Cluster Detection Tests in Spatial Epidemiology: A Global Indicator for Performance Assessment

Science.gov (United States)

Guttmann, Aline; Li, Xinran; Feschet, Fabien; Gaudart, Jean; Demongeot, Jacques; Boire, Jean-Yves; Ouchchane, Lemlih

2015-01-01

In cluster detection of disease, the use of local cluster detection tests (CDTs) is current. These methods aim both at locating likely clusters and testing for their statistical significance. New or improved CDTs are regularly proposed to epidemiologists and must be subjected to performance assessment. Because location accuracy has to be considered, performance assessment goes beyond the raw estimation of type I or II errors. As no consensus exists for performance evaluations, heterogeneous methods are used, and therefore studies are rarely comparable. A global indicator of performance, which assesses both spatial accuracy and usual power, would facilitate the exploration of CDTs behaviour and help between-studies comparisons. The Tanimoto coefficient (TC) is a well-known measure of similarity that can assess location accuracy but only for one detected cluster. In a simulation study, performance is measured for many tests. From the TC, we here propose two statistics, the averaged TC and the cumulated TC, as indicators able to provide a global overview of CDTs performance for both usual power and location accuracy. We evidence the properties of these two indicators and the superiority of the cumulated TC to assess performance. We tested these indicators to conduct a systematic spatial assessment displayed through performance maps. PMID:26086911

Analysis of sea-surface radar signatures by means of wavelet-based edge detection and detection of regularities; Analyse von Radarsignaturen der Meeresoberflaeche mittels auf Wavelets basierender Kantenerkennung und Regularitaetsbestimmung

Energy Technology Data Exchange (ETDEWEB)

Wolff, U. [GKSS-Forschungszentrum Geesthacht GmbH (Germany). Inst. fuer Gewaesserphysik

2000-07-01

The derivation and implementation of an algorithm for edge detection in images and for the detection of the Lipschitz regularity in edge points are described. The method is based on the use of the wavelet transform for edge detection at different resolutions. The Lipschitz regularity is a measure that characterizes the edges. The description of the derivation is first performed in one dimension. The approach of Mallat is formulated consistently and proved. Subsequently, the two-dimensional case is addressed, for which the derivation, as well as the description of the algorithm, is analogous. The algorithm is applied to detect edges in nautical radar images using images collected at the island of Sylt. The edges discernible in the images and the Lipschitz values provide information about the position and nature of spatial variations in the depth of the seafloor. By comparing images from different periods of measurement, temporal changes in the bottom structures can be localized at different resolutions and interpreted. The method is suited to the monitoring of coastal areas. It is an inexpensive way to observe long-term changes in the seafloor character. Thus, the results of this technique may be used by the authorities responsible for coastal protection to decide whether measures should be taken or not. (orig.)

Real time QRS complex detection using DFA and regular grammar.

Science.gov (United States)

Hamdi, Salah; Ben Abdallah, Asma; Bedoui, Mohamed Hedi

2017-02-28

The sequence of Q, R, and S peaks (QRS) complex detection is a crucial procedure in electrocardiogram (ECG) processing and analysis. We propose a novel approach for QRS complex detection based on the deterministic finite automata with the addition of some constraints. This paper confirms that regular grammar is useful for extracting QRS complexes and interpreting normalized ECG signals. A QRS is assimilated to a pair of adjacent peaks which meet certain criteria of standard deviation and duration. The proposed method was applied on several kinds of ECG signals issued from the standard MIT-BIH arrhythmia database. A total of 48 signals were used. For an input signal, several parameters were determined, such as QRS durations, RR distances, and the peaks' amplitudes. σRR and σQRS parameters were added to quantify the regularity of RR distances and QRS durations, respectively. The sensitivity rate of the suggested method was 99.74% and the specificity rate was 99.86%. Moreover, the sensitivity and the specificity rates variations according to the Signal-to-Noise Ratio were performed. Regular grammar with the addition of some constraints and deterministic automata proved functional for ECG signals diagnosis. Compared to statistical methods, the use of grammar provides satisfactory and competitive results and indices that are comparable to or even better than those cited in the literature.

Spatial Outlier Detection of CO2 Monitoring Data Based on Spatial Local Outlier Factor

OpenAIRE

Liu Xin; Zhang Shaoliang; Zheng Pulin

2015-01-01

Spatial local outlier factor (SLOF) algorithm was adopted in this study for spatial outlier detection because of the limitations of the traditional static threshold detection. Based on the spatial characteristics of CO2 monitoring data obtained in the carbon capture and storage (CCS) project, the K-Nearest Neighbour (KNN) graph was constructed using the latitude and longitude information of the monitoring points to identify the spatial neighbourhood of the monitoring points. Then ...

Spatial Outlier Detection of CO2 Monitoring Data Based on Spatial Local Outlier Factor

Directory of Open Access Journals (Sweden)

Liu Xin

2015-12-01

Full Text Available Spatial local outlier factor (SLOF algorithm was adopted in this study for spatial outlier detection because of the limitations of the traditional static threshold detection. Based on the spatial characteristics of CO2 monitoring data obtained in the carbon capture and storage (CCS project, the K-Nearest Neighbour (KNN graph was constructed using the latitude and longitude information of the monitoring points to identify the spatial neighbourhood of the monitoring points. Then SLOF was adopted to calculate the outlier degrees of the monitoring points and the 3σ rule was employed to identify the spatial outlier. Finally, the selection of K value was analysed and the optimal one was selected. The results show that, compared with the static threshold method, the proposed algorithm has a higher detection precision. It can overcome the shortcomings of the static threshold method and improve the accuracy and diversity of local outlier detection, which provides a reliable reference for the safety assessment and warning of CCS monitoring.

Detecting spatial regimes in ecosystems | Science Inventory ...

Science.gov (United States)

Research on early warning indicators has generally focused on assessing temporal transitions with limited application of these methods to detecting spatial regimes. Traditional spatial boundary detection procedures that result in ecoregion maps are typically based on ecological potential (i.e. potential vegetation), and often fail to account for ongoing changes due to stressors such as land use change and climate change and their effects on plant and animal communities. We use Fisher information, an information theory based method, on both terrestrial and aquatic animal data (US Breeding Bird Survey and marine zooplankton) to identify ecological boundaries, and compare our results to traditional early warning indicators, conventional ecoregion maps, and multivariate analysis such as nMDS (non-metric Multidimensional Scaling) and cluster analysis. We successfully detect spatial regimes and transitions in both terrestrial and aquatic systems using Fisher information. Furthermore, Fisher information provided explicit spatial information about community change that is absent from other multivariate approaches. Our results suggest that defining spatial regimes based on animal communities may better reflect ecological reality than do traditional ecoregion maps, especially in our current era of rapid and unpredictable ecological change. Use an information theory based method to identify ecological boundaries and compare our results to traditional early warning

High-resolution seismic data regularization and wavefield separation

Science.gov (United States)

Cao, Aimin; Stump, Brian; DeShon, Heather

2018-04-01

We present a new algorithm, non-equispaced fast antileakage Fourier transform (NFALFT), for irregularly sampled seismic data regularization. Synthetic tests from 1-D to 5-D show that the algorithm may efficiently remove leaked energy in the frequency wavenumber domain, and its corresponding regularization process is accurate and fast. Taking advantage of the NFALFT algorithm, we suggest a new method (wavefield separation) for the detection of the Earth's inner core shear wave with irregularly distributed seismic arrays or networks. All interfering seismic phases that propagate along the minor arc are removed from the time window around the PKJKP arrival. The NFALFT algorithm is developed for seismic data, but may also be used for other irregularly sampled temporal or spatial data processing.

Detecting regular sound changes in linguistics as events of concerted evolution.

Science.gov (United States)

Hruschka, Daniel J; Branford, Simon; Smith, Eric D; Wilkins, Jon; Meade, Andrew; Pagel, Mark; Bhattacharya, Tanmoy

2015-01-05

Concerted evolution is normally used to describe parallel changes at different sites in a genome, but it is also observed in languages where a specific phoneme changes to the same other phoneme in many words in the lexicon—a phenomenon known as regular sound change. We develop a general statistical model that can detect concerted changes in aligned sequence data and apply it to study regular sound changes in the Turkic language family. Linguistic evolution, unlike the genetic substitutional process, is dominated by events of concerted evolutionary change. Our model identified more than 70 historical events of regular sound change that occurred throughout the evolution of the Turkic language family, while simultaneously inferring a dated phylogenetic tree. Including regular sound changes yielded an approximately 4-fold improvement in the characterization of linguistic change over a simpler model of sporadic change, improved phylogenetic inference, and returned more reliable and plausible dates for events on the phylogenies. The historical timings of the concerted changes closely follow a Poisson process model, and the sound transition networks derived from our model mirror linguistic expectations. We demonstrate that a model with no prior knowledge of complex concerted or regular changes can nevertheless infer the historical timings and genealogical placements of events of concerted change from the signals left in contemporary data. Our model can be applied wherever discrete elements—such as genes, words, cultural trends, technologies, or morphological traits—can change in parallel within an organism or other evolving group. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Laplacian manifold regularization method for fluorescence molecular tomography

Science.gov (United States)

He, Xuelei; Wang, Xiaodong; Yi, Huangjian; Chen, Yanrong; Zhang, Xu; Yu, Jingjing; He, Xiaowei

2017-04-01

Sparse regularization methods have been widely used in fluorescence molecular tomography (FMT) for stable three-dimensional reconstruction. Generally, ℓ1-regularization-based methods allow for utilizing the sparsity nature of the target distribution. However, in addition to sparsity, the spatial structure information should be exploited as well. A joint ℓ1 and Laplacian manifold regularization model is proposed to improve the reconstruction performance, and two algorithms (with and without Barzilai-Borwein strategy) are presented to solve the regularization model. Numerical studies and in vivo experiment demonstrate that the proposed Gradient projection-resolved Laplacian manifold regularization method for the joint model performed better than the comparative algorithm for ℓ1 minimization method in both spatial aggregation and location accuracy.

Spatial-Temporal Event Detection from Geo-Tagged Tweets

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Yuqian Huang

2018-04-01

Full Text Available As one of the most popular social networking services in the world, Twitter allows users to post messages along with their current geographic locations. Such georeferenced or geo-tagged Twitter datasets can benefit location-based services, targeted advertising and geosocial studies. Our study focused on the detection of small-scale spatial-temporal events and their textual content. First, we used Spatial-Temporal Density-Based Spatial Clustering of Applications with Noise (ST-DBSCAN to spatially-temporally cluster the tweets. Then, the word frequencies were summarized for each cluster and the potential topics were modeled by the Latent Dirichlet Allocation (LDA algorithm. Using two years of Twitter data from four college cities in the U.S., we were able to determine the spatial-temporal patterns of two known events, two unknown events and one recurring event, which then were further explored and modeled to identify the semantic content about the events. This paper presents our process and recommendations for both finding event-related tweets as well as understanding the spatial-temporal behaviors and semantic natures of the detected events.

Grid-texture mechanisms in human vision: Contrast detection of regular sparse micro-patterns requires specialist templates.

Science.gov (United States)

Baker, Daniel H; Meese, Tim S

2016-07-27

Previous work has shown that human vision performs spatial integration of luminance contrast energy, where signals are squared and summed (with internal noise) over area at detection threshold. We tested that model here in an experiment using arrays of micro-pattern textures that varied in overall stimulus area and sparseness of their target elements, where the contrast of each element was normalised for sensitivity across the visual field. We found a power-law improvement in performance with stimulus area, and a decrease in sensitivity with sparseness. While the contrast integrator model performed well when target elements constituted 50-100% of the target area (replicating previous results), observers outperformed the model when texture elements were sparser than this. This result required the inclusion of further templates in our model, selective for grids of various regular texture densities. By assuming a MAX operation across these noisy mechanisms the model also accounted for the increase in the slope of the psychometric function that occurred as texture density decreased. Thus, for the first time, mechanisms that are selective for texture density have been revealed at contrast detection threshold. We suggest that these mechanisms have a role to play in the perception of visual textures.

Analysis of Regularly and Irregularly Sampled Spatial, Multivariate, and Multi-temporal Data

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

1994-01-01

This thesis describes different methods that are useful in the analysis of multivariate data. Some methods focus on spatial data (sampled regularly or irregularly), others focus on multitemporal data or data from multiple sources. The thesis covers selected and not all aspects of relevant data......-variograms are described. As a new way of setting up a well-balanced kriging support the Delaunay triangulation is suggested. Two case studies show the usefulness of 2-D semivariograms of geochemical data from areas in central Spain (with a geologist's comment) and South Greenland, and kriging/cokriging of an undersampled...... are considered as repetitions. Three case studies show the strength of the methods; one uses SPOT High Resolution Visible (HRV) multispectral (XS) data covering economically important pineapple and coffee plantations near Thika, Kiambu District, Kenya, the other two use Landsat Thematic Mapper (TM) data covering...

Cardiac C-arm computed tomography using a 3D + time ROI reconstruction method with spatial and temporal regularization

Energy Technology Data Exchange (ETDEWEB)

Mory, Cyril, E-mail: cyril.mory@philips.com [Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, Université Lyon 1, F-69621 Villeurbanne Cedex (France); Philips Research Medisys, 33 rue de Verdun, 92156 Suresnes (France); Auvray, Vincent; Zhang, Bo [Philips Research Medisys, 33 rue de Verdun, 92156 Suresnes (France); Grass, Michael; Schäfer, Dirk [Philips Research, Röntgenstrasse 24–26, D-22335 Hamburg (Germany); Chen, S. James; Carroll, John D. [Department of Medicine, Division of Cardiology, University of Colorado Denver, 12605 East 16th Avenue, Aurora, Colorado 80045 (United States); Rit, Simon [Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, Université Lyon 1 (France); Centre Léon Bérard, 28 rue Laënnec, F-69373 Lyon (France); Peyrin, Françoise [Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, Université Lyon 1, F-69621 Villeurbanne Cedex (France); X-ray Imaging Group, European Synchrotron, Radiation Facility, BP 220, F-38043 Grenoble Cedex (France); Douek, Philippe; Boussel, Loïc [Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, Université Lyon 1 (France); Hospices Civils de Lyon, 28 Avenue du Doyen Jean Lépine, 69500 Bron (France)

2014-02-15

Purpose: Reconstruction of the beating heart in 3D + time in the catheter laboratory using only the available C-arm system would improve diagnosis, guidance, device sizing, and outcome control for intracardiac interventions, e.g., electrophysiology, valvular disease treatment, structural or congenital heart disease. To obtain such a reconstruction, the patient's electrocardiogram (ECG) must be recorded during the acquisition and used in the reconstruction. In this paper, the authors present a 4D reconstruction method aiming to reconstruct the heart from a single sweep 10 s acquisition. Methods: The authors introduce the 4D RecOnstructiOn using Spatial and TEmporal Regularization (short 4D ROOSTER) method, which reconstructs all cardiac phases at once, as a 3D + time volume. The algorithm alternates between a reconstruction step based on conjugate gradient and four regularization steps: enforcing positivity, averaging along time outside a motion mask that contains the heart and vessels, 3D spatial total variation minimization, and 1D temporal total variation minimization. Results: 4D ROOSTER recovers the different temporal representations of a moving Shepp and Logan phantom, and outperforms both ECG-gated simultaneous algebraic reconstruction technique and prior image constrained compressed sensing on a clinical case. It generates 3D + time reconstructions with sharp edges which can be used, for example, to estimate the patient's left ventricular ejection fraction. Conclusions: 4D ROOSTER can be applied for human cardiac C-arm CT, and potentially in other dynamic tomography areas. It can easily be adapted to other problems as regularization is decoupled from projection and back projection.

Cardiac C-arm computed tomography using a 3D + time ROI reconstruction method with spatial and temporal regularization

International Nuclear Information System (INIS)

Mory, Cyril; Auvray, Vincent; Zhang, Bo; Grass, Michael; Schäfer, Dirk; Chen, S. James; Carroll, John D.; Rit, Simon; Peyrin, Françoise; Douek, Philippe; Boussel, Loïc

2014-01-01

Purpose: Reconstruction of the beating heart in 3D + time in the catheter laboratory using only the available C-arm system would improve diagnosis, guidance, device sizing, and outcome control for intracardiac interventions, e.g., electrophysiology, valvular disease treatment, structural or congenital heart disease. To obtain such a reconstruction, the patient's electrocardiogram (ECG) must be recorded during the acquisition and used in the reconstruction. In this paper, the authors present a 4D reconstruction method aiming to reconstruct the heart from a single sweep 10 s acquisition. Methods: The authors introduce the 4D RecOnstructiOn using Spatial and TEmporal Regularization (short 4D ROOSTER) method, which reconstructs all cardiac phases at once, as a 3D + time volume. The algorithm alternates between a reconstruction step based on conjugate gradient and four regularization steps: enforcing positivity, averaging along time outside a motion mask that contains the heart and vessels, 3D spatial total variation minimization, and 1D temporal total variation minimization. Results: 4D ROOSTER recovers the different temporal representations of a moving Shepp and Logan phantom, and outperforms both ECG-gated simultaneous algebraic reconstruction technique and prior image constrained compressed sensing on a clinical case. It generates 3D + time reconstructions with sharp edges which can be used, for example, to estimate the patient's left ventricular ejection fraction. Conclusions: 4D ROOSTER can be applied for human cardiac C-arm CT, and potentially in other dynamic tomography areas. It can easily be adapted to other problems as regularization is decoupled from projection and back projection

Spatial filtering precedes motion detection.

Science.gov (United States)

Morgan, M J

1992-01-23

When we perceive motion on a television or cinema screen, there must be some process that allows us to track moving objects over time: if not, the result would be a conflicting mass of motion signals in all directions. A possible mechanism, suggested by studies of motion displacement in spatially random patterns, is that low-level motion detectors have a limited spatial range, which ensures that they tend to be stimulated over time by the same object. This model predicts that the direction of displacement of random patterns cannot be detected reliably above a critical absolute displacement value (Dmax) that is independent of the size or density of elements in the display. It has been inferred that Dmax is a measure of the size of motion detectors in the visual pathway. Other studies, however, have shown that Dmax increases with element size, in which case the most likely interpretation is that Dmax depends on the probability of false matches between pattern elements following a displacement. These conflicting accounts are reconciled here by showing that Dmax is indeed determined by the spacing between the elements in the pattern, but only after fine detail has been removed by a physiological prefiltering stage: the filter required to explain the data has a similar size to the receptive field of neurons in the primate magnocellular pathway. The model explains why Dmax can be increased by removing high spatial frequencies from random patterns, and simplifies our view of early motion detection.

Iterative Method of Regularization with Application of Advanced Technique for Detection of Contours

International Nuclear Information System (INIS)

Niedziela, T.; Stankiewicz, A.

2000-01-01

This paper proposes a novel iterative method of regularization with application of an advanced technique for detection of contours. To eliminate noises, the properties of convolution of functions are utilized. The method can be accomplished in a simple neural cellular network, which creates the possibility of extraction of contours by automatic image recognition equipment. (author)

Enhancing spatial detection accuracy for syndromic surveillance with street level incidence data

Directory of Open Access Journals (Sweden)

Alemi Farrokh

2010-01-01

Full Text Available Abstract Background The Department of Defense Military Health System operates a syndromic surveillance system that monitors medical records at more than 450 non-combat Military Treatment Facilities (MTF worldwide. The Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE uses both temporal and spatial algorithms to detect disease outbreaks. This study focuses on spatial detection and attempts to improve the effectiveness of the ESSENCE implementation of the spatial scan statistic by increasing the spatial resolution of incidence data from zip codes to street address level. Methods Influenza-Like Illness (ILI was used as a test syndrome to develop methods to improve the spatial accuracy of detected alerts. Simulated incident clusters of various sizes were superimposed on real ILI incidents from the 2008/2009 influenza season. Clusters were detected using the spatial scan statistic and their displacement from simulated loci was measured. Detected cluster size distributions were also evaluated for compliance with simulated cluster sizes. Results Relative to the ESSENCE zip code based method, clusters detected using street level incidents were displaced on average 65% less for 2 and 5 mile radius clusters and 31% less for 10 mile radius clusters. Detected cluster size distributions for the street address method were quasi normal and sizes tended to slightly exceed simulated radii. ESSENCE methods yielded fragmented distributions and had high rates of zero radius and oversized clusters. Conclusions Spatial detection accuracy improved notably with regard to both location and size when incidents were geocoded to street addresses rather than zip code centroids. Since street address geocoding success rates were only 73.5%, zip codes were still used for more than one quarter of ILI cases. Thus, further advances in spatial detection accuracy are dependant on systematic improvements in the collection of individual

The patterning of retinal horizontal cells: normalizing the regularity index enhances the detection of genomic linkage

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Patrick W. Keeley

2014-10-01

Full Text Available Retinal neurons are often arranged as non-random distributions called mosaics, as their somata minimize proximity to neighboring cells of the same type. The horizontal cells serve as an example of such a mosaic, but little is known about the developmental mechanisms that underlie their patterning. To identify genes involved in this process, we have used three different spatial statistics to assess the patterning of the horizontal cell mosaic across a panel of genetically distinct recombinant inbred strains. To avoid the confounding effect cell density, which varies two-fold across these different strains, we computed the real/random regularity ratio, expressing the regularity of a mosaic relative to a randomly distributed simulation of similarly sized cells. To test whether this latter statistic better reflects the variation in biological processes that contribute to horizontal cell spacing, we subsequently compared the genetic linkage for each of these two traits, the regularity index and the real/random regularity ratio, each computed from the distribution of nearest neighbor (NN distances and from the Voronoi domain (VD areas. Finally, we compared each of these analyses with another index of patterning, the packing factor. Variation in the regularity indexes, as well as their real/random regularity ratios, and the packing factor, mapped quantitative trait loci (QTL to the distal ends of Chromosomes 1 and 14. For the NN and VD analyses, we found that the degree of linkage was greater when using the real/random regularity ratio rather than the respective regularity index. Using informatic resources, we narrow the list of prospective genes positioned at these two intervals to a small collection of six genes that warrant further investigation to determine their potential role in shaping the patterning of the horizontal cell mosaic.

Spatial Cluster Detection for Repeatedly Measured Outcomes while Accounting for Residential History

OpenAIRE

Cook, Andrea J.; Gold, Diane R.; Li, Yi

2009-01-01

Spatial cluster detection has become an important methodology in quantifying the effect of hazardous exposures. Previous methods have focused on cross-sectional outcomes that are binary or continuous. There are virtually no spatial cluster detection methods proposed for longitudinal outcomes. This paper proposes a new spatial cluster detection method for repeated outcomes using cumulative geographic residuals. A major advantage of this method is its ability to readily incorporate information ...

Fractional Regularization Term for Variational Image Registration

Directory of Open Access Journals (Sweden)

Rafael Verdú-Monedero

2009-01-01

Full Text Available Image registration is a widely used task of image analysis with applications in many fields. Its classical formulation and current improvements are given in the spatial domain. In this paper a regularization term based on fractional order derivatives is formulated. This term is defined and implemented in the frequency domain by translating the energy functional into the frequency domain and obtaining the Euler-Lagrange equations which minimize it. The new regularization term leads to a simple formulation and design, being applicable to higher dimensions by using the corresponding multidimensional Fourier transform. The proposed regularization term allows for a real gradual transition from a diffusion registration to a curvature registration which is best suited to some applications and it is not possible in the spatial domain. Results with 3D actual images show the validity of this approach.

Incorporating Spatial Information for Microaneurysm Detection in Retinal Images

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Mohamed M. Habib

2017-06-01

Full Text Available The presence of microaneurysms(MAs in retinal images is a pathognomonic sign of Diabetic Retinopathy (DR. This is one of the leading causes of blindness in the working population worldwide. This paper introduces a novel algorithm that combines information from spatial views of the retina for the purpose of MA detection. Most published research in the literature has addressed the problem of detecting MAs from single retinal images. This work proposes the incorporation of information from two spatial views during the detection process. The algorithm is evaluated using 160 images from 40 patients seen as part of a UK diabetic eye screening programme which contained 207 MAs. An improvement in performance compared to detection from an algorithm that relies on a single image is shown as an increase of 2% ROC score, hence demonstrating the potential of this method.

Improvements in GRACE Gravity Fields Using Regularization

Science.gov (United States)

Save, H.; Bettadpur, S.; Tapley, B. D.

2008-12-01

The unconstrained global gravity field models derived from GRACE are susceptible to systematic errors that show up as broad "stripes" aligned in a North-South direction on the global maps of mass flux. These errors are believed to be a consequence of both systematic and random errors in the data that are amplified by the nature of the gravity field inverse problem. These errors impede scientific exploitation of the GRACE data products, and limit the realizable spatial resolution of the GRACE global gravity fields in certain regions. We use regularization techniques to reduce these "stripe" errors in the gravity field products. The regularization criteria are designed such that there is no attenuation of the signal and that the solutions fit the observations as well as an unconstrained solution. We have used a computationally inexpensive method, normally referred to as "L-ribbon", to find the regularization parameter. This paper discusses the characteristics and statistics of a 5-year time-series of regularized gravity field solutions. The solutions show markedly reduced stripes, are of uniformly good quality over time, and leave little or no systematic observation residuals, which is a frequent consequence of signal suppression from regularization. Up to degree 14, the signal in regularized solution shows correlation greater than 0.8 with the un-regularized CSR Release-04 solutions. Signals from large-amplitude and small-spatial extent events - such as the Great Sumatra Andaman Earthquake of 2004 - are visible in the global solutions without using special post-facto error reduction techniques employed previously in the literature. Hydrological signals as small as 5 cm water-layer equivalent in the small river basins, like Indus and Nile for example, are clearly evident, in contrast to noisy estimates from RL04. The residual variability over the oceans relative to a seasonal fit is small except at higher latitudes, and is evident without the need for de-striping or

Spatial cluster detection for repeatedly measured outcomes while accounting for residential history.

Science.gov (United States)

Cook, Andrea J; Gold, Diane R; Li, Yi

2009-10-01

Spatial cluster detection has become an important methodology in quantifying the effect of hazardous exposures. Previous methods have focused on cross-sectional outcomes that are binary or continuous. There are virtually no spatial cluster detection methods proposed for longitudinal outcomes. This paper proposes a new spatial cluster detection method for repeated outcomes using cumulative geographic residuals. A major advantage of this method is its ability to readily incorporate information on study participants relocation, which most cluster detection statistics cannot. Application of these methods will be illustrated by the Home Allergens and Asthma prospective cohort study analyzing the relationship between environmental exposures and repeated measured outcome, occurrence of wheeze in the last 6 months, while taking into account mobile locations.

Near-Regular Structure Discovery Using Linear Programming

KAUST Repository

Huang, Qixing

2014-06-02

Near-regular structures are common in manmade and natural objects. Algorithmic detection of such regularity greatly facilitates our understanding of shape structures, leads to compact encoding of input geometries, and enables efficient generation and manipulation of complex patterns on both acquired and synthesized objects. Such regularity manifests itself both in the repetition of certain geometric elements, as well as in the structured arrangement of the elements. We cast the regularity detection problem as an optimization and efficiently solve it using linear programming techniques. Our optimization has a discrete aspect, that is, the connectivity relationships among the elements, as well as a continuous aspect, namely the locations of the elements of interest. Both these aspects are captured by our near-regular structure extraction framework, which alternates between discrete and continuous optimizations. We demonstrate the effectiveness of our framework on a variety of problems including near-regular structure extraction, structure-preserving pattern manipulation, and markerless correspondence detection. Robustness results with respect to geometric and topological noise are presented on synthesized, real-world, and also benchmark datasets. © 2014 ACM.

Reconstruction of signal in plastic scintillator of PET using Tikhonov regularization.

Science.gov (United States)

Raczynski, Lech

2015-08-01

The new concept of Time of Flight Positron Emission Tomography (TOF-PET) detection system, which allows for single bed imaging of the whole human body, is currently under development at the Jagiellonian University. The Jagiellonian-PET (J-PET) detector improves the TOF resolution due to the use of fast plastic scintillators. Since registration of the waveform of signals with duration times of few nanoseconds is not feasible, a novel front-end electronics allowing for sampling in a voltage domain at four thresholds was developed. To take fully advantage of these fast signals a novel scheme of recovery of the waveform of the signal, based on idea from the Tikhonov regularization method, is presented. From the Bayes theory the properties of regularized solution, especially its covariance matrix, may be easily derived. This step is crucial to introduce and prove the formula for calculations of the signal recovery error. The method is tested using signals registered by means of the single detection module of the J-PET detector built out from the 30 cm long plastic scintillator strip. It is shown that using the recovered waveform of the signals, instead of samples at four voltage levels alone, improves the spatial resolution of the hit position reconstruction from 1.05 cm to 0.94 cm. Moreover, the obtained result is only slightly worse than the one evaluated using the original raw-signal. The spatial resolution calculated under these conditions is equal to 0.93 cm.

Task-based detectability in CT image reconstruction by filtered backprojection and penalized likelihood estimation

Energy Technology Data Exchange (ETDEWEB)

Gang, Grace J. [Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario M5G 2M9, Canada and Department of Biomedical Engineering, Johns Hopkins University, Baltimore Maryland 21205 (Canada); Stayman, J. Webster; Zbijewski, Wojciech [Department of Biomedical Engineering, Johns Hopkins University, Baltimore Maryland 21205 (United States); Siewerdsen, Jeffrey H., E-mail: jeff.siewerdsen@jhu.edu [Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario M5G 2M9, Canada and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205 (United States)

2014-08-15

Purpose: Nonstationarity is an important aspect of imaging performance in CT and cone-beam CT (CBCT), especially for systems employing iterative reconstruction. This work presents a theoretical framework for both filtered-backprojection (FBP) and penalized-likelihood (PL) reconstruction that includes explicit descriptions of nonstationary noise, spatial resolution, and task-based detectability index. Potential utility of the model was demonstrated in the optimal selection of regularization parameters in PL reconstruction. Methods: Analytical models for local modulation transfer function (MTF) and noise-power spectrum (NPS) were investigated for both FBP and PL reconstruction, including explicit dependence on the object and spatial location. For FBP, a cascaded systems analysis framework was adapted to account for nonstationarity by separately calculating fluence and system gains for each ray passing through any given voxel. For PL, the point-spread function and covariance were derived using the implicit function theorem and first-order Taylor expansion according toFessler [“Mean and variance of implicitly defined biased estimators (such as penalized maximum likelihood): Applications to tomography,” IEEE Trans. Image Process. 5(3), 493–506 (1996)]. Detectability index was calculated for a variety of simple tasks. The model for PL was used in selecting the regularization strength parameter to optimize task-based performance, with both a constant and a spatially varying regularization map. Results: Theoretical models of FBP and PL were validated in 2D simulated fan-beam data and found to yield accurate predictions of local MTF and NPS as a function of the object and the spatial location. The NPS for both FBP and PL exhibit similar anisotropic nature depending on the pathlength (and therefore, the object and spatial location within the object) traversed by each ray, with the PL NPS experiencing greater smoothing along directions with higher noise. The MTF of FBP

Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters

Directory of Open Access Journals (Sweden)

Fonseca Carlos M

2010-10-01

Full Text Available Abstract Background Irregularly shaped spatial clusters are difficult to delineate. A cluster found by an algorithm often spreads through large portions of the map, impacting its geographical meaning. Penalized likelihood methods for Kulldorff's spatial scan statistics have been used to control the excessive freedom of the shape of clusters. Penalty functions based on cluster geometry and non-connectivity have been proposed recently. Another approach involves the use of a multi-objective algorithm to maximize two objectives: the spatial scan statistics and the geometric penalty function. Results & Discussion We present a novel scan statistic algorithm employing a function based on the graph topology to penalize the presence of under-populated disconnection nodes in candidate clusters, the disconnection nodes cohesion function. A disconnection node is defined as a region within a cluster, such that its removal disconnects the cluster. By applying this function, the most geographically meaningful clusters are sifted through the immense set of possible irregularly shaped candidate cluster solutions. To evaluate the statistical significance of solutions for multi-objective scans, a statistical approach based on the concept of attainment function is used. In this paper we compared different penalized likelihoods employing the geometric and non-connectivity regularity functions and the novel disconnection nodes cohesion function. We also build multi-objective scans using those three functions and compare them with the previous penalized likelihood scans. An application is presented using comprehensive state-wide data for Chagas' disease in puerperal women in Minas Gerais state, Brazil. Conclusions We show that, compared to the other single-objective algorithms, multi-objective scans present better performance, regarding power, sensitivity and positive predicted value. The multi-objective non-connectivity scan is faster and better suited for the

Spatially adaptive mixture modeling for analysis of FMRI time series.

Science.gov (United States)

Vincent, Thomas; Risser, Laurent; Ciuciu, Philippe

2010-04-01

Within-subject analysis in fMRI essentially addresses two problems, the detection of brain regions eliciting evoked activity and the estimation of the underlying dynamics. In Makni et aL, 2005 and Makni et aL, 2008, a detection-estimation framework has been proposed to tackle these problems jointly, since they are connected to one another. In the Bayesian formalism, detection is achieved by modeling activating and nonactivating voxels through independent mixture models (IMM) within each region while hemodynamic response estimation is performed at a regional scale in a nonparametric way. Instead of IMMs, in this paper we take advantage of spatial mixture models (SMM) for their nonlinear spatial regularizing properties. The proposed method is unsupervised and spatially adaptive in the sense that the amount of spatial correlation is automatically tuned from the data and this setting automatically varies across brain regions. In addition, the level of regularization is specific to each experimental condition since both the signal-to-noise ratio and the activation pattern may vary across stimulus types in a given brain region. These aspects require the precise estimation of multiple partition functions of underlying Ising fields. This is addressed efficiently using first path sampling for a small subset of fields and then using a recently developed fast extrapolation technique for the large remaining set. Simulation results emphasize that detection relying on supervised SMM outperforms its IMM counterpart and that unsupervised spatial mixture models achieve similar results without any hand-tuning of the correlation parameter. On real datasets, the gain is illustrated in a localizer fMRI experiment: brain activations appear more spatially resolved using SMM in comparison with classical general linear model (GLM)-based approaches, while estimating a specific parcel-based HRF shape. Our approach therefore validates the treatment of unsmoothed fMRI data without fixed GLM

Spatial-temporal event detection in climate parameter imagery.

Energy Technology Data Exchange (ETDEWEB)

McKenna, Sean Andrew; Gutierrez, Karen A.

2011-10-01

Previously developed techniques that comprise statistical parametric mapping, with applications focused on human brain imaging, are examined and tested here for new applications in anomaly detection within remotely-sensed imagery. Two approaches to analysis are developed: online, regression-based anomaly detection and conditional differences. These approaches are applied to two example spatial-temporal data sets: data simulated with a Gaussian field deformation approach and weekly NDVI images derived from global satellite coverage. Results indicate that anomalies can be identified in spatial temporal data with the regression-based approach. Additionally, la Nina and el Nino climatic conditions are used as different stimuli applied to the earth and this comparison shows that el Nino conditions lead to significant decreases in NDVI in both the Amazon Basin and in Southern India.

Contour Propagation With Riemannian Elasticity Regularization

DEFF Research Database (Denmark)

Bjerre, Troels; Hansen, Mads Fogtmann; Sapru, W.

2011-01-01

Purpose/Objective(s): Adaptive techniques allow for correction of spatial changes during the time course of the fractionated radiotherapy. Spatial changes include tumor shrinkage and weight loss, causing tissue deformation and residual positional errors even after translational and rotational image...... the planning CT onto the rescans and correcting to reflect actual anatomical changes. For deformable registration, a free-form, multi-level, B-spline deformation model with Riemannian elasticity, penalizing non-rigid local deformations, and volumetric changes, was used. Regularization parameters was defined...... on the original delineation and tissue deformation in the time course between scans form a better starting point than rigid propagation. There was no significant difference of locally and globally defined regularization. The method used in the present study suggests that deformed contours need to be reviewed...

Phantom experiments using soft-prior regularization EIT for breast cancer imaging.

Science.gov (United States)

Murphy, Ethan K; Mahara, Aditya; Wu, Xiaotian; Halter, Ryan J

2017-06-01

A soft-prior regularization (SR) electrical impedance tomography (EIT) technique for breast cancer imaging is described, which shows an ability to accurately reconstruct tumor/inclusion conductivity values within a dense breast model investigated using a cylindrical and a breast-shaped tank. The SR-EIT method relies on knowing the spatial location of a suspicious lesion initially detected from a second imaging modality. Standard approaches (using Laplace smoothing and total variation regularization) without prior structural information are unable to accurately reconstruct or detect the tumors. The soft-prior approach represents a very significant improvement to these standard approaches, and has the potential to improve conventional imaging techniques, such as automated whole breast ultrasound (AWB-US), by providing electrical property information of suspicious lesions to improve AWB-US's ability to discriminate benign from cancerous lesions. Specifically, the best soft-regularization technique found average absolute tumor/inclusion errors of 0.015 S m -1 for the cylindrical test and 0.055 S m -1 and 0.080 S m -1 for the breast-shaped tank for 1.8 cm and 2.5 cm inclusions, respectively. The standard approaches were statistically unable to distinguish the tumor from the mammary gland tissue. An analysis of false tumors (benign suspicious lesions) provides extra insight into the potential and challenges EIT has for providing clinically relevant information. The ability to obtain accurate conductivity values of a suspicious lesion (>1.8 cm) detected from another modality (e.g. AWB-US) could significantly reduce false positives and result in a clinically important technology.

Spiking Regularity and Coherence in Complex Hodgkin–Huxley Neuron Networks

International Nuclear Information System (INIS)

Zhi-Qiang, Sun; Ping, Xie; Wei, Li; Peng-Ye, Wang

2010-01-01

We study the effects of the strength of coupling between neurons on the spiking regularity and coherence in a complex network with randomly connected Hodgkin–Huxley neurons driven by colored noise. It is found that for the given topology realization and colored noise correlation time, there exists an optimal strength of coupling, at which the spiking regularity of the network reaches the best level. Moreover, when the temporal regularity reaches the best level, the spatial coherence of the system has already increased to a relatively high level. In addition, for the given number of neurons and noise correlation time, the values of average regularity and spatial coherence at the optimal strength of coupling are nearly independent of the topology realization. Furthermore, there exists an optimal value of colored noise correlation time at which the spiking regularity can reach its best level. These results may be helpful for understanding of the real neuron world. (cross-disciplinary physics and related areas of science and technology)

Analysis of absence seizure generation using EEG spatial-temporal regularity measures.

Science.gov (United States)

Mammone, Nadia; Labate, Domenico; Lay-Ekuakille, Aime; Morabito, Francesco C

2012-12-01

Epileptic seizures are thought to be generated and to evolve through an underlying anomaly of synchronization in the activity of groups of neuronal populations. The related dynamic scenario of state transitions is revealed by detecting changes in the dynamical properties of Electroencephalography (EEG) signals. The recruitment procedure ending with the crisis can be explored through a spatial-temporal plot from which to extract suitable descriptors that are able to monitor and quantify the evolving synchronization level from the EEG tracings. In this paper, a spatial-temporal analysis of EEG recordings based on the concept of permutation entropy (PE) is proposed. The performance of PE are tested on a database of 24 patients affected by absence (generalized) seizures. The results achieved are compared to the dynamical behavior of the EEG of 40 healthy subjects. Being PE a feature which is dependent on two parameters, an extensive study of the sensitivity of the performance of PE with respect to the parameters' setting was carried out on scalp EEG. Once the optimal PE configuration was determined, its ability to detect the different brain states was evaluated. According to the results here presented, it seems that the widely accepted model of "jump" transition to absence seizure should be in some cases coupled (or substituted) by a gradual transition model characteristic of self-organizing networks. Indeed, it appears that the transition to the epileptic status is heralded before the preictal state, ever since the interictal stages. As a matter of fact, within the limits of the analyzed database, the frontal-temporal scalp areas appear constantly associated to PE levels higher compared to the remaining electrodes, whereas the parieto-occipital areas appear associated to lower PE values. The EEG of healthy subjects neither shows any similar dynamic behavior nor exhibits any recurrent portrait in PE topography.

Regularized non-stationary morphological reconstruction algorithm for weak signal detection in microseismic monitoring: methodology

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Huang, Weilin; Wang, Runqiu; Chen, Yangkang

2018-05-01

Microseismic signal is typically weak compared with the strong background noise. In order to effectively detect the weak signal in microseismic data, we propose a mathematical morphology based approach. We decompose the initial data into several morphological multiscale components. For detection of weak signal, a non-stationary weighting operator is proposed and introduced into the process of reconstruction of data by morphological multiscale components. The non-stationary weighting operator can be obtained by solving an inversion problem. The regularized non-stationary method can be understood as a non-stationary matching filtering method, where the matching filter has the same size as the data to be filtered. In this paper, we provide detailed algorithmic descriptions and analysis. The detailed algorithm framework, parameter selection and computational issue for the regularized non-stationary morphological reconstruction (RNMR) method are presented. We validate the presented method through a comprehensive analysis through different data examples. We first test the proposed technique using a synthetic data set. Then the proposed technique is applied to a field project, where the signals induced from hydraulic fracturing are recorded by 12 three-component geophones in a monitoring well. The result demonstrates that the RNMR can improve the detectability of the weak microseismic signals. Using the processed data, the short-term-average over long-term average picking algorithm and Geiger's method are applied to obtain new locations of microseismic events. In addition, we show that the proposed RNMR method can be used not only in microseismic data but also in reflection seismic data to detect the weak signal. We also discussed the extension of RNMR from 1-D to 2-D or a higher dimensional version.

FIND: difFerential chromatin INteractions Detection using a spatial Poisson process.

Science.gov (United States)

Djekidel, Mohamed Nadhir; Chen, Yang; Zhang, Michael Q

2018-02-12

Polymer-based simulations and experimental studies indicate the existence of a spatial dependency between the adjacent DNA fibers involved in the formation of chromatin loops. However, the existing strategies for detecting differential chromatin interactions assume that the interacting segments are spatially independent from the other segments nearby. To resolve this issue, we developed a new computational method, FIND, which considers the local spatial dependency between interacting loci. FIND uses a spatial Poisson process to detect differential chromatin interactions that show a significant difference in their interaction frequency and the interaction frequency of their neighbors. Simulation and biological data analysis show that FIND outperforms the widely used count-based methods and has a better signal-to-noise ratio. © 2018 Djekidel et al.; Published by Cold Spring Harbor Laboratory Press.

Bypassing the Limits of Ll Regularization: Convex Sparse Signal Processing Using Non-Convex Regularization

Science.gov (United States)

Parekh, Ankit

Sparsity has become the basis of some important signal processing methods over the last ten years. Many signal processing problems (e.g., denoising, deconvolution, non-linear component analysis) can be expressed as inverse problems. Sparsity is invoked through the formulation of an inverse problem with suitably designed regularization terms. The regularization terms alone encode sparsity into the problem formulation. Often, the ℓ1 norm is used to induce sparsity, so much so that ℓ1 regularization is considered to be `modern least-squares'. The use of ℓ1 norm, as a sparsity-inducing regularizer, leads to a convex optimization problem, which has several benefits: the absence of extraneous local minima, well developed theory of globally convergent algorithms, even for large-scale problems. Convex regularization via the ℓ1 norm, however, tends to under-estimate the non-zero values of sparse signals. In order to estimate the non-zero values more accurately, non-convex regularization is often favored over convex regularization. However, non-convex regularization generally leads to non-convex optimization, which suffers from numerous issues: convergence may be guaranteed to only a stationary point, problem specific parameters may be difficult to set, and the solution is sensitive to the initialization of the algorithm. The first part of this thesis is aimed toward combining the benefits of non-convex regularization and convex optimization to estimate sparse signals more effectively. To this end, we propose to use parameterized non-convex regularizers with designated non-convexity and provide a range for the non-convex parameter so as to ensure that the objective function is strictly convex. By ensuring convexity of the objective function (sum of data-fidelity and non-convex regularizer), we can make use of a wide variety of convex optimization algorithms to obtain the unique global minimum reliably. The second part of this thesis proposes a non-linear signal

Regularity and predictability of human mobility in personal space.

Directory of Open Access Journals (Sweden)

Daniel Austin

Full Text Available Fundamental laws governing human mobility have many important applications such as forecasting and controlling epidemics or optimizing transportation systems. These mobility patterns, studied in the context of out of home activity during travel or social interactions with observations recorded from cell phone use or diffusion of money, suggest that in extra-personal space humans follow a high degree of temporal and spatial regularity - most often in the form of time-independent universal scaling laws. Here we show that mobility patterns of older individuals in their home also show a high degree of predictability and regularity, although in a different way than has been reported for out-of-home mobility. Studying a data set of almost 15 million observations from 19 adults spanning up to 5 years of unobtrusive longitudinal home activity monitoring, we find that in-home mobility is not well represented by a universal scaling law, but that significant structure (predictability and regularity is uncovered when explicitly accounting for contextual data in a model of in-home mobility. These results suggest that human mobility in personal space is highly stereotyped, and that monitoring discontinuities in routine room-level mobility patterns may provide an opportunity to predict individual human health and functional status or detect adverse events and trends.

Quantifying and mapping spatial variability in simulated forest plots

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Gavin R. Corral; Harold E. Burkhart

2016-01-01

We used computer simulations to test the efficacy of multivariate statistical methods to detect, quantify, and map spatial variability of forest stands. Simulated stands were developed of regularly-spaced plantations of loblolly pine (Pinus taeda L.). We assumed no affects of competition or mortality, but random variability was added to individual tree characteristics...

Mixture models with entropy regularization for community detection in networks

Science.gov (United States)

Chang, Zhenhai; Yin, Xianjun; Jia, Caiyan; Wang, Xiaoyang

2018-04-01

Community detection is a key exploratory tool in network analysis and has received much attention in recent years. NMM (Newman's mixture model) is one of the best models for exploring a range of network structures including community structure, bipartite and core-periphery structures, etc. However, NMM needs to know the number of communities in advance. Therefore, in this study, we have proposed an entropy regularized mixture model (called EMM), which is capable of inferring the number of communities and identifying network structure contained in a network, simultaneously. In the model, by minimizing the entropy of mixing coefficients of NMM using EM (expectation-maximization) solution, the small clusters contained little information can be discarded step by step. The empirical study on both synthetic networks and real networks has shown that the proposed model EMM is superior to the state-of-the-art methods.

Detecting abrupt dynamic change based on changes in the fractal properties of spatial images

Science.gov (United States)

Liu, Qunqun; He, Wenping; Gu, Bin; Jiang, Yundi

2017-10-01

Many abrupt climate change events often cannot be detected timely by conventional abrupt detection methods until a few years after these events have occurred. The reason for this lag in detection is that abundant and long-term observational data are required for accurate abrupt change detection by these methods, especially for the detection of a regime shift. So, these methods cannot help us understand and forecast the evolution of the climate system in a timely manner. Obviously, spatial images, generated by a coupled spatiotemporal dynamical model, contain more information about a dynamic system than a single time series, and we find that spatial images show the fractal properties. The fractal properties of spatial images can be quantitatively characterized by the Hurst exponent, which can be estimated by two-dimensional detrended fluctuation analysis (TD-DFA). Based on this, TD-DFA is used to detect an abrupt dynamic change of a coupled spatiotemporal model. The results show that the TD-DFA method can effectively detect abrupt parameter changes in the coupled model by monitoring the changing in the fractal properties of spatial images. The present method provides a new way for abrupt dynamic change detection, which can achieve timely and efficient abrupt change detection results.

A flexible spatial scan statistic with a restricted likelihood ratio for detecting disease clusters.

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Tango, Toshiro; Takahashi, Kunihiko

2012-12-30

Spatial scan statistics are widely used tools for detection of disease clusters. Especially, the circular spatial scan statistic proposed by Kulldorff (1997) has been utilized in a wide variety of epidemiological studies and disease surveillance. However, as it cannot detect noncircular, irregularly shaped clusters, many authors have proposed different spatial scan statistics, including the elliptic version of Kulldorff's scan statistic. The flexible spatial scan statistic proposed by Tango and Takahashi (2005) has also been used for detecting irregularly shaped clusters. However, this method sets a feasible limitation of a maximum of 30 nearest neighbors for searching candidate clusters because of heavy computational load. In this paper, we show a flexible spatial scan statistic implemented with a restricted likelihood ratio proposed by Tango (2008) to (1) eliminate the limitation of 30 nearest neighbors and (2) to have surprisingly much less computational time than the original flexible spatial scan statistic. As a side effect, it is shown to be able to detect clusters with any shape reasonably well as the relative risk of the cluster becomes large via Monte Carlo simulation. We illustrate the proposed spatial scan statistic with data on mortality from cerebrovascular disease in the Tokyo Metropolitan area, Japan. Copyright © 2012 John Wiley & Sons, Ltd.

The Regularized Iteratively Reweighted MAD Method for Change Detection in Multi- and Hyperspectral Data

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

2007-01-01

This paper describes new extensions to the previously published multivariate alteration detection (MAD) method for change detection in bi-temporal, multi- and hypervariate data such as remote sensing imagery. Much like boosting methods often applied in data mining work, the iteratively reweighted...... to observations that show little change, i.e., for which the sum of squared, standardized MAD variates is small, and small weights are assigned to observations for which the sum is large. Like the original MAD method, the iterative extension is invariant to linear (affine) transformations of the original...... an agricultural region in Kenya, and hyperspectral airborne HyMap data from a small rural area in southeastern Germany are given. The latter case demonstrates the need for regularization....

Predicting detection performance with model observers: Fourier domain or spatial domain?

Science.gov (United States)

Chen, Baiyu; Yu, Lifeng; Leng, Shuai; Kofler, James; Favazza, Christopher; Vrieze, Thomas; McCollough, Cynthia

2016-02-27

The use of Fourier domain model observer is challenged by iterative reconstruction (IR), because IR algorithms are nonlinear and IR images have noise texture different from that of FBP. A modified Fourier domain model observer, which incorporates nonlinear noise and resolution properties, has been proposed for IR and needs to be validated with human detection performance. On the other hand, the spatial domain model observer is theoretically applicable to IR, but more computationally intensive than the Fourier domain method. The purpose of this study is to compare the modified Fourier domain model observer to the spatial domain model observer with both FBP and IR images, using human detection performance as the gold standard. A phantom with inserts of various low contrast levels and sizes was repeatedly scanned 100 times on a third-generation, dual-source CT scanner at 5 dose levels and reconstructed using FBP and IR algorithms. The human detection performance of the inserts was measured via a 2-alternative-forced-choice (2AFC) test. In addition, two model observer performances were calculated, including a Fourier domain non-prewhitening model observer and a spatial domain channelized Hotelling observer. The performance of these two mode observers was compared in terms of how well they correlated with human observer performance. Our results demonstrated that the spatial domain model observer correlated well with human observers across various dose levels, object contrast levels, and object sizes. The Fourier domain observer correlated well with human observers using FBP images, but overestimated the detection performance using IR images.

Minimum detection limit and spatial resolution of thin-sample field-emission electron probe microanalysis

International Nuclear Information System (INIS)

Kubo, Yugo; Hamada, Kotaro; Urano, Akira

2013-01-01

The minimum detection limit and spatial resolution for a thinned semiconductor sample were determined by electron probe microanalysis (EPMA) using a Schottky field emission (FE) electron gun and wavelength dispersive X-ray spectrometry. Comparison of the FE-EPMA results with those obtained using energy dispersive X-ray spectrometry in conjunction with scanning transmission electron microscopy, confirmed that FE-EPMA is largely superior in terms of detection sensitivity. Thin-sample FE-EPMA is demonstrated as a very effective method for high resolution, high sensitivity analysis in a laboratory environment because a high probe current and high signal-to-noise ratio can be achieved. - Highlights: • Minimum detection limit and spatial resolution determined for FE-EPMA. • Detection sensitivity of FE-EPMA greatly superior to that of STEM-EDX. • Minimum detection limit and spatial resolution controllable by probe current

SPATIAL MODELING OF SOLID-STATE REGULAR POLYHEDRA (SOLIDS OF PLATON IN AUTOCAD SYSTEM

Directory of Open Access Journals (Sweden)

P. V. Bezditko

2009-03-01

Full Text Available This article describes the technology of modeling regular polyhedra by graphic methods. The authors came to the conclusion that in order to create solid models of regular polyhedra the method of extrusion is best to use.

Moving target detection based on temporal-spatial information fusion for infrared image sequences

Science.gov (United States)

Toing, Wu-qin; Xiong, Jin-yu; Zeng, An-jun; Wu, Xiao-ping; Xu, Hao-peng

2009-07-01

Moving target detection and localization is one of the most fundamental tasks in visual surveillance. In this paper, through analyzing the advantages and disadvantages of the traditional approaches about moving target detection, a novel approach based on temporal-spatial information fusion is proposed for moving target detection. The proposed method combines the spatial feature in single frame and the temporal properties within multiple frames of an image sequence of moving target. First, the method uses the spatial image segmentation for target separation from background and uses the local temporal variance for extracting targets and wiping off the trail artifact. Second, the logical "and" operator is used to fuse the temporal and spatial information. In the end, to the fusion image sequence, the morphological filtering and blob analysis are used to acquire exact moving target. The algorithm not only requires minimal computation and memory but also quickly adapts to the change of background and environment. Comparing with other methods, such as the KDE, the Mixture of K Gaussians, etc., the simulation results show the proposed method has better validity and higher adaptive for moving target detection, especially in infrared image sequences with complex illumination change, noise change, and so on.

Neutron detection in the frame of spatial magnetic spin resonance

Energy Technology Data Exchange (ETDEWEB)

Jericha, Erwin, E-mail: jericha@ati.ac.at [TU Wien, Atominstitut, Stadionallee 2, 1020 Wien (Austria); Bosina, Joachim [TU Wien, Atominstitut, Stadionallee 2, 1020 Wien (Austria); Austrian Academy of Sciences, Stefan Meyer Institute, Boltzmanngasse 3, 1090 Wien (Austria); Institut Laue–Langevin, 71 Avenue des Martyrs, 38042 Grenoble (France); Geltenbort, Peter [Institut Laue–Langevin, 71 Avenue des Martyrs, 38042 Grenoble (France); Hino, Masahiro [Kyoto University, Research Reactor Institute, Kumatori, Osaka 590-0494 (Japan); Mach, Wilfried [TU Wien, Atominstitut, Stadionallee 2, 1020 Wien (Austria); Oda, Tatsuro [Kyoto University, Department of Nuclear Engineering, Kyoto 615-8540 (Japan); Badurek, Gerald [TU Wien, Atominstitut, Stadionallee 2, 1020 Wien (Austria)

2017-02-11

This work is related to neutron detection in the context of the polarised neutron optics technique of spatial magnetic spin resonance. By this technique neutron beams may be tailored in their spectral distribution and temporal structure. We have performed experiments with very cold neutrons (VCN) at the high-flux research reactor of the Institut Laue Langevin (ILL) in Grenoble to demonstrate the potential of this method. A combination of spatially and temporally resolving neutron detection allowed us to characterize a prototype neutron resonator. With this detector we were able to record neutron time-of-flight spectra, assess and minimise neutron background and provide for normalisation of the spectra owing to variations in reactor power and ambient conditions at the same time.

Three regularities of recognition memory: the role of bias.

Science.gov (United States)

Hilford, Andrew; Maloney, Laurence T; Glanzer, Murray; Kim, Kisok

2015-12-01

A basic assumption of Signal Detection Theory is that decisions are made on the basis of likelihood ratios. In a preceding paper, Glanzer, Hilford, and Maloney (Psychonomic Bulletin & Review, 16, 431-455, 2009) showed that the likelihood ratio assumption implies that three regularities will occur in recognition memory: (1) the Mirror Effect, (2) the Variance Effect, (3) the normalized Receiver Operating Characteristic (z-ROC) Length Effect. The paper offered formal proofs and computational demonstrations that decisions based on likelihood ratios produce the three regularities. A survey of data based on group ROCs from 36 studies validated the likelihood ratio assumption by showing that its three implied regularities are ubiquitous. The study noted, however, that bias, another basic factor in Signal Detection Theory, can obscure the Mirror Effect. In this paper we examine how bias affects the regularities at the theoretical level. The theoretical analysis shows: (1) how bias obscures the Mirror Effect, not the other two regularities, and (2) four ways to counter that obscuring. We then report the results of five experiments that support the theoretical analysis. The analyses and the experimental results also demonstrate: (1) that the three regularities govern individual, as well as group, performance, (2) alternative explanations of the regularities are ruled out, and (3) that Signal Detection Theory, correctly applied, gives a simple and unified explanation of recognition memory data.

Likelihood ratio decisions in memory: three implied regularities.

Science.gov (United States)

Glanzer, Murray; Hilford, Andrew; Maloney, Laurence T

2009-06-01

We analyze four general signal detection models for recognition memory that differ in their distributional assumptions. Our analyses show that a basic assumption of signal detection theory, the likelihood ratio decision axis, implies three regularities in recognition memory: (1) the mirror effect, (2) the variance effect, and (3) the z-ROC length effect. For each model, we present the equations that produce the three regularities and show, in computed examples, how they do so. We then show that the regularities appear in data from a range of recognition studies. The analyses and data in our study support the following generalization: Individuals make efficient recognition decisions on the basis of likelihood ratios.

Breast cancer mitosis detection in histopathological images with spatial feature extraction

Science.gov (United States)

Albayrak, Abdülkadir; Bilgin, Gökhan

2013-12-01

In this work, cellular mitosis detection in histopathological images has been investigated. Mitosis detection is very expensive and time consuming process. Development of digital imaging in pathology has enabled reasonable and effective solution to this problem. Segmentation of digital images provides easier analysis of cell structures in histopathological data. To differentiate normal and mitotic cells in histopathological images, feature extraction step is very crucial step for the system accuracy. A mitotic cell has more distinctive textural dissimilarities than the other normal cells. Hence, it is important to incorporate spatial information in feature extraction or in post-processing steps. As a main part of this study, Haralick texture descriptor has been proposed with different spatial window sizes in RGB and La*b* color spaces. So, spatial dependencies of normal and mitotic cellular pixels can be evaluated within different pixel neighborhoods. Extracted features are compared with various sample sizes by Support Vector Machines using k-fold cross validation method. According to the represented results, it has been shown that separation accuracy on mitotic and non-mitotic cellular pixels gets better with the increasing size of spatial window.

The regularized monotonicity method: detecting irregular indefinite inclusions

DEFF Research Database (Denmark)

Garde, Henrik; Staboulis, Stratos

2018-01-01

inclusions, where the conductivity distribution has both more and less conductive parts relative to the background conductivity; one such method is the monotonicity method of Harrach, Seo, and Ullrich. We formulate the method for irregular indefinite inclusions, meaning that we make no regularity assumptions...

Regular Topographic Patterning of Karst Depressions Suggests Landscape Self-Organization

Science.gov (United States)

Quintero, C.; Cohen, M. J.

2017-12-01

Thousands of wetland depressions that are commonly host to cypress domes dot the sub-tropical limestone landscape of South Florida. The origin of these depression features has been the topic of debate. Here we build upon the work of previous surveyors of this landscape to analyze the morphology and spatial distribution of depressions on the Big Cypress landscape. We took advantage of the emergence and availability of high resolution Light Direction and Ranging (LiDAR) technology and ArcMap GIS software to analyze the structure and regularity of landscape features with methods unavailable to past surveyors. Six 2.25 km2 LiDAR plots within the preserve were selected for remote analysis and one depression feature within each plot was selected for more intensive sediment and water depth surveying. Depression features on the Big Cypress landscape were found to show strong evidence of regular spatial patterning. Periodicity, a feature of regularly patterned landscapes, is apparent in both Variograms and Radial Spectrum Analyses. Size class distributions of the identified features indicate constrained feature sizes while Average Nearest Neighbor analyses support the inference of dispersed features with non-random spacing. The presence of regular patterning on this landscape strongly implies biotic reinforcement of spatial structure by way of the scale dependent feedback. In characterizing the structure of this wetland landscape we add to the growing body of work dedicated to documenting how water, life and geology may interact to shape the natural landscapes we see today.

UNFOLDED REGULAR AND SEMI-REGULAR POLYHEDRA

Directory of Open Access Journals (Sweden)

IONIŢĂ Elena

2015-06-01

Full Text Available This paper proposes a presentation unfolding regular and semi-regular polyhedra. Regular polyhedra are convex polyhedra whose faces are regular and equal polygons, with the same number of sides, and whose polyhedral angles are also regular and equal. Semi-regular polyhedra are convex polyhedra with regular polygon faces, several types and equal solid angles of the same type. A net of a polyhedron is a collection of edges in the plane which are the unfolded edges of the solid. Modeling and unfolding Platonic and Arhimediene polyhedra will be using 3dsMAX program. This paper is intended as an example of descriptive geometry applications.

EIT image reconstruction with four dimensional regularization.

Science.gov (United States)

Dai, Tao; Soleimani, Manuchehr; Adler, Andy

2008-09-01

Electrical impedance tomography (EIT) reconstructs internal impedance images of the body from electrical measurements on body surface. The temporal resolution of EIT data can be very high, although the spatial resolution of the images is relatively low. Most EIT reconstruction algorithms calculate images from data frames independently, although data are actually highly correlated especially in high speed EIT systems. This paper proposes a 4-D EIT image reconstruction for functional EIT. The new approach is developed to directly use prior models of the temporal correlations among images and 3-D spatial correlations among image elements. A fast algorithm is also developed to reconstruct the regularized images. Image reconstruction is posed in terms of an augmented image and measurement vector which are concatenated from a specific number of previous and future frames. The reconstruction is then based on an augmented regularization matrix which reflects the a priori constraints on temporal and 3-D spatial correlations of image elements. A temporal factor reflecting the relative strength of the image correlation is objectively calculated from measurement data. Results show that image reconstruction models which account for inter-element correlations, in both space and time, show improved resolution and noise performance, in comparison to simpler image models.

Temporal Modulation Detection Depends on Sharpness of Spatial Tuning.

Science.gov (United States)

Zhou, Ning; Cadmus, Matthew; Dong, Lixue; Mathews, Juliana

2018-04-25

Prior research has shown that in electrical hearing, cochlear implant (CI) users' speech recognition performance is related in part to their ability to detect temporal modulation (i.e., modulation sensitivity). Previous studies have also shown better speech recognition when selectively stimulating sites with good modulation sensitivity rather than all stimulation sites. Site selection based on channel interaction measures, such as those using imaging or psychophysical estimates of spread of neural excitation, has also been shown to improve speech recognition. This led to the question of whether temporal modulation sensitivity and spatial selectivity of neural excitation are two related variables. In the present study, CI users' modulation sensitivity was compared for sites with relatively broad or narrow neural excitation patterns. This was achieved by measuring temporal modulation detection thresholds (MDTs) at stimulation sites that were significantly different in their sharpness of the psychophysical spatial tuning curves (PTCs) and measuring MDTs at the same sites in monopolar (MP) and bipolar (BP) stimulation modes. Nine postlingually deafened subjects implanted with Cochlear Nucleus® device took part in the study. Results showed a significant correlation between the sharpness of PTCs and MDTs, indicating that modulation detection benefits from a more spatially restricted neural activation pattern. There was a significant interaction between stimulation site and mode. That is, using BP stimulation only improved MDTs at stimulation sites with broad PTCs but had no effect or sometimes a detrimental effect on MDTs at stimulation sites with sharp PTCs. This interaction could suggest that a criterion number of nerve fibers is needed to achieve optimal temporal resolution, and, to achieve optimized speech recognition outcomes, individualized selection of site-specific current focusing strategies may be necessary. These results also suggest that the removal of

Temporal Data-Driven Sleep Scheduling and Spatial Data-Driven Anomaly Detection for Clustered Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Gang Li

2016-09-01

Full Text Available The spatial–temporal correlation is an important feature of sensor data in wireless sensor networks (WSNs. Most of the existing works based on the spatial–temporal correlation can be divided into two parts: redundancy reduction and anomaly detection. These two parts are pursued separately in existing works. In this work, the combination of temporal data-driven sleep scheduling (TDSS and spatial data-driven anomaly detection is proposed, where TDSS can reduce data redundancy. The TDSS model is inspired by transmission control protocol (TCP congestion control. Based on long and linear cluster structure in the tunnel monitoring system, cooperative TDSS and spatial data-driven anomaly detection are then proposed. To realize synchronous acquisition in the same ring for analyzing the situation of every ring, TDSS is implemented in a cooperative way in the cluster. To keep the precision of sensor data, spatial data-driven anomaly detection based on the spatial correlation and Kriging method is realized to generate an anomaly indicator. The experiment results show that cooperative TDSS can realize non-uniform sensing effectively to reduce the energy consumption. In addition, spatial data-driven anomaly detection is quite significant for maintaining and improving the precision of sensor data.

Improving PET spatial resolution and detectability for prostate cancer imaging

International Nuclear Information System (INIS)

Bal, H; Guerin, L; Casey, M E; Conti, M; Eriksson, L; Michel, C; Fanti, S; Pettinato, C; Adler, S; Choyke, P

2014-01-01

Prostate cancer, one of the most common forms of cancer among men, can benefit from recent improvements in positron emission tomography (PET) technology. In particular, better spatial resolution, lower noise and higher detectability of small lesions could be greatly beneficial for early diagnosis and could provide a strong support for guiding biopsy and surgery. In this article, the impact of improved PET instrumentation with superior spatial resolution and high sensitivity are discussed, together with the latest development in PET technology: resolution recovery and time-of-flight reconstruction. Using simulated cancer lesions, inserted in clinical PET images obtained with conventional protocols, we show that visual identification of the lesions and detectability via numerical observers can already be improved using state of the art PET reconstruction methods. This was achieved using both resolution recovery and time-of-flight reconstruction, and a high resolution image with 2 mm pixel size. Channelized Hotelling numerical observers showed an increase in the area under the LROC curve from 0.52 to 0.58. In addition, a relationship between the simulated input activity and the area under the LROC curve showed that the minimum detectable activity was reduced by more than 23%. (paper)

A visibility-based approach using regularization for imaging-spectroscopy in solar X-ray astronomy

Energy Technology Data Exchange (ETDEWEB)

Prato, M; Massone, A M; Piana, M [CNR - INFM LAMIA, Via Dodecaneso 33 1-16146 Genova (Italy); Emslie, A G [Department of Physics, Oklahoma State University, Stillwater, OK 74078 (United States); Hurford, G J [Space Sciences Laboratory, University of California at Berkeley, 8 Gauss Way, Berkeley, CA 94720-7450 (United States); Kontar, E P [Department of Physics and Astronomy, The University, Glasgow G12 8QQ, Scotland (United Kingdom); Schwartz, R A [CUA - Catholic University and LSSP at NASA Goddard Space Flight Center, code 671.1 Greenbelt, MD 20771 (United States)], E-mail: massone@ge.infm.it

2008-11-01

The Reuven Ramaty High-Energy Solar Spectroscopic Imager (RHESSI) is a nine-collimators satellite detecting X-rays and {gamma}-rays emitted by the Sun during flares. As the spacecraft rotates, imaging information is encoded as rapid time-variations of the detected flux. We recently proposed a method for the construction of electron flux maps at different electron energies from sets of count visibilities (i.e., direct, calibrated measurements of specific Fourier components of the source spatial structure) measured by RHESSI. The method requires the application of regularized inversion for the synthesis of electron visibility spectra and of imaging techniques for the reconstruction of two-dimensional electron flux maps. The method, already tested on real events registered by RHESSI, is validated in this paper by means of simulated realistic data.

Impacts of memory on a regular lattice for different population sizes with asynchronous update in spatial snowdrift game

Science.gov (United States)

Shu, Feng; Liu, Xingwen; Li, Min

2018-05-01

Memory is an important factor on the evolution of cooperation in spatial structure. For evolutionary biologists, the problem is often how cooperation acts can emerge in an evolving system. In the case of snowdrift game, it is found that memory can boost cooperation level for large cost-to-benefit ratio r, while inhibit cooperation for small r. Thus, how to enlarge the range of r for the purpose of enhancing cooperation becomes a hot issue recently. This paper addresses a new memory-based approach and its core lies in: Each agent applies the given rule to compare its own historical payoffs in a certain memory size, and take the obtained maximal one as virtual payoff. In order to get the optimal strategy, each agent randomly selects one of its neighbours to compare their virtual payoffs, which can lead to the optimal strategy. Both constant-size memory and size-varying memory are investigated by means of a scenario of asynchronous updating algorithm on regular lattices with different sizes. Simulation results show that this approach effectively enhances cooperation level in spatial structure and makes the high cooperation level simultaneously emerge for both small and large r. Moreover, it is discovered that population sizes have a significant influence on the effects of cooperation.

Detecting Temporal and Spatial Effects of Epithelial Cancers with Raman Spectroscopy

Directory of Open Access Journals (Sweden)

Matthew D. Keller

2008-01-01

Full Text Available Epithelial cancers, including those of the skin and cervix, are the most common type of cancers in humans. Many recent studies have attempted to use Raman spectroscopy to diagnose these cancers. In this paper, Raman spectral markers related to the temporal and spatial effects of cervical and skin cancers are examined through four separate but related studies. Results from a clinical cervix study show that previous disease has a significant effect on the Raman signatures of the cervix, which allow for near 100% classification for discriminating previous disease versus a true normal. A Raman microspectroscopy study showed that Raman can detect changes due to adjacent regions of dysplasia or HPV that cannot be detected histologically, while a clinical skin study showed that Raman spectra may be detecting malignancy associated changes in tissues surrounding nonmelanoma skin cancers. Finally, results of an organotypic raft culture study provided support for both the skin and the in vitro cervix results. These studies add to the growing body of evidence that optical spectroscopy, in this case Raman spectral markers, can be used to detect subtle temporal and spatial effects in tissue near cancerous sites that go otherwise undetected by conventional histology.

A hierarchical model for estimating the spatial distribution and abundance of animals detected by continuous-time recorders.

Directory of Open Access Journals (Sweden)

Robert M Dorazio

Full Text Available Several spatial capture-recapture (SCR models have been developed to estimate animal abundance by analyzing the detections of individuals in a spatial array of traps. Most of these models do not use the actual dates and times of detection, even though this information is readily available when using continuous-time recorders, such as microphones or motion-activated cameras. Instead most SCR models either partition the period of trap operation into a set of subjectively chosen discrete intervals and ignore multiple detections of the same individual within each interval, or they simply use the frequency of detections during the period of trap operation and ignore the observed times of detection. Both practices make inefficient use of potentially important information in the data.We developed a hierarchical SCR model to estimate the spatial distribution and abundance of animals detected with continuous-time recorders. Our model includes two kinds of point processes: a spatial process to specify the distribution of latent activity centers of individuals within the region of sampling and a temporal process to specify temporal patterns in the detections of individuals. We illustrated this SCR model by analyzing spatial and temporal patterns evident in the camera-trap detections of tigers living in and around the Nagarahole Tiger Reserve in India. We also conducted a simulation study to examine the performance of our model when analyzing data sets of greater complexity than the tiger data.Our approach provides three important benefits: First, it exploits all of the information in SCR data obtained using continuous-time recorders. Second, it is sufficiently versatile to allow the effects of both space use and behavior of animals to be specified as functions of covariates that vary over space and time. Third, it allows both the spatial distribution and abundance of individuals to be estimated, effectively providing a species distribution model, even in

Experimental study on the crack detection with optimized spatial wavelet analysis and windowing

Science.gov (United States)

Ghanbari Mardasi, Amir; Wu, Nan; Wu, Christine

2018-05-01

In this paper, a high sensitive crack detection is experimentally realized and presented on a beam under certain deflection by optimizing spatial wavelet analysis. Due to the crack existence in the beam structure, a perturbation/slop singularity is induced in the deflection profile. Spatial wavelet transformation works as a magnifier to amplify the small perturbation signal at the crack location to detect and localize the damage. The profile of a deflected aluminum cantilever beam is obtained for both intact and cracked beams by a high resolution laser profile sensor. Gabor wavelet transformation is applied on the subtraction of intact and cracked data sets. To improve detection sensitivity, scale factor in spatial wavelet transformation and the transformation repeat times are optimized. Furthermore, to detect the possible crack close to the measurement boundaries, wavelet transformation edge effect, which induces large values of wavelet coefficient around the measurement boundaries, is efficiently reduced by introducing different windowing functions. The result shows that a small crack with depth of less than 10% of the beam height can be localized with a clear perturbation. Moreover, the perturbation caused by a crack at 0.85 mm away from one end of the measurement range, which is covered by wavelet transform edge effect, emerges by applying proper window functions.

Research on photodiode detector-based spatial transient light detection and processing system

Science.gov (United States)

Liu, Meiying; Wang, Hu; Liu, Yang; Zhao, Hui; Nan, Meng

2016-10-01

In order to realize real-time signal identification and processing of spatial transient light, the features and the energy of the captured target light signal are first described and quantitatively calculated. Considering that the transient light signal has random occurrence, a short duration and an evident beginning and ending, a photodiode detector based spatial transient light detection and processing system is proposed and designed in this paper. This system has a large field of view and is used to realize non-imaging energy detection of random, transient and weak point target under complex background of spatial environment. Weak signal extraction under strong background is difficult. In this paper, considering that the background signal changes slowly and the target signal changes quickly, filter is adopted for signal's background subtraction. A variable speed sampling is realized by the way of sampling data points with a gradually increased interval. The two dilemmas that real-time processing of large amount of data and power consumption required by the large amount of data needed to be stored are solved. The test results with self-made simulative signal demonstrate the effectiveness of the design scheme. The practical system could be operated reliably. The detection and processing of the target signal under the strong sunlight background was realized. The results indicate that the system can realize real-time detection of target signal's characteristic waveform and monitor the system working parameters. The prototype design could be used in a variety of engineering applications.

Spatial correlation analysis of urban traffic state under a perspective of community detection

Science.gov (United States)

Yang, Yanfang; Cao, Jiandong; Qin, Yong; Jia, Limin; Dong, Honghui; Zhang, Aomuhan

2018-05-01

Understanding the spatial correlation of urban traffic state is essential for identifying the evolution patterns of urban traffic state. However, the distribution of traffic state always has characteristics of large spatial span and heterogeneity. This paper adapts the concept of community detection to the correlation network of urban traffic state and proposes a new perspective to identify the spatial correlation patterns of traffic state. In the proposed urban traffic network, the nodes represent road segments, and an edge between a pair of nodes is added depending on the result of significance test for the corresponding correlation of traffic state. Further, the process of community detection in the urban traffic network (named GWPA-K-means) is applied to analyze the spatial dependency of traffic state. The proposed method extends the traditional K-means algorithm in two steps: (i) redefines the initial cluster centers by two properties of nodes (the GWPA value and the minimum shortest path length); (ii) utilizes the weight signal propagation process to transfer the topological information of the urban traffic network into a node similarity matrix. Finally, numerical experiments are conducted on a simple network and a real urban road network in Beijing. The results show that GWPA-K-means algorithm is valid in spatial correlation analysis of traffic state. The network science and community structure analysis perform well in describing the spatial heterogeneity of traffic state on a large spatial scale.

Incrementally Detecting Change Types of Spatial Area Object: A Hierarchical Matching Method Considering Change Process

Directory of Open Access Journals (Sweden)

Yanhui Wang

2018-01-01

Full Text Available Detecting and extracting the change types of spatial area objects can track area objects’ spatiotemporal change pattern and provide the change backtracking mechanism for incrementally updating spatial datasets. To respond to the problems of high complexity of detection methods, high redundancy rate of detection factors, and the low automation degree during incrementally update process, we take into account the change process of area objects in an integrated way and propose a hierarchical matching method to detect the nine types of changes of area objects, while minimizing the complexity of the algorithm and the redundancy rate of detection factors. We illustrate in details the identification, extraction, and database entry of change types, and how we achieve a close connection and organic coupling of incremental information extraction and object type-of-change detection so as to characterize the whole change process. The experimental results show that this method can successfully detect incremental information about area objects in practical applications, with the overall accuracy reaching above 90%, which is much higher than the existing weighted matching method, making it quite feasible and applicable. It helps establish the corresponding relation between new-version and old-version objects, and facilitate the linked update processing and quality control of spatial data.

Improved resolution and reliability in dynamic PET using Bayesian regularization of MRTM2

DEFF Research Database (Denmark)

Agn, Mikael; Svarer, Claus; Frokjaer, Vibe G.

2014-01-01

This paper presents a mathematical model that regularizes dynamic PET data by using a Bayesian framework. We base the model on the well known two-parameter multilinear reference tissue method MRTM2 and regularize on the assumption that spatially close regions have similar parameters. The developed...... model is compared to the conventional approach of improving the low signal-to-noise ratio of PET data, i.e., spatial filtering of each time frame independently by a Gaussian kernel. We show that the model handles high levels of noise better than the conventional approach, while at the same time...

Chimeric mitochondrial peptides from contiguous regular and swinger RNA.

Science.gov (United States)

Seligmann, Hervé

2016-01-01

Previous mass spectrometry analyses described human mitochondrial peptides entirely translated from swinger RNAs, RNAs where polymerization systematically exchanged nucleotides. Exchanges follow one among 23 bijective transformation rules, nine symmetric exchanges (X ↔ Y, e.g. A ↔ C) and fourteen asymmetric exchanges (X → Y → Z → X, e.g. A → C → G → A), multiplying by 24 DNA's protein coding potential. Abrupt switches from regular to swinger polymerization produce chimeric RNAs. Here, human mitochondrial proteomic analyses assuming abrupt switches between regular and swinger transcriptions, detect chimeric peptides, encoded by part regular, part swinger RNA. Contiguous regular- and swinger-encoded residues within single peptides are stronger evidence for translation of swinger RNA than previously detected, entirely swinger-encoded peptides: regular parts are positive controls matched with contiguous swinger parts, increasing confidence in results. Chimeric peptides are 200 × rarer than swinger peptides (3/100,000 versus 6/1000). Among 186 peptides with > 8 residues for each regular and swinger parts, regular parts of eleven chimeric peptides correspond to six among the thirteen recognized, mitochondrial protein-coding genes. Chimeric peptides matching partly regular proteins are rarer and less expressed than chimeric peptides matching non-coding sequences, suggesting targeted degradation of misfolded proteins. Present results strengthen hypotheses that the short mitogenome encodes far more proteins than hitherto assumed. Entirely swinger-encoded proteins could exist.

Reliability and Minimum Detectable Change of Temporal-Spatial, Kinematic, and Dynamic Stability Measures during Perturbed Gait.

Directory of Open Access Journals (Sweden)

Christopher A Rábago

Full Text Available Temporal-spatial, kinematic variability, and dynamic stability measures collected during perturbation-based assessment paradigms are often used to identify dysfunction associated with gait instability. However, it remains unclear which measures are most reliable for detecting and tracking responses to perturbations. This study systematically determined the between-session reliability and minimum detectable change values of temporal-spatial, kinematic variability, and dynamic stability measures during three types of perturbed gait. Twenty young healthy adults completed two identical testing sessions two weeks apart, comprised of an unperturbed and three perturbed (cognitive, physical, and visual walking conditions in a virtual reality environment. Within each session, perturbation responses were compared to unperturbed walking using paired t-tests. Between-session reliability and minimum detectable change values were also calculated for each measure and condition. All temporal-spatial, kinematic variability and dynamic stability measures demonstrated fair to excellent between-session reliability. Minimal detectable change values, normalized to mean values ranged from 1-50%. Step width mean and variability measures demonstrated the greatest response to perturbations with excellent between-session reliability and low minimum detectable change values. Orbital stability measures demonstrated specificity to perturbation direction and sensitivity with excellent between-session reliability and low minimum detectable change values. We observed substantially greater between-session reliability and lower minimum detectable change values for local stability measures than previously described which may be the result of averaging across trials within a session and using velocity versus acceleration data for reconstruction of state spaces. Across all perturbation types, temporal-spatial, orbital and local measures were the most reliable measures with the

4D PET iterative deconvolution with spatiotemporal regularization for quantitative dynamic PET imaging.

Science.gov (United States)

Reilhac, Anthonin; Charil, Arnaud; Wimberley, Catriona; Angelis, Georgios; Hamze, Hasar; Callaghan, Paul; Garcia, Marie-Paule; Boisson, Frederic; Ryder, Will; Meikle, Steven R; Gregoire, Marie-Claude

2015-09-01

Quantitative measurements in dynamic PET imaging are usually limited by the poor counting statistics particularly in short dynamic frames and by the low spatial resolution of the detection system, resulting in partial volume effects (PVEs). In this work, we present a fast and easy to implement method for the restoration of dynamic PET images that have suffered from both PVE and noise degradation. It is based on a weighted least squares iterative deconvolution approach of the dynamic PET image with spatial and temporal regularization. Using simulated dynamic [(11)C] Raclopride PET data with controlled biological variations in the striata between scans, we showed that the restoration method provides images which exhibit less noise and better contrast between emitting structures than the original images. In addition, the method is able to recover the true time activity curve in the striata region with an error below 3% while it was underestimated by more than 20% without correction. As a result, the method improves the accuracy and reduces the variability of the kinetic parameter estimates calculated from the corrected images. More importantly it increases the accuracy (from less than 66% to more than 95%) of measured biological variations as well as their statistical detectivity. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

Tools for Multimode Quantum Information: Modulation, Detection, and Spatial Quantum Correlations

DEFF Research Database (Denmark)

Lassen, Mikael Østergaard; Delaubert, Vincent; Janousek, Jirí

2007-01-01

We present here all the tools required for continuous variable parallel quantum information protocols based on spatial multi-mode quantum correlations and entanglement. We describe techniques for encoding and detecting this quantum information with high efficiency in the individual modes. We use ...

Sharp spatially constrained inversion

DEFF Research Database (Denmark)

Vignoli, Giulio G.; Fiandaca, Gianluca G.; Christiansen, Anders Vest C A.V.C.

2013-01-01

We present sharp reconstruction of multi-layer models using a spatially constrained inversion with minimum gradient support regularization. In particular, its application to airborne electromagnetic data is discussed. Airborne surveys produce extremely large datasets, traditionally inverted...... by using smoothly varying 1D models. Smoothness is a result of the regularization constraints applied to address the inversion ill-posedness. The standard Occam-type regularized multi-layer inversion produces results where boundaries between layers are smeared. The sharp regularization overcomes...... inversions are compared against classical smooth results and available boreholes. With the focusing approach, the obtained blocky results agree with the underlying geology and allow for easier interpretation by the end-user....

Web-based GIS for spatial pattern detection: application to malaria incidence in Vietnam.

Science.gov (United States)

Bui, Thanh Quang; Pham, Hai Minh

2016-01-01

There is a great concern on how to build up an interoperable health information system of public health and health information technology within the development of public information and health surveillance programme. Technically, some major issues remain regarding to health data visualization, spatial processing of health data, health information dissemination, data sharing and the access of local communities to health information. In combination with GIS, we propose a technical framework for web-based health data visualization and spatial analysis. Data was collected from open map-servers and geocoded by open data kit package and data geocoding tools. The Web-based system is designed based on Open-source frameworks and libraries. The system provides Web-based analyst tool for pattern detection through three spatial tests: Nearest neighbour, K function, and Spatial Autocorrelation. The result is a web-based GIS, through which end users can detect disease patterns via selecting area, spatial test parameters and contribute to managers and decision makers. The end users can be health practitioners, educators, local communities, health sector authorities and decision makers. This web-based system allows for the improvement of health related services to public sector users as well as citizens in a secure manner. The combination of spatial statistics and web-based GIS can be a solution that helps empower health practitioners in direct and specific intersectional actions, thus provide for better analysis, control and decision-making.

Salient Object Detection via Structured Matrix Decomposition.

Science.gov (United States)

Peng, Houwen; Li, Bing; Ling, Haibin; Hu, Weiming; Xiong, Weihua; Maybank, Stephen J

2016-05-04

Low-rank recovery models have shown potential for salient object detection, where a matrix is decomposed into a low-rank matrix representing image background and a sparse matrix identifying salient objects. Two deficiencies, however, still exist. First, previous work typically assumes the elements in the sparse matrix are mutually independent, ignoring the spatial and pattern relations of image regions. Second, when the low-rank and sparse matrices are relatively coherent, e.g., when there are similarities between the salient objects and background or when the background is complicated, it is difficult for previous models to disentangle them. To address these problems, we propose a novel structured matrix decomposition model with two structural regularizations: (1) a tree-structured sparsity-inducing regularization that captures the image structure and enforces patches from the same object to have similar saliency values, and (2) a Laplacian regularization that enlarges the gaps between salient objects and the background in feature space. Furthermore, high-level priors are integrated to guide the matrix decomposition and boost the detection. We evaluate our model for salient object detection on five challenging datasets including single object, multiple objects and complex scene images, and show competitive results as compared with 24 state-of-the-art methods in terms of seven performance metrics.

A spatially offset Raman spectroscopy method for non-destructive detection of gelatin-encapsulated powders

Science.gov (United States)

Non-destructive subsurface detection of encapsulated, coated, or seal-packaged foods and pharmaceuticals can help prevent distribution and consumption of counterfeit or hazardous products. This study used a Spatially Offset Raman Spectroscopy (SORS) method to detect and identify urea, ibuprofen, and...

An Improved Fitness Evaluation Mechanism with Memory in Spatial Prisoner's Dilemma Game on Regular Lattices

International Nuclear Information System (INIS)

Wang Juan; Liu Li-Na; Dong En-Zeng; Wang Li

2013-01-01

To deeply understand the emergence of cooperation in natural, social and economical systems, we present an improved fitness evaluation mechanism with memory in spatial prisoner's dilemma game on regular lattices. In our model, the individual fitness is not only determined by the payoff in the current game round, but also by the payoffs in previous round bins. A tunable parameter, termed as the memory strength (μ), which lies between 0 and 1, is introduced into the model to regulate the ratio of payoffs of current and previous game rounds in the individual fitness calculation. When μ = 0, our model is reduced to the standard prisoner's dilemma game; while μ = 1 represents the case in which the payoff is totally determined by the initial strategies and thus it is far from the realistic ones. Extensive numerical simulations indicate that the memory effect can substantially promote the evolution of cooperation. For μ < 1, the stronger the memory effect, the higher the cooperation level, but μ = 1 leads to a pathological state of cooperation, but can partially enhance the cooperation in the very large temptation parameter. The current results are of great significance for us to account for the role of memory effect during the evolution of cooperation among selfish players. (general)

Spatially valid proprioceptive cues improve the detection of a visual stimulus

DEFF Research Database (Denmark)

Jackson, Carl P T; Miall, R Chris; Balslev, Daniela

2010-01-01

, which has been demonstrated for other modality pairings. The aim of this study was to test whether proprioceptive signals can spatially cue a visual target to improve its detection. Participants were instructed to use a planar manipulandum in a forward reaching action and determine during this movement...

An Examination of Three Spatial Event Cluster Detection Methods

Directory of Open Access Journals (Sweden)

Hensley H. Mariathas

2015-03-01

Full Text Available In spatial disease surveillance, geographic areas with large numbers of disease cases are to be identified, so that targeted investigations can be pursued. Geographic areas with high disease rates are called disease clusters and statistical cluster detection tests are used to identify geographic areas with higher disease rates than expected by chance alone. In some situations, disease-related events rather than individuals are of interest for geographical surveillance, and methods to detect clusters of disease-related events are called event cluster detection methods. In this paper, we examine three distributional assumptions for the events in cluster detection: compound Poisson, approximate normal and multiple hypergeometric (exact. The methods differ on the choice of distributional assumption for the potentially multiple correlated events per individual. The methods are illustrated on emergency department (ED presentations by children and youth (age < 18 years because of substance use in the province of Alberta, Canada, during 1 April 2007, to 31 March 2008. Simulation studies are conducted to investigate Type I error and the power of the clustering methods.

Early non-destructive biofouling detection and spatial distribution: Application of oxygen sensing optodes

KAUST Repository

Farhat, Nadia

2015-06-11

Biofouling is a serious problem in reverse osmosis/nanofiltration (RO/NF) applications, reducing membrane performance. Early detection of biofouling plays an essential role in an adequate anti-biofouling strategy. Presently, fouling of membrane filtration systems is mainly determined by measuring changes in pressure drop, which is not exclusively linked to biofouling. Non-destructive imaging of oxygen concentrations (i) is specific for biological activity of biofilms and (ii) may enable earlier detection of biofilm accumulation than pressure drop. The objective of this study was to test whether transparent luminescent planar O2 optodes, in combination with a simple imaging system, can be used for early non-destructive biofouling detection. This biofouling detection is done by mapping the two-dimensional distribution of O2 concentrations and O2 decrease rates inside a membrane fouling simulator (MFS). Results show that at an early stage, biofouling development was detected by the oxygen sensing optodes while no significant increase in pressure drop was yet observed. Additionally, optodes could detect spatial heterogeneities in biofouling distribution at a micro scale. Biofilm development started mainly at the feed spacer crossings. The spatial and quantitative information on biological activity will lead to better understanding of the biofouling processes, contributing to the development of more effective biofouling control strategies.

Chord length distributions between hard disks and spheres in regular, semi-regular, and quasi-random structures

International Nuclear Information System (INIS)

Olson, Gordon L.

2008-01-01

In binary stochastic media in two- and three-dimensions consisting of randomly placed impenetrable disks or spheres, the chord lengths in the background material between disks and spheres closely follow exponential distributions if the disks and spheres occupy less than 10% of the medium. This work demonstrates that for regular spatial structures of disks and spheres, the tails of the chord length distributions (CLDs) follow power laws rather than exponentials. In dilute media, when the disks and spheres are widely spaced, the slope of the power law seems to be independent of the details of the structure. When approaching a close-packed arrangement, the exact placement of the spheres can make a significant difference. When regular structures are perturbed by small random displacements, the CLDs become power laws with steeper slopes. An example CLD from a quasi-random distribution of spheres in clusters shows a modified exponential distribution

Chord length distributions between hard disks and spheres in regular, semi-regular, and quasi-random structures

Energy Technology Data Exchange (ETDEWEB)

Olson, Gordon L. [Computer and Computational Sciences Division (CCS-2), Los Alamos National Laboratory, 5 Foxglove Circle, Madison, WI 53717 (United States)], E-mail: olson99@tds.net

2008-11-15

In binary stochastic media in two- and three-dimensions consisting of randomly placed impenetrable disks or spheres, the chord lengths in the background material between disks and spheres closely follow exponential distributions if the disks and spheres occupy less than 10% of the medium. This work demonstrates that for regular spatial structures of disks and spheres, the tails of the chord length distributions (CLDs) follow power laws rather than exponentials. In dilute media, when the disks and spheres are widely spaced, the slope of the power law seems to be independent of the details of the structure. When approaching a close-packed arrangement, the exact placement of the spheres can make a significant difference. When regular structures are perturbed by small random displacements, the CLDs become power laws with steeper slopes. An example CLD from a quasi-random distribution of spheres in clusters shows a modified exponential distribution.

Detection and removal of spatial bias in multiwell assays.

Science.gov (United States)

Lachmann, Alexander; Giorgi, Federico M; Alvarez, Mariano J; Califano, Andrea

2016-07-01

Multiplex readout assays are now increasingly being performed using microfluidic automation in multiwell format. For instance, the Library of Integrated Network-based Cellular Signatures (LINCS) has produced gene expression measurements for tens of thousands of distinct cell perturbations using a 384-well plate format. This dataset is by far the largest 384-well gene expression measurement assay ever performed. We investigated the gene expression profiles of a million samples from the LINCS dataset and found that the vast majority (96%) of the tested plates were affected by a significant 2D spatial bias. Using a novel algorithm combining spatial autocorrelation detection and principal component analysis, we could remove most of the spatial bias from the LINCS dataset and show in parallel a dramatic improvement of similarity between biological replicates assayed in different plates. The proposed methodology is fully general and can be applied to any highly multiplexed assay performed in multiwell format. ac2248@columbia.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Diverse Regular Employees and Non-regular Employment (Japanese)

OpenAIRE

MORISHIMA Motohiro

2011-01-01

Currently there are high expectations for the introduction of policies related to diverse regular employees. These policies are a response to the problem of disparities between regular and non-regular employees (part-time, temporary, contract and other non-regular employees) and will make it more likely that workers can balance work and their private lives while companies benefit from the advantages of regular employment. In this paper, I look at two issues that underlie this discussion. The ...

Zero-crossing detection algorithm for arrays of optical spatial filtering velocimetry sensors

DEFF Research Database (Denmark)

Jakobsen, Michael Linde; Pedersen, Finn; Hanson, Steen Grüner

2008-01-01

This paper presents a zero-crossing detection algorithm for arrays of compact low-cost optical sensors based on spatial filtering for measuring fluctuations in angular velocity of rotating solid structures. The algorithm is applicable for signals with moderate signal-to-noise ratios, and delivers...... repeating the same measurement error for each revolution of the target, and to gain high performance measurement of angular velocity. The traditional zero-crossing detection is extended by 1) inserting an appropriate band-pass filter before the zero-crossing detection, 2) measuring time periods between zero...

Effective action for scalar fields and generalized zeta-function regularization

International Nuclear Information System (INIS)

Cognola, Guido; Zerbini, Sergio

2004-01-01

Motivated by the study of quantum fields in a Friedmann-Robertson-Walker space-time, the one-loop effective action for a scalar field defined in the ultrastatic manifold RxH 3 /Γ, H 3 /Γ being the finite volume, noncompact, hyperbolic spatial section, is investigated by a generalization of zeta-function regularization. It is shown that additional divergences may appear at the one-loop level. The one-loop renormalizability of the model is discussed and, making use of a generalization of zeta-function regularization, the one-loop renormalization group equations are derived

Analysis of laser-printed spatial resolution for mammographic microcalcification detection

International Nuclear Information System (INIS)

Smathers, R.L.; Kowarski, D.

1987-01-01

The detectability of microcalfications in mammograms was compared in Kodak Min-R screen-film mammograms versus digitized laser-printed films. Pulverized bone specks were used as the phantoms to produce the original mammograms. The mammograms were then digitized to a spatial resolution of 2,048 x, 2048 with 4,096 gray levels and laser-printed at spatial resolutions of 512 x 512, 1,024 x 1,024, and 2,048 x 2,048 with 256 gray levels. The number of bone specks was determined on a region-by region basis. The 512 x 512 resolution laser-printed images were nondiagnostic, 1,024 x 1,024 images were better, and 2,048 x 2,048 images were quite comparable to the original screen-film mammograms

Energy functions for regularization algorithms

Science.gov (United States)

Delingette, H.; Hebert, M.; Ikeuchi, K.

1991-01-01

Regularization techniques are widely used for inverse problem solving in computer vision such as surface reconstruction, edge detection, or optical flow estimation. Energy functions used for regularization algorithms measure how smooth a curve or surface is, and to render acceptable solutions these energies must verify certain properties such as invariance with Euclidean transformations or invariance with parameterization. The notion of smoothness energy is extended here to the notion of a differential stabilizer, and it is shown that to void the systematic underestimation of undercurvature for planar curve fitting, it is necessary that circles be the curves of maximum smoothness. A set of stabilizers is proposed that meet this condition as well as invariance with rotation and parameterization.

Fast entanglement detection for unknown states of two spatial qutrits

International Nuclear Information System (INIS)

Lima, G.; Gomez, E. S.; Saavedra, C.; Vargas, A.; Vianna, R. O.

2010-01-01

We investigate the practicality of the method proposed by Maciel et al. [Phys. Rev. A. 80, 032325 (2009).] for detecting the entanglement of two spatial qutrits (three-dimensional quantum systems), which are encoded in the discrete transverse momentum of single photons transmitted through a multislit aperture. The method is based on the acquisition of partial information of the quantum state through projective measurements, and a data processing analysis done with semidefinite programs. This analysis relies on generating gradually an optimal entanglement witness operator, and numerical investigations have shown that it allows for the entanglement detection of unknown states with a cost much lower than full state tomography.

Deep Spatial-Temporal Joint Feature Representation for Video Object Detection.

Science.gov (United States)

Zhao, Baojun; Zhao, Boya; Tang, Linbo; Han, Yuqi; Wang, Wenzheng

2018-03-04

With the development of deep neural networks, many object detection frameworks have shown great success in the fields of smart surveillance, self-driving cars, and facial recognition. However, the data sources are usually videos, and the object detection frameworks are mostly established on still images and only use the spatial information, which means that the feature consistency cannot be ensured because the training procedure loses temporal information. To address these problems, we propose a single, fully-convolutional neural network-based object detection framework that involves temporal information by using Siamese networks. In the training procedure, first, the prediction network combines the multiscale feature map to handle objects of various sizes. Second, we introduce a correlation loss by using the Siamese network, which provides neighboring frame features. This correlation loss represents object co-occurrences across time to aid the consistent feature generation. Since the correlation loss should use the information of the track ID and detection label, our video object detection network has been evaluated on the large-scale ImageNet VID dataset where it achieves a 69.5% mean average precision (mAP).

Complex sparse spatial filter for decoding mixed frequency and phase coded steady-state visually evoked potentials.

Science.gov (United States)

Morikawa, Naoki; Tanaka, Toshihisa; Islam, Md Rabiul

2018-07-01

Mixed frequency and phase coding (FPC) can achieve the significant increase of the number of commands in steady-state visual evoked potential-based brain-computer interface (SSVEP-BCI). However, the inconsistent phases of the SSVEP over channels in a trial and the existence of non-contributing channels due to noise effects can decrease accurate detection of stimulus frequency. We propose a novel command detection method based on a complex sparse spatial filter (CSSF) by solving ℓ 1 - and ℓ 2,1 -regularization problems for a mixed-coded SSVEP-BCI. In particular, ℓ 2,1 -regularization (aka group sparsification) can lead to the rejection of electrodes that are not contributing to the SSVEP detection. A calibration data based canonical correlation analysis (CCA) and CSSF with ℓ 1 - and ℓ 2,1 -regularization cases were demonstrated for a 16-target stimuli with eleven subjects. The results of statistical test suggest that the proposed method with ℓ 1 - and ℓ 2,1 -regularization significantly achieved the highest ITR. The proposed approaches do not need any reference signals, automatically select prominent channels, and reduce the computational cost compared to the other mixed frequency-phase coding (FPC)-based BCIs. The experimental results suggested that the proposed method can be usable implementing BCI effectively with reduce visual fatigue. Copyright © 2018 Elsevier B.V. All rights reserved.

Optimal Design of the Adaptive Normalized Matched Filter Detector using Regularized Tyler Estimators

KAUST Repository

Kammoun, Abla; Couillet, Romain; Pascal, Frederic; Alouini, Mohamed-Slim

2017-01-01

This article addresses improvements on the design of the adaptive normalized matched filter (ANMF) for radar detection. It is well-acknowledged that the estimation of the noise-clutter covariance matrix is a fundamental step in adaptive radar detection. In this paper, we consider regularized estimation methods which force by construction the eigenvalues of the covariance estimates to be greater than a positive regularization parameter ρ. This makes them more suitable for high dimensional problems with a limited number of secondary data samples than traditional sample covariance estimates. The motivation behind this work is to understand the effect and properly set the value of ρthat improves estimate conditioning while maintaining a low estimation bias. More specifically, we consider the design of the ANMF detector for two kinds of regularized estimators, namely the regularized sample covariance matrix (RSCM), the regularized Tyler estimator (RTE). The rationale behind this choice is that the RTE is efficient in mitigating the degradation caused by the presence of impulsive noises while inducing little loss when the noise is Gaussian. Based on asymptotic results brought by recent tools from random matrix theory, we propose a design for the regularization parameter that maximizes the asymptotic detection probability under constant asymptotic false alarm rates. Provided Simulations support the efficiency of the proposed method, illustrating its gain over conventional settings of the regularization parameter.

Optimal Design of the Adaptive Normalized Matched Filter Detector using Regularized Tyler Estimators

KAUST Repository

Kammoun, Abla

2017-10-25

This article addresses improvements on the design of the adaptive normalized matched filter (ANMF) for radar detection. It is well-acknowledged that the estimation of the noise-clutter covariance matrix is a fundamental step in adaptive radar detection. In this paper, we consider regularized estimation methods which force by construction the eigenvalues of the covariance estimates to be greater than a positive regularization parameter ρ. This makes them more suitable for high dimensional problems with a limited number of secondary data samples than traditional sample covariance estimates. The motivation behind this work is to understand the effect and properly set the value of ρthat improves estimate conditioning while maintaining a low estimation bias. More specifically, we consider the design of the ANMF detector for two kinds of regularized estimators, namely the regularized sample covariance matrix (RSCM), the regularized Tyler estimator (RTE). The rationale behind this choice is that the RTE is efficient in mitigating the degradation caused by the presence of impulsive noises while inducing little loss when the noise is Gaussian. Based on asymptotic results brought by recent tools from random matrix theory, we propose a design for the regularization parameter that maximizes the asymptotic detection probability under constant asymptotic false alarm rates. Provided Simulations support the efficiency of the proposed method, illustrating its gain over conventional settings of the regularization parameter.

Deep Spatial-Temporal Joint Feature Representation for Video Object Detection

Directory of Open Access Journals (Sweden)

Baojun Zhao

2018-03-01

Full Text Available With the development of deep neural networks, many object detection frameworks have shown great success in the fields of smart surveillance, self-driving cars, and facial recognition. However, the data sources are usually videos, and the object detection frameworks are mostly established on still images and only use the spatial information, which means that the feature consistency cannot be ensured because the training procedure loses temporal information. To address these problems, we propose a single, fully-convolutional neural network-based object detection framework that involves temporal information by using Siamese networks. In the training procedure, first, the prediction network combines the multiscale feature map to handle objects of various sizes. Second, we introduce a correlation loss by using the Siamese network, which provides neighboring frame features. This correlation loss represents object co-occurrences across time to aid the consistent feature generation. Since the correlation loss should use the information of the track ID and detection label, our video object detection network has been evaluated on the large-scale ImageNet VID dataset where it achieves a 69.5% mean average precision (mAP.

Filter Bank Regularized Common Spatial Pattern Ensemble for Small Sample Motor Imagery Classification.

Science.gov (United States)

Park, Sang-Hoon; Lee, David; Lee, Sang-Goog

2018-02-01

For the last few years, many feature extraction methods have been proposed based on biological signals. Among these, the brain signals have the advantage that they can be obtained, even by people with peripheral nervous system damage. Motor imagery electroencephalograms (EEG) are inexpensive to measure, offer a high temporal resolution, and are intuitive. Therefore, these have received a significant amount of attention in various fields, including signal processing, cognitive science, and medicine. The common spatial pattern (CSP) algorithm is a useful method for feature extraction from motor imagery EEG. However, performance degradation occurs in a small-sample setting (SSS), because the CSP depends on sample-based covariance. Since the active frequency range is different for each subject, it is also inconvenient to set the frequency range to be different every time. In this paper, we propose the feature extraction method based on a filter bank to solve these problems. The proposed method consists of five steps. First, motor imagery EEG is divided by a using filter bank. Second, the regularized CSP (R-CSP) is applied to the divided EEG. Third, we select the features according to mutual information based on the individual feature algorithm. Fourth, parameter sets are selected for the ensemble. Finally, we classify using ensemble based on features. The brain-computer interface competition III data set IVa is used to evaluate the performance of the proposed method. The proposed method improves the mean classification accuracy by 12.34%, 11.57%, 9%, 4.95%, and 4.47% compared with CSP, SR-CSP, R-CSP, filter bank CSP (FBCSP), and SR-FBCSP. Compared with the filter bank R-CSP ( , ), which is a parameter selection version of the proposed method, the classification accuracy is improved by 3.49%. In particular, the proposed method shows a large improvement in performance in the SSS.

Coordinate-invariant regularization

International Nuclear Information System (INIS)

Halpern, M.B.

1987-01-01

A general phase-space framework for coordinate-invariant regularization is given. The development is geometric, with all regularization contained in regularized DeWitt Superstructures on field deformations. Parallel development of invariant coordinate-space regularization is obtained by regularized functional integration of the momenta. As representative examples of the general formulation, the regularized general non-linear sigma model and regularized quantum gravity are discussed. copyright 1987 Academic Press, Inc

Temporal and spatial predictability of an irrelevant event differently affect detection and memory of items in a visual sequence

Directory of Open Access Journals (Sweden)

Junji eOhyama

2016-02-01

Full Text Available We examined how the temporal and spatial predictability of a task-irrelevant visual event affects the detection and memory of a visual item embedded in a continuously changing sequence. Participants observed 11 sequentially presented letters, during which a task-irrelevant visual event was either present or absent. Predictabilities of spatial location and temporal position of the event were controlled in 2 × 2 conditions. In the spatially predictable conditions, the event occurred at the same location within the stimulus sequence or at another location, while, in the spatially unpredictable conditions, it occurred at random locations. In the temporally predictable conditions, the event timing was fixed relative to the order of the letters, while in the temporally unpredictable condition, it could not be predicted from the letter order. Participants performed a working memory task and a target detection reaction time task. Memory accuracy was higher for a letter simultaneously presented at the same location as the event in the temporally unpredictable conditions, irrespective of the spatial predictability of the event. On the other hand, the detection reaction times were only faster for a letter simultaneously presented at the same location as the event when the event was both temporally and spatially predictable. Thus, to facilitate ongoing detection processes, an event must be predictable both in space and time, while memory processes are enhanced by temporally unpredictable (i.e., surprising events. Evidently, temporal predictability has differential effects on detection and memory of a visual item embedded in a sequence of images.

Comparisons of lesion detectability in ultrasound images acquired using time-shift compensation and spatial compounding.

Science.gov (United States)

Lacefield, James C; Pilkington, Wayne C; Waag, Robert C

2004-12-01

The effects of aberration, time-shift compensation, and spatial compounding on the discrimination of positive-contrast lesions in ultrasound b-scan images are investigated using a two-dimensional (2-D) array system and tissue-mimicking phantoms. Images were acquired within an 8.8 x 12-mm2 field of view centered on one of four statistically similar 4-mm diameter spherical lesions. Each lesion was imaged in four planes offset by successive 45 degree rotations about the central scan line. Images of the lesions were acquired using conventional geometric focusing through a water path, geometric focusing through a 35-mm thick distributed aberration phantom, and time-shift compensated transmit and receive focusing through the aberration phantom. The views of each lesion were averaged to form sets of water path, aberrated, and time-shift compensated 4:1 compound images and 16:1 compound images. The contrast ratio and detectability index of each image were computed to assess lesion differentiation. In the presence of aberration representative of breast or abdominal wall tissue, time-shift compensation provided statistically significant improvements of contrast ratio but did not consistently affect the detectability index, and spatial compounding significantly increased the detectability index but did not alter the contrast ratio. Time-shift compensation and spatial compounding thus provide complementary benefits to lesion detection.

Network hydraulics inclusion in water quality event detection using multiple sensor stations data.

Science.gov (United States)

Oliker, Nurit; Ostfeld, Avi

2015-09-01

Event detection is one of the current most challenging topics in water distribution systems analysis: how regular on-line hydraulic (e.g., pressure, flow) and water quality (e.g., pH, residual chlorine, turbidity) measurements at different network locations can be efficiently utilized to detect water quality contamination events. This study describes an integrated event detection model which combines multiple sensor stations data with network hydraulics. To date event detection modelling is likely limited to single sensor station location and dataset. Single sensor station models are detached from network hydraulics insights and as a result might be significantly exposed to false positive alarms. This work is aimed at decreasing this limitation through integrating local and spatial hydraulic data understanding into an event detection model. The spatial analysis complements the local event detection effort through discovering events with lower signatures by exploring the sensors mutual hydraulic influences. The unique contribution of this study is in incorporating hydraulic simulation information into the overall event detection process of spatially distributed sensors. The methodology is demonstrated on two example applications using base runs and sensitivity analyses. Results show a clear advantage of the suggested model over single-sensor event detection schemes. Copyright © 2015 Elsevier Ltd. All rights reserved.

Spatial capture-recapture: a promising method for analyzing data collected using artificial cover objects

Science.gov (United States)

Sutherland, Chris; Munoz, David; Miller, David A.W.; Grant, Evan H. Campbell

2016-01-01

Spatial capture–recapture (SCR) is a relatively recent development in ecological statistics that provides a spatial context for estimating abundance and space use patterns, and improves inference about absolute population density. SCR has been applied to individual encounter data collected noninvasively using methods such as camera traps, hair snares, and scat surveys. Despite the widespread use of capture-based surveys to monitor amphibians and reptiles, there are few applications of SCR in the herpetological literature. We demonstrate the utility of the application of SCR for studies of reptiles and amphibians by analyzing capture–recapture data from Red-Backed Salamanders, Plethodon cinereus, collected using artificial cover boards. Using SCR to analyze spatial encounter histories of marked individuals, we found evidence that density differed little among four sites within the same forest (on average, 1.59 salamanders/m2) and that salamander detection probability peaked in early October (Julian day 278) reflecting expected surface activity patterns of the species. The spatial scale of detectability, a measure of space use, indicates that the home range size for this population of Red-Backed Salamanders in autumn was 16.89 m2. Surveying reptiles and amphibians using artificial cover boards regularly generates spatial encounter history data of known individuals, which can readily be analyzed using SCR methods, providing estimates of absolute density and inference about the spatial scale of habitat use.

A simple homogeneous model for regular and irregular metallic wire media samples

Science.gov (United States)

Kosulnikov, S. Y.; Mirmoosa, M. S.; Simovski, C. R.

2018-02-01

To simplify the solution of electromagnetic problems with wire media samples, it is reasonable to treat them as the samples of a homogeneous material without spatial dispersion. The account of spatial dispersion implies additional boundary conditions and makes the solution of boundary problems difficult especially if the sample is not an infinitely extended layer. Moreover, for a novel type of wire media - arrays of randomly tilted wires - a spatially dispersive model has not been developed. Here, we introduce a simplistic heuristic model of wire media samples shaped as bricks. Our model covers WM of both regularly and irregularly stretched wires.

Spatial resolution and chest nodule detection: an interesting incidental finding

Science.gov (United States)

Toomey, R. J.; McEntee, M. F.; Ryan, J. T.; Evanoff, M. G.; Hayes, A.; Brennan, P. C.

2010-02-01

This study reports an incidental finding from a larger work. It examines the relationship between spatial resolution and nodule detection for chest radiographs. Twelve examining radiologists with the American Board of Radiology read thirty chest radiographs in two conditions - full (1500 × 1500 pixel) resolution, and 300 × 300 pixel resolution linearly interpolated to 1500 × 1500 pixels. All images were surrounded by a 10-pixel sharp grey border to aid in focussing the observer's eye when viewing the comparatively unsharp interpolated images. Fifteen of the images contained a single simulated pulmonary nodule. Observers were asked to rate their confidence that a nodule was present on each radiograph on a scale of 1 (least confidence, certain no lesion is present) to 6 (most confidence, certain a lesion was present). All other abnormalities were to be ignored. No windowing, levelling or magnification of the images was permitted and viewing distance was constrained to approximately 70cm. Images were displayed on a 3 megapixel greyscale monitor. Receiver operating characteristic (ROC) analysis was applied to the results of the readings using the Dorfman-Berbaum-Metz multiplereader, multiple-case method. No statistically significant differences were found with either readers and cases treated as random or with cases treated as fixed. Low spatial frequency information appears to be sufficient for the detection of chest lesion of the type used in this study.

Improved QRD-M Detection Algorithm for Generalized Spatial Modulation Scheme

Directory of Open Access Journals (Sweden)

Xiaorong Jing

2017-01-01

Full Text Available Generalized spatial modulation (GSM is a spectral and energy efficient multiple-input multiple-output (MIMO transmission scheme. It will lead to imperfect detection performance with relatively high computational complexity by directly applying the original QR-decomposition with M algorithm (QRD-M to the GSM scheme. In this paper an improved QRD-M algorithm is proposed for GSM signal detection, which achieves near-optimal performance but with relatively low complexity. Based on the QRD, the improved algorithm firstly transforms the maximum likelihood (ML detection of the GSM signals into searching an inverted tree structure. Then, in the searching process of the M branches, the branches corresponding to the illegitimate transmit antenna combinations (TACs and related to invalid number of active antennas are cut in order to improve the validity of the resultant branches at each level by taking advantage of characteristics of GSM signals. Simulation results show that the improved QRD-M detection algorithm provides similar performance to maximum likelihood (ML with the reduced computational complexity compared to the original QRD-M algorithm, and the optimal value of parameter M of the improved QRD-M algorithm for detection of the GSM scheme is equal to modulation order plus one.

Simultaneous detection of landmarks and key-frame in cardiac perfusion MRI using a joint spatial-temporal context model

Science.gov (United States)

Lu, Xiaoguang; Xue, Hui; Jolly, Marie-Pierre; Guetter, Christoph; Kellman, Peter; Hsu, Li-Yueh; Arai, Andrew; Zuehlsdorff, Sven; Littmann, Arne; Georgescu, Bogdan; Guehring, Jens

2011-03-01

Cardiac perfusion magnetic resonance imaging (MRI) has proven clinical significance in diagnosis of heart diseases. However, analysis of perfusion data is time-consuming, where automatic detection of anatomic landmarks and key-frames from perfusion MR sequences is helpful for anchoring structures and functional analysis of the heart, leading toward fully automated perfusion analysis. Learning-based object detection methods have demonstrated their capabilities to handle large variations of the object by exploring a local region, i.e., context. Conventional 2D approaches take into account spatial context only. Temporal signals in perfusion data present a strong cue for anchoring. We propose a joint context model to encode both spatial and temporal evidence. In addition, our spatial context is constructed not only based on the landmark of interest, but also the landmarks that are correlated in the neighboring anatomies. A discriminative model is learned through a probabilistic boosting tree. A marginal space learning strategy is applied to efficiently learn and search in a high dimensional parameter space. A fully automatic system is developed to simultaneously detect anatomic landmarks and key frames in both RV and LV from perfusion sequences. The proposed approach was evaluated on a database of 373 cardiac perfusion MRI sequences from 77 patients. Experimental results of a 4-fold cross validation show superior landmark detection accuracies of the proposed joint spatial-temporal approach to the 2D approach that is based on spatial context only. The key-frame identification results are promising.

Early detection of tuberculosis outbreaks among the San Francisco homeless: trade-offs between spatial resolution and temporal scale.

Directory of Open Access Journals (Sweden)

Brandon W Higgs

Full Text Available BACKGROUND: San Francisco has the highest rate of tuberculosis (TB in the U.S. with recurrent outbreaks among the homeless and marginally housed. It has been shown for syndromic data that when exact geographic coordinates of individual patients are used as the spatial base for outbreak detection, higher detection rates and accuracy are achieved compared to when data are aggregated into administrative regions such as zip codes and census tracts. We examine the effect of varying the spatial resolution in the TB data within the San Francisco homeless population on detection sensitivity, timeliness, and the amount of historical data needed to achieve better performance measures. METHODS AND FINDINGS: We apply a variation of space-time permutation scan statistic to the TB data in which a patient's location is either represented by its exact coordinates or by the centroid of its census tract. We show that the detection sensitivity and timeliness of the method generally improve when exact locations are used to identify real TB outbreaks. When outbreaks are simulated, while the detection timeliness is consistently improved when exact coordinates are used, the detection sensitivity varies depending on the size of the spatial scanning window and the number of tracts in which cases are simulated. Finally, we show that when exact locations are used, smaller amount of historical data is required for training the model. CONCLUSION: Systematic characterization of the spatio-temporal distribution of TB cases can widely benefit real time surveillance and guide public health investigations of TB outbreaks as to what level of spatial resolution results in improved detection sensitivity and timeliness. Trading higher spatial resolution for better performance is ultimately a tradeoff between maintaining patient confidentiality and improving public health when sharing data. Understanding such tradeoffs is critical to managing the complex interplay between public

A sparsity-regularized Born iterative method for reconstruction of two-dimensional piecewise continuous inhomogeneous domains

KAUST Repository

Sandhu, Ali Imran; Desmal, Abdulla; Bagci, Hakan

2016-01-01

A sparsity-regularized Born iterative method (BIM) is proposed for efficiently reconstructing two-dimensional piecewise-continuous inhomogeneous dielectric profiles. Such profiles are typically not spatially sparse, which reduces the efficiency of the sparsity-promoting regularization. To overcome this problem, scattered fields are represented in terms of the spatial derivative of the dielectric profile and reconstruction is carried out over samples of the dielectric profile's derivative. Then, like the conventional BIM, the nonlinear problem is iteratively converted into a sequence of linear problems (in derivative samples) and sparsity constraint is enforced on each linear problem using the thresholded Landweber iterations. Numerical results, which demonstrate the efficiency and accuracy of the proposed method in reconstructing piecewise-continuous dielectric profiles, are presented.

Modelling aggregation on the large scale and regularity on the small scale in spatial point pattern datasets

DEFF Research Database (Denmark)

Lavancier, Frédéric; Møller, Jesper

We consider a dependent thinning of a regular point process with the aim of obtaining aggregation on the large scale and regularity on the small scale in the resulting target point process of retained points. Various parametric models for the underlying processes are suggested and the properties...

Evaluating species richness: biased ecological inference results from spatial heterogeneity in species detection probabilities

Science.gov (United States)

McNew, Lance B.; Handel, Colleen M.

2015-01-01

Accurate estimates of species richness are necessary to test predictions of ecological theory and evaluate biodiversity for conservation purposes. However, species richness is difficult to measure in the field because some species will almost always be overlooked due to their cryptic nature or the observer's failure to perceive their cues. Common measures of species richness that assume consistent observability across species are inviting because they may require only single counts of species at survey sites. Single-visit estimation methods ignore spatial and temporal variation in species detection probabilities related to survey or site conditions that may confound estimates of species richness. We used simulated and empirical data to evaluate the bias and precision of raw species counts, the limiting forms of jackknife and Chao estimators, and multi-species occupancy models when estimating species richness to evaluate whether the choice of estimator can affect inferences about the relationships between environmental conditions and community size under variable detection processes. Four simulated scenarios with realistic and variable detection processes were considered. Results of simulations indicated that (1) raw species counts were always biased low, (2) single-visit jackknife and Chao estimators were significantly biased regardless of detection process, (3) multispecies occupancy models were more precise and generally less biased than the jackknife and Chao estimators, and (4) spatial heterogeneity resulting from the effects of a site covariate on species detection probabilities had significant impacts on the inferred relationships between species richness and a spatially explicit environmental condition. For a real dataset of bird observations in northwestern Alaska, the four estimation methods produced different estimates of local species richness, which severely affected inferences about the effects of shrubs on local avian richness. Overall, our results

Right hemisphere dominance during spatial selective attention and target detection occurs outside the dorsal fronto-parietal network

Science.gov (United States)

Shulman, Gordon L.; Pope, Daniel L. W.; Astafiev, Serguei V.; McAvoy, Mark P.; Snyder, Abraham Z.; Corbetta, Maurizio

2010-01-01

Spatial selective attention is widely considered to be right hemisphere dominant. Previous functional magnetic resonance imaging (fMRI) studies, however, have reported bilateral blood-oxygenation-level-dependent (BOLD) responses in dorsal fronto-parietal regions during anticipatory shifts of attention to a location (Kastner et al., 1999; Corbetta et al., 2000; Hopfinger et al., 2000). Right-lateralized activity has mainly been reported in ventral fronto-parietal regions for shifts of attention to an unattended target stimulus (Arrington et al., 2000; Corbetta et al., 2000). However, clear conclusions cannot be drawn from these studies because hemispheric asymmetries were not assessed using direct voxel-wise comparisons of activity in left and right hemispheres. Here, we used this technique to measure hemispheric asymmetries during shifts of spatial attention evoked by a peripheral cue stimulus and during target detection at the cued location. Stimulus-driven shifts of spatial attention in both visual fields evoked right-hemisphere dominant activity in temporo-parietal junction (TPJ). Target detection at the attended location produced a more widespread right hemisphere dominance in frontal, parietal, and temporal cortex, including the TPJ region asymmetrically activated during shifts of spatial attention. However, hemispheric asymmetries were not observed during either shifts of attention or target detection in the dorsal fronto-parietal regions (anterior precuneus, medial intraparietal sulcus, frontal eye fields) that showed the most robust activations for shifts of attention. Therefore, right hemisphere dominance during stimulus-driven shifts of spatial attention and target detection reflects asymmetries in cortical regions that are largely distinct from the dorsal fronto-parietal network involved in the control of selective attention. PMID:20219998

Total variation regularization for fMRI-based prediction of behavior

Science.gov (United States)

Michel, Vincent; Gramfort, Alexandre; Varoquaux, Gaël; Eger, Evelyn; Thirion, Bertrand

2011-01-01

While medical imaging typically provides massive amounts of data, the extraction of relevant information for predictive diagnosis remains a difficult challenge. Functional MRI (fMRI) data, that provide an indirect measure of task-related or spontaneous neuronal activity, are classically analyzed in a mass-univariate procedure yielding statistical parametric maps. This analysis framework disregards some important principles of brain organization: population coding, distributed and overlapping representations. Multivariate pattern analysis, i.e., the prediction of behavioural variables from brain activation patterns better captures this structure. To cope with the high dimensionality of the data, the learning method has to be regularized. However, the spatial structure of the image is not taken into account in standard regularization methods, so that the extracted features are often hard to interpret. More informative and interpretable results can be obtained with the ℓ1 norm of the image gradient, a.k.a. its Total Variation (TV), as regularization. We apply for the first time this method to fMRI data, and show that TV regularization is well suited to the purpose of brain mapping while being a powerful tool for brain decoding. Moreover, this article presents the first use of TV regularization for classification. PMID:21317080

Considering the Spatial Layout Information of Bag of Features (BoF) Framework for Image Classification.

Science.gov (United States)

Mu, Guangyu; Liu, Ying; Wang, Limin

2015-01-01

The spatial pooling method such as spatial pyramid matching (SPM) is very crucial in the bag of features model used in image classification. SPM partitions the image into a set of regular grids and assumes that the spatial layout of all visual words obey the uniform distribution over these regular grids. However, in practice, we consider that different visual words should obey different spatial layout distributions. To improve SPM, we develop a novel spatial pooling method, namely spatial distribution pooling (SDP). The proposed SDP method uses an extension model of Gauss mixture model to estimate the spatial layout distributions of the visual vocabulary. For each visual word type, SDP can generate a set of flexible grids rather than the regular grids from the traditional SPM. Furthermore, we can compute the grid weights for visual word tokens according to their spatial coordinates. The experimental results demonstrate that SDP outperforms the traditional spatial pooling methods, and is competitive with the state-of-the-art classification accuracy on several challenging image datasets.

A multiresolution method for solving the Poisson equation using high order regularization

DEFF Research Database (Denmark)

Hejlesen, Mads Mølholm; Walther, Jens Honore

2016-01-01

We present a novel high order multiresolution Poisson solver based on regularized Green's function solutions to obtain exact free-space boundary conditions while using fast Fourier transforms for computational efficiency. Multiresolution is a achieved through local refinement patches and regulari......We present a novel high order multiresolution Poisson solver based on regularized Green's function solutions to obtain exact free-space boundary conditions while using fast Fourier transforms for computational efficiency. Multiresolution is a achieved through local refinement patches...... and regularized Green's functions corresponding to the difference in the spatial resolution between the patches. The full solution is obtained utilizing the linearity of the Poisson equation enabling super-position of solutions. We show that the multiresolution Poisson solver produces convergence rates...

Spatial-temporal features of thermal images for Carpal Tunnel Syndrome detection

Science.gov (United States)

Estupinan Roldan, Kevin; Ortega Piedrahita, Marco A.; Benitez, Hernan D.

2014-02-01

Disorders associated with repeated trauma account for about 60% of all occupational illnesses, Carpal Tunnel Syndrome (CTS) being the most consulted today. Infrared Thermography (IT) has come to play an important role in the field of medicine. IT is non-invasive and detects diseases based on measuring temperature variations. IT represents a possible alternative to prevalent methods for diagnosis of CTS (i.e. nerve conduction studies and electromiography). This work presents a set of spatial-temporal features extracted from thermal images taken in healthy and ill patients. Support Vector Machine (SVM) classifiers test this feature space with Leave One Out (LOO) validation error. The results of the proposed approach show linear separability and lower validation errors when compared to features used in previous works that do not account for temperature spatial variability.

Detection of auditory signals in quiet and noisy backgrounds while performing a visuo-spatial task

Directory of Open Access Journals (Sweden)

Vishakha W Rawool

2016-01-01

Full Text Available Context: The ability to detect important auditory signals while performing visual tasks may be further compounded by background chatter. Thus, it is important to know how task performance may interact with background chatter to hinder signal detection. Aim: To examine any interactive effects of speech spectrum noise and task performance on the ability to detect signals. Settings and Design: The setting was a sound-treated booth. A repeated measures design was used. Materials and Methods: Auditory thresholds of 20 normal adults were determined at 0.5, 1, 2 and 4 kHz in the following conditions presented in a random order: (1 quiet with attention; (2 quiet with a visuo-spatial task or puzzle (distraction; (3 noise with attention and (4 noise with task. Statistical Analysis: Multivariate analyses of variance (MANOVA with three repeated factors (quiet versus noise, visuo-spatial task versus no task, signal frequency. Results: MANOVA revealed significant main effects for noise and signal frequency and significant noise–frequency and task–frequency interactions. Distraction caused by performing the task worsened the thresholds for tones presented at the beginning of the experiment and had no effect on tones presented in the middle. At the end of the experiment, thresholds (4 kHz were better while performing the task than those obtained without performing the task. These effects were similar across the quiet and noise conditions. Conclusion: Detection of auditory signals is difficult at the beginning of a distracting visuo-spatial task but over time, task learning and auditory training effects can nullify the effect of distraction and may improve detection of high frequency sounds.

A Spatially Offset Raman Spectroscopy Method for Non-Destructive Detection of Gelatin-Encapsulated Powders

Directory of Open Access Journals (Sweden)

Kuanglin Chao

2017-03-01

Full Text Available Non-destructive subsurface detection of encapsulated, coated, or seal-packaged foods and pharmaceuticals can help prevent distribution and consumption of counterfeit or hazardous products. This study used a Spatially Offset Raman Spectroscopy (SORS method to detect and identify urea, ibuprofen, and acetaminophen powders contained within one or more (up to eight layers of gelatin capsules to demonstrate subsurface chemical detection and identification. A 785-nm point-scan Raman spectroscopy system was used to acquire spatially offset Raman spectra for an offset range of 0 to 10 mm from the surfaces of 24 encapsulated samples, using a step size of 0.1 mm to obtain 101 spectral measurements per sample. As the offset distance was increased, the spectral contribution from the subsurface powder gradually outweighed that of the surface capsule layers, allowing for detection of the encapsulated powders. Containing mixed contributions from the powder and capsule, the SORS spectra for each sample were resolved into pure component spectra using self-modeling mixture analysis (SMA and the corresponding components were identified using spectral information divergence values. As demonstrated here for detecting chemicals contained inside thick capsule layers, this SORS measurement technique coupled with SMA has the potential to be a reliable non-destructive method for subsurface inspection and authentication of foods, health supplements, and pharmaceutical products that are prepared or packaged with semi-transparent materials.

Integrated circuit-based electrochemical sensor for spatially resolved detection of redox-active metabolites in biofilms.

Science.gov (United States)

Bellin, Daniel L; Sakhtah, Hassan; Rosenstein, Jacob K; Levine, Peter M; Thimot, Jordan; Emmett, Kevin; Dietrich, Lars E P; Shepard, Kenneth L

2014-01-01

Despite advances in monitoring spatiotemporal expression patterns of genes and proteins with fluorescent probes, direct detection of metabolites and small molecules remains challenging. A technique for spatially resolved detection of small molecules would benefit the study of redox-active metabolites that are produced by microbial biofilms and can affect their development. Here we present an integrated circuit-based electrochemical sensing platform featuring an array of working electrodes and parallel potentiostat channels. 'Images' over a 3.25 × 0.9 mm(2) area can be captured with a diffusion-limited spatial resolution of 750 μm. We demonstrate that square wave voltammetry can be used to detect, identify and quantify (for concentrations as low as 2.6 μM) four distinct redox-active metabolites called phenazines. We characterize phenazine production in both wild-type and mutant Pseudomonas aeruginosa PA14 colony biofilms, and find correlations with fluorescent reporter imaging of phenazine biosynthetic gene expression.

Effort variation regularization in sound field reproduction

DEFF Research Database (Denmark)

Stefanakis, Nick; Jacobsen, Finn; Sarris, Ioannis

2010-01-01

In this paper, active control is used in order to reproduce a given sound field in an extended spatial region. A method is proposed which minimizes the reproduction error at a number of control positions with the reproduction sources holding a certain relation within their complex strengths......), and adaptive wave field synthesis (AWFS), both under free-field conditions and in reverberant rooms. It is shown that effort variation regularization overcomes the problems associated with small spaces and with a low ratio of direct to reverberant energy, improving thus the reproduction accuracy...

A sparsity-regularized Born iterative method for reconstruction of two-dimensional piecewise continuous inhomogeneous domains

KAUST Repository

Sandhu, Ali Imran

2016-04-10

A sparsity-regularized Born iterative method (BIM) is proposed for efficiently reconstructing two-dimensional piecewise-continuous inhomogeneous dielectric profiles. Such profiles are typically not spatially sparse, which reduces the efficiency of the sparsity-promoting regularization. To overcome this problem, scattered fields are represented in terms of the spatial derivative of the dielectric profile and reconstruction is carried out over samples of the dielectric profile\\'s derivative. Then, like the conventional BIM, the nonlinear problem is iteratively converted into a sequence of linear problems (in derivative samples) and sparsity constraint is enforced on each linear problem using the thresholded Landweber iterations. Numerical results, which demonstrate the efficiency and accuracy of the proposed method in reconstructing piecewise-continuous dielectric profiles, are presented.

Detection thresholds of macaque otolith afferents.

Science.gov (United States)

Yu, Xiong-Jie; Dickman, J David; Angelaki, Dora E

2012-06-13

The vestibular system is our sixth sense and is important for spatial perception functions, yet the sensory detection and discrimination properties of vestibular neurons remain relatively unexplored. Here we have used signal detection theory to measure detection thresholds of otolith afferents using 1 Hz linear accelerations delivered along three cardinal axes. Direction detection thresholds were measured by comparing mean firing rates centered on response peak and trough (full-cycle thresholds) or by comparing peak/trough firing rates with spontaneous activity (half-cycle thresholds). Thresholds were similar for utricular and saccular afferents, as well as for lateral, fore/aft, and vertical motion directions. When computed along the preferred direction, full-cycle direction detection thresholds were 7.54 and 3.01 cm/s(2) for regular and irregular firing otolith afferents, respectively. Half-cycle thresholds were approximately double, with excitatory thresholds being half as large as inhibitory thresholds. The variability in threshold among afferents was directly related to neuronal gain and did not depend on spike count variance. The exact threshold values depended on both the time window used for spike count analysis and the filtering method used to calculate mean firing rate, although differences between regular and irregular afferent thresholds were independent of analysis parameters. The fact that minimum thresholds measured in macaque otolith afferents are of the same order of magnitude as human behavioral thresholds suggests that the vestibular periphery might determine the limit on our ability to detect or discriminate small differences in head movement, with little noise added during downstream processing.

Regularized forecasting of chaotic dynamical systems

International Nuclear Information System (INIS)

Bollt, Erik M.

2017-01-01

While local models of dynamical systems have been highly successful in terms of using extensive data sets observing even a chaotic dynamical system to produce useful forecasts, there is a typical problem as follows. Specifically, with k-near neighbors, kNN method, local observations occur due to recurrences in a chaotic system, and this allows for local models to be built by regression to low dimensional polynomial approximations of the underlying system estimating a Taylor series. This has been a popular approach, particularly in context of scalar data observations which have been represented by time-delay embedding methods. However such local models can generally allow for spatial discontinuities of forecasts when considered globally, meaning jumps in predictions because the collected near neighbors vary from point to point. The source of these discontinuities is generally that the set of near neighbors varies discontinuously with respect to the position of the sample point, and so therefore does the model built from the near neighbors. It is possible to utilize local information inferred from near neighbors as usual but at the same time to impose a degree of regularity on a global scale. We present here a new global perspective extending the general local modeling concept. In so doing, then we proceed to show how this perspective allows us to impose prior presumed regularity into the model, by involving the Tikhonov regularity theory, since this classic perspective of optimization in ill-posed problems naturally balances fitting an objective with some prior assumed form of the result, such as continuity or derivative regularity for example. This all reduces to matrix manipulations which we demonstrate on a simple data set, with the implication that it may find much broader context.

Uncertainty of a detected spatial cluster in 1D: quantification and visualization

KAUST Repository

Lee, Junho; Gangnon, Ronald E.; Zhu, Jun; Liang, Jingjing

2017-01-01

Spatial cluster detection is an important problem in a variety of scientific disciplines such as environmental sciences, epidemiology and sociology. However, there appears to be very limited statistical methodology for quantifying the uncertainty of a detected cluster. In this paper, we develop a new method for the quantification and visualization of uncertainty associated with a detected cluster. Our approach is defining a confidence set for the true cluster and visualizing the confidence set, based on the maximum likelihood, in time or in one-dimensional space. We evaluate the pivotal property of the statistic used to construct the confidence set and the coverage rate for the true cluster via empirical distributions. For illustration, our methodology is applied to both simulated data and an Alaska boreal forest dataset. Copyright © 2017 John Wiley & Sons, Ltd.

Uncertainty of a detected spatial cluster in 1D: quantification and visualization

KAUST Repository

Lee, Junho

2017-10-19

Spatial cluster detection is an important problem in a variety of scientific disciplines such as environmental sciences, epidemiology and sociology. However, there appears to be very limited statistical methodology for quantifying the uncertainty of a detected cluster. In this paper, we develop a new method for the quantification and visualization of uncertainty associated with a detected cluster. Our approach is defining a confidence set for the true cluster and visualizing the confidence set, based on the maximum likelihood, in time or in one-dimensional space. We evaluate the pivotal property of the statistic used to construct the confidence set and the coverage rate for the true cluster via empirical distributions. For illustration, our methodology is applied to both simulated data and an Alaska boreal forest dataset. Copyright © 2017 John Wiley & Sons, Ltd.

Built-Up Area Detection from High-Resolution Satellite Images Using Multi-Scale Wavelet Transform and Local Spatial Statistics

Science.gov (United States)

Chen, Y.; Zhang, Y.; Gao, J.; Yuan, Y.; Lv, Z.

2018-04-01

Recently, built-up area detection from high-resolution satellite images (HRSI) has attracted increasing attention because HRSI can provide more detailed object information. In this paper, multi-resolution wavelet transform and local spatial autocorrelation statistic are introduced to model the spatial patterns of built-up areas. First, the input image is decomposed into high- and low-frequency subbands by wavelet transform at three levels. Then the high-frequency detail information in three directions (horizontal, vertical and diagonal) are extracted followed by a maximization operation to integrate the information in all directions. Afterward, a cross-scale operation is implemented to fuse different levels of information. Finally, local spatial autocorrelation statistic is introduced to enhance the saliency of built-up features and an adaptive threshold algorithm is used to achieve the detection of built-up areas. Experiments are conducted on ZY-3 and Quickbird panchromatic satellite images, and the results show that the proposed method is very effective for built-up area detection.

A spatial approach of magnitude-squared coherence applied to selective attention detection.

Science.gov (United States)

Bonato Felix, Leonardo; de Souza Ranaudo, Fernando; D'affonseca Netto, Aluizio; Ferreira Leite Miranda de Sá, Antonio Mauricio

2014-05-30

Auditory selective attention is the human ability of actively focusing in a certain sound stimulus while avoiding all other ones. This ability can be used, for example, in behavioral studies and brain-machine interface. In this work we developed an objective method - called Spatial Coherence - to detect the side where a subject is focusing attention to. This method takes into consideration the Magnitude Squared Coherence and the topographic distribution of responses among electroencephalogram electrodes. The individuals were stimulated with amplitude-modulated tones binaurally and were oriented to focus attention to only one of the stimuli. The results indicate a contralateral modulation of ASSR in the attention condition and are in agreement with prior studies. Furthermore, the best combination of electrodes led to a hit rate of 82% for 5.03 commands per minute. Using a similar paradigm, in a recent work, a maximum hit rate of 84.33% was achieved, but with a greater a classification time (20s, i.e. 3 commands per minute). It seems that Spatial Coherence is a useful technique for detecting focus of auditory selective attention. Copyright © 2014 Elsevier B.V. All rights reserved.

Regularized Adaptive Notch Filters for Acoustic Howling Suppression

DEFF Research Database (Denmark)

Gil-Cacho, Pepe; van Waterschoot, Toon; Moonen, Marc

2009-01-01

In this paper, a method for the suppression of acoustic howling is developed, based on adaptive notch filters (ANF) with regularization (RANF). The method features three RANFs working in parallel to achieve frequency tracking, howling detection and suppression. The ANF-based approach to howling...

Spatially patterned matrix elasticity directs stem cell fate

Science.gov (United States)

Yang, Chun; DelRio, Frank W.; Ma, Hao; Killaars, Anouk R.; Basta, Lena P.; Kyburz, Kyle A.; Anseth, Kristi S.

2016-08-01

There is a growing appreciation for the functional role of matrix mechanics in regulating stem cell self-renewal and differentiation processes. However, it is largely unknown how subcellular, spatial mechanical variations in the local extracellular environment mediate intracellular signal transduction and direct cell fate. Here, the effect of spatial distribution, magnitude, and organization of subcellular matrix mechanical properties on human mesenchymal stem cell (hMSCs) function was investigated. Exploiting a photodegradation reaction, a hydrogel cell culture substrate was fabricated with regions of spatially varied and distinct mechanical properties, which were subsequently mapped and quantified by atomic force microscopy (AFM). The variations in the underlying matrix mechanics were found to regulate cellular adhesion and transcriptional events. Highly spread, elongated morphologies and higher Yes-associated protein (YAP) activation were observed in hMSCs seeded on hydrogels with higher concentrations of stiff regions in a dose-dependent manner. However, when the spatial organization of the mechanically stiff regions was altered from a regular to randomized pattern, lower levels of YAP activation with smaller and more rounded cell morphologies were induced in hMSCs. We infer from these results that irregular, disorganized variations in matrix mechanics, compared with regular patterns, appear to disrupt actin organization, and lead to different cell fates; this was verified by observations of lower alkaline phosphatase (ALP) activity and higher expression of CD105, a stem cell marker, in hMSCs in random versus regular patterns of mechanical properties. Collectively, this material platform has allowed innovative experiments to elucidate a novel spatial mechanical dosing mechanism that correlates to both the magnitude and organization of spatial stiffness.

Mediastinal lymph node detection and station mapping on chest CT using spatial priors and random forest

Energy Technology Data Exchange (ETDEWEB)

Liu, Jiamin; Hoffman, Joanne; Zhao, Jocelyn; Yao, Jianhua; Lu, Le; Kim, Lauren; Turkbey, Evrim B.; Summers, Ronald M., E-mail: rms@nih.gov [Imaging Biomarkers and Computer-aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center Building, 10 Room 1C224 MSC 1182, Bethesda, Maryland 20892-1182 (United States)

2016-07-15

Purpose: To develop an automated system for mediastinal lymph node detection and station mapping for chest CT. Methods: The contextual organs, trachea, lungs, and spine are first automatically identified to locate the region of interest (ROI) (mediastinum). The authors employ shape features derived from Hessian analysis, local object scale, and circular transformation that are computed per voxel in the ROI. Eight more anatomical structures are simultaneously segmented by multiatlas label fusion. Spatial priors are defined as the relative multidimensional distance vectors corresponding to each structure. Intensity, shape, and spatial prior features are integrated and parsed by a random forest classifier for lymph node detection. The detected candidates are then segmented by the following curve evolution process. Texture features are computed on the segmented lymph nodes and a support vector machine committee is used for final classification. For lymph node station labeling, based on the segmentation results of the above anatomical structures, the textual definitions of mediastinal lymph node map according to the International Association for the Study of Lung Cancer are converted into patient-specific color-coded CT image, where the lymph node station can be automatically assigned for each detected node. Results: The chest CT volumes from 70 patients with 316 enlarged mediastinal lymph nodes are used for validation. For lymph node detection, their system achieves 88% sensitivity at eight false positives per patient. For lymph node station labeling, 84.5% of lymph nodes are correctly assigned to their stations. Conclusions: Multiple-channel shape, intensity, and spatial prior features aggregated by a random forest classifier improve mediastinal lymph node detection on chest CT. Using the location information of segmented anatomic structures from the multiatlas formulation enables accurate identification of lymph node stations.

Mediastinal lymph node detection and station mapping on chest CT using spatial priors and random forest

International Nuclear Information System (INIS)

Liu, Jiamin; Hoffman, Joanne; Zhao, Jocelyn; Yao, Jianhua; Lu, Le; Kim, Lauren; Turkbey, Evrim B.; Summers, Ronald M.

2016-01-01

Purpose: To develop an automated system for mediastinal lymph node detection and station mapping for chest CT. Methods: The contextual organs, trachea, lungs, and spine are first automatically identified to locate the region of interest (ROI) (mediastinum). The authors employ shape features derived from Hessian analysis, local object scale, and circular transformation that are computed per voxel in the ROI. Eight more anatomical structures are simultaneously segmented by multiatlas label fusion. Spatial priors are defined as the relative multidimensional distance vectors corresponding to each structure. Intensity, shape, and spatial prior features are integrated and parsed by a random forest classifier for lymph node detection. The detected candidates are then segmented by the following curve evolution process. Texture features are computed on the segmented lymph nodes and a support vector machine committee is used for final classification. For lymph node station labeling, based on the segmentation results of the above anatomical structures, the textual definitions of mediastinal lymph node map according to the International Association for the Study of Lung Cancer are converted into patient-specific color-coded CT image, where the lymph node station can be automatically assigned for each detected node. Results: The chest CT volumes from 70 patients with 316 enlarged mediastinal lymph nodes are used for validation. For lymph node detection, their system achieves 88% sensitivity at eight false positives per patient. For lymph node station labeling, 84.5% of lymph nodes are correctly assigned to their stations. Conclusions: Multiple-channel shape, intensity, and spatial prior features aggregated by a random forest classifier improve mediastinal lymph node detection on chest CT. Using the location information of segmented anatomic structures from the multiatlas formulation enables accurate identification of lymph node stations.

Dim small targets detection based on self-adaptive caliber temporal-spatial filtering

Science.gov (United States)

Fan, Xiangsuo; Xu, Zhiyong; Zhang, Jianlin; Huang, Yongmei; Peng, Zhenming

2017-09-01

To boost the detect ability of dim small targets, this paper began by using improved anisotropy for background prediction (IABP), followed by target enhancement by improved high-order cumulates (HQS). Finally, on the basis of image pre-processing, to address the problem of missed and wrong detection caused by fixed caliber of traditional pipeline filtering, this paper used targets' multi-frame movement correlation in the time-space domain, combined with the scale-space theory, to propose a temporal-spatial filtering algorithm which allows the caliber to make self-adaptive changes according to the changes of the targets' scale, effectively solving the detection-related issues brought by unchanged caliber and decreased/increased size of the targets. Experiments showed that the improved anisotropic background predication could be loyal to the true background of the original image to the maximum extent, presenting a superior overall performance to other background prediction methods; the improved HQS significantly increased the signal-noise ratio of images; when the signal-noise ratio was lower than 2.6 dB, this detection algorithm could effectively eliminate noise and detect targets. For the algorithm, the lowest signal-to-noise ratio of the detectable target is 0.37.

A structural framework for anomalous change detection and characterization

Energy Technology Data Exchange (ETDEWEB)

Prasad, Lakshman [Los Alamos National Laboratory; Theiler, James P [Los Alamos National Laboratory

2009-01-01

We present a spatially adaptive scheme for automatically searching a pair of images of a scene for unusual and interesting changes. Our motivation is to bring into play structural aspects of image features alongside the spectral attributes used for anomalous change detection (ACD). We leverage a small but informative subset of pixels, namely edge pixels of the images, as anchor points of a Delaunay triangulation to jointly decompose the images into a set of triangular regions, called trixels, which are spectrally uniform. Such decomposition helps in image regularization by simple-function approximation on a feature-adaptive grid. Applying ACD to this trixel grid instead of pixels offers several advantages. It allows: (1) edge-preserving smoothing of images, (2) speed-up of spatial computations by significantly reducing the representation of the images, and (3) the easy recovery of structure of the detected anomalous changes by associating anomalous trixels with polygonal image features. The latter facility further enables the application of shape-theoretic criteria and algorithms to characterize the changes and recognize them as interesting or not. This incorporation of spatial information has the potential to filter out some spurious changes, such as due to parallax, shadows, and misregistration, by identifying and filtering out those that are structurally similar and spatially pervasive. Our framework supports the joint spatial and spectral analysis of images, potentially enabling the design of more robust ACD algorithms.

From regular text to artistic writing and artworks: Fourier statistics of images with low and high aesthetic appeal

Directory of Open Access Journals (Sweden)

Tamara eMelmer

2013-04-01

Full Text Available The spatial characteristics of letters and their influence on readability and letter identification have been intensely studied during the last decades. There have been few studies, however, on statistical image properties that reflect more global aspects of text, for example properties that may relate to its aesthetic appeal. It has been shown that natural scenes and a large variety of visual artworks possess a scale-invariant Fourier power spectrum that falls off linearly with increasing frequency in log-log plots. We asked whether images of text share this property. As expected, the Fourier spectrum of images of regular typed or handwritten text is highly anisotropic, i.e. the spectral image properties in vertical, horizontal and oblique orientations differ. Moreover, the spatial frequency spectra of text images are not scale invariant in any direction. The decline is shallower in the low-frequency part of the spectrum for text than for aesthetic artworks, whereas, in the high-frequency part, it is steeper. These results indicate that, in general, images of regular text contain less global structure (low spatial frequencies relative to fine detail (high spatial frequencies than images of aesthetics artworks. Moreover, we studied images of text with artistic claim (ornate print and calligraphy and ornamental art. For some measures, these images assume average values intermediate between regular text and aesthetic artworks. Finally, to answer the question of whether the statistical properties measured by us are universal amongst humans or are subject to intercultural differences, we compared images from three different cultural backgrounds (Western, East Asian and Arabic. Results for different categories (regular text, aesthetic writing, ornamental art and fine art were similar across cultures.

From regular text to artistic writing and artworks: Fourier statistics of images with low and high aesthetic appeal

Science.gov (United States)

Melmer, Tamara; Amirshahi, Seyed A.; Koch, Michael; Denzler, Joachim; Redies, Christoph

2013-01-01

The spatial characteristics of letters and their influence on readability and letter identification have been intensely studied during the last decades. There have been few studies, however, on statistical image properties that reflect more global aspects of text, for example properties that may relate to its aesthetic appeal. It has been shown that natural scenes and a large variety of visual artworks possess a scale-invariant Fourier power spectrum that falls off linearly with increasing frequency in log-log plots. We asked whether images of text share this property. As expected, the Fourier spectrum of images of regular typed or handwritten text is highly anisotropic, i.e., the spectral image properties in vertical, horizontal, and oblique orientations differ. Moreover, the spatial frequency spectra of text images are not scale-invariant in any direction. The decline is shallower in the low-frequency part of the spectrum for text than for aesthetic artworks, whereas, in the high-frequency part, it is steeper. These results indicate that, in general, images of regular text contain less global structure (low spatial frequencies) relative to fine detail (high spatial frequencies) than images of aesthetics artworks. Moreover, we studied images of text with artistic claim (ornate print and calligraphy) and ornamental art. For some measures, these images assume average values intermediate between regular text and aesthetic artworks. Finally, to answer the question of whether the statistical properties measured by us are universal amongst humans or are subject to intercultural differences, we compared images from three different cultural backgrounds (Western, East Asian, and Arabic). Results for different categories (regular text, aesthetic writing, ornamental art, and fine art) were similar across cultures. PMID:23554592

Efficient L1 regularization-based reconstruction for fluorescent molecular tomography using restarted nonlinear conjugate gradient.

Science.gov (United States)

Shi, Junwei; Zhang, Bin; Liu, Fei; Luo, Jianwen; Bai, Jing

2013-09-15

For the ill-posed fluorescent molecular tomography (FMT) inverse problem, the L1 regularization can protect the high-frequency information like edges while effectively reduce the image noise. However, the state-of-the-art L1 regularization-based algorithms for FMT reconstruction are expensive in memory, especially for large-scale problems. An efficient L1 regularization-based reconstruction algorithm based on nonlinear conjugate gradient with restarted strategy is proposed to increase the computational speed with low memory consumption. The reconstruction results from phantom experiments demonstrate that the proposed algorithm can obtain high spatial resolution and high signal-to-noise ratio, as well as high localization accuracy for fluorescence targets.

Detecting Activation in fMRI Data: An Approach Based on Sparse Representation of BOLD Signal

Directory of Open Access Journals (Sweden)

Blanca Guillen

2018-01-01

Full Text Available This paper proposes a simple yet effective approach for detecting activated voxels in fMRI data by exploiting the inherent sparsity property of the BOLD signal in temporal and spatial domains. In the time domain, the approach combines the General Linear Model (GLM with a Least Absolute Deviation (LAD based regression method regularized by the pseudonorm l0 to promote sparsity in the parameter vector of the model. In the spatial domain, detection of activated regions is based on thresholding the spatial map of estimated parameters associated with a particular stimulus. The threshold is calculated by exploiting the sparseness of the BOLD signal in the spatial domain assuming a Laplacian distribution model. The proposed approach is validated using synthetic and real fMRI data. For synthetic data, results show that the proposed approach is able to detect most activated voxels without any false activation. For real data, the method is evaluated through comparison with the SPM software. Results indicate that this approach can effectively find activated regions that are similar to those found by SPM, but using a much simpler approach. This study may lead to the development of robust spatial approaches to further simplifying the complexity of classical schemes.

A New Pansharpening Method Based on Spatial and Spectral Sparsity Priors.

Science.gov (United States)

He, Xiyan; Condat, Laurent; Bioucas-Diaz, Jose; Chanussot, Jocelyn; Xia, Junshi

2014-06-27

The development of multisensor systems in recent years has led to great increase in the amount of available remote sensing data. Image fusion techniques aim at inferring high quality images of a given area from degraded versions of the same area obtained by multiple sensors. This paper focuses on pansharpening, which is the inference of a high spatial resolution multispectral image from two degraded versions with complementary spectral and spatial resolution characteristics: a) a low spatial resolution multispectral image; and b) a high spatial resolution panchromatic image. We introduce a new variational model based on spatial and spectral sparsity priors for the fusion. In the spectral domain we encourage low-rank structure, whereas in the spatial domain we promote sparsity on the local differences. Given the fact that both panchromatic and multispectral images are integrations of the underlying continuous spectra using different channel responses, we propose to exploit appropriate regularizations based on both spatial and spectral links between panchromatic and the fused multispectral images. A weighted version of the vector Total Variation (TV) norm of the data matrix is employed to align the spatial information of the fused image with that of the panchromatic image. With regard to spectral information, two different types of regularization are proposed to promote a soft constraint on the linear dependence between the panchromatic and the fused multispectral images. The first one estimates directly the linear coefficients from the observed panchromatic and low resolution multispectral images by Linear Regression (LR) while the second one employs the Principal Component Pursuit (PCP) to obtain a robust recovery of the underlying low-rank structure. We also show that the two regularizers are strongly related. The basic idea of both regularizers is that the fused image should have low-rank and preserve edge locations. We use a variation of the recently proposed

Spatial-time-state fusion algorithm for defect detection through eddy current pulsed thermography

Science.gov (United States)

Xiao, Xiang; Gao, Bin; Woo, Wai Lok; Tian, Gui Yun; Xiao, Xiao Ting

2018-05-01

Eddy Current Pulsed Thermography (ECPT) has received extensive attention due to its high sensitive of detectability on surface and subsurface cracks. However, it remains as a difficult challenge in unsupervised detection as to identify defects without knowing any prior knowledge. This paper presents a spatial-time-state features fusion algorithm to obtain fully profile of the defects by directional scanning. The proposed method is intended to conduct features extraction by using independent component analysis (ICA) and automatic features selection embedding genetic algorithm. Finally, the optimal feature of each step is fused to obtain defects reconstruction by applying common orthogonal basis extraction (COBE) method. Experiments have been conducted to validate the study and verify the efficacy of the proposed method on blind defect detection.

Applying spatial analysis tools in public health: an example using SaTScan to detect geographic targets for colorectal cancer screening interventions.

Science.gov (United States)

Sherman, Recinda L; Henry, Kevin A; Tannenbaum, Stacey L; Feaster, Daniel J; Kobetz, Erin; Lee, David J

2014-03-20

Epidemiologists are gradually incorporating spatial analysis into health-related research as geocoded cases of disease become widely available and health-focused geospatial computer applications are developed. One health-focused application of spatial analysis is cluster detection. Using cluster detection to identify geographic areas with high-risk populations and then screening those populations for disease can improve cancer control. SaTScan is a free cluster-detection software application used by epidemiologists around the world to describe spatial clusters of infectious and chronic disease, as well as disease vectors and risk factors. The objectives of this article are to describe how spatial analysis can be used in cancer control to detect geographic areas in need of colorectal cancer screening intervention, identify issues commonly encountered by SaTScan users, detail how to select the appropriate methods for using SaTScan, and explain how method selection can affect results. As an example, we used various methods to detect areas in Florida where the population is at high risk for late-stage diagnosis of colorectal cancer. We found that much of our analysis was underpowered and that no single method detected all clusters of statistical or public health significance. However, all methods detected 1 area as high risk; this area is potentially a priority area for a screening intervention. Cluster detection can be incorporated into routine public health operations, but the challenge is to identify areas in which the burden of disease can be alleviated through public health intervention. Reliance on SaTScan's default settings does not always produce pertinent results.

Cabri 3D - assisted collaborative learning to enhance junior high school students’ spatial ability

Science.gov (United States)

Muntazhimah; Miatun, A.

2018-01-01

The main purpose of this quasi-experimental study was to determine the enhancement of spatial ability of junior high school students who learned through Cabri-3D assisted collaborative learning. The methodology of this study was the nonequivalent group that was conducted to students of the eighth grade in a junior high school as a population. Samples consisted one class of the experimental group who studied with Cabri-3D assisted collaborative learning and one class as a control group who got regular learning activity. The instrument used in this study was a spatial ability test. Analyzing normalized gain of students’ spatial ability based on mathemathical prior knowledge (MPK) and its interactions was tested by two-way ANOVA at a significance level of 5% then continued with using Post Hoc Scheffe test. The research results showed that there was significant difference in enhancement of the spatial ability between students who learnt with Cabri 3D assisted collaborative learning and students who got regular learning, there was significant difference in enhancement of the spatial ability between students who learnt with cabri 3D assisted collaborative learning and students who got regular learning in terms of MPK and there is no significant interaction between learning (Cabri-3D assisted collaborative learning and regular learning) with students’ MPK (high, medium, and low) toward the enhancement of students’ spatial abilities. From the above findings, it can be seen that cabri-3D assisted collaborative learning could enhance spatial ability of junior high school students.

Use of regularized algebraic methods in tomographic reconstruction

International Nuclear Information System (INIS)

Koulibaly, P.M.; Darcourt, J.; Blanc-Ferraud, L.; Migneco, O.; Barlaud, M.

1997-01-01

The algebraic methods are used in emission tomography to facilitate the compensation of attenuation and of Compton scattering. We have tested on a phantom the use of a regularization (a priori introduction of information), as well as the taking into account of spatial resolution variation with the depth (SRVD). Hence, we have compared the performances of the two methods by back-projection filtering (BPF) and of the two algebraic methods (AM) in terms of FWHM (by means of a point source), of the reduction of background noise (σ/m) on the homogeneous part of Jaszczak's phantom and of reconstruction speed (time unit = BPF). The BPF methods make use of a grade filter (maximal resolution, no noise treatment), single or associated with a Hann's low-pass (f c = 0.4), as well as of an attenuation correction. The AM which embody attenuation and scattering corrections are, on one side, the OS EM (Ordered Subsets, partitioning and rearranging of the projection matrix; Expectation Maximization) without regularization or SRVD correction, and, on the other side, the OS MAP EM (Maximum a posteriori), regularized and embodying the SRVD correction. A table is given containing for each used method (grade, Hann, OS EM and OS MAP EM) the values of FWHM, σ/m and time, respectively. One can observe that the OS MAP EM algebraic method allows ameliorating both the resolution, by taking into account the SRVD in the reconstruction process and noise treatment by regularization. In addition, due to the OS technique the reconstruction times are acceptable

Multivariate spatial Gaussian mixture modeling for statistical clustering of hemodynamic parameters in functional MRI

International Nuclear Information System (INIS)

Fouque, A.L.; Ciuciu, Ph.; Risser, L.; Fouque, A.L.; Ciuciu, Ph.; Risser, L.

2009-01-01

In this paper, a novel statistical parcellation of intra-subject functional MRI (fMRI) data is proposed. The key idea is to identify functionally homogenous regions of interest from their hemodynamic parameters. To this end, a non-parametric voxel-based estimation of hemodynamic response function is performed as a prerequisite. Then, the extracted hemodynamic features are entered as the input data of a Multivariate Spatial Gaussian Mixture Model (MSGMM) to be fitted. The goal of the spatial aspect is to favor the recovery of connected components in the mixture. Our statistical clustering approach is original in the sense that it extends existing works done on univariate spatially regularized Gaussian mixtures. A specific Gibbs sampler is derived to account for different covariance structures in the feature space. On realistic artificial fMRI datasets, it is shown that our algorithm is helpful for identifying a parsimonious functional parcellation required in the context of joint detection estimation of brain activity. This allows us to overcome the classical assumption of spatial stationarity of the BOLD signal model. (authors)

Distance-regular graphs

NARCIS (Netherlands)

van Dam, Edwin R.; Koolen, Jack H.; Tanaka, Hajime

2016-01-01

This is a survey of distance-regular graphs. We present an introduction to distance-regular graphs for the reader who is unfamiliar with the subject, and then give an overview of some developments in the area of distance-regular graphs since the monograph 'BCN'[Brouwer, A.E., Cohen, A.M., Neumaier,

Detection of the power lines in UAV remote sensed images using spectral-spatial methods.

Science.gov (United States)

Bhola, Rishav; Krishna, Nandigam Hari; Ramesh, K N; Senthilnath, J; Anand, Gautham

2018-01-15

In this paper, detection of the power lines on images acquired by Unmanned Aerial Vehicle (UAV) based remote sensing is carried out using spectral-spatial methods. Spectral clustering was performed using Kmeans and Expectation Maximization (EM) algorithm to classify the pixels into the power lines and non-power lines. The spectral clustering methods used in this study are parametric in nature, to automate the number of clusters Davies-Bouldin index (DBI) is used. The UAV remote sensed image is clustered into the number of clusters determined by DBI. The k clustered image is merged into 2 clusters (power lines and non-power lines). Further, spatial segmentation was performed using morphological and geometric operations, to eliminate the non-power line regions. In this study, UAV images acquired at different altitudes and angles were analyzed to validate the robustness of the proposed method. It was observed that the EM with spatial segmentation (EM-Seg) performed better than the Kmeans with spatial segmentation (Kmeans-Seg) on most of the UAV images. Copyright © 2017 Elsevier Ltd. All rights reserved.

Regular expressions cookbook

CERN Document Server

Goyvaerts, Jan

2009-01-01

This cookbook provides more than 100 recipes to help you crunch data and manipulate text with regular expressions. Every programmer can find uses for regular expressions, but their power doesn't come worry-free. Even seasoned users often suffer from poor performance, false positives, false negatives, or perplexing bugs. Regular Expressions Cookbook offers step-by-step instructions for some of the most common tasks involving this tool, with recipes for C#, Java, JavaScript, Perl, PHP, Python, Ruby, and VB.NET. With this book, you will: Understand the basics of regular expressions through a

LL-regular grammars

NARCIS (Netherlands)

Nijholt, Antinus

1980-01-01

Culik II and Cogen introduced the class of LR-regular grammars, an extension of the LR(k) grammars. In this paper we consider an analogous extension of the LL(k) grammars called the LL-regular grammars. The relation of this class of grammars to other classes of grammars will be shown. Any LL-regular

Single-trial detection of visual evoked potentials by common spatial patterns and wavelet filtering for brain-computer interface.

Science.gov (United States)

Tu, Yiheng; Huang, Gan; Hung, Yeung Sam; Hu, Li; Hu, Yong; Zhang, Zhiguo

2013-01-01

Event-related potentials (ERPs) are widely used in brain-computer interface (BCI) systems as input signals conveying a subject's intention. A fast and reliable single-trial ERP detection method can be used to develop a BCI system with both high speed and high accuracy. However, most of single-trial ERP detection methods are developed for offline EEG analysis and thus have a high computational complexity and need manual operations. Therefore, they are not applicable to practical BCI systems, which require a low-complexity and automatic ERP detection method. This work presents a joint spatial-time-frequency filter that combines common spatial patterns (CSP) and wavelet filtering (WF) for improving the signal-to-noise (SNR) of visual evoked potentials (VEP), which can lead to a single-trial ERP-based BCI.

Blind detection of isolated astrophysical pulses in the spatial Fourier transform domain

Science.gov (United States)

Schmid, Natalia A.; Prestage, Richard M.

2018-04-01

We present a novel approach for the detection of isolated transients in pulsar surveys and fast radio transient observations. Rather than the conventional approach of performing a computationally expensive blind DM search, we take the spatial Fourier transform (SFT) of short (˜ few seconds) sections of data. A transient will have a characteristic signature in the SFT domain, and we present a blind statistic which may be used to detect this signature at an empirical zero False Alarm Rate (FAR). The method has been evaluated using simulations, and also applied to two fast radio burst observations. In addition to its use for current observations, we expect this method will be extremely beneficial for future multi-beam observations made by telescopes equipped with phased array feeds.

A spatial hazard model for cluster detection on continuous indicators of disease: application to somatic cell score.

Science.gov (United States)

Gay, Emilie; Senoussi, Rachid; Barnouin, Jacques

2007-01-01

Methods for spatial cluster detection dealing with diseases quantified by continuous variables are few, whereas several diseases are better approached by continuous indicators. For example, subclinical mastitis of the dairy cow is evaluated using a continuous marker of udder inflammation, the somatic cell score (SCS). Consequently, this study proposed to analyze spatialized risk and cluster components of herd SCS through a new method based on a spatial hazard model. The dataset included annual SCS for 34 142 French dairy herds for the year 2000, and important SCS risk factors: mean parity, percentage of winter and spring calvings, and herd size. The model allowed the simultaneous estimation of the effects of known risk factors and of potential spatial clusters on SCS, and the mapping of the estimated clusters and their range. Mean parity and winter and spring calvings were significantly associated with subclinical mastitis risk. The model with the presence of 3 clusters was highly significant, and the 3 clusters were attractive, i.e. closeness to cluster center increased the occurrence of high SCS. The three localizations were the following: close to the city of Troyes in the northeast of France; around the city of Limoges in the center-west; and in the southwest close to the city of Tarbes. The semi-parametric method based on spatial hazard modeling applies to continuous variables, and takes account of both risk factors and potential heterogeneity of the background population. This tool allows a quantitative detection but assumes a spatially specified form for clusters.

The spatial resolution of the porcine multifocal electroretinogram for detection of laser-induced retinal lesions

DEFF Research Database (Denmark)

Kyhn, Maria Voss; Kiilgaard, Jens Folke; Scherfig, Erik

2008-01-01

This study aimed to investigate the spatial resolution of a porcine multifocal electroretinogram (mfERG) protocol by testing its ability to detect laser-induced retinal lesions. Furthermore, we wanted to describe time-dependent changes in implicit time and amplitude of the different mfERG peaks...

Regularization Techniques for ECG Imaging during Atrial Fibrillation: a Computational Study

Directory of Open Access Journals (Sweden)

Carlos Figuera

2016-10-01

Full Text Available The inverse problem of electrocardiography is usually analyzed during stationary rhythms. However, the performance of the regularization methods under fibrillatory conditions has not been fully studied. In this work, we assessed different regularization techniques during atrial fibrillation (AF for estimating four target parameters, namely, epicardial potentials, dominant frequency (DF, phase maps, and singularity point (SP location. We use a realistic mathematical model of atria and torso anatomy with three different electrical activity patterns (i.e. sinus rhythm, simple AF and complex AF. Body surface potentials (BSP were simulated using Boundary Element Method and corrupted with white Gaussian noise of different powers. Noisy BSPs were used to obtain the epicardial potentials on the atrial surface, using fourteen different regularization techniques. DF, phase maps and SP location were computed from estimated epicardial potentials. Inverse solutions were evaluated using a set of performance metrics adapted to each clinical target. For the case of SP location, an assessment methodology based on the spatial mass function of the SP location and four spatial error metrics was proposed. The role of the regularization parameter for Tikhonov-based methods, and the effect of noise level and imperfections in the knowledge of the transfer matrix were also addressed. Results showed that the Bayes maximum-a-posteriori method clearly outperforms the rest of the techniques but requires a priori information about the epicardial potentials. Among the purely non-invasive techniques, Tikhonov-based methods performed as well as more complex techniques in realistic fibrillatory conditions, with a slight gain between 0.02 and 0.2 in terms of the correlation coefficient. Also, the use of a constant regularization parameter may be advisable since the performance was similar to that obtained with a variable parameter (indeed there was no difference for the zero

Combining time-frequency and spatial information for the detection of sleep spindles

Directory of Open Access Journals (Sweden)

Christian eO'Reilly

2015-02-01

Full Text Available EEG sleep spindles are short (0.5-2.0 s bursts of activity in the 11-16 Hz band occurring during non-rapid eye movement (NREM sleep. This sporadic activity is thought to play a role in memory consolidation, brain plasticity, and protection of sleep integrity. Many automatic detectors have been proposed to assist or replace experts for sleep spindle scoring. However, these algorithms usually detect too many events making it difficult to achieve a good tradeoff between sensitivity (Se and false detection rate (FDr. In this work, we propose a semi-automatic detector comprising a sensitivity phase based on well-established criteria followed by a specificity phase using spatial and spectral criteria.In the sensitivity phase, selected events are those which amplitude in the 10 – 16 Hz band and spectral ratio characteristics both reject a null hypothesis (p <0.1 stating that the considered event is not a spindle. This null hypothesis is constructed from events occurring during rapid eye movement (REM sleep epochs. In the specificity phase, a hierarchical clustering of the selected candidates is done based on events’ frequency and spatial position along the anterior-posterior axis. Only events from the classes grouping most (at least 80% spindles scored by an expert are kept. We obtain Se = 93.2% and FDr = 93.0% in the first phase and Se = 85.4% and FDr = 86.2% in the second phase. For these two phases, Matthew’s correlation coefficients are respectively 0.228 and 0.324. Results suggest that spindles are defined by specific spatio-spectral properties and that automatic detection methods can be improved by considering these features.

Infants use temporal regularities to chunk objects in memory.

Science.gov (United States)

Kibbe, Melissa M; Feigenson, Lisa

2016-01-01

Infants, like adults, can maintain only a few items in working memory, but can overcome this limit by creating more efficient representations, or "chunks." Previous research shows that infants can form chunks using shared features or spatial proximity between objects. Here we asked whether infants also can create chunked representations using regularities that unfold over time. Thirteen-month old infants first were familiarized with four objects of different shapes and colors, presented in successive pairs. For some infants, the identities of objects in each pair varied randomly across familiarization (Experiment 1). For others, the objects within a pair always co-occurred, either in consistent relative spatial positions (Experiment 2a) or varying spatial positions (Experiment 2b). Following familiarization, infants saw all four objects hidden behind a screen and then saw the screen lifted to reveal either four objects or only three. Infants in Experiment 1, who had been familiarized with random object pairings, failed to look longer at the unexpected 3-object outcome; they showed the same inability to concurrently represent four objects as in other studies of infant working memory. In contrast, infants in Experiments 2a and 2b, who had been familiarized with regularly co-occurring pairs, looked longer at the unexpected outcome. These infants apparently used the co-occurrence between individual objects during familiarization to form chunked representations that were later deployed to track the objects as they were hidden at test. In Experiment 3, we confirmed that the familiarization affected infants' ability to remember the occluded objects rather than merely establishing longer-term memory for object pairs. Following familiarization to consistent pairs, infants who were not shown a hiding event (but merely saw the same test outcomes as in Experiments 2a and b) showed no preference for arrays of three versus four objects. Finally, in Experiments 4 and 5, we asked

Non-Linear Detection for Joint Space-Frequency Block Coding and Spatial Multiplexing in OFDM-MIMO Systems

DEFF Research Database (Denmark)

Rahman, Imadur Mohamed; Marchetti, Nicola; Fitzek, Frank

2005-01-01

(SIC) receiver where the detection is done on subcarrier by sub-carrier basis based on both Zero Forcing (ZF) and Minimum Mean Square Error (MMSE) nulling criterion for the system. In terms of Frame Error Rate (FER), MMSE based SIC receiver performs better than all other receivers compared......In this work, we have analyzed a joint spatial diversity and multiplexing transmission structure for MIMO-OFDM system, where Orthogonal Space-Frequency Block Coding (OSFBC) is used across all spatial multiplexing branches. We have derived a BLAST-like non-linear Successive Interference Cancellation...... in this paper. We have found that a linear two-stage receiver for the proposed system [1] performs very close to the non-linear receiver studied in this work. Finally, we compared the system performance in spatially correlated scenario. It is found that higher amount of spatial correlation at the transmitter...

Spatial- and Time-Correlated Detection of Fission Fragments

Directory of Open Access Journals (Sweden)

Platkevic M.

2012-02-01

Full Text Available With the goal to measure angular correlations of fission fragments in rare fission decay (e.g. ternary and quaternary fission, a multi-detector coincidence system based on two and up to four position sensitive pixel detectors Timepix has been built. In addition to the high granularity, wide dynamic range and per pixel signal threshold, these devices are equipped with per pixel energy and time sensitivity providing more information (position, energy, time, enhances particle-type identification and selectivity of event-by-event detection. Operation of the device with the integrated USB 2.0 based readout interface FITPix and the control and data acquisition software tool Pixelman enables online visualization and flexible/adjustable operation for a different type of experiments. Spatially correlated fission fragments can be thus registered in coincidence. Similarly triggered measurements are performed using an integrated spectrometric module with analogue signal chain electronics. The current status of development together with demonstration of the technique with a 252Cf source is presented.

Quantifying Forest Spatial Pattern Trends at Multiple Extents: An Approach to Detect Significant Changes at Different Scales

Directory of Open Access Journals (Sweden)

Ludovico Frate

2014-09-01

Full Text Available We propose a procedure to detect significant changes in forest spatial patterns and relevant scales. Our approach consists of four sequential steps. First, based on a series of multi-temporal forest maps, a set of geographic windows of increasing extents are extracted. Second, for each extent and date, specific stochastic simulations that replicate real-world spatial pattern characteristics are run. Third, by computing pattern metrics on both simulated and real maps, their empirical distributions and confidence intervals are derived. Finally, multi-temporal scalograms are built for each metric. Based on cover maps (1954, 2011 with a resolution of 10 m we analyze forest pattern changes in a central Apennines (Italy reserve at multiple spatial extents (128, 256 and 512 pixels. We identify three types of multi-temporal scalograms, depending on pattern metric behaviors, describing different dynamics of natural reforestation process. The statistical distribution and variability of pattern metrics at multiple extents offers a new and powerful tool to detect forest variations over time. Similar procedures can (i help to identify significant changes in spatial patterns and provide the bases to relate them to landscape processes; (ii minimize the bias when comparing pattern metrics at a single extent and (iii be extended to other landscapes and scales.

Spatial distribution

DEFF Research Database (Denmark)

Borregaard, Michael Krabbe; Hendrichsen, Ditte Katrine; Nachman, Gøsta Støger

2008-01-01

, depending on the nature of intraspecific interactions between them: while the individuals of some species repel each other and partition the available area, others form groups of varying size, determined by the fitness of each group member. The spatial distribution pattern of individuals again strongly......Living organisms are distributed over the entire surface of the planet. The distribution of the individuals of each species is not random; on the contrary, they are strongly dependent on the biology and ecology of the species, and vary over different spatial scale. The structure of whole...... populations reflects the location and fragmentation pattern of the habitat types preferred by the species, and the complex dynamics of migration, colonization, and population growth taking place over the landscape. Within these, individuals are distributed among each other in regular or clumped patterns...

An iterative method for Tikhonov regularization with a general linear regularization operator

NARCIS (Netherlands)

Hochstenbach, M.E.; Reichel, L.

2010-01-01

Tikhonov regularization is one of the most popular approaches to solve discrete ill-posed problems with error-contaminated data. A regularization operator and a suitable value of a regularization parameter have to be chosen. This paper describes an iterative method, based on Golub-Kahan

Spatial-Temporal Synchrophasor Data Characterization and Analytics in Smart Grid Fault Detection, Identification, and Impact Causal Analysis

Energy Technology Data Exchange (ETDEWEB)

Jiang, Huaiguang; Dai, Xiaoxiao; Gao, David Wenzhong; Zhang, Jun Jason; Zhang, Yingchen; Muljadi, Eduard

2016-09-01

An approach of big data characterization for smart grids (SGs) and its applications in fault detection, identification, and causal impact analysis is proposed in this paper, which aims to provide substantial data volume reduction while keeping comprehensive information from synchrophasor measurements in spatial and temporal domains. Especially, based on secondary voltage control (SVC) and local SG observation algorithm, a two-layer dynamic optimal synchrophasor measurement devices selection algorithm (OSMDSA) is proposed to determine SVC zones, their corresponding pilot buses, and the optimal synchrophasor measurement devices. Combining the two-layer dynamic OSMDSA and matching pursuit decomposition, the synchrophasor data is completely characterized in the spatial-temporal domain. To demonstrate the effectiveness of the proposed characterization approach, SG situational awareness is investigated based on hidden Markov model based fault detection and identification using the spatial-temporal characteristics generated from the reduced data. To identify the major impact buses, the weighted Granger causality for SGs is proposed to investigate the causal relationship of buses during system disturbance. The IEEE 39-bus system and IEEE 118-bus system are employed to validate and evaluate the proposed approach.

Regularized quasinormal modes for plasmonic resonators and open cavities

Science.gov (United States)

Kamandar Dezfouli, Mohsen; Hughes, Stephen

2018-03-01

Optical mode theory and analysis of open cavities and plasmonic particles is an essential component of optical resonator physics, offering considerable insight and efficiency for connecting to classical and quantum optical properties such as the Purcell effect. However, obtaining the dissipative modes in normalized form for arbitrarily shaped open-cavity systems is notoriously difficult, often involving complex spatial integrations, even after performing the necessary full space solutions to Maxwell's equations. The formal solutions are termed quasinormal modes, which are known to diverge in space, and additional techniques are frequently required to obtain more accurate field representations in the far field. In this work, we introduce a finite-difference time-domain technique that can be used to obtain normalized quasinormal modes using a simple dipole-excitation source, and an inverse Green function technique, in real frequency space, without having to perform any spatial integrations. Moreover, we show how these modes are naturally regularized to ensure the correct field decay behavior in the far field, and thus can be used at any position within and outside the resonator. We term these modes "regularized quasinormal modes" and show the reliability and generality of the theory by studying the generalized Purcell factor of dipole emitters near metallic nanoresonators, hybrid devices with metal nanoparticles coupled to dielectric waveguides, as well as coupled cavity-waveguides in photonic crystals slabs. We also directly compare our results with full-dipole simulations of Maxwell's equations without any approximations, and show excellent agreement.

Evolution of radial profiles in regular Lemaitre-Tolman-Bondi dust models

International Nuclear Information System (INIS)

Sussman, Roberto A

2010-01-01

We undertake a comprehensive and rigorous analytic study of the evolution of radial profiles of covariant scalars in regular LemaItre-Tolman-Bondi (LTB) dust models. We consider specifically the phenomenon of 'profile inversions' in which an initial clump profile of density, spatial curvature or the expansion scalar might evolve into a void profile (and vice versa). Previous work in the literature on models with density void profiles and/or allowing for density profile inversions is given full generalization, with some erroneous results corrected. We prove rigorously that if an evolution without shell crossings is assumed, then only the 'clump to void' inversion can occur in density profiles, and only in hyperbolic models or regions with negative spatial curvature. The profiles of spatial curvature follow similar patterns as those of the density, with 'clump to void' inversions only possible for hyperbolic models or regions. However, profiles of the expansion scalar are less restrictive, with profile inversions necessarily taking place in elliptic models. We also examine radial profiles in special LTB configurations: closed elliptic models, models with a simultaneous big bang singularity, as well as a locally collapsing elliptic region surrounded by an expanding hyperbolic background. The general analytic statements that we obtain allow for setting up the right initial conditions to construct fully regular LTB models with any specific qualitative requirements for the profiles of all scalars and their time evolution. The results presented can be very useful in guiding future numerical work on these models and in revising previous analytic work on all their applications.

Effective spatial database support for acquiring spatial information from remote sensing images

Science.gov (United States)

Jin, Peiquan; Wan, Shouhong; Yue, Lihua

2009-12-01

In this paper, a new approach to maintain spatial information acquiring from remote-sensing images is presented, which is based on Object-Relational DBMS. According to this approach, the detected and recognized results of targets are stored and able to be further accessed in an ORDBMS-based spatial database system, and users can access the spatial information using the standard SQL interface. This approach is different from the traditional ArcSDE-based method, because the spatial information management module is totally integrated into the DBMS and becomes one of the core modules in the DBMS. We focus on three issues, namely the general framework for the ORDBMS-based spatial database system, the definitions of the add-in spatial data types and operators, and the process to develop a spatial Datablade on Informix. The results show that the ORDBMS-based spatial database support for image-based target detecting and recognition is easy and practical to be implemented.

Effect of harmonicity on the detection of a signal in a complex masker and on spatial release from masking.

Directory of Open Access Journals (Sweden)

Astrid Klinge

Full Text Available The amount of masking of sounds from one source (signals by sounds from a competing source (maskers heavily depends on the sound characteristics of the masker and the signal and on their relative spatial location. Numerous studies investigated the ability to detect a signal in a speech or a noise masker or the effect of spatial separation of signal and masker on the amount of masking, but there is a lack of studies investigating the combined effects of many cues on the masking as is typical for natural listening situations. The current study using free-field listening systematically evaluates the combined effects of harmonicity and inharmonicity cues in multi-tone maskers and cues resulting from spatial separation of target signal and masker on the detection of a pure tone in a multi-tone or a noise masker. A linear binaural processing model was implemented to predict the masked thresholds in order to estimate whether the observed thresholds can be accounted for by energetic masking in the auditory periphery or whether other effects are involved. Thresholds were determined for combinations of two target frequencies (1 and 8 kHz, two spatial configurations (masker and target either co-located or spatially separated by 90 degrees azimuth, and five different masker types (four complex multi-tone stimuli, one noise masker. A spatial separation of target and masker resulted in a release from masking for all masker types. The amount of masking significantly depended on the masker type and frequency range. The various harmonic and inharmonic relations between target and masker or between components of the masker resulted in a complex pattern of increased or decreased masked thresholds in comparison to the predicted energetic masking. The results indicate that harmonicity cues affect the detectability of a tonal target in a complex masker.

The perception of regularity in an isochronous stimulus in zebra finches (Taeniopygia guttata) and humans

NARCIS (Netherlands)

van der Aa, J.; Honing, H.; ten Cate, C.

2015-01-01

Perceiving temporal regularity in an auditory stimulus is considered one of the basic features of musicality. Here we examine whether zebra finches can detect regularity in an isochronous stimulus. Using a go/no go paradigm we show that zebra finches are able to distinguish between an isochronous

Regular Expression Pocket Reference

CERN Document Server

Stubblebine, Tony

2007-01-01

This handy little book offers programmers a complete overview of the syntax and semantics of regular expressions that are at the heart of every text-processing application. Ideal as a quick reference, Regular Expression Pocket Reference covers the regular expression APIs for Perl 5.8, Ruby (including some upcoming 1.9 features), Java, PHP, .NET and C#, Python, vi, JavaScript, and the PCRE regular expression libraries. This concise and easy-to-use reference puts a very powerful tool for manipulating text and data right at your fingertips. Composed of a mixture of symbols and text, regular exp

Detecting violations of temporal regularities in waking and sleeping two-month-old infants

NARCIS (Netherlands)

Otte, R.A.; Winkler, I.; Braeken, M.A.K.A.; Stekelenburg, J.J.; van der Stelt, O.; Van den Bergh, B.R.H.

2013-01-01

Correctly processing rapid sequences of sounds is essential for developmental milestones, such as language acquisition. We investigated the sensitivity of two-month-old infants to violations of a temporal regularity, by recording event-related brain potentials (ERPs) in an auditory oddball paradigm

Guidance of Spatial Attention by Incidental Learning and Endogenous Cuing

Science.gov (United States)

Jiang, Yuhong V.; Swallow, Khena M.; Rosenbaum, Gail M.

2013-01-01

Our visual system is highly sensitive to regularities in the environment. Locations that were important in one's previous experience are often prioritized during search, even though observers may not be aware of the learning. In this study we characterized the guidance of spatial attention by incidental learning of a target's spatial probability,…

DARK MATTER SUBSTRUCTURE DETECTION USING SPATIALLY RESOLVED SPECTROSCOPY OF LENSED DUSTY GALAXIES

International Nuclear Information System (INIS)

Hezaveh, Yashar; Holder, Gilbert; Dalal, Neal; Kuhlen, Michael; Marrone, Daniel; Murray, Norman; Vieira, Joaquin

2013-01-01

We investigate how strong lensing of dusty, star-forming galaxies (DSFGs) by foreground galaxies can be used as a probe of dark matter halo substructure. We find that spatially resolved spectroscopy of lensed sources allows dramatic improvements to measurements of lens parameters. In particular, we find that modeling of the full, three-dimensional (angular position and radial velocity) data can significantly facilitate substructure detection, increasing the sensitivity of observables to lower mass subhalos. We carry out simulations of lensed dusty sources observed by early ALMA (Cycle 1) and use a Fisher matrix analysis to study the parameter degeneracies and mass detection limits of this method. We find that even with conservative assumptions, it is possible to detect galactic dark matter subhalos of ∼10 8 M ☉ with high significance in most lensed DSFGs. Specifically, we find that in typical DSFG lenses, there is a ∼55% probability of detecting a substructure with M > 10 8 M ☉ with more than 5σ detection significance in each lens, if the abundance of substructure is consistent with previous lensing results. The full ALMA array, with its significantly enhanced sensitivity and resolution, should improve these estimates considerably. Given the sample of ∼100 lenses provided by surveys such as the South Pole Telescope, our understanding of dark matter substructure in typical galaxy halos is poised to improve dramatically over the next few years.

Statistical regularities in art: Relations with visual coding and perception.

Science.gov (United States)

Graham, Daniel J; Redies, Christoph

2010-07-21

Since at least 1935, vision researchers have used art stimuli to test human response to complex scenes. This is sensible given the "inherent interestingness" of art and its relation to the natural visual world. The use of art stimuli has remained popular, especially in eye tracking studies. Moreover, stimuli in common use by vision scientists are inspired by the work of famous artists (e.g., Mondrians). Artworks are also popular in vision science as illustrations of a host of visual phenomena, such as depth cues and surface properties. However, until recently, there has been scant consideration of the spatial, luminance, and color statistics of artwork, and even less study of ways that regularities in such statistics could affect visual processing. Furthermore, the relationship between regularities in art images and those in natural scenes has received little or no attention. In the past few years, there has been a concerted effort to study statistical regularities in art as they relate to neural coding and visual perception, and art stimuli have begun to be studied in rigorous ways, as natural scenes have been. In this minireview, we summarize quantitative studies of links between regular statistics in artwork and processing in the visual stream. The results of these studies suggest that art is especially germane to understanding human visual coding and perception, and it therefore warrants wider study. Copyright 2010 Elsevier Ltd. All rights reserved.

Redistribution population data across a regular spatial grid according to buildings characteristics

Science.gov (United States)

Calka, Beata; Bielecka, Elzbieta; Zdunkiewicz, Katarzyna

2016-12-01

Population data are generally provided by state census organisations at the predefined census enumeration units. However, these datasets very are often required at userdefined spatial units that differ from the census output levels. A number of population estimation techniques have been developed to address these problems. This article is one of those attempts aimed at improving county level population estimates by using spatial disaggregation models with support of buildings characteristic, derived from national topographic database, and average area of a flat. The experimental gridded population surface was created for Opatów county, sparsely populated rural region located in Central Poland. The method relies on geolocation of population counts in buildings, taking into account the building volume and structural building type and then aggregation the people total in 1 km quadrilateral grid. The overall quality of population distribution surface expressed by the mean of RMSE equals 9 persons, and the MAE equals 0.01. We also discovered that nearly 20% of total county area is unpopulated and 80% of people lived on 33% of the county territory.

Mixed Total Variation and L1 Regularization Method for Optical Tomography Based on Radiative Transfer Equation

Directory of Open Access Journals (Sweden)

Jinping Tang

2017-01-01

Full Text Available Optical tomography is an emerging and important molecular imaging modality. The aim of optical tomography is to reconstruct optical properties of human tissues. In this paper, we focus on reconstructing the absorption coefficient based on the radiative transfer equation (RTE. It is an ill-posed parameter identification problem. Regularization methods have been broadly applied to reconstruct the optical coefficients, such as the total variation (TV regularization and the L1 regularization. In order to better reconstruct the piecewise constant and sparse coefficient distributions, TV and L1 norms are combined as the regularization. The forward problem is discretized with the discontinuous Galerkin method on the spatial space and the finite element method on the angular space. The minimization problem is solved by a Jacobian-based Levenberg-Marquardt type method which is equipped with a split Bregman algorithms for the L1 regularization. We use the adjoint method to compute the Jacobian matrix which dramatically improves the computation efficiency. By comparing with the other imaging reconstruction methods based on TV and L1 regularizations, the simulation results show the validity and efficiency of the proposed method.

The geometry of continuum regularization

International Nuclear Information System (INIS)

Halpern, M.B.

1987-03-01

This lecture is primarily an introduction to coordinate-invariant regularization, a recent advance in the continuum regularization program. In this context, the program is seen as fundamentally geometric, with all regularization contained in regularized DeWitt superstructures on field deformations

Regular expression containment

DEFF Research Database (Denmark)

Henglein, Fritz; Nielsen, Lasse

2011-01-01

We present a new sound and complete axiomatization of regular expression containment. It consists of the conventional axiomatiza- tion of concatenation, alternation, empty set and (the singleton set containing) the empty string as an idempotent semiring, the fixed- point rule E* = 1 + E × E......* for Kleene-star, and a general coin- duction rule as the only additional rule. Our axiomatization gives rise to a natural computational inter- pretation of regular expressions as simple types that represent parse trees, and of containment proofs as coercions. This gives the axiom- atization a Curry......-Howard-style constructive interpretation: Con- tainment proofs do not only certify a language-theoretic contain- ment, but, under our computational interpretation, constructively transform a membership proof of a string in one regular expres- sion into a membership proof of the same string in another regular expression. We...

Supersymmetric dimensional regularization

International Nuclear Information System (INIS)

Siegel, W.; Townsend, P.K.; van Nieuwenhuizen, P.

1980-01-01

There is a simple modification of dimension regularization which preserves supersymmetry: dimensional reduction to real D < 4, followed by analytic continuation to complex D. In terms of component fields, this means fixing the ranges of all indices on the fields (and therefore the numbers of Fermi and Bose components). For superfields, it means continuing in the dimensionality of x-space while fixing the dimensionality of theta-space. This regularization procedure allows the simple manipulation of spinor derivatives in supergraph calculations. The resulting rules are: (1) First do all algebra exactly as in D = 4; (2) Then do the momentum integrals as in ordinary dimensional regularization. This regularization procedure needs extra rules before one can say that it is consistent. Such extra rules needed for superconformal anomalies are discussed. Problems associated with renormalizability and higher order loops are also discussed

Influence of chewing behaviour on memory and spatial learning in albino BALB/c mice.

Science.gov (United States)

Aguirre Siancas, E E

2017-05-01

Since the relationship between chewing and cognitive functions has not been fully elucidated, this study aimed to determine the impact of chewing behaviour on spatial learning and memory in albino male BALB/c mice. Twenty mice aged 8 weeks were divided into 2 equal groups. The regular chewing group was fed with uncrushed grains (the same diet given to all 20 mice since they were weaned) and the limited chewing group was fed with crushed grains. At 16 weeks of age, the mice were evaluated over 5 days, including a 4-day acquisition phase prior to a probe test of spatial learning and memory in the Morris water maze on the fifth day. A comparison of the regular chewing group and the limited chewing group found no significant differences in either the acquisition phase or the probe test. However, there were significant differences in the acquisition phase for just the regular chewing group when comparing results from the first day to those from the other 3 days. The results suggest that regular chewing affects spatial learning and memory since mice in the regular chewing group decreased their times to find the hidden platform during the acquisition phase. Copyright © 2015 Sociedad Española de Neurología. Publicado por Elsevier España, S.L.U. All rights reserved.

Spatial Modeling of Industrial Windfall on Soils to Detect Woody Species with Potential for Bioremediation

Science.gov (United States)

S. Salazar; M. Mendoza; A. M. Tejeda

2006-01-01

A spatial model is presented to explain the concentration of heavy metals (Fe, Cu, Zn, Ni, Cr, Co and Pb), in the soils around the industrial complex near the Port of Veracruz, Mexico. Unexpected low concentration sites where then tested to detect woody plant species that may have the capability to hiperacumulate these contaminants, hence having a potential for...

Regularization by External Variables

DEFF Research Database (Denmark)

Bossolini, Elena; Edwards, R.; Glendinning, P. A.

2016-01-01

Regularization was a big topic at the 2016 CRM Intensive Research Program on Advances in Nonsmooth Dynamics. There are many open questions concerning well known kinds of regularization (e.g., by smoothing or hysteresis). Here, we propose a framework for an alternative and important kind of regula......Regularization was a big topic at the 2016 CRM Intensive Research Program on Advances in Nonsmooth Dynamics. There are many open questions concerning well known kinds of regularization (e.g., by smoothing or hysteresis). Here, we propose a framework for an alternative and important kind...

Crosstalk elimination in the detection of dual-beam optical tweezers by spatial filtering

International Nuclear Information System (INIS)

Ott, Dino; Oddershede, Lene B.; Reihani, S. Nader S.

2014-01-01

In dual-beam optical tweezers, the accuracy of position and force measurements is often compromised by crosstalk between the two detected signals, this crosstalk leading to systematic and significant errors on the measured forces and distances. This is true both for dual-beam optical traps where the splitting of the two traps is done by polarization optics and for dual optical traps constructed by other methods, e.g., holographic tweezers. If the two traps are orthogonally polarized, most often crosstalk is minimized by inserting polarization optics in front of the detector; however, this method is not perfect because of the de-polarization of the trapping beam introduced by the required high numerical aperture optics. Here we present a simple and easy-to-implement method to efficiently eliminate crosstalk. The method is based on spatial filtering by simply inserting a pinhole at the correct position and is highly compatible with standard back focal plane photodiode based detection of position and force. Our spatial filtering method reduces crosstalk up to five times better than polarization filtering alone. The effectiveness is dependent on pinhole size and distance between the traps and is here quantified experimentally and reproduced by theoretical modeling. The method here proposed will improve the accuracy of force-distance measurements, e.g., of single molecules, performed by dual-beam optical traps and hence give much more scientific value for the experimental efforts

Regular Single Valued Neutrosophic Hypergraphs

Directory of Open Access Journals (Sweden)

Muhammad Aslam Malik

2016-12-01

Full Text Available In this paper, we define the regular and totally regular single valued neutrosophic hypergraphs, and discuss the order and size along with properties of regular and totally regular single valued neutrosophic hypergraphs. We also extend work on completeness of single valued neutrosophic hypergraphs.

Spatial and temporal control of thermal waves by using DMDs for interference based crack detection

Science.gov (United States)

Thiel, Erik; Kreutzbruck, Marc; Ziegler, Mathias

2016-02-01

Active Thermography is a well-established non-destructive testing method and used to detect cracks, voids or material inhomogeneities. It is based on applying thermal energy to a samples' surface whereas inner defects alter the nonstationary heat flow. Conventional excitation of a sample is hereby done spatially, either planar (e.g. using a lamp) or local (e.g. using a focused laser) and temporally, either pulsed or periodical. In this work we combine a high power laser with a Digital Micromirror Device (DMD) allowing us to merge all degrees of freedom to a spatially and temporally controlled heat source. This enables us to exploit the possibilities of coherent thermal wave shaping. Exciting periodically while controlling at the same time phase and amplitude of the illumination source induces - via absorption at the sample's surface - a defined thermal wave propagation through a sample. That means thermal waves can be controlled almost like acoustical or optical waves. However, in contrast to optical or acoustical waves, thermal waves are highly damped due to the diffusive character of the thermal heat flow and therefore limited in penetration depth in relation to the achievable resolution. Nevertheless, the coherence length of thermal waves can be chosen in the mmrange for modulation frequencies below 10 Hz which is perfectly met by DMD technology. This approach gives us the opportunity to transfer known technologies from wave shaping techniques to thermography methods. We will present experiments on spatial and temporal wave shaping, demonstrating interference based crack detection.

Detection of the spatial accuracy of an O-arm in the region of surgical interest

Science.gov (United States)

Koivukangas, Tapani; Katisko, Jani P. A.; Koivukangsa, John P.

2013-03-01

Medical imaging is an essential component of a wide range of surgical procedures1. For image guided surgical (IGS) procedures, medical images are the main source of information2. The IGS procedures rely largely on obtained image data, so the data needs to provide differentiation between normal and abnormal tissues, especially when other surgical guidance devices are used in the procedures. The image data also needs to provide accurate spatial representation of the patient3. This research has concentrated on the concept of accuracy assessment of IGS devices to meet the needs of quality assurance in the hospital environment. For this purpose, two precision engineered accuracy assessment phantoms have been developed as advanced materials and methods for the community. The phantoms were designed to mimic the volume of a human head as the common region of surgical interest (ROSI). This paper introduces the utilization of the phantoms in spatial accuracy assessment of a commercial surgical 3D CT scanner, the O-Arm. The study presents methods and results of image quality detection of possible geometrical distortions in the region of surgical interest. The results show that in the pre-determined ROSI there are clear image distortion and artefacts using too high imaging parameters when scanning the objects. On the other hand, when using optimal parameters, the O-Arm causes minimal error in IGS accuracy. The detected spatial inaccuracy of the O-Arm with used parameters was in the range of less than 1.00 mm.

People Detection Based on Spatial Mapping of Friendliness and Floor Boundary Points for a Mobile Navigation Robot

Directory of Open Access Journals (Sweden)

Tsuyoshi Tasaki

2011-01-01

Full Text Available Navigation robots must single out partners requiring navigation and move in the cluttered environment where people walk around. Developing such robots requires two different people detections: detecting partners and detecting all moving people around the robots. For detecting partners, we design divided spaces based on the spatial relationships and sensing ranges. Mapping the friendliness of each divided space based on the stimulus from the multiple sensors to detect people calling robots positively, robots detect partners on the highest friendliness space. For detecting moving people, we regard objects’ floor boundary points in an omnidirectional image as obstacles. We classify obstacles as moving people by comparing movement of each point with robot movement using odometry data, dynamically changing thresholds to detect. Our robot detected 95.0% of partners while it stands by and interacts with people and detected 85.0% of moving people while robot moves, which was four times higher than previous methods did.

DARK MATTER SUBSTRUCTURE DETECTION USING SPATIALLY RESOLVED SPECTROSCOPY OF LENSED DUSTY GALAXIES

Energy Technology Data Exchange (ETDEWEB)

Hezaveh, Yashar; Holder, Gilbert [Department of Physics, McGill University, 3600 Rue University, Montreal, Quebec H3A 2T8 (Canada); Dalal, Neal [Astronomy Department, University of Illinois at Urbana-Champaign, 1002 West Green Street, Urbana, IL 61801 (United States); Kuhlen, Michael [Theoretical Astrophysics Center, University of California, Berkeley, CA 94720 (United States); Marrone, Daniel [Steward Observatory, University of Arizona, 933 North Cherry Avenue, Tucson, AZ 85721 (United States); Murray, Norman [CITA, University of Toronto, 60 St. George Street, Toronto, ON M5S 3H8 (Canada); Vieira, Joaquin [California Institute of Technology, 1200 East California Blvd, MC 249-17, Pasadena, CA 91125 (United States)

2013-04-10

We investigate how strong lensing of dusty, star-forming galaxies (DSFGs) by foreground galaxies can be used as a probe of dark matter halo substructure. We find that spatially resolved spectroscopy of lensed sources allows dramatic improvements to measurements of lens parameters. In particular, we find that modeling of the full, three-dimensional (angular position and radial velocity) data can significantly facilitate substructure detection, increasing the sensitivity of observables to lower mass subhalos. We carry out simulations of lensed dusty sources observed by early ALMA (Cycle 1) and use a Fisher matrix analysis to study the parameter degeneracies and mass detection limits of this method. We find that even with conservative assumptions, it is possible to detect galactic dark matter subhalos of {approx}10{sup 8} M{sub Sun} with high significance in most lensed DSFGs. Specifically, we find that in typical DSFG lenses, there is a {approx}55% probability of detecting a substructure with M > 10{sup 8} M{sub Sun} with more than 5{sigma} detection significance in each lens, if the abundance of substructure is consistent with previous lensing results. The full ALMA array, with its significantly enhanced sensitivity and resolution, should improve these estimates considerably. Given the sample of {approx}100 lenses provided by surveys such as the South Pole Telescope, our understanding of dark matter substructure in typical galaxy halos is poised to improve dramatically over the next few years.

Relativistic time-dependent Fermion-mass renormalization using statistical regularization

Science.gov (United States)

Kutnink, Timothy; McMurray, Christian; Santrach, Amelia; Hockett, Sarah; Barcus, Scott; Petridis, Athanasios

2017-09-01

The time-dependent electromagnetically self-coupled Dirac equation is solved numerically by means of the staggered-leap-frog algorithm with reflecting boundary conditions. The stability region of the method versus the interaction strength and the spatial-grid size over time-step ratio is established. The expectation values of several dynamic operators are then evaluated as functions of time. These include the fermion and electromagnetic energies and the fermion dynamic mass. There is a characteristic, non-exponential, oscillatory dependence leading to asymptotic constants of these expectation values. In the case of the fermion mass this amounts to renormalization. The dependence of the expectation values on the spatial-grid size is evaluated in detail. Furthermore, the contribution of positive and negative energy states to the asymptotic values and the gauge fields is analyzed. Statistical regularization, employing a canonical ensemble whose temperature is the inverse of the grid size, is used to remove the grid-size and momentum-dependence and produce a finite result in the continuum limit.

The Evolution of Reputation-Based Cooperation in Regular Networks

Directory of Open Access Journals (Sweden)

Tatsuya Sasaki

2017-01-01

Full Text Available Despite recent advances in reputation technologies, it is not clear how reputation systems can affect human cooperation in social networks. Although it is known that two of the major mechanisms in the evolution of cooperation are spatial selection and reputation-based reciprocity, theoretical study of the interplay between both mechanisms remains almost uncharted. Here, we present a new individual-based model for the evolution of reciprocal cooperation between reputation and networks. We comparatively analyze four of the leading moral assessment rules—shunning, image scoring, stern judging, and simple standing—and base the model on the giving game in regular networks for Cooperators, Defectors, and Discriminators. Discriminators rely on a proper moral assessment rule. By using individual-based models, we show that the four assessment rules are differently characterized in terms of how cooperation evolves, depending on the benefit-to-cost ratio, the network-node degree, and the observation and error conditions. Our findings show that the most tolerant rule—simple standing—is the most robust among the four assessment rules in promoting cooperation in regular networks.

Quantifying spatial and temporal trends in beach-dune volumetric changes using spatial statistics

Science.gov (United States)

Eamer, Jordan B. R.; Walker, Ian J.

2013-06-01

Spatial statistics are generally underutilized in coastal geomorphology, despite offering great potential for identifying and quantifying spatial-temporal trends in landscape morphodynamics. In particular, local Moran's Ii provides a statistical framework for detecting clusters of significant change in an attribute (e.g., surface erosion or deposition) and quantifying how this changes over space and time. This study analyzes and interprets spatial-temporal patterns in sediment volume changes in a beach-foredune-transgressive dune complex following removal of invasive marram grass (Ammophila spp.). Results are derived by detecting significant changes in post-removal repeat DEMs derived from topographic surveys and airborne LiDAR. The study site was separated into discrete, linked geomorphic units (beach, foredune, transgressive dune complex) to facilitate sub-landscape scale analysis of volumetric change and sediment budget responses. Difference surfaces derived from a pixel-subtraction algorithm between interval DEMs and the LiDAR baseline DEM were filtered using the local Moran's Ii method and two different spatial weights (1.5 and 5 m) to detect statistically significant change. Moran's Ii results were compared with those derived from a more spatially uniform statistical method that uses a simpler student's t distribution threshold for change detection. Morphodynamic patterns and volumetric estimates were similar between the uniform geostatistical method and Moran's Ii at a spatial weight of 5 m while the smaller spatial weight (1.5 m) consistently indicated volumetric changes of less magnitude. The larger 5 m spatial weight was most representative of broader site morphodynamics and spatial patterns while the smaller spatial weight provided volumetric changes consistent with field observations. All methods showed foredune deflation immediately following removal with increased sediment volumes into the spring via deposition at the crest and on lobes in the lee

A self-adapting and altitude-dependent regularization method for atmospheric profile retrievals

Directory of Open Access Journals (Sweden)

M. Ridolfi

2009-03-01

Full Text Available MIPAS is a Fourier transform spectrometer, operating onboard of the ENVISAT satellite since July 2002. The online retrieval algorithm produces geolocated profiles of temperature and of volume mixing ratios of six key atmospheric constituents: H₂O, O₃, HNO₃, CH₄, N₂O and NO₂. In the validation phase, oscillations beyond the error bars were observed in several profiles, particularly in CH₄ and N₂O.

To tackle this problem, a Tikhonov regularization scheme has been implemented in the retrieval algorithm. The applied regularization is however rather weak in order to preserve the vertical resolution of the profiles.

In this paper we present a self-adapting and altitude-dependent regularization approach that detects whether the analyzed observations contain information about small-scale profile features, and determines the strength of the regularization accordingly. The objective of the method is to smooth out artificial oscillations as much as possible, while preserving the fine detail features of the profile when related information is detected in the observations.

The proposed method is checked for self consistency, its performance is tested on MIPAS observations and compared with that of some other regularization schemes available in the literature. In all the considered cases the proposed scheme achieves a good performance, thanks to its altitude dependence and to the constraints employed, which are specific of the inversion problem under consideration. The proposed method is generally applicable to iterative Gauss-Newton algorithms for the retrieval of vertical distribution profiles from atmospheric remote sounding measurements.

On a correspondence between regular and non-regular operator monotone functions

DEFF Research Database (Denmark)

Gibilisco, P.; Hansen, Frank; Isola, T.

2009-01-01

We prove the existence of a bijection between the regular and the non-regular operator monotone functions satisfying a certain functional equation. As an application we give a new proof of the operator monotonicity of certain functions related to the Wigner-Yanase-Dyson skew information....

Clustering, randomness, and regularity in cloud fields: 2. Cumulus cloud fields

Science.gov (United States)

Zhu, T.; Lee, J.; Weger, R. C.; Welch, R. M.

1992-12-01

During the last decade a major controversy has been brewing concerning the proper characterization of cumulus convection. The prevailing view has been that cumulus clouds form in clusters, in which cloud spacing is closer than that found for the overall cloud field and which maintains its identity over many cloud lifetimes. This "mutual protection hypothesis" of Randall and Huffman (1980) has been challenged by the "inhibition hypothesis" of Ramirez et al. (1990) which strongly suggests that the spatial distribution of cumuli must tend toward a regular distribution. A dilemma has resulted because observations have been reported to support both hypotheses. The present work reports a detailed analysis of cumulus cloud field spatial distributions based upon Landsat, Advanced Very High Resolution Radiometer, and Skylab data. Both nearest-neighbor and point-to-cloud cumulative distribution function statistics are investigated. The results show unequivocally that when both large and small clouds are included in the cloud field distribution, the cloud field always has a strong clustering signal. The strength of clustering is largest at cloud diameters of about 200-300 m, diminishing with increasing cloud diameter. In many cases, clusters of small clouds are found which are not closely associated with large clouds. As the small clouds are eliminated from consideration, the cloud field typically tends towards regularity. Thus it would appear that the "inhibition hypothesis" of Ramirez and Bras (1990) has been verified for the large clouds. However, these results are based upon the analysis of point processes. A more exact analysis also is made which takes into account the cloud size distributions. Since distinct clouds are by definition nonoverlapping, cloud size effects place a restriction upon the possible locations of clouds in the cloud field. The net effect of this analysis is that the large clouds appear to be randomly distributed, with only weak tendencies towards

A scan statistic for binary outcome based on hypergeometric probability model, with an application to detecting spatial clusters of Japanese encephalitis.

Science.gov (United States)

Zhao, Xing; Zhou, Xiao-Hua; Feng, Zijian; Guo, Pengfei; He, Hongyan; Zhang, Tao; Duan, Lei; Li, Xiaosong

2013-01-01

As a useful tool for geographical cluster detection of events, the spatial scan statistic is widely applied in many fields and plays an increasingly important role. The classic version of the spatial scan statistic for the binary outcome is developed by Kulldorff, based on the Bernoulli or the Poisson probability model. In this paper, we apply the Hypergeometric probability model to construct the likelihood function under the null hypothesis. Compared with existing methods, the likelihood function under the null hypothesis is an alternative and indirect method to identify the potential cluster, and the test statistic is the extreme value of the likelihood function. Similar with Kulldorff's methods, we adopt Monte Carlo test for the test of significance. Both methods are applied for detecting spatial clusters of Japanese encephalitis in Sichuan province, China, in 2009, and the detected clusters are identical. Through a simulation to independent benchmark data, it is indicated that the test statistic based on the Hypergeometric model outweighs Kulldorff's statistics for clusters of high population density or large size; otherwise Kulldorff's statistics are superior.

Self-Organisation in Spatial Systems-From Fractal Chaos to Regular Patterns and Vice Versa.

Directory of Open Access Journals (Sweden)

Michal Banaszak

Full Text Available This study offers a new perspective on the evolutionary patterns of cities or urban agglomerations. Such developments can range from chaotic to fully ordered. We demonstrate that in a dynamic space of interactive human behaviour cities produce a wealth of gravitational attractors whose size and shape depend on the resistance of space emerging inter alia from transport friction costs. This finding offers original insights into the complex evolution of spatial systems and appears to be consistent with the principles of central place theory known from the spatial sciences and geography. Our approach is dynamic in nature and forms a generalisation of hierarchical principles in geographic space.

Stochastic dynamic modeling of regular and slow earthquakes

Science.gov (United States)

Aso, N.; Ando, R.; Ide, S.

2017-12-01

Both regular and slow earthquakes are slip phenomena on plate boundaries and are simulated by a (quasi-)dynamic modeling [Liu and Rice, 2005]. In these numerical simulations, spatial heterogeneity is usually considered not only for explaining real physical properties but also for evaluating the stability of the calculations or the sensitivity of the results on the condition. However, even though we discretize the model space with small grids, heterogeneity at smaller scales than the grid size is not considered in the models with deterministic governing equations. To evaluate the effect of heterogeneity at the smaller scales we need to consider stochastic interactions between slip and stress in a dynamic modeling. Tidal stress is known to trigger or affect both regular and slow earthquakes [Yabe et al., 2015; Ide et al., 2016], and such an external force with fluctuation can also be considered as a stochastic external force. A healing process of faults may also be stochastic, so we introduce stochastic friction law. In the present study, we propose a stochastic dynamic model to explain both regular and slow earthquakes. We solve mode III problem, which corresponds to the rupture propagation along the strike direction. We use BIEM (boundary integral equation method) scheme to simulate slip evolution, but we add stochastic perturbations in the governing equations, which is usually written in a deterministic manner. As the simplest type of perturbations, we adopt Gaussian deviations in the formulation of the slip-stress kernel, external force, and friction. By increasing the amplitude of perturbations of the slip-stress kernel, we reproduce complicated rupture process of regular earthquakes including unilateral and bilateral ruptures. By perturbing external force, we reproduce slow rupture propagation at a scale of km/day. The slow propagation generated by a combination of fast interaction at S-wave velocity is analogous to the kinetic theory of gasses: thermal

Stochastic analytic regularization

International Nuclear Information System (INIS)

Alfaro, J.

1984-07-01

Stochastic regularization is reexamined, pointing out a restriction on its use due to a new type of divergence which is not present in the unregulated theory. Furthermore, we introduce a new form of stochastic regularization which permits the use of a minimal subtraction scheme to define the renormalized Green functions. (author)

Impact of respiratory motion correction and spatial resolution on lesion detection in PET: a simulation study based on real MR dynamic data

Science.gov (United States)

Polycarpou, Irene; Tsoumpas, Charalampos; King, Andrew P.; Marsden, Paul K.

2014-02-01

The aim of this study is to investigate the impact of respiratory motion correction and spatial resolution on lesion detectability in PET as a function of lesion size and tracer uptake. Real respiratory signals describing different breathing types are combined with a motion model formed from real dynamic MR data to simulate multiple dynamic PET datasets acquired from a continuously moving subject. Lung and liver lesions were simulated with diameters ranging from 6 to 12 mm and lesion to background ratio ranging from 3:1 to 6:1. Projection data for 6 and 3 mm PET scanner resolution were generated using analytic simulations and reconstructed without and with motion correction. Motion correction was achieved using motion compensated image reconstruction. The detectability performance was quantified by a receiver operating characteristic (ROC) analysis obtained using a channelized Hotelling observer and the area under the ROC curve (AUC) was calculated as the figure of merit. The results indicate that respiratory motion limits the detectability of lung and liver lesions, depending on the variation of the breathing cycle length and amplitude. Patients with large quiescent periods had a greater AUC than patients with regular breathing cycles and patients with long-term variability in respiratory cycle or higher motion amplitude. In addition, small (less than 10 mm diameter) or low contrast (3:1) lesions showed the greatest improvement in AUC as a result of applying motion correction. In particular, after applying motion correction the AUC is improved by up to 42% with current PET resolution (i.e. 6 mm) and up to 51% for higher PET resolution (i.e. 3 mm). Finally, the benefit of increasing the scanner resolution is small unless motion correction is applied. This investigation indicates high impact of respiratory motion correction on lesion detectability in PET and highlights the importance of motion correction in order to benefit from the increased resolution of future

Impact of respiratory motion correction and spatial resolution on lesion detection in PET: a simulation study based on real MR dynamic data

International Nuclear Information System (INIS)

Polycarpou, Irene; Tsoumpas, Charalampos; King, Andrew P; Marsden, Paul K

2014-01-01

The aim of this study is to investigate the impact of respiratory motion correction and spatial resolution on lesion detectability in PET as a function of lesion size and tracer uptake. Real respiratory signals describing different breathing types are combined with a motion model formed from real dynamic MR data to simulate multiple dynamic PET datasets acquired from a continuously moving subject. Lung and liver lesions were simulated with diameters ranging from 6 to 12 mm and lesion to background ratio ranging from 3:1 to 6:1. Projection data for 6 and 3 mm PET scanner resolution were generated using analytic simulations and reconstructed without and with motion correction. Motion correction was achieved using motion compensated image reconstruction. The detectability performance was quantified by a receiver operating characteristic (ROC) analysis obtained using a channelized Hotelling observer and the area under the ROC curve (AUC) was calculated as the figure of merit. The results indicate that respiratory motion limits the detectability of lung and liver lesions, depending on the variation of the breathing cycle length and amplitude. Patients with large quiescent periods had a greater AUC than patients with regular breathing cycles and patients with long-term variability in respiratory cycle or higher motion amplitude. In addition, small (less than 10 mm diameter) or low contrast (3:1) lesions showed the greatest improvement in AUC as a result of applying motion correction. In particular, after applying motion correction the AUC is improved by up to 42% with current PET resolution (i.e. 6 mm) and up to 51% for higher PET resolution (i.e. 3 mm). Finally, the benefit of increasing the scanner resolution is small unless motion correction is applied. This investigation indicates high impact of respiratory motion correction on lesion detectability in PET and highlights the importance of motion correction in order to benefit from the increased resolution of future

Detection and Classification of Multiple Objects using an RGB-D Sensor and Linear Spatial Pyramid Matching

DEFF Research Database (Denmark)

Dimitriou, Michalis; Kounalakis, Tsampikos; Vidakis, Nikolaos

2013-01-01

, connected components detection and filtering approaches, in order to design a complete image processing algorithm for efficient object detection of multiple individual objects in a single scene, even in complex scenes with many objects. Besides, we apply the Linear Spatial Pyramid Matching (LSPM) [1] method......This paper presents a complete system for multiple object detection and classification in a 3D scene using an RGB-D sensor such as the Microsoft Kinect sensor. Successful multiple object detection and classification are crucial features in many 3D computer vision applications. The main goal...... is making machines see and understand objects like humans do. To this goal, the new RGB-D sensors can be utilized since they provide real-time depth map which can be used along with the RGB images for our tasks. In our system we employ effective depth map processing techniques, along with edge detection...

EBSD spatial resolution for detecting sigma phase in steels

Energy Technology Data Exchange (ETDEWEB)

Bordín, S. Fernandez; Limandri, S. [Instituto de Física Enrique Gaviola, CONICET. M. Allende s/n, Ciudad Universitaria, 5000 Córdoba (Argentina); Ranalli, J.M. [Comisión Nacional de Energía Atómica, Av. Gral. Paz 1499, San Martín, 1650 Buenos Aires (Argentina); Castellano, G. [Instituto de Física Enrique Gaviola, CONICET. M. Allende s/n, Ciudad Universitaria, 5000 Córdoba (Argentina)

2016-12-15

The spatial resolution of the electron backscatter diffraction signal is explored by Monte Carlo simulation for the sigma phase in steel at a typical instrumental set-up. In order to estimate the active volume corresponding to the diffracted electrons, the fraction of the backscattered electrons contributing to the diffraction signal was inferred by extrapolating the Kikuchi pattern contrast measured by other authors, as a function of the diffracted electron energy. In the resulting estimation, the contribution of the intrinsic incident beam size and the software capability to deconvolve patterns were included. A strong influence of the beam size on the lateral resolution was observed, resulting in 20 nm for the aperture considered. For longitudinal and depth directions the resolutions obtained were 75 nm and 16 nm, respectively. The reliability of this last result is discussed in terms of the survey of the last large-angle deflection undergone by the backscattered electrons involved in the diffraction process. Bearing in mind the mean transversal resolution found, it was possible to detect small area grains of sigma phase by EBSD measurements, for a stabilized austenitic AISI 347 stainless steel under heat treatments, simulating post welding (40 h at 600 °C) and aging (284 h at 484 °C) effects—as usually occurring in nuclear reactor pressure vessels. - Highlights: • EBSD spatial resolution is studied by Monte Carlo simulation for σ-phase in steel. • The contribution of the intrinsic incident beam size was included. • A stabilized austenitic stainless steel under heat treatments was measured by EBSD. • With the transversal resolution found, small area σ-phase grains could be identified.

Calculation and simulation on mid-spatial frequency error in continuous polishing

International Nuclear Information System (INIS)

Xie Lei; Zhang Yunfan; You Yunfeng; Ma Ping; Liu Yibin; Yan Dingyao

2013-01-01

Based on theoretical model of continuous polishing, the influence of processing parameters on the polishing result was discussed. Possible causes of mid-spatial frequency error in the process were analyzed. The simulation results demonstrated that the low spatial frequency error was mainly caused by large rotating ratio. The mid-spatial frequency error would decrease as the low spatial frequency error became lower. The regular groove shape was the primary reason of the mid-spatial frequency error. When irregular and fitful grooves were adopted, the mid-spatial frequency error could be lessened. Moreover, the workpiece swing could make the polishing process more uniform and reduce the mid-spatial frequency error caused by the fix-eccentric plane polishing. (authors)

Spatial statistical analysis of organs for intelligent CAD and its application to disease detection

International Nuclear Information System (INIS)

Takizawa, Hotaka

2009-01-01

The present article reports our research that was performed in a research project supported by a Grantin-Aid for Scientific Research on Priority Area from the Ministry of Education, Culture Sports, Science and Technology, JAPAN, from 2003 to 2006. Our method developed in the research acquired the trend of variation of spatial relations between true diseases, false positives and image features through statistical analysis of a set of medical images and improved the accuracy of disease detection by predicting their occurrence positions in an image based on the trend. This article describes the formulation of the method in general form and shows the results obtained by applying the method to chest X-ray CT images for detection of pulmonary nodules. (author)

Multi-index Stochastic Collocation Convergence Rates for Random PDEs with Parametric Regularity

KAUST Repository

Haji Ali, Abdul Lateef; Nobile, Fabio; Tamellini, Lorenzo; Tempone, Raul

2016-01-01

We analyze the recent Multi-index Stochastic Collocation (MISC) method for computing statistics of the solution of a partial differential equation (PDE) with random data, where the random coefficient is parametrized by means of a countable sequence of terms in a suitable expansion. MISC is a combination technique based on mixed differences of spatial approximations and quadratures over the space of random data, and naturally, the error analysis uses the joint regularity of the solution with respect to both the variables in the physical domain and parametric variables. In MISC, the number of problem solutions performed at each discretization level is not determined by balancing the spatial and stochastic components of the error, but rather by suitably extending the knapsack-problem approach employed in the construction of the quasi-optimal sparse-grids and Multi-index Monte Carlo methods, i.e., we use a greedy optimization procedure to select the most effective mixed differences to include in the MISC estimator. We apply our theoretical estimates to a linear elliptic PDE in which the log-diffusion coefficient is modeled as a random field, with a covariance similar to a Matérn model, whose realizations have spatial regularity determined by a scalar parameter. We conduct a complexity analysis based on a summability argument showing algebraic rates of convergence with respect to the overall computational work. The rate of convergence depends on the smoothness parameter, the physical dimensionality and the efficiency of the linear solver. Numerical experiments show the effectiveness of MISC in this infinite dimensional setting compared with the Multi-index Monte Carlo method and compare the convergence rate against the rates predicted in our theoretical analysis. © 2016 SFoCM

Multi-index Stochastic Collocation Convergence Rates for Random PDEs with Parametric Regularity

KAUST Repository

Haji Ali, Abdul Lateef

2016-08-26

We analyze the recent Multi-index Stochastic Collocation (MISC) method for computing statistics of the solution of a partial differential equation (PDE) with random data, where the random coefficient is parametrized by means of a countable sequence of terms in a suitable expansion. MISC is a combination technique based on mixed differences of spatial approximations and quadratures over the space of random data, and naturally, the error analysis uses the joint regularity of the solution with respect to both the variables in the physical domain and parametric variables. In MISC, the number of problem solutions performed at each discretization level is not determined by balancing the spatial and stochastic components of the error, but rather by suitably extending the knapsack-problem approach employed in the construction of the quasi-optimal sparse-grids and Multi-index Monte Carlo methods, i.e., we use a greedy optimization procedure to select the most effective mixed differences to include in the MISC estimator. We apply our theoretical estimates to a linear elliptic PDE in which the log-diffusion coefficient is modeled as a random field, with a covariance similar to a Matérn model, whose realizations have spatial regularity determined by a scalar parameter. We conduct a complexity analysis based on a summability argument showing algebraic rates of convergence with respect to the overall computational work. The rate of convergence depends on the smoothness parameter, the physical dimensionality and the efficiency of the linear solver. Numerical experiments show the effectiveness of MISC in this infinite dimensional setting compared with the Multi-index Monte Carlo method and compare the convergence rate against the rates predicted in our theoretical analysis. © 2016 SFoCM

The effects of incidentally learned temporal and spatial predictability on response times and visual fixations during target detection and discrimination.

Directory of Open Access Journals (Sweden)

Melissa R Beck

Full Text Available Responses are quicker to predictable stimuli than if the time and place of appearance is uncertain. Studies that manipulate target predictability often involve overt cues to speed up response times. However, less is known about whether individuals will exhibit faster response times when target predictability is embedded within the inter-trial relationships. The current research examined the combined effects of spatial and temporal target predictability on reaction time (RT and allocation of overt attention in a sustained attention task. Participants responded as quickly as possible to stimuli while their RT and eye movements were measured. Target temporal and spatial predictability were manipulated by altering the number of: 1 different time intervals between a response and the next target; and 2 possible spatial locations of the target. The effects of target predictability on target detection (Experiment 1 and target discrimination (Experiment 2 were tested. For both experiments, shorter RTs as target predictability increased across both space and time were found. In addition, the influences of spatial and temporal target predictability on RT and the overt allocation of attention were task dependent; suggesting that effective orienting of attention relies on both spatial and temporal predictability. These results indicate that stimulus predictability can be increased without overt cues and detected purely through inter-trial relationships over the course of repeated stimulus presentations.

Fast joint detection-estimation of evoked brain activity in event-related FMRI using a variational approach

Science.gov (United States)

Chaari, Lotfi; Vincent, Thomas; Forbes, Florence; Dojat, Michel; Ciuciu, Philippe

2013-01-01

In standard within-subject analyses of event-related fMRI data, two steps are usually performed separately: detection of brain activity and estimation of the hemodynamic response. Because these two steps are inherently linked, we adopt the so-called region-based Joint Detection-Estimation (JDE) framework that addresses this joint issue using a multivariate inference for detection and estimation. JDE is built by making use of a regional bilinear generative model of the BOLD response and constraining the parameter estimation by physiological priors using temporal and spatial information in a Markovian model. In contrast to previous works that use Markov Chain Monte Carlo (MCMC) techniques to sample the resulting intractable posterior distribution, we recast the JDE into a missing data framework and derive a Variational Expectation-Maximization (VEM) algorithm for its inference. A variational approximation is used to approximate the Markovian model in the unsupervised spatially adaptive JDE inference, which allows automatic fine-tuning of spatial regularization parameters. It provides a new algorithm that exhibits interesting properties in terms of estimation error and computational cost compared to the previously used MCMC-based approach. Experiments on artificial and real data show that VEM-JDE is robust to model mis-specification and provides computational gain while maintaining good performance in terms of activation detection and hemodynamic shape recovery. PMID:23096056

Speech detection in noise and spatial unmasking in children with simultaneous versus sequential bilateral cochlear implants.

Science.gov (United States)

Chadha, Neil K; Papsin, Blake C; Jiwani, Salima; Gordon, Karen A

2011-09-01

To measure speech detection in noise performance for children with bilateral cochlear implants (BiCI), to compare performance in children with simultaneous implant versus those with sequential implant, and to compare performance to normal-hearing children. Prospective cohort study. Tertiary academic pediatric center. Children with early-onset bilateral deafness and 2-year BiCI experience, comprising the "sequential" group (>2 yr interimplantation delay, n = 12) and "simultaneous group" (no interimplantation delay, n = 10) and normal-hearing controls (n = 8). Thresholds to speech detection (at 0-degree azimuth) were measured with noise at 0-degree azimuth or ± 90-degree azimuth. Spatial unmasking (SU) as the noise condition changed from 0-degree azimuth to ± 90-degree azimuth and binaural summation advantage (BSA) of 2 over 1 CI. Speech detection in noise was significantly poorer than controls for both BiCI groups (p simultaneous group approached levels found in normal controls (7.2 ± 0.6 versus 8.6 ± 0.6 dB, p > 0.05) and was significantly better than that in the sequential group (3.9 ± 0.4 dB, p simultaneous group but, in the sequential group, was significantly better when noise was moved to the second rather than the first implanted ear (4.8 ± 0.5 versus 3.0 ± 0.4 dB, p sequential group's second rather than first CI. Children with simultaneously implanted BiCI demonstrated an advantage over children with sequential implant by using spatial cues to improve speech detection in noise.

Effective field theory dimensional regularization

International Nuclear Information System (INIS)

Lehmann, Dirk; Prezeau, Gary

2002-01-01

A Lorentz-covariant regularization scheme for effective field theories with an arbitrary number of propagating heavy and light particles is given. This regularization scheme leaves the low-energy analytic structure of Greens functions intact and preserves all the symmetries of the underlying Lagrangian. The power divergences of regularized loop integrals are controlled by the low-energy kinematic variables. Simple diagrammatic rules are derived for the regularization of arbitrary one-loop graphs and the generalization to higher loops is discussed

Effective field theory dimensional regularization

Science.gov (United States)

Lehmann, Dirk; Prézeau, Gary

2002-01-01

A Lorentz-covariant regularization scheme for effective field theories with an arbitrary number of propagating heavy and light particles is given. This regularization scheme leaves the low-energy analytic structure of Greens functions intact and preserves all the symmetries of the underlying Lagrangian. The power divergences of regularized loop integrals are controlled by the low-energy kinematic variables. Simple diagrammatic rules are derived for the regularization of arbitrary one-loop graphs and the generalization to higher loops is discussed.

Detection of Dendritic Spines Using Wavelet-Based Conditional Symmetric Analysis and Regularized Morphological Shared-Weight Neural Networks

Directory of Open Access Journals (Sweden)

Shuihua Wang

2015-01-01

Full Text Available Identification and detection of dendritic spines in neuron images are of high interest in diagnosis and treatment of neurological and psychiatric disorders (e.g., Alzheimer’s disease, Parkinson’s diseases, and autism. In this paper, we have proposed a novel automatic approach using wavelet-based conditional symmetric analysis and regularized morphological shared-weight neural networks (RMSNN for dendritic spine identification involving the following steps: backbone extraction, localization of dendritic spines, and classification. First, a new algorithm based on wavelet transform and conditional symmetric analysis has been developed to extract backbone and locate the dendrite boundary. Then, the RMSNN has been proposed to classify the spines into three predefined categories (mushroom, thin, and stubby. We have compared our proposed approach against the existing methods. The experimental result demonstrates that the proposed approach can accurately locate the dendrite and accurately classify the spines into three categories with the accuracy of 99.1% for “mushroom” spines, 97.6% for “stubby” spines, and 98.6% for “thin” spines.

Spatial analysis of "crazy quilts", a class of potentially random aesthetic artefacts.

Directory of Open Access Journals (Sweden)

Gesche Westphal-Fitch

Full Text Available Human artefacts in general are highly structured and often display ordering principles such as translational, reflectional or rotational symmetry. In contrast, human artefacts that are intended to appear random and non symmetrical are very rare. Furthermore, many studies show that humans find it extremely difficult to recognize or reproduce truly random patterns or sequences. Here, we attempt to model two-dimensional decorative spatial patterns produced by humans that show no obvious order. "Crazy quilts" represent a historically important style of quilt making that became popular in the 1870s, and lasted about 50 years. Crazy quilts are unusual because unlike most human artefacts, they are specifically intended to appear haphazard and unstructured. We evaluate the degree to which this intention was achieved by using statistical techniques of spatial point pattern analysis to compare crazy quilts with regular quilts from the same region and era and to evaluate the fit of various random distributions to these two quilt classes. We found that the two quilt categories exhibit fundamentally different spatial characteristics: The patch areas of crazy quilts derive from a continuous random distribution, while area distributions of regular quilts consist of Gaussian mixtures. These Gaussian mixtures derive from regular pattern motifs that are repeated and we suggest that such a mixture is a distinctive signature of human-made visual patterns. In contrast, the distribution found in crazy quilts is shared with many other naturally occurring spatial patterns. Centroids of patches in the two quilt classes are spaced differently and in general, crazy quilts but not regular quilts are well-fitted by a random Strauss process. These results indicate that, within the constraints of the quilt format, Victorian quilters indeed achieved their goal of generating random structures.

Spatial analysis of "crazy quilts", a class of potentially random aesthetic artefacts.

Science.gov (United States)

Westphal-Fitch, Gesche; Fitch, W Tecumseh

2013-01-01

Human artefacts in general are highly structured and often display ordering principles such as translational, reflectional or rotational symmetry. In contrast, human artefacts that are intended to appear random and non symmetrical are very rare. Furthermore, many studies show that humans find it extremely difficult to recognize or reproduce truly random patterns or sequences. Here, we attempt to model two-dimensional decorative spatial patterns produced by humans that show no obvious order. "Crazy quilts" represent a historically important style of quilt making that became popular in the 1870s, and lasted about 50 years. Crazy quilts are unusual because unlike most human artefacts, they are specifically intended to appear haphazard and unstructured. We evaluate the degree to which this intention was achieved by using statistical techniques of spatial point pattern analysis to compare crazy quilts with regular quilts from the same region and era and to evaluate the fit of various random distributions to these two quilt classes. We found that the two quilt categories exhibit fundamentally different spatial characteristics: The patch areas of crazy quilts derive from a continuous random distribution, while area distributions of regular quilts consist of Gaussian mixtures. These Gaussian mixtures derive from regular pattern motifs that are repeated and we suggest that such a mixture is a distinctive signature of human-made visual patterns. In contrast, the distribution found in crazy quilts is shared with many other naturally occurring spatial patterns. Centroids of patches in the two quilt classes are spaced differently and in general, crazy quilts but not regular quilts are well-fitted by a random Strauss process. These results indicate that, within the constraints of the quilt format, Victorian quilters indeed achieved their goal of generating random structures.

Hierarchical regular small-world networks

International Nuclear Information System (INIS)

Boettcher, Stefan; Goncalves, Bruno; Guclu, Hasan

2008-01-01

Two new networks are introduced that resemble small-world properties. These networks are recursively constructed but retain a fixed, regular degree. They possess a unique one-dimensional lattice backbone overlaid by a hierarchical sequence of long-distance links, mixing real-space and small-world features. Both networks, one 3-regular and the other 4-regular, lead to distinct behaviors, as revealed by renormalization group studies. The 3-regular network is planar, has a diameter growing as √N with system size N, and leads to super-diffusion with an exact, anomalous exponent d w = 1.306..., but possesses only a trivial fixed point T c = 0 for the Ising ferromagnet. In turn, the 4-regular network is non-planar, has a diameter growing as ∼2 √(log 2 N 2 ) , exhibits 'ballistic' diffusion (d w = 1), and a non-trivial ferromagnetic transition, T c > 0. It suggests that the 3-regular network is still quite 'geometric', while the 4-regular network qualifies as a true small world with mean-field properties. As an engineering application we discuss synchronization of processors on these networks. (fast track communication)

75 FR 76006 - Regular Meeting

Science.gov (United States)

2010-12-07

... FARM CREDIT SYSTEM INSURANCE CORPORATION Regular Meeting AGENCY: Farm Credit System Insurance Corporation Board. ACTION: Regular meeting. SUMMARY: Notice is hereby given of the regular meeting of the Farm Credit System Insurance Corporation Board (Board). Date and Time: The meeting of the Board will be held...

General inverse problems for regular variation

DEFF Research Database (Denmark)

Damek, Ewa; Mikosch, Thomas Valentin; Rosinski, Jan

2014-01-01

Regular variation of distributional tails is known to be preserved by various linear transformations of some random structures. An inverse problem for regular variation aims at understanding whether the regular variation of a transformed random object is caused by regular variation of components ...

The characteristics and spatial distributions of initially missed and rebiopsy-detected prostate cancers

Directory of Open Access Journals (Sweden)

Myung-Won You

2016-07-01

Full Text Available Purpose: The purpose of this study was to analyze the characteristics of initially missed and rebiopsy-detected prostate cancers following 12-core transrectal biopsy. Methods: A total of 45 patients with prostate cancers detected on rebiopsy and 45 patients with prostate cancers initially detected on transrectal ultrasound-guided biopsy were included in the study. For result analysis, the prostate was divided into six compartments, and the cancer positive rates, estimated tumor burden, and agreement rates between biopsy and surgical specimens, along with clinical data, were evaluated. Results: The largest mean tumor burden was located in the medial apex in both groups. There were significantly more tumors in this location in the rebiopsy group (44.9% than in the control group (30.1%, P=0.015. The overall sensitivity of biopsy was significantly lower in the rebiopsy group (22.5% vs. 43.4%, P<0.001. The agreement rate of cancer positive cores between biopsy and surgical specimens was significantly lower in the medial apex in the rebiopsy group compared with that of the control group (50.0% vs. 65.6%, P=0.035. The cancer positive rates of target biopsy cores and premalignant lesions in the rebiopsy group were 63.1% and 42.3%, respectively. Conclusion: Rebiopsy-detected prostate cancers showed different spatial distribution and lower cancer detection rate of biopsy cores compared with initially diagnosed cancers. To overcome lower cancer detection rate, target biopsy of abnormal sonographic findings, premalignant lesions and medial apex which revealed larger tumor burden would be recommended when performing rebiopsy.

Wind turbine extraction from high spatial resolution remote sensing images based on saliency detection

Science.gov (United States)

Chen, Jingbo; Yue, Anzhi; Wang, Chengyi; Huang, Qingqing; Chen, Jiansheng; Meng, Yu; He, Dongxu

2018-01-01

The wind turbine is a device that converts the wind's kinetic energy into electrical power. Accurate and automatic extraction of wind turbine is instructive for government departments to plan wind power plant projects. A hybrid and practical framework based on saliency detection for wind turbine extraction, using Google Earth image at spatial resolution of 1 m, is proposed. It can be viewed as a two-phase procedure: coarsely detection and fine extraction. In the first stage, we introduced a frequency-tuned saliency detection approach for initially detecting the area of interest of the wind turbines. This method exploited features of color and luminance, was simple to implement, and was computationally efficient. Taking into account the complexity of remote sensing images, in the second stage, we proposed a fast method for fine-tuning results in frequency domain and then extracted wind turbines from these salient objects by removing the irrelevant salient areas according to the special properties of the wind turbines. Experiments demonstrated that our approach consistently obtains higher precision and better recall rates. Our method was also compared with other techniques from the literature and proves that it is more applicable and robust.

Near-real-time radiography detects 0.1% changes in areal density with 1-millimeter spatial resolution

International Nuclear Information System (INIS)

Stupin, D.M.

1987-06-01

Using digital subtraction radiography, the author detects an 0.1% change in areal density in a phantom. Areal density is the product rho x, where rho is the material density and x is the material thickness. Therefore, it is possible to detect an 0.1% difference in either density or thickness in unknown samples. A special x-ray television camera detects the areal density change on the phantom. In a difference image, formed by subtracting the 128-television-frame averages of the phantom image from the phantom-and-step image, the step is resolved with a 1-mm spatial resolution. Surprisingly, crossed 2-μm-diam tungsten wires that overlie the phantom are also detected. This procedure takes a few seconds. The performance of any digital imaging x-ray system will improve by using the averaging and digital subtraction techniques. 8 refs., 6 figs

Hessian regularization based non-negative matrix factorization for gene expression data clustering.

Science.gov (United States)

Liu, Xiao; Shi, Jun; Wang, Congzhi

2015-01-01

Since a key step in the analysis of gene expression data is to detect groups of genes that have similar expression patterns, clustering technique is then commonly used to analyze gene expression data. Data representation plays an important role in clustering analysis. The non-negative matrix factorization (NMF) is a widely used data representation method with great success in machine learning. Although the traditional manifold regularization method, Laplacian regularization (LR), can improve the performance of NMF, LR still suffers from the problem of its weak extrapolating power. Hessian regularization (HR) is a newly developed manifold regularization method, whose natural properties make it more extrapolating, especially for small sample data. In this work, we propose the HR-based NMF (HR-NMF) algorithm, and then apply it to represent gene expression data for further clustering task. The clustering experiments are conducted on five commonly used gene datasets, and the results indicate that the proposed HR-NMF outperforms LR-based NMM and original NMF, which suggests the potential application of HR-NMF for gene expression data.

Continuum-regularized quantum gravity

International Nuclear Information System (INIS)

Chan Huesum; Halpern, M.B.

1987-01-01

The recent continuum regularization of d-dimensional Euclidean gravity is generalized to arbitrary power-law measure and studied in some detail as a representative example of coordinate-invariant regularization. The weak-coupling expansion of the theory illustrates a generic geometrization of regularized Schwinger-Dyson rules, generalizing previous rules in flat space and flat superspace. The rules are applied in a non-trivial explicit check of Einstein invariance at one loop: the cosmological counterterm is computed and its contribution is included in a verification that the graviton mass is zero. (orig.)

Online co-regularized algorithms

NARCIS (Netherlands)

Ruijter, T. de; Tsivtsivadze, E.; Heskes, T.

2012-01-01

We propose an online co-regularized learning algorithm for classification and regression tasks. We demonstrate that by sequentially co-regularizing prediction functions on unlabeled data points, our algorithm provides improved performance in comparison to supervised methods on several UCI benchmarks

Geometric continuum regularization of quantum field theory

International Nuclear Information System (INIS)

Halpern, M.B.

1989-01-01

An overview of the continuum regularization program is given. The program is traced from its roots in stochastic quantization, with emphasis on the examples of regularized gauge theory, the regularized general nonlinear sigma model and regularized quantum gravity. In its coordinate-invariant form, the regularization is seen as entirely geometric: only the supermetric on field deformations is regularized, and the prescription provides universal nonperturbative invariant continuum regularization across all quantum field theory. 54 refs

Regularization destriping of remote sensing imagery

Science.gov (United States)

Basnayake, Ranil; Bollt, Erik; Tufillaro, Nicholas; Sun, Jie; Gierach, Michelle

2017-07-01

We illustrate the utility of variational destriping for ocean color images from both multispectral and hyperspectral sensors. In particular, we examine data from a filter spectrometer, the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar Partnership (NPP) orbiter, and an airborne grating spectrometer, the Jet Population Laboratory's (JPL) hyperspectral Portable Remote Imaging Spectrometer (PRISM) sensor. We solve the destriping problem using a variational regularization method by giving weights spatially to preserve the other features of the image during the destriping process. The target functional penalizes the neighborhood of stripes (strictly, directionally uniform features) while promoting data fidelity, and the functional is minimized by solving the Euler-Lagrange equations with an explicit finite-difference scheme. We show the accuracy of our method from a benchmark data set which represents the sea surface temperature off the coast of Oregon, USA. Technical details, such as how to impose continuity across data gaps using inpainting, are also described.

A new formulation of the linear sampling method: spatial resolution and post-processing

International Nuclear Information System (INIS)

Piana, M; Aramini, R; Brignone, M; Coyle, J

2008-01-01

A new formulation of the linear sampling method is described, which requires the regularized solution of a single functional equation set in a direct sum of L 2 spaces. This new approach presents the following notable advantages: it is computationally more effective than the traditional implementation, since time consuming samplings of the Tikhonov minimum problem and of the generalized discrepancy equation are avoided; it allows a quantitative estimate of the spatial resolution achievable by the method; it facilitates a post-processing procedure for the optimal selection of the scatterer profile by means of edge detection techniques. The formulation is described in a two-dimensional framework and in the case of obstacle scattering, although generalizations to three dimensions and penetrable inhomogeneities are straightforward

Regularized maximum correntropy machine

KAUST Repository

Wang, Jim Jing-Yan; Wang, Yunji; Jing, Bing-Yi; Gao, Xin

2015-01-01

In this paper we investigate the usage of regularized correntropy framework for learning of classifiers from noisy labels. The class label predictors learned by minimizing transitional loss functions are sensitive to the noisy and outlying labels of training samples, because the transitional loss functions are equally applied to all the samples. To solve this problem, we propose to learn the class label predictors by maximizing the correntropy between the predicted labels and the true labels of the training samples, under the regularized Maximum Correntropy Criteria (MCC) framework. Moreover, we regularize the predictor parameter to control the complexity of the predictor. The learning problem is formulated by an objective function considering the parameter regularization and MCC simultaneously. By optimizing the objective function alternately, we develop a novel predictor learning algorithm. The experiments on two challenging pattern classification tasks show that it significantly outperforms the machines with transitional loss functions.

Regularized maximum correntropy machine

KAUST Repository

Wang, Jim Jing-Yan

2015-02-12

In this paper we investigate the usage of regularized correntropy framework for learning of classifiers from noisy labels. The class label predictors learned by minimizing transitional loss functions are sensitive to the noisy and outlying labels of training samples, because the transitional loss functions are equally applied to all the samples. To solve this problem, we propose to learn the class label predictors by maximizing the correntropy between the predicted labels and the true labels of the training samples, under the regularized Maximum Correntropy Criteria (MCC) framework. Moreover, we regularize the predictor parameter to control the complexity of the predictor. The learning problem is formulated by an objective function considering the parameter regularization and MCC simultaneously. By optimizing the objective function alternately, we develop a novel predictor learning algorithm. The experiments on two challenging pattern classification tasks show that it significantly outperforms the machines with transitional loss functions.

Drone-based Object Counting by Spatially Regularized Regional Proposal Network

OpenAIRE

Hsieh, Meng-Ru; Lin, Yen-Liang; Hsu, Winston H.

2017-01-01

Existing counting methods often adopt regression-based approaches and cannot precisely localize the target objects, which hinders the further analysis (e.g., high-level understanding and fine-grained classification). In addition, most of prior work mainly focus on counting objects in static environments with fixed cameras. Motivated by the advent of unmanned flying vehicles (i.e., drones), we are interested in detecting and counting objects in such dynamic environments. We propose Layout Prop...

Spatial Analysis of “Crazy Quilts”, a Class of Potentially Random Aesthetic Artefacts

Science.gov (United States)

Westphal-Fitch, Gesche; Fitch, W. Tecumseh

2013-01-01

Human artefacts in general are highly structured and often display ordering principles such as translational, reflectional or rotational symmetry. In contrast, human artefacts that are intended to appear random and non symmetrical are very rare. Furthermore, many studies show that humans find it extremely difficult to recognize or reproduce truly random patterns or sequences. Here, we attempt to model two-dimensional decorative spatial patterns produced by humans that show no obvious order. “Crazy quilts” represent a historically important style of quilt making that became popular in the 1870s, and lasted about 50 years. Crazy quilts are unusual because unlike most human artefacts, they are specifically intended to appear haphazard and unstructured. We evaluate the degree to which this intention was achieved by using statistical techniques of spatial point pattern analysis to compare crazy quilts with regular quilts from the same region and era and to evaluate the fit of various random distributions to these two quilt classes. We found that the two quilt categories exhibit fundamentally different spatial characteristics: The patch areas of crazy quilts derive from a continuous random distribution, while area distributions of regular quilts consist of Gaussian mixtures. These Gaussian mixtures derive from regular pattern motifs that are repeated and we suggest that such a mixture is a distinctive signature of human-made visual patterns. In contrast, the distribution found in crazy quilts is shared with many other naturally occurring spatial patterns. Centroids of patches in the two quilt classes are spaced differently and in general, crazy quilts but not regular quilts are well-fitted by a random Strauss process. These results indicate that, within the constraints of the quilt format, Victorian quilters indeed achieved their goal of generating random structures. PMID:24066095

Unimodal and crossmodal gradients of spatial attention

DEFF Research Database (Denmark)

Föcker, J.; Hötting, K.; Gondan, Matthias

2010-01-01

Behavioral and event-related potential (ERP) studies have shown that spatial attention is gradually distributed around the center of the attentional focus. The present study compared uni- and crossmodal gradients of spatial attention to investigate whether the orienting of auditory and visual...... spatial attention is based on modality specific or supramodal representations of space. Auditory and visual stimuli were presented from five speaker locations positioned in the right hemifield. Participants had to attend to the innermost or outmost right position in order to detect either visual...... or auditory deviant stimuli. Detection rates and event-related potentials (ERPs) indicated that spatial attention is distributed as a gradient. Unimodal spatial ERP gradients correlated with the spatial resolution of the modality. Crossmodal spatial gradients were always broader than the corresponding...

Evaluating the impact of spatio-temporal smoothness constraints on the BOLD hemodynamic response function estimation: an analysis based on Tikhonov regularization

International Nuclear Information System (INIS)

Casanova, R; Yang, L; Hairston, W D; Laurienti, P J; Maldjian, J A

2009-01-01

Recently we have proposed the use of Tikhonov regularization with temporal smoothness constraints to estimate the BOLD fMRI hemodynamic response function (HRF). The temporal smoothness constraint was imposed on the estimates by using second derivative information while the regularization parameter was selected based on the generalized cross-validation function (GCV). Using one-dimensional simulations, we previously found this method to produce reliable estimates of the HRF time course, especially its time to peak (TTP), being at the same time fast and robust to over-sampling in the HRF estimation. Here, we extend the method to include simultaneous temporal and spatial smoothness constraints. This method does not need Gaussian smoothing as a pre-processing step as usually done in fMRI data analysis. We carried out two-dimensional simulations to compare the two methods: Tikhonov regularization with temporal (Tik-GCV-T) and spatio-temporal (Tik-GCV-ST) smoothness constraints on the estimated HRF. We focus our attention on quantifying the influence of the Gaussian data smoothing and the presence of edges on the performance of these techniques. Our results suggest that the spatial smoothing introduced by regularization is less severe than that produced by Gaussian smoothing. This allows more accurate estimates of the response amplitudes while producing similar estimates of the TTP. We illustrate these ideas using real data. (note)

Detecting spatial patterns with the cumulant function – Part 1: The theory

Directory of Open Access Journals (Sweden)

P. Naveau

2008-02-01

Full Text Available In climate studies, detecting spatial patterns that largely deviate from the sample mean still remains a statistical challenge. Although a Principal Component Analysis (PCA, or equivalently a Empirical Orthogonal Functions (EOF decomposition, is often applied for this purpose, it provides meaningful results only if the underlying multivariate distribution is Gaussian. Indeed, PCA is based on optimizing second order moments, and the covariance matrix captures the full dependence structure of multivariate Gaussian vectors. Whenever the application at hand can not satisfy this normality hypothesis (e.g. precipitation data, alternatives and/or improvements to PCA have to be developed and studied. To go beyond this second order statistics constraint, that limits the applicability of the PCA, we take advantage of the cumulant function that can produce higher order moments information. The cumulant function, well-known in the statistical literature, allows us to propose a new, simple and fast procedure to identify spatial patterns for non-Gaussian data. Our algorithm consists in maximizing the cumulant function. Three families of multivariate random vectors, for which explicit computations are obtained, are implemented to illustrate our approach. In addition, we show that our algorithm corresponds to selecting the directions along which projected data display the largest spread over the marginal probability density tails.

Two-way regularization for MEG source reconstruction via multilevel coordinate descent

KAUST Repository

Siva Tian, Tian

2013-12-01

Magnetoencephalography (MEG) source reconstruction refers to the inverse problem of recovering the neural activity from the MEG time course measurements. A spatiotemporal two-way regularization (TWR) method was recently proposed by Tian et al. to solve this inverse problem and was shown to outperform several one-way regularization methods and spatiotemporal methods. This TWR method is a two-stage procedure that first obtains a raw estimate of the source signals and then refines the raw estimate to ensure spatial focality and temporal smoothness using spatiotemporal regularized matrix decomposition. Although proven to be effective, the performance of two-stage TWR depends on the quality of the raw estimate. In this paper we directly solve the MEG source reconstruction problem using a multivariate penalized regression where the number of variables is much larger than the number of cases. A special feature of this regression is that the regression coefficient matrix has a spatiotemporal two-way structure that naturally invites a two-way penalty. Making use of this structure, we develop a computationally efficient multilevel coordinate descent algorithm to implement the method. This new one-stage TWR method has shown its superiority to the two-stage TWR method in three simulation studies with different levels of complexity and a real-world MEG data analysis. © 2013 Wiley Periodicals, Inc., A Wiley Company.

Effect of spatial noise of medical grade Liquid Crystal Displays (LCD) on the detection of micro-calcification

Science.gov (United States)

Roehrig, Hans; Fan, Jiahua; Dallas, William J.; Krupinski, Elizabeth A.; Johnson, Jeffrey

2009-08-01

This presentation describes work in progress that is the result of an NIH SBIR Phase 1 project that addresses the wide- spread concern for the large number of breast-cancers and cancer victims [1,2]. The primary goal of the project is to increase the detection rate of microcalcifications as a result of the decrease of spatial noise of the LCDs used to display the mammograms [3,4]. Noise reduction is to be accomplished with the aid of a high performance CCD camera and subsequent application of local-mean equalization and error diffusion [5,6]. A second goal of the project is the actual detection of breast cancer. Contrary to the approach to mammography, where the mammograms typically have a pixel matrix of approximately 1900 x 2300 pixels, otherwise known as FFDM or Full-Field Digital Mammograms, we will only use sections of mammograms with a pixel matrix of 256 x 256 pixels. This is because at this time, reduction of spatial noise on an LCD can only be done on relatively small areas like 256 x 256 pixels. In addition, judging the efficacy for detection of breast cancer will be done using two methods: One is a conventional ROC study [7], the other is a vision model developed over several years starting at the Sarnoff Research Center and continuing at the Siemens Corporate Research in Princeton NJ [8].

On tests of randomness for spatial point patterns

International Nuclear Information System (INIS)

Doguwa, S.I.

1990-11-01

New tests of randomness for spatial point patterns are introduced. These test statistics are then compared in a power study with the existing alternatives. These results of the power study suggest that one of the tests proposed is extremely powerful against both aggregated and regular alternatives. (author). 9 refs, 7 figs, 3 tabs

System identification via sparse multiple kernel-based regularization using sequential convex optimization techniques

DEFF Research Database (Denmark)

Chen, Tianshi; Andersen, Martin Skovgaard; Ljung, Lennart

2014-01-01

Model estimation and structure detection with short data records are two issues that receive increasing interests in System Identification. In this paper, a multiple kernel-based regularization method is proposed to handle those issues. Multiple kernels are conic combinations of fixed kernels...

Troubles detected during regular inspection of No.1 plant in Oi Power Station, Kansai Electric Power Co., Inc

International Nuclear Information System (INIS)

1990-01-01

No. 1 plant in Oi Power Station, Kansai Electric Power Co., Inc. is a PWR plant with rated output of 1175 MW, and its regular inspection is carried out since August 14, 1989. When eddy current flaw detection inspection was carried out on the total number (11426 except already plugged tubes) of the heating tubes of steam generators, significant indication was observed in tube supporting plate part of 279 tubes, at the boundary of tube plate expanded part of 34 tubes, and in the tube plate expanded part of 99 tubes, 411 heating tubes in total (all on high temperature side). Consequently, it was decided to repair 367 tubes using sleeves, and to plug other 44 tubes. Besides, among the heating tubes plugged in the past, it was decided to remove plugs from 161 tubes, and by repairing them with sleeves, to use them again. Total number of heating tubes 13552 (3388 tubes x 4 steam generators), Number of plugged tubes 2009 (decrease by 117 this time), Ratio of plugging 14.8%. (K.I.)

Spatial early warning signals in a lake manipulation

Science.gov (United States)

Butitta, Vince L.; Carpenter, Stephen R.; Loken, Luke; Pace, Michael L.; Stanley, Emily H.

2017-01-01

Rapid changes in state have been documented for many of Earth's ecosystems. Despite a growing toolbox of methods for detecting declining resilience or early warning indicators (EWIs) of ecosystem transitions, these methods have rarely been evaluated in whole-ecosystem trials using reference ecosystems. In this study, we experimentally tested EWIs of cyanobacteria blooms based on changes in the spatial structure of a lake. We induced a cyanobacteria bloom by adding nutrients to an experimental lake and mapped fine-resolution spatial patterning of cyanobacteria using a mobile sensor platform. Prior to the bloom, we detected theoretically predicted spatial EWIs based on variance and spatial autocorrelation, as well as a new index based on the extreme values. Changes in EWIs were not discernible in an unenriched reference lake. Despite the fluid environment of a lake where spatial heterogeneity driven by biological processes may be overwhelmed by physical mixing, spatial EWIs detected an approaching bloom suggesting the utility of spatial metrics for signaling ecological thresholds.

Regularities of Multifractal Measures

Indian Academy of Sciences (India)

First, we prove the decomposition theorem for the regularities of multifractal Hausdorff measure and packing measure in R R d . This decomposition theorem enables us to split a set into regular and irregular parts, so that we can analyze each separately, and recombine them without affecting density properties. Next, we ...

Adaptive Regularization of Neural Classifiers

DEFF Research Database (Denmark)

Andersen, Lars Nonboe; Larsen, Jan; Hansen, Lars Kai

1997-01-01

We present a regularization scheme which iteratively adapts the regularization parameters by minimizing the validation error. It is suggested to use the adaptive regularization scheme in conjunction with optimal brain damage pruning to optimize the architecture and to avoid overfitting. Furthermo......, we propose an improved neural classification architecture eliminating an inherent redundancy in the widely used SoftMax classification network. Numerical results demonstrate the viability of the method...

A Comparison of Grizzly Bear Demographic Parameters Estimated from Non-Spatial and Spatial Open Population Capture-Recapture Models.

Science.gov (United States)

Whittington, Jesse; Sawaya, Michael A

2015-01-01

Capture-recapture studies are frequently used to monitor the status and trends of wildlife populations. Detection histories from individual animals are used to estimate probability of detection and abundance or density. The accuracy of abundance and density estimates depends on the ability to model factors affecting detection probability. Non-spatial capture-recapture models have recently evolved into spatial capture-recapture models that directly include the effect of distances between an animal's home range centre and trap locations on detection probability. Most studies comparing non-spatial and spatial capture-recapture biases focussed on single year models and no studies have compared the accuracy of demographic parameter estimates from open population models. We applied open population non-spatial and spatial capture-recapture models to three years of grizzly bear DNA-based data from Banff National Park and simulated data sets. The two models produced similar estimates of grizzly bear apparent survival, per capita recruitment, and population growth rates but the spatial capture-recapture models had better fit. Simulations showed that spatial capture-recapture models produced more accurate parameter estimates with better credible interval coverage than non-spatial capture-recapture models. Non-spatial capture-recapture models produced negatively biased estimates of apparent survival and positively biased estimates of per capita recruitment. The spatial capture-recapture grizzly bear population growth rates and 95% highest posterior density averaged across the three years were 0.925 (0.786-1.071) for females, 0.844 (0.703-0.975) for males, and 0.882 (0.779-0.981) for females and males combined. The non-spatial capture-recapture population growth rates were 0.894 (0.758-1.024) for females, 0.825 (0.700-0.948) for males, and 0.863 (0.771-0.957) for both sexes. The combination of low densities, low reproductive rates, and predominantly negative population growth

A Comparison of Grizzly Bear Demographic Parameters Estimated from Non-Spatial and Spatial Open Population Capture-Recapture Models.

Directory of Open Access Journals (Sweden)

Jesse Whittington

Full Text Available Capture-recapture studies are frequently used to monitor the status and trends of wildlife populations. Detection histories from individual animals are used to estimate probability of detection and abundance or density. The accuracy of abundance and density estimates depends on the ability to model factors affecting detection probability. Non-spatial capture-recapture models have recently evolved into spatial capture-recapture models that directly include the effect of distances between an animal's home range centre and trap locations on detection probability. Most studies comparing non-spatial and spatial capture-recapture biases focussed on single year models and no studies have compared the accuracy of demographic parameter estimates from open population models. We applied open population non-spatial and spatial capture-recapture models to three years of grizzly bear DNA-based data from Banff National Park and simulated data sets. The two models produced similar estimates of grizzly bear apparent survival, per capita recruitment, and population growth rates but the spatial capture-recapture models had better fit. Simulations showed that spatial capture-recapture models produced more accurate parameter estimates with better credible interval coverage than non-spatial capture-recapture models. Non-spatial capture-recapture models produced negatively biased estimates of apparent survival and positively biased estimates of per capita recruitment. The spatial capture-recapture grizzly bear population growth rates and 95% highest posterior density averaged across the three years were 0.925 (0.786-1.071 for females, 0.844 (0.703-0.975 for males, and 0.882 (0.779-0.981 for females and males combined. The non-spatial capture-recapture population growth rates were 0.894 (0.758-1.024 for females, 0.825 (0.700-0.948 for males, and 0.863 (0.771-0.957 for both sexes. The combination of low densities, low reproductive rates, and predominantly negative

Efficient operator splitting algorithm for joint sparsity-regularized SPIRiT-based parallel MR imaging reconstruction.

Science.gov (United States)

Duan, Jizhong; Liu, Yu; Jing, Peiguang

2018-02-01

Self-consistent parallel imaging (SPIRiT) is an auto-calibrating model for the reconstruction of parallel magnetic resonance imaging, which can be formulated as a regularized SPIRiT problem. The Projection Over Convex Sets (POCS) method was used to solve the formulated regularized SPIRiT problem. However, the quality of the reconstructed image still needs to be improved. Though methods such as NonLinear Conjugate Gradients (NLCG) can achieve higher spatial resolution, these methods always demand very complex computation and converge slowly. In this paper, we propose a new algorithm to solve the formulated Cartesian SPIRiT problem with the JTV and JL1 regularization terms. The proposed algorithm uses the operator splitting (OS) technique to decompose the problem into a gradient problem and a denoising problem with two regularization terms, which is solved by our proposed split Bregman based denoising algorithm, and adopts the Barzilai and Borwein method to update step size. Simulation experiments on two in vivo data sets demonstrate that the proposed algorithm is 1.3 times faster than ADMM for datasets with 8 channels. Especially, our proposal is 2 times faster than ADMM for the dataset with 32 channels. Copyright © 2017 Elsevier Inc. All rights reserved.

Condition Number Regularized Covariance Estimation.

Science.gov (United States)

Won, Joong-Ho; Lim, Johan; Kim, Seung-Jean; Rajaratnam, Bala

2013-06-01

Estimation of high-dimensional covariance matrices is known to be a difficult problem, has many applications, and is of current interest to the larger statistics community. In many applications including so-called the "large p small n " setting, the estimate of the covariance matrix is required to be not only invertible, but also well-conditioned. Although many regularization schemes attempt to do this, none of them address the ill-conditioning problem directly. In this paper, we propose a maximum likelihood approach, with the direct goal of obtaining a well-conditioned estimator. No sparsity assumption on either the covariance matrix or its inverse are are imposed, thus making our procedure more widely applicable. We demonstrate that the proposed regularization scheme is computationally efficient, yields a type of Steinian shrinkage estimator, and has a natural Bayesian interpretation. We investigate the theoretical properties of the regularized covariance estimator comprehensively, including its regularization path, and proceed to develop an approach that adaptively determines the level of regularization that is required. Finally, we demonstrate the performance of the regularized estimator in decision-theoretic comparisons and in the financial portfolio optimization setting. The proposed approach has desirable properties, and can serve as a competitive procedure, especially when the sample size is small and when a well-conditioned estimator is required.

Group-regularized individual prediction: theory and application to pain.

Science.gov (United States)

Lindquist, Martin A; Krishnan, Anjali; López-Solà, Marina; Jepma, Marieke; Woo, Choong-Wan; Koban, Leonie; Roy, Mathieu; Atlas, Lauren Y; Schmidt, Liane; Chang, Luke J; Reynolds Losin, Elizabeth A; Eisenbarth, Hedwig; Ashar, Yoni K; Delk, Elizabeth; Wager, Tor D

2017-01-15

Multivariate pattern analysis (MVPA) has become an important tool for identifying brain representations of psychological processes and clinical outcomes using fMRI and related methods. Such methods can be used to predict or 'decode' psychological states in individual subjects. Single-subject MVPA approaches, however, are limited by the amount and quality of individual-subject data. In spite of higher spatial resolution, predictive accuracy from single-subject data often does not exceed what can be accomplished using coarser, group-level maps, because single-subject patterns are trained on limited amounts of often-noisy data. Here, we present a method that combines population-level priors, in the form of biomarker patterns developed on prior samples, with single-subject MVPA maps to improve single-subject prediction. Theoretical results and simulations motivate a weighting based on the relative variances of biomarker-based prediction-based on population-level predictive maps from prior groups-and individual-subject, cross-validated prediction. Empirical results predicting pain using brain activity on a trial-by-trial basis (single-trial prediction) across 6 studies (N=180 participants) confirm the theoretical predictions. Regularization based on a population-level biomarker-in this case, the Neurologic Pain Signature (NPS)-improved single-subject prediction accuracy compared with idiographic maps based on the individuals' data alone. The regularization scheme that we propose, which we term group-regularized individual prediction (GRIP), can be applied broadly to within-person MVPA-based prediction. We also show how GRIP can be used to evaluate data quality and provide benchmarks for the appropriateness of population-level maps like the NPS for a given individual or study. Copyright © 2015 Elsevier Inc. All rights reserved.

Nonparametric Bayesian models for a spatial covariance.

Science.gov (United States)

Reich, Brian J; Fuentes, Montserrat

2012-01-01

A crucial step in the analysis of spatial data is to estimate the spatial correlation function that determines the relationship between a spatial process at two locations. The standard approach to selecting the appropriate correlation function is to use prior knowledge or exploratory analysis, such as a variogram analysis, to select the correct parametric correlation function. Rather that selecting a particular parametric correlation function, we treat the covariance function as an unknown function to be estimated from the data. We propose a flexible prior for the correlation function to provide robustness to the choice of correlation function. We specify the prior for the correlation function using spectral methods and the Dirichlet process prior, which is a common prior for an unknown distribution function. Our model does not require Gaussian data or spatial locations on a regular grid. The approach is demonstrated using a simulation study as well as an analysis of California air pollution data.

Spatial distribution measured by the modulation transfer function

International Nuclear Information System (INIS)

Rossi, P.; Brice, D.K.; Doyle, B.L.

2003-01-01

Spatial distributions in ion micro-beam and IBA experimental practice are regularly characterized through the parameters of FWHM and tail area percentage (TF, tail fraction). Linear and stationary transducer theory allows these distributions to be described in the Fourier-dual frequency space, and provides an indirect method to evaluate them through measurement of the modulation transfer function (MTF). We suggest direct measurement of MTF by employing bar pattern grids, similar to those used for calibration of radiological equipment. Assuming spatial distributions of the form exp(-(|αx|) η ), we are able to relate the MTF measurements to the more popular FWHM and TF. This new approach to determine spatial resolution can become a standard for use by the micro-beam community

A Third-Generation Adaptive Statistical Iterative Reconstruction Technique: Phantom Study of Image Noise, Spatial Resolution, Lesion Detectability, and Dose Reduction Potential.

Science.gov (United States)

Euler, André; Solomon, Justin; Marin, Daniele; Nelson, Rendon C; Samei, Ehsan

2018-06-01

The purpose of this study was to assess image noise, spatial resolution, lesion detectability, and the dose reduction potential of a proprietary third-generation adaptive statistical iterative reconstruction (ASIR-V) technique. A phantom representing five different body sizes (12-37 cm) and a contrast-detail phantom containing lesions of five low-contrast levels (5-20 HU) and three sizes (2-6 mm) were deployed. Both phantoms were scanned on a 256-MDCT scanner at six different radiation doses (1.25-10 mGy). Images were reconstructed with filtered back projection (FBP), ASIR-V with 50% blending with FBP (ASIR-V 50%), and ASIR-V without blending (ASIR-V 100%). In the first phantom, noise properties were assessed by noise power spectrum analysis. Spatial resolution properties were measured by use of task transfer functions for objects of different contrasts. Noise magnitude, noise texture, and resolution were compared between the three groups. In the second phantom, low-contrast detectability was assessed by nine human readers independently for each condition. The dose reduction potential of ASIR-V was estimated on the basis of a generalized linear statistical regression model. On average, image noise was reduced 37.3% with ASIR-V 50% and 71.5% with ASIR-V 100% compared with FBP. ASIR-V shifted the noise power spectrum toward lower frequencies compared with FBP. The spatial resolution of ASIR-V was equivalent or slightly superior to that of FBP, except for the low-contrast object, which had lower resolution. Lesion detection significantly increased with both ASIR-V levels (p = 0.001), with an estimated radiation dose reduction potential of 15% ± 5% (SD) for ASIR-V 50% and 31% ± 9% for ASIR-V 100%. ASIR-V reduced image noise and improved lesion detection compared with FBP and had potential for radiation dose reduction while preserving low-contrast detectability.

A Mobile Outdoor Augmented Reality Method Combining Deep Learning Object Detection and Spatial Relationships for Geovisualization.

Science.gov (United States)

Rao, Jinmeng; Qiao, Yanjun; Ren, Fu; Wang, Junxing; Du, Qingyun

2017-08-24

The purpose of this study was to develop a robust, fast and markerless mobile augmented reality method for registration, geovisualization and interaction in uncontrolled outdoor environments. We propose a lightweight deep-learning-based object detection approach for mobile or embedded devices; the vision-based detection results of this approach are combined with spatial relationships by means of the host device's built-in Global Positioning System receiver, Inertial Measurement Unit and magnetometer. Virtual objects generated based on geospatial information are precisely registered in the real world, and an interaction method based on touch gestures is implemented. The entire method is independent of the network to ensure robustness to poor signal conditions. A prototype system was developed and tested on the Wuhan University campus to evaluate the method and validate its results. The findings demonstrate that our method achieves a high detection accuracy, stable geovisualization results and interaction.

A Mobile Outdoor Augmented Reality Method Combining Deep Learning Object Detection and Spatial Relationships for Geovisualization

Directory of Open Access Journals (Sweden)

Jinmeng Rao

2017-08-01

Full Text Available The purpose of this study was to develop a robust, fast and markerless mobile augmented reality method for registration, geovisualization and interaction in uncontrolled outdoor environments. We propose a lightweight deep-learning-based object detection approach for mobile or embedded devices; the vision-based detection results of this approach are combined with spatial relationships by means of the host device’s built-in Global Positioning System receiver, Inertial Measurement Unit and magnetometer. Virtual objects generated based on geospatial information are precisely registered in the real world, and an interaction method based on touch gestures is implemented. The entire method is independent of the network to ensure robustness to poor signal conditions. A prototype system was developed and tested on the Wuhan University campus to evaluate the method and validate its results. The findings demonstrate that our method achieves a high detection accuracy, stable geovisualization results and interaction.

A Mobile Outdoor Augmented Reality Method Combining Deep Learning Object Detection and Spatial Relationships for Geovisualization

Science.gov (United States)

Rao, Jinmeng; Qiao, Yanjun; Ren, Fu; Wang, Junxing; Du, Qingyun

2017-01-01

The purpose of this study was to develop a robust, fast and markerless mobile augmented reality method for registration, geovisualization and interaction in uncontrolled outdoor environments. We propose a lightweight deep-learning-based object detection approach for mobile or embedded devices; the vision-based detection results of this approach are combined with spatial relationships by means of the host device’s built-in Global Positioning System receiver, Inertial Measurement Unit and magnetometer. Virtual objects generated based on geospatial information are precisely registered in the real world, and an interaction method based on touch gestures is implemented. The entire method is independent of the network to ensure robustness to poor signal conditions. A prototype system was developed and tested on the Wuhan University campus to evaluate the method and validate its results. The findings demonstrate that our method achieves a high detection accuracy, stable geovisualization results and interaction. PMID:28837096

A novel airport extraction model based on saliency region detection for high spatial resolution remote sensing images

Science.gov (United States)

Lv, Wen; Zhang, Libao; Zhu, Yongchun

2017-06-01

The airport is one of the most crucial traffic facilities in military and civil fields. Automatic airport extraction in high spatial resolution remote sensing images has many applications such as regional planning and military reconnaissance. Traditional airport extraction strategies usually base on prior knowledge and locate the airport target by template matching and classification, which will cause high computation complexity and large costs of computing resources for high spatial resolution remote sensing images. In this paper, we propose a novel automatic airport extraction model based on saliency region detection, airport runway extraction and adaptive threshold segmentation. In saliency region detection, we choose frequency-tuned (FT) model for computing airport saliency using low level features of color and luminance that is easy and fast to implement and can provide full-resolution saliency maps. In airport runway extraction, Hough transform is adopted to count the number of parallel line segments. In adaptive threshold segmentation, the Otsu threshold segmentation algorithm is proposed to obtain more accurate airport regions. The experimental results demonstrate that the proposed model outperforms existing saliency analysis models and shows good performance in the extraction of the airport.

Spatial Processing in Infancy Predicts Both Spatial and Mathematical Aptitude in Childhood.

Science.gov (United States)

Lauer, Jillian E; Lourenco, Stella F

2016-10-01

Despite considerable interest in the role of spatial intelligence in science, technology, engineering, and mathematics (STEM) achievement, little is known about the ontogenetic origins of individual differences in spatial aptitude or their relation to later accomplishments in STEM disciplines. The current study provides evidence that spatial processes present in infancy predict interindividual variation in both spatial and mathematical competence later in development. Using a longitudinal design, we found that children's performance on a brief visuospatial change-detection task administered between 6 and 13 months of age was related to their spatial aptitude (i.e., mental-transformation skill) and mastery of symbolic-math concepts at 4 years of age, even when we controlled for general cognitive abilities and spatial memory. These results suggest that nascent spatial processes present in the first year of life not only act as precursors to later spatial intelligence but also predict math achievement during childhood.

Condition Number Regularized Covariance Estimation*

Science.gov (United States)

Won, Joong-Ho; Lim, Johan; Kim, Seung-Jean; Rajaratnam, Bala

2012-01-01

Estimation of high-dimensional covariance matrices is known to be a difficult problem, has many applications, and is of current interest to the larger statistics community. In many applications including so-called the “large p small n” setting, the estimate of the covariance matrix is required to be not only invertible, but also well-conditioned. Although many regularization schemes attempt to do this, none of them address the ill-conditioning problem directly. In this paper, we propose a maximum likelihood approach, with the direct goal of obtaining a well-conditioned estimator. No sparsity assumption on either the covariance matrix or its inverse are are imposed, thus making our procedure more widely applicable. We demonstrate that the proposed regularization scheme is computationally efficient, yields a type of Steinian shrinkage estimator, and has a natural Bayesian interpretation. We investigate the theoretical properties of the regularized covariance estimator comprehensively, including its regularization path, and proceed to develop an approach that adaptively determines the level of regularization that is required. Finally, we demonstrate the performance of the regularized estimator in decision-theoretic comparisons and in the financial portfolio optimization setting. The proposed approach has desirable properties, and can serve as a competitive procedure, especially when the sample size is small and when a well-conditioned estimator is required. PMID:23730197

A spatial scan statistic for compound Poisson data.

Science.gov (United States)

Rosychuk, Rhonda J; Chang, Hsing-Ming

2013-12-20

The topic of spatial cluster detection gained attention in statistics during the late 1980s and early 1990s. Effort has been devoted to the development of methods for detecting spatial clustering of cases and events in the biological sciences, astronomy and epidemiology. More recently, research has examined detecting clusters of correlated count data associated with health conditions of individuals. Such a method allows researchers to examine spatial relationships of disease-related events rather than just incident or prevalent cases. We introduce a spatial scan test that identifies clusters of events in a study region. Because an individual case may have multiple (repeated) events, we base the test on a compound Poisson model. We illustrate our method for cluster detection on emergency department visits, where individuals may make multiple disease-related visits. Copyright © 2013 John Wiley & Sons, Ltd.

Remote sensing and spatial analysis based study for detecting deforestation and the associated drivers

Science.gov (United States)

El-Abbas, Mustafa M.; Csaplovics, Elmar; Deafalla, Taisser H.

2013-10-01

Nowadays, remote-sensing technologies are becoming increasingly interlinked to the issue of deforestation. They offer a systematized and objective strategy to document, understand and simulate the deforestation process and its associated causes. In this context, the main goal of this study, conducted in the Blue Nile region of Sudan, in which most of the natural habitats were dramatically destroyed, was to develop spatial methodologies to assess the deforestation dynamics and its associated factors. To achieve that, optical multispectral satellite scenes (i.e., ASTER and LANDSAT) integrated with field survey in addition to multiple data sources were used for the analyses. Spatiotemporal Object Based Image Analysis (STOBIA) was applied to assess the change dynamics within the period of study. Broadly, the above mentioned analyses include; Object Based (OB) classifications, post-classification change detection, data fusion, information extraction and spatial analysis. Hierarchical multi-scale segmentation thresholds were applied and each class was delimited with semantic meanings by a set of rules associated with membership functions. Consequently, the fused multi-temporal data were introduced to create detailed objects of change classes from the input LU/LC classes. The dynamic changes were quantified and spatially located as well as the spatial and contextual relations from adjacent areas were analyzed. The main finding of the present study is that, the forest areas were drastically decreased, while the agrarian structure in conversion of forest into agricultural fields and grassland was the main force of deforestation. In contrast, the capability of the area to recover was clearly observed. The study concludes with a brief assessment of an 'oriented' framework, focused on the alarming areas where serious dynamics are located and where urgent plans and interventions are most critical, guided with potential solutions based on the identified driving forces.

Mesoscopic effects in an agent-based bargaining model in regular lattices.

Science.gov (United States)

Poza, David J; Santos, José I; Galán, José M; López-Paredes, Adolfo

2011-03-09

The effect of spatial structure has been proved very relevant in repeated games. In this work we propose an agent based model where a fixed finite population of tagged agents play iteratively the Nash demand game in a regular lattice. The model extends the multiagent bargaining model by Axtell, Epstein and Young modifying the assumption of global interaction. Each agent is endowed with a memory and plays the best reply against the opponent's most frequent demand. We focus our analysis on the transient dynamics of the system, studying by computer simulation the set of states in which the system spends a considerable fraction of the time. The results show that all the possible persistent regimes in the global interaction model can also be observed in this spatial version. We also find that the mesoscopic properties of the interaction networks that the spatial distribution induces in the model have a significant impact on the diffusion of strategies, and can lead to new persistent regimes different from those found in previous research. In particular, community structure in the intratype interaction networks may cause that communities reach different persistent regimes as a consequence of the hindering diffusion effect of fluctuating agents at their borders.

Mesoscopic effects in an agent-based bargaining model in regular lattices.

Directory of Open Access Journals (Sweden)

David J Poza

Full Text Available The effect of spatial structure has been proved very relevant in repeated games. In this work we propose an agent based model where a fixed finite population of tagged agents play iteratively the Nash demand game in a regular lattice. The model extends the multiagent bargaining model by Axtell, Epstein and Young modifying the assumption of global interaction. Each agent is endowed with a memory and plays the best reply against the opponent's most frequent demand. We focus our analysis on the transient dynamics of the system, studying by computer simulation the set of states in which the system spends a considerable fraction of the time. The results show that all the possible persistent regimes in the global interaction model can also be observed in this spatial version. We also find that the mesoscopic properties of the interaction networks that the spatial distribution induces in the model have a significant impact on the diffusion of strategies, and can lead to new persistent regimes different from those found in previous research. In particular, community structure in the intratype interaction networks may cause that communities reach different persistent regimes as a consequence of the hindering diffusion effect of fluctuating agents at their borders.

Task-Driven Optimization of Fluence Field and Regularization for Model-Based Iterative Reconstruction in Computed Tomography.

Science.gov (United States)

Gang, Grace J; Siewerdsen, Jeffrey H; Stayman, J Webster

2017-12-01

This paper presents a joint optimization of dynamic fluence field modulation (FFM) and regularization in quadratic penalized-likelihood reconstruction that maximizes a task-based imaging performance metric. We adopted a task-driven imaging framework for prospective designs of the imaging parameters. A maxi-min objective function was adopted to maximize the minimum detectability index ( ) throughout the image. The optimization algorithm alternates between FFM (represented by low-dimensional basis functions) and local regularization (including the regularization strength and directional penalty weights). The task-driven approach was compared with three FFM strategies commonly proposed for FBP reconstruction (as well as a task-driven TCM strategy) for a discrimination task in an abdomen phantom. The task-driven FFM assigned more fluence to less attenuating anteroposterior views and yielded approximately constant fluence behind the object. The optimal regularization was almost uniform throughout image. Furthermore, the task-driven FFM strategy redistribute fluence across detector elements in order to prescribe more fluence to the more attenuating central region of the phantom. Compared with all strategies, the task-driven FFM strategy not only improved minimum by at least 17.8%, but yielded higher over a large area inside the object. The optimal FFM was highly dependent on the amount of regularization, indicating the importance of a joint optimization. Sample reconstructions of simulated data generally support the performance estimates based on computed . The improvements in detectability show the potential of the task-driven imaging framework to improve imaging performance at a fixed dose, or, equivalently, to provide a similar level of performance at reduced dose.

Regular-, irregular-, and pseudo-character processing in Chinese: The regularity effect in normal adult readers

Directory of Open Access Journals (Sweden)

Dustin Kai Yan Lau

2014-03-01

Full Text Available Background Unlike alphabetic languages, Chinese uses a logographic script. However, the pronunciation of many character’s phonetic radical has the same pronunciation as the character as a whole. These are considered regular characters and can be read through a lexical non-semantic route (Weekes & Chen, 1999. Pseudocharacters are another way to study this non-semantic route. A pseudocharacter is the combination of existing semantic and phonetic radicals in their legal positions resulting in a non-existing character (Ho, Chan, Chung, Lee, & Tsang, 2007. Pseudocharacters can be pronounced by direct derivation from the sound of its phonetic radical. Conversely, if the pronunciation of a character does not follow that of the phonetic radical, it is considered as irregular and can only be correctly read through the lexical-semantic route. The aim of the current investigation was to examine reading aloud in normal adults. We hypothesized that the regularity effect, previously described for alphabetical scripts and acquired dyslexic patients of Chinese (Weekes & Chen, 1999; Wu, Liu, Sun, Chromik, & Zhang, 2014, would also be present in normal adult Chinese readers. Method Participants. Thirty (50% female native Hong Kong Cantonese speakers with a mean age of 19.6 years and a mean education of 12.9 years. Stimuli. Sixty regular-, 60 irregular-, and 60 pseudo-characters (with at least 75% of name agreement in Chinese were matched by initial phoneme, number of strokes and family size. Additionally, regular- and irregular-characters were matched by frequency (low and consistency. Procedure. Each participant was asked to read aloud the stimuli presented on a laptop using the DMDX software. The order of stimuli presentation was randomized. Data analysis. ANOVAs were carried out by participants and items with RTs and errors as dependent variables and type of stimuli (regular-, irregular- and pseudo-character as repeated measures (F1 or between subject�

Detecting the Land-Cover Changes Induced by Large-Physical Disturbances Using Landscape Metrics, Spatial Sampling, Simulation and Spatial Analysis

Directory of Open Access Journals (Sweden)

Hone-Jay Chu

2009-08-01

Full Text Available The objectives of the study are to integrate the conditional Latin Hypercube Sampling (cLHS, sequential Gaussian simulation (SGS and spatial analysis in remotely sensed images, to monitor the effects of large chronological disturbances on spatial characteristics of landscape changes including spatial heterogeneity and variability. The multiple NDVI images demonstrate that spatial patterns of disturbed landscapes were successfully delineated by spatial analysis such as variogram, Moran’I and landscape metrics in the study area. The hybrid method delineates the spatial patterns and spatial variability of landscapes caused by these large disturbances. The cLHS approach is applied to select samples from Normalized Difference Vegetation Index (NDVI images from SPOT HRV images in the Chenyulan watershed of Taiwan, and then SGS with sufficient samples is used to generate maps of NDVI images. In final, the NDVI simulated maps are verified using indexes such as the correlation coefficient and mean absolute error (MAE. Therefore, the statistics and spatial structures of multiple NDVI images present a very robust behavior, which advocates the use of the index for the quantification of the landscape spatial patterns and land cover change. In addition, the results transferred by Open Geospatial techniques can be accessed from web-based and end-user applications of the watershed management.

Spatial and temporal resolution requirements for quench detection in (RE)Ba2Cu3Ox magnets using Rayleigh-scattering-based fiber optic distributed sensing

International Nuclear Information System (INIS)

Chan, W K; Schwartz, J; Flanagan, G

2013-01-01

One of the key remaining challenges to safe and reliable operation of large, high temperature superconductor (HTS)-based magnet systems is quench detection and protection. Due to the slow quench propagation in HTS systems, the conventional discrete voltage-tap approach developed for NbTi and Nb 3 Sn magnets may not be sufficient. In contrast, a distributed temperature profile, generated by a distributed temperature sensor and facilitating continuous monitoring of the temperature at any monitored locations within a magnet with high spatial resolution, may be required. One such distributed temperature sensing option is the use of Rayleigh-based fiber optic sensors (FOS), which are immune to electromagnetic interference. The detection of a quench via Rayleigh-based FOS relies on converting the spectral shifts in the Rayleigh scattering spectra into temperature variations. As a result, the higher the spatial sampling resolution the larger the data processing volume, and thus the lower the temporal sampling resolution. So, for effective quench detection, which requires the quick and accurate identification of a hot spot, it is important to find a balance between the spatial and temporal resolutions executable on a given data acquisition and processing (DAQ) system. This paper discusses a method for finding an appropriate DAQ technology that matches the characteristic of a superconducting coil, and determining the acceptable resolutions for efficient and safe quench detection. A quench detection algorithm based on distributed temperature sensing is proposed and its implementation challenges are discussed. (paper)

Regularity effect in prospective memory during aging

Directory of Open Access Journals (Sweden)

Geoffrey Blondelle

2016-10-01

Full Text Available Background: Regularity effect can affect performance in prospective memory (PM, but little is known on the cognitive processes linked to this effect. Moreover, its impacts with regard to aging remain unknown. To our knowledge, this study is the first to examine regularity effect in PM in a lifespan perspective, with a sample of young, intermediate, and older adults. Objective and design: Our study examined the regularity effect in PM in three groups of participants: 28 young adults (18–30, 16 intermediate adults (40–55, and 25 older adults (65–80. The task, adapted from the Virtual Week, was designed to manipulate the regularity of the various activities of daily life that were to be recalled (regular repeated activities vs. irregular non-repeated activities. We examine the role of several cognitive functions including certain dimensions of executive functions (planning, inhibition, shifting, and binding, short-term memory, and retrospective episodic memory to identify those involved in PM, according to regularity and age. Results: A mixed-design ANOVA showed a main effect of task regularity and an interaction between age and regularity: an age-related difference in PM performances was found for irregular activities (older < young, but not for regular activities. All participants recalled more regular activities than irregular ones with no age effect. It appeared that recalling of regular activities only involved planning for both intermediate and older adults, while recalling of irregular ones were linked to planning, inhibition, short-term memory, binding, and retrospective episodic memory. Conclusion: Taken together, our data suggest that planning capacities seem to play a major role in remembering to perform intended actions with advancing age. Furthermore, the age-PM-paradox may be attenuated when the experimental design is adapted by implementing a familiar context through the use of activities of daily living. The clinical

J-regular rings with injectivities

OpenAIRE

Shen, Liang

2010-01-01

A ring $R$ is called a J-regular ring if R/J(R) is von Neumann regular, where J(R) is the Jacobson radical of R. It is proved that if R is J-regular, then (i) R is right n-injective if and only if every homomorphism from an $n$-generated small right ideal of $R$ to $R_{R}$ can be extended to one from $R_{R}$ to $R_{R}$; (ii) R is right FP-injective if and only if R is right (J, R)-FP-injective. Some known results are improved.

Regularization based on steering parameterized Gaussian filters and a Bhattacharyya distance functional

Science.gov (United States)

Lopes, Emerson P.

2001-08-01

Template regularization embeds the problem of class separability. In the machine vision perspective, this problem is critical when a textural classification procedure is applied to non-stationary pattern mosaic images. These applications often present low accuracy performance due to disturbance of the classifiers produced by exogenous or endogenous signal regularity perturbations. Natural scene imaging, where the images present certain degree of homogeneity in terms of texture element size or shape (primitives) shows a variety of behaviors, especially varying the preferential spatial directionality. The space-time image pattern characterization is only solved if classification procedures are designed considering the most robust tools within a parallel and hardware perspective. The results to be compared in this paper are obtained using a framework based on multi-resolution, frame and hypothesis approach. Two strategies for the bank of Gabor filters applications are considered: adaptive strategy using the KL transform and fix configuration strategy. The regularization under discussion is accomplished in the pyramid building system instance. The filterings are steering Gaussians controlled by free parameters which are adjusted in accordance with a feedback process driven by hints obtained from sequence of frames interaction functionals pos-processed in the training process and including classification of training set samples as examples. Besides these adjustments there is continuous input data sensitive adaptiveness. The experimental result assessments are focused on two basic issues: Bhattacharyya distance as pattern characterization feature and the combination of KL transform as feature selection and adaptive criterion with the regularization of the pattern Bhattacharyya distance functional (BDF) behavior, using the BDF state separability and symmetry as the main indicators of an optimum framework parameter configuration.

Regular self-microstructuring on CR39 using high UV laser dose

International Nuclear Information System (INIS)

Parvin, P.; Refahizadeh, M.; Mortazavi, S.Z.; Silakhori, K.; Mahdiloo, A.; Aghaii, P.

2014-01-01

The UV laser induced replicas in the form of self-lining microstructures are created by high dose (with high fluence) ArF laser irradiation on CR39. Microstructures as the self-induced contours, in the form of concentric circles, appear when the laser fluence is well above the ablation threshold. It leads to the regular periodic parallel lines, i.e. circles with large radii having spatial separation 100–200 nm and line width 300–600 nm, where the number of shots increases to achieve higher UV doses. The surface wettability is also investigated after laser texturing to exhibit that a notable hydrophilicity takes place at high doses.

Iterative Regularization with Minimum-Residual Methods

DEFF Research Database (Denmark)

Jensen, Toke Koldborg; Hansen, Per Christian

2007-01-01

subspaces. We provide a combination of theory and numerical examples, and our analysis confirms the experience that MINRES and MR-II can work as general regularization methods. We also demonstrate theoretically and experimentally that the same is not true, in general, for GMRES and RRGMRES their success......We study the regularization properties of iterative minimum-residual methods applied to discrete ill-posed problems. In these methods, the projection onto the underlying Krylov subspace acts as a regularizer, and the emphasis of this work is on the role played by the basis vectors of these Krylov...... as regularization methods is highly problem dependent....

Iterative regularization with minimum-residual methods

DEFF Research Database (Denmark)

Jensen, Toke Koldborg; Hansen, Per Christian

2006-01-01

subspaces. We provide a combination of theory and numerical examples, and our analysis confirms the experience that MINRES and MR-II can work as general regularization methods. We also demonstrate theoretically and experimentally that the same is not true, in general, for GMRES and RRGMRES - their success......We study the regularization properties of iterative minimum-residual methods applied to discrete ill-posed problems. In these methods, the projection onto the underlying Krylov subspace acts as a regularizer, and the emphasis of this work is on the role played by the basis vectors of these Krylov...... as regularization methods is highly problem dependent....

Abstract spatial concept priming dynamically influences real-world actions

Directory of Open Access Journals (Sweden)

Sarah M Tower-Richardi

2012-09-01

Full Text Available Experienced regularities in our perceptions and actions play important roles in grounding abstract concepts such as social status, time, and emotion. Might we similarly ground abstract spatial concepts in more experienced-based domains? The present experiment explores this possibility by implicitly priming abstract spatial terms (north, south, east, west and then measuring participants’ hand movement trajectories while they respond to a body-referenced spatial target (up, down, left, right in a verbal (Exp. 1 or spatial (Exp. 2 format. Results from two experiments demonstrate temporally-dynamic and prime-biased movement trajectories when the primes are incongruent with the targets (e.g., north – left, west – up. That is, priming abstract coordinate directions influences subsequent actions in response to concrete target directions. These findings provide the first evidence that abstract concepts of world-centered coordinate axes are implicitly understood in the context of concrete body-referenced axes; critically, this abstract-concrete relationship manifests in motor movements, and may have implications for spatial memory organization.

A wavelet-based regularized reconstruction algorithm for SENSE parallel MRI with applications to neuroimaging

International Nuclear Information System (INIS)

Chaari, L.; Pesquet, J.Ch.; Chaari, L.; Ciuciu, Ph.; Benazza-Benyahia, A.

2011-01-01

To reduce scanning time and/or improve spatial/temporal resolution in some Magnetic Resonance Imaging (MRI) applications, parallel MRI acquisition techniques with multiple coils acquisition have emerged since the early 1990's as powerful imaging methods that allow a faster acquisition process. In these techniques, the full FOV image has to be reconstructed from the resulting acquired under sampled k-space data. To this end, several reconstruction techniques have been proposed such as the widely-used Sensitivity Encoding (SENSE) method. However, the reconstructed image generally presents artifacts when perturbations occur in both the measured data and the estimated coil sensitivity profiles. In this paper, we aim at achieving accurate image reconstruction under degraded experimental conditions (low magnetic field and high reduction factor), in which neither the SENSE method nor the Tikhonov regularization in the image domain give convincing results. To this end, we present a novel method for SENSE-based reconstruction which proceeds with regularization in the complex wavelet domain by promoting sparsity. The proposed approach relies on a fast algorithm that enables the minimization of regularized non-differentiable criteria including more general penalties than a classical l 1 term. To further enhance the reconstructed image quality, local convex constraints are added to the regularization process. In vivo human brain experiments carried out on Gradient-Echo (GRE) anatomical and Echo Planar Imaging (EPI) functional MRI data at 1.5 T indicate that our algorithm provides reconstructed images with reduced artifacts for high reduction factors. (authors)

Multiple graph regularized protein domain ranking.

Science.gov (United States)

Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin

2012-11-19

Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods. To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods. The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications.

Spatially modulated imaging system

International Nuclear Information System (INIS)

Barrett, H.H.

1975-01-01

Noncoherent radiation, such as x-rays, is spatially coded, directed through an object and spatially detected to form a spatially coded pattern, from which an image of the object may be reconstructed. The x-ray source may be formed by x-ray fluorescence and substration of the holographic images formed by two sources having energy levels predominantly above and below the maximum absorption range of an agent in the object may be used to enhance contrast in the reproduced image. (Patent Office Record)

Representing Spatial Layout According to Intrinsic Frames of Reference.

Science.gov (United States)

Xie, Chaoxiang; Li, Shiyi; Tao, Weidong; Wei, Yiping; Sun, Hong-Jin

2017-01-01

Mou and McNamara have suggested that object locations are represented according to intrinsic reference frames. In three experiments, we investigated the limitations of intrinsic reference frames as a mean to represent object locations in spatial memory. Participants learned the locations of seven or eight common objects in a rectangular room and then made judgments of relative direction based on their memory of the layout. The results of all experiments showed that when all objects were positioned regularly, judgments of relative direction were faster or more accurate for novel headings that were aligned with the primary intrinsic structure than for other novel headings; however, when one irregularly positioned object was added to the layout, this advantage was eliminated. The experiments further indicated that with a single view at study, participants could represent the layout from either an egocentric orientation or a different orientation, according to experimental instructions. Together, these results suggest that environmental reference frames and intrinsic axes can influence performance for novel headings, but their role in spatial memory depends on egocentric experience, layout regularity, and instructions.

Detection of precursory deformation using a TLS. Application to spatial prediction of rockfalls.

Science.gov (United States)

Abellán, Antonio; Vilaplana, Joan Manuel; Calvet, Jaume; Rodriguez, Xavier

2010-05-01

Different applications on the use of Terrestrial Laser Scanner (TLS) on rock slopes are undergoing rapid development, mainly in the characterization of 3D discontinuities and the monitoring of rock slopes. The emphasis of this research is on detection of precursory deformation and its application to spatial prediction of rockfalls. The pilot study area corresponds to the main scarp of an old slide located at Puigcercós (Catalonia, Spain). 3D temporal variations of the terrain were analyzed by comparing sequential TLS datasets. Five areas characterized by centimetric precursory deformations were detected in the study area. Of these deformations, (a) growing deformation across three areas culminated in a rockfall occurrence; and (b) another growing deformation across two areas was detected, making a subsequent rockfall likely. The areas with precursory deformations detected in Puigcercós showed the following characteristics: (a) a sub-vertical fracture delimiting the moving part from the rest of the slope; (b) an increase in the horizontal displacement upwards, typical of a toppling failure mechanism (Muller 1968; Goodman and Bray, 1976). In addition, decimetric-scale rockfalls were observed in the upper part of the moving areas, which is consistent with the observations of Rosser et al., (2007). TLS ILRIS 3D technical characteristics are as follows: high accuracy (7.2 mm at a range of 50 meters), high angular resolution (e.g. 1 point every few cm), fast data acquisition: 2,500 points/second; broad coverage; high maximum range on natural slopes: ~600m. The single point distances between the surface of reference and the successive data point clouds were computed using a conventional methodology (data vs. reference comparison). The direction of comparison was defined as the normal vector of the rock face at its central part. We focused in the study of the small scale displacements towards the origin of coordinates, which reflect the pre-failure deformation on part of

Noise-induced tinnitus using individualized gap detection analysis and its relationship with hyperacusis, anxiety, and spatial cognition.

Directory of Open Access Journals (Sweden)

Edward Pace

Full Text Available Tinnitus has a complex etiology that involves auditory and non-auditory factors and may be accompanied by hyperacusis, anxiety and cognitive changes. Thus far, investigations of the interrelationship between tinnitus and auditory and non-auditory impairment have yielded conflicting results. To further address this issue, we noise exposed rats and assessed them for tinnitus using a gap detection behavioral paradigm combined with statistically-driven analysis to diagnose tinnitus in individual rats. We also tested rats for hearing detection, responsivity, and loss using prepulse inhibition and auditory brainstem response, and for spatial cognition and anxiety using Morris water maze and elevated plus maze. We found that our tinnitus diagnosis method reliably separated noise-exposed rats into tinnitus((+ and tinnitus((- groups and detected no evidence of tinnitus in tinnitus((- and control rats. In addition, the tinnitus((+ group demonstrated enhanced startle amplitude, indicating hyperacusis-like behavior. Despite these results, neither tinnitus, hyperacusis nor hearing loss yielded any significant effects on spatial learning and memory or anxiety, though a majority of rats with the highest anxiety levels had tinnitus. These findings showed that we were able to develop a clinically relevant tinnitus((+ group and that our diagnosis method is sound. At the same time, like clinical studies, we found that tinnitus does not always result in cognitive-emotional dysfunction, although tinnitus may predispose subjects to certain impairment like anxiety. Other behavioral assessments may be needed to further define the relationship between tinnitus and anxiety, cognitive deficits, and other impairments.

Noise-Induced Tinnitus Using Individualized Gap Detection Analysis and Its Relationship with Hyperacusis, Anxiety, and Spatial Cognition

Science.gov (United States)

Pace, Edward; Zhang, Jinsheng

2013-01-01

Tinnitus has a complex etiology that involves auditory and non-auditory factors and may be accompanied by hyperacusis, anxiety and cognitive changes. Thus far, investigations of the interrelationship between tinnitus and auditory and non-auditory impairment have yielded conflicting results. To further address this issue, we noise exposed rats and assessed them for tinnitus using a gap detection behavioral paradigm combined with statistically-driven analysis to diagnose tinnitus in individual rats. We also tested rats for hearing detection, responsivity, and loss using prepulse inhibition and auditory brainstem response, and for spatial cognition and anxiety using Morris water maze and elevated plus maze. We found that our tinnitus diagnosis method reliably separated noise-exposed rats into tinnitus(+) and tinnitus(−) groups and detected no evidence of tinnitus in tinnitus(−) and control rats. In addition, the tinnitus(+) group demonstrated enhanced startle amplitude, indicating hyperacusis-like behavior. Despite these results, neither tinnitus, hyperacusis nor hearing loss yielded any significant effects on spatial learning and memory or anxiety, though a majority of rats with the highest anxiety levels had tinnitus. These findings showed that we were able to develop a clinically relevant tinnitus(+) group and that our diagnosis method is sound. At the same time, like clinical studies, we found that tinnitus does not always result in cognitive-emotional dysfunction, although tinnitus may predispose subjects to certain impairment like anxiety. Other behavioral assessments may be needed to further define the relationship between tinnitus and anxiety, cognitive deficits, and other impairments. PMID:24069375

Detecting spatial patterns with the cumulant function – Part 2: An application to El Niño

Directory of Open Access Journals (Sweden)

P. Yiou

2008-02-01

Full Text Available The spatial coherence of a measured variable (e.g. temperature or pressure is often studied to determine the regions of high variability or to find teleconnections, i.e. correlations between specific regions. While usual methods to find spatial patterns, such as Principal Components Analysis (PCA, are constrained by linear symmetries, the dependence of variables such as temperature or pressure at different locations is generally nonlinear. In particular, large deviations from the sample mean are expected to be strongly affected by such nonlinearities. Here we apply a newly developed nonlinear technique (Maxima of Cumulant Function, MCF for detection of typical spatial patterns that largely deviate from the mean. In order to test the technique and to introduce the methodology, we focus on the El Niño/Southern Oscillation and its spatial patterns. We find nonsymmetric temperature patterns corresponding to El Niño and La Niña, and we compare the results of MCF with other techniques, such as the symmetric solutions of PCA, and the nonsymmetric solutions of Nonlinear PCA (NLPCA. We found that MCF solutions are more reliable than the NLPCA fits, and can capture mixtures of principal components. Finally, we apply Extreme Value Theory on the temporal variations extracted from our methodology. We find that the tails of the distribution of extreme temperatures during La Niña episodes is bounded, while the tail during El Niños is less likely to be bounded. This implies that the mean spatial patterns of the two phases are asymmetric, as well as the behaviour of their extremes.

Application of L1/2 regularization logistic method in heart disease diagnosis.

Science.gov (United States)

Zhang, Bowen; Chai, Hua; Yang, Ziyi; Liang, Yong; Chu, Gejin; Liu, Xiaoying

2014-01-01

Heart disease has become the number one killer of human health, and its diagnosis depends on many features, such as age, blood pressure, heart rate and other dozens of physiological indicators. Although there are so many risk factors, doctors usually diagnose the disease depending on their intuition and experience, which requires a lot of knowledge and experience for correct determination. To find the hidden medical information in the existing clinical data is a noticeable and powerful approach in the study of heart disease diagnosis. In this paper, sparse logistic regression method is introduced to detect the key risk factors using L(1/2) regularization on the real heart disease data. Experimental results show that the sparse logistic L(1/2) regularization method achieves fewer but informative key features than Lasso, SCAD, MCP and Elastic net regularization approaches. Simultaneously, the proposed method can cut down the computational complexity, save cost and time to undergo medical tests and checkups, reduce the number of attributes needed to be taken from patients.

Detection of Tuberculosis Infection Hotspots Using Activity Spaces Based Spatial Approach in an Urban Tokyo, from 2003 to 2011.

Science.gov (United States)

Izumi, Kiyohiko; Ohkado, Akihiro; Uchimura, Kazuhiro; Murase, Yoshiro; Tatsumi, Yuriko; Kayebeta, Aya; Watanabe, Yu; Ishikawa, Nobukatsu

2015-01-01

Identifying ongoing tuberculosis infection sites is crucial for breaking chains of transmission in tuberculosis-prevalent urban areas. Previous studies have pointed out that detection of local accumulation of tuberculosis patients based on their residential addresses may be limited by a lack of matching between residences and tuberculosis infection sites. This study aimed to identify possible tuberculosis hotspots using TB genotype clustering statuses and a concept of "activity space", a place where patients spend most of their waking hours. We further compared the spatial distribution by different residential statuses and describe urban environmental features of the detected hotspots. Culture-positive tuberculosis patients notified to Shinjuku city from 2003 to 2011 were enrolled in this case-based cross-sectional study, and their demographic and clinical information, TB genotype clustering statuses, and activity space were collected. Spatial statistics (Global Moran's I and Getis-Ord Gi* statistics) identified significant hotspots in 152 census tracts, and urban environmental features and tuberculosis patients' characteristics in these hotspots were assessed. Of the enrolled 643 culture-positive tuberculosis patients, 416 (64.2%) were general inhabitants, 42 (6.5%) were foreign-born people, and 184 were homeless people (28.6%). The percentage of overall genotype clustering was 43.7%. Genotype-clustered general inhabitants and homeless people formed significant hotspots around a major railway station, whereas the non-clustered general inhabitants formed no hotspots. This suggested the detected hotspots of activity spaces may reflect ongoing tuberculosis transmission sites and were characterized by smaller residential floor size and a higher proportion of non-working households. Activity space-based spatial analysis suggested possible TB transmission sites around the major railway station and it can assist in further comprehension of TB transmission dynamics in an

Detection of Tuberculosis Infection Hotspots Using Activity Spaces Based Spatial Approach in an Urban Tokyo, from 2003 to 2011.

Directory of Open Access Journals (Sweden)

Kiyohiko Izumi

Full Text Available Identifying ongoing tuberculosis infection sites is crucial for breaking chains of transmission in tuberculosis-prevalent urban areas. Previous studies have pointed out that detection of local accumulation of tuberculosis patients based on their residential addresses may be limited by a lack of matching between residences and tuberculosis infection sites. This study aimed to identify possible tuberculosis hotspots using TB genotype clustering statuses and a concept of "activity space", a place where patients spend most of their waking hours. We further compared the spatial distribution by different residential statuses and describe urban environmental features of the detected hotspots.Culture-positive tuberculosis patients notified to Shinjuku city from 2003 to 2011 were enrolled in this case-based cross-sectional study, and their demographic and clinical information, TB genotype clustering statuses, and activity space were collected. Spatial statistics (Global Moran's I and Getis-Ord Gi* statistics identified significant hotspots in 152 census tracts, and urban environmental features and tuberculosis patients' characteristics in these hotspots were assessed.Of the enrolled 643 culture-positive tuberculosis patients, 416 (64.2% were general inhabitants, 42 (6.5% were foreign-born people, and 184 were homeless people (28.6%. The percentage of overall genotype clustering was 43.7%. Genotype-clustered general inhabitants and homeless people formed significant hotspots around a major railway station, whereas the non-clustered general inhabitants formed no hotspots. This suggested the detected hotspots of activity spaces may reflect ongoing tuberculosis transmission sites and were characterized by smaller residential floor size and a higher proportion of non-working households.Activity space-based spatial analysis suggested possible TB transmission sites around the major railway station and it can assist in further comprehension of TB transmission

An iterative detection method of MIMO over spatial correlated frequency selective channel: using list sphere decoding for simplification

Science.gov (United States)

Shi, Zhiping; Yan, Bing

2010-08-01

In multiple-input multiple-output(MIMO) wireless systems, combining good channel codes(e.g., Non-binary Repeat Accumulate codes) with adaptive turbo equalization is a good option to get better performance and lower complexity under Spatial Correlated Frequency Selective(SCFS) Channel. The key of this method is after joint antennas MMSE detection (JAD/MMSE) based on interruption cancelling using soft information, considering the detection result as an output of a Gaussian equivalent flat fading channel, and performing maximum likelihood detection(ML) to get more correct estimated result. But the using of ML brings great complexity increase, which is not allowed. In this paper, a low complexity method called list sphere decoding is introduced and applied to replace the ML in order to simplify the adaptive iterative turbo equalization system.

Higher derivative regularization and chiral anomaly

International Nuclear Information System (INIS)

Nagahama, Yoshinori.

1985-02-01

A higher derivative regularization which automatically leads to the consistent chiral anomaly is analyzed in detail. It explicitly breaks all the local gauge symmetry but preserves global chiral symmetry and leads to the chirally symmetric consistent anomaly. This regularization thus clarifies the physics content contained in the consistent anomaly. We also briefly comment on the application of this higher derivative regularization to massless QED. (author)

Intrinsic spatial resolution limitations due to differences between positron emission position and annihilation detection localization

International Nuclear Information System (INIS)

Perez, Pedro; Malano, Francisco; Valente, Mauro

2012-01-01

Since its successful implementation for clinical diagnostic, positron emission tomography (PET) represents the most promising medical imaging technique. The recent major growth of PET imaging is mainly due to its ability to trace the biologic pathways of different compounds in the patient's body, assuming the patient can be labeled with some PET isotope. Regardless of the type of isotope, the PET imaging method is based on the detection of two 511-keV gamma photons being emitted in opposite directions, with almost 180 deg between them, as a consequence of electron-positron annihilation. Therefore, this imaging method is intrinsically limited by random uncertainties in spatial resolutions, related with differences between the actual position of positron emission and the location of the detected annihilation. This study presents an approach with the Monte Carlo method to analyze the influence of this effect on different isotopes of potential implementation in PET. (author)

Hamiltonian description of the parametrized scalar field in bounded spatial regions

International Nuclear Information System (INIS)

Barbero G, J Fernando; Margalef-Bentabol, Juan; Villaseñor, Eduardo J S

2016-01-01

We study the Hamiltonian formulation for a parametrized scalar field in a regular bounded spatial region subject to Dirichlet, Neumann and Robin boundary conditions. We generalize the work carried out by a number of authors on parametrized field systems to the interesting case where spatial boundaries are present. The configuration space of our models contains both smooth scalar fields defined on the spatial manifold and spacelike embeddings from the spatial manifold to a target spacetime endowed with a fixed Lorentzian background metric. We pay particular attention to the geometry of the infinite dimensional manifold of embeddings and the description of the relevant geometric objects: the symplectic form on the primary constraint submanifold and the Hamiltonian vector fields defined on it. (paper)

Differentiation of Forebrain and Hippocampal Dopamine 1-Class Receptors, D1R and D5R, in Spatial Learning and Memory

Science.gov (United States)

Sariñana, Joshua; Tonegawa, Susumu

2017-01-01

Activation of prefrontal cortical (PFC), striatal, and hippocampal dopamine 1-class receptors (D1R and D5R) is necessary for normal spatial information processing. Yet the precise role of the D1R versus the D5R in the aforementioned structures, and their specific contribution to the water-maze spatial learning task remains unknown. D1R- and D5R- specific in situ hybridization probes showed that forebrain restricted D1R and D5R KO mice (F-D1R/D5R KO) displayed D1R mRNA deletion in the medial (m)PFC, dorsal and ventral striatum, and the dentate gyrus (DG) of the hippocampus. D5R mRNA deletion was limited to the mPFC, the CA1 and DG hippocampal subregions. F-D1R/D5R KO mice were given water-maze training and displayed subtle spatial latency differences between genotypes and spatial memory deficits during both regular and reversal training. To differentiate forebrain D1R from D5R activation, forebrain restricted D1R KO (F-D1R KO) and D5R KO (F-D5R KO) mice were trained on the water-maze task. F-D1R KO animals exhibited escape latency deficits throughout regular and reversal training as well as spatial memory deficits during reversal training. F-D1R KO mice also showed perseverative behavior during the reversal spatial memory probe test. In contrast, F-D5R KO animals did not present observable deficits on the water-maze task. Because F-D1R KO mice showed water-maze deficits we tested the necessity of hippocampal D1R activation for spatial learning and memory. We trained DG restricted D1R KO (DG-D1R KO) mice on the water-maze task. DG-D1R KO mice did not present detectable spatial memory deficit, but did show subtle deficits during specific days of training. Our data provides evidence that forebrain D5R activation plays a unique role in spatial learning and memory in conjunction with D1R activation. Moreover, these data suggest that mPFC and striatal, but not DG D1R activation are essential for spatial learning and memory. PMID:26174222

One-way spatial integration of Navier-Stokes equations: stability of wall-bounded flows

Science.gov (United States)

Rigas, Georgios; Colonius, Tim; Towne, Aaron; Beyar, Michael

2016-11-01

For three-dimensional flows, questions of stability, receptivity, secondary flows, and coherent structures require the solution of large partial-derivative eigenvalue problems. Reduced-order approximations are thus required for engineering prediction since these problems are often computationally intractable or prohibitively expensive. For spatially slowly evolving flows, such as jets and boundary layers, a regularization of the equations of motion sometimes permits a fast spatial marching procedure that results in a huge reduction in computational cost. Recently, a novel one-way spatial marching algorithm has been developed by Towne & Colonius. The new method overcomes the principle flaw observed in Parabolized Stability Equations (PSE), namely the ad hoc regularization that removes upstream propagating modes. The one-way method correctly parabolizes the flow equations based on estimating, in a computationally efficient way, the local spectrum in each cross-stream plane and an efficient spectral filter eliminates modes with upstream group velocity. Results from the application of the method to wall-bounded flows will be presented and compared with predictions from the full linearized compressible Navier-Stokes equations and PSE.

Multiple graph regularized protein domain ranking

KAUST Repository

Wang, Jim Jing-Yan

2012-11-19

Background: Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods.Results: To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods.Conclusion: The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications. 2012 Wang et al; licensee BioMed Central Ltd.

Multiple graph regularized protein domain ranking

KAUST Repository

Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin

2012-01-01

Background: Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods.Results: To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods.Conclusion: The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications. 2012 Wang et al; licensee BioMed Central Ltd.

Multiple graph regularized protein domain ranking

Directory of Open Access Journals (Sweden)

Wang Jim

2012-11-01

Full Text Available Abstract Background Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods. Results To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods. Conclusion The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications.

75 FR 53966 - Regular Meeting

Science.gov (United States)

2010-09-02

... FARM CREDIT SYSTEM INSURANCE CORPORATION Regular Meeting AGENCY: Farm Credit System Insurance Corporation Board. SUMMARY: Notice is hereby given of the regular meeting of the Farm Credit System Insurance Corporation Board (Board). DATE AND TIME: The meeting of the Board will be held at the offices of the Farm...

Work and family life of childrearing women workers in Japan: comparison of non-regular employees with short working hours, non-regular employees with long working hours, and regular employees.

Science.gov (United States)

Seto, Masako; Morimoto, Kanehisa; Maruyama, Soichiro

2006-05-01

This study assessed the working and family life characteristics, and the degree of domestic and work strain of female workers with different employment statuses and weekly working hours who are rearing children. Participants were the mothers of preschoolers in a large Japanese city. We classified the women into three groups according to the hours they worked and their employment conditions. The three groups were: non-regular employees working less than 30 h a week (n=136); non-regular employees working 30 h or more per week (n=141); and regular employees working 30 h or more a week (n=184). We compared among the groups the subjective values of work, financial difficulties, childcare and housework burdens, psychological effects, and strains such as work and family strain, work-family conflict, and work dissatisfaction. Regular employees were more likely to report job pressures and inflexible work schedules and to experience more strain related to work and family than non-regular employees. Non-regular employees were more likely to be facing financial difficulties. In particular, non-regular employees working longer hours tended to encounter socioeconomic difficulties and often lacked support from family and friends. Female workers with children may have different social backgrounds and different stressors according to their working hours and work status.

Land cover mapping and change detection in urban watersheds using QuickBird high spatial resolution satellite imagery

Science.gov (United States)

Hester, David Barry

The objective of this research was to develop methods for urban land cover analysis using QuickBird high spatial resolution satellite imagery. Such imagery has emerged as a rich commercially available remote sensing data source and has enjoyed high-profile broadcast news media and Internet applications, but methods of quantitative analysis have not been thoroughly explored. The research described here consists of three studies focused on the use of pan-sharpened 61-cm spatial resolution QuickBird imagery, the spatial resolution of which is the highest of any commercial satellite. In the first study, a per-pixel land cover classification method is developed for use with this imagery. This method utilizes a per-pixel classification approach to generate an accurate six-category high spatial resolution land cover map of a developing suburban area. The primary objective of the second study was to develop an accurate land cover change detection method for use with QuickBird land cover products. This work presents an efficient fuzzy framework for transforming map uncertainty into accurate and meaningful high spatial resolution land cover change analysis. The third study described here is an urban planning application of the high spatial resolution QuickBird-based land cover product developed in the first study. This work both meaningfully connects this exciting new data source to urban watershed management and makes an important empirical contribution to the study of suburban watersheds. Its analysis of residential roads and driveways as well as retail parking lots sheds valuable light on the impact of transportation-related land use on the suburban landscape. Broadly, these studies provide new methods for using state-of-the-art remote sensing data to inform land cover analysis and urban planning. These methods are widely adaptable and produce land cover products that are both meaningful and accurate. As additional high spatial resolution satellites are launched and the

Incremental projection approach of regularization for inverse problems

Energy Technology Data Exchange (ETDEWEB)

Souopgui, Innocent, E-mail: innocent.souopgui@usm.edu [The University of Southern Mississippi, Department of Marine Science (United States); Ngodock, Hans E., E-mail: hans.ngodock@nrlssc.navy.mil [Naval Research Laboratory (United States); Vidard, Arthur, E-mail: arthur.vidard@imag.fr; Le Dimet, François-Xavier, E-mail: ledimet@imag.fr [Laboratoire Jean Kuntzmann (France)

2016-10-15

This paper presents an alternative approach to the regularized least squares solution of ill-posed inverse problems. Instead of solving a minimization problem with an objective function composed of a data term and a regularization term, the regularization information is used to define a projection onto a convex subspace of regularized candidate solutions. The objective function is modified to include the projection of each iterate in the place of the regularization. Numerical experiments based on the problem of motion estimation for geophysical fluid images, show the improvement of the proposed method compared with regularization methods. For the presented test case, the incremental projection method uses 7 times less computation time than the regularization method, to reach the same error target. Moreover, at convergence, the incremental projection is two order of magnitude more accurate than the regularization method.

Geometric regularizations and dual conifold transitions

International Nuclear Information System (INIS)

Landsteiner, Karl; Lazaroiu, Calin I.

2003-01-01

We consider a geometric regularization for the class of conifold transitions relating D-brane systems on noncompact Calabi-Yau spaces to certain flux backgrounds. This regularization respects the SL(2,Z) invariance of the flux superpotential, and allows for computation of the relevant periods through the method of Picard-Fuchs equations. The regularized geometry is a noncompact Calabi-Yau which can be viewed as a monodromic fibration, with the nontrivial monodromy being induced by the regulator. It reduces to the original, non-monodromic background when the regulator is removed. Using this regularization, we discuss the simple case of the local conifold, and show how the relevant field-theoretic information can be extracted in this approach. (author)

Studies on factors of disorder and regularity in the streeet view. Part 1. ; Studies on disorder and regularity in the central business district. Gairo keikan no ranzatsuter dot seizensei yoin ni kansuru kenkyu. 1. ; Chushin shigaichi ni okeru ranzatsuter dot seizensei ni kansuru kenkyu

Energy Technology Data Exchange (ETDEWEB)

Matsunoto, N; Teranishi, N; Senda, M [Nagoya Institute of Technology, Nagoya (Japan). Faculty of Engineering

1991-11-30

This study intends to identify the visual confusion in spatial views by the routine human conveption of regularity and disorder, and elucidate physical factors that cause the disorder and regularity in street views. The disorder factors include the additions annexed to the view afterwards, and the regularity factors include those flat objects that decide the pattern of a view. Many factors can be either disorder or reguarity factors according to their sizes, how they look, and how they are affected by surrounding objects. The disorder in a street view is approximately determined of its degree by such distribution patterns as the spread and convergence of the disorder factors. The disorder can be intensified stronger by the spread of the factors widely over an entire area or the existence of objects that give disorderly impressions, rather than by the number of disorder factors. The regularity is affected easily by the disorder factors, it being lowered by intensities of the disorder. The deciding factors for the disorder or regularity are the kinds of objects, how they are laid out and what they are surrounded with. The factors that govern the degrees of the disorder and regularity are how large the area they are distributed in, the number, how they look, and where they are positioned. 24 refs., 9 figs., 6 tabs.

Cognitive components of regularity processing in the auditory domain.

Directory of Open Access Journals (Sweden)

Stefan Koelsch

Full Text Available BACKGROUND: Music-syntactic irregularities often co-occur with the processing of physical irregularities. In this study we constructed chord-sequences such that perceived differences in the cognitive processing between regular and irregular chords could not be due to the sensory processing of acoustic factors like pitch repetition or pitch commonality (the major component of 'sensory dissonance'. METHODOLOGY/PRINCIPAL FINDINGS: Two groups of subjects (musicians and nonmusicians were investigated with electroencephalography (EEG. Irregular chords elicited an early right anterior negativity (ERAN in the event-related brain potentials (ERPs. The ERAN had a latency of around 180 ms after the onset of the music-syntactically irregular chords, and had maximum amplitude values over right anterior electrode sites. CONCLUSIONS/SIGNIFICANCE: Because irregular chords were hardly detectable based on acoustical factors (such as pitch repetition and sensory dissonance, this ERAN effect reflects for the most part cognitive (not sensory components of regularity-based, music-syntactic processing. Our study represents a methodological advance compared to previous ERP-studies investigating the neural processing of music-syntactically irregular chords.

A composite likelihood approach for spatially correlated survival data

Science.gov (United States)

Paik, Jane; Ying, Zhiliang

2013-01-01

The aim of this paper is to provide a composite likelihood approach to handle spatially correlated survival data using pairwise joint distributions. With e-commerce data, a recent question of interest in marketing research has been to describe spatially clustered purchasing behavior and to assess whether geographic distance is the appropriate metric to describe purchasing dependence. We present a model for the dependence structure of time-to-event data subject to spatial dependence to characterize purchasing behavior from the motivating example from e-commerce data. We assume the Farlie-Gumbel-Morgenstern (FGM) distribution and then model the dependence parameter as a function of geographic and demographic pairwise distances. For estimation of the dependence parameters, we present pairwise composite likelihood equations. We prove that the resulting estimators exhibit key properties of consistency and asymptotic normality under certain regularity conditions in the increasing-domain framework of spatial asymptotic theory. PMID:24223450

A composite likelihood approach for spatially correlated survival data.

Science.gov (United States)

Paik, Jane; Ying, Zhiliang

2013-01-01

The aim of this paper is to provide a composite likelihood approach to handle spatially correlated survival data using pairwise joint distributions. With e-commerce data, a recent question of interest in marketing research has been to describe spatially clustered purchasing behavior and to assess whether geographic distance is the appropriate metric to describe purchasing dependence. We present a model for the dependence structure of time-to-event data subject to spatial dependence to characterize purchasing behavior from the motivating example from e-commerce data. We assume the Farlie-Gumbel-Morgenstern (FGM) distribution and then model the dependence parameter as a function of geographic and demographic pairwise distances. For estimation of the dependence parameters, we present pairwise composite likelihood equations. We prove that the resulting estimators exhibit key properties of consistency and asymptotic normality under certain regularity conditions in the increasing-domain framework of spatial asymptotic theory.

Adaptive regularization

DEFF Research Database (Denmark)

Hansen, Lars Kai; Rasmussen, Carl Edward; Svarer, C.

1994-01-01

Regularization, e.g., in the form of weight decay, is important for training and optimization of neural network architectures. In this work the authors provide a tool based on asymptotic sampling theory, for iterative estimation of weight decay parameters. The basic idea is to do a gradient desce...

Spatial variability of the wave bottom boundary layer over movable rippled beds

NARCIS (Netherlands)

Rodriguez-Abudo, S.; Foster, D.L.; Henriquez, M.

2013-01-01

Observations of the spatially dependent velocity field over movable bed forms subjected to slightly skewed and asymmetric regular wave forcing were collected. The dynamics between the ripple elements is dominated by coherent vortices, characterized by the swirling strength, and evidenced in the

Periodicity in spatial data and geostatistical models: autocorrelation between patches

Science.gov (United States)

Volker C. Radeloff; Todd F. Miller; Hong S. He; David J. Mladenoff

2000-01-01

Several recent studies in landscape ecology have found periodicity in correlograms or semi-variograms calculated, for instance, from spatial data of soils, forests, or animal populations. Some of the studies interpreted this as an indication of regular or periodic landscape patterns. This interpretation is in disagreement with other studies that doubt whether such...

Regularizing portfolio optimization

International Nuclear Information System (INIS)

Still, Susanne; Kondor, Imre

2010-01-01

The optimization of large portfolios displays an inherent instability due to estimation error. This poses a fundamental problem, because solutions that are not stable under sample fluctuations may look optimal for a given sample, but are, in effect, very far from optimal with respect to the average risk. In this paper, we approach the problem from the point of view of statistical learning theory. The occurrence of the instability is intimately related to over-fitting, which can be avoided using known regularization methods. We show how regularized portfolio optimization with the expected shortfall as a risk measure is related to support vector regression. The budget constraint dictates a modification. We present the resulting optimization problem and discuss the solution. The L2 norm of the weight vector is used as a regularizer, which corresponds to a diversification 'pressure'. This means that diversification, besides counteracting downward fluctuations in some assets by upward fluctuations in others, is also crucial because it improves the stability of the solution. The approach we provide here allows for the simultaneous treatment of optimization and diversification in one framework that enables the investor to trade off between the two, depending on the size of the available dataset.

Regularizing portfolio optimization

Science.gov (United States)

Still, Susanne; Kondor, Imre

2010-07-01

The optimization of large portfolios displays an inherent instability due to estimation error. This poses a fundamental problem, because solutions that are not stable under sample fluctuations may look optimal for a given sample, but are, in effect, very far from optimal with respect to the average risk. In this paper, we approach the problem from the point of view of statistical learning theory. The occurrence of the instability is intimately related to over-fitting, which can be avoided using known regularization methods. We show how regularized portfolio optimization with the expected shortfall as a risk measure is related to support vector regression. The budget constraint dictates a modification. We present the resulting optimization problem and discuss the solution. The L2 norm of the weight vector is used as a regularizer, which corresponds to a diversification 'pressure'. This means that diversification, besides counteracting downward fluctuations in some assets by upward fluctuations in others, is also crucial because it improves the stability of the solution. The approach we provide here allows for the simultaneous treatment of optimization and diversification in one framework that enables the investor to trade off between the two, depending on the size of the available dataset.

Low-dose 4D cone-beam CT via joint spatiotemporal regularization of tensor framelet and nonlocal total variation

Science.gov (United States)

Han, Hao; Gao, Hao; Xing, Lei

2017-08-01

Excessive radiation exposure is still a major concern in 4D cone-beam computed tomography (4D-CBCT) due to its prolonged scanning duration. Radiation dose can be effectively reduced by either under-sampling the x-ray projections or reducing the x-ray flux. However, 4D-CBCT reconstruction under such low-dose protocols is prone to image artifacts and noise. In this work, we propose a novel joint regularization-based iterative reconstruction method for low-dose 4D-CBCT. To tackle the under-sampling problem, we employ spatiotemporal tensor framelet (STF) regularization to take advantage of the spatiotemporal coherence of the patient anatomy in 4D images. To simultaneously suppress the image noise caused by photon starvation, we also incorporate spatiotemporal nonlocal total variation (SNTV) regularization to make use of the nonlocal self-recursiveness of anatomical structures in the spatial and temporal domains. Under the joint STF-SNTV regularization, the proposed iterative reconstruction approach is evaluated first using two digital phantoms and then using physical experiment data in the low-dose context of both under-sampled and noisy projections. Compared with existing approaches via either STF or SNTV regularization alone, the presented hybrid approach achieves improved image quality, and is particularly effective for the reconstruction of low-dose 4D-CBCT data that are not only sparse but noisy.

Tessellating the Sphere with Regular Polygons

Science.gov (United States)

Soto-Johnson, Hortensia; Bechthold, Dawn

2004-01-01

Tessellations in the Euclidean plane and regular polygons that tessellate the sphere are reviewed. The regular polygons that can possibly tesellate the sphere are spherical triangles, squares and pentagons.

Accretion onto some well-known regular black holes

International Nuclear Information System (INIS)

Jawad, Abdul; Shahzad, M.U.

2016-01-01

In this work, we discuss the accretion onto static spherically symmetric regular black holes for specific choices of the equation of state parameter. The underlying regular black holes are charged regular black holes using the Fermi-Dirac distribution, logistic distribution, nonlinear electrodynamics, respectively, and Kehagias-Sftesos asymptotically flat regular black holes. We obtain the critical radius, critical speed, and squared sound speed during the accretion process near the regular black holes. We also study the behavior of radial velocity, energy density, and the rate of change of the mass for each of the regular black holes. (orig.)

Accretion onto some well-known regular black holes

Energy Technology Data Exchange (ETDEWEB)

Jawad, Abdul; Shahzad, M.U. [COMSATS Institute of Information Technology, Department of Mathematics, Lahore (Pakistan)

2016-03-15

In this work, we discuss the accretion onto static spherically symmetric regular black holes for specific choices of the equation of state parameter. The underlying regular black holes are charged regular black holes using the Fermi-Dirac distribution, logistic distribution, nonlinear electrodynamics, respectively, and Kehagias-Sftesos asymptotically flat regular black holes. We obtain the critical radius, critical speed, and squared sound speed during the accretion process near the regular black holes. We also study the behavior of radial velocity, energy density, and the rate of change of the mass for each of the regular black holes. (orig.)

Accretion onto some well-known regular black holes

Science.gov (United States)

Jawad, Abdul; Shahzad, M. Umair

2016-03-01

In this work, we discuss the accretion onto static spherically symmetric regular black holes for specific choices of the equation of state parameter. The underlying regular black holes are charged regular black holes using the Fermi-Dirac distribution, logistic distribution, nonlinear electrodynamics, respectively, and Kehagias-Sftesos asymptotically flat regular black holes. We obtain the critical radius, critical speed, and squared sound speed during the accretion process near the regular black holes. We also study the behavior of radial velocity, energy density, and the rate of change of the mass for each of the regular black holes.

Asymptotic analysis of the role of spatial sampling for covariance parameter estimation of Gaussian processes

International Nuclear Information System (INIS)

Bachoc, Francois

2014-01-01

Covariance parameter estimation of Gaussian processes is analyzed in an asymptotic framework. The spatial sampling is a randomly perturbed regular grid and its deviation from the perfect regular grid is controlled by a single scalar regularity parameter. Consistency and asymptotic normality are proved for the Maximum Likelihood and Cross Validation estimators of the covariance parameters. The asymptotic covariance matrices of the covariance parameter estimators are deterministic functions of the regularity parameter. By means of an exhaustive study of the asymptotic covariance matrices, it is shown that the estimation is improved when the regular grid is strongly perturbed. Hence, an asymptotic confirmation is given to the commonly admitted fact that using groups of observation points with small spacing is beneficial to covariance function estimation. Finally, the prediction error, using a consistent estimator of the covariance parameters, is analyzed in detail. (authors)

Diagrammatic methods in phase-space regularization

International Nuclear Information System (INIS)

Bern, Z.; Halpern, M.B.; California Univ., Berkeley

1987-11-01

Using the scalar prototype and gauge theory as the simplest possible examples, diagrammatic methods are developed for the recently proposed phase-space form of continuum regularization. A number of one-loop and all-order applications are given, including general diagrammatic discussions of the nogrowth theorem and the uniqueness of the phase-space stochastic calculus. The approach also generates an alternate derivation of the equivalence of the large-β phase-space regularization to the more conventional coordinate-space regularization. (orig.)

Metric regularity and subdifferential calculus

International Nuclear Information System (INIS)

Ioffe, A D

2000-01-01

The theory of metric regularity is an extension of two classical results: the Lyusternik tangent space theorem and the Graves surjection theorem. Developments in non-smooth analysis in the 1980s and 1990s paved the way for a number of far-reaching extensions of these results. It was also well understood that the phenomena behind the results are of metric origin, not connected with any linear structure. At the same time it became clear that some basic hypotheses of the subdifferential calculus are closely connected with the metric regularity of certain set-valued maps. The survey is devoted to the metric theory of metric regularity and its connection with subdifferential calculus in Banach spaces

Implementation of 3D spatial indexing and compression in a large-scale molecular dynamics simulation database for rapid atomic contact detection

Directory of Open Access Journals (Sweden)

Toofanny Rudesh D

2011-08-01

Full Text Available Abstract Background Molecular dynamics (MD simulations offer the ability to observe the dynamics and interactions of both whole macromolecules and individual atoms as a function of time. Taken in context with experimental data, atomic interactions from simulation provide insight into the mechanics of protein folding, dynamics, and function. The calculation of atomic interactions or contacts from an MD trajectory is computationally demanding and the work required grows exponentially with the size of the simulation system. We describe the implementation of a spatial indexing algorithm in our multi-terabyte MD simulation database that significantly reduces the run-time required for discovery of contacts. The approach is applied to the Dynameomics project data. Spatial indexing, also known as spatial hashing, is a method that divides the simulation space into regular sized bins and attributes an index to each bin. Since, the calculation of contacts is widely employed in the simulation field, we also use this as the basis for testing compression of data tables. We investigate the effects of compression of the trajectory coordinate tables with different options of data and index compression within MS SQL SERVER 2008. Results Our implementation of spatial indexing speeds up the calculation of contacts over a 1 nanosecond (ns simulation window by between 14% and 90% (i.e., 1.2 and 10.3 times faster. For a 'full' simulation trajectory (51 ns spatial indexing reduces the calculation run-time between 31 and 81% (between 1.4 and 5.3 times faster. Compression resulted in reduced table sizes but resulted in no significant difference in the total execution time for neighbour discovery. The greatest compression (~36% was achieved using page level compression on both the data and indexes. Conclusions The spatial indexing scheme significantly decreases the time taken to calculate atomic contacts and could be applied to other multidimensional neighbor discovery

Implementation of 3D spatial indexing and compression in a large-scale molecular dynamics simulation database for rapid atomic contact detection.

Science.gov (United States)

Toofanny, Rudesh D; Simms, Andrew M; Beck, David A C; Daggett, Valerie

2011-08-10

Molecular dynamics (MD) simulations offer the ability to observe the dynamics and interactions of both whole macromolecules and individual atoms as a function of time. Taken in context with experimental data, atomic interactions from simulation provide insight into the mechanics of protein folding, dynamics, and function. The calculation of atomic interactions or contacts from an MD trajectory is computationally demanding and the work required grows exponentially with the size of the simulation system. We describe the implementation of a spatial indexing algorithm in our multi-terabyte MD simulation database that significantly reduces the run-time required for discovery of contacts. The approach is applied to the Dynameomics project data. Spatial indexing, also known as spatial hashing, is a method that divides the simulation space into regular sized bins and attributes an index to each bin. Since, the calculation of contacts is widely employed in the simulation field, we also use this as the basis for testing compression of data tables. We investigate the effects of compression of the trajectory coordinate tables with different options of data and index compression within MS SQL SERVER 2008. Our implementation of spatial indexing speeds up the calculation of contacts over a 1 nanosecond (ns) simulation window by between 14% and 90% (i.e., 1.2 and 10.3 times faster). For a 'full' simulation trajectory (51 ns) spatial indexing reduces the calculation run-time between 31 and 81% (between 1.4 and 5.3 times faster). Compression resulted in reduced table sizes but resulted in no significant difference in the total execution time for neighbour discovery. The greatest compression (~36%) was achieved using page level compression on both the data and indexes. The spatial indexing scheme significantly decreases the time taken to calculate atomic contacts and could be applied to other multidimensional neighbor discovery problems. The speed up enables on-the-fly calculation

Asymptotic properties of the development of conformally flat data near spatial infinity

International Nuclear Information System (INIS)

Valiente Kroon, Juan Antonio

2007-01-01

The analysis of the relation between Bondi-type systems (NP-gauge) and a gauge used in the analysis of the structure of spatial infinity (F-gauge) which was carried out by Friedrich and Kannar (2000 J. Math Phys. 41 2195) is retaken and applied to the development of a suitable class of conformally flat initial data sets with non-vanishing second fundamental form. The calculations presented depend on a certain assumption about the existence and regularity of the solutions to the conformal Einstein field equations close to null and spatial infinity. As a result of the calculations the Newman-Penrose constants of both future and past null infinity are calculated in terms of initial data and are shown to be equal. It is also shown that the asymptotic shear goes to zero as one approaches spatial infinity along the generators of null infinity so that it is possible to select, in a canonical fashion, the Poincare group out of the BMS group. An expansion-again in terms of initial data quantities-of the Bondi mass close to spatial infinity is calculated. This expansion shows that if the existence and regularity assumptions hold, the Bondi mass approaches the ADM mass. A discussion of possible conditions on the initial data which would render a peeling development is presented

Outlier detection using autoencoders

CERN Document Server

Lyudchik, Olga

2016-01-01

Outlier detection is a crucial part of any data analysis applications. The goal of outlier detection is to separate a core of regular observations from some polluting ones, called “outliers”. We propose an outlier detection method using deep autoencoder. In our research the invented method was applied to detect outlier points in the MNIST dataset of handwriting digits. The experimental results show that the proposed method has a potential to be used for anomaly detection.

Temporal regularity of the environment drives time perception

OpenAIRE

van Rijn, H; Rhodes, D; Di Luca, M

2016-01-01

It’s reasonable to assume that a regularly paced sequence should be perceived as regular, but here we show that perceived regularity depends on the context in which the sequence is embedded. We presented one group of participants with perceptually regularly paced sequences, and another group of participants with mostly irregularly paced sequences (75% irregular, 25% regular). The timing of the final stimulus in each sequence could be var- ied. In one experiment, we asked whether the last stim...

The performance of spatially offset Raman spectroscopy for liquid explosive detection

Science.gov (United States)

Loeffen, Paul W.; Maskall, Guy; Bonthron, Stuart; Bloomfield, Matthew; Tombling, Craig; Matousek, Pavel

2016-10-01

Aviation security requirements adopted in 2014 require liquids to be screened at most airports throughout Europe, North America and Australia. Cobalt's unique Spatially Offset Raman Spectroscopy (SORS™) technology has proven extremely effective at screening liquids, aerosols and gels (LAGS) with extremely low false alarm rates. SORS is compatible with a wide range of containers, including coloured, opaque or clear plastics, glass and paper, as well as duty-free bottles in STEBs (secure tamper-evident bags). Our award-winning Insight range has been specially developed for table-top screening at security checkpoints. Insight systems use our patented SORS technology for rapid and accurate chemical analysis of substances in unopened non-metallic containers. Insight100M™ and the latest member of the range - Insight200M™ - also screen metallic containers. Our unique systems screen liquids, aerosols and gels with the highest detection capability and lowest false alarm rates of any ECAC-approved scanner, with several hundred units already in use at airports including eight of the top ten European hubs. This paper presents an analysis of real performance data for these systems.

Application of Fourier-wavelet regularized deconvolution for improving image quality of free space propagation x-ray phase contrast imaging.

Science.gov (United States)

Zhou, Zhongxing; Gao, Feng; Zhao, Huijuan; Zhang, Lixin

2012-11-21

New x-ray phase contrast imaging techniques without using synchrotron radiation confront a common problem from the negative effects of finite source size and limited spatial resolution. These negative effects swamp the fine phase contrast fringes and make them almost undetectable. In order to alleviate this problem, deconvolution procedures should be applied to the blurred x-ray phase contrast images. In this study, three different deconvolution techniques, including Wiener filtering, Tikhonov regularization and Fourier-wavelet regularized deconvolution (ForWaRD), were applied to the simulated and experimental free space propagation x-ray phase contrast images of simple geometric phantoms. These algorithms were evaluated in terms of phase contrast improvement and signal-to-noise ratio. The results demonstrate that the ForWaRD algorithm is most appropriate for phase contrast image restoration among above-mentioned methods; it can effectively restore the lost information of phase contrast fringes while reduce the amplified noise during Fourier regularization.

Land Cover Change Detection in Urban Lake Areas Using Multi-Temporary Very High Spatial Resolution Aerial Images

Directory of Open Access Journals (Sweden)

Wenyuan Zhang

2018-01-01

Full Text Available The availability of very high spatial resolution (VHR remote sensing imagery provides unique opportunities to exploit meaningful change information in detail with object-oriented image analysis. This study investigated land cover (LC changes in Shahu Lake of Wuhan using multi-temporal VHR aerial images in the years 1978, 1981, 1989, 1995, 2003, and 2011. A multi-resolution segmentation algorithm and CART (classification and regression trees classifier were employed to perform highly accurate LC classification of the individual images, while a post-classification comparison method was used to detect changes. The experiments demonstrated that significant changes in LC occurred along with the rapid urbanization during 1978–2011. The dominant changes that took place in the study area were lake and vegetation shrinking, replaced by high density buildings and roads. The total area of Shahu Lake decreased from ~7.64 km2 to ~3.60 km2 during the past 33 years, where 52.91% of its original area was lost. The presented results also indicated that urban expansion and inadequate legislative protection are the main factors in Shahu Lake’s shrinking. The object-oriented change detection schema presented in this manuscript enables us to better understand the specific spatial changes of Shahu Lake, which can be used to make reasonable decisions for lake protection and urban development.

Accounting for regional background and population size in the detection of spatial clusters and outliers using geostatistical filtering and spatial neutral models: the case of lung cancer in Long Island, New York

Directory of Open Access Journals (Sweden)

Goovaerts Pierre

2004-07-01

methodology allows one to identify geographic pattern above and beyond background variation. The implementation of this approach in spatial statistical software will facilitate the detection of spatial disparities in mortality rates, establishing the rationale for targeted cancer control interventions, including consideration of health services needs, and resource allocation for screening and diagnostic testing. It will allow researchers to systematically evaluate how sensitive their results are to assumptions implicit under alternative null hypotheses.

Temporal regularization of ultrasound-based liver motion estimation for image-guided radiation therapy

Energy Technology Data Exchange (ETDEWEB)

O’Shea, Tuathan P., E-mail: tuathan.oshea@icr.ac.uk; Bamber, Jeffrey C.; Harris, Emma J. [Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS foundation Trust, Sutton, London SM2 5PT (United Kingdom)

2016-01-15

Purpose: Ultrasound-based motion estimation is an expanding subfield of image-guided radiation therapy. Although ultrasound can detect tissue motion that is a fraction of a millimeter, its accuracy is variable. For controlling linear accelerator tracking and gating, ultrasound motion estimates must remain highly accurate throughout the imaging sequence. This study presents a temporal regularization method for correlation-based template matching which aims to improve the accuracy of motion estimates. Methods: Liver ultrasound sequences (15–23 Hz imaging rate, 2.5–5.5 min length) from ten healthy volunteers under free breathing were used. Anatomical features (blood vessels) in each sequence were manually annotated for comparison with normalized cross-correlation based template matching. Five sequences from a Siemens Acuson™ scanner were used for algorithm development (training set). Results from incremental tracking (IT) were compared with a temporal regularization method, which included a highly specific similarity metric and state observer, known as the α–β filter/similarity threshold (ABST). A further five sequences from an Elekta Clarity™ system were used for validation, without alteration of the tracking algorithm (validation set). Results: Overall, the ABST method produced marked improvements in vessel tracking accuracy. For the training set, the mean and 95th percentile (95%) errors (defined as the difference from manual annotations) were 1.6 and 1.4 mm, respectively (compared to 6.2 and 9.1 mm, respectively, for IT). For each sequence, the use of the state observer leads to improvement in the 95% error. For the validation set, the mean and 95% errors for the ABST method were 0.8 and 1.5 mm, respectively. Conclusions: Ultrasound-based motion estimation has potential to monitor liver translation over long time periods with high accuracy. Nonrigid motion (strain) and the quality of the ultrasound data are likely to have an impact on tracking

Wave dynamics of regular and chaotic rays

International Nuclear Information System (INIS)

McDonald, S.W.

1983-09-01

In order to investigate general relationships between waves and rays in chaotic systems, I study the eigenfunctions and spectrum of a simple model, the two-dimensional Helmholtz equation in a stadium boundary, for which the rays are ergodic. Statistical measurements are performed so that the apparent randomness of the stadium modes can be quantitatively contrasted with the familiar regularities observed for the modes in a circular boundary (with integrable rays). The local spatial autocorrelation of the eigenfunctions is constructed in order to indirectly test theoretical predictions for the nature of the Wigner distribution corresponding to chaotic waves. A portion of the large-eigenvalue spectrum is computed and reported in an appendix; the probability distribution of successive level spacings is analyzed and compared with theoretical predictions. The two principal conclusions are: 1) waves associated with chaotic rays may exhibit randomly situated localized regions of high intensity; 2) the Wigner function for these waves may depart significantly from being uniformly distributed over the surface of constant frequency in the ray phase space

Locating sensors for detecting source-to-target patterns of special nuclear material smuggling: a spatial information theoretic approach.

Science.gov (United States)

Przybyla, Jay; Taylor, Jeffrey; Zhou, Xuesong

2010-01-01

In this paper, a spatial information-theoretic model is proposed to locate sensors for detecting source-to-target patterns of special nuclear material (SNM) smuggling. In order to ship the nuclear materials from a source location with SNM production to a target city, the smugglers must employ global and domestic logistics systems. This paper focuses on locating a limited set of fixed and mobile radiation sensors in a transportation network, with the intent to maximize the expected information gain and minimize the estimation error for the subsequent nuclear material detection stage. A Kalman filtering-based framework is adapted to assist the decision-maker in quantifying the network-wide information gain and SNM flow estimation accuracy.

Locating Sensors for Detecting Source-to-Target Patterns of Special Nuclear Material Smuggling: A Spatial Information Theoretic Approach

Directory of Open Access Journals (Sweden)

Xuesong Zhou

2010-08-01

Full Text Available In this paper, a spatial information-theoretic model is proposed to locate sensors for detecting source-to-target patterns of special nuclear material (SNM smuggling. In order to ship the nuclear materials from a source location with SNM production to a target city, the smugglers must employ global and domestic logistics systems. This paper focuses on locating a limited set of fixed and mobile radiation sensors in a transportation network, with the intent to maximize the expected information gain and minimize the estimation error for the subsequent nuclear material detection stage. A Kalman filtering-based framework is adapted to assist the decision-maker in quantifying the network-wide information gain and SNM flow estimation accuracy.

Detection of infarct lesions from single MRI modality using inconsistency between voxel intensity and spatial location--a 3-D automatic approach.

Science.gov (United States)

Shen, Shan; Szameitat, André J; Sterr, Annette

2008-07-01

Detection of infarct lesions using traditional segmentation methods is always problematic due to intensity similarity between lesions and normal tissues, so that multispectral MRI modalities were often employed for this purpose. However, the high costs of MRI scan and the severity of patient conditions restrict the collection of multiple images. Therefore, in this paper, a new 3-D automatic lesion detection approach was proposed, which required only a single type of anatomical MRI scan. It was developed on a theory that, when lesions were present, the voxel-intensity-based segmentation and the spatial-location-based tissue distribution should be inconsistent in the regions of lesions. The degree of this inconsistency was calculated, which indicated the likelihood of tissue abnormality. Lesions were identified when the inconsistency exceeded a defined threshold. In this approach, the intensity-based segmentation was implemented by the conventional fuzzy c-mean (FCM) algorithm, while the spatial location of tissues was provided by prior tissue probability maps. The use of simulated MRI lesions allowed us to quantitatively evaluate the performance of the proposed method, as the size and location of lesions were prespecified. The results showed that our method effectively detected lesions with 40-80% signal reduction compared to normal tissues (similarity index > 0.7). The capability of the proposed method in practice was also demonstrated on real infarct lesions from 15 stroke patients, where the lesions detected were in broad agreement with true lesions. Furthermore, a comparison to a statistical segmentation approach presented in the literature suggested that our 3-D lesion detection approach was more reliable. Future work will focus on adapting the current method to multiple sclerosis lesion detection.

Downscaling Satellite Precipitation with Emphasis on Extremes: A Variational 1-Norm Regularization in the Derivative Domain

Science.gov (United States)

Foufoula-Georgiou, E.; Ebtehaj, A. M.; Zhang, S. Q.; Hou, A. Y.

2013-01-01

The increasing availability of precipitation observations from space, e.g., from the Tropical Rainfall Measuring Mission (TRMM) and the forthcoming Global Precipitation Measuring (GPM) Mission, has fueled renewed interest in developing frameworks for downscaling and multi-sensor data fusion that can handle large data sets in computationally efficient ways while optimally reproducing desired properties of the underlying rainfall fields. Of special interest is the reproduction of extreme precipitation intensities and gradients, as these are directly relevant to hazard prediction. In this paper, we present a new formalism for downscaling satellite precipitation observations, which explicitly allows for the preservation of some key geometrical and statistical properties of spatial precipitation. These include sharp intensity gradients (due to high-intensity regions embedded within lower-intensity areas), coherent spatial structures (due to regions of slowly varying rainfall),and thicker-than-Gaussian tails of precipitation gradients and intensities. Specifically, we pose the downscaling problem as a discrete inverse problem and solve it via a regularized variational approach (variational downscaling) where the regularization term is selected to impose the desired smoothness in the solution while allowing for some steep gradients(called 1-norm or total variation regularization). We demonstrate the duality between this geometrically inspired solution and its Bayesian statistical interpretation, which is equivalent to assuming a Laplace prior distribution for the precipitation intensities in the derivative (wavelet) space. When the observation operator is not known, we discuss the effect of its misspecification and explore a previously proposed dictionary-based sparse inverse downscaling methodology to indirectly learn the observation operator from a database of coincidental high- and low-resolution observations. The proposed method and ideas are illustrated in case

The uniqueness of the regularization procedure

International Nuclear Information System (INIS)

Brzezowski, S.

1981-01-01

On the grounds of the BPHZ procedure, the criteria of correct regularization in perturbation calculations of QFT are given, together with the prescription for dividing the regularized formulas into the finite and infinite parts. (author)

SQL injection detection system

OpenAIRE

Vargonas, Vytautas

2017-01-01

SQL injection detection system Programmers do not always ensure security of developed systems. That is why it is important to look for solutions outside being reliant on developers. In this work SQL injection detection system is proposed. The system analyzes HTTP request parameters and detects intrusions. It is based on unsupervised machine learning. Trained by regular request data system detects outlier user parameters. Since training is not reliant on previous knowledge of SQL injections, t...

Coupling regularizes individual units in noisy populations

International Nuclear Information System (INIS)

Ly Cheng; Ermentrout, G. Bard

2010-01-01

The regularity of a noisy system can modulate in various ways. It is well known that coupling in a population can lower the variability of the entire network; the collective activity is more regular. Here, we show that diffusive (reciprocal) coupling of two simple Ornstein-Uhlenbeck (O-U) processes can regularize the individual, even when it is coupled to a noisier process. In cellular networks, the regularity of individual cells is important when a select few play a significant role. The regularizing effect of coupling surprisingly applies also to general nonlinear noisy oscillators. However, unlike with the O-U process, coupling-induced regularity is robust to different kinds of coupling. With two coupled noisy oscillators, we derive an asymptotic formula assuming weak noise and coupling for the variance of the period (i.e., spike times) that accurately captures this effect. Moreover, we find that reciprocal coupling can regularize the individual period of higher dimensional oscillators such as the Morris-Lecar and Brusselator models, even when coupled to noisier oscillators. Coupling can have a counterintuitive and beneficial effect on noisy systems. These results have implications for the role of connectivity with noisy oscillators and the modulation of variability of individual oscillators.

Learning regularization parameters for general-form Tikhonov

International Nuclear Information System (INIS)

Chung, Julianne; Español, Malena I

2017-01-01

Computing regularization parameters for general-form Tikhonov regularization can be an expensive and difficult task, especially if multiple parameters or many solutions need to be computed in real time. In this work, we assume training data is available and describe an efficient learning approach for computing regularization parameters that can be used for a large set of problems. We consider an empirical Bayes risk minimization framework for finding regularization parameters that minimize average errors for the training data. We first extend methods from Chung et al (2011 SIAM J. Sci. Comput. 33 3132–52) to the general-form Tikhonov problem. Then we develop a learning approach for multi-parameter Tikhonov problems, for the case where all involved matrices are simultaneously diagonalizable. For problems where this is not the case, we describe an approach to compute near-optimal regularization parameters by using operator approximations for the original problem. Finally, we propose a new class of regularizing filters, where solutions correspond to multi-parameter Tikhonov solutions, that requires less data than previously proposed optimal error filters, avoids the generalized SVD, and allows flexibility and novelty in the choice of regularization matrices. Numerical results for 1D and 2D examples using different norms on the errors show the effectiveness of our methods. (paper)

5 CFR 551.421 - Regular working hours.

Science.gov (United States)

2010-01-01

... 5 Administrative Personnel 1 2010-01-01 2010-01-01 false Regular working hours. 551.421 Section... Activities § 551.421 Regular working hours. (a) Under the Act there is no requirement that a Federal employee... distinction based on whether the activity is performed by an employee during regular working hours or outside...

Regular extensions of some classes of grammars

NARCIS (Netherlands)

Nijholt, Antinus

Culik and Cohen introduced the class of LR-regular grammars, an extension of the LR(k) grammars. In this report we consider the analogous extension of the LL(k) grammers, called the LL-regular grammars. The relations of this class of grammars to other classes of grammars are shown. Every LL-regular

HIERARCHICAL REGULARIZATION OF POLYGONS FOR PHOTOGRAMMETRIC POINT CLOUDS OF OBLIQUE IMAGES

Directory of Open Access Journals (Sweden)

L. Xie

2017-05-01

Full Text Available Despite the success of multi-view stereo (MVS reconstruction from massive oblique images in city scale, only point clouds and triangulated meshes are available from existing MVS pipelines, which are topologically defect laden, free of semantical information and hard to edit and manipulate interactively in further applications. On the other hand, 2D polygons and polygonal models are still the industrial standard. However, extraction of the 2D polygons from MVS point clouds is still a non-trivial task, given the fact that the boundaries of the detected planes are zigzagged and regularities, such as parallel and orthogonal, cannot preserve. Aiming to solve these issues, this paper proposes a hierarchical polygon regularization method for the photogrammetric point clouds from existing MVS pipelines, which comprises of local and global levels. After boundary points extraction, e.g. using alpha shapes, the local level is used to consolidate the original points, by refining the orientation and position of the points using linear priors. The points are then grouped into local segments by forward searching. In the global level, regularities are enforced through a labeling process, which encourage the segments share the same label and the same label represents segments are parallel or orthogonal. This is formulated as Markov Random Field and solved efficiently. Preliminary results are made with point clouds from aerial oblique images and compared with two classical regularization methods, which have revealed that the proposed method are more powerful in abstracting a single building and is promising for further 3D polygonal model reconstruction and GIS applications.

Regular non-twisting S-branes

International Nuclear Information System (INIS)

Obregon, Octavio; Quevedo, Hernando; Ryan, Michael P.

2004-01-01

We construct a family of time and angular dependent, regular S-brane solutions which corresponds to a simple analytical continuation of the Zipoy-Voorhees 4-dimensional vacuum spacetime. The solutions are asymptotically flat and turn out to be free of singularities without requiring a twist in space. They can be considered as the simplest non-singular generalization of the singular S0-brane solution. We analyze the properties of a representative of this family of solutions and show that it resembles to some extent the asymptotic properties of the regular Kerr S-brane. The R-symmetry corresponds, however, to the general lorentzian symmetry. Several generalizations of this regular solution are derived which include a charged S-brane and an additional dilatonic field. (author)

Interactive facades analysis and synthesis of semi-regular facades

KAUST Repository

AlHalawani, Sawsan; Yang, Yongliang; Liu, Han; Mitra, Niloy J.

2013-01-01

Urban facades regularly contain interesting variations due to allowed deformations of repeated elements (e.g., windows in different open or close positions) posing challenges to state-of-the-art facade analysis algorithms. We propose a semi-automatic framework to recover both repetition patterns of the elements and their individual deformation parameters to produce a factored facade representation. Such a representation enables a range of applications including interactive facade images, improved multi-view stereo reconstruction, facade-level change detection, and novel image editing possibilities. © 2013 The Author(s) Computer Graphics Forum © 2013 The Eurographics Association and Blackwell Publishing Ltd.

Interactive facades analysis and synthesis of semi-regular facades

KAUST Repository

AlHalawani, Sawsan

2013-05-01

Urban facades regularly contain interesting variations due to allowed deformations of repeated elements (e.g., windows in different open or close positions) posing challenges to state-of-the-art facade analysis algorithms. We propose a semi-automatic framework to recover both repetition patterns of the elements and their individual deformation parameters to produce a factored facade representation. Such a representation enables a range of applications including interactive facade images, improved multi-view stereo reconstruction, facade-level change detection, and novel image editing possibilities. © 2013 The Author(s) Computer Graphics Forum © 2013 The Eurographics Association and Blackwell Publishing Ltd.

Spatial frequency characteristics at image decision-point locations for observers with different radiological backgrounds in lung nodule detection

Science.gov (United States)

Pietrzyk, Mariusz W.; Manning, David J.; Dix, Alan; Donovan, Tim

2009-02-01

Aim: The goal of the study is to determine the spatial frequency characteristics at locations in the image of overt and covert observers' decisions and find out if there are any similarities in different observers' groups: the same radiological experience group or the same accuracy scored level. Background: The radiological task is described as a visual searching decision making procedure involving visual perception and cognitive processing. Humans perceive the world through a number of spatial frequency channels, each sensitive to visual information carried by different spatial frequency ranges and orientations. Recent studies have shown that particular physical properties of local and global image-based elements are correlated with the performance and the level of experience of human observers in breast cancer and lung nodule detections. Neurological findings in visual perception were an inspiration for wavelet applications in vision research because the methodology tries to mimic the brain processing algorithms. Methods: The wavelet approach to the set of postero-anterior chest radiographs analysis has been used to characterize perceptual preferences observers with different levels of experience in the radiological task. Psychophysical methodology has been applied to track eye movements over the image, where particular ROIs related to the observers' fixation clusters has been analysed in the spaces frame by Daubechies functions. Results: Significance differences have been found between the spatial frequency characteristics at the location of different decisions.

Rapid Fear Detection Relies on High Spatial Frequencies

NARCIS (Netherlands)

Stein, T.; Seymour, K.; Hebart, M.N.; Sterzer, P.

Signals of threat—such as fearful faces—are processed with priority and have privileged access to awareness. This fear advantage is commonly believed to engage a specialized subcortical pathway to the amygdala that bypasses visual cortex and processes predominantly low-spatial-frequency information

Regular Expression Matching and Operational Semantics

Directory of Open Access Journals (Sweden)

Asiri Rathnayake

2011-08-01

Full Text Available Many programming languages and tools, ranging from grep to the Java String library, contain regular expression matchers. Rather than first translating a regular expression into a deterministic finite automaton, such implementations typically match the regular expression on the fly. Thus they can be seen as virtual machines interpreting the regular expression much as if it were a program with some non-deterministic constructs such as the Kleene star. We formalize this implementation technique for regular expression matching using operational semantics. Specifically, we derive a series of abstract machines, moving from the abstract definition of matching to increasingly realistic machines. First a continuation is added to the operational semantics to describe what remains to be matched after the current expression. Next, we represent the expression as a data structure using pointers, which enables redundant searches to be eliminated via testing for pointer equality. From there, we arrive both at Thompson's lockstep construction and a machine that performs some operations in parallel, suitable for implementation on a large number of cores, such as a GPU. We formalize the parallel machine using process algebra and report some preliminary experiments with an implementation on a graphics processor using CUDA.

Tetravalent one-regular graphs of order 4p2

DEFF Research Database (Denmark)

Feng, Yan-Quan; Kutnar, Klavdija; Marusic, Dragan

2014-01-01

A graph is one-regular if its automorphism group acts regularly on the set of its arcs. In this paper tetravalent one-regular graphs of order 4p2, where p is a prime, are classified.......A graph is one-regular if its automorphism group acts regularly on the set of its arcs. In this paper tetravalent one-regular graphs of order 4p2, where p is a prime, are classified....

The perception of regularity in an isochronous stimulus in zebra finches (Taeniopygia guttata) and humans.

Science.gov (United States)

van der Aa, Jeroen; Honing, Henkjan; ten Cate, Carel

2015-06-01

Perceiving temporal regularity in an auditory stimulus is considered one of the basic features of musicality. Here we examine whether zebra finches can detect regularity in an isochronous stimulus. Using a go/no go paradigm we show that zebra finches are able to distinguish between an isochronous and an irregular stimulus. However, when the tempo of the isochronous stimulus is changed, it is no longer treated as similar to the training stimulus. Training with three isochronous and three irregular stimuli did not result in improvement of the generalization. In contrast, humans, exposed to the same stimuli, readily generalized across tempo changes. Our results suggest that zebra finches distinguish the different stimuli by learning specific local temporal features of each individual stimulus rather than attending to the global structure of the stimuli, i.e., to the temporal regularity. Copyright © 2015 Elsevier B.V. All rights reserved.

Regularization and error assignment to unfolded distributions

CERN Document Server

Zech, Gunter

2011-01-01

The commonly used approach to present unfolded data only in graphical formwith the diagonal error depending on the regularization strength is unsatisfac-tory. It does not permit the adjustment of parameters of theories, the exclusionof theories that are admitted by the observed data and does not allow the com-bination of data from different experiments. We propose fixing the regulariza-tion strength by a p-value criterion, indicating the experimental uncertaintiesindependent of the regularization and publishing the unfolded data in additionwithout regularization. These considerations are illustrated with three differentunfolding and smoothing approaches applied to a toy example.

Variability in Regularity: Mining Temporal Mobility Patterns in London, Singapore and Beijing Using Smart-Card Data.

Science.gov (United States)

Zhong, Chen; Batty, Michael; Manley, Ed; Wang, Jiaqiu; Wang, Zijia; Chen, Feng; Schmitt, Gerhard

2016-01-01

To discover regularities in human mobility is of fundamental importance to our understanding of urban dynamics, and essential to city and transport planning, urban management and policymaking. Previous research has revealed universal regularities at mainly aggregated spatio-temporal scales but when we zoom into finer scales, considerable heterogeneity and diversity is observed instead. The fundamental question we address in this paper is at what scales are the regularities we detect stable, explicable, and sustainable. This paper thus proposes a basic measure of variability to assess the stability of such regularities focusing mainly on changes over a range of temporal scales. We demonstrate this by comparing regularities in the urban mobility patterns in three world cities, namely London, Singapore and Beijing using one-week of smart-card data. The results show that variations in regularity scale as non-linear functions of the temporal resolution, which we measure over a scale from 1 minute to 24 hours thus reflecting the diurnal cycle of human mobility. A particularly dramatic increase in variability occurs up to the temporal scale of about 15 minutes in all three cities and this implies that limits exist when we look forward or backward with respect to making short-term predictions. The degree of regularity varies in fact from city to city with Beijing and Singapore showing higher regularity in comparison to London across all temporal scales. A detailed discussion is provided, which relates the analysis to various characteristics of the three cities. In summary, this work contributes to a deeper understanding of regularities in patterns of transit use from variations in volumes of travellers entering subway stations, it establishes a generic analytical framework for comparative studies using urban mobility data, and it provides key points for the management of variability by policy-makers intent on for making the travel experience more amenable.

Variability in Regularity: Mining Temporal Mobility Patterns in London, Singapore and Beijing Using Smart-Card Data

Science.gov (United States)

Zhong, Chen; Batty, Michael; Manley, Ed; Wang, Jiaqiu; Wang, Zijia; Chen, Feng; Schmitt, Gerhard

2016-01-01

To discover regularities in human mobility is of fundamental importance to our understanding of urban dynamics, and essential to city and transport planning, urban management and policymaking. Previous research has revealed universal regularities at mainly aggregated spatio-temporal scales but when we zoom into finer scales, considerable heterogeneity and diversity is observed instead. The fundamental question we address in this paper is at what scales are the regularities we detect stable, explicable, and sustainable. This paper thus proposes a basic measure of variability to assess the stability of such regularities focusing mainly on changes over a range of temporal scales. We demonstrate this by comparing regularities in the urban mobility patterns in three world cities, namely London, Singapore and Beijing using one-week of smart-card data. The results show that variations in regularity scale as non-linear functions of the temporal resolution, which we measure over a scale from 1 minute to 24 hours thus reflecting the diurnal cycle of human mobility. A particularly dramatic increase in variability occurs up to the temporal scale of about 15 minutes in all three cities and this implies that limits exist when we look forward or backward with respect to making short-term predictions. The degree of regularity varies in fact from city to city with Beijing and Singapore showing higher regularity in comparison to London across all temporal scales. A detailed discussion is provided, which relates the analysis to various characteristics of the three cities. In summary, this work contributes to a deeper understanding of regularities in patterns of transit use from variations in volumes of travellers entering subway stations, it establishes a generic analytical framework for comparative studies using urban mobility data, and it provides key points for the management of variability by policy-makers intent on for making the travel experience more amenable. PMID:26872333

Variability in Regularity: Mining Temporal Mobility Patterns in London, Singapore and Beijing Using Smart-Card Data.

Directory of Open Access Journals (Sweden)

Chen Zhong

Full Text Available To discover regularities in human mobility is of fundamental importance to our understanding of urban dynamics, and essential to city and transport planning, urban management and policymaking. Previous research has revealed universal regularities at mainly aggregated spatio-temporal scales but when we zoom into finer scales, considerable heterogeneity and diversity is observed instead. The fundamental question we address in this paper is at what scales are the regularities we detect stable, explicable, and sustainable. This paper thus proposes a basic measure of variability to assess the stability of such regularities focusing mainly on changes over a range of temporal scales. We demonstrate this by comparing regularities in the urban mobility patterns in three world cities, namely London, Singapore and Beijing using one-week of smart-card data. The results show that variations in regularity scale as non-linear functions of the temporal resolution, which we measure over a scale from 1 minute to 24 hours thus reflecting the diurnal cycle of human mobility. A particularly dramatic increase in variability occurs up to the temporal scale of about 15 minutes in all three cities and this implies that limits exist when we look forward or backward with respect to making short-term predictions. The degree of regularity varies in fact from city to city with Beijing and Singapore showing higher regularity in comparison to London across all temporal scales. A detailed discussion is provided, which relates the analysis to various characteristics of the three cities. In summary, this work contributes to a deeper understanding of regularities in patterns of transit use from variations in volumes of travellers entering subway stations, it establishes a generic analytical framework for comparative studies using urban mobility data, and it provides key points for the management of variability by policy-makers intent on for making the travel experience more

Beamforming Through Regularized Inverse Problems in Ultrasound Medical Imaging.

Science.gov (United States)

Szasz, Teodora; Basarab, Adrian; Kouame, Denis

2016-12-01

Beamforming (BF) in ultrasound (US) imaging has significant impact on the quality of the final image, controlling its resolution and contrast. Despite its low spatial resolution and contrast, delay-and-sum (DAS) is still extensively used nowadays in clinical applications, due to its real-time capabilities. The most common alternatives are minimum variance (MV) method and its variants, which overcome the drawbacks of DAS, at the cost of higher computational complexity that limits its utilization in real-time applications. In this paper, we propose to perform BF in US imaging through a regularized inverse problem based on a linear model relating the reflected echoes to the signal to be recovered. Our approach presents two major advantages: 1) its flexibility in the choice of statistical assumptions on the signal to be beamformed (Laplacian and Gaussian statistics are tested herein) and 2) its robustness to a reduced number of pulse emissions. The proposed framework is flexible and allows for choosing the right tradeoff between noise suppression and sharpness of the resulted image. We illustrate the performance of our approach on both simulated and experimental data, with in vivo examples of carotid and thyroid. Compared with DAS, MV, and two other recently published BF techniques, our method offers better spatial resolution, respectively contrast, when using Laplacian and Gaussian priors.

Higher order total variation regularization for EIT reconstruction.

Science.gov (United States)

Gong, Bo; Schullcke, Benjamin; Krueger-Ziolek, Sabine; Zhang, Fan; Mueller-Lisse, Ullrich; Moeller, Knut

2018-01-08

Electrical impedance tomography (EIT) attempts to reveal the conductivity distribution of a domain based on the electrical boundary condition. This is an ill-posed inverse problem; its solution is very unstable. Total variation (TV) regularization is one of the techniques commonly employed to stabilize reconstructions. However, it is well known that TV regularization induces staircase effects, which are not realistic in clinical applications. To reduce such artifacts, modified TV regularization terms considering a higher order differential operator were developed in several previous studies. One of them is called total generalized variation (TGV) regularization. TGV regularization has been successively applied in image processing in a regular grid context. In this study, we adapted TGV regularization to the finite element model (FEM) framework for EIT reconstruction. Reconstructions using simulation and clinical data were performed. First results indicate that, in comparison to TV regularization, TGV regularization promotes more realistic images. Graphical abstract Reconstructed conductivity changes located on selected vertical lines. For each of the reconstructed images as well as the ground truth image, conductivity changes located along the selected left and right vertical lines are plotted. In these plots, the notation GT in the legend stands for ground truth, TV stands for total variation method, and TGV stands for total generalized variation method. Reconstructed conductivity distributions from the GREIT algorithm are also demonstrated.

Motion-aware temporal regularization for improved 4D cone-beam computed tomography

Science.gov (United States)

Mory, Cyril; Janssens, Guillaume; Rit, Simon

2016-09-01

Four-dimensional cone-beam computed tomography (4D-CBCT) of the free-breathing thorax is a valuable tool in image-guided radiation therapy of the thorax and the upper abdomen. It allows the determination of the position of a tumor throughout the breathing cycle, while only its mean position can be extracted from three-dimensional CBCT. The classical approaches are not fully satisfactory: respiration-correlated methods allow one to accurately locate high-contrast structures in any frame, but contain strong streak artifacts unless the acquisition is significantly slowed down. Motion-compensated methods can yield streak-free, but static, reconstructions. This work proposes a 4D-CBCT method that can be seen as a trade-off between respiration-correlated and motion-compensated reconstruction. It builds upon the existing reconstruction using spatial and temporal regularization (ROOSTER) and is called motion-aware ROOSTER (MA-ROOSTER). It performs temporal regularization along curved trajectories, following the motion estimated on a prior 4D CT scan. MA-ROOSTER does not involve motion-compensated forward and back projections: the input motion is used only during temporal regularization. MA-ROOSTER is compared to ROOSTER, motion-compensated Feldkamp-Davis-Kress (MC-FDK), and two respiration-correlated methods, on CBCT acquisitions of one physical phantom and two patients. It yields streak-free reconstructions, visually similar to MC-FDK, and robust information on tumor location throughout the breathing cycle. MA-ROOSTER also allows a variation of the lung tissue density during the breathing cycle, similar to that of planning CT, which is required for quantitative post-processing.

Application of Turchin's method of statistical regularization

Science.gov (United States)

Zelenyi, Mikhail; Poliakova, Mariia; Nozik, Alexander; Khudyakov, Alexey

2018-04-01

During analysis of experimental data, one usually needs to restore a signal after it has been convoluted with some kind of apparatus function. According to Hadamard's definition this problem is ill-posed and requires regularization to provide sensible results. In this article we describe an implementation of the Turchin's method of statistical regularization based on the Bayesian approach to the regularization strategy.

Asymmetric Spatial Processing Under Cognitive Load.

Science.gov (United States)

Naert, Lien; Bonato, Mario; Fias, Wim

2018-01-01

Spatial attention allows us to selectively process information within a certain location in space. Despite the vast literature on spatial attention, the effect of cognitive load on spatial processing is still not fully understood. In this study we added cognitive load to a spatial processing task, so as to see whether it would differentially impact upon the processing of visual information in the left versus the right hemispace. The main paradigm consisted of a detection task that was performed during the maintenance interval of a verbal working memory task. We found that increasing cognitive working memory load had a more negative impact on detecting targets presented on the left side compared to those on the right side. The strength of the load effect correlated with the strength of the interaction on an individual level. The implications of an asymmetric attentional bias with a relative disadvantage for the left (vs the right) hemispace under high verbal working memory (WM) load are discussed.

Asymmetric Spatial Processing Under Cognitive Load

Directory of Open Access Journals (Sweden)

Lien Naert

2018-04-01

Full Text Available Spatial attention allows us to selectively process information within a certain location in space. Despite the vast literature on spatial attention, the effect of cognitive load on spatial processing is still not fully understood. In this study we added cognitive load to a spatial processing task, so as to see whether it would differentially impact upon the processing of visual information in the left versus the right hemispace. The main paradigm consisted of a detection task that was performed during the maintenance interval of a verbal working memory task. We found that increasing cognitive working memory load had a more negative impact on detecting targets presented on the left side compared to those on the right side. The strength of the load effect correlated with the strength of the interaction on an individual level. The implications of an asymmetric attentional bias with a relative disadvantage for the left (vs the right hemispace under high verbal working memory (WM load are discussed.

Using demographic characteristics of populations to detect spatial fragmentation following suspected ebola outbreaks in great apes.

Science.gov (United States)

Genton, Céline; Cristescu, Romane; Gatti, Sylvain; Levréro, Florence; Bigot, Elodie; Motsch, Peggy; Le Gouar, Pascaline; Pierre, Jean-Sébastien; Ménard, Nelly

2017-09-01

Demographic crashes due to emerging diseases can contribute to population fragmentation and increase extinction risk of small populations. Ebola outbreaks in 2002-2004 are suspected to have caused a decline of more than 80% in some Western lowland gorilla (Gorilla gorilla gorilla) populations. We investigated whether demographic indicators of this event allowed for the detection of spatial fragmentation in gorilla populations. We collected demographic data from two neighbouring populations: the Lokoué population, suspected to have been affected by an Ebola outbreak (followed from 2001 to 2014), and the Romani population, of unknown demographic status before Ebola outbreaks (followed from 2005 to 2014). Ten years after the outbreak, the Lokoué population is slowly recovering and the short-term demographic indicators of a population crash were no longer detectable. The Lokoué population has not experienced any additional demographic perturbation over the past decade. The Romani population did not show any of the demographic indicators of a population crash over the past decade. Its demographic structure remained similar to that of unaffected populations. Our results highlighted that the Ebola disease could contribute to fragmentation of gorilla populations due to the spatially heterogeneous impact of its outbreaks. The demographic structure of populations (i.e., age-sex and group structure) can be useful indicators of a possible occurrence of recent Ebola outbreaks in populations without known history, and may be more broadly used in other emerging disease/species systems. Longitudinal data are critical to our understanding of the impact of emerging diseases on wild populations and their conservation. © 2017 Wiley Periodicals, Inc.

Extinction threshold for spatial forest dynamics with height structure.

Science.gov (United States)

Garcia-Domingo, Josep L; Saldaña, Joan

2011-05-07

We present a pair-approximation model for spatial forest dynamics defined on a regular lattice. The model assumes three possible states for a lattice site: empty (gap site), occupied by an immature tree, and occupied by a mature tree, and considers three nonlinearities in the dynamics associated to the processes of light interference, gap expansion, and recruitment. We obtain an expression of the basic reproduction number R(0) which, in contrast to the one obtained under the mean-field approach, uses information about the spatial arrangement of individuals close to extinction. Moreover, we analyze the corresponding survival-extinction transition of the forest and the spatial correlations among gaps, immature and mature trees close to this critical point. Predictions of the pair-approximation model are compared with those of a cellular automaton. Copyright © 2011 Elsevier Ltd. All rights reserved.

On the regularized fermionic projector of the vacuum

Science.gov (United States)

Finster, Felix

2008-03-01

We construct families of fermionic projectors with spherically symmetric regularization, which satisfy the condition of a distributional MP-product. The method is to analyze regularization tails with a power law or logarithmic scaling in composite expressions in the fermionic projector. The resulting regularizations break the Lorentz symmetry and give rise to a multilayer structure of the fermionic projector near the light cone. Furthermore, we construct regularizations which go beyond the distributional MP-product in that they yield additional distributional contributions supported at the origin. The remaining freedom for the regularization parameters and the consequences for the normalization of the fermionic states are discussed.

On the regularized fermionic projector of the vacuum

International Nuclear Information System (INIS)

Finster, Felix

2008-01-01

We construct families of fermionic projectors with spherically symmetric regularization, which satisfy the condition of a distributional MP-product. The method is to analyze regularization tails with a power law or logarithmic scaling in composite expressions in the fermionic projector. The resulting regularizations break the Lorentz symmetry and give rise to a multilayer structure of the fermionic projector near the light cone. Furthermore, we construct regularizations which go beyond the distributional MP-product in that they yield additional distributional contributions supported at the origin. The remaining freedom for the regularization parameters and the consequences for the normalization of the fermionic states are discussed

Routes to chaos in continuous mechanical systems: Part 2. Modelling transitions from regular to chaotic dynamics

International Nuclear Information System (INIS)

Krysko, A.V.; Awrejcewicz, J.; Papkova, I.V.; Krysko, V.A.

2012-01-01

In second part of the paper both classical and novel scenarios of transition from regular to chaotic dynamics of dissipative continuous mechanical systems are studied. A detailed analysis allowed us to detect the already known classical scenarios of transition from periodic to chaotic dynamics, and in particular the Feigenbaum scenario. The Feigenbaum constant was computed for all continuous mechanical objects studied in the first part of the paper. In addition, we illustrate and discuss different and novel scenarios of transition of the analysed systems from regular to chaotic dynamics, and we show that the type of scenario depends essentially on excitation parameters.

Efficient regularization with wavelet sparsity constraints in photoacoustic tomography

Science.gov (United States)

Frikel, Jürgen; Haltmeier, Markus

2018-02-01

In this paper, we consider the reconstruction problem of photoacoustic tomography (PAT) with a flat observation surface. We develop a direct reconstruction method that employs regularization with wavelet sparsity constraints. To that end, we derive a wavelet-vaguelette decomposition (WVD) for the PAT forward operator and a corresponding explicit reconstruction formula in the case of exact data. In the case of noisy data, we combine the WVD reconstruction formula with soft-thresholding, which yields a spatially adaptive estimation method. We demonstrate that our method is statistically optimal for white random noise if the unknown function is assumed to lie in any Besov-ball. We present generalizations of this approach and, in particular, we discuss the combination of PAT-vaguelette soft-thresholding with a total variation (TV) prior. We also provide an efficient implementation of the PAT-vaguelette transform that leads to fast image reconstruction algorithms supported by numerical results.

Mode regularization of the supersymmetric sphaleron and kink: Zero modes and discrete gauge symmetry

International Nuclear Information System (INIS)

Goldhaber, Alfred Scharff; Litvintsev, Andrei; Nieuwenhuizen, Peter van

2001-01-01

To obtain the one-loop corrections to the mass of a kink by mode regularization, one may take one-half the result for the mass of a widely separated kink-antikink (or sphaleron) system, where the two bosonic zero modes count as two degrees of freedom, but the two fermionic zero modes as only one degree of freedom in the sums over modes. For a single kink, there is one bosonic zero mode degree of freedom, but it is necessary to average over four sets of fermionic boundary conditions in order (i) to preserve the fermionic Z 2 gauge invariance ψ→-ψ, (ii) to satisfy the basic principle of mode regularization that the boundary conditions in the trivial and the kink sector should be the same, (iii) that the energy stored at the boundaries cancels and (iv) to avoid obtaining a finite, uniformly distributed energy which would violate cluster decomposition. The average number of fermionic zero-energy degrees of freedom in the presence of the kink is then indeed 1/2. For boundary conditions leading to only one fermionic zero-energy solution, the Z 2 gauge invariance identifies two seemingly distinct 'vacua' as the same physical ground state, and the single fermionic zero-energy solution does not correspond to a degree of freedom. Other boundary conditions lead to two spatially separated ω∼0 solutions, corresponding to one (spatially delocalized) degree of freedom. This nonlocality is consistent with the principle of cluster decomposition for correlators of observables

Assessment of tuberculosis spatial hotspot areas in Antananarivo, Madagascar, by combining spatial analysis and genotyping.

Science.gov (United States)

Ratovonirina, Noël Harijaona; Rakotosamimanana, Niaina; Razafimahatratra, Solohery Lalaina; Raherison, Mamy Serge; Refrégier, Guislaine; Sola, Christophe; Rakotomanana, Fanjasoa; Rasolofo Razanamparany, Voahangy

2017-08-14

Tuberculosis (TB) remains a public health problem in Madagascar. A crucial element of TB control is the development of an easy and rapid method for the orientation of TB control strategies in the country. Our main objective was to develop a TB spatial hotspot identification method by combining spatial analysis and TB genotyping method in Antananarivo. Sputa of new pulmonary TB cases from 20 TB diagnosis and treatment centers (DTCs) in Antananarivo were collected from August 2013 to May 2014 for culture. Mycobacterium tuberculosis complex (MTBC) clinical isolates were typed by spoligotyping on a Luminex® 200 platform. All TB patients were respectively localized according to their neighborhood residence and the spatial distribution of all pulmonary TB patients and patients with genotypic clustered isolates were scanned respectively by the Kulldorff spatial scanning method for identification of significant spatial clustering. Areas exhibiting spatial clustering of patients with genotypic clustered isolates were considered as hotspot TB areas for transmission. Overall, 467 new cases were included in the study, and 394 spoligotypes were obtained (84.4%). New TB cases were distributed in 133 of the 192 Fokontany (administrative neighborhoods) of Antananarivo (1 to 15 clinical patients per Fokontany) and patients with genotypic clustered isolates were distributed in 127 of the 192 Fokontany (1 to 13 per Fokontany). A single spatial focal point of epidemics was detected when ignoring genotypic data (p = 0.039). One Fokontany of this focal point and three additional ones were detected to be spatially clustered when taking genotypes into account (p Madagascar and will allow better TB control strategies by public health authorities.

Regularization modeling for large-eddy simulation

NARCIS (Netherlands)

Geurts, Bernardus J.; Holm, D.D.

2003-01-01

A new modeling approach for large-eddy simulation (LES) is obtained by combining a "regularization principle" with an explicit filter and its inversion. This regularization approach allows a systematic derivation of the implied subgrid model, which resolves the closure problem. The central role of

Computer Game Play as an Imaginary Stage for Reading: Implicit Spatial Effects of Computer Games Embedded in Hard Copy Books

Science.gov (United States)

Smith, Glenn Gordon

2012-01-01

This study compared books with embedded computer games (via pentop computers with microdot paper and audio feedback) with regular books with maps, in terms of fifth graders' comprehension and retention of spatial details from stories. One group read a story in hard copy with embedded computer games, the other group read it in regular book format…

Charge-coupled devices for particle detection with high spatial resolution

International Nuclear Information System (INIS)

Farley, F.J.; Damerell, C.J.S.; Gillman, A.R.; Wickens, F.J.

1980-10-01

The results of a study of the possible application of a thin microelectronic device (the charge-coupled device) to high energy physics as particle detectors with good spatial resolution which can distinguish between tracks emerging from the primary vertex and those from secondary vertices due to the decay of short lived particles with higher flavours, are reported. Performance characteristics indicating the spatial resolution, particle discrimination, time resolution, readout time and lifetime of such detectors have been obtained. (U.K.)

Manifold Regularized Correlation Object Tracking.

Science.gov (United States)

Hu, Hongwei; Ma, Bo; Shen, Jianbing; Shao, Ling

2018-05-01

In this paper, we propose a manifold regularized correlation tracking method with augmented samples. To make better use of the unlabeled data and the manifold structure of the sample space, a manifold regularization-based correlation filter is introduced, which aims to assign similar labels to neighbor samples. Meanwhile, the regression model is learned by exploiting the block-circulant structure of matrices resulting from the augmented translated samples over multiple base samples cropped from both target and nontarget regions. Thus, the final classifier in our method is trained with positive, negative, and unlabeled base samples, which is a semisupervised learning framework. A block optimization strategy is further introduced to learn a manifold regularization-based correlation filter for efficient online tracking. Experiments on two public tracking data sets demonstrate the superior performance of our tracker compared with the state-of-the-art tracking approaches.

From recreational to regular drug use

DEFF Research Database (Denmark)

Järvinen, Margaretha; Ravn, Signe

2011-01-01

This article analyses the process of going from recreational use to regular and problematic use of illegal drugs. We present a model containing six career contingencies relevant for young people’s progress from recreational to regular drug use: the closing of social networks, changes in forms...

Genet-specific DNA methylation probabilities detected in a spatial epigenetic analysis of a clonal plant population.

Directory of Open Access Journals (Sweden)

Kiwako S Araki

Full Text Available In sessile organisms such as plants, spatial genetic structures of populations show long-lasting patterns. These structures have been analyzed across diverse taxa to understand the processes that determine the genetic makeup of organismal populations. For many sessile organisms that mainly propagate via clonal spread, epigenetic status can vary between clonal individuals in the absence of genetic changes. However, fewer previous studies have explored the epigenetic properties in comparison to the genetic properties of natural plant populations. Here, we report the simultaneous evaluation of the spatial structure of genetic and epigenetic variation in a natural population of the clonal plant Cardamine leucantha. We applied a hierarchical Bayesian model to evaluate the effects of membership of a genet (a group of individuals clonally derived from a single seed and vegetation cover on the epigenetic variation between ramets (clonal plants that are physiologically independent individuals. We sampled 332 ramets in a 20 m × 20 m study plot that contained 137 genets (identified using eight SSR markers. We detected epigenetic variation in DNA methylation at 24 methylation-sensitive amplified fragment length polymorphism (MS-AFLP loci. There were significant genet effects at all 24 MS-AFLP loci in the distribution of subepiloci. Vegetation cover had no statistically significant effect on variation in the majority of MS-AFLP loci. The spatial aggregation of epigenetic variation is therefore largely explained by the aggregation of ramets that belong to the same genets. By applying hierarchical Bayesian analyses, we successfully identified a number of genet-specific changes in epigenetic status within a natural plant population in a complex context, where genotypes and environmental factors are unevenly distributed. This finding suggests that it requires further studies on the spatial epigenetic structure of natural populations of diverse organisms

Genet-specific DNA methylation probabilities detected in a spatial epigenetic analysis of a clonal plant population.

Science.gov (United States)

Araki, Kiwako S; Kubo, Takuya; Kudoh, Hiroshi

2017-01-01

In sessile organisms such as plants, spatial genetic structures of populations show long-lasting patterns. These structures have been analyzed across diverse taxa to understand the processes that determine the genetic makeup of organismal populations. For many sessile organisms that mainly propagate via clonal spread, epigenetic status can vary between clonal individuals in the absence of genetic changes. However, fewer previous studies have explored the epigenetic properties in comparison to the genetic properties of natural plant populations. Here, we report the simultaneous evaluation of the spatial structure of genetic and epigenetic variation in a natural population of the clonal plant Cardamine leucantha. We applied a hierarchical Bayesian model to evaluate the effects of membership of a genet (a group of individuals clonally derived from a single seed) and vegetation cover on the epigenetic variation between ramets (clonal plants that are physiologically independent individuals). We sampled 332 ramets in a 20 m × 20 m study plot that contained 137 genets (identified using eight SSR markers). We detected epigenetic variation in DNA methylation at 24 methylation-sensitive amplified fragment length polymorphism (MS-AFLP) loci. There were significant genet effects at all 24 MS-AFLP loci in the distribution of subepiloci. Vegetation cover had no statistically significant effect on variation in the majority of MS-AFLP loci. The spatial aggregation of epigenetic variation is therefore largely explained by the aggregation of ramets that belong to the same genets. By applying hierarchical Bayesian analyses, we successfully identified a number of genet-specific changes in epigenetic status within a natural plant population in a complex context, where genotypes and environmental factors are unevenly distributed. This finding suggests that it requires further studies on the spatial epigenetic structure of natural populations of diverse organisms, particularly for

Downscaling Satellite Precipitation with Emphasis on Extremes: A Variational ℓ1-Norm Regularization in the Derivative Domain

Science.gov (United States)

Foufoula-Georgiou, E.; Ebtehaj, A. M.; Zhang, S. Q.; Hou, A. Y.

2014-05-01

The increasing availability of precipitation observations from space, e.g., from the Tropical Rainfall Measuring Mission (TRMM) and the forthcoming Global Precipitation Measuring (GPM) Mission, has fueled renewed interest in developing frameworks for downscaling and multi-sensor data fusion that can handle large data sets in computationally efficient ways while optimally reproducing desired properties of the underlying rainfall fields. Of special interest is the reproduction of extreme precipitation intensities and gradients, as these are directly relevant to hazard prediction. In this paper, we present a new formalism for downscaling satellite precipitation observations, which explicitly allows for the preservation of some key geometrical and statistical properties of spatial precipitation. These include sharp intensity gradients (due to high-intensity regions embedded within lower-intensity areas), coherent spatial structures (due to regions of slowly varying rainfall), and thicker-than-Gaussian tails of precipitation gradients and intensities. Specifically, we pose the downscaling problem as a discrete inverse problem and solve it via a regularized variational approach (variational downscaling) where the regularization term is selected to impose the desired smoothness in the solution while allowing for some steep gradients (called ℓ1-norm or total variation regularization). We demonstrate the duality between this geometrically inspired solution and its Bayesian statistical interpretation, which is equivalent to assuming a Laplace prior distribution for the precipitation intensities in the derivative (wavelet) space. When the observation operator is not known, we discuss the effect of its misspecification and explore a previously proposed dictionary-based sparse inverse downscaling methodology to indirectly learn the observation operator from a data base of coincidental high- and low-resolution observations. The proposed method and ideas are illustrated in case

Regular variation on measure chains

Czech Academy of Sciences Publication Activity Database

Řehák, Pavel; Vitovec, J.

2010-01-01

Roč. 72, č. 1 (2010), s. 439-448 ISSN 0362-546X R&D Projects: GA AV ČR KJB100190701 Institutional research plan: CEZ:AV0Z10190503 Keywords : regularly varying function * regularly varying sequence * measure chain * time scale * embedding theorem * representation theorem * second order dynamic equation * asymptotic properties Subject RIV: BA - General Mathematics Impact factor: 1.279, year: 2010 http://www.sciencedirect.com/science/article/pii/S0362546X09008475

New regular black hole solutions

International Nuclear Information System (INIS)

Lemos, Jose P. S.; Zanchin, Vilson T.

2011-01-01

In the present work we consider general relativity coupled to Maxwell's electromagnetism and charged matter. Under the assumption of spherical symmetry, there is a particular class of solutions that correspond to regular charged black holes whose interior region is de Sitter, the exterior region is Reissner-Nordstroem and there is a charged thin-layer in-between the two. The main physical and geometrical properties of such charged regular black holes are analyzed.

Manifold Regularized Correlation Object Tracking

OpenAIRE

Hu, Hongwei; Ma, Bo; Shen, Jianbing; Shao, Ling

2017-01-01

In this paper, we propose a manifold regularized correlation tracking method with augmented samples. To make better use of the unlabeled data and the manifold structure of the sample space, a manifold regularization-based correlation filter is introduced, which aims to assign similar labels to neighbor samples. Meanwhile, the regression model is learned by exploiting the block-circulant structure of matrices resulting from the augmented translated samples over multiple base samples cropped fr...

On geodesics in low regularity

Science.gov (United States)

Sämann, Clemens; Steinbauer, Roland

2018-02-01

We consider geodesics in both Riemannian and Lorentzian manifolds with metrics of low regularity. We discuss existence of extremal curves for continuous metrics and present several old and new examples that highlight their subtle interrelation with solutions of the geodesic equations. Then we turn to the initial value problem for geodesics for locally Lipschitz continuous metrics and generalize recent results on existence, regularity and uniqueness of solutions in the sense of Filippov.

The Analysis of Two-Way Functional Data Using Two-Way Regularized Singular Value Decompositions

KAUST Repository

Huang, Jianhua Z.

2009-12-01

Two-way functional data consist of a data matrix whose row and column domains are both structured, for example, temporally or spatially, as when the data are time series collected at different locations in space. We extend one-way functional principal component analysis (PCA) to two-way functional data by introducing regularization of both left and right singular vectors in the singular value decomposition (SVD) of the data matrix. We focus on a penalization approach and solve the nontrivial problem of constructing proper two-way penalties from oneway regression penalties. We introduce conditional cross-validated smoothing parameter selection whereby left-singular vectors are cross- validated conditional on right-singular vectors, and vice versa. The concept can be realized as part of an alternating optimization algorithm. In addition to the penalization approach, we briefly consider two-way regularization with basis expansion. The proposed methods are illustrated with one simulated and two real data examples. Supplemental materials available online show that several "natural" approaches to penalized SVDs are flawed and explain why so. © 2009 American Statistical Association.

Collective behavior in the spatial spreading of obesity

Science.gov (United States)

Gallos, Lazaros K.; Barttfeld, Pablo; Havlin, Shlomo; Sigman, Mariano; Makse, Hernán A.

2012-06-01

Obesity prevalence is increasing in many countries at alarming levels. A difficulty in the conception of policies to reverse these trends is the identification of the drivers behind the obesity epidemics. Here, we implement a spatial spreading analysis to investigate whether obesity shows spatial correlations, revealing the effect of collective and global factors acting above individual choices. We find a regularity in the spatial fluctuations of their prevalence revealed by a pattern of scale-free long-range correlations. The fluctuations are anomalous, deviating in a fundamental way from the weaker correlations found in the underlying population distribution indicating the presence of collective behavior, i.e., individual habits may have negligible influence in shaping the patterns of spreading. Interestingly, we find the same scale-free correlations in economic activities associated with food production. These results motivate future interventions to investigate the causality of this relation providing guidance for the implementation of preventive health policies.

Sharp switches between regular and swinger mitochondrial replication: 16S rDNA systematically exchanging nucleotides AT+CG in the mitogenome of Kamimuria wangi.

Science.gov (United States)

Seligmann, Hervé

2016-07-01

Swinger DNAs are sequences whose homology with known sequences is detected only by assuming systematic exchanges between nucleotides. Nine symmetric (XY, i.e. AC) and fourteen asymmetric (X->Y->Z, i.e. A->C->G) exchanges exist. All swinger DNA previously detected in GenBank follow the AT+CG exchange, while mitochondrial swinger RNAs distribute among different swinger types. Here different alignment criteria detect 87 additional swinger mitochondrial DNAs (86 from insects), including the first swinger gene embedded within a complete genome, corresponding to the mitochondrial 16S rDNA of the stonefly Kamimuria wangi. Other Kamimuria mt genome regions are "regular", stressing unanswered questions on (a) swinger polymerization regulation; (b) swinger 16S rDNA functions; and (c) specificity to rDNA, in particular 16S rDNA. Sharp switches between regular and swinger replication, together with previous observations on swinger transcription, suggest that swinger replication might be due to a switch in polymerization mode of regular polymerases and the possibility of swinger-encoded information, predicted in primordial genes such as rDNA.

Manifold Regularized Reinforcement Learning.

Science.gov (United States)

Li, Hongliang; Liu, Derong; Wang, Ding

2018-04-01

This paper introduces a novel manifold regularized reinforcement learning scheme for continuous Markov decision processes. Smooth feature representations for value function approximation can be automatically learned using the unsupervised manifold regularization method. The learned features are data-driven, and can be adapted to the geometry of the state space. Furthermore, the scheme provides a direct basis representation extension for novel samples during policy learning and control. The performance of the proposed scheme is evaluated on two benchmark control tasks, i.e., the inverted pendulum and the energy storage problem. Simulation results illustrate the concepts of the proposed scheme and show that it can obtain excellent performance.

Auditory Stimulus Processing and Task Learning Are Adequate in Dyslexia, but Benefits from Regularities Are Reduced

Science.gov (United States)

Daikhin, Luba; Raviv, Ofri; Ahissar, Merav

2017-01-01

Purpose: The reading deficit for people with dyslexia is typically associated with linguistic, memory, and perceptual-discrimination difficulties, whose relation to reading impairment is disputed. We proposed that automatic detection and usage of serial sound regularities for individuals with dyslexia is impaired (anchoring deficit hypothesis),…

Spatial self-organized patterning in seagrasses along a depth gradient of an intertidal ecosystem

NARCIS (Netherlands)

van der Heide, Tjisse; Bouma, Tjeerd J.; Nes, Egbert H. van; van de Koppel, Johan; Scheffer, Marten; Roelofs, Jan G.M.; Katwijk, Marieke M. van; Smolders, Alfons J.P.

2010-01-01

The spatial structure of seagrass landscapes is typically ascribed to the direct influence of physical factors such as hydrodynamics, light, and sediment transport. We studied regularly interspaced banded patterns, formed by elongated patches of seagrass, in a small-scale intertidal ecosystem. We

Homotopic non-local regularized reconstruction from sparse positron emission tomography measurements

International Nuclear Information System (INIS)

Wong, Alexander; Liu, Chenyi; Wang, Xiao Yu; Fieguth, Paul; Bie, Hongxia

2015-01-01

Positron emission tomography scanners collect measurements of a patient’s in vivo radiotracer distribution. The system detects pairs of gamma rays emitted indirectly by a positron-emitting radionuclide (tracer), which is introduced into the body on a biologically active molecule, and the tomograms must be reconstructed from projections. The reconstruction of tomograms from the acquired PET data is an inverse problem that requires regularization. The use of tightly packed discrete detector rings, although improves signal-to-noise ratio, are often associated with high costs of positron emission tomography systems. Thus a sparse reconstruction, which would be capable of overcoming the noise effect while allowing for a reduced number of detectors, would have a great deal to offer. In this study, we introduce and investigate the potential of a homotopic non-local regularization reconstruction framework for effectively reconstructing positron emission tomograms from such sparse measurements. Results obtained using the proposed approach are compared with traditional filtered back-projection as well as expectation maximization reconstruction with total variation regularization. A new reconstruction method was developed for the purpose of improving the quality of positron emission tomography reconstruction from sparse measurements. We illustrate that promising reconstruction performance can be achieved for the proposed approach even at low sampling fractions, which allows for the use of significantly fewer detectors and have the potential to reduce scanner costs

Learning Sparse Visual Representations with Leaky Capped Norm Regularizers

OpenAIRE

Wangni, Jianqiao; Lin, Dahua

2017-01-01

Sparsity inducing regularization is an important part for learning over-complete visual representations. Despite the popularity of $\\ell_1$ regularization, in this paper, we investigate the usage of non-convex regularizations in this problem. Our contribution consists of three parts. First, we propose the leaky capped norm regularization (LCNR), which allows model weights below a certain threshold to be regularized more strongly as opposed to those above, therefore imposes strong sparsity and...

Adaptive regularization of noisy linear inverse problems

DEFF Research Database (Denmark)

Hansen, Lars Kai; Madsen, Kristoffer Hougaard; Lehn-Schiøler, Tue

2006-01-01

In the Bayesian modeling framework there is a close relation between regularization and the prior distribution over parameters. For prior distributions in the exponential family, we show that the optimal hyper-parameter, i.e., the optimal strength of regularization, satisfies a simple relation: T......: The expectation of the regularization function, i.e., takes the same value in the posterior and prior distribution. We present three examples: two simulations, and application in fMRI neuroimaging....

Clustering, randomness, and regularity in cloud fields. 4: Stratocumulus cloud fields

Science.gov (United States)

Lee, J.; Chou, J.; Weger, R. C.; Welch, R. M.

1994-01-01

To complete the analysis of the spatial distribution of boundary layer cloudiness, the present study focuses on nine stratocumulus Landsat scenes. The results indicate many similarities between stratocumulus and cumulus spatial distributions. Most notably, at full spatial resolution all scenes exhibit a decidedly clustered distribution. The strength of the clustering signal decreases with increasing cloud size; the clusters themselves consist of a few clouds (less than 10), occupy a small percentage of the cloud field area (less than 5%), contain between 20% and 60% of the cloud field population, and are randomly located within the scene. In contrast, stratocumulus in almost every respect are more strongly clustered than are cumulus cloud fields. For instance, stratocumulus clusters contain more clouds per cluster, occupy a larger percentage of the total area, and have a larger percentage of clouds participating in clusters than the corresponding cumulus examples. To investigate clustering at intermediate spatial scales, the local dimensionality statistic is introduced. Results obtained from this statistic provide the first direct evidence for regularity among large (more than 900 m in diameter) clouds in stratocumulus and cumulus cloud fields, in support of the inhibition hypothesis of Ramirez and Bras (1990). Also, the size compensated point-to-cloud cumulative distribution function statistic is found to be necessary to obtain a consistent description of stratocumulus cloud distributions. A hypothesis regarding the underlying physical mechanisms responsible for cloud clustering is presented. It is suggested that cloud clusters often arise from 4 to 10 triggering events localized within regions less than 2 km in diameter and randomly distributed within the cloud field. As the size of the cloud surpasses the scale of the triggering region, the clustering signal weakens and the larger cloud locations become more random.

Clustering, randomness, and regularity in cloud fields. 4. Stratocumulus cloud fields

Science.gov (United States)

Lee, J.; Chou, J.; Weger, R. C.; Welch, R. M.

1994-07-01

To complete the analysis of the spatial distribution of boundary layer cloudiness, the present study focuses on nine stratocumulus Landsat scenes. The results indicate many similarities between stratocumulus and cumulus spatial distributions. Most notably, at full spatial resolution all scenes exhibit a decidedly clustered distribution. The strength of the clustering signal decreases with increasing cloud size; the clusters themselves consist of a few clouds (less than 10), occupy a small percentage of the cloud field area (less than 5%), contain between 20% and 60% of the cloud field population, and are randomly located within the scene. In contrast, stratocumulus in almost every respect are more strongly clustered than are cumulus cloud fields. For instance, stratocumulus clusters contain more clouds per cluster, occupy a larger percentage of the total area, and have a larger percentage of clouds participating in clusters than the corresponding cumulus examples. To investigate clustering at intermediate spatial scales, the local dimensionality statistic is introduced. Results obtained from this statistic provide the first direct evidence for regularity among large (>900 m in diameter) clouds in stratocumulus and cumulus cloud fields, in support of the inhibition hypothesis of Ramirez and Bras (1990). Also, the size compensated point-to-cloud cumulative distribution function statistic is found to be necessary to obtain a consistent description of stratocumulus cloud distributions. A hypothesis regarding the underlying physical mechanisms responsible for cloud clustering is presented. It is suggested that cloud clusters often arise from 4 to 10 triggering events localized within regions less than 2 km in diameter and randomly distributed within the cloud field. As the size of the cloud surpasses the scale of the triggering region, the clustering signal weakens and the larger cloud locations become more random.

Particle detector spatial resolution

International Nuclear Information System (INIS)

Perez-Mendez, V.

1992-01-01

Method and apparatus for producing separated columns of scintillation layer material, for use in detection of X-rays and high energy charged particles with improved spatial resolution is disclosed. A pattern of ridges or projections is formed on one surface of a substrate layer or in a thin polyimide layer, and the scintillation layer is grown at controlled temperature and growth rate on the ridge-containing material. The scintillation material preferentially forms cylinders or columns, separated by gaps conforming to the pattern of ridges, and these columns direct most of the light produced in the scintillation layer along individual columns for subsequent detection in a photodiode layer. The gaps may be filled with a light-absorbing material to further enhance the spatial resolution of the particle detector. 12 figs

Strong lensing of a regular black hole with an electrodynamics source

Science.gov (United States)

Manna, Tuhina; Rahaman, Farook; Molla, Sabiruddin; Bhadra, Jhumpa; Shah, Hasrat Hussain

2018-05-01

In this paper we have investigated the gravitational lensing phenomenon in the strong field regime for a regular, charged, static black holes with non-linear electrodynamics source. We have obtained the angle of deflection and compared it to a Schwarzschild black hole and Reissner Nordström black hole with similar properties. We have also done a graphical study of the relativistic image positions and magnifications. We hope that this method may be useful in the detection of non-luminous bodies like this current black hole.

Exclusion of children with intellectual disabilities from regular ...

African Journals Online (AJOL)

Study investigated why teachers exclude children with intellectual disability from the regular classrooms in Nigeria. Participants were, 169 regular teachers randomly selected from Oyo and Ogun states. Questionnaire was used to collect data result revealed that 57.4% regular teachers could not cope with children with ID ...

The Bayesian group lasso for confounded spatial data

Science.gov (United States)

Hefley, Trevor J.; Hooten, Mevin B.; Hanks, Ephraim M.; Russell, Robin E.; Walsh, Daniel P.

2017-01-01

Generalized linear mixed models for spatial processes are widely used in applied statistics. In many applications of the spatial generalized linear mixed model (SGLMM), the goal is to obtain inference about regression coefficients while achieving optimal predictive ability. When implementing the SGLMM, multicollinearity among covariates and the spatial random effects can make computation challenging and influence inference. We present a Bayesian group lasso prior with a single tuning parameter that can be chosen to optimize predictive ability of the SGLMM and jointly regularize the regression coefficients and spatial random effect. We implement the group lasso SGLMM using efficient Markov chain Monte Carlo (MCMC) algorithms and demonstrate how multicollinearity among covariates and the spatial random effect can be monitored as a derived quantity. To test our method, we compared several parameterizations of the SGLMM using simulated data and two examples from plant ecology and disease ecology. In all examples, problematic levels multicollinearity occurred and influenced sampling efficiency and inference. We found that the group lasso prior resulted in roughly twice the effective sample size for MCMC samples of regression coefficients and can have higher and less variable predictive accuracy based on out-of-sample data when compared to the standard SGLMM.

On infinite regular and chiral maps

OpenAIRE

Arredondo, John A.; Valdez, Camilo Ramírez y Ferrán

2015-01-01

We prove that infinite regular and chiral maps take place on surfaces with at most one end. Moreover, we prove that an infinite regular or chiral map on an orientable surface with genus can only be realized on the Loch Ness monster, that is, the topological surface of infinite genus with one end.

29 CFR 779.18 - Regular rate.

Science.gov (United States)

2010-07-01

... employee under subsection (a) or in excess of the employee's normal working hours or regular working hours... Relating to Labor (Continued) WAGE AND HOUR DIVISION, DEPARTMENT OF LABOR STATEMENTS OF GENERAL POLICY OR... not less than one and one-half times their regular rates of pay. Section 7(e) of the Act defines...

Detecting, anticipating, and predicting critical transitions in spatially extended systems.

Science.gov (United States)

Kwasniok, Frank

2018-03-01

A data-driven linear framework for detecting, anticipating, and predicting incipient bifurcations in spatially extended systems based on principal oscillation pattern (POP) analysis is discussed. The dynamics are assumed to be governed by a system of linear stochastic differential equations which is estimated from the data. The principal modes of the system together with corresponding decay or growth rates and oscillation frequencies are extracted as the eigenvectors and eigenvalues of the system matrix. The method can be applied to stationary datasets to identify the least stable modes and assess the proximity to instability; it can also be applied to nonstationary datasets using a sliding window approach to track the changing eigenvalues and eigenvectors of the system. As a further step, a genuinely nonstationary POP analysis is introduced. Here, the system matrix of the linear stochastic model is time-dependent, allowing for extrapolation and prediction of instabilities beyond the learning data window. The methods are demonstrated and explored using the one-dimensional Swift-Hohenberg equation as an example, focusing on the dynamics of stochastic fluctuations around the homogeneous stable state prior to the first bifurcation. The POP-based techniques are able to extract and track the least stable eigenvalues and eigenvectors of the system; the nonstationary POP analysis successfully predicts the timing of the first instability and the unstable mode well beyond the learning data window.

Continuum regularized Yang-Mills theory

International Nuclear Information System (INIS)

Sadun, L.A.

1987-01-01

Using the machinery of stochastic quantization, Z. Bern, M. B. Halpern, C. Taubes and I recently proposed a continuum regularization technique for quantum field theory. This regularization may be implemented by applying a regulator to either the (d + 1)-dimensional Parisi-Wu Langevin equation or, equivalently, to the d-dimensional second order Schwinger-Dyson (SD) equations. This technique is non-perturbative, respects all gauge and Lorentz symmetries, and is consistent with a ghost-free gauge fixing (Zwanziger's). This thesis is a detailed study of this regulator, and of regularized Yang-Mills theory, using both perturbative and non-perturbative techniques. The perturbative analysis comes first. The mechanism of stochastic quantization is reviewed, and a perturbative expansion based on second-order SD equations is developed. A diagrammatic method (SD diagrams) for evaluating terms of this expansion is developed. We apply the continuum regulator to a scalar field theory. Using SD diagrams, we show that all Green functions can be rendered finite to all orders in perturbation theory. Even non-renormalizable theories can be regularized. The continuum regulator is then applied to Yang-Mills theory, in conjunction with Zwanziger's gauge fixing. A perturbative expansion of the regulator is incorporated into the diagrammatic method. It is hoped that the techniques discussed in this thesis will contribute to the construction of a renormalized Yang-Mills theory is 3 and 4 dimensions

Invariance Detection within an Interactive System: A Perceptual Gateway to Language Development

Science.gov (United States)

Gogate, Lakshmi J.; Hollich, George

2010-01-01

In this article, we hypothesize that "invariance detection," a general perceptual phenomenon whereby organisms attend to relatively stable patterns or regularities, is an important means by which infants tune in to various aspects of spoken language. In so doing, we synthesize a substantial body of research on detection of regularities across the…

INTERSECTION DETECTION BASED ON QUALITATIVE SPATIAL REASONING ON STOPPING POINT CLUSTERS

Directory of Open Access Journals (Sweden)

S. Zourlidou

2016-06-01

Full Text Available The purpose of this research is to propose and test a method for detecting intersections by analysing collectively acquired trajectories of moving vehicles. Instead of solely relying on the geometric features of the trajectories, such as heading changes, which may indicate turning points and consequently intersections, we extract semantic features of the trajectories in form of sequences of stops and moves. Under this spatiotemporal prism, the extracted semantic information which indicates where vehicles stop can reveal important locations, such as junctions. The advantage of the proposed approach in comparison with existing turning-points oriented approaches is that it can detect intersections even when not all the crossing road segments are sampled and therefore no turning points are observed in the trajectories. The challenge with this approach is that first of all, not all vehicles stop at the same location – thus, the stop-location is blurred along the direction of the road; this, secondly, leads to the effect that nearby junctions can induce similar stop-locations. As a first step, a density-based clustering is applied on the layer of stop observations and clusters of stop events are found. Representative points of the clusters are determined (one per cluster and in a last step the existence of an intersection is clarified based on spatial relational cluster reasoning, with which less informative geospatial clusters, in terms of whether a junction exists and where its centre lies, are transformed in more informative ones. Relational reasoning criteria, based on the relative orientation of the clusters with their adjacent ones are discussed for making sense of the relation that connects them, and finally for forming groups of stop events that belong to the same junction.

Search for Spatially Extended Fermi-LAT Sources Using Two Years of Data

Energy Technology Data Exchange (ETDEWEB)

Lande, Joshua; Ackermann, Markus; Allafort, Alice; Ballet, Jean; Bechtol, Keith; Burnett, Toby; Cohen-Tanugi, Johann; Drlica-Wagner, Alex; Funk, Stefan; Giordano, Francesco; Grondin, Marie-Helene; Kerr, Matthew; Lemoine-Goumard, Marianne

2012-07-13

Spatial extension is an important characteristic for correctly associating {gamma}-ray-emitting sources with their counterparts at other wavelengths and for obtaining an unbiased model of their spectra. We present a new method for quantifying the spatial extension of sources detected by the Large Area Telescope (LAT), the primary science instrument on the Fermi Gamma-ray Space Telescope (Fermi). We perform a series of Monte Carlo simulations to validate this tool and calculate the LAT threshold for detecting the spatial extension of sources. We then test all sources in the second Fermi -LAT catalog (2FGL) for extension. We report the detection of seven new spatially extended sources.

Evaluating Middle School Students' Spatial-scientific Performance in Earth-space Science

Science.gov (United States)

Wilhelm, Jennifer; Jackson, C.; Toland, M. D.; Cole, M.; Wilhelm, R. J.

2013-06-01

Many astronomical concepts cannot be understood without a developed understanding of four spatial-mathematics domains defined as follows: a) Geometric Spatial Visualization (GSV) - Visualizing the geometric features of a system as it appears above, below, and within the system’s plane; b) Spatial Projection (SP) - Projecting to a different location and visualizing from that global perspective; c) Cardinal Directions (CD) - Distinguishing directions (N, S, E, W) in order to document an object’s vector position in space; and d) Periodic Patterns - (PP) Recognizing occurrences at regular intervals of time and/or space. For this study, differences were examined between groups of sixth grade students’ spatial-scientific development pre/post implementation of an Earth/Space unit. Treatment teachers employed a NASA-based curriculum (Realistic Explorations in Astronomical Learning), while control teachers implemented their regular Earth/Space units. A 2-level hierarchical linear model was used to evaluate student performance on the Lunar Phases Concept Inventory (LPCI) and four spatial-mathematics domains, while controlling for two variables (gender and ethnicity) at the student level and one variable (teaching experience) at the teacher level. Overall LPCI results show pre-test scores predicted post-test scores, boys performed better than girls, and Whites performed better than non-Whites. We also compared experimental and control groups’ by spatial-mathematics domain outcomes. For GSV, it was found that boys, in general, tended to have higher GSV post-scores. For domains CD and SP, no statistically significant differences were observed. PP results show Whites performed better than non-Whites. Also for PP, a significant cross-level interaction term (gender-treatment) was observed, which means differences in control and experimental groups are dependent on students’ gender. These findings can be interpreted as: (a) the experimental girls scored higher than the

Temporal and spatial distribution characteristics in the natural plague foci of Chinese Mongolian gerbils based on spatial autocorrelation.

Science.gov (United States)

Du, Hai-Wen; Wang, Yong; Zhuang, Da-Fang; Jiang, Xiao-San

2017-08-07

The nest flea index of Meriones unguiculatus is a critical indicator for the prevention and control of plague, which can be used not only to detect the spatial and temporal distributions of Meriones unguiculatus, but also to reveal its cluster rule. This research detected the temporal and spatial distribution characteristics of the plague natural foci of Mongolian gerbils by body flea index from 2005 to 2014, in order to predict plague outbreaks. Global spatial autocorrelation was used to describe the entire spatial distribution pattern of the body flea index in the natural plague foci of typical Chinese Mongolian gerbils. Cluster and outlier analysis and hot spot analysis were also used to detect the intensity of clusters based on geographic information system methods. The quantity of M. unguiculatus nest fleas in the sentinel surveillance sites from 2005 to 2014 and host density data of the study area from 2005 to 2010 used in this study were provided by Chinese Center for Disease Control and Prevention. The epidemic focus regions of the Mongolian gerbils remain the same as the hot spot regions relating to the body flea index. High clustering areas possess a similar pattern as the distribution pattern of the body flea index indicating that the transmission risk of plague is relatively high. In terms of time series, the area of the epidemic focus gradually increased from 2005 to 2007, declined rapidly in 2008 and 2009, and then decreased slowly and began trending towards stability from 2009 to 2014. For the spatial change, the epidemic focus regions began moving northward from the southwest epidemic focus of the Mongolian gerbils from 2005 to 2007, and then moved from north to south in 2007 and 2008. The body flea index of Chinese gerbil foci reveals significant spatial and temporal aggregation characteristics through the employing of spatial autocorrelation. The diversity of temporary and spatial distribution is mainly affected by seasonal variation, the human

Regularity effect in prospective memory during aging

OpenAIRE

Blondelle, Geoffrey; Hainselin, Mathieu; Gounden, Yannick; Heurley, Laurent; Voisin, Hélène; Megalakaki, Olga; Bressous, Estelle; Quaglino, Véronique

2016-01-01

Background: Regularity effect can affect performance in prospective memory (PM), but little is known on the cognitive processes linked to this effect. Moreover, its impacts with regard to aging remain unknown. To our knowledge, this study is the first to examine regularity effect in PM in a lifespan perspective, with a sample of young, intermediate, and older adults.Objective and design: Our study examined the regularity effect in PM in three groups of participants: 28 young adults (18–30), 1...

20 CFR 226.14 - Employee regular annuity rate.

Science.gov (United States)

2010-04-01

... 20 Employees' Benefits 1 2010-04-01 2010-04-01 false Employee regular annuity rate. 226.14 Section... COMPUTING EMPLOYEE, SPOUSE, AND DIVORCED SPOUSE ANNUITIES Computing an Employee Annuity § 226.14 Employee regular annuity rate. The regular annuity rate payable to the employee is the total of the employee tier I...

Spatial Distribution of Soil Fauna In Long Term No Tillage

Science.gov (United States)

Corbo, J. Z. F.; Vieira, S. R.; Siqueira, G. M.

2012-04-01

The soil is a complex system constituted by living beings, organic and mineral particles, whose components define their physical, chemical and biological properties. Soil fauna plays an important role in soil and may reflect and interfere in its functionality. These organisms' populations may be influenced by management practices, fertilization, liming and porosity, among others. Such changes may reduce the composition and distribution of soil fauna community. Thus, this study aimed to determine the spatial variability of soil fauna in consolidated no-tillage system. The experimental area is located at Instituto Agronômico in Campinas (São Paulo, Brazil). The sampling was conducted in a Rhodic Eutrudox, under no tillage system and 302 points distributed in a 3.2 hectare area in a regular grid of 10.00 m x 10.00 m were sampled. The soil fauna was sampled with "Pitfall Traps" method and traps remained in the area for seven days. Data were analyzed using descriptive statistics to determine the main statistical moments (mean variance, coefficient of variation, standard deviation, skewness and kurtosis). Geostatistical tools were used to determine the spatial variability of the attributes using the experimental semivariogram. For the biodiversity analysis, Shannon and Pielou indexes and richness were calculated for each sample. Geostatistics has proven to be a great tool for mapping the spatial variability of groups from the soil epigeal fauna. The family Formicidae proved to be the most abundant and dominant in the study area. The parameters of descriptive statistics showed that all attributes studied showed lognormal frequency distribution for groups from the epigeal soil fauna. The exponential model was the most suited for the obtained data, for both groups of epigeal soil fauna (Acari, Araneae, Coleoptera, Formicidae and Coleoptera larva), and the other biodiversity indexes. The sampling scheme (10.00 m x 10.00 m) was not sufficient to detect the spatial

Regular algebra and finite machines

CERN Document Server

Conway, John Horton

2012-01-01

World-famous mathematician John H. Conway based this classic text on a 1966 course he taught at Cambridge University. Geared toward graduate students of mathematics, it will also prove a valuable guide to researchers and professional mathematicians.His topics cover Moore's theory of experiments, Kleene's theory of regular events and expressions, Kleene algebras, the differential calculus of events, factors and the factor matrix, and the theory of operators. Additional subjects include event classes and operator classes, some regulator algebras, context-free languages, communicative regular alg

Detecting regularities in soccer dynamics: A T-pattern approach

Directory of Open Access Journals (Sweden)

Valentino Zurloni

2014-01-01

Full Text Available La dinámica del juego en partidos de fútbol profesional es un fenómeno complejo que no ha estado resuelto de forma óptima a través delas vías tradicionales que han pretendido la cuantificación en deportes de equipo. El objetivo de este estudio es el de detectar la dinámica existente mediante un análisis de patrones temporales. Específicamente, se pretenden revelar las estructuras ocultas pero estables que subyacen a las situaciones interactivas que determinan las acciones de ataque en el fútbol. El planteamiento metodológico se basa en un diseño observacional, y con apoyo de registros digitales y análisis informatizados. Los datos se analizaron mediante el programa Theme 6 beta, el cual permite detectar la estructura temporaly secuencial de las series de datos, poniendo de manifiesto patrones que regular o irregularmente ocurren repetidamente en un período de observación. El Theme ha detectado muchos patrones temporales (T-patterns en los partidos de fútbol analizados. Se hallaron notables diferencias entre los partidos ganados y perdidos. El número de distintos T-patterns detectados fue mayor para los partidos perdidos, y menor para los ganados, mientras que el número de eventos codificados fue similar. El programa Theme y los T-patterns mejoran las posibilidades investigadoras respecto a un análisis de rendimiento basado en la frecuencia, y hacen que esta metodología sea eficaz para la investigación y constituya un apoyo procedimental en el análisis del deporte. Nuestros resultados indican que se requieren posteriores investigaciones relativas a posibles conexiones entre la detección de estas estructuras temporales y las observaciones humanas respecto al rendimiento en el fútbol. Este planteamiento sería un apoyo tanto para los miembros de los equipos como para los entrenadores, permitiendo alcanzar una mejor comprensión de la dinámica del juego y aportando una información que no ofrecen los métodos tradicionales.

39 CFR 6.1 - Regular meetings, annual meeting.

Science.gov (United States)

2010-07-01

... 39 Postal Service 1 2010-07-01 2010-07-01 false Regular meetings, annual meeting. 6.1 Section 6.1 Postal Service UNITED STATES POSTAL SERVICE THE BOARD OF GOVERNORS OF THE U.S. POSTAL SERVICE MEETINGS (ARTICLE VI) § 6.1 Regular meetings, annual meeting. The Board shall meet regularly on a schedule...

Shade tree spatial structure and pod production explain frosty pod rot intensity in cacao agroforests, Costa Rica.

Science.gov (United States)

Gidoin, Cynthia; Avelino, Jacques; Deheuvels, Olivier; Cilas, Christian; Bieng, Marie Ange Ngo

2014-03-01

Vegetation composition and plant spatial structure affect disease intensity through resource and microclimatic variation effects. The aim of this study was to evaluate the independent effect and relative importance of host composition and plant spatial structure variables in explaining disease intensity at the plot scale. For that purpose, frosty pod rot intensity, a disease caused by Moniliophthora roreri on cacao pods, was monitored in 36 cacao agroforests in Costa Rica in order to assess the vegetation composition and spatial structure variables conducive to the disease. Hierarchical partitioning was used to identify the most causal factors. Firstly, pod production, cacao tree density and shade tree spatial structure had significant independent effects on disease intensity. In our case study, the amount of susceptible tissue was the most relevant host composition variable for explaining disease intensity by resource dilution. Indeed, cacao tree density probably affected disease intensity more by the creation of self-shading rather than by host dilution. Lastly, only regularly distributed forest trees, and not aggregated or randomly distributed forest trees, reduced disease intensity in comparison to plots with a low forest tree density. A regular spatial structure is probably crucial to the creation of moderate and uniform shade as recommended for frosty pod rot management. As pod production is an important service expected from these agroforests, shade tree spatial structure may be a lever for integrated management of frosty pod rot in cacao agroforests.

Spatial Patterns and Driving Forces of Greenhouse Land Change in Shouguang City, China

Directory of Open Access Journals (Sweden)

Bohua Yu

2017-03-01

Full Text Available As an important facet of modern agricultural development, greenhouses satisfy ever-increasing demands for agricultural production and, therefore, constitute a growing proportion of global agriculture. However, just a handful of countries regularly collect statistics on the land cover of greenhouse infrastructure. Even when collected, these data cannot provide the detailed spatial information required for environmental risk assessment. It is, therefore, important to map spatial changes in greenhouse land cover using remote sensing (RS approaches to determine the underlying factors driving these changes. In this paper, we apply a support vector machine (SVM algorithm to identify greenhouse land cover in Shouguang City, China. Enhanced thematic mapper (ETM images were selected as the data source for land use classification in this study as they can be freely acquired and offer the necessary spatial resolution. We then used a binary logistic regression model to quantitatively discern the mechanisms underlying changes in greenhouse land cover. The results of this study show that greenhouse land cover in Shouguang increased by 50.51% between 2000 and 2015, and that 90.39% of this expansion took place between 2010 and 2015. Elevation, slope, precipitation, and the distance to the nearest rural settlements and coastline are all significant factors driving expansion in greenhouse land cover, while distance to the nearest urban areas, rivers, roads, railways, and coastline have contributed to contractions in this land use type. Our research provided a practical approach to allow the detection of changes in greenhouse land cover in the countries with using free or low-cost satellite images.

A regularized stationary mean-field game

KAUST Repository

Yang, Xianjin

2016-01-01

In the thesis, we discuss the existence and numerical approximations of solutions of a regularized mean-field game with a low-order regularization. In the first part, we prove a priori estimates and use the continuation method to obtain the existence of a solution with a positive density. Finally, we introduce the monotone flow method and solve the system numerically.

A regularized stationary mean-field game

KAUST Repository

Yang, Xianjin

2016-04-19

In the thesis, we discuss the existence and numerical approximations of solutions of a regularized mean-field game with a low-order regularization. In the first part, we prove a priori estimates and use the continuation method to obtain the existence of a solution with a positive density. Finally, we introduce the monotone flow method and solve the system numerically.

Automating InDesign with Regular Expressions

CERN Document Server

Kahrel, Peter

2006-01-01

If you need to make automated changes to InDesign documents beyond what basic search and replace can handle, you need regular expressions, and a bit of scripting to make them work. This Short Cut explains both how to write regular expressions, so you can find and replace the right things, and how to use them in InDesign specifically.

Optimal behaviour can violate the principle of regularity.

Science.gov (United States)

Trimmer, Pete C

2013-07-22

Understanding decisions is a fundamental aim of behavioural ecology, psychology and economics. The regularity axiom of utility theory holds that a preference between options should be maintained when other options are made available. Empirical studies have shown that animals violate regularity but this has not been understood from a theoretical perspective, such decisions have therefore been labelled as irrational. Here, I use models of state-dependent behaviour to demonstrate that choices can violate regularity even when behavioural strategies are optimal. I also show that the range of conditions over which regularity should be violated can be larger when options do not always persist into the future. Consequently, utility theory--based on axioms, including transitivity, regularity and the independence of irrelevant alternatives--is undermined, because even alternatives that are never chosen by an animal (in its current state) can be relevant to a decision.

Bias correction for magnetic resonance images via joint entropy regularization.

Science.gov (United States)

Wang, Shanshan; Xia, Yong; Dong, Pei; Luo, Jianhua; Huang, Qiu; Feng, Dagan; Li, Yuanxiang

2014-01-01

Due to the imperfections of the radio frequency (RF) coil or object-dependent electrodynamic interactions, magnetic resonance (MR) images often suffer from a smooth and biologically meaningless bias field, which causes severe troubles for subsequent processing and quantitative analysis. To effectively restore the original signal, this paper simultaneously exploits the spatial and gradient features of the corrupted MR images for bias correction via the joint entropy regularization. With both isotropic and anisotropic total variation (TV) considered, two nonparametric bias correction algorithms have been proposed, namely IsoTVBiasC and AniTVBiasC. These two methods have been applied to simulated images under various noise levels and bias field corruption and also tested on real MR data. The test results show that the proposed two methods can effectively remove the bias field and also present comparable performance compared to the state-of-the-art methods.

Dimensional regularization in configuration space

International Nuclear Information System (INIS)

Bollini, C.G.; Giambiagi, J.J.

1995-09-01

Dimensional regularization is introduced in configuration space by Fourier transforming in D-dimensions the perturbative momentum space Green functions. For this transformation, Bochner theorem is used, no extra parameters, such as those of Feynman or Bogoliubov-Shirkov are needed for convolutions. The regularized causal functions in x-space have ν-dependent moderated singularities at the origin. They can be multiplied together and Fourier transformed (Bochner) without divergence problems. The usual ultraviolet divergences appear as poles of the resultant functions of ν. Several example are discussed. (author). 9 refs

Matrix regularization of 4-manifolds

OpenAIRE

Trzetrzelewski, M.

2012-01-01

We consider products of two 2-manifolds such as S^2 x S^2, embedded in Euclidean space and show that the corresponding 4-volume preserving diffeomorphism algebra can be approximated by a tensor product SU(N)xSU(N) i.e. functions on a manifold are approximated by the Kronecker product of two SU(N) matrices. A regularization of the 4-sphere is also performed by constructing N^2 x N^2 matrix representations of the 4-algebra (and as a byproduct of the 3-algebra which makes the regularization of S...

Regular Breakfast and Blood Lead Levels among Preschool Children

Directory of Open Access Journals (Sweden)

Needleman Herbert

2011-04-01

Full Text Available Abstract Background Previous studies have shown that fasting increases lead absorption in the gastrointestinal tract of adults. Regular meals/snacks are recommended as a nutritional intervention for lead poisoning in children, but epidemiological evidence of links between fasting and blood lead levels (B-Pb is rare. The purpose of this study was to examine the association between eating a regular breakfast and B-Pb among children using data from the China Jintan Child Cohort Study. Methods Parents completed a questionnaire regarding children's breakfast-eating habit (regular or not, demographics, and food frequency. Whole blood samples were collected from 1,344 children for the measurements of B-Pb and micronutrients (iron, copper, zinc, calcium, and magnesium. B-Pb and other measures were compared between children with and without regular breakfast. Linear regression modeling was used to evaluate the association between regular breakfast and log-transformed B-Pb. The association between regular breakfast and risk of lead poisoning (B-Pb≥10 μg/dL was examined using logistic regression modeling. Results Median B-Pb among children who ate breakfast regularly and those who did not eat breakfast regularly were 6.1 μg/dL and 7.2 μg/dL, respectively. Eating breakfast was also associated with greater zinc blood levels. Adjusting for other relevant factors, the linear regression model revealed that eating breakfast regularly was significantly associated with lower B-Pb (beta = -0.10 units of log-transformed B-Pb compared with children who did not eat breakfast regularly, p = 0.02. Conclusion The present study provides some initial human data supporting the notion that eating a regular breakfast might reduce B-Pb in young children. To our knowledge, this is the first human study exploring the association between breakfast frequency and B-Pb in young children.

On the equivalence of different regularization methods

International Nuclear Information System (INIS)

Brzezowski, S.

1985-01-01

The R-circunflex-operation preceded by the regularization procedure is discussed. Some arguments are given, according to which the results may depend on the method of regularization, introduced in order to avoid divergences in perturbation calculations. 10 refs. (author)

Accreting fluids onto regular black holes via Hamiltonian approach

Energy Technology Data Exchange (ETDEWEB)

Jawad, Abdul [COMSATS Institute of Information Technology, Department of Mathematics, Lahore (Pakistan); Shahzad, M.U. [COMSATS Institute of Information Technology, Department of Mathematics, Lahore (Pakistan); University of Central Punjab, CAMS, UCP Business School, Lahore (Pakistan)

2017-08-15

We investigate the accretion of test fluids onto regular black holes such as Kehagias-Sfetsos black holes and regular black holes with Dagum distribution function. We analyze the accretion process when different test fluids are falling onto these regular black holes. The accreting fluid is being classified through the equation of state according to the features of regular black holes. The behavior of fluid flow and the existence of sonic points is being checked for these regular black holes. It is noted that the three-velocity depends on critical points and the equation of state parameter on phase space. (orig.)

AN INVESTIGATION OF AUTOMATIC CHANGE DETECTION FOR TOPOGRAPHIC MAP UPDATING

Directory of Open Access Journals (Sweden)

P. Duncan

2012-08-01

Full Text Available Changes to the landscape are constantly occurring and it is essential for geospatial and mapping organisations that these changes are regularly detected and captured, so that map databases can be updated to reflect the current status of the landscape. The Chief Directorate of National Geospatial Information (CD: NGI, South Africa's national mapping agency, currently relies on manual methods of detecting changes and capturing these changes. These manual methods are time consuming and labour intensive, and rely on the skills and interpretation of the operator. It is therefore necessary to move towards more automated methods in the production process at CD: NGI. The aim of this research is to do an investigation into a methodology for automatic or semi-automatic change detection for the purpose of updating topographic databases. The method investigated for detecting changes is through image classification as well as spatial analysis and is focussed on urban landscapes. The major data input into this study is high resolution aerial imagery and existing topographic vector data. Initial results indicate the traditional pixel-based image classification approaches are unsatisfactory for large scale land-use mapping and that object-orientated approaches hold more promise. Even in the instance of object-oriented image classification generalization of techniques on a broad-scale has provided inconsistent results. A solution may lie with a hybrid approach of pixel and object-oriented techniques.

Spatial distribution of trachoma cases in the City of Bauru, State of São Paulo, Brazil, detected in 2006: defining key areas for improvement of health resources

Directory of Open Access Journals (Sweden)

Carlos Alberto Macharelli

2013-04-01

Full Text Available Introduction The objective of this study was to analyze the spatial behavior of the occurrence of trachoma cases detected in the City of Bauru, State of São Paulo, Brazil, in 2006 in order to use the information collected to set priority areas for optimization of health resources. Methods the trachoma cases identified in 2006 were georeferenced. The data evaluated were: schools where the trachoma cases studied, data from the 2000 Census, census tract, type of housing, water supply conditions, distribution of income and levels of education of household heads. In the Google Earth® software and TerraView® were made descriptive spatial analysis and estimates of the Kernel. Each area was studied by interpolation of the density surfaces exposing events to facilitate to recognize the clusters. Results Of the 66 cases detected, only one (1.5% was not a resident of the city's outskirts. A positive association was detected of trachoma cases and the percentage of heads of household with income below three minimum wages and schooling under eight years of education. Conclusions The recognition of the spatial distribution of trachoma cases coincided with the areas of greatest social inequality in Bauru City. The micro-areas identified are those that should be prioritized in the rationalization of health resources. There is the possibility of using the trachoma cases detected as an indicator of performance of micro priority health programs.

Spatial distribution of trachoma cases in the City of Bauru, State of São Paulo, Brazil, detected in 2006: defining key areas for improvement of health resources

Directory of Open Access Journals (Sweden)

Carlos Alberto Macharelli

2013-02-01

Full Text Available Introduction The objective of this study was to analyze the spatial behavior of the occurrence of trachoma cases detected in the City of Bauru, State of São Paulo, Brazil, in 2006 in order to use the information collected to set priority areas for optimization of health resources. Methods the trachoma cases identified in 2006 were georeferenced. The data evaluated were: schools where the trachoma cases studied, data from the 2000 Census, census tract, type of housing, water supply conditions, distribution of income and levels of education of household heads. In the Google Earth® software and TerraView® were made descriptive spatial analysis and estimates of the Kernel. Each area was studied by interpolation of the density surfaces exposing events to facilitate to recognize the clusters. Results Of the 66 cases detected, only one (1.5% was not a resident of the city's outskirts. A positive association was detected of trachoma cases and the percentage of heads of household with income below three minimum wages and schooling under eight years of education. Conclusions The recognition of the spatial distribution of trachoma cases coincided with the areas of greatest social inequality in Bauru City. The micro-areas identified are those that should be prioritized in the rationalization of health resources. There is the possibility of using the trachoma cases detected as an indicator of performance of micro priority health programs.

Noise suppression for dual-energy CT via penalized weighted least-square optimization with similarity-based regularization

Energy Technology Data Exchange (ETDEWEB)

Harms, Joseph; Wang, Tonghe; Petrongolo, Michael; Zhu, Lei, E-mail: leizhu@gatech.edu [Nuclear and Radiological Engineering and Medical Physics Programs, The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332 (United States); Niu, Tianye [Sir Run Run Shaw Hospital, Zhejiang University School of Medicine (China); Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang, 310016 (China)

2016-05-15

Purpose: Dual-energy CT (DECT) expands applications of CT imaging in its capability to decompose CT images into material images. However, decomposition via direct matrix inversion leads to large noise amplification and limits quantitative use of DECT. Their group has previously developed a noise suppression algorithm via penalized weighted least-square optimization with edge-preservation regularization (PWLS-EPR). In this paper, the authors improve method performance using the same framework of penalized weighted least-square optimization but with similarity-based regularization (PWLS-SBR), which substantially enhances the quality of decomposed images by retaining a more uniform noise power spectrum (NPS). Methods: The design of PWLS-SBR is based on the fact that averaging pixels of similar materials gives a low-noise image. For each pixel, the authors calculate the similarity to other pixels in its neighborhood by comparing CT values. Using an empirical Gaussian model, the authors assign high/low similarity value to one neighboring pixel if its CT value is close/far to the CT value of the pixel of interest. These similarity values are organized in matrix form, such that multiplication of the similarity matrix to the image vector reduces image noise. The similarity matrices are calculated on both high- and low-energy CT images and averaged. In PWLS-SBR, the authors include a regularization term to minimize the L-2 norm of the difference between the images without and with noise suppression via similarity matrix multiplication. By using all pixel information of the initial CT images rather than just those lying on or near edges, PWLS-SBR is superior to the previously developed PWLS-EPR, as supported by comparison studies on phantoms and a head-and-neck patient. Results: On the line-pair slice of the Catphan{sup ©}600 phantom, PWLS-SBR outperforms PWLS-EPR and retains spatial resolution of 8 lp/cm, comparable to the original CT images, even at 90% reduction in noise

Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery

Science.gov (United States)

Zhong, Yanfei; Han, Xiaobing; Zhang, Liangpei

2018-04-01

Multi-class geospatial object detection from high spatial resolution (HSR) remote sensing imagery is attracting increasing attention in a wide range of object-related civil and engineering applications. However, the distribution of objects in HSR remote sensing imagery is location-variable and complicated, and how to accurately detect the objects in HSR remote sensing imagery is a critical problem. Due to the powerful feature extraction and representation capability of deep learning, the deep learning based region proposal generation and object detection integrated framework has greatly promoted the performance of multi-class geospatial object detection for HSR remote sensing imagery. However, due to the translation caused by the convolution operation in the convolutional neural network (CNN), although the performance of the classification stage is seldom influenced, the localization accuracies of the predicted bounding boxes in the detection stage are easily influenced. The dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage has not been addressed for HSR remote sensing imagery, and causes position accuracy problems for multi-class geospatial object detection with region proposal generation and object detection. In order to further improve the performance of the region proposal generation and object detection integrated framework for HSR remote sensing imagery object detection, a position-sensitive balancing (PSB) framework is proposed in this paper for multi-class geospatial object detection from HSR remote sensing imagery. The proposed PSB framework takes full advantage of the fully convolutional network (FCN), on the basis of a residual network, and adopts the PSB framework to solve the dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage. In addition, a pre-training mechanism is utilized to accelerate the training procedure

Bounded Perturbation Regularization for Linear Least Squares Estimation

KAUST Repository

Ballal, Tarig

2017-10-18

This paper addresses the problem of selecting the regularization parameter for linear least-squares estimation. We propose a new technique called bounded perturbation regularization (BPR). In the proposed BPR method, a perturbation with a bounded norm is allowed into the linear transformation matrix to improve the singular-value structure. Following this, the problem is formulated as a min-max optimization problem. Next, the min-max problem is converted to an equivalent minimization problem to estimate the unknown vector quantity. The solution of the minimization problem is shown to converge to that of the ℓ2 -regularized least squares problem, with the unknown regularizer related to the norm bound of the introduced perturbation through a nonlinear constraint. A procedure is proposed that combines the constraint equation with the mean squared error (MSE) criterion to develop an approximately optimal regularization parameter selection algorithm. Both direct and indirect applications of the proposed method are considered. Comparisons with different Tikhonov regularization parameter selection methods, as well as with other relevant methods, are carried out. Numerical results demonstrate that the proposed method provides significant improvement over state-of-the-art methods.

Influence of whitening and regular dentifrices on orthodontic clear ligature color stability.

Science.gov (United States)

Oliveira, Adauê S; Kaizer, Marina R; Salgado, Vinícius E; Soldati, Dener C; Silva, Roberta C; Moraes, Rafael R

2015-01-01

This study evaluated the effect of brushing orthodontic clear ligatures with a whitening dentifrice containing a blue pigment (Close Up White Now, Unilever, London, UK) on their color stability, when exposed to a staining agent. Ligatures from 3M Unitek (Monrovia, CA, USA) and Morelli (Sorocaba, SP, Brazil) were tested. Baseline color measurements were performed and nonstained groups (control) were stored in distilled water whereas test groups were exposed for 1 hour daily to red wine. Specimens were brushed daily using regular or whitening dentifrice. Color measurements were repeated after 7, 14, 21, and 28 days using a spectrophotometer based on the CIE L*a*b* system. Decreased luminosity (CIE L*), increased red discoloration (CIE a* axis), and increased yellow discoloration (CIE b* axis) were generally observed for ligatures exposed to the staining agent. Color variation was generally lower in specimens brushed with regular dentifrice, but ligatures brushed with whitening dentifrice were generally less red and less yellow than regular dentifrice. The whitening dentifrice led to blue discoloration trend, with visually detectable differences particularly apparent according to storage condition and ligature brand. The whitening dentifrice containing blue pigment did not improve the ligature color stability, but it decreased yellow discoloration and increased a blue coloration. The use of a whitening dentifrice containing blue pigment during orthodontic treatment might decrease the yellow discoloration of elastic ligatures. © 2015 Wiley Periodicals, Inc.

MRI reconstruction with joint global regularization and transform learning.

Science.gov (United States)

Tanc, A Korhan; Eksioglu, Ender M

2016-10-01

Sparsity based regularization has been a popular approach to remedy the measurement scarcity in image reconstruction. Recently, sparsifying transforms learned from image patches have been utilized as an effective regularizer for the Magnetic Resonance Imaging (MRI) reconstruction. Here, we infuse additional global regularization terms to the patch-based transform learning. We develop an algorithm to solve the resulting novel cost function, which includes both patchwise and global regularization terms. Extensive simulation results indicate that the introduced mixed approach has improved MRI reconstruction performance, when compared to the algorithms which use either of the patchwise transform learning or global regularization terms alone. Copyright © 2016 Elsevier Ltd. All rights reserved.

A spatial scan statistic for nonisotropic two-level risk cluster.

Science.gov (United States)

Li, Xiao-Zhou; Wang, Jin-Feng; Yang, Wei-Zhong; Li, Zhong-Jie; Lai, Sheng-Jie

2012-01-30

Spatial scan statistic methods are commonly used for geographical disease surveillance and cluster detection. The standard spatial scan statistic does not model any variability in the underlying risks of subregions belonging to a detected cluster. For a multilevel risk cluster, the isotonic spatial scan statistic could model a centralized high-risk kernel in the cluster. Because variations in disease risks are anisotropic owing to different social, economical, or transport factors, the real high-risk kernel will not necessarily take the central place in a whole cluster area. We propose a spatial scan statistic for a nonisotropic two-level risk cluster, which could be used to detect a whole cluster and a noncentralized high-risk kernel within the cluster simultaneously. The performance of the three methods was evaluated through an intensive simulation study. Our proposed nonisotropic two-level method showed better power and geographical precision with two-level risk cluster scenarios, especially for a noncentralized high-risk kernel. Our proposed method is illustrated using the hand-foot-mouth disease data in Pingdu City, Shandong, China in May 2009, compared with two other methods. In this practical study, the nonisotropic two-level method is the only way to precisely detect a high-risk area in a detected whole cluster. Copyright © 2011 John Wiley & Sons, Ltd.

Lead-position dependent regular oscillations and random fluctuations of conductance in graphene quantum dots

International Nuclear Information System (INIS)

Huang Liang; Yang Rui; Lai Yingcheng; Ferry, David K

2013-01-01

Quantum interference causes a wavefunction to have sensitive spatial dependence, and this has a significant effect on quantum transport. For example, in a quantum-dot system, the conductance can depend on the lead positions. We investigate, for graphene quantum dots, the conductance variations with the lead positions. Since for graphene the types of boundaries, e.g., zigzag and armchair, can fundamentally affect the quantum transport characteristics, we focus on rectangular graphene quantum dots, for which the effects of boundaries can be systematically studied. For both zigzag and armchair horizontal boundaries, we find that changing the positions of the leads can induce significant conductance variations. Depending on the Fermi energy, the variations can be either regular oscillations or random conductance fluctuations. We develop a physical theory to elucidate the origin of the conductance oscillation/fluctuation patterns. In particular, quantum interference leads to standing-wave-like-patterns in the quantum dot which, in the absence of leads, are regulated by the energy-band structure of the corresponding vertical graphene ribbon. The observed ‘coexistence’ of regular oscillations and random fluctuations in the conductance can be exploited for the development of graphene-based nanodevices. (paper)

Strictly-regular number system and data structures

DEFF Research Database (Denmark)

Elmasry, Amr Ahmed Abd Elmoneim; Jensen, Claus; Katajainen, Jyrki

2010-01-01

We introduce a new number system that we call the strictly-regular system, which efficiently supports the operations: digit-increment, digit-decrement, cut, concatenate, and add. Compared to other number systems, the strictly-regular system has distinguishable properties. It is superior to the re...

Analysis of regularized Navier-Stokes equations, 2

Science.gov (United States)

Ou, Yuh-Roung; Sritharan, S. S.

1989-01-01

A practically important regularization of the Navier-Stokes equations was analyzed. As a continuation of the previous work, the structure of the attractors characterizing the solutins was studied. Local as well as global invariant manifolds were found. Regularity properties of these manifolds are analyzed.

Regularities, Natural Patterns and Laws of Nature

Directory of Open Access Journals (Sweden)

Stathis Psillos

2014-02-01

Full Text Available  The goal of this paper is to sketch an empiricist metaphysics of laws of nature. The key idea is that there are regularities without regularity-enforcers. Differently put, there are natural laws without law-makers of a distinct metaphysical kind. This sketch will rely on the concept of a natural pattern and more significantly on the existence of a network of natural patterns in nature. The relation between a regularity and a pattern will be analysed in terms of mereology.  Here is the road map. In section 2, I will briefly discuss the relation between empiricism and metaphysics, aiming to show that an empiricist metaphysics is possible. In section 3, I will offer arguments against stronger metaphysical views of laws. Then, in section 4 I will motivate nomic objectivism. In section 5, I will address the question ‘what is a regularity?’ and will develop a novel answer to it, based on the notion of a natural pattern. In section 6, I will raise the question: ‘what is a law of nature?’, the answer to which will be: a law of nature is a regularity that is characterised by the unity of a natural pattern.

First-impression bias effects on mismatch negativity to auditory spatial deviants.

Science.gov (United States)

Fitzgerald, Kaitlin; Provost, Alexander; Todd, Juanita

2018-04-01

Internal models of regularities in the world serve to facilitate perception as redundant input can be predicted and neural resources conserved for that which is new or unexpected. In the auditory system, this is reflected in an evoked potential component known as mismatch negativity (MMN). MMN is elicited by the violation of an established regularity to signal the inaccuracy of the current model and direct resources to the unexpected event. Prevailing accounts suggest that MMN amplitude will increase with stability in regularity; however, observations of first-impression bias contradict stability effects. If tones rotate probabilities as a rare deviant (p = .125) and common standard (p = .875), MMN elicited to the initial deviant tone reaches maximal amplitude faster than MMN to the first standard when later encountered as deviant-a differential pattern that persists throughout rotations. Sensory inference is therefore biased by longer-term contextual information beyond local probability statistics. Using the same multicontext sequence structure, we examined whether this bias generalizes to MMN elicited by spatial sound cues using monaural sounds (n = 19, right first deviant and n = 22, left first deviant) and binaural sounds (n = 19, right first deviant). The characteristic differential modulation of MMN to the two tones was observed in two of three groups, providing partial support for the generalization of first-impression bias to spatially deviant sounds. We discuss possible explanations for its absence when the initial deviant was delivered monaurally to the right ear. © 2017 Society for Psychophysiological Research.

Consistent Partial Least Squares Path Modeling via Regularization.

Science.gov (United States)

Jung, Sunho; Park, JaeHong

2018-01-01

Partial least squares (PLS) path modeling is a component-based structural equation modeling that has been adopted in social and psychological research due to its data-analytic capability and flexibility. A recent methodological advance is consistent PLS (PLSc), designed to produce consistent estimates of path coefficients in structural models involving common factors. In practice, however, PLSc may frequently encounter multicollinearity in part because it takes a strategy of estimating path coefficients based on consistent correlations among independent latent variables. PLSc has yet no remedy for this multicollinearity problem, which can cause loss of statistical power and accuracy in parameter estimation. Thus, a ridge type of regularization is incorporated into PLSc, creating a new technique called regularized PLSc. A comprehensive simulation study is conducted to evaluate the performance of regularized PLSc as compared to its non-regularized counterpart in terms of power and accuracy. The results show that our regularized PLSc is recommended for use when serious multicollinearity is present.

Consistent Partial Least Squares Path Modeling via Regularization

Directory of Open Access Journals (Sweden)

Sunho Jung

2018-02-01

Full Text Available Partial least squares (PLS path modeling is a component-based structural equation modeling that has been adopted in social and psychological research due to its data-analytic capability and flexibility. A recent methodological advance is consistent PLS (PLSc, designed to produce consistent estimates of path coefficients in structural models involving common factors. In practice, however, PLSc may frequently encounter multicollinearity in part because it takes a strategy of estimating path coefficients based on consistent correlations among independent latent variables. PLSc has yet no remedy for this multicollinearity problem, which can cause loss of statistical power and accuracy in parameter estimation. Thus, a ridge type of regularization is incorporated into PLSc, creating a new technique called regularized PLSc. A comprehensive simulation study is conducted to evaluate the performance of regularized PLSc as compared to its non-regularized counterpart in terms of power and accuracy. The results show that our regularized PLSc is recommended for use when serious multicollinearity is present.

Depreciation of public goods in spatial public goods games

Science.gov (United States)

Shi, Dong-Mei; Zhuang, Yong; Li, Yu-Jian; Wang, Bing-Hong

2011-10-01

In real situations, the value of public goods will be reduced or even lost because of external factors or for intrinsic reasons. In this work, we investigate the evolution of cooperation by considering the effect of depreciation of public goods in spatial public goods games on a square lattice. It is assumed that each individual gains full advantage if the number of the cooperators nc within a group centered on that individual equals or exceeds the critical mass (CM). Otherwise, there is depreciation of the public goods, which is realized by rescaling the multiplication factor r to (nc/CM)r. It is shown that the emergence of cooperation is remarkably promoted for CM > 1 even at small values of r, and a global cooperative level is achieved at an intermediate value of CM = 4 at a small r. We further study the effect of depreciation of public goods on different topologies of a regular lattice, and find that the system always reaches global cooperation at a moderate value of CM = G - 1 regardless of whether or not there exist overlapping triangle structures on the regular lattice, where G is the group size of the associated regular lattice.

Regularization of the Boundary-Saddle-Node Bifurcation

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Xia Liu

2018-01-01

Full Text Available In this paper we treat a particular class of planar Filippov systems which consist of two smooth systems that are separated by a discontinuity boundary. In such systems one vector field undergoes a saddle-node bifurcation while the other vector field is transversal to the boundary. The boundary-saddle-node (BSN bifurcation occurs at a critical value when the saddle-node point is located on the discontinuity boundary. We derive a local topological normal form for the BSN bifurcation and study its local dynamics by applying the classical Filippov’s convex method and a novel regularization approach. In fact, by the regularization approach a given Filippov system is approximated by a piecewise-smooth continuous system. Moreover, the regularization process produces a singular perturbation problem where the original discontinuous set becomes a center manifold. Thus, the regularization enables us to make use of the established theories for continuous systems and slow-fast systems to study the local behavior around the BSN bifurcation.

Low-Complexity Regularization Algorithms for Image Deblurring

KAUST Repository

Alanazi, Abdulrahman

2016-11-01

Image restoration problems deal with images in which information has been degraded by blur or noise. In practice, the blur is usually caused by atmospheric turbulence, motion, camera shake, and several other mechanical or physical processes. In this study, we present two regularization algorithms for the image deblurring problem. We first present a new method based on solving a regularized least-squares (RLS) problem. This method is proposed to find a near-optimal value of the regularization parameter in the RLS problems. Experimental results on the non-blind image deblurring problem are presented. In all experiments, comparisons are made with three benchmark methods. The results demonstrate that the proposed method clearly outperforms the other methods in terms of both the output PSNR and structural similarity, as well as the visual quality of the deblurred images. To reduce the complexity of the proposed algorithm, we propose a technique based on the bootstrap method to estimate the regularization parameter in low and high-resolution images. Numerical results show that the proposed technique can effectively reduce the computational complexity of the proposed algorithms. In addition, for some cases where the point spread function (PSF) is separable, we propose using a Kronecker product so as to reduce the computations. Furthermore, in the case where the image is smooth, it is always desirable to replace the regularization term in the RLS problems by a total variation term. Therefore, we propose a novel method for adaptively selecting the regularization parameter in a so-called square root regularized total variation (SRTV). Experimental results demonstrate that our proposed method outperforms the other benchmark methods when applied to smooth images in terms of PSNR, SSIM and the restored image quality. In this thesis, we focus on the non-blind image deblurring problem, where the blur kernel is assumed to be known. However, we developed algorithms that also work

On the regularities of gamma-ray initiated emission of really-secondary electrons

International Nuclear Information System (INIS)

Grudskij, M.Ya.; Roldugin, N.N.; Smirnov, V.V.

1982-01-01

Emission regularities of the really-secondary electrons from metals are discussed on the basis of experimental data on electron emission characteristics under gamma radiation of incident quanta produced for a wide energy range (Esub(γ)=0.03+-2 MeV) and atomic numbers of target materials (Z=13+-79). Comparison with published experimental and calculated data is performed. It is shown that yield of the really-secondary electrons into vacuum from the target surface bombarded with a normally incident collimated beam of gamma radiation calculating on energy unit absorbed in the yield zone of the really-secondary electrons is determined only with the target material emittivity and can be calculated if spatial-energy distributions and the number of secondary fast electrons emitted out of the target are known

Spatial recurrence analysis: A sensitive and fast detection tool in digital mammography

International Nuclear Information System (INIS)

Prado, T. L.; Galuzio, P. P.; Lopes, S. R.; Viana, R. L.

2014-01-01

Efficient diagnostics of breast cancer requires fast digital mammographic image processing. Many breast lesions, both benign and malignant, are barely visible to the untrained eye and requires accurate and reliable methods of image processing. We propose a new method of digital mammographic image analysis that meets both needs. It uses the concept of spatial recurrence as the basis of a spatial recurrence quantification analysis, which is the spatial extension of the well-known time recurrence analysis. The recurrence-based quantifiers are able to evidence breast lesions in a way as good as the best standard image processing methods available, but with a better control over the spurious fragments in the image

Deterministic automata for extended regular expressions

Directory of Open Access Journals (Sweden)

Syzdykov Mirzakhmet

2017-12-01

Full Text Available In this work we present the algorithms to produce deterministic finite automaton (DFA for extended operators in regular expressions like intersection, subtraction and complement. The method like “overriding” of the source NFA(NFA not defined with subset construction rules is used. The past work described only the algorithm for AND-operator (or intersection of regular languages; in this paper the construction for the MINUS-operator (and complement is shown.

Regularities of intermediate adsorption complex relaxation

International Nuclear Information System (INIS)

Manukova, L.A.

1982-01-01

The experimental data, characterizing the regularities of intermediate adsorption complex relaxation in the polycrystalline Mo-N 2 system at 77 K are given. The method of molecular beam has been used in the investigation. The analytical expressions of change regularity in the relaxation process of full and specific rates - of transition from intermediate state into ''non-reversible'', of desorption into the gas phase and accumUlation of the particles in the intermediate state are obtained

Sparse structure regularized ranking

KAUST Repository

Wang, Jim Jing-Yan; Sun, Yijun; Gao, Xin

2014-01-01

Learning ranking scores is critical for the multimedia database retrieval problem. In this paper, we propose a novel ranking score learning algorithm by exploring the sparse structure and using it to regularize ranking scores. To explore the sparse

Sparse structure regularized ranking

KAUST Repository

Wang, Jim Jing-Yan

2014-04-17

Learning ranking scores is critical for the multimedia database retrieval problem. In this paper, we propose a novel ranking score learning algorithm by exploring the sparse structure and using it to regularize ranking scores. To explore the sparse structure, we assume that each multimedia object could be represented as a sparse linear combination of all other objects, and combination coefficients are regarded as a similarity measure between objects and used to regularize their ranking scores. Moreover, we propose to learn the sparse combination coefficients and the ranking scores simultaneously. A unified objective function is constructed with regard to both the combination coefficients and the ranking scores, and is optimized by an iterative algorithm. Experiments on two multimedia database retrieval data sets demonstrate the significant improvements of the propose algorithm over state-of-the-art ranking score learning algorithms.