstance, 3D graphics with summary, difference of versions of 3D studio and system, and Auto CAD application.
Graphic filter library implemented in CUDA language
Peroutková, Hedvika
2009-01-01
This thesis deals with the problem of reducing computation time of raster image processing by parallel computing on graphics processing unit. Raster image processing thereby refers to the application of graphic filters, which can be applied in sequence with different settings. This thesis evaluates the suitability of using parallelization on graphic card for raster image adjustments based on multicriterial choice. Filters are implemented for graphics processing unit in CUDA language. Opacity ...
Computer graphics in heat-transfer simulations
International Nuclear Information System (INIS)
Hamlin, G.A. Jr.
1980-01-01
Computer graphics can be very useful in the setup of heat transfer simulations and in the display of the results of such simulations. The potential use of recently available low-cost graphics devices in the setup of such simulations has not been fully exploited. Several types of graphics devices and their potential usefulness are discussed, and some configurations of graphics equipment are presented in the low-, medium-, and high-price ranges
Hierarchical data structures for graphics program languages
International Nuclear Information System (INIS)
Gonauser, M.; Schinner, P.; Weiss, J.
1978-01-01
Graphic data processing with a computer makes exacting demands on the interactive capability of the program language and the management of the graphic data. A description of the structure of a graphics program language which has been shown by initial practical experiments to possess a particularly favorable interactive capability is followed by the evaluation of various data structures (list, tree, ring) with respect to their interactive capability in processing graphics. A practical structure is proposed. (orig.) [de
Computer graphics from basic to application
Energy Technology Data Exchange (ETDEWEB)
Kim, Do Hyeong; Mun, Sung Min
1998-04-15
This book mentions conception of computer graphics, background history, necessity and applied field like construction design, image processing, auto mobile design, fashion design and TV broadcast, basic principle of computer, computer graphics hardware, computer graphics software such as adobe illustrator tool box and adobe photo shop, quarkXpress like introduction, application and operating circumstance, 3D graphics with summary, difference of versions of 3D studio and system, and Auto CAD application.
Data structures, computer graphics, and pattern recognition
Klinger, A; Kunii, T L
1977-01-01
Data Structures, Computer Graphics, and Pattern Recognition focuses on the computer graphics and pattern recognition applications of data structures methodology.This book presents design related principles and research aspects of the computer graphics, system design, data management, and pattern recognition tasks. The topics include the data structure design, concise structuring of geometric data for computer aided design, and data structures for pattern recognition algorithms. The survey of data structures for computer graphics systems, application of relational data structures in computer gr
Childhood trauma in the graphic memoir
Beskow, Sara H.
2011-01-01
In this thesis I examine why the graphic memoir has become such a popular platform for authors to explore their childhood, and how these authors use graphic memoirs in an attempt to understand any trauma that they experienced during their formative years. Graphic memoirs in this context allow for a dual outlet to express these complex traumatic events, both through vivid illustrations and descriptive text. To illustrate how graphic memoirs are used as an outlet for traumatic experiences I hav...
Narrative Problems of Graphic Design History.
Margolin, Victor
1994-01-01
Discusses three major accounts (by Philip Meggs, Enric Satue and Richard Hollis) of graphic design history. Notes that these texts address the history of graphic design, but each raises questions about what material to include, as well as how graphic design is both related to and distinct from other visual practices such as typography, art…
Graphical interpretation of numerical model results
International Nuclear Information System (INIS)
Drewes, D.R.
1979-01-01
Computer software has been developed to produce high quality graphical displays of data from a numerical grid model. The code uses an existing graphical display package (DISSPLA) and overcomes some of the problems of both line-printer output and traditional graphics. The software has been designed to be flexible enough to handle arbitrarily placed computation grids and a variety of display requirements
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...
Elastic SCAD as a novel penalization method for SVM classification tasks in high-dimensional data.
Becker, Natalia; Toedt, Grischa; Lichter, Peter; Benner, Axel
2011-05-09
Classification and variable selection play an important role in knowledge discovery in high-dimensional data. Although Support Vector Machine (SVM) algorithms are among the most powerful classification and prediction methods with a wide range of scientific applications, the SVM does not include automatic feature selection and therefore a number of feature selection procedures have been developed. Regularisation approaches extend SVM to a feature selection method in a flexible way using penalty functions like LASSO, SCAD and Elastic Net.We propose a novel penalty function for SVM classification tasks, Elastic SCAD, a combination of SCAD and ridge penalties which overcomes the limitations of each penalty alone.Since SVM models are extremely sensitive to the choice of tuning parameters, we adopted an interval search algorithm, which in comparison to a fixed grid search finds rapidly and more precisely a global optimal solution. Feature selection methods with combined penalties (Elastic Net and Elastic SCAD SVMs) are more robust to a change of the model complexity than methods using single penalties. Our simulation study showed that Elastic SCAD SVM outperformed LASSO (L1) and SCAD SVMs. Moreover, Elastic SCAD SVM provided sparser classifiers in terms of median number of features selected than Elastic Net SVM and often better predicted than Elastic Net in terms of misclassification error.Finally, we applied the penalization methods described above on four publicly available breast cancer data sets. Elastic SCAD SVM was the only method providing robust classifiers in sparse and non-sparse situations. The proposed Elastic SCAD SVM algorithm provides the advantages of the SCAD penalty and at the same time avoids sparsity limitations for non-sparse data. We were first to demonstrate that the integration of the interval search algorithm and penalized SVM classification techniques provides fast solutions on the optimization of tuning parameters.The penalized SVM
Robust and sparse correlation matrix estimation for the analysis of high-dimensional genomics data.
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
Short, Jeremy C.; Randolph-Seng, Brandon; McKenny, Aaron F.
2013-01-01
Graphic novels have been increasingly incorporated into business communication forums. Despite potential benefits, little research has examined the merits of the graphic novel approach. In response, we engage in a two-study approach. Study 1 explores the potential of graphic novels to affect learning outcomes and finds that the graphic novel was…
Measuring Cognitive Load in Test Items: Static Graphics versus Animated Graphics
Dindar, M.; Kabakçi Yurdakul, I.; Inan Dönmez, F.
2015-01-01
The majority of multimedia learning studies focus on the use of graphics in learning process but very few of them examine the role of graphics in testing students' knowledge. This study investigates the use of static graphics versus animated graphics in a computer-based English achievement test from a cognitive load theory perspective. Three…
A handbook of statistical graphics using SAS ODS
Der, Geoff
2014-01-01
An Introduction to Graphics: Good Graphics, Bad Graphics, Catastrophic Graphics and Statistical GraphicsThe Challenger DisasterGraphical DisplaysA Little History and Some Early Graphical DisplaysGraphical DeceptionAn Introduction to ODS GraphicsGenerating ODS GraphsODS DestinationsStatistical Graphics ProceduresODS Graphs from Statistical ProceduresControlling ODS GraphicsControlling Labelling in GraphsODS Graphics EditorGraphs for Displaying the Characteristics of Univariate Data: Horse Racing, Mortality Rates, Forearm Lengths, Survival Times and Geyser EruptionsIntroductionPie Chart, Bar Cha
Some Thoughts on Contemporary Graphic Print
Directory of Open Access Journals (Sweden)
Stefan Skiba
2016-09-01
Full Text Available The production requirements of original graphic works of art have changed since 1980. The development of digital printing using lightfast colors now rivals traditional techniques such as wood cut, screen print, lithography, etching etc. Today, with respect to artistic legitimacy, original graphics using traditional printing techniques compete with original graphics produced by digital printing techniques on the art market. What criteria distinguish traditional printing techniques from those of digital printing in the production and acquisition of original graphics? What consequences is the serious artist faced with when deciding to implement digital print production? How does digital print change original graphic acquisition decisions?
Graphical debugging of combinational geometry
International Nuclear Information System (INIS)
Burns, T.J.; Smith, M.S.
1992-01-01
A graphical debugger for combinatorial geometry being developed at Oak Ridge National Laboratory is described. The prototype debugger consists of two parts: a FORTRAN-based ''view'' generator and a Microsoft Windows application for displaying the geometry. Options and features of both modules are discussed. Examples illustrating the various options available are presented. The potential for utilizing the images produced using the debugger as a visualization tool for the output of the radiation transport codes is discussed as is the future direction of the development
HISPLT: A history graphics postprocessor
International Nuclear Information System (INIS)
Thompson, S.L.; Kmetyk, L.N.
1991-09-01
HISPLT is a graphics postprocessor designed to plot time histories for wave propagation codes. HISPLT is available for CRAY UNICOS, CRAY CTSS, VAX VMS computer systems, and a variety of UNIX workstations. The original HISPLT code employs a database structure that allows the program to be used without modification to process data generated by many wave propagation codes. HISPLT has recently been modified to process time histories for the reactor safety analysis code, MELCOR. This report provides a complete set of input instructions for HISPLT and provides examples of the types of plotted output that can be generated using HISPLT. 6 refs., 8 figs., 5 tabs
Wang Tiles in Computer Graphics
Lagae, Ares
2009-01-01
Many complex signals in computer graphics, such as point distributions and textures, cannot be efficiently synthesized and stored. This book presents tile-based methods based on Wang tiles and corner tiles to solve both these problems. Instead of synthesizing a complex signal when needed, the signal is synthesized beforehand over a small set of Wang tiles or corner tiles. Arbitrary large amounts of that signal can then efficiently be generated when needed by generating a stochastic tiling, and storing only a small set of tiles reduces storage requirements. A tile-based method for generating a
Graphical presentation of participants' results
International Nuclear Information System (INIS)
Anon.
1991-01-01
The results obtained by 19 participating dosimetry systems are presented in this section. Section 3 of the preceding report, ''Evaluation of the Results of the First IAEA Coordinated Research Program on Intercomparison for Individual Monitoring'', explains the format and conventions used for the graphical presentation. The system number appearing in the upper left hand corner of each page can be used to correlate the dosimeter design information found in Table 2 of the preceding report with the results. However, care should be used in drawing inferences from this correlation, since system performance depends on a number of factors other than detector selection and design characteristics. (orig.)
Graphics workflow optimization when editing standard tasks using modern graphics editing programs
Khabirova, Maja
2012-01-01
This work focuses on the description and characteristics of common problems which graphic designers face daily when working for advertising agencies. This work describes tasks and organises them according to the type of graphic being processed and the types of output. In addition, this work describes the ways these common tasks can be completed using modern graphics editing software. It also provides a practical definition of a graphic designer and graphic agency. The aim of this work is to m...
Prediction-Oriented Marker Selection (PROMISE): With Application to High-Dimensional Regression.
Kim, Soyeon; Baladandayuthapani, Veerabhadran; Lee, J Jack
2017-06-01
In personalized medicine, biomarkers are used to select therapies with the highest likelihood of success based on an individual patient's biomarker/genomic profile. Two goals are to choose important biomarkers that accurately predict treatment outcomes and to cull unimportant biomarkers to reduce the cost of biological and clinical verifications. These goals are challenging due to the high dimensionality of genomic data. Variable selection methods based on penalized regression (e.g., the lasso and elastic net) have yielded promising results. However, selecting the right amount of penalization is critical to simultaneously achieving these two goals. Standard approaches based on cross-validation (CV) typically provide high prediction accuracy with high true positive rates but at the cost of too many false positives. Alternatively, stability selection (SS) controls the number of false positives, but at the cost of yielding too few true positives. To circumvent these issues, we propose prediction-oriented marker selection (PROMISE), which combines SS with CV to conflate the advantages of both methods. Our application of PROMISE with the lasso and elastic net in data analysis shows that, compared to CV, PROMISE produces sparse solutions, few false positives, and small type I + type II error, and maintains good prediction accuracy, with a marginal decrease in the true positive rates. Compared to SS, PROMISE offers better prediction accuracy and true positive rates. In summary, PROMISE can be applied in many fields to select regularization parameters when the goals are to minimize false positives and maximize prediction accuracy.
Diagonal Likelihood Ratio Test for Equality of Mean Vectors in High-Dimensional Data
Hu, Zongliang
2017-10-27
We propose a likelihood ratio test framework for testing normal mean vectors in high-dimensional data under two common scenarios: the one-sample test and the two-sample test with equal covariance matrices. We derive the test statistics under the assumption that the covariance matrices follow a diagonal matrix structure. In comparison with the diagonal Hotelling\\'s tests, our proposed test statistics display some interesting characteristics. In particular, they are a summation of the log-transformed squared t-statistics rather than a direct summation of those components. More importantly, to derive the asymptotic normality of our test statistics under the null and local alternative hypotheses, we do not require the assumption that the covariance matrix follows a diagonal matrix structure. As a consequence, our proposed test methods are very flexible and can be widely applied in practice. Finally, simulation studies and a real data analysis are also conducted to demonstrate the advantages of our likelihood ratio test method.
Biomarker identification and effect estimation on schizophrenia –a high dimensional data analysis
Directory of Open Access Journals (Sweden)
Yuanzhang eLi
2015-05-01
Full Text Available Biomarkers have been examined in schizophrenia research for decades. Medical morbidity and mortality rates, as well as personal and societal costs, are associated with schizophrenia patients. The identification of biomarkers and alleles, which often have a small effect individually, may help to develop new diagnostic tests for early identification and treatment. Currently, there is not a commonly accepted statistical approach to identify predictive biomarkers from high dimensional data. We used space Decomposition-Gradient-Regression method (DGR to select biomarkers, which are associated with the risk of schizophrenia. Then, we used the gradient scores, generated from the selected biomarkers, as the prediction factor in regression to estimate their effects. We also used an alternative approach, classification and regression tree (CART, to compare the biomarker selected by DGR and found about 70% of the selected biomarkers were the same. However, the advantage of DGR is that it can evaluate individual effects for each biomarker from their combined effect. In DGR analysis of serum specimens of US military service members with a diagnosis of schizophrenia from 1992 to 2005 and their controls, Alpha-1-Antitrypsin (AAT, Interleukin-6 receptor (IL-6r and Connective Tissue Growth Factor (CTGF were selected to identify schizophrenia for males; and Alpha-1-Antitrypsin (AAT, Apolipoprotein B (Apo B and Sortilin were selected for females. If these findings from military subjects are replicated by other studies, they suggest the possibility of a novel biomarker panel as an adjunct to earlier diagnosis and initiation of treatment.
A sparse grid based method for generative dimensionality reduction of high-dimensional data
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.
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.
Directory of Open Access Journals (Sweden)
Enkelejda Miho
2018-02-01
Full Text Available The adaptive immune system recognizes antigens via an immense array of antigen-binding antibodies and T-cell receptors, the immune repertoire. The interrogation of immune repertoires is of high relevance for understanding the adaptive immune response in disease and infection (e.g., autoimmunity, cancer, HIV. Adaptive immune receptor repertoire sequencing (AIRR-seq has driven the quantitative and molecular-level profiling of immune repertoires, thereby revealing the high-dimensional complexity of the immune receptor sequence landscape. Several methods for the computational and statistical analysis of large-scale AIRR-seq data have been developed to resolve immune repertoire complexity and to understand the dynamics of adaptive immunity. Here, we review the current research on (i diversity, (ii clustering and network, (iii phylogenetic, and (iv machine learning methods applied to dissect, quantify, and compare the architecture, evolution, and specificity of immune repertoires. We summarize outstanding questions in computational immunology and propose future directions for systems immunology toward coupling AIRR-seq with the computational discovery of immunotherapeutics, vaccines, and immunodiagnostics.
Construction of high-dimensional neural network potentials using environment-dependent atom pairs.
Jose, K V Jovan; Artrith, Nongnuch; Behler, Jörg
2012-05-21
An accurate determination of the potential energy is the crucial step in computer simulations of chemical processes, but using electronic structure methods on-the-fly in molecular dynamics (MD) is computationally too demanding for many systems. Constructing more efficient interatomic potentials becomes intricate with increasing dimensionality of the potential-energy surface (PES), and for numerous systems the accuracy that can be achieved is still not satisfying and far from the reliability of first-principles calculations. Feed-forward neural networks (NNs) have a very flexible functional form, and in recent years they have been shown to be an accurate tool to construct efficient PESs. High-dimensional NN potentials based on environment-dependent atomic energy contributions have been presented for a number of materials. Still, these potentials may be improved by a more detailed structural description, e.g., in form of atom pairs, which directly reflect the atomic interactions and take the chemical environment into account. We present an implementation of an NN method based on atom pairs, and its accuracy and performance are compared to the atom-based NN approach using two very different systems, the methanol molecule and metallic copper. We find that both types of NN potentials provide an excellent description of both PESs, with the pair-based method yielding a slightly higher accuracy making it a competitive alternative for addressing complex systems in MD simulations.
Xia, Yin; Cai, Tianxi; Cai, T Tony
2018-01-01
Motivated by applications in genomics, we consider in this paper global and multiple testing for the comparisons of two high-dimensional linear regression models. A procedure for testing the equality of the two regression vectors globally is proposed and shown to be particularly powerful against sparse alternatives. We then introduce a multiple testing procedure for identifying unequal coordinates while controlling the false discovery rate and false discovery proportion. Theoretical justifications are provided to guarantee the validity of the proposed tests and optimality results are established under sparsity assumptions on the regression coefficients. The proposed testing procedures are easy to implement. Numerical properties of the procedures are investigated through simulation and data analysis. The results show that the proposed tests maintain the desired error rates under the null and have good power under the alternative at moderate sample sizes. The procedures are applied to the Framingham Offspring study to investigate the interactions between smoking and cardiovascular related genetic mutations important for an inflammation marker.
Individual-based models for adaptive diversification in high-dimensional phenotype spaces.
Ispolatov, Iaroslav; Madhok, Vaibhav; Doebeli, Michael
2016-02-07
Most theories of evolutionary diversification are based on equilibrium assumptions: they are either based on optimality arguments involving static fitness landscapes, or they assume that populations first evolve to an equilibrium state before diversification occurs, as exemplified by the concept of evolutionary branching points in adaptive dynamics theory. Recent results indicate that adaptive dynamics may often not converge to equilibrium points and instead generate complicated trajectories if evolution takes place in high-dimensional phenotype spaces. Even though some analytical results on diversification in complex phenotype spaces are available, to study this problem in general we need to reconstruct individual-based models from the adaptive dynamics generating the non-equilibrium dynamics. Here we first provide a method to construct individual-based models such that they faithfully reproduce the given adaptive dynamics attractor without diversification. We then show that a propensity to diversify can be introduced by adding Gaussian competition terms that generate frequency dependence while still preserving the same adaptive dynamics. For sufficiently strong competition, the disruptive selection generated by frequency-dependence overcomes the directional evolution along the selection gradient and leads to diversification in phenotypic directions that are orthogonal to the selection gradient. Copyright © 2015 Elsevier Ltd. All rights reserved.
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.
Energy Technology Data Exchange (ETDEWEB)
Dan Maljovec; Bei Wang; Valerio Pascucci; Peer-Timo Bremer; Michael Pernice; Robert Nourgaliev
2013-05-01
The next generation of methodologies for nuclear reactor Probabilistic Risk Assessment (PRA) explicitly accounts for the time element in modeling the probabilistic system evolution and uses numerical simulation tools to account for possible dependencies between failure events. The Monte-Carlo (MC) and the Dynamic Event Tree (DET) approaches belong to this new class of dynamic PRA methodologies. A challenge of dynamic PRA algorithms is the large amount of data they produce which may be difficult to visualize and analyze in order to extract useful information. We present a software tool that is designed to address these goals. We model a large-scale nuclear simulation dataset as a high-dimensional scalar function defined over a discrete sample of the domain. First, we provide structural analysis of such a function at multiple scales and provide insight into the relationship between the input parameters and the output. Second, we enable exploratory analysis for users, where we help the users to differentiate features from noise through multi-scale analysis on an interactive platform, based on domain knowledge and data characterization. Our analysis is performed by exploiting the topological and geometric properties of the domain, building statistical models based on its topological segmentations and providing interactive visual interfaces to facilitate such explorations. We provide a user’s guide to our software tool by highlighting its analysis and visualization capabilities, along with a use case involving dataset from a nuclear reactor safety simulation.
Schran, Christoph; Uhl, Felix; Behler, Jörg; Marx, Dominik
2018-03-01
The design of accurate helium-solute interaction potentials for the simulation of chemically complex molecules solvated in superfluid helium has long been a cumbersome task due to the rather weak but strongly anisotropic nature of the interactions. We show that this challenge can be met by using a combination of an effective pair potential for the He-He interactions and a flexible high-dimensional neural network potential (NNP) for describing the complex interaction between helium and the solute in a pairwise additive manner. This approach yields an excellent agreement with a mean absolute deviation as small as 0.04 kJ mol-1 for the interaction energy between helium and both hydronium and Zundel cations compared with coupled cluster reference calculations with an energetically converged basis set. The construction and improvement of the potential can be performed in a highly automated way, which opens the door for applications to a variety of reactive molecules to study the effect of solvation on the solute as well as the solute-induced structuring of the solvent. Furthermore, we show that this NNP approach yields very convincing agreement with the coupled cluster reference for properties like many-body spatial and radial distribution functions. This holds for the microsolvation of the protonated water monomer and dimer by a few helium atoms up to their solvation in bulk helium as obtained from path integral simulations at about 1 K.
Multi-Scale Factor Analysis of High-Dimensional Brain Signals
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.
Meng, Xi; Nguyen, Bao D; Ridge, Clark; Shaka, A J
2009-01-01
High-dimensional (HD) NMR spectra have poorer digital resolution than low-dimensional (LD) spectra, for a fixed amount of experiment time. This has led to "reduced-dimensionality" strategies, in which several LD projections of the HD NMR spectrum are acquired, each with higher digital resolution; an approximate HD spectrum is then inferred by some means. We propose a strategy that moves in the opposite direction, by adding more time dimensions to increase the information content of the data set, even if only a very sparse time grid is used in each dimension. The full HD time-domain data can be analyzed by the filter diagonalization method (FDM), yielding very narrow resonances along all of the frequency axes, even those with sparse sampling. Integrating over the added dimensions of HD FDM NMR spectra reconstitutes LD spectra with enhanced resolution, often more quickly than direct acquisition of the LD spectrum with a larger number of grid points in each of the fewer dimensions. If the extra-dimensions do not appear in the final spectrum, and are used solely to boost information content, we propose the moniker hidden-dimension NMR. This work shows that HD peaks have unmistakable frequency signatures that can be detected as single HD objects by an appropriate algorithm, even though their patterns would be tricky for a human operator to visualize or recognize, and even if digital resolution in an HD FT spectrum is very coarse compared with natural line widths.
Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification.
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.
Simulation-based hypothesis testing of high dimensional means under covariance heterogeneity.
Chang, Jinyuan; Zheng, Chao; Zhou, Wen-Xin; Zhou, Wen
2017-12-01
In this article, we study the problem of testing the mean vectors of high dimensional data in both one-sample and two-sample cases. The proposed testing procedures employ maximum-type statistics and the parametric bootstrap techniques to compute the critical values. Different from the existing tests that heavily rely on the structural conditions on the unknown covariance matrices, the proposed tests allow general covariance structures of the data and therefore enjoy wide scope of applicability in practice. To enhance powers of the tests against sparse alternatives, we further propose two-step procedures with a preliminary feature screening step. Theoretical properties of the proposed tests are investigated. Through extensive numerical experiments on synthetic data sets and an human acute lymphoblastic leukemia gene expression data set, we illustrate the performance of the new tests and how they may provide assistance on detecting disease-associated gene-sets. The proposed methods have been implemented in an R-package HDtest and are available on CRAN. © 2017, The International Biometric Society.
Multi-SOM: an Algorithm for High-Dimensional, Small Size Datasets
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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.
Identification of computer graphics objects
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Rossinskyi Yu.M.
2016-04-01
Full Text Available The article is devoted to the use of computer graphics methods in problems of creating drawings, charts, drafting, etc. The widespread use of these methods requires the development of efficient algorithms for the identification of objects of drawings. The article analyzes the model-making algorithms for this problem and considered the possibility of reducing the time using graphics editing operations. Editing results in such operations as copying, moving and deleting objects specified images. These operations allow the use of a reliable identification of images of objects methods. For information on the composition of the image of the object along with information about the identity and the color should include information about the spatial location and other characteristics of the object (the thickness and style of contour lines, fill style, and so on. In order to enable the pixel image analysis to structure the information it is necessary to enable the initial code image objects color. The article shows the results of the implementation of the algorithm of encoding object identifiers. To simplify the process of building drawings of any kind, and reduce time-consuming, method of drawing objects identification is proposed based on the use as the ID information of the object color.
Graphical programming at Sandia National Laboratories
International Nuclear Information System (INIS)
McDonald, M.J.; Palmquist, R.D.; Desjarlais, L.
1993-09-01
Sandia has developed an advanced operational control system approach, called Graphical Programming, to design, program, and operate robotic systems. The Graphical Programming approach produces robot systems that are faster to develop and use, safer in operation, and cheaper overall than altemative teleoperation or autonomous robot control systems. Graphical Programming also provides an efficient and easy-to-use interface to traditional robot systems for use in setup and programming tasks. This paper provides an overview of the Graphical Programming approach and lists key features of Graphical Programming systems. Graphical Programming uses 3-D visualization and simulation software with intuitive operator interfaces for the programming and control of complex robotic systems. Graphical Programming Supervisor software modules allow an operator to command and simulate complex tasks in a graphic preview mode and, when acceptable, command the actual robots and monitor their motions with the graphic system. Graphical Programming Supervisors maintain registration with the real world and allow the robot to perform tasks that cannot be accurately represented with models alone by using a combination of model and sensor-based control
Packaging printed circuit boards: A production application of interactive graphics
Perrill, W. A.
1975-01-01
The structure and use of an Interactive Graphics Packaging Program (IGPP), conceived to apply computer graphics to the design of packaging electronic circuits onto printed circuit boards (PCB), were described. The intent was to combine the data storage and manipulative power of the computer with the imaginative, intuitive power of a human designer. The hardware includes a CDC 6400 computer and two CDC 777 terminals with CRT screens, light pens, and keyboards. The program is written in FORTRAN 4 extended with the exception of a few functions coded in COMPASS (assembly language). The IGPP performs four major functions for the designer: (1) data input and display, (2) component placement (automatic or manual), (3) conductor path routing (automatic or manual), and (4) data output. The most complex PCB packaged to date measured 16.5 cm by 19 cm and contained 380 components, two layers of ground planes and four layers of conductors mixed with ground planes.
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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.
International Nuclear Information System (INIS)
Guerrieri, A.
2009-01-01
In this report the largest Lyapunov characteristic exponent of a high dimensional atmospheric global circulation model of intermediate complexity has been estimated numerically. A sensitivity analysis has been carried out by varying the equator-to-pole temperature difference, the space resolution and the value of some parameters employed by the model. Chaotic and non-chaotic regimes of circulation have been found. [it
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
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
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
International Nuclear Information System (INIS)
Langrene, Nicolas
2014-01-01
This thesis deals with the numerical solution of general stochastic control problems, with notable applications for electricity markets. We first propose a structural model for the price of electricity, allowing for price spikes well above the marginal fuel price under strained market conditions. This model allows to price and partially hedge electricity derivatives, using fuel forwards as hedging instruments. Then, we propose an algorithm, which combines Monte-Carlo simulations with local basis regressions, to solve general optimal switching problems. A comprehensive rate of convergence of the method is provided. Moreover, we manage to make the algorithm parsimonious in memory (and hence suitable for high dimensional problems) by generalizing to this framework a memory reduction method that avoids the storage of the sample paths. We illustrate this on the problem of investments in new power plants (our structural power price model allowing the new plants to impact the price of electricity). Finally, we study more general stochastic control problems (the control can be continuous and impact the drift and volatility of the state process), the solutions of which belong to the class of fully nonlinear Hamilton-Jacobi-Bellman equations, and can be handled via constrained Backward Stochastic Differential Equations, for which we develop a backward algorithm based on control randomization and parametric optimizations. A rate of convergence between the constraPned BSDE and its discrete version is provided, as well as an estimate of the optimal control. This algorithm is then applied to the problem of super replication of options under uncertain volatilities (and correlations). (author)
Evaluation of a new high-dimensional miRNA profiling platform
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Lamblin Anne-Francoise
2009-08-01
Full Text Available Abstract Background MicroRNAs (miRNAs are a class of approximately 22 nucleotide long, widely expressed RNA molecules that play important regulatory roles in eukaryotes. To investigate miRNA function, it is essential that methods to quantify their expression levels be available. Methods We evaluated a new miRNA profiling platform that utilizes Illumina's existing robust DASL chemistry as the basis for the assay. Using total RNA from five colon cancer patients and four cell lines, we evaluated the reproducibility of miRNA expression levels across replicates and with varying amounts of input RNA. The beta test version was comprised of 735 miRNA targets of Illumina's miRNA profiling application. Results Reproducibility between sample replicates within a plate was good (Spearman's correlation 0.91 to 0.98 as was the plate-to-plate reproducibility replicates run on different days (Spearman's correlation 0.84 to 0.98. To determine whether quality data could be obtained from a broad range of input RNA, data obtained from amounts ranging from 25 ng to 800 ng were compared to those obtained at 200 ng. No effect across the range of RNA input was observed. Conclusion These results indicate that very small amounts of starting material are sufficient to allow sensitive miRNA profiling using the Illumina miRNA high-dimensional platform. Nonlinear biases were observed between replicates, indicating the need for abundance-dependent normalization. Overall, the performance characteristics of the Illumina miRNA profiling system were excellent.
Multivariate linear regression of high-dimensional fMRI data with multiple target variables.
Valente, Giancarlo; Castellanos, Agustin Lage; Vanacore, Gianluca; Formisano, Elia
2014-05-01
Multivariate regression is increasingly used to study the relation between fMRI spatial activation patterns and experimental stimuli or behavioral ratings. With linear models, informative brain locations are identified by mapping the model coefficients. This is a central aspect in neuroimaging, as it provides the sought-after link between the activity of neuronal populations and subject's perception, cognition or behavior. Here, we show that mapping of informative brain locations using multivariate linear regression (MLR) may lead to incorrect conclusions and interpretations. MLR algorithms for high dimensional data are designed to deal with targets (stimuli or behavioral ratings, in fMRI) separately, and the predictive map of a model integrates information deriving from both neural activity patterns and experimental design. Not accounting explicitly for the presence of other targets whose associated activity spatially overlaps with the one of interest may lead to predictive maps of troublesome interpretation. We propose a new model that can correctly identify the spatial patterns associated with a target while achieving good generalization. For each target, the training is based on an augmented dataset, which includes all remaining targets. The estimation on such datasets produces both maps and interaction coefficients, which are then used to generalize. The proposed formulation is independent of the regression algorithm employed. We validate this model on simulated fMRI data and on a publicly available dataset. Results indicate that our method achieves high spatial sensitivity and good generalization and that it helps disentangle specific neural effects from interaction with predictive maps associated with other targets. Copyright © 2013 Wiley Periodicals, Inc.
Gomez, Luis J; Yücel, Abdulkadir C; Hernandez-Garcia, Luis; Taylor, Stephan F; Michielssen, Eric
2015-01-01
A computational framework for uncertainty quantification in transcranial magnetic stimulation (TMS) is presented. The framework leverages high-dimensional model representations (HDMRs), which approximate observables (i.e., quantities of interest such as electric (E) fields induced inside targeted cortical regions) via series of iteratively constructed component functions involving only the most significant random variables (i.e., parameters that characterize the uncertainty in a TMS setup such as the position and orientation of TMS coils, as well as the size, shape, and conductivity of the head tissue). The component functions of HDMR expansions are approximated via a multielement probabilistic collocation (ME-PC) method. While approximating each component function, a quasi-static finite-difference simulator is used to compute observables at integration/collocation points dictated by the ME-PC method. The proposed framework requires far fewer simulations than traditional Monte Carlo methods for providing highly accurate statistical information (e.g., the mean and standard deviation) about the observables. The efficiency and accuracy of the proposed framework are demonstrated via its application to the statistical characterization of E-fields generated by TMS inside cortical regions of an MRI-derived realistic head model. Numerical results show that while uncertainties in tissue conductivities have negligible effects on TMS operation, variations in coil position/orientation and brain size significantly affect the induced E-fields. Our numerical results have several implications for the use of TMS during depression therapy: 1) uncertainty in the coil position and orientation may reduce the response rates of patients; 2) practitioners should favor targets on the crest of a gyrus to obtain maximal stimulation; and 3) an increasing scalp-to-cortex distance reduces the magnitude of E-fields on the surface and inside the cortex.
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Datta Susmita
2010-08-01
Full Text Available Abstract Background Generally speaking, different classifiers tend to work well for certain types of data and conversely, it is usually not known a priori which algorithm will be optimal in any given classification application. In addition, for most classification problems, selecting the best performing classification algorithm amongst a number of competing algorithms is a difficult task for various reasons. As for example, the order of performance may depend on the performance measure employed for such a comparison. In this work, we present a novel adaptive ensemble classifier constructed by combining bagging and rank aggregation that is capable of adaptively changing its performance depending on the type of data that is being classified. The attractive feature of the proposed classifier is its multi-objective nature where the classification results can be simultaneously optimized with respect to several performance measures, for example, accuracy, sensitivity and specificity. We also show that our somewhat complex strategy has better predictive performance as judged on test samples than a more naive approach that attempts to directly identify the optimal classifier based on the training data performances of the individual classifiers. Results We illustrate the proposed method with two simulated and two real-data examples. In all cases, the ensemble classifier performs at the level of the best individual classifier comprising the ensemble or better. Conclusions For complex high-dimensional datasets resulting from present day high-throughput experiments, it may be wise to consider a number of classification algorithms combined with dimension reduction techniques rather than a fixed standard algorithm set a priori.
Landfors, Mattias; Philip, Philge; Rydén, Patrik; Stenberg, Per
2011-01-01
Genome-wide analysis of gene expression or protein binding patterns using different array or sequencing based technologies is now routinely performed to compare different populations, such as treatment and reference groups. It is often necessary to normalize the data obtained to remove technical variation introduced in the course of conducting experimental work, but standard normalization techniques are not capable of eliminating technical bias in cases where the distribution of the truly altered variables is skewed, i.e. when a large fraction of the variables are either positively or negatively affected by the treatment. However, several experiments are likely to generate such skewed distributions, including ChIP-chip experiments for the study of chromatin, gene expression experiments for the study of apoptosis, and SNP-studies of copy number variation in normal and tumour tissues. A preliminary study using spike-in array data established that the capacity of an experiment to identify altered variables and generate unbiased estimates of the fold change decreases as the fraction of altered variables and the skewness increases. We propose the following work-flow for analyzing high-dimensional experiments with regions of altered variables: (1) Pre-process raw data using one of the standard normalization techniques. (2) Investigate if the distribution of the altered variables is skewed. (3) If the distribution is not believed to be skewed, no additional normalization is needed. Otherwise, re-normalize the data using a novel HMM-assisted normalization procedure. (4) Perform downstream analysis. Here, ChIP-chip data and simulated data were used to evaluate the performance of the work-flow. It was found that skewed distributions can be detected by using the novel DSE-test (Detection of Skewed Experiments). Furthermore, applying the HMM-assisted normalization to experiments where the distribution of the truly altered variables is skewed results in considerably higher
From Ambiguities to Insights: Query-based Comparisons of High-Dimensional Data
Kowalski, Jeanne; Talbot, Conover; Tsai, Hua L.; Prasad, Nijaguna; Umbricht, Christopher; Zeiger, Martha A.
2007-11-01
Genomic technologies will revolutionize drag discovery and development; that much is universally agreed upon. The high dimension of data from such technologies has challenged available data analytic methods; that much is apparent. To date, large-scale data repositories have not been utilized in ways that permit their wealth of information to be efficiently processed for knowledge, presumably due in large part to inadequate analytical tools to address numerous comparisons of high-dimensional data. In candidate gene discovery, expression comparisons are often made between two features (e.g., cancerous versus normal), such that the enumeration of outcomes is manageable. With multiple features, the setting becomes more complex, in terms of comparing expression levels of tens of thousands transcripts across hundreds of features. In this case, the number of outcomes, while enumerable, become rapidly large and unmanageable, and scientific inquiries become more abstract, such as "which one of these (compounds, stimuli, etc.) is not like the others?" We develop analytical tools that promote more extensive, efficient, and rigorous utilization of the public data resources generated by the massive support of genomic studies. Our work innovates by enabling access to such metadata with logically formulated scientific inquires that define, compare and integrate query-comparison pair relations for analysis. We demonstrate our computational tool's potential to address an outstanding biomedical informatics issue of identifying reliable molecular markers in thyroid cancer. Our proposed query-based comparison (QBC) facilitates access to and efficient utilization of metadata through logically formed inquires expressed as query-based comparisons by organizing and comparing results from biotechnologies to address applications in biomedicine.
Spectra processing with computer graphics
International Nuclear Information System (INIS)
Kruse, H.
1979-01-01
A program of processng gamma-ray spectra in rock analysis is described. The peak search was performed by applying a cross-correlation function. The experimental data were approximated by an analytical function represented by the sum of a polynomial and a multiple peak function. The latter is Gaussian, joined with the low-energy side by an exponential. A modified Gauss-Newton algorithm is applied for the purpose of fitting the data to the function. The processing of the values derived from a lunar sample demonstrates the effect of different choices of polynomial orders for approximating the background for various fitting intervals. Observations on applications of interactive graphics are presented. 3 figures, 1 table
The HEASARC graphical user interface
White, N.; Barrett, P.; Jacobs, P.; Oneel, B.
1992-01-01
An OSF/Motif-based graphical user interface has been developed to facilitate the use of the database and data analysis software packages available from the High Energy Astrophysics Science Archive Research Center (HEASARC). It can also be used as an interface to other, similar, routines. A small number of tables are constructed to specify the possible commands and command parameters for a given set of analysis routines. These tables can be modified by a designer to affect the appearance of the interface screens. They can also be dynamically changed in response to parameter adjustments made while the underlying program is running. Additionally, a communication protocol has been designed so that the interface can operate locally or across a network. It is intended that this software be able to run on a variety of workstations and X terminals.
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.
CONSIDERATIONS ABOUT APPLICATION OF GRAPHICAL CALCULUS
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Anghel Alina Angelica
2015-06-01
Full Text Available Some typical engineering problems can be solved using graphical methods in an integrated way for knowledge of design, mathematics, mechanics and physics. This paper presents some graphical methods which improve student’s performances on streigth of intuitive interpretation, visualization and understanding solutions of engineering problems. The increased use of graphical methods adds another layer of interpretation to a given task requiring the coordination of different knowledge, in order to produce a correct solution and to improve the product design.
Programming Language Software For Graphics Applications
Beckman, Brian C.
1993-01-01
New approach reduces repetitive development of features common to different applications. High-level programming language and interactive environment with access to graphical hardware and software created by adding graphical commands and other constructs to standardized, general-purpose programming language, "Scheme". Designed for use in developing other software incorporating interactive computer-graphics capabilities into application programs. Provides alternative to programming entire applications in C or FORTRAN, specifically ameliorating design and implementation of complex control and data structures typifying applications with interactive graphics. Enables experimental programming and rapid development of prototype software, and yields high-level programs serving as executable versions of software-design documentation.
Geometrical and Graphical Solutions of Quadratic Equations.
Hornsby, E. John, Jr.
1990-01-01
Presented are several geometrical and graphical methods of solving quadratic equations. Discussed are Greek origins, Carlyle's method, von Staudt's method, fixed graph methods and imaginary solutions. (CW)
Integrating high dimensional bi-directional parsing models for gene mention tagging.
Hsu, Chun-Nan; Chang, Yu-Ming; Kuo, Cheng-Ju; Lin, Yu-Shi; Huang, Han-Shen; Chung, I-Fang
2008-07-01
Tagging gene and gene product mentions in scientific text is an important initial step of literature mining. In this article, we describe in detail our gene mention tagger participated in BioCreative 2 challenge and analyze what contributes to its good performance. Our tagger is based on the conditional random fields model (CRF), the most prevailing method for the gene mention tagging task in BioCreative 2. Our tagger is interesting because it accomplished the highest F-scores among CRF-based methods and second over all. Moreover, we obtained our results by mostly applying open source packages, making it easy to duplicate our results. We first describe in detail how we developed our CRF-based tagger. We designed a very high dimensional feature set that includes most of information that may be relevant. We trained bi-directional CRF models with the same set of features, one applies forward parsing and the other backward, and integrated two models based on the output scores and dictionary filtering. One of the most prominent factors that contributes to the good performance of our tagger is the integration of an additional backward parsing model. However, from the definition of CRF, it appears that a CRF model is symmetric and bi-directional parsing models will produce the same results. We show that due to different feature settings, a CRF model can be asymmetric and the feature setting for our tagger in BioCreative 2 not only produces different results but also gives backward parsing models slight but constant advantage over forward parsing model. To fully explore the potential of integrating bi-directional parsing models, we applied different asymmetric feature settings to generate many bi-directional parsing models and integrate them based on the output scores. Experimental results show that this integrated model can achieve even higher F-score solely based on the training corpus for gene mention tagging. Data sets, programs and an on-line service of our gene
Greedy algorithms for high-dimensional non-symmetric linear problems***
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Cancès E.
2013-12-01
Full Text Available In this article, we present a family of numerical approaches to solve high-dimensional linear non-symmetric problems. The principle of these methods is to approximate a function which depends on a large number of variates by a sum of tensor product functions, each term of which is iteratively computed via a greedy algorithm ? . There exists a good theoretical framework for these methods in the case of (linear and nonlinear symmetric elliptic problems. However, the convergence results are not valid any more as soon as the problems under consideration are not symmetric. We present here a review of the main algorithms proposed in the literature to circumvent this difficulty, together with some new approaches. The theoretical convergence results and the practical implementation of these algorithms are discussed. Their behaviors are illustrated through some numerical examples. Dans cet article, nous présentons une famille de méthodes numériques pour résoudre des problèmes linéaires non symétriques en grande dimension. Le principe de ces approches est de représenter une fonction dépendant d’un grand nombre de variables sous la forme d’une somme de fonctions produit tensoriel, dont chaque terme est calculé itérativement via un algorithme glouton ? . Ces méthodes possèdent de bonnes propriétés théoriques dans le cas de problèmes elliptiques symétriques (linéaires ou non linéaires, mais celles-ci ne sont plus valables dès lors que les problèmes considérés ne sont plus symétriques. Nous présentons une revue des principaux algorithmes proposés dans la littérature pour contourner cette difficulté ainsi que de nouvelles approches que nous proposons. Les résultats de convergence théoriques et la mise en oeuvre pratique de ces algorithmes sont détaillés et leur comportement est illustré au travers d’exemples numériques.
THE USING OF GRAPHICAL EDITOR IN THE ENGINEERING GRAPHICS AND THE COURSE DESIGNING
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KARPYUK L. V.
2016-08-01
Full Text Available The problems of learning students of the engineering and computer graphics of the course on the base of computer-aided design (CAD were described in the article. The examples of training tasks for acquiring knowledge of work in the environment of graphical editor of AutoCAD were shown. These examples are needed to perform drawings on The Engineering Graphics, and also for a graphic part of Course Projects for students of mechanical specialties.
Graphical User Interface in Art
Gwilt, Ian
This essay discusses the use of the Graphical User Interface (GUI) as a site of creative practice. By creatively repositioning the GUI as a work of art it is possible to challenge our understanding and expectations of the conventional computer interface wherein the icons and navigational architecture of the GUI no longer function as a technological tool. These artistic recontextualizations are often used to question our engagement with technology and to highlight the pivotal place that the domestic computer has taken in our everyday social, cultural and (increasingly), creative domains. Through these works the media specificity of the screen-based GUI can broken by dramatic changes in scale, form and configuration. This can be seen through the work of new media artists who have re-imagined the GUI in a number of creative forms both, within the digital, as image, animation, net and interactive art, and in the analogue, as print, painting, sculpture, installation and performative event. Furthermore as a creative work, the GUI can also be utilized as a visual way-finder to explore the relationship between the dynamic potentials of the digital and the concretized qualities of the material artifact.
A Theoretical Analysis of Learning with Graphics--Implications for Computer Graphics Design.
ChanLin, Lih-Juan
This paper reviews the literature pertinent to learning with graphics. The dual coding theory provides explanation about how graphics are stored and precessed in semantic memory. The level of processing theory suggests how graphics can be employed in learning to encourage deeper processing. In addition to dual coding theory and level of processing…
On the Role of Computer Graphics in Engineering Design Graphics Courses.
Pleck, Michael H.
The implementation of two- and three-dimensional computer graphics in a freshmen engineering design course at the university level is described. An assessment of the capabilities and limitations of computer graphics is made, along with a presentation of the fundamental role which computer graphics plays in engineering design instruction.…
Q-Technique and Graphics Research.
Kahle, Roger R.
Because Q-technique is as appropriate for use with visual and design items as for use with words, it is not stymied by the topics one is likely to encounter in graphics research. In particular Q-technique is suitable for studying the so-called "congeniality" of typography, for various copytesting usages, and for multivariate graphics research. The…
Graphic Organizers: Outlets for Your Thoughts.
Ekhaml, Leticia
1998-01-01
Graphs, bars, charts, and diagrams have been used by designers, writers, and scientists to communicate. Now, research suggests that graphic organizers benefit teaching and learning. This article describes graphic organizers: sequential, conceptual, hierarchical, cyclical, Venn, fishbone or Ishikawa, squeeze and stretch, why-why, t-chart, KWL…
Deconstruction and Graphic Design: History Meets Theory.
Lupton, Ellen; Miller, J. Abbott
1994-01-01
Considers the reception and use of deconstruction in the recent history of graphic design. Considers the place of graphics within the theory of deconstruction in the work of philosopher Jacques Derrida. Argues that deconstruction is not a style but a mode of questioning through and about the technologies, formal devices, social institutions and…
Introduction to Graphics Programming in Java
DEFF Research Database (Denmark)
Rosendahl, Mads
Writing graphics applications in Java using Swing can be quite a daunting experience which requires understanding of some large libraries, and fairly advanced aspects of Java. In these notes we will show that by using a small subset of the Swing package we can write a write range of graphics...
A Laboratory Application of Microcomputer Graphics.
Gehring, Kalle B.; Moore, John W.
1983-01-01
A PASCAL graphics and instrument interface program for a Z80/S-100 based microcomputer was developed. The computer interfaces to a stopped-flow spectrophotometer replacing a storage oscilloscope and polaroid camera. Applications of this system are discussed, indicating that graphics and analog-to-digital boards have transformed the computer into…
Using Graphic Organizers in Intercultural Education
Ciascai, Liliana
2009-01-01
Graphic organizers are instruments of representation, illustration and modeling of information. In the educational practice they are used for building, and systematization of knowledge. Graphic organizers are instruments that addressed mostly visual learning style, but their use is beneficial to all learners. In this paper we illustrate the use of…
Making Art Connections with Graphic Organizers
Stephens, Pam; Hermus, Cindy
2007-01-01
Posters, slide shows, videos, diagrams, charts, written or illustrated class notes, daily logs, to do lists, and written instructions are all helpful modes of teaching for visual learners. Another form of instruction that is helpful for visual learners is the graphic organizers. Sometimes called "mind maps", graphic organizers are illustrative…
Spatial Ability through Engineering Graphics Education
Marunic, Gordana; Glazar, Vladimir
2013-01-01
Spatial ability has been confirmed to be of particular importance for successful engineering graphics education and to be a component of human intelligence that can be improved through instruction and training. Consequently, the creation and communication by means of graphics demand careful development of spatial skills provided by the balanced…
Graphics with Special Interfaces for Disabled People.
Tronconi, A.; And Others
The paper describes new software and special input devices to allow physically impaired children to utilize the graphic capabilities of personal computers. Special input devices for computer graphics access--the voice recognition card, the single switch, or the mouse emulator--can be used either singly or in combination by the disabled to control…
Standards of Multimedia Graphic Design in Education
Aldalalah, Osamah Ahmad; Ababneh, Ziad Waleed Mohamed
2015-01-01
This study aims to determine Standards of Multimedia Graphic Design in Education through the analysis of the theoretical basis and previous studies related to this subject. This study has identified the list of standards of Multimedia, Graphic Design, each of which has a set indicator through which the quality of Multimedia can be evaluated in…
Determining Normal-Distribution Tolerance Bounds Graphically
Mezzacappa, M. A.
1983-01-01
Graphical method requires calculations and table lookup. Distribution established from only three points: mean upper and lower confidence bounds and lower confidence bound of standard deviation. Method requires only few calculations with simple equations. Graphical procedure establishes best-fit line for measured data and bounds for selected confidence level and any distribution percentile.
ABOUT THE ROMANIAN SOCIETY FOR ENGINEERING GRAPHICS
Directory of Open Access Journals (Sweden)
SIMION Ionel
2015-06-01
Full Text Available SORGING is a non-profit, non-governmental society, opened to all professionals interested in Engineering Graphics and Design. It aims to promote the research, development and innovation activities, together with the dissemination of best practices and assistance for educational purposes. In this paper the research and educational activities of the Romanian Society for Engineering Graphics will be briefly reviewed.
Teaching Graphics in Technical Communication Classes.
Spurgeon, Kristene C.
Perhaps because the United States is undergoing a video revolution, perhaps because of its increasing sales of goods to non-English speaking markets where graphics can help explain the products, perhaps because of the decreasing communication skills of the work force, graphic aids are becoming more and more widely used and more and more important.…
Graphic Design in Libraries: A Conceptual Process
Ruiz, Miguel
2014-01-01
Providing successful library services requires efficient and effective communication with users; therefore, it is important that content creators who develop visual materials understand key components of design and, specifically, develop a holistic graphic design process. Graphic design, as a form of visual communication, is the process of…
User-Extensible Graphics Using Abstract Structure,
1987-08-01
Flex 6 The Algol68 model of the graphical abstract structure 5 The creation of a PictureDefinition 6 The making of a picture from a PictureDefinition 7...data together with the operations that can be performed on that data. i 7! ś I _ § 4, The Alqol68 model of the graphical abstract structure Every
Collaborating on a Graphic Medicine Novel
DEFF Research Database (Denmark)
Frølunde, Lisbeth
2018-01-01
The presentation centers on establishing creative collaborations to support the production of my graphic novel (Family Anecdotes) about mourning and mental health. I explore various challenges of authoring an “autobiofictional" graphic medicine novel – as an arts-based communication researcher, a...
Adolescents' Motivations for Viewing Graphic Horror.
Johnston, Deirdre D.
1995-01-01
Identifies four motivations adolescents report for viewing graphic horror films: gore watching, thrill watching, independent watching, and problem watching. Argues that viewing motivations are predictors of responses to graphic horror. Finds that viewing motivations were related to viewers' cognitive and affective responses and a tendency to…
A study of perceptions of graphical passwords
CSIR Research Space (South Africa)
Vorster, JS
2015-10-01
Full Text Available Depending on the graphical password schema, the key-space can be even bigger than alpha-numeric passwords. However, in conventional passwords, users will re-use letters within a password. This study investigates graphical passwords for symbol...
Mastering probabilistic graphical models using Python
Ankan, Ankur
2015-01-01
If you are a researcher or a machine learning enthusiast, or are working in the data science field and have a basic idea of Bayesian learning or probabilistic graphical models, this book will help you to understand the details of graphical models and use them in your data science problems.
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.
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
Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks.
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.
The graphics future in scientific applications
International Nuclear Information System (INIS)
Enderle, G.
1982-01-01
Computer graphics methods and tools are being used to a great extent in scientific research. The future development in this area will be influenced both by new hardware developments and by software advances. On the hardware sector, the development of the raster technology will lead to the increased use of colour workstations with more local processing power. Colour hardcopy devices for creating plots, slides, or movies will be available at a lower price than today. The first real 3D-workstations appear on the marketplace. One of the main activities on the software sector is the standardization of computer graphics systems, graphical files, and device interfaces. This will lead to more portable graphical application programs and to a common base for computer graphics education. (orig.)
Structural identifiability of cyclic graphical models of biological networks with latent variables.
Wang, Yulin; Lu, Na; Miao, Hongyu
2016-06-13
is thus of higher resolution in comparison with many existing approaches. Overall, this study provides a basis for systematic examination and refinement of graphical models of biological networks from the identifiability point of view, and it has a significant potential to be extended to more complex network structures or high-dimensional systems.
International Nuclear Information System (INIS)
Oganesian, A.G.
1998-01-01
A method is proposed for estimating unknown vacuum expectation values of high-dimensional operators. The method is based on the idea that the factorization hypothesis is self-consistent. Results are obtained for all vacuum expectation values of dimension-7 operators, and some estimates for dimension-10 operators are presented as well. The resulting values are used to compute corrections of higher dimensions to the Bjorken and Ellis-Jaffe sum rules
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.
FUn: a framework for interactive visualizations of large, high-dimensional datasets on the web.
Probst, Daniel; Reymond, Jean-Louis
2018-04-15
During the past decade, big data have become a major tool in scientific endeavors. Although statistical methods and algorithms are well-suited for analyzing and summarizing enormous amounts of data, the results do not allow for a visual inspection of the entire data. Current scientific software, including R packages and Python libraries such as ggplot2, matplotlib and plot.ly, do not support interactive visualizations of datasets exceeding 100 000 data points on the web. Other solutions enable the web-based visualization of big data only through data reduction or statistical representations. However, recent hardware developments, especially advancements in graphical processing units, allow for the rendering of millions of data points on a wide range of consumer hardware such as laptops, tablets and mobile phones. Similar to the challenges and opportunities brought to virtually every scientific field by big data, both the visualization of and interaction with copious amounts of data are both demanding and hold great promise. Here we present FUn, a framework consisting of a client (Faerun) and server (Underdark) module, facilitating the creation of web-based, interactive 3D visualizations of large datasets, enabling record level visual inspection. We also introduce a reference implementation providing access to SureChEMBL, a database containing patent information on more than 17 million chemical compounds. The source code and the most recent builds of Faerun and Underdark, Lore.js and the data preprocessing toolchain used in the reference implementation, are available on the project website (http://doc.gdb.tools/fun/). daniel.probst@dcb.unibe.ch or jean-louis.reymond@dcb.unibe.ch.
Graphic user interface for COSMOS code
International Nuclear Information System (INIS)
Oh, Je Yong; Koo, Yang Hyun; Lee, Byung Ho; Cheon, Jin Sik; Sohn, Dong Seong
2003-06-01
The Graphic User Interface (GUI) - which consisted of graphical elements such as windows, menu, button, icon, and so on - made it possible that the computer could be easily used for common users. Hence, the GUI was introduced to improve the efficiency to input parameters in COSMOS code. The functions to output graphs on the screen and postscript files were also added. And the graph library can be applied to the other codes. The details of principles of GUI and graphic library were described in the report
Practical Implementation of a Graphics Turing Test
DEFF Research Database (Denmark)
Borg, Mathias; Johansen, Stine Schmieg; Thomsen, Dennis Lundgaard
2012-01-01
We present a practical implementation of a variation of the Turing Test for realistic computer graphics. The test determines whether virtual representations of objects appear as real as genuine objects. Two experiments were conducted wherein a real object and a similar virtual object is presented...... graphics. Based on the results from these experiments, future versions of the Graphics Turing Test could ease the restrictions currently necessary in order to test object telepresence under more general conditions. Furthermore, the test could be used to determine the minimum requirements to achieve object...
Future of motion graphics and particle systems
Warambo, Bryan
2012-01-01
The purpose of this research is to study the use of particle systems in motion graphics, which is known to be the most popular graphics tool for multiple animated elements. It is known to be a procedural animation because as the emitter builds up more particles are formed to create a motion effect. At the same time exploring the future of motion graphics and Particle systems connection and the relevance it has in terms of longevity in being a major post-production element in digital media. Th...
OAP- OFFICE AUTOMATION PILOT GRAPHICS DATABASE SYSTEM
Ackerson, T.
1994-01-01
The Office Automation Pilot (OAP) Graphics Database system offers the IBM PC user assistance in producing a wide variety of graphs and charts. OAP uses a convenient database system, called a chartbase, for creating and maintaining data associated with the charts, and twelve different graphics packages are available to the OAP user. Each of the graphics capabilities is accessed in a similar manner. The user chooses creation, revision, or chartbase/slide show maintenance options from an initial menu. The user may then enter or modify data displayed on a graphic chart. The cursor moves through the chart in a "circular" fashion to facilitate data entries and changes. Various "help" functions and on-screen instructions are available to aid the user. The user data is used to generate the graphics portion of the chart. Completed charts may be displayed in monotone or color, printed, plotted, or stored in the chartbase on the IBM PC. Once completed, the charts may be put in a vector format and plotted for color viewgraphs. The twelve graphics capabilities are divided into three groups: Forms, Structured Charts, and Block Diagrams. There are eight Forms available: 1) Bar/Line Charts, 2) Pie Charts, 3) Milestone Charts, 4) Resources Charts, 5) Earned Value Analysis Charts, 6) Progress/Effort Charts, 7) Travel/Training Charts, and 8) Trend Analysis Charts. There are three Structured Charts available: 1) Bullet Charts, 2) Organization Charts, and 3) Work Breakdown Structure (WBS) Charts. The Block Diagram available is an N x N Chart. Each graphics capability supports a chartbase. The OAP graphics database system provides the IBM PC user with an effective means of managing data which is best interpreted as a graphic display. The OAP graphics database system is written in IBM PASCAL 2.0 and assembler for interactive execution on an IBM PC or XT with at least 384K of memory, and a color graphics adapter and monitor. Printed charts require an Epson, IBM, OKIDATA, or HP Laser
Inferring network structure in non-normal and mixed discrete-continuous genomic data.
Bhadra, Anindya; Rao, Arvind; Baladandayuthapani, Veerabhadran
2018-03-01
Inferring dependence structure through undirected graphs is crucial for uncovering the major modes of multivariate interaction among high-dimensional genomic markers that are potentially associated with cancer. Traditionally, conditional independence has been studied using sparse Gaussian graphical models for continuous data and sparse Ising models for discrete data. However, there are two clear situations when these approaches are inadequate. The first occurs when the data are continuous but display non-normal marginal behavior such as heavy tails or skewness, rendering an assumption of normality inappropriate. The second occurs when a part of the data is ordinal or discrete (e.g., presence or absence of a mutation) and the other part is continuous (e.g., expression levels of genes or proteins). In this case, the existing Bayesian approaches typically employ a latent variable framework for the discrete part that precludes inferring conditional independence among the data that are actually observed. The current article overcomes these two challenges in a unified framework using Gaussian scale mixtures. Our framework is able to handle continuous data that are not normal and data that are of mixed continuous and discrete nature, while still being able to infer a sparse conditional sign independence structure among the observed data. Extensive performance comparison in simulations with alternative techniques and an analysis of a real cancer genomics data set demonstrate the effectiveness of the proposed approach. © 2017, The International Biometric Society.
Every finite strategic form game has at least one mixed strategy ...
Indian Academy of Sciences (India)
First page Back Continue Last page Overview Graphics. Every finite strategic form game has at least one mixed strategy Nash equilibrium. Every finite strategic form game has at least one mixed strategy Nash equilibrium.
High Dimensional Spectral Graph Theory and Non-backtracking Random Walks on Graphs
Kempton, Mark
This thesis has two primary areas of focus. First we study connection graphs, which are weighted graphs in which each edge is associated with a d-dimensional rotation matrix for some fixed dimension d, in addition to a scalar weight. Second, we study non-backtracking random walks on graphs, which are random walks with the additional constraint that they cannot return to the immediately previous state at any given step. Our work in connection graphs is centered on the notion of consistency, that is, the product of rotations moving from one vertex to another is independent of the path taken, and a generalization called epsilon-consistency. We present higher dimensional versions of the combinatorial Laplacian matrix and normalized Laplacian matrix from spectral graph theory, and give results characterizing the consistency of a connection graph in terms of the spectra of these matrices. We generalize several tools from classical spectral graph theory, such as PageRank and effective resistance, to apply to connection graphs. We use these tools to give algorithms for sparsification, clustering, and noise reduction on connection graphs. In non-backtracking random walks, we address the question raised by Alon et. al. concerning how the mixing rate of a non-backtracking random walk to its stationary distribution compares to the mixing rate for an ordinary random walk. Alon et. al. address this question for regular graphs. We take a different approach, and use a generalization of Ihara's Theorem to give a new proof of Alon's result for regular graphs, and to extend the result to biregular graphs. Finally, we give a non-backtracking version of Polya's Random Walk Theorem for 2-dimensional grids.
Group Design Problems in Engineering Design Graphics.
Kelley, David
2001-01-01
Describes group design techniques used within the engineering design graphics sequence at Western Washington University. Engineering and design philosophies such as concurrent engineering place an emphasis on group collaboration for the solving of design problems. (Author/DDR)
The Visual Communication or Graphic Communication Dilemma
Fecik, John T.
1975-01-01
The author reviews the history of communication and communications technology, considers differences between "visual communication" and "graphic communication," and comments on "seeds of revolution" in the industry. He offers four components of an educational structure or organization titled "graphic…
Heuristic attacks against graphical password generators
CSIR Research Space (South Africa)
Peach, S
2010-05-01
Full Text Available In this paper the authors explore heuristic attacks against graphical password generators. A new trend is emerging to use user clickable pictures to generate passwords. This technique of authentication can be successfully used for - for example...
Toolkit Design for Interactive Structured Graphics
National Research Council Canada - National Science Library
Bederson, Benjamin B; Grosjean, Jesse; Meyer, Jon
2003-01-01
.... We describe Jazz (a polylithic toolkit) and Piccolo (a monolithic toolkit), each of which we built to support interactive 2D structured graphics applications in general, and Zoomable User Interface applications in particular...
Graphical modelling software in R - status
DEFF Research Database (Denmark)
Detlefsen, Claus; Højsgaard, Søren; Lauritzen, Steffen L
2007-01-01
Graphical models in their modern form have been around for nearly a quarter of a century. Various computer programs for inference in graphical models have been developed over that period. Some examples of free software programs are BUGS (Thomas 1994), CoCo (Badsberg2001), Digram (Klein, Keiding......, and Kreiner 1995), MIM (Edwards 2000), and Tetrad (Glymour, Scheines, Spirtes, and Kelley 1987). The gR initiative (Lauritzen 2002) aims at making graphical models available in R (R Development Core Team 2006). A small grant from the Danish Science Foundation supported this initiative. We will summarize...... the results of the initiative so far. Specifically we will illustrate some of the R packages for graphical modelling currently on CRAN and discuss their strengths and weaknesses....
Graphic Designer/Production Coordinator | IDRC - International ...
International Development Research Centre (IDRC) Digital Library (Canada)
Provides design and graphic services for print- and Web-based publishing;; Initiates designs and carries out ... process, ensuring that such suppliers meet appropriate standards of quality and service at reasonable cost; ... Internal Services.
Full Plant STWAVE: SMS Graphical Interface
National Research Council Canada - National Science Library
McKee Smith, Jane; Zundel, Alan
2006-01-01
The purpose of this Coastal and Hydraulics Engineering Technical Note (CHETN) is to describe the graphical interface for the full-plane version of the wave model STWAVE (Smith et al. 2001; Smith 2001...
DDP-516 Computer Graphics System Capabilities
1972-06-01
This report describes the capabilities of the DDP-516 Computer Graphics System. One objective of this report is to acquaint DOT management and project planners with the system's current capabilities, applications hardware and software. The Appendix i...
Graphical passwords: a qualitative study of password patterns
CSIR Research Space (South Africa)
Vorster, J
2015-03-01
Full Text Available Graphical passwords schemas are becoming more main-stream. There are many different approaches to graphical passwords, each with its own drawbacks and advantages. There has been many studies to suggest that graphical passwords should be stronger...
History And Importance Of Graphic Design
Lyallya, Kirill
2016-01-01
This thesis is about history and importance of graphic design in different periods, from ancient times until today. The features inherent in different countries are considered. The techniques, basic methods for creating projects and computer software that designers have used are mentioned. In order to understand the importance of graphic design in our lives, it is considered from the side of ordinary people, how it manifests itself in daily lives and how it affects business. The thesis provid...
Rough surface scattering simulations using graphics cards
International Nuclear Information System (INIS)
Klapetek, Petr; Valtr, Miroslav; Poruba, Ales; Necas, David; Ohlidal, Miloslav
2010-01-01
In this article we present results of rough surface scattering calculations using a graphical processing unit implementation of the Finite Difference in Time Domain algorithm. Numerical results are compared to real measurements and computational performance is compared to computer processor implementation of the same algorithm. As a basis for computations, atomic force microscope measurements of surface morphology are used. It is shown that the graphical processing unit capabilities can be used to speedup presented computationally demanding algorithms without loss of precision.
Computer graphics in reactor safety analysis
International Nuclear Information System (INIS)
Fiala, C.; Kulak, R.F.
1989-01-01
This paper describes a family of three computer graphics codes designed to assist the analyst in three areas: the modelling of complex three-dimensional finite element models of reactor structures; the interpretation of computational results; and the reporting of the results of numerical simulations. The purpose and key features of each code are presented. The graphics output used in actual safety analysis are used to illustrate the capabilities of each code. 5 refs., 10 figs
Light reflection models for computer graphics.
Greenberg, D P
1989-04-14
During the past 20 years, computer graphic techniques for simulating the reflection of light have progressed so that today images of photorealistic quality can be produced. Early algorithms considered direct lighting only, but global illumination phenomena with indirect lighting, surface interreflections, and shadows can now be modeled with ray tracing, radiosity, and Monte Carlo simulations. This article describes the historical development of computer graphic algorithms for light reflection and pictorially illustrates what will be commonly available in the near future.
Computer communications and graphics for clinical radiology
International Nuclear Information System (INIS)
Rhodes, M.L.; Azzawi, Y.; Tivattanasuk, E.S.; Pang, A.T.; Ly, K.; Panicker, H.
1985-01-01
Computer graphics has many forms. When applied in medicine, it can range from simple two dimensional charts and graphs to rendering of three-dimensional scenes. Computer graphic displays of molecular or large anatomic structures have been used to great advantage by numerous medical researchers. In addition, graphic presentations can be dynamic where displays are controlled by physician-user commands, or the presentations can be static, where views are recorded in discrete frames for later distribution or permanent archival. In medicine both interactive and static forms of computer graphics have their proper place in the effective delivery of health care. Computer graphics, however, changes constantly in the area of software techniques, hardware improvements and its clinical application. What may be medically appropriate today in the use of computer graphics can soon become inadequate and well behind the new advances that so quickly follow. In this paper the key feature of computer communication is discussed that aids in the clinical utility of computer graphics in medicine. It is distribution. Distribution in terms of instantaneous computer graphic software updates and more importantly, distribution of meaningful three-dimensional presentations to referring physicians. Physicians who, working in their private offices, have no routine access to medical work stations. In this environment three dimensional presentations of anatomy are static in nature, but must deliver realistic views of critical structures. This paper outlines how computer communication provides the essential ingredient to the provision of this service. As an illustration, the electronic distribution of software to generate three dimensional views of complex anatomoic structures is discussed. Sample views are included
Anthroposcenes: Towards an Environmental Graphic Novel
Directory of Open Access Journals (Sweden)
Laura Perry
2018-02-01
Full Text Available In this article, I consider how two contemporary graphic novels, Richard McGuire’s 'Here' (2015 and Lauren Redniss’s 'Radioactive' (2010, take up the challenge posed by the Anthropocene to represent both geologic and human scales. I argue that graphic novels prove a fruitful site for investigating the capacities of both visual art and literature to respond to such a refiguring of the boundaries of the human subject and narrative. While the most commercially popular and frequently studied texts in climate fiction tend to be novels or films, I turn from considering the patterns of genre fiction to the affordances of form. I explore how the conceptual and aesthetic frameworks of the graphic novel form encompass environmental phenomena that are often difficult to visualize elsewhere, stretching beyond human perspectives. In particular, I show how the aesthetics of temporality, or visual time, in graphic novels encourages readings that take notice of the nonhuman presence in plots and narrative events. In arguing for the environmental, more-than-human implications of visual time in the graphic novel form, I focus on how representations of domestic habits and daily routines in 'Here' and 'Radioactive' are articulated within and implicated by unruly scales of time and space (too small and too large to contain. I argue that the aesthetics of time in the form of the graphic novel address representational challenges central to the Anthropocene, environmental justice, and slow violence, in particular, the mediation between the planetary and the domestic.
A Linux Workstation for High Performance Graphics
Geist, Robert; Westall, James
2000-01-01
The primary goal of this effort was to provide a low-cost method of obtaining high-performance 3-D graphics using an industry standard library (OpenGL) on PC class computers. Previously, users interested in doing substantial visualization or graphical manipulation were constrained to using specialized, custom hardware most often found in computers from Silicon Graphics (SGI). We provided an alternative to expensive SGI hardware by taking advantage of third-party, 3-D graphics accelerators that have now become available at very affordable prices. To make use of this hardware our goal was to provide a free, redistributable, and fully-compatible OpenGL work-alike library so that existing bodies of code could simply be recompiled. for PC class machines running a free version of Unix. This should allow substantial cost savings while greatly expanding the population of people with access to a serious graphics development and viewing environment. This should offer a means for NASA to provide a spectrum of graphics performance to its scientists, supplying high-end specialized SGI hardware for high-performance visualization while fulfilling the requirements of medium and lower performance applications with generic, off-the-shelf components and still maintaining compatibility between the two.
Picturing Illness: History, Poetics, and Graphic Medicine
Directory of Open Access Journals (Sweden)
Raghavi Ravi Kasthuri
2015-05-01
Full Text Available Comics have often been treated as a juvenile and sub-literary art form; however, taking cues from the new-found cultural acceptance of comics, particularly with the publication of Art Spiegelman’s Maus (1986, Chris Ware’s Jimmy Corrigan: The Smartest Kid on Earth (2000, and Alison Bechdel’s Fun Home: A Family Tragedy (2006, there have emerged, over the past decade, a new breed of comics dealing with the patient/caregivers’ experiences, perspectives and identities. Christened as graphic medicine, these illness narratives use comics as a medium to address wide ranging disease/illness related issues. The present review examines the following issues: What is graphic medicine? Is there a tangible relationship between underground comics and graphic medicine? If so, can we regard underground comics as historical precedent to graphic medicine? What are the uses of comics in medicine? Broadly put, drawing examples from various graphic medical narratives, the paper seeks to trace the history and poetics of graphic medicine.
Designing Raster Cells as the Basis for Developing Personal Graphic Language
Directory of Open Access Journals (Sweden)
Jana Z. Vujić
2011-05-01
Full Text Available Continuous work in creating new designer solutions points towards the need to create personal routines as personalcommunication in the relation comprising design, algorithms, and original computer graphics. This paper showsprocedures for developing a control language for creating graphic designs with individual raster elements (screeningelement obtaint by halftoning. Personal commands should set routines in a language understood by the printer andthe designer. The PostScript basis is used because we mix vector and pixel graphics in the same program stream, aswell as different colour systems, and our own raster forms. The printing raster is set with the target of special designmulti-use, and this includes the field of security graphics and art computer reproduction. Each raster form assumesmodifications, creating their raster family. The raster cell content is transformed with PostScript, allowing the settingof basic values, angle and liniature for each pixel separately. Raster cells are mixed in multi-colour graphics to thelevel of individual designs with variable values of parameters determining them.
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
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
Garashchuk, Sophya; Rassolov, Vitaly A
2008-07-14
Semiclassical implementation of the quantum trajectory formalism [J. Chem. Phys. 120, 1181 (2004)] is further developed to give a stable long-time description of zero-point energy in anharmonic systems of high dimensionality. The method is based on a numerically cheap linearized quantum force approach; stabilizing terms compensating for the linearization errors are added into the time-evolution equations for the classical and nonclassical components of the momentum operator. The wave function normalization and energy are rigorously conserved. Numerical tests are performed for model systems of up to 40 degrees of freedom.
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.
Directory of Open Access Journals (Sweden)
Saumyadipta Pyne
Full Text Available In biomedical applications, an experimenter encounters different potential sources of variation in data such as individual samples, multiple experimental conditions, and multivariate responses of a panel of markers such as from a signaling network. In multiparametric cytometry, which is often used for analyzing patient samples, such issues are critical. While computational methods can identify cell populations in individual samples, without the ability to automatically match them across samples, it is difficult to compare and characterize the populations in typical experiments, such as those responding to various stimulations or distinctive of particular patients or time-points, especially when there are many samples. Joint Clustering and Matching (JCM is a multi-level framework for simultaneous modeling and registration of populations across a cohort. JCM models every population with a robust multivariate probability distribution. Simultaneously, JCM fits a random-effects model to construct an overall batch template--used for registering populations across samples, and classifying new samples. By tackling systems-level variation, JCM supports practical biomedical applications involving large cohorts. Software for fitting the JCM models have been implemented in an R package EMMIX-JCM, available from http://www.maths.uq.edu.au/~gjm/mix_soft/EMMIX-JCM/.
Animated graphics for comparing two risks: a cautionary tale.
Zikmund-Fisher, Brian J; Witteman, Holly O; Fuhrel-Forbis, Andrea; Exe, Nicole L; Kahn, Valerie C; Dickson, Mark
2012-07-25
The increasing use of computer-administered risk communications affords the potential to replace static risk graphics with animations that use motion cues to reinforce key risk messages. Research on the use of animated graphics, however, has yielded mixed findings, and little research exists to identify the specific animations that might improve risk knowledge and patients' decision making. To test whether viewing animated forms of standard pictograph (icon array) risk graphics displaying risks of side effects would improve people's ability to select the treatment with the lowest risk profile, as compared with viewing static images of the same risks. A total of 4198 members of a demographically diverse Internet panel read a scenario about two hypothetical treatments for thyroid cancer. Each treatment was described as equally effective but varied in side effects (with one option slightly better than the other). Participants were randomly assigned to receive all risk information in 1 of 10 pictograph formats in a quasi-factorial design. We compared a control condition of static grouped icons with a static scattered icon display and with 8 Flash-based animated versions that incorporated different combinations of (1) building the risk 1 icon at a time, (2) having scattered risk icons settle into a group, or (3) having scattered risk icons shuffle themselves (either automatically or by user control). We assessed participants' ability to choose the better treatment (choice accuracy), their gist knowledge of side effects (knowledge accuracy), and their graph evaluation ratings, controlling for subjective numeracy and need for cognition. When compared against static grouped-icon arrays, no animations significantly improved any outcomes, and most showed significant performance degradations. However, participants who received animations of grouped icons in which at-risk icons appeared 1 at a time performed as well on all outcomes as the static grouped-icon control group
Prospective Validation of a High Dimensional Shape Model for Organ Motion in Intact Cervical Cancer
Energy Technology Data Exchange (ETDEWEB)
Williamson, Casey W.; Green, Garrett; Noticewala, Sonal S.; Li, Nan; Shen, Hanjie [Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, California (United States); Vaida, Florin [Division of Biostatistics and Bioinformatics, Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, California (United States); Mell, Loren K., E-mail: lmell@ucsd.edu [Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, California (United States)
2016-11-15
Purpose: Validated models are needed to justify strategies to define planning target volumes (PTVs) for intact cervical cancer used in clinical practice. Our objective was to independently validate a previously published shape model, using data collected prospectively from clinical trials. Methods and Materials: We analyzed 42 patients with intact cervical cancer treated with daily fractionated pelvic intensity modulated radiation therapy and concurrent chemotherapy in one of 2 prospective clinical trials. We collected online cone beam computed tomography (CBCT) scans before each fraction. Clinical target volume (CTV) structures from the planning computed tomography scan were cast onto each CBCT scan after rigid registration and manually redrawn to account for organ motion and deformation. We applied the 95% isodose cloud from the planning computed tomography scan to each CBCT scan and computed any CTV outside the 95% isodose cloud. The primary aim was to determine the proportion of CTVs that were encompassed within the 95% isodose volume. A 1-sample t test was used to test the hypothesis that the probability of complete coverage was different from 95%. We used mixed-effects logistic regression to assess effects of time and patient variability. Results: The 95% isodose line completely encompassed 92.3% of all CTVs (95% confidence interval, 88.3%-96.4%), not significantly different from the 95% probability anticipated a priori (P=.19). The overall proportion of missed CTVs was small: the grand mean of covered CTVs was 99.9%, and 95.2% of misses were located in the anterior body of the uterus. Time did not affect coverage probability (P=.71). Conclusions: With the clinical implementation of a previously proposed PTV definition strategy based on a shape model for intact cervical cancer, the probability of CTV coverage was high and the volume of CTV missed was low. This PTV expansion strategy is acceptable for clinical trials and practice; however, we recommend daily
DbAccess: Interactive Statistics and Graphics for Plasma Physics Databases
International Nuclear Information System (INIS)
Davis, W.; Mastrovito, D.
2003-01-01
DbAccess is an X-windows application, written in IDL(reg s ign), meeting many specialized statistical and graphical needs of NSTX [National Spherical Torus Experiment] plasma physicists, such as regression statistics and the analysis of variance. Flexible ''views'' and ''joins,'' which include options for complex SQL expressions, facilitate mixing data from different database tables. General Atomics Plot Objects add extensive graphical and interactive capabilities. An example is included for plasma confinement-time scaling analysis using a multiple linear regression least-squares power fit
Directory of Open Access Journals (Sweden)
Omid Hamidi
2014-01-01
Full Text Available Microarray technology results in high-dimensional and low-sample size data sets. Therefore, fitting sparse models is substantial because only a small number of influential genes can reliably be identified. A number of variable selection approaches have been proposed for high-dimensional time-to-event data based on Cox proportional hazards where censoring is present. The present study applied three sparse variable selection techniques of Lasso, smoothly clipped absolute deviation and the smooth integration of counting, and absolute deviation for gene expression survival time data using the additive risk model which is adopted when the absolute effects of multiple predictors on the hazard function are of interest. The performances of used techniques were evaluated by time dependent ROC curve and bootstrap .632+ prediction error curves. The selected genes by all methods were highly significant (P<0.001. The Lasso showed maximum median of area under ROC curve over time (0.95 and smoothly clipped absolute deviation showed the lowest prediction error (0.105. It was observed that the selected genes by all methods improved the prediction of purely clinical model indicating the valuable information containing in the microarray features. So it was concluded that used approaches can satisfactorily predict survival based on selected gene expression measurements.
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.
Ren, Jie; He, Tao; Li, Ye; Liu, Sai; Du, Yinhao; Jiang, Yu; Wu, Cen
2017-05-16
Over the past decades, the prevalence of type 2 diabetes mellitus (T2D) has been steadily increasing around the world. Despite large efforts devoted to better understand the genetic basis of the disease, the identified susceptibility loci can only account for a small portion of the T2D heritability. Some of the existing approaches proposed for the high dimensional genetic data from the T2D case-control study are limited by analyzing a few number of SNPs at a time from a large pool of SNPs, by ignoring the correlations among SNPs and by adopting inefficient selection techniques. We propose a network constrained regularization method to select important SNPs by taking the linkage disequilibrium into account. To accomodate the case control study, an iteratively reweighted least square algorithm has been developed within the coordinate descent framework where optimization of the regularized logistic loss function is performed with respect to one parameter at a time and iteratively cycle through all the parameters until convergence. In this article, a novel approach is developed to identify important SNPs more effectively through incorporating the interconnections among them in the regularized selection. A coordinate descent based iteratively reweighed least squares (IRLS) algorithm has been proposed. Both the simulation study and the analysis of the Nurses's Health Study, a case-control study of type 2 diabetes data with high dimensional SNP measurements, demonstrate the advantage of the network based approach over the competing alternatives.
International Nuclear Information System (INIS)
Grossman, Y.
1997-10-01
In supersymmetric models with nonvanishing Majorana neutrino masses, the sneutrino and antisneutrino mix. The conditions under which this mixing is experimentally observable are studied, and mass-splitting of the sneutrino mass eigenstates and sneutrino oscillation phenomena are analyzed
Interactive voxel graphics in virtual reality
Brody, Bill; Chappell, Glenn G.; Hartman, Chris
2002-06-01
Interactive voxel graphics in virtual reality poses significant research challenges in terms of interface, file I/O, and real-time algorithms. Voxel graphics is not so new, as it is the focus of a good deal of scientific visualization. Interactive voxel creation and manipulation is a more innovative concept. Scientists are understandably reluctant to manipulate data. They collect or model data. A scientific analogy to interactive graphics is the generation of initial conditions for some model. It is used as a method to test those models. We, however, are in the business of creating new data in the form of graphical imagery. In our endeavor, science is a tool and not an end. Nevertheless, there is a whole class of interactions and associated data generation scenarios that are natural to our way of working and that are also appropriate to scientific inquiry. Annotation by sketching or painting to point to and distinguish interesting and important information is very significant for science as well as art. Annotation in 3D is difficult without a good 3D interface. Interactive graphics in virtual reality is an appropriate approach to this problem.
Graphical calculus for Gaussian pure states
International Nuclear Information System (INIS)
Menicucci, Nicolas C.; Flammia, Steven T.; Loock, Peter van
2011-01-01
We provide a unified graphical calculus for all Gaussian pure states, including graph transformation rules for all local and semilocal Gaussian unitary operations, as well as local quadrature measurements. We then use this graphical calculus to analyze continuous-variable (CV) cluster states, the essential resource for one-way quantum computing with CV systems. Current graphical approaches to CV cluster states are only valid in the unphysical limit of infinite squeezing, and the associated graph transformation rules only apply when the initial and final states are of this form. Our formalism applies to all Gaussian pure states and subsumes these rules in a natural way. In addition, the term 'CV graph state' currently has several inequivalent definitions in use. Using this formalism we provide a single unifying definition that encompasses all of them. We provide many examples of how the formalism may be used in the context of CV cluster states: defining the 'closest' CV cluster state to a given Gaussian pure state and quantifying the error in the approximation due to finite squeezing; analyzing the optimality of certain methods of generating CV cluster states; drawing connections between this graphical formalism and bosonic Hamiltonians with Gaussian ground states, including those useful for CV one-way quantum computing; and deriving a graphical measure of bipartite entanglement for certain classes of CV cluster states. We mention other possible applications of this formalism and conclude with a brief note on fault tolerance in CV one-way quantum computing.
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.
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.
The graphics future in scientific applications-trends and developments in computer graphics
Enderle, G
1982-01-01
Computer graphics methods and tools are being used to a great extent in scientific research. The future development in this area will be influenced both by new hardware developments and by software advances. On the hardware sector, the development of the raster technology will lead to the increased use of colour workstations with more local processing power. Colour hardcopy devices for creating plots, slides, or movies will be available at a lower price than today. The first real 3D-workstations will appear on the marketplace. One of the main activities on the software sector is the standardization of computer graphics systems, graphical files, and device interfaces. This will lead to more portable graphical application programs and to a common base for computer graphics education.
International Nuclear Information System (INIS)
Mueller, R.
1992-01-01
A new control system based on a distributed computing environment is gradually installed at BESSY, a 800 MeV storage ring dedicated to the generation of synchrotron light in the VUV and soft X-ray region. The new operator consoles are large high resolution, bitmap oriented color graphic screens with mouse and keyboard. A new graphical user interface has been developed with a user interface management system. A graphics server encapsulates completely representational aspects, mediates between user interactions and application variables and takes care of a consistent state of graphical and applicational objects. Graphical representations, semantics of user interactions and interpreter instructions are defined in a database written in a simple and comprehensible user interface definition language. (R.P.) 7 refs.; 5 figs
Formal Analysis of Graphical Security Models
DEFF Research Database (Denmark)
Aslanyan, Zaruhi
, software components and human actors interacting with each other to form so-called socio-technical systems. The importance of socio-technical systems to modern societies requires verifying their security properties formally, while their inherent complexity makes manual analyses impracticable. Graphical...... models for security offer an unrivalled opportunity to describe socio-technical systems, for they allow to represent different aspects like human behaviour, computation and physical phenomena in an abstract yet uniform manner. Moreover, these models can be assigned a formal semantics, thereby allowing...... formal verification of their properties. Finally, their appealing graphical notations enable to communicate security concerns in an understandable way also to non-experts, often in charge of the decision making. This dissertation argues that automated techniques can be developed on graphical security...
Graphical Model Theory for Wireless Sensor Networks
International Nuclear Information System (INIS)
Davis, William B.
2002-01-01
Information processing in sensor networks, with many small processors, demands a theory of computation that allows the minimization of processing effort, and the distribution of this effort throughout the network. Graphical model theory provides a probabilistic theory of computation that explicitly addresses complexity and decentralization for optimizing network computation. The junction tree algorithm, for decentralized inference on graphical probability models, can be instantiated in a variety of applications useful for wireless sensor networks, including: sensor validation and fusion; data compression and channel coding; expert systems, with decentralized data structures, and efficient local queries; pattern classification, and machine learning. Graphical models for these applications are sketched, and a model of dynamic sensor validation and fusion is presented in more depth, to illustrate the junction tree algorithm
CT applications of medical computer graphics
International Nuclear Information System (INIS)
Rhodes, M.L.
1985-01-01
Few applications of computer graphics show as much promise and early success as that for CT. Unlike electron microscopy, ultrasound, business, military, and animation applications, CT image data are inherently digital. CT pictures can be processed directly by programs well established in the fields of computer graphics and digital image processing. Methods for reformatting digital pictures, enhancing structure shape, reducing image noise, and rendering three-dimensional (3D) scenes of anatomic structures have all become routine at many CT centers. In this chapter, the authors provide a brief introduction to computer graphics terms and techniques commonly applied to CT pictures and, when appropriate, to those showing promise for magnetic resonance images. Topics discussed here are image-processing options that are applied to digital images already constructed. In the final portion of this chapter techniques for ''slicing'' CT image data are presented, and geometric principles that describe the specification of oblique and curved images are outlined. Clinical examples are included
Graphics supercomputer for computational fluid dynamics research
Liaw, Goang S.
1994-11-01
The objective of this project is to purchase a state-of-the-art graphics supercomputer to improve the Computational Fluid Dynamics (CFD) research capability at Alabama A & M University (AAMU) and to support the Air Force research projects. A cutting-edge graphics supercomputer system, Onyx VTX, from Silicon Graphics Computer Systems (SGI), was purchased and installed. Other equipment including a desktop personal computer, PC-486 DX2 with a built-in 10-BaseT Ethernet card, a 10-BaseT hub, an Apple Laser Printer Select 360, and a notebook computer from Zenith were also purchased. A reading room has been converted to a research computer lab by adding some furniture and an air conditioning unit in order to provide an appropriate working environments for researchers and the purchase equipment. All the purchased equipment were successfully installed and are fully functional. Several research projects, including two existing Air Force projects, are being performed using these facilities.
Design considerations for parallel graphics libraries
Crockett, Thomas W.
1994-01-01
Applications which run on parallel supercomputers are often characterized by massive datasets. Converting these vast collections of numbers to visual form has proven to be a powerful aid to comprehension. For a variety of reasons, it may be desirable to provide this visual feedback at runtime. One way to accomplish this is to exploit the available parallelism to perform graphics operations in place. In order to do this, we need appropriate parallel rendering algorithms and library interfaces. This paper provides a tutorial introduction to some of the issues which arise in designing parallel graphics libraries and their underlying rendering algorithms. The focus is on polygon rendering for distributed memory message-passing systems. We illustrate our discussion with examples from PGL, a parallel graphics library which has been developed on the Intel family of parallel systems.
Graphic Design for the Real World?
DEFF Research Database (Denmark)
Elisabeth Bichler, Katrine; Beier, Sofie
2016-01-01
This article examines graphic design’s role within design activism. It outlines design activism in general and its relation to commercial design culture in a consumerist economy. Thereafter it discusses persuasive tendencies in graphic design and questions if its current contribution to design...... activism is limited to its predominant narrow role of persuading for “the good cause.” To illustrate the hypothesis that such a persuasive approach lacks activist potential and thus social impact, cases that represent traditional graphic-design activism are compared to alternative approaches...... solely solving communicative problems for commissioning clients. It is argued that in this way visual communication can intervene into problems on a functional level, similarly to artifacts from design disciplines such as architecture and industrial or product design....
Network performance for graphical control systems
International Nuclear Information System (INIS)
Clout, P.; Geib, M.; Westervelt, R.
1992-01-01
Vsystem is a toolbox for building graphically-based control systems. The real-tiem database component, Vaccess, includes all the networking support necessary to build multi-computer control systems. Vaccess has two modes of database access, synchronous and asynchronous. Vdraw is another component of Vsystem that allows developers and users to develop control screens and windows by drawing rather than programming. Based on X-windows, Vsystem provides the possibility of running Vdraw either on the workstation with the graphics or on the computer with the database. We have made some measurements on the cpu loading, elapsed time and the network loading to give some guidance in system configuration performance. It will be seen that asynchronous network access gives large performance increases and that the network database change notification protocol can be either more or less efficient than the X-window network protocol, depending on the graphical representation of the data. (author)
A memorable reading experience with motion graphics
Sandhu, Cecilia
2017-01-01
Studien omfattar ämnet motion graphics (rörlig grafik) och hur det kan förmedla en skriven text. En textbaserad rörlig grafik togs fram för att undersöka om rörelserna gjorde textinnehållet lättare att minnas och förstå jämfört med att läsa texten statiskt. Skillnader men också likheter gällande korttidsminne och läsupplevelse undersöktes genom två testgrupper. Studien visar på att läsarna mindes bättre med motion graphics, att motion graphics i vissa fall kan förmedla en mer positiv och spec...
Data Sorting Using Graphics Processing Units
Directory of Open Access Journals (Sweden)
M. J. Mišić
2012-06-01
Full Text Available Graphics processing units (GPUs have been increasingly used for general-purpose computation in recent years. The GPU accelerated applications are found in both scientific and commercial domains. Sorting is considered as one of the very important operations in many applications, so its efficient implementation is essential for the overall application performance. This paper represents an effort to analyze and evaluate the implementations of the representative sorting algorithms on the graphics processing units. Three sorting algorithms (Quicksort, Merge sort, and Radix sort were evaluated on the Compute Unified Device Architecture (CUDA platform that is used to execute applications on NVIDIA graphics processing units. Algorithms were tested and evaluated using an automated test environment with input datasets of different characteristics. Finally, the results of this analysis are briefly discussed.
Visualization of graphical information fusion results
Blasch, Erik; Levchuk, Georgiy; Staskevich, Gennady; Burke, Dustin; Aved, Alex
2014-06-01
Graphical fusion methods are popular to describe distributed sensor applications such as target tracking and pattern recognition. Additional graphical methods include network analysis for social, communications, and sensor management. With the growing availability of various data modalities, graphical fusion methods are widely used to combine data from multiple sensors and modalities. To better understand the usefulness of graph fusion approaches, we address visualization to increase user comprehension of multi-modal data. The paper demonstrates a use case that combines graphs from text reports and target tracks to associate events and activities of interest visualization for testing Measures of Performance (MOP) and Measures of Effectiveness (MOE). The analysis includes the presentation of the separate graphs and then graph-fusion visualization for linking network graphs for tracking and classification.
Graphic medicine: comics as medical narrative.
Williams, Ian C M
2012-06-01
Among the growing number of works of graphic fiction, a number of titles dealing directly with the patient experience of illness or caring for others with an illness are to be found. Thanks in part to the Medical Humanities movement, many medical schools now encourage the reading of classic literature to gain insight into the human condition. Until recently, the medium of comics (the term is used in the plural to refer to both the physical objects and the attendant philosophy and practice surrounding them) has received little attention from healthcare scholars, even though some authors argue that graphic fiction is, in fact, a form of literature. This paper suggests that it is time that the medium was examined by healthcare professionals and studies some acclaimed comic works. Drawing on the principles of narrative medicine, this paper will ask whether comics and graphic novels could be used as a resource for health professionals, patients and carers.
Computer graphics aid mission operations. [NASA missions
Jeletic, James F.
1990-01-01
The application of computer graphics techniques in NASA space missions is reviewed. Telemetric monitoring of the Space Shuttle and its components is discussed, noting the use of computer graphics for real-time visualization problems in the retrieval and repair of the Solar Maximum Mission. The use of the world map display for determining a spacecraft's location above the earth and the problem of verifying the relative position and orientation of spacecraft to celestial bodies are examined. The Flight Dynamics/STS Three-dimensional Monitoring System and the Trajectroy Computations and Orbital Products System world map display are described, emphasizing Space Shuttle applications. Also, consideration is given to the development of monitoring systems such as the Shuttle Payloads Mission Monitoring System and the Attitude Heads-Up Display and the use of the NASA-Goddard Two-dimensional Graphics Monitoring System during Shuttle missions and to support the Hubble Space Telescope.
Engineering Design Graphics: Into the 21st Century
Harris, La Verne Abe; Meyers, Frederick
2007-01-01
Graphical plans for construction of machinery and architecture have evolved over the last 6,000 years beginning from hieroglyphics to drawings on printable media, from the "Golden Age" of engineering graphics to the innovation of computer graphics and prototyping. The evolution of engineering design graphics as a profession has also evolved. Years…
An Agent Framework for Recognition of Graphic Units in Drawings
Achten, H.H.; Jessurun, A.J.; Koszewski, K.; Wrona, S.
2002-01-01
Architects use graphic conventions in their drawings that have meaningful content to the design task. In previous work, such well-defined sets of graphic entities have been identified and defined. These sets are called graphic units. In this paper, we discuss how graphic unit recognition in drawings
Analysis of graphic representation ability in oscillation phenomena
Dewi, A. R. C.; Putra, N. M. D.; Susilo
2018-03-01
This study aims to investigates how the ability of students to representation graphs of linear function and harmonic function in understanding of oscillation phenomena. Method of this research used mix methods with concurrent embedded design. The subjects were 35 students of class X MIA 3 SMA 1 Bae Kudus. Data collection through giving essays and interviews that lead to the ability to read and draw graphs in material of Hooke's law and oscillation characteristics. The results of study showed that most of the students had difficulty in drawing graph of linear function and harmonic function of deviation with time. Students’ difficulties in drawing the graph of linear function is the difficulty of analyzing the variable data needed in graph making, confusing the placement of variable data on the coordinate axis, the difficulty of determining the scale interval on each coordinate, and the variation of how to connect the dots forming the graph. Students’ difficulties in representing the graph of harmonic function is to determine the time interval of sine harmonic function, the difficulty to determine the initial deviation point of the drawing, the difficulty of finding the deviation equation of the case of oscillation characteristics and the confusion to different among the maximum deviation (amplitude) with the length of the spring caused the load.Complexity of the characteristic attributes of the oscillation phenomena graphs, students tend to show less well the ability of graphical representation of harmonic functions than the performance of the graphical representation of linear functions.
Efficiently adapting graphical models for selectivity estimation
DEFF Research Database (Denmark)
Tzoumas, Kostas; Deshpande, Amol; Jensen, Christian S.
2013-01-01
cardinality estimation without making the independence assumption. By carefully using concepts from the field of graphical models, we are able to factor the joint probability distribution over all the attributes in the database into small, usually two-dimensional distributions, without a significant loss...... in estimation accuracy. We show how to efficiently construct such a graphical model from the database using only two-way join queries, and we show how to perform selectivity estimation in a highly efficient manner. We integrate our algorithms into the PostgreSQL DBMS. Experimental results indicate...
Graphical Model Debugger Framework for Embedded Systems
DEFF Research Database (Denmark)
Zeng, Kebin
2010-01-01
Model Driven Software Development has offered a faster way to design and implement embedded real-time software by moving the design to a model level, and by transforming models to code. However, the testing of embedded systems has remained at the code level. This paper presents a Graphical Model...... Debugger Framework, providing an auxiliary avenue of analysis of system models at runtime by executing generated code and updating models synchronously, which allows embedded developers to focus on the model level. With the model debugger, embedded developers can graphically test their design model...
Development of INFRA graphic user interface
International Nuclear Information System (INIS)
Yang, Y. S.; Lee, C. B.; Kim, Y. M.; Kim, D. H.; Kim, S. K.
2004-01-01
GUI(Graphic User Interface) has been developed for high burnup fuel performance code INFRA. Based upon FORTRAN program language, INFRA was developed by COMPAQ Visual FORTRAN 6.5. Graphic user input and output interface have been developed by using Visual Basic and MDB which are the most widely used program language and database for windows application development. Various input parameters, which are required for INFRA calculation, can be input more conveniently by newly developed input interface. Without any additional data handling, INFRA calculation results can be investigated intuitively by 2D or 3D graphs on screen and animation function
Object-oriented graphics programming in C++
Stevens, Roger T
2014-01-01
Object-Oriented Graphics Programming in C++ provides programmers with the information needed to produce realistic pictures on a PC monitor screen.The book is comprised of 20 chapters that discuss the aspects of graphics programming in C++. The book starts with a short introduction discussing the purpose of the book. It also includes the basic concepts of programming in C++ and the basic hardware requirement. Subsequent chapters cover related topics in C++ programming such as the various display modes; displaying TGA files, and the vector class. The text also tackles subjects on the processing
Fast Gridding on Commodity Graphics Hardware
DEFF Research Database (Denmark)
Sørensen, Thomas Sangild; Schaeffter, Tobias; Noe, Karsten Østergaard
2007-01-01
is the far most time consuming of the three steps (Table 1). Modern graphics cards (GPUs) can be utilised as a fast parallel processor provided that algorithms are reformulated in a parallel solution. The purpose of this work is to test the hypothesis, that a non-cartesian reconstruction can be efficiently...... implemented on graphics hardware giving a significant speedup compared to CPU based alternatives. We present a novel GPU implementation of the convolution step that overcomes the problems of memory bandwidth that has limited the speed of previous GPU gridding algorithms [2]....
A graphics based remote handling control system
International Nuclear Information System (INIS)
Leinemann, K.
1984-08-01
A control and simulation system with an interactive graphic man-machine interface is proposed for the articulated boom in JET. The system shall support 1. the study of boom movements in the planning phase, 2. the training of operators by appropriate simulations, 3. the programming of boom movements, and 4. the on-line control of the boom. A combination of computer graphic display and TV-images is proposed for providing optimum recognition of the actual situation and for echoing to the operator actions. (orig.) [de
Alarm annunciation in a graphical environment
International Nuclear Information System (INIS)
Adams, D.G.
1994-01-01
Well-designed graphical user interfaces, such as Microsoft reg-sign Windows trademark or UNIX trademark--based X-Windows reg-sign, provide a capability for enhanced display of security alarm information. Conversely, a poorly designed interface can quickly overwhelm an operator. This paper describes types of graphical information that can be displayed and offers guidance on how to best display that information. Limits are proposed for the complexity of the user interface, and guidelines are suggested for the display of maps and sensors
Alonso Valdivieso, Concepción
2016-01-01
[EN] What exactly are ‘Motion Graphics’? Many people still aren’t entirely sure. Taken literally they are just graphics in motion, but they also mean much more than that. Motion graphics use incredibly expressive techniques and as a result are often found in advertising, corporate videos, credit sequences, etc. They can translate a complex idea into a clear message with just a few seconds of animation.At the same time they have a very particular, simple and stylish aesthetic. But, if any anim...
International Nuclear Information System (INIS)
Brochard, P.; Colin De Verdiere, G.; Nomine, J.P.; Perros, J.P.
1993-01-01
Within the framework of the development of a new version of the Psyche software, the author reports a benchmark study on different graphical libraries and systems and on the Psyche application. The author outlines the current context of development of graphical tools which still lacks of standardisation. This makes the comparison somehow limited and finally related to envisaged applications. The author presents the various systems and libraries, test principles, and characteristics of machines. Results and interpretations are then presented with reference to faced problems
Building probabilistic graphical models with Python
Karkera, Kiran R
2014-01-01
This is a short, practical guide that allows data scientists to understand the concepts of Graphical models and enables them to try them out using small Python code snippets, without being too mathematically complicated. If you are a data scientist who knows about machine learning and want to enhance your knowledge of graphical models, such as Bayes network, in order to use them to solve real-world problems using Python libraries, this book is for you. This book is intended for those who have some Python and machine learning experience, or are exploring the machine learning field.
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
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.)
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.
Directory of Open Access Journals (Sweden)
F. C. Cooper
2013-04-01
Full Text Available The fluctuation-dissipation theorem (FDT has been proposed as a method of calculating the response of the earth's atmosphere to a forcing. For this problem the high dimensionality of the relevant data sets makes truncation necessary. Here we propose a method of truncation based upon the assumption that the response to a localised forcing is spatially localised, as an alternative to the standard method of choosing a number of the leading empirical orthogonal functions. For systems where this assumption holds, the response to any sufficiently small non-localised forcing may be estimated using a set of truncations that are chosen algorithmically. We test our algorithm using 36 and 72 variable versions of a stochastic Lorenz 95 system of ordinary differential equations. We find that, for long integrations, the bias in the response estimated by the FDT is reduced from ~75% of the true response to ~30%.
Wang, Xueyi
2012-02-08
The k-nearest neighbors (k-NN) algorithm is a widely used machine learning method that finds nearest neighbors of a test object in a feature space. We present a new exact k-NN algorithm called kMkNN (k-Means for k-Nearest Neighbors) that uses the k-means clustering and the triangle inequality to accelerate the searching for nearest neighbors in a high dimensional space. The kMkNN algorithm has two stages. In the buildup stage, instead of using complex tree structures such as metric trees, kd-trees, or ball-tree, kMkNN uses a simple k-means clustering method to preprocess the training dataset. In the searching stage, given a query object, kMkNN finds nearest training objects starting from the nearest cluster to the query object and uses the triangle inequality to reduce the distance calculations. Experiments show that the performance of kMkNN is surprisingly good compared to the traditional k-NN algorithm and tree-based k-NN algorithms such as kd-trees and ball-trees. On a collection of 20 datasets with up to 10(6) records and 10(4) dimensions, kMkNN shows a 2-to 80-fold reduction of distance calculations and a 2- to 60-fold speedup over the traditional k-NN algorithm for 16 datasets. Furthermore, kMkNN performs significant better than a kd-tree based k-NN algorithm for all datasets and performs better than a ball-tree based k-NN algorithm for most datasets. The results show that kMkNN is effective for searching nearest neighbors in high dimensional spaces.
Fast Sparse Level Sets on Graphics Hardware
Jalba, Andrei C.; Laan, Wladimir J. van der; Roerdink, Jos B.T.M.
The level-set method is one of the most popular techniques for capturing and tracking deformable interfaces. Although level sets have demonstrated great potential in visualization and computer graphics applications, such as surface editing and physically based modeling, their use for interactive
Codesign Analysis of a Computer Graphics Application
DEFF Research Database (Denmark)
Madsen, Jan; Brage, Jens P.
1996-01-01
This paper describes a codesign case study where a computer graphics application is examined with the intention to speed up its execution. The application is specified as a C program, and is characterized by the lack of a simple compute-intensive kernel. The hardware/software partitioning is based...
A Codesign Case Study in Computer Graphics
DEFF Research Database (Denmark)
Brage, Jens P.; Madsen, Jan
1994-01-01
The paper describes a codesign case study where a computer graphics application is examined with the intention to speed up its execution. The application is specified as a C program, and is characterized by the lack of a simple compute-intensive kernel. The hardware/software partitioning is based...
Graphical user interfaces and visually disabled users
Poll, L.H.D.; Waterham, R.P.
1995-01-01
From February 1992 until the end of 1993, the authors ((IPO) Institute for Perception Research) participated in a European ((TIDE) Technology Initiative for Disabled and Elderly) project which addressed the problem arising for visually disabled computer-users from the growing use of Graphical User
Graphical Method for Determining Projectile Trajectory
Moore, J. C.; Baker, J. C.; Franzel, L.; McMahon, D.; Songer, D.
2010-01-01
We present a nontrigonometric graphical method for predicting the trajectory of a projectile when the angle and initial velocity are known. Students enrolled in a general education conceptual physics course typically have weak backgrounds in trigonometry, making inaccessible the standard analytical calculation of projectile range. Furthermore,…
Textbook Graphics and Maps: Keys to Learning.
Danzer, Gerald A.
1980-01-01
Explains how social studies pupils can use an awareness of textbook design to become better students. Suggestions include reproducing the collage on an American history textbook as a large poster for classroom use and directing students to design a graphic unit opener in the same style as the ones in their textbooks. (DB)
'Grafic'. A subroutine for the graphic displays
International Nuclear Information System (INIS)
Yunta Carretero, J.
1977-01-01
The subroutire Grafic allows the drawing of different standard graphics in a pploter Calcomp and avoid the users the preparation of calls to several Plot subroutines, which are necessary for the plotting. The possibilities of this subroutine, user's guide, suggestions about better use and examples, and also the carry out in Fortran language, are described. (author) [es
Probabilistic reasoning with graphical security models
Kordy, Barbara; Pouly, Marc; Schweitzer, Patrick
This work provides a computational framework for meaningful probabilistic evaluation of attack–defense scenarios involving dependent actions. We combine the graphical security modeling technique of attack–defense trees with probabilistic information expressed in terms of Bayesian networks. In order
Integrating Rapid Prototyping into Graphic Communications
Xu, Renmei; Flowers, Jim
2015-01-01
Integrating different science, technology, engineering, and mathematics (STEM) areas can help students learn and leverage both the equipment and expertise at a single school. In comparing graphic communications classes with classes that involve rapid prototyping (RP) technologies like 3D printing, there are sufficient similarities between goals,…
Using scalable vector graphics to evolve art
den Heijer, E.; Eiben, A. E.
2016-01-01
In this paper, we describe our investigations of the use of scalable vector graphics as a genotype representation in evolutionary art. We describe the technical aspects of using SVG in evolutionary art, and explain our custom, SVG specific operators initialisation, mutation and crossover. We perform
Introduction to 3D Graphics through Excel
Benacka, Jan
2013-01-01
The article presents a method of explaining the principles of 3D graphics through making a revolvable and sizable orthographic parallel projection of cuboid in Excel. No programming is used. The method was tried in fourteen 90 minute lessons with 181 participants, which were Informatics teachers, undergraduates of Applied Informatics and gymnasium…
Interactive Learning for Graphic Design Foundations
Chu, Sauman; Ramirez, German Mauricio Mejia
2012-01-01
One of the biggest problems for students majoring in pre-graphic design is students' inability to apply their knowledge to different design solutions. The purpose of this study is to examine the effectiveness of interactive learning modules in facilitating knowledge acquisition during the learning process and to create interactive learning modules…