Multidimensional gray-wavelet processing in interferometric fiber-optic gyroscopes

NASA Astrophysics Data System (ADS)

Yang, Yi; Wang, Zinan; Peng, Chao; Li, Zhengbin

2013-11-01

A multidimensional signal processing method for a single interferometric fiber-optic gyroscope (IFOG) is proposed, to the best of our knowledge, for the first time. The proposed method, based on a novel IFOG structure with quadrature demodulation, combines a multidimensional gray model (GM) and a wavelet compression technique for noise suppression and sensitivity enhancement. In the IFOG, two series of measured rotation rates are obtained simultaneously: an in-phase component and a quadrature component. Together with the traditionally measured rate, the three measured rates are processed by the combined gray-wavelet method. Simulations show that the intensity noise and non-reciprocal phase fluctuations are effectively suppressed by this method. Experimental comparisons with a one-dimensional GM(1, 1) model show that the proposed three-dimensional method achieves much better denoising performance. This advantage is validated by the Allan variance analysis: in a low-SNR (signal-to-noise ratio) experiment, our method reduces the angle random walk (ARW) and the bias instability (BI) from 1 × 10-2 deg h-1/2 and 3 × 10-2 deg h-1 to 1 × 10-3 deg h-1/2 and 3 × 10-3 deg h-1, respectively; in a high-SNR experiment, our method reduces the ARW and the BI from 9 × 10-4 deg h-1/2 and 5 × 10-3 deg h-1 to 4 × 10-4 deg h-1/2 and 3 × 10-3 deg h-1, respectively. Further, our method increases the dimension of the state-of-the-art IFOG technique from one to three, thus obtaining higher IFOG sensitivity and stability by exploiting the increase in available information.

Decomposed multidimensional control grid interpolation for common consumer electronic image processing applications

NASA Astrophysics Data System (ADS)

Zwart, Christine M.; Venkatesan, Ragav; Frakes, David H.

2012-10-01

Interpolation is an essential and broadly employed function of signal processing. Accordingly, considerable development has focused on advancing interpolation algorithms toward optimal accuracy. Such development has motivated a clear shift in the state-of-the art from classical interpolation to more intelligent and resourceful approaches, registration-based interpolation for example. As a natural result, many of the most accurate current algorithms are highly complex, specific, and computationally demanding. However, the diverse hardware destinations for interpolation algorithms present unique constraints that often preclude use of the most accurate available options. For example, while computationally demanding interpolators may be suitable for highly equipped image processing platforms (e.g., computer workstations and clusters), only more efficient interpolators may be practical for less well equipped platforms (e.g., smartphones and tablet computers). The latter examples of consumer electronics present a design tradeoff in this regard: high accuracy interpolation benefits the consumer experience but computing capabilities are limited. It follows that interpolators with favorable combinations of accuracy and efficiency are of great practical value to the consumer electronics industry. We address multidimensional interpolation-based image processing problems that are common to consumer electronic devices through a decomposition approach. The multidimensional problems are first broken down into multiple, independent, one-dimensional (1-D) interpolation steps that are then executed with a newly modified registration-based one-dimensional control grid interpolator. The proposed approach, decomposed multidimensional control grid interpolation (DMCGI), combines the accuracy of registration-based interpolation with the simplicity, flexibility, and computational efficiency of a 1-D interpolation framework. Results demonstrate that DMCGI provides improved interpolation accuracy (and other benefits) in image resizing, color sample demosaicing, and video deinterlacing applications, at a computational cost that is manageable or reduced in comparison to popular alternatives.

Investigation of multidimensional control systems in the state space and wavelet medium

NASA Astrophysics Data System (ADS)

Fedosenkov, D. B.; Simikova, A. A.; Fedosenkov, B. A.

2018-05-01

The notions are introduced of “one-dimensional-point” and “multidimensional-point” automatic control systems. To demonstrate the joint use of approaches based on the concepts of state space and wavelet transforms, a method for optimal control in a state space medium represented in the form of time-frequency representations (maps), is considered. The computer-aided control system is formed on the basis of the similarity transformation method, which makes it possible to exclude the use of reduced state variable observers. 1D-material flow signals formed by primary transducers are converted by means of wavelet transformations into multidimensional concentrated-at-a point variables in the form of time-frequency distributions of Cohen’s class. The algorithm for synthesizing a stationary controller for feeding processes is given here. The conclusion is made that the formation of an optimal control law with time-frequency distributions available contributes to the improvement of transient processes quality in feeding subsystems and the mixing unit. Confirming the efficiency of the method presented is illustrated by an example of the current registration of material flows in the multi-feeding unit. The first section in your paper.

GLO-Roots: an imaging platform enabling multidimensional characterization of soil-grown root systems

PubMed Central

Rellán-Álvarez, Rubén; Lobet, Guillaume; Lindner, Heike; Pradier, Pierre-Luc; Sebastian, Jose; Yee, Muh-Ching; Geng, Yu; Trontin, Charlotte; LaRue, Therese; Schrager-Lavelle, Amanda; Haney, Cara H; Nieu, Rita; Maloof, Julin; Vogel, John P; Dinneny, José R

2015-01-01

Root systems develop different root types that individually sense cues from their local environment and integrate this information with systemic signals. This complex multi-dimensional amalgam of inputs enables continuous adjustment of root growth rates, direction, and metabolic activity that define a dynamic physical network. Current methods for analyzing root biology balance physiological relevance with imaging capability. To bridge this divide, we developed an integrated-imaging system called Growth and Luminescence Observatory for Roots (GLO-Roots) that uses luminescence-based reporters to enable studies of root architecture and gene expression patterns in soil-grown, light-shielded roots. We have developed image analysis algorithms that allow the spatial integration of soil properties, gene expression, and root system architecture traits. We propose GLO-Roots as a system that has great utility in presenting environmental stimuli to roots in ways that evoke natural adaptive responses and in providing tools for studying the multi-dimensional nature of such processes. DOI: http://dx.doi.org/10.7554/eLife.07597.001 PMID:26287479

GLO-Roots: An imaging platform enabling multidimensional characterization of soil-grown root systems

DOE PAGES

Rellan-Alvarez, Ruben; Lobet, Guillaume; Lindner, Heike; ...

2015-08-19

Root systems develop different root types that individually sense cues from their local environment and integrate this information with systemic signals. This complex multi-dimensional amalgam of inputs enables continuous adjustment of root growth rates, direction, and metabolic activity that define a dynamic physical network. Current methods for analyzing root biology balance physiological relevance with imaging capability. To bridge this divide, we developed an integrated-imaging system called Growth and Luminescence Observatory for Roots (GLO-Roots) that uses luminescence-based reporters to enable studies of root architecture and gene expression patterns in soil-grown, light-shielded roots. We have developed image analysis algorithms that allow themore » spatial integration of soil properties, gene expression, and root system architecture traits. We propose GLO-Roots as a system that has great utility in presenting environmental stimuli to roots in ways that evoke natural adaptive responses and in providing tools for studying the multi-dimensional nature of such processes.« less

Software-defined microwave photonic filter with high reconfigurable resolution

PubMed Central

Wei, Wei; Yi, Lilin; Jaouën, Yves; Hu, Weisheng

2016-01-01

Microwave photonic filters (MPFs) are of great interest in radio frequency systems since they provide prominent flexibility on microwave signal processing. Although filter reconfigurability and tunability have been demonstrated repeatedly, it is still difficult to control the filter shape with very high precision. Thus the MPF application is basically limited to signal selection. Here we present a polarization-insensitive single-passband arbitrary-shaped MPF with ~GHz bandwidth based on stimulated Brillouin scattering (SBS) in optical fibre. For the first time the filter shape, bandwidth and central frequency can all be precisely defined by software with ~MHz resolution. The unprecedented multi-dimensional filter flexibility offers new possibilities to process microwave signals directly in optical domain with high precision thus enhancing the MPF functionality. Nanosecond pulse shaping by implementing precisely defined filters is demonstrated to prove the filter superiority and practicability. PMID:27759062

Software-defined microwave photonic filter with high reconfigurable resolution.

PubMed

Wei, Wei; Yi, Lilin; Jaouën, Yves; Hu, Weisheng

2016-10-19

Microwave photonic filters (MPFs) are of great interest in radio frequency systems since they provide prominent flexibility on microwave signal processing. Although filter reconfigurability and tunability have been demonstrated repeatedly, it is still difficult to control the filter shape with very high precision. Thus the MPF application is basically limited to signal selection. Here we present a polarization-insensitive single-passband arbitrary-shaped MPF with ~GHz bandwidth based on stimulated Brillouin scattering (SBS) in optical fibre. For the first time the filter shape, bandwidth and central frequency can all be precisely defined by software with ~MHz resolution. The unprecedented multi-dimensional filter flexibility offers new possibilities to process microwave signals directly in optical domain with high precision thus enhancing the MPF functionality. Nanosecond pulse shaping by implementing precisely defined filters is demonstrated to prove the filter superiority and practicability.

Multidimensional Single-Cell Analysis of BCR Signaling Reveals Proximal Activation Defect As a Hallmark of Chronic Lymphocytic Leukemia B Cells

PubMed Central

Palomba, M. Lia; Piersanti, Kelly; Ziegler, Carly G. K.; Decker, Hugo; Cotari, Jesse W.; Bantilan, Kurt; Rijo, Ivelise; Gardner, Jeff R.; Heaney, Mark; Bemis, Debra; Balderas, Robert; Malek, Sami N.; Seymour, Erlene; Zelenetz, Andrew D.

2014-01-01

Purpose Chronic Lymphocytic Leukemia (CLL) is defined by a perturbed B-cell receptor-mediated signaling machinery. We aimed to model differential signaling behavior between B cells from CLL and healthy individuals to pinpoint modes of dysregulation. Experimental Design We developed an experimental methodology combining immunophenotyping, multiplexed phosphospecific flow cytometry, and multifactorial statistical modeling. Utilizing patterns of signaling network covariance, we modeled BCR signaling in 67 CLL patients using Partial Least Squares Regression (PLSR). Results from multidimensional modeling were validated using an independent test cohort of 38 patients. Results We identified a dynamic and variable imbalance between proximal (pSYK, pBTK) and distal (pPLCγ2, pBLNK, ppERK) phosphoresponses. PLSR identified the relationship between upstream tyrosine kinase SYK and its target, PLCγ2, as maximally predictive and sufficient to distinguish CLL from healthy samples, pointing to this juncture in the signaling pathway as a hallmark of CLL B cells. Specific BCR pathway signaling signatures that correlate with the disease and its degree of aggressiveness were identified. Heterogeneity in the PLSR response variable within the B cell population is both a characteristic mark of healthy samples and predictive of disease aggressiveness. Conclusion Single-cell multidimensional analysis of BCR signaling permitted focused analysis of the variability and heterogeneity of signaling behavior from patient-to-patient, and from cell-to-cell. Disruption of the pSYK/pPLCγ2 relationship is uncovered as a robust hallmark of CLL B cell signaling behavior. Together, these observations implicate novel elements of the BCR signal transduction as potential therapeutic targets. PMID:24489640

Method of multi-dimensional moment analysis for the characterization of signal peaks

DOEpatents

Pfeifer, Kent B; Yelton, William G; Kerr, Dayle R; Bouchier, Francis A

2012-10-23

A method of multi-dimensional moment analysis for the characterization of signal peaks can be used to optimize the operation of an analytical system. With a two-dimensional Peclet analysis, the quality and signal fidelity of peaks in a two-dimensional experimental space can be analyzed and scored. This method is particularly useful in determining optimum operational parameters for an analytical system which requires the automated analysis of large numbers of analyte data peaks. For example, the method can be used to optimize analytical systems including an ion mobility spectrometer that uses a temperature stepped desorption technique for the detection of explosive mixtures.

Single-indicator-based Multidimensional Sensing: Detection and Identification of Heavy Metal Ions and Understanding the Foundations from Experiment to Simulation

PubMed Central

Leng, Yumin; Qian, Sihua; Wang, Yuhui; Lu, Cheng; Ji, Xiaoxu; Lu, Zhiwen; Lin, Hengwei

2016-01-01

Multidimensional sensing offers advantages in accuracy, diversity and capability for the simultaneous detection and discrimination of multiple analytes, however, the previous reports usually require complicated synthesis/fabrication process and/or need a variety of techniques (or instruments) to acquire signals. Therefore, to take full advantages of this concept, simple designs are highly desirable. Herein, a novel concept is conceived to construct multidimensional sensing platforms based on a single indicator that has capability of showing diverse color/fluorescence responses with the addition of different analytes. Through extracting hidden information from these responses, such as red, green and blue (RGB) alterations, a triple-channel-based multidimensional sensing platform could consequently be fabricated, and the RGB alterations are further applicable to standard statistical methods. As a proof-of-concept study, a triple-channel sensing platform is fabricated solely using dithizone with assistance of cetyltrimethylammonium bromide (CTAB) for hyperchromicity and sensitization, which demonstrates superior capabilities in detection and identification of ten common heavy metal ions at their standard concentrations of wastewater-discharge of China. Moreover, this sensing platform exhibits promising applications in semi-quantitative and even quantitative analysis individuals of these heavy metal ions with high sensitivity as well. Finally, density functional theory calculations are performed to reveal the foundations for this analysis. PMID:27146105

Non-uniform sampling: post-Fourier era of NMR data collection and processing.

PubMed

Kazimierczuk, Krzysztof; Orekhov, Vladislav

2015-11-01

The invention of multidimensional techniques in the 1970s revolutionized NMR, making it the general tool of structural analysis of molecules and materials. In the most straightforward approach, the signal sampling in the indirect dimensions of a multidimensional experiment is performed in the same manner as in the direct dimension, i.e. with a grid of equally spaced points. This results in lengthy experiments with a resolution often far from optimum. To circumvent this problem, numerous sparse-sampling techniques have been developed in the last three decades, including two traditionally distinct approaches: the radial sampling and non-uniform sampling. This mini review discusses the sparse signal sampling and reconstruction techniques from the point of view of an underdetermined linear algebra problem that arises when a full, equally spaced set of sampled points is replaced with sparse sampling. Additional assumptions that are introduced to solve the problem, as well as the shape of the undersampled Fourier transform operator (visualized as so-called point spread function), are shown to be the main differences between various sparse-sampling methods. Copyright © 2015 John Wiley & Sons, Ltd.

On simplified application of multidimensional Savitzky-Golay filters and differentiators

NASA Astrophysics Data System (ADS)

Shekhar, Chandra

2016-02-01

I propose a simplified approach for multidimensional Savitzky-Golay filtering, to enable its fast and easy implementation in scientific and engineering applications. The proposed method, which is derived from a generalized framework laid out by Thornley (D. J. Thornley, "Novel anisotropic multidimensional convolution filters for derivative estimation and reconstruction" in Proceedings of International Conference on Signal Processing and Communications, November 2007), first transforms any given multidimensional problem into a unique one, by transforming coordinates of the sampled data nodes to unity-spaced, uniform data nodes, and then performs filtering and calculates partial derivatives on the unity-spaced nodes. It is followed by transporting the calculated derivatives back onto the original data nodes by using the chain rule of differentiation. The burden to performing the most cumbersome task, which is to carry out the filtering and to obtain derivatives on the unity-spaced nodes, is almost eliminated by providing convolution coefficients for a number of convolution kernel sizes and polynomial orders, up to four spatial dimensions. With the availability of the convolution coefficients, the task of filtering at a data node reduces merely to multiplication of two known matrices. Simplified strategies to adequately address near-boundary data nodes and to calculate partial derivatives there are also proposed. Finally, the proposed methodologies are applied to a three-dimensional experimentally obtained data set, which shows that multidimensional Savitzky-Golay filters and differentiators perform well in both the internal and the near-boundary regions of the domain.

Coding Strategies and Implementations of Compressive Sensing

NASA Astrophysics Data System (ADS)

Tsai, Tsung-Han

This dissertation studies the coding strategies of computational imaging to overcome the limitation of conventional sensing techniques. The information capacity of conventional sensing is limited by the physical properties of optics, such as aperture size, detector pixels, quantum efficiency, and sampling rate. These parameters determine the spatial, depth, spectral, temporal, and polarization sensitivity of each imager. To increase sensitivity in any dimension can significantly compromise the others. This research implements various coding strategies subject to optical multidimensional imaging and acoustic sensing in order to extend their sensing abilities. The proposed coding strategies combine hardware modification and signal processing to exploiting bandwidth and sensitivity from conventional sensors. We discuss the hardware architecture, compression strategies, sensing process modeling, and reconstruction algorithm of each sensing system. Optical multidimensional imaging measures three or more dimensional information of the optical signal. Traditional multidimensional imagers acquire extra dimensional information at the cost of degrading temporal or spatial resolution. Compressive multidimensional imaging multiplexes the transverse spatial, spectral, temporal, and polarization information on a two-dimensional (2D) detector. The corresponding spectral, temporal and polarization coding strategies adapt optics, electronic devices, and designed modulation techniques for multiplex measurement. This computational imaging technique provides multispectral, temporal super-resolution, and polarization imaging abilities with minimal loss in spatial resolution and noise level while maintaining or gaining higher temporal resolution. The experimental results prove that the appropriate coding strategies may improve hundreds times more sensing capacity. Human auditory system has the astonishing ability in localizing, tracking, and filtering the selected sound sources or information from a noisy environment. Using engineering efforts to accomplish the same task usually requires multiple detectors, advanced computational algorithms, or artificial intelligence systems. Compressive acoustic sensing incorporates acoustic metamaterials in compressive sensing theory to emulate the abilities of sound localization and selective attention. This research investigates and optimizes the sensing capacity and the spatial sensitivity of the acoustic sensor. The well-modeled acoustic sensor allows localizing multiple speakers in both stationary and dynamic auditory scene; and distinguishing mixed conversations from independent sources with high audio recognition rate.

Dopaminergic Balance between Reward Maximization and Policy Complexity

PubMed Central

Parush, Naama; Tishby, Naftali; Bergman, Hagai

2011-01-01

Previous reinforcement-learning models of the basal ganglia network have highlighted the role of dopamine in encoding the mismatch between prediction and reality. Far less attention has been paid to the computational goals and algorithms of the main-axis (actor). Here, we construct a top-down model of the basal ganglia with emphasis on the role of dopamine as both a reinforcement learning signal and as a pseudo-temperature signal controlling the general level of basal ganglia excitability and motor vigilance of the acting agent. We argue that the basal ganglia endow the thalamic-cortical networks with the optimal dynamic tradeoff between two constraints: minimizing the policy complexity (cost) and maximizing the expected future reward (gain). We show that this multi-dimensional optimization processes results in an experience-modulated version of the softmax behavioral policy. Thus, as in classical softmax behavioral policies, probability of actions are selected according to their estimated values and the pseudo-temperature, but in addition also vary according to the frequency of previous choices of these actions. We conclude that the computational goal of the basal ganglia is not to maximize cumulative (positive and negative) reward. Rather, the basal ganglia aim at optimization of independent gain and cost functions. Unlike previously suggested single-variable maximization processes, this multi-dimensional optimization process leads naturally to a softmax-like behavioral policy. We suggest that beyond its role in the modulation of the efficacy of the cortico-striatal synapses, dopamine directly affects striatal excitability and thus provides a pseudo-temperature signal that modulates the tradeoff between gain and cost. The resulting experience and dopamine modulated softmax policy can then serve as a theoretical framework to account for the broad range of behaviors and clinical states governed by the basal ganglia and dopamine systems. PMID:21603228

Invited Article: Single-shot THz detection techniques optimized for multidimensional THz spectroscopy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Teo, Stephanie M.; Ofori-Okai, Benjamin K.; Werley, Christopher A.

Multidimensional spectroscopy at visible and infrared frequencies has opened a window into the transfer of energy and quantum coherences at ultrafast time scales. For these measurements to be performed in a manageable amount of time, one spectral axis is typically recorded in a single laser shot. An analogous rapid-scanning capability for THz measurements will unlock the multidimensional toolkit in this frequency range. Here, we first review the merits of existing single-shot THz schemes and discuss their potential in multidimensional THz spectroscopy. We then introduce improved experimental designs and noise suppression techniques for the two most promising methods: frequency-to-time encoding withmore » linear spectral interferometry and angle-to-time encoding with dual echelons. Both methods, each using electro-optic detection in the linear regime, were able to reproduce the THz temporal waveform acquired with a traditional scanning delay line. Although spectral interferometry had mediocre performance in terms of signal-to-noise, the dual echelon method was easily implemented and achieved the same level of signal-to-noise as the scanning delay line in only 4.5% of the laser pulses otherwise required (or 22 times faster). This reduction in acquisition time will compress day-long scans to hours and hence provides a practical technique for multidimensional THz measurements.« less

Invited Article: Single-shot THz detection techniques optimized for multidimensional THz spectroscopy.

PubMed

Teo, Stephanie M; Ofori-Okai, Benjamin K; Werley, Christopher A; Nelson, Keith A

2015-05-01

Multidimensional spectroscopy at visible and infrared frequencies has opened a window into the transfer of energy and quantum coherences at ultrafast time scales. For these measurements to be performed in a manageable amount of time, one spectral axis is typically recorded in a single laser shot. An analogous rapid-scanning capability for THz measurements will unlock the multidimensional toolkit in this frequency range. Here, we first review the merits of existing single-shot THz schemes and discuss their potential in multidimensional THz spectroscopy. We then introduce improved experimental designs and noise suppression techniques for the two most promising methods: frequency-to-time encoding with linear spectral interferometry and angle-to-time encoding with dual echelons. Both methods, each using electro-optic detection in the linear regime, were able to reproduce the THz temporal waveform acquired with a traditional scanning delay line. Although spectral interferometry had mediocre performance in terms of signal-to-noise, the dual echelon method was easily implemented and achieved the same level of signal-to-noise as the scanning delay line in only 4.5% of the laser pulses otherwise required (or 22 times faster). This reduction in acquisition time will compress day-long scans to hours and hence provides a practical technique for multidimensional THz measurements.

MBA Quality Signals.

ERIC Educational Resources Information Center

Chapman, Randall S.

1998-01-01

A study identified quality signals for master's programs in business administration (MBAs). Traditional scholarly oriented academic signals are apparently not valued as such by external customer groups. MBA academic quality appears to be a multidimensional construct, with subdimensions of real-worldness; placement; student satisfaction; and…

Multidimensional Data Modeling for Business Process Analysis

NASA Astrophysics Data System (ADS)

Mansmann, Svetlana; Neumuth, Thomas; Scholl, Marc H.

The emerging area of business process intelligence attempts to enhance the analytical capabilities of business process management systems by employing data warehousing and mining technologies. This paper presents an approach to re-engineering the business process modeling in conformity with the multidimensional data model. Since the business process and the multidimensional model are driven by rather different objectives and assumptions, there is no straightforward solution to converging these models.

Proteases Revisited: Roles and Therapeutic Implications in Fibrosis

PubMed Central

Kryczka, Jakub

2017-01-01

Proteases target many substrates, triggering changes in distinct biological processes correlated with cell migration, EMT/EndMT and fibrosis. Extracellular protease activity, demonstrated by secreted and membrane-bound protease forms, leads to ECM degradation, activation of other proteases (i.e., proteolysis of nonactive zymogens), decomposition of cell-cell junctions, release of sequestered growth factors (TGF-β and VEGF), activation of signal proteins and receptors, degradation of inflammatory inhibitors or inflammation-related proteins, and changes in cell mechanosensing and motility. Intracellular proteases, mainly caspases and cathepsins, modulate lysosome activity and signal transduction pathways. Herein, we discuss the current knowledge on the multidimensional impact of proteases on the development of fibrosis. PMID:28642633

Physiological correlates of mental workload

NASA Technical Reports Server (NTRS)

Zacharias, G. L.

1980-01-01

A literature review was conducted to assess the basis of and techniques for physiological assessment of mental workload. The study findings reviewed had shortcomings involving one or more of the following basic problems: (1) physiologic arousal can be easily driven by nonworkload factors, confounding any proposed metric; (2) the profound absence of underlying physiologic models has promulgated a multiplicity of seemingly arbitrary signal processing techniques; (3) the unspecified multidimensional nature of physiological "state" has given rise to a broad spectrum of competing noncommensurate metrics; and (4) the lack of an adequate definition of workload compels physiologic correlations to suffer either from the vagueness of implicit workload measures or from the variance of explicit subjective assessments. Using specific studies as examples, two basic signal processing/data reduction techniques in current use, time and ensemble averaging are discussed.

A multidimensional subdiffusion model: An arbitrage-free market

NASA Astrophysics Data System (ADS)

Li, Guo-Hua; Zhang, Hong; Luo, Mao-Kang

2012-12-01

To capture the subdiffusive characteristics of financial markets, the subordinated process, directed by the inverse α-stale subordinator Sα(t) for 0 < α < 1, has been employed as the model of asset prices. In this article, we introduce a multidimensional subdiffusion model that has a bond and K correlated stocks. The stock price process is a multidimensional subdiffusion process directed by the inverse α-stable subordinator. This model describes the period of stagnation for each stock and the behavior of the dependency between multiple stocks. Moreover, we derive the multidimensional fractional backward Kolmogorov equation for the subordinated process using the Laplace transform technique. Finally, using a martingale approach, we prove that the multidimensional subdiffusion model is arbitrage-free, and also gives an arbitrage-free pricing rule for contingent claims associated with the martingale measure.

Compact stochastic models for multidimensional quasiballistic thermal transport

NASA Astrophysics Data System (ADS)

Vermeersch, Bjorn

2016-11-01

The Boltzmann transport equation (BTE) has proven indispensable in elucidating quasiballistic heat dynamics. The experimental observations of nondiffusive thermal transients, however, are interpreted almost exclusively through purely diffusive formalisms that merely extract "effective" Fourier conductivities. Here, we build upon stochastic transport theory to provide a characterisation framework that blends the rich physics contained within the BTE solutions with the convenience of conventional analyses. The multidimensional phonon dynamics are described in terms of an isotropic Poissonian flight process with a rigorous Fourier-Laplace single pulse response P (ξ → ,s )=1 /[s +ψ(∥ ξ → ∥ )] . The spatial propagator ψ(∥ ξ → ∥ ) , unlike commonly reconstructed mean free path spectra κΣ(Λ) , serves as a genuine thermal blueprint of the medium that can be identified in a compact form directly from the raw measurement signals. Practical illustrations for transient thermal grating and time domain thermoreflectance experiments on respectively GaAs and InGaAs are provided.

Switchable in-line monitor for multi-dimensional multiplexed photonic integrated circuit.

PubMed

Chen, Guanyu; Yu, Yu; Ye, Mengyuan; Zhang, Xinliang

2016-06-27

A flexible monitor suitable for the discrimination of on-chip transmitted mode division multiplexed (MDM) and wavelength division multiplexed (WDM) signals is proposed and fabricated. By selectively extracting part of the incoming signals through the tunable wavelength and mode dependent drop filter, the in-line and switchable monitor can discriminate the wavelength, mode and power information of the transmitted signals. Being different from a conventional mode and wavelength demultiplexer, the monitor is specifically designed to ensure a flexible in-line monitoring. For demonstration, three mode and three wavelength multiplexed signals are successfully processed. Assisted by the integrated photodetectors (PDs), both the measured photo currents and eye diagrams validate the performance of the proposed device. The bit error ratio (BER) measurement results show less than 0.4 dB power penalty between different modes and ~2 dB power penalty for single wavelength and WDM cases under 10^-9 BER level.

Perceptual Salience and Children's Multidimensional Problem Solving

ERIC Educational Resources Information Center

Odom, Richard D.; Corbin, David W.

1973-01-01

Uni- and multidimensional processing of 6- to 9-year olds was studied using recall tasks in which an array of stimuli was reconstructed to match a model array. Results indicated that both age groups were able to solve multidimensional problems, but that solution rate was retarded by the unidimensional processing of highly salient dimensions.…

Multidimensional optical spectroscopy of a single molecule in a current-carrying state

NASA Astrophysics Data System (ADS)

Rahav, S.; Mukamel, S.

2010-12-01

The nonlinear optical signals from an open system consisting of a molecule connected to metallic leads, in response to a sequence of impulsive pulses, are calculated using a superoperator formalism. Two detection schemes are considered: coherent stimulated emission and incoherent fluorescence. The two provide similar but not identical information. The necessary superoperator correlation functions are evaluated either by converting them to ordinary (Hilbert space) operators which are then expanded in many-body states, or by using Wick's theorem for superoperators to factorize them into nonequilibrium two point Green's functions. As an example we discuss a stimulated Raman process that shows resonances involving two different charge states of the molecule in the same signal.

Dynamic analysis of heartbeat rate signals of epileptics using multidimensional phase space reconstruction approach

NASA Astrophysics Data System (ADS)

Su, Zhi-Yuan; Wu, Tzuyin; Yang, Po-Hua; Wang, Yeng-Tseng

2008-04-01

The heartbeat rate signal provides an invaluable means of assessing the sympathetic-parasympathetic balance of the human autonomic nervous system and thus represents an ideal diagnostic mechanism for detecting a variety of disorders such as epilepsy, cardiac disease and so forth. The current study analyses the dynamics of the heartbeat rate signal of known epilepsy sufferers in order to obtain a detailed understanding of the heart rate pattern during a seizure event. In the proposed approach, the ECG signals are converted into heartbeat rate signals and the embedology theorem is then used to construct the corresponding multidimensional phase space. The dynamics of the heartbeat rate signal are then analyzed before, during and after an epileptic seizure by examining the maximum Lyapunov exponent and the correlation dimension of the attractors in the reconstructed phase space. In general, the results reveal that the heartbeat rate signal transits from an aperiodic, highly-complex behaviour before an epileptic seizure to a low dimensional chaotic motion during the seizure event. Following the seizure, the signal trajectories return to a highly-complex state, and the complex signal patterns associated with normal physiological conditions reappear.

On the bandwidth of the plenoptic function.

PubMed

Do, Minh N; Marchand-Maillet, Davy; Vetterli, Martin

2012-02-01

The plenoptic function (POF) provides a powerful conceptual tool for describing a number of problems in image/video processing, vision, and graphics. For example, image-based rendering is shown as sampling and interpolation of the POF. In such applications, it is important to characterize the bandwidth of the POF. We study a simple but representative model of the scene where band-limited signals (e.g., texture images) are "painted" on smooth surfaces (e.g., of objects or walls). We show that, in general, the POF is not band limited unless the surfaces are flat. We then derive simple rules to estimate the essential bandwidth of the POF for this model. Our analysis reveals that, in addition to the maximum and minimum depths and the maximum frequency of painted signals, the bandwidth of the POF also depends on the maximum surface slope. With a unifying formalism based on multidimensional signal processing, we can verify several key results in POF processing, such as induced filtering in space and depth-corrected interpolation, and quantify the necessary sampling rates. © 2011 IEEE

Multidimensional Recurrence Quantification Analysis (MdRQA) for the Analysis of Multidimensional Time-Series: A Software Implementation in MATLAB and Its Application to Group-Level Data in Joint Action

PubMed Central

Wallot, Sebastian; Roepstorff, Andreas; Mønster, Dan

2016-01-01

We introduce Multidimensional Recurrence Quantification Analysis (MdRQA) as a tool to analyze multidimensional time-series data. We show how MdRQA can be used to capture the dynamics of high-dimensional signals, and how MdRQA can be used to assess coupling between two or more variables. In particular, we describe applications of the method in research on joint and collective action, as it provides a coherent analysis framework to systematically investigate dynamics at different group levels—from individual dynamics, to dyadic dynamics, up to global group-level of arbitrary size. The Appendix in Supplementary Material contains a software implementation in MATLAB to calculate MdRQA measures. PMID:27920748

Multidimensional Recurrence Quantification Analysis (MdRQA) for the Analysis of Multidimensional Time-Series: A Software Implementation in MATLAB and Its Application to Group-Level Data in Joint Action.

PubMed

Wallot, Sebastian; Roepstorff, Andreas; Mønster, Dan

2016-01-01

We introduce Multidimensional Recurrence Quantification Analysis (MdRQA) as a tool to analyze multidimensional time-series data. We show how MdRQA can be used to capture the dynamics of high-dimensional signals, and how MdRQA can be used to assess coupling between two or more variables. In particular, we describe applications of the method in research on joint and collective action, as it provides a coherent analysis framework to systematically investigate dynamics at different group levels-from individual dynamics, to dyadic dynamics, up to global group-level of arbitrary size. The Appendix in Supplementary Material contains a software implementation in MATLAB to calculate MdRQA measures.

Temporal masking of multidimensional tactual stimuli

NASA Astrophysics Data System (ADS)

Tan, Hong Z.; Reed, Charlotte M.; Delhorne, Lorraine A.; Durlach, Nathaniel I.; Wan, Natasha

2003-12-01

Experiments were performed to examine the temporal masking properties of multidimensional tactual stimulation patterns delivered to the left index finger. The stimuli consisted of fixed-frequency sinusoidal motions in the kinesthetic (2 or 4 Hz), midfrequency (30 Hz), and cutaneous (300 Hz) frequency ranges. Seven stimuli composed of one, two, or three spectral components were constructed at each of two signal durations (125 or 250 ms). Subjects identified target signals under three different masking paradigms: forward masking, backward masking, and sandwiched masking (in which the target is presented between two maskers). Target identification was studied as a function of interstimulus interval (ISI) in the range 0 to 640 ms. For both signal durations, percent-correct scores increased with ISI for each of the three masking paradigms. Scores with forward and backward masking were similar and significantly higher than scores obtained with sandwiched masking. Analyses of error trials revealed that subjects showed a tendency to respond, more often than chance, with the masker, the composite of the masker and target, or the combination of the target and a component of the masker. The current results are compared to those obtained in previous studies of tactual recognition masking with brief cutaneous spatial patterns. The results are also discussed in terms of estimates of information transfer (IT) and IT rate, are compared to previous studies with multidimensional tactual signals, and are related to research on the development of tactual aids for the deaf.

Artifacts in time-resolved NUS: A case study of NOE build-up curves from 2D NOESY.

PubMed

Dass, Rupashree; Kasprzak, Paweł; Koźmiński, Wiktor; Kazimierczuk, Krzysztof

2016-04-01

Multidimensional NMR spectroscopy requires time-consuming sampling of indirect dimensions and so is usually used to study stable samples. However, dynamically changing compounds or their mixtures commonly occur in problems of natural science. Monitoring them requires the use multidimensional NMR in a time-resolved manner - in other words, a series of quick spectra must be acquired at different points in time. Among the many solutions that have been proposed to achieve this goal, time-resolved non-uniform sampling (TR-NUS) is one of the simplest. In a TR-NUS experiment, the signal is sampled using a shuffled random schedule and then divided into overlapping subsets. These subsets are then processed using one of the NUS reconstruction methods, for example compressed sensing (CS). The resulting stack of spectra forms a temporal "pseudo-dimension" that shows the changes caused by the process occurring in the sample. CS enables the use of small subsets of data, which minimizes the averaging of the effects studied. Yet, even within these limited timeframes, the sample undergoes certain changes. In this paper we discuss the effect of varying signal amplitude in a TR-NUS experiment. Our theoretical calculations show that the variations within the subsets lead to t1-noise, which is dependent on the rate of change of the signal amplitude. We verify these predictions experimentally. As a model case we choose a novel 2D TR-NOESY experiment in which mixing time is varied in parallel with shuffled NUS in the indirect dimension. The experiment, performed on a sample of strychnine, provides a near-continuous NOE build-up curve, whose shape closely reflects the t1-noise level. 2D TR-NOESY reduces the measurement time compared to the conventional approach and makes it possible to verify the theoretical predictions about signal variations during TR-NUS. Copyright © 2016 Elsevier Inc. All rights reserved.

Genten: Software for Generalized Tensor Decompositions v. 1.0.0

DOE Office of Scientific and Technical Information (OSTI.GOV)

Phipps, Eric T.; Kolda, Tamara G.; Dunlavy, Daniel

Tensors, or multidimensional arrays, are a powerful mathematical means of describing multiway data. This software provides computational means for decomposing or approximating a given tensor in terms of smaller tensors of lower dimension, focusing on decomposition of large, sparse tensors. These techniques have applications in many scientific areas, including signal processing, linear algebra, computer vision, numerical analysis, data mining, graph analysis, neuroscience and more. The software is designed to take advantage of parallelism present emerging computer architectures such has multi-core CPUs, many-core accelerators such as the Intel Xeon Phi, and computation-oriented GPUs to enable efficient processing of large tensors.

Random phase detection in multidimensional NMR.

PubMed

Maciejewski, Mark W; Fenwick, Matthew; Schuyler, Adam D; Stern, Alan S; Gorbatyuk, Vitaliy; Hoch, Jeffrey C

2011-10-04

Despite advances in resolution accompanying the development of high-field superconducting magnets, biomolecular applications of NMR require multiple dimensions in order to resolve individual resonances, and the achievable resolution is typically limited by practical constraints on measuring time. In addition to the need for measuring long evolution times to obtain high resolution, the need to distinguish the sign of the frequency constrains the ability to shorten measuring times. Sign discrimination is typically accomplished by sampling the signal with two different receiver phases or by selecting a reference frequency outside the range of frequencies spanned by the signal and then sampling at a higher rate. In the parametrically sampled (indirect) time dimensions of multidimensional NMR experiments, either method imposes an additional factor of 2 sampling burden for each dimension. We demonstrate that by using a single detector phase at each time sample point, but randomly altering the phase for different points, the sign ambiguity that attends fixed single-phase detection is resolved. Random phase detection enables a reduction in experiment time by a factor of 2 for each indirect dimension, amounting to a factor of 8 for a four-dimensional experiment, albeit at the cost of introducing sampling artifacts. Alternatively, for fixed measuring time, random phase detection can be used to double resolution in each indirect dimension. Random phase detection is complementary to nonuniform sampling methods, and their combination offers the potential for additional benefits. In addition to applications in biomolecular NMR, random phase detection could be useful in magnetic resonance imaging and other signal processing contexts.

Development of a Computerized Adaptive Testing for Diagnosing the Cognitive Process of Grade 7 Students in Learning Algebra, Using Multidimensional Item Response Theory

ERIC Educational Resources Information Center

Senarat, Somprasong; Tayraukham, Sombat; Piyapimonsit, Chatsiri; Tongkhambanjong, Sakesan

2013-01-01

The purpose of this research is to develop a multidimensional computerized adaptive test for diagnosing the cognitive process of grade 7 students in learning algebra by applying multidimensional item response theory. The research is divided into 4 steps: 1) the development of item bank of algebra, 2) the development of the multidimensional…

Measurement model as a means for studying the process of emotion origination

NASA Astrophysics Data System (ADS)

Taymanov, R.; Baksheeva, Iu; Sapozhnikova, K.; Chunovkina, A.

2016-11-01

In the last edition of the International Vocabulary of Metrology the concept “measurement” was spread outside the field of physical quantities. This fact makes it relevant to analyze the experience of developing the models of multidimensional quantity measurements. The model of measurements of expected emotions caused by musical and other acoustic impacts, is considered. The model relies upon a hypothesis of a nonlinear conversion of acoustic signals to a neurophysiological reaction giving rise to emotion. Methods for checking this hypothesis as well as experimental results are given.

Image matrix processor for fast multi-dimensional computations

DOEpatents

Roberson, George P.; Skeate, Michael F.

1996-01-01

An apparatus for multi-dimensional computation which comprises a computation engine, including a plurality of processing modules. The processing modules are configured in parallel and compute respective contributions to a computed multi-dimensional image of respective two dimensional data sets. A high-speed, parallel access storage system is provided which stores the multi-dimensional data sets, and a switching circuit routes the data among the processing modules in the computation engine and the storage system. A data acquisition port receives the two dimensional data sets representing projections through an image, for reconstruction algorithms such as encountered in computerized tomography. The processing modules include a programmable local host, by which they may be configured to execute a plurality of different types of multi-dimensional algorithms. The processing modules thus include an image manipulation processor, which includes a source cache, a target cache, a coefficient table, and control software for executing image transformation routines using data in the source cache and the coefficient table and loading resulting data in the target cache. The local host processor operates to load the source cache with a two dimensional data set, loads the coefficient table, and transfers resulting data out of the target cache to the storage system, or to another destination.

Acoustic Remote Sensing

NASA Astrophysics Data System (ADS)

Dowling, David R.; Sabra, Karim G.

2015-01-01

Acoustic waves carry information about their source and collect information about their environment as they propagate. This article reviews how these information-carrying and -collecting features of acoustic waves that travel through fluids can be exploited for remote sensing. In nearly all cases, modern acoustic remote sensing involves array-recorded sounds and array signal processing to recover multidimensional results. The application realm for acoustic remote sensing spans an impressive range of signal frequencies (10-2 to 107 Hz) and distances (10-2 to 107 m) and involves biomedical ultrasound imaging, nondestructive evaluation, oil and gas exploration, military systems, and Nuclear Test Ban Treaty monitoring. In the past two decades, approaches have been developed to robustly localize remote sources; remove noise and multipath distortion from recorded signals; and determine the acoustic characteristics of the environment through which the sound waves have traveled, even when the recorded sounds originate from uncooperative sources or are merely ambient noise.

Application of random coherence order selection in gradient-enhanced multidimensional NMR

NASA Astrophysics Data System (ADS)

Bostock, Mark J.; Nietlispach, Daniel

2016-03-01

Development of multidimensional NMR is essential to many applications, for example in high resolution structural studies of biomolecules. Multidimensional techniques enable separation of NMR signals over several dimensions, improving signal resolution, whilst also allowing identification of new connectivities. However, these advantages come at a significant cost. The Fourier transform theorem requires acquisition of a grid of regularly spaced points to satisfy the Nyquist criterion, while frequency discrimination and acquisition of a pure phase spectrum require acquisition of both quadrature components for each time point in every indirect (non-acquisition) dimension, adding a factor of 2 N -1 to the number of free- induction decays which must be acquired, where N is the number of dimensions. Compressed sensing (CS) ℓ 1-norm minimisation in combination with non-uniform sampling (NUS) has been shown to be extremely successful in overcoming the Nyquist criterion. Previously, maximum entropy reconstruction has also been used to overcome the limitation of frequency discrimination, processing data acquired with only one quadrature component at a given time interval, known as random phase detection (RPD), allowing a factor of two reduction in the number of points for each indirect dimension (Maciejewski et al. 2011 PNAS 108 16640). However, whilst this approach can be easily applied in situations where the quadrature components are acquired as amplitude modulated data, the same principle is not easily extended to phase modulated (P-/N-type) experiments where data is acquired in the form exp (iωt) or exp (-iωt), and which make up many of the multidimensional experiments used in modern NMR. Here we demonstrate a modification of the CS ℓ 1-norm approach to allow random coherence order selection (RCS) for phase modulated experiments; we generalise the nomenclature for RCS and RPD as random quadrature detection (RQD). With this method, the power of RQD can be extended to the full suite of experiments available to modern NMR spectroscopy, allowing resolution enhancements for all indirect dimensions; alone or in combination with NUS, RQD can be used to improve experimental resolution, or shorten experiment times, of considerable benefit to the challenging applications undertaken by modern NMR.

Frequency-Comb Based Double-Quantum Two-Dimensional Spectrum Identifies Collective Hyperfine Resonances in Atomic Vapor Induced by Dipole-Dipole Interactions

NASA Astrophysics Data System (ADS)

Lomsadze, Bachana; Cundiff, Steven T.

2018-06-01

Frequency-comb based multidimensional coherent spectroscopy is a novel optical method that enables high-resolution measurement in a short acquisition time. The method's resolution makes multidimensional coherent spectroscopy relevant for atomic systems that have narrow resonances. We use double-quantum multidimensional coherent spectroscopy to reveal collective hyperfine resonances in rubidium vapor at 100 °C induced by dipole-dipole interactions. We observe tilted and elongated line shapes in the double-quantum 2D spectra, which have never been reported for Doppler-broadened systems. The elongated line shapes suggest that the signal is predominately from the interacting atoms that have a near zero relative velocity.

Coherent multi-dimensional spectroscopy at optical frequencies in a single beam with optical readout

NASA Astrophysics Data System (ADS)

Seiler, Hélène; Palato, Samuel; Kambhampati, Patanjali

2017-09-01

Ultrafast coherent multi-dimensional spectroscopies form a powerful set of techniques to unravel complex processes, ranging from light-harvesting, chemical exchange in biological systems to many-body interactions in quantum-confined materials. Yet these spectroscopies remain complex to implement at the high frequencies of vibrational and electronic transitions, thereby limiting their widespread use. Here we demonstrate the feasibility of two-dimensional spectroscopy at optical frequencies in a single beam. Femtosecond optical pulses are spectrally broadened to a relevant bandwidth and subsequently shaped into phase coherent pulse trains. By suitably modulating the phases of the pulses within the beam, we show that it is possible to directly read out the relevant optical signals. This work shows that one needs neither complex beam geometries nor complex detection schemes in order to measure two-dimensional spectra at optical frequencies. Our setup provides not only a simplified experimental design over standard two-dimensional spectrometers but its optical readout also enables novel applications in microscopy.

Fast acquisition of multidimensional NMR spectra of solids and mesophases using alternative sampling methods.

PubMed

Lesot, Philippe; Kazimierczuk, Krzysztof; Trébosc, Julien; Amoureux, Jean-Paul; Lafon, Olivier

2015-11-01

Unique information about the atom-level structure and dynamics of solids and mesophases can be obtained by the use of multidimensional nuclear magnetic resonance (NMR) experiments. Nevertheless, the acquisition of these experiments often requires long acquisition times. We review here alternative sampling methods, which have been proposed to circumvent this issue in the case of solids and mesophases. Compared to the spectra of solutions, those of solids and mesophases present some specificities because they usually display lower signal-to-noise ratios, non-Lorentzian line shapes, lower spectral resolutions and wider spectral widths. We highlight herein the advantages and limitations of these alternative sampling methods. A first route to accelerate the acquisition time of multidimensional NMR spectra consists in the use of sparse sampling schemes, such as truncated, radial or random sampling ones. These sparsely sampled datasets are generally processed by reconstruction methods differing from the Discrete Fourier Transform (DFT). A host of non-DFT methods have been applied for solids and mesophases, including the G-matrix Fourier transform, the linear least-square procedures, the covariance transform, the maximum entropy and the compressed sensing. A second class of alternative sampling consists in departing from the Jeener paradigm for multidimensional NMR experiments. These non-Jeener methods include Hadamard spectroscopy as well as spatial or orientational encoding of the evolution frequencies. The increasing number of high field NMR magnets and the development of techniques to enhance NMR sensitivity will contribute to widen the use of these alternative sampling methods for the study of solids and mesophases in the coming years. Copyright © 2015 John Wiley & Sons, Ltd.

A novel second-order standard addition analytical method based on data processing with multidimensional partial least-squares and residual bilinearization.

PubMed

Lozano, Valeria A; Ibañez, Gabriela A; Olivieri, Alejandro C

2009-10-05

In the presence of analyte-background interactions and a significant background signal, both second-order multivariate calibration and standard addition are required for successful analyte quantitation achieving the second-order advantage. This report discusses a modified second-order standard addition method, in which the test data matrix is subtracted from the standard addition matrices, and quantitation proceeds via the classical external calibration procedure. It is shown that this novel data processing method allows one to apply not only parallel factor analysis (PARAFAC) and multivariate curve resolution-alternating least-squares (MCR-ALS), but also the recently introduced and more flexible partial least-squares (PLS) models coupled to residual bilinearization (RBL). In particular, the multidimensional variant N-PLS/RBL is shown to produce the best analytical results. The comparison is carried out with the aid of a set of simulated data, as well as two experimental data sets: one aimed at the determination of salicylate in human serum in the presence of naproxen as an additional interferent, and the second one devoted to the analysis of danofloxacin in human serum in the presence of salicylate.

Development of morphogen gradient: The role of dimension and discreteness

DOE Office of Scientific and Technical Information (OSTI.GOV)

Teimouri, Hamid; Kolomeisky, Anatoly B.

2014-02-28

The fundamental processes of biological development are governed by multiple signaling molecules that create non-uniform concentration profiles known as morphogen gradients. It is widely believed that the establishment of morphogen gradients is a result of complex processes that involve diffusion and degradation of locally produced signaling molecules. We developed a multi-dimensional discrete-state stochastic approach for investigating the corresponding reaction-diffusion models. It provided a full analytical description for stationary profiles and for important dynamic properties such as local accumulation times, variances, and mean first-passage times. The role of discreteness in developing of morphogen gradients is analyzed by comparing with available continuummore » descriptions. It is found that the continuum models prediction about multiple time scales near the source region in two-dimensional and three-dimensional systems is not supported in our analysis. Using ideas that view the degradation process as an effective potential, the effect of dimensionality on establishment of morphogen gradients is also discussed. In addition, we investigated how these reaction-diffusion processes are modified with changing the size of the source region.« less

Multidimensional upwind hydrodynamics on unstructured meshes using graphics processing units - I. Two-dimensional uniform meshes

NASA Astrophysics Data System (ADS)

Paardekooper, S.-J.

2017-08-01

We present a new method for numerical hydrodynamics which uses a multidimensional generalization of the Roe solver and operates on an unstructured triangular mesh. The main advantage over traditional methods based on Riemann solvers, which commonly use one-dimensional flux estimates as building blocks for a multidimensional integration, is its inherently multidimensional nature, and as a consequence its ability to recognize multidimensional stationary states that are not hydrostatic. A second novelty is the focus on graphics processing units (GPUs). By tailoring the algorithms specifically to GPUs, we are able to get speedups of 100-250 compared to a desktop machine. We compare the multidimensional upwind scheme to a traditional, dimensionally split implementation of the Roe solver on several test problems, and we find that the new method significantly outperforms the Roe solver in almost all cases. This comes with increased computational costs per time-step, which makes the new method approximately a factor of 2 slower than a dimensionally split scheme acting on a structured grid.

Image matrix processor for fast multi-dimensional computations

DOEpatents

Roberson, G.P.; Skeate, M.F.

1996-10-15

An apparatus for multi-dimensional computation is disclosed which comprises a computation engine, including a plurality of processing modules. The processing modules are configured in parallel and compute respective contributions to a computed multi-dimensional image of respective two dimensional data sets. A high-speed, parallel access storage system is provided which stores the multi-dimensional data sets, and a switching circuit routes the data among the processing modules in the computation engine and the storage system. A data acquisition port receives the two dimensional data sets representing projections through an image, for reconstruction algorithms such as encountered in computerized tomography. The processing modules include a programmable local host, by which they may be configured to execute a plurality of different types of multi-dimensional algorithms. The processing modules thus include an image manipulation processor, which includes a source cache, a target cache, a coefficient table, and control software for executing image transformation routines using data in the source cache and the coefficient table and loading resulting data in the target cache. The local host processor operates to load the source cache with a two dimensional data set, loads the coefficient table, and transfers resulting data out of the target cache to the storage system, or to another destination. 10 figs.

Heteronuclear Multidimensional Protein NMR in a Teaching Laboratory

ERIC Educational Resources Information Center

Wright, Nathan T.

2016-01-01

Heteronuclear multidimensional NMR techniques are commonly used to study protein structure, function, and dynamics, yet they are rarely taught at the undergraduate level. Here, we describe a senior undergraduate laboratory where students collect, process, and analyze heteronuclear multidimensional NMR experiments using an unstudied Ig domain (Ig2…

Feature Sampling in Detection: Implications for the Measurement of Perceptual Independence

ERIC Educational Resources Information Center

Macho, Siegfried

2007-01-01

The article presents the feature sampling signal detection (FS-SDT) model, an extension of the multivariate signal detection (SDT) model. The FS-SDT model assumes that, because of attentional shifts, different subsets of features are sampled for different presentations of the same multidimensional stimulus. Contrary to the SDT model, the FS-SDT…

The Multidimensional Assessment of Interoceptive Awareness (MAIA)

PubMed Central

Mehling, Wolf E.; Price, Cynthia; Daubenmier, Jennifer J.; Acree, Mike; Bartmess, Elizabeth; Stewart, Anita

2012-01-01

This paper describes the development of a multidimensional self-report measure of interoceptive body awareness. The systematic mixed-methods process involved reviewing the current literature, specifying a multidimensional conceptual framework, evaluating prior instruments, developing items, and analyzing focus group responses to scale items by instructors and patients of body awareness-enhancing therapies. Following refinement by cognitive testing, items were field-tested in students and instructors of mind-body approaches. Final item selection was achieved by submitting the field test data to an iterative process using multiple validation methods, including exploratory cluster and confirmatory factor analyses, comparison between known groups, and correlations with established measures of related constructs. The resulting 32-item multidimensional instrument assesses eight concepts. The psychometric properties of these final scales suggest that the Multidimensional Assessment of Interoceptive Awareness (MAIA) may serve as a starting point for research and further collaborative refinement. PMID:23133619

Multi-dimensional spatial light communication made with on-chip InGaN photonic integration

NASA Astrophysics Data System (ADS)

Yang, Yongchao; Zhu, Bingcheng; Shi, Zheng; Wang, Jinyuan; Li, Xin; Gao, Xumin; Yuan, Jialei; Li, Yuanhang; Jiang, Yan; Wang, Yongjin

2017-04-01

Here, we propose, fabricate and characterize suspended photonic integration of InGaN multiple-quantum-well light-emitting diode (MQW-LED), waveguide and InGaN MQW-photodetector on a single chip. The unique light emission property of InGaN MQW-LED makes it feasible to establish multi-dimensional spatial data transmission using visible light. The in-plane light communication system is comprised of InGaN MQW-LED, waveguide and InGaN MQW-photodetector, and the out-of-plane data transmission is realized by detecting the free-space light emission via a commercial photodiode module. Moreover, a full-duplex light communication is experimentally demonstrated at a data transmission rate of 50 Mbps when both InGaN MQW-diodes operate under simultaneous light emission and detection mode. The in-plane superimposed signals are able to be extracted through the self-interference cancellation method, and the out-of-plane superimposed signals are in good agreement with the calculated signals according to the extracted transmitted signals. These results are promising for the development of on-chip InGaN photonic integration for diverse applications.

Energy-efficient spatial-domain-based hybrid multidimensional coded-modulations enabling multi-Tb/s optical transport.

PubMed

Djordjevic, Ivan B

2011-08-15

In addition to capacity, the future high-speed optical transport networks will also be constrained by energy consumption. In order to solve the capacity and energy constraints simultaneously, in this paper we propose the use of energy-efficient hybrid D-dimensional signaling (D>4) by employing all available degrees of freedom for conveyance of the information over a single carrier including amplitude, phase, polarization and orbital angular momentum (OAM). Given the fact that the OAM eigenstates, associated with the azimuthal phase dependence of the complex electric field, are orthogonal, they can be used as basis functions for multidimensional signaling. Since the information capacity is a linear function of number of dimensions, through D-dimensional signal constellations we can significantly improve the overall optical channel capacity. The energy-efficiency problem is solved, in this paper, by properly designing the D-dimensional signal constellation such that the mutual information is maximized, while taking the energy constraint into account. We demonstrate high-potential of proposed energy-efficient hybrid D-dimensional coded-modulation scheme by Monte Carlo simulations. © 2011 Optical Society of America

SAR processing on the MPP

NASA Technical Reports Server (NTRS)

Batcher, K. E.; Eddey, E. E.; Faiss, R. O.; Gilmore, P. A.

1981-01-01

The processing of synthetic aperture radar (SAR) signals using the massively parallel processor (MPP) is discussed. The fast Fourier transform convolution procedures employed in the algorithms are described. The MPP architecture comprises an array unit (ARU) which processes arrays of data; an array control unit which controls the operation of the ARU and performs scalar arithmetic; a program and data management unit which controls the flow of data; and a unique staging memory (SM) which buffers and permutes data. The ARU contains a 128 by 128 array of bit-serial processing elements (PE). Two-by-four surarrays of PE's are packaged in a custom VLSI HCMOS chip. The staging memory is a large multidimensional-access memory which buffers and permutes data flowing with the system. Efficient SAR processing is achieved via ARU communication paths and SM data manipulation. Real time processing capability can be realized via a multiple ARU, multiple SM configuration.

Noninvasive Electroencephalogram Based Control of a Robotic Arm for Writing Task Using Hybrid BCI System.

PubMed

Gao, Qiang; Dou, Lixiang; Belkacem, Abdelkader Nasreddine; Chen, Chao

2017-01-01

A novel hybrid brain-computer interface (BCI) based on the electroencephalogram (EEG) signal which consists of a motor imagery- (MI-) based online interactive brain-controlled switch, "teeth clenching" state detector, and a steady-state visual evoked potential- (SSVEP-) based BCI was proposed to provide multidimensional BCI control. MI-based BCI was used as single-pole double throw brain switch (SPDTBS). By combining the SPDTBS with 4-class SSEVP-based BCI, movement of robotic arm was controlled in three-dimensional (3D) space. In addition, muscle artifact (EMG) of "teeth clenching" condition recorded from EEG signal was detected and employed as interrupter, which can initialize the statement of SPDTBS. Real-time writing task was implemented to verify the reliability of the proposed noninvasive hybrid EEG-EMG-BCI. Eight subjects participated in this study and succeeded to manipulate a robotic arm in 3D space to write some English letters. The mean decoding accuracy of writing task was 0.93 ± 0.03. Four subjects achieved the optimal criteria of writing the word "HI" which is the minimum movement of robotic arm directions (15 steps). Other subjects had needed to take from 2 to 4 additional steps to finish the whole process. These results suggested that our proposed hybrid noninvasive EEG-EMG-BCI was robust and efficient for real-time multidimensional robotic arm control.

Noninvasive Electroencephalogram Based Control of a Robotic Arm for Writing Task Using Hybrid BCI System

PubMed Central

Gao, Qiang

2017-01-01

A novel hybrid brain-computer interface (BCI) based on the electroencephalogram (EEG) signal which consists of a motor imagery- (MI-) based online interactive brain-controlled switch, “teeth clenching” state detector, and a steady-state visual evoked potential- (SSVEP-) based BCI was proposed to provide multidimensional BCI control. MI-based BCI was used as single-pole double throw brain switch (SPDTBS). By combining the SPDTBS with 4-class SSEVP-based BCI, movement of robotic arm was controlled in three-dimensional (3D) space. In addition, muscle artifact (EMG) of “teeth clenching” condition recorded from EEG signal was detected and employed as interrupter, which can initialize the statement of SPDTBS. Real-time writing task was implemented to verify the reliability of the proposed noninvasive hybrid EEG-EMG-BCI. Eight subjects participated in this study and succeeded to manipulate a robotic arm in 3D space to write some English letters. The mean decoding accuracy of writing task was 0.93 ± 0.03. Four subjects achieved the optimal criteria of writing the word “HI” which is the minimum movement of robotic arm directions (15 steps). Other subjects had needed to take from 2 to 4 additional steps to finish the whole process. These results suggested that our proposed hybrid noninvasive EEG-EMG-BCI was robust and efficient for real-time multidimensional robotic arm control. PMID:28660211

Quantum and Multidimensional Explanations in a Neurobiological Context of Mind.

PubMed

Korf, Jakob

2015-08-01

This article examines the possible relevance of physical-mathematical multidimensional or quantum concepts aiming at understanding the (human) mind in a neurobiological context. Some typical features of the quantum and multidimensional concepts are briefly introduced, including entanglement, superposition, holonomic, and quantum field theories. Next, we consider neurobiological principles, such as the brain and its emerging (physical) mind, evolutionary and ontological origins, entropy, syntropy/neg-entropy, causation, and brain energy metabolism. In many biological processes, including biochemical conversions, protein folding, and sensory perception, the ubiquitous involvement of quantum mechanisms is well recognized. Quantum and multidimensional approaches might be expected to help describe and model both brain and mental processes, but an understanding of their direct involvement in mental activity, that is, without mediation by molecular processes, remains elusive. More work has to be done to bridge the gap between current neurobiological and physical-mathematical concepts with their associated quantum-mind theories. © The Author(s) 2014.

ANALYSIS OF TRACE-LEVEL ORGANIC COMBUSTION PROCESS EMISSIONS USING NOVEL MULTIDIMENSIONAL GAS CHROMATOGRAPHY-MASS SPECTROMETRY PROCEDURES

EPA Science Inventory

The paper discusses the analysis of trace-level organic combustion process emissions using novel multidimensional gas chromatography-mass spectrometry (MDGC-MS) procedures. It outlines the application of the technique through the analyses of various incinerator effluent and produ...

A high performance biometric signal and image processing method to reveal blood perfusion towards 3D oxygen saturation mapping

NASA Astrophysics Data System (ADS)

Imms, Ryan; Hu, Sijung; Azorin-Peris, Vicente; Trico, Michaël.; Summers, Ron

2014-03-01

Non-contact imaging photoplethysmography (PPG) is a recent development in the field of physiological data acquisition, currently undergoing a large amount of research to characterize and define the range of its capabilities. Contact-based PPG techniques have been broadly used in clinical scenarios for a number of years to obtain direct information about the degree of oxygen saturation for patients. With the advent of imaging techniques, there is strong potential to enable access to additional information such as multi-dimensional blood perfusion and saturation mapping. The further development of effective opto-physiological monitoring techniques is dependent upon novel modelling techniques coupled with improved sensor design and effective signal processing methodologies. The biometric signal and imaging processing platform (bSIPP) provides a comprehensive set of features for extraction and analysis of recorded iPPG data, enabling direct comparison with other biomedical diagnostic tools such as ECG and EEG. Additionally, utilizing information about the nature of tissue structure has enabled the generation of an engineering model describing the behaviour of light during its travel through the biological tissue. This enables the estimation of the relative oxygen saturation and blood perfusion in different layers of the tissue to be calculated, which has the potential to be a useful diagnostic tool.

Using the Pearson Distribution for Synthesis of the Suboptimal Algorithms for Filtering Multi-Dimensional Markov Processes

NASA Astrophysics Data System (ADS)

Mit'kin, A. S.; Pogorelov, V. A.; Chub, E. G.

2015-08-01

We consider the method of constructing the suboptimal filter on the basis of approximating the a posteriori probability density of the multidimensional Markov process by the Pearson distributions. The proposed method can efficiently be used for approximating asymmetric, excessive, and finite densities.

Sensitivity Gains, Linearity, and Spectral Reproducibility in Nonuniformly Sampled Multidimensional MAS NMR Spectra of High Dynamic Range.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Suiter, Christopher L.; Paramasivam, Sivakumar; Hou, Guangjin

Recently, we have demonstrated that considerable inherent sensitivity gains are attained in MAS NMR spectra acquired by nonuniform sampling (NUS) and introduced maximum entropy interpolation (MINT) processing that assures the linearity of transformation between the time and frequency domains. In this report, we examine the utility of the NUS/MINT approach in multidimensional datasets possessing high dynamic range, such as homonuclear 13C–13C correlation spectra. We demonstrate on model compounds and on 1–73-(U-13C,15N)/74–108-(U-15N) E. coli thioredoxin reassembly, that with appropriately constructed 50 % NUS schedules inherent sensitivity gains of 1.7–2.1-fold are readily reached in such datasets. We show that both linearity andmore » line width are retained under these experimental conditions throughout the entire dynamic range of the signals. Furthermore, we demonstrate that the reproducibility of the peak intensities is excellent in the NUS/MINT approach when experiments are repeated multiple times and identical experimental and processing conditions are employed. Finally, we discuss the principles for design and implementation of random exponentially biased NUS sampling schedules for homonuclear 13C–13C MAS correlation experiments that yield high quality artifact-free datasets.« less

Sensitivity gains, linearity, and spectral reproducibility in nonuniformly sampled multidimensional MAS NMR spectra of high dynamic range

PubMed Central

Suiter, Christopher L.; Paramasivam, Sivakumar; Hou, Guangjin; Sun, Shangjin; Rice, David; Hoch, Jeffrey C.; Rovnyak, David

2014-01-01

Recently, we have demonstrated that considerable inherent sensitivity gains are attained in MAS NMR spectra acquired by nonuniform sampling (NUS) and introduced maximum entropy interpolation (MINT) processing that assures the linearity of transformation between the time and frequency domains. In this report, we examine the utility of the NUS/MINT approach in multidimensional datasets possessing high dynamic range, such as homonuclear 13C–13C correlation spectra. We demonstrate on model compounds and on 1–73-(U-13C, 15N)/74–108-(U-15N) E. coli thioredoxin reassembly, that with appropriately constructed 50 % NUS schedules inherent sensitivity gains of 1.7–2.1-fold are readily reached in such datasets. We show that both linearity and line width are retained under these experimental conditions throughout the entire dynamic range of the signals. Furthermore, we demonstrate that the reproducibility of the peak intensities is excellent in the NUS/MINT approach when experiments are repeated multiple times and identical experimental and processing conditions are employed. Finally, we discuss the principles for design and implementation of random exponentially biased NUS sampling schedules for homonuclear 13C–13C MAS correlation experiments that yield high-quality artifact-free datasets. PMID:24752819

EDA-gram: designing electrodermal activity fingerprints for visualization and feature extraction.

PubMed

Chaspari, Theodora; Tsiartas, Andreas; Stein Duker, Leah I; Cermak, Sharon A; Narayanan, Shrikanth S

2016-08-01

Wearable technology permeates every aspect of our daily life increasing the need of reliable and interpretable models for processing the large amount of biomedical data. We propose the EDA-Gram, a multidimensional fingerprint of the electrodermal activity (EDA) signal, inspired by the widely-used notion of spectrogram. The EDA-Gram is based on the sparse decomposition of EDA from a knowledge-driven set of dictionary atoms. The time axis reflects the analysis frames, the spectral dimension depicts the width of selected dictionary atoms, while intensity values are computed from the atom coefficients. In this way, EDA-Gram incorporates the amplitude and shape of Skin Conductance Responses (SCR), which comprise an essential part of the signal. EDA-Gram is further used as a foundation for signal-specific feature design. Our results indicate that the proposed representation can accentuate fine-grain signal fluctuations, which might not always be apparent through simple visual inspection. Statistical analysis and classification/regression experiments further suggest that the derived features can differentiate between multiple arousal levels and stress-eliciting environments for two datasets.

A multidimensional evaluation of a nursing information-literacy program.

PubMed Central

Fox, L M; Richter, J M; White, N E

1996-01-01

The goal of an information-literacy program is to develop student skills in locating, evaluating, and applying information for use in critical thinking and problem solving. This paper describes a multidimensional evaluation process for determining nursing students' growth in cognitive and affective domains. Results indicate improvement in student skills as a result of a nursing information-literacy program. Multidimensional evaluation produces a well-rounded picture of student progress based on formal measurement as well as informal feedback. Developing new educational programs can be a time-consuming challenge. It is important, when expending so much effort, to ensure that the goals of the new program are achieved and benefits to students demonstrated. A multidimensional approach to evaluation can help to accomplish those ends. In 1988, The University of Northern Colorado School of Nursing began working with a librarian to integrate an information-literacy component, entitled Pathways to Information Literacy, into the curriculum. This article describes the program and discusses how a multidimensional evaluation process was used to assess program effectiveness. The evaluation process not only helped to measure the effectiveness of the program but also allowed the instructors to use several different approaches to evaluation. PMID:8826621

Multidimensional preparative liquid chromatography to isolate flavonoids from bergamot juice and evaluation of their anti-inflammatory potential.

PubMed

Russo, Marina; Dugo, Paola; Marzocco, Stefania; Inferrera, Veronica; Mondello, Luigi

2015-12-01

Important objectives of a high-performance liquid chromatography preparative process are: purity of products isolated, yield, and throughput. The multidimensional preparative liquid chromatography method used in this work was developed mainly to increase the throughput; moreover purity and yield are increased thanks to the automated collection of the molecules based on the intensity of a signal generated from the mass spectrometer detector, in this way only a specific product can be targeted. This preparative system allowed, in few analyses both in the first and second dimensions, the isolation of eight pure compounds present at very different concentration in the original sample with high purity (>95%) and yield, which showed how the system is efficient and versatile. Pure molecules were used to validate the analytical method and to test the anti-inflammatory and antiproliferative potential of flavonoids. The contemporary presence, in bergamot juice, of all the flavonoids together increases the anti-inflammatory effect with respect to the single compound alone. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The Use of the Visualisation of Multidimensional Data Using PCA to Evaluate Possibilities of the Division of Coal Samples Space Due to their Suitability for Fluidised Gasification

NASA Astrophysics Data System (ADS)

Jamróz, Dariusz; Niedoba, Tomasz; Surowiak, Agnieszka; Tumidajski, Tadeusz

2016-09-01

Methods serving to visualise multidimensional data through the transformation of multidimensional space into two-dimensional space, enable to present the multidimensional data on the computer screen. Thanks to this, qualitative analysis of this data can be performed in the most natural way for humans, through the sense of sight. An example of such a method of multidimensional data visualisation is PCA (principal component analysis) method. This method was used in this work to present and analyse a set of seven-dimensional data (selected seven properties) describing coal samples obtained from Janina and Wieczorek coal mines. Coal from these mines was previously subjected to separation by means of a laboratory ring jig, consisting of ten rings. With 5 layers of both types of coal (with 2 rings each) were obtained in this way. It was decided to check if the method of multidimensional data visualisation enables to divide the space of such divided samples into areas with different suitability for the fluidised gasification process. To that end, the card of technological suitability of coal was used (Sobolewski et al., 2012; 2013), in which key, relevant and additional parameters, having effect on the gasification process, were described. As a result of analyses, it was stated that effective determination of coal samples suitability for the on-surface gasification process in a fluidised reactor is possible. The PCA method enables the visualisation of the optimal subspace containing the set requirements concerning the properties of coals intended for this process.

Reducing seed dependent variability of non-uniformly sampled multidimensional NMR data

NASA Astrophysics Data System (ADS)

Mobli, Mehdi

2015-07-01

The application of NMR spectroscopy to study the structure, dynamics and function of macromolecules requires the acquisition of several multidimensional spectra. The one-dimensional NMR time-response from the spectrometer is extended to additional dimensions by introducing incremented delays in the experiment that cause oscillation of the signal along "indirect" dimensions. For a given dimension the delay is incremented at twice the rate of the maximum frequency (Nyquist rate). To achieve high-resolution requires acquisition of long data records sampled at the Nyquist rate. This is typically a prohibitive step due to time constraints, resulting in sub-optimal data records to the detriment of subsequent analyses. The multidimensional NMR spectrum itself is typically sparse, and it has been shown that in such cases it is possible to use non-Fourier methods to reconstruct a high-resolution multidimensional spectrum from a random subset of non-uniformly sampled (NUS) data. For a given acquisition time, NUS has the potential to improve the sensitivity and resolution of a multidimensional spectrum, compared to traditional uniform sampling. The improvements in sensitivity and/or resolution achieved by NUS are heavily dependent on the distribution of points in the random subset acquired. Typically, random points are selected from a probability density function (PDF) weighted according to the NMR signal envelope. In extreme cases as little as 1% of the data is subsampled. The heavy under-sampling can result in poor reproducibility, i.e. when two experiments are carried out where the same number of random samples is selected from the same PDF but using different random seeds. Here, a jittered sampling approach is introduced that is shown to improve random seed dependent reproducibility of multidimensional spectra generated from NUS data, compared to commonly applied NUS methods. It is shown that this is achieved due to the low variability of the inherent sensitivity of the random subset chosen from a given PDF. Finally, it is demonstrated that metrics used to find optimal NUS distributions are heavily dependent on the inherent sensitivity of the random subset, and such optimisation is therefore less critical when using the proposed sampling scheme.

PMv Neuronal Firing May Be Driven by a Movement Command Trajectory within Multidimensional Gaussian Fields.

PubMed

Agarwal, Rahul; Thakor, Nitish V; Sarma, Sridevi V; Massaquoi, Steve G

2015-06-24

The premotor cortex (PM) is known to be a site of visuo-somatosensory integration for the production of movement. We sought to better understand the ventral PM (PMv) by modeling its signal encoding in greater detail. Neuronal firing data was obtained from 110 PMv neurons in two male rhesus macaques executing four reach-grasp-manipulate tasks. We found that in the large majority of neurons (∼90%) the firing patterns across the four tasks could be explained by assuming that a high-dimensional position/configuration trajectory-like signal evolving ∼250 ms before movement was encoded within a multidimensional Gaussian field (MGF). Our findings are consistent with the possibility that PMv neurons process a visually specified reference command for the intended arm/hand position trajectory with respect to a proprioceptively or visually sensed initial configuration. The estimated MGF were (hyper) disc-like, such that each neuron's firing modulated strongly only with commands that evolved along a single direction within position/configuration space. Thus, many neurons appeared to be tuned to slices of this input signal space that as a collection appeared to well cover the space. The MGF encoding models appear to be consistent with the arm-referent, bell-shaped, visual target tuning curves and target selectivity patterns observed in PMV visual-motor neurons. These findings suggest that PMv may implement a lookup table-like mechanism that helps translate intended movement trajectory into time-varying patterns of activation in motor cortex and spinal cord. MGFs provide an improved nonlinear framework for potentially decoding visually specified, intended multijoint arm/hand trajectories well in advance of movement. Copyright © 2015 the authors 0270-6474/15/359508-18$15.00/0.

Multidimensional Framework for the Analysis of Innovations at Universities in Catalonia

ERIC Educational Resources Information Center

Tomas, Marina; Castro, Diego

2011-01-01

The purpose of this paper is to contribute to a better understanding of the nature of change processes and dynamics at Catalan universities. A multidimensional approach was adopted to examine the change processes and to analyse organizational innovation in higher education. The paper draws involved in each particular innovation. Analysis of these…

Information-Processing Architectures in Multidimensional Classification: A Validation Test of the Systems Factorial Technology

ERIC Educational Resources Information Center

Fific, Mario; Nosofsky, Robert M.; Townsend, James T.

2008-01-01

A growing methodology, known as the systems factorial technology (SFT), is being developed to diagnose the types of information-processing architectures (serial, parallel, or coactive) and stopping rules (exhaustive or self-terminating) that operate in tasks of multidimensional perception. Whereas most previous applications of SFT have been in…

Multidimensional Profiling of Task Stress States for Human Factors: A Brief Review.

PubMed

Matthews, Gerald

2016-09-01

This article advocates multidimensional assessment of task stress in human factors and reviews the use of the Dundee Stress State Questionnaire (DSSQ) for evaluation of systems and operators. Contemporary stress research has progressed from an exclusive focus on environmental stressors to transactional perspectives on the stress process. Performance impacts of stress reflect the operator's dynamic attempts to understand and cope with task demands. Multidimensional stress assessments are necessary to gauge the different forms of system-operator interaction. This review discusses the theoretical and practical use of the DSSQ in evaluating multidimensional patterns of stress response. It presents psychometric evidence for the multidimensional perspective and illustrative profiles of subjective state response to task stressors and environments. Evidence is also presented on stress state correlations with related variables, including personality, stress process measures, psychophysiological response, and objective task performance. Evidence supports the validity of the DSSQ as a task stress measure. Studies of various simulated environments show that different tasks elicit different profiles of stress state response. Operator characteristics such as resilience predict individual differences in state response to stressors. Structural equation modeling may be used to understand performance impacts of stress states. Multidimensional assessment affords insight into the stress process in a variety of human factors contexts. Integrating subjective and psychophysiological assessment is a priority for future research. Stress state measurement contributes to evaluating system design, countermeasures to stress and fatigue, and performance vulnerabilities. It may also support personnel selection and diagnostic monitoring of operators. © 2016, Human Factors and Ergonomics Society.

Emotion Recognition from EEG Signals Using Multidimensional Information in EMD Domain.

PubMed

Zhuang, Ning; Zeng, Ying; Tong, Li; Zhang, Chi; Zhang, Hanming; Yan, Bin

2017-01-01

This paper introduces a method for feature extraction and emotion recognition based on empirical mode decomposition (EMD). By using EMD, EEG signals are decomposed into Intrinsic Mode Functions (IMFs) automatically. Multidimensional information of IMF is utilized as features, the first difference of time series, the first difference of phase, and the normalized energy. The performance of the proposed method is verified on a publicly available emotional database. The results show that the three features are effective for emotion recognition. The role of each IMF is inquired and we find that high frequency component IMF1 has significant effect on different emotional states detection. The informative electrodes based on EMD strategy are analyzed. In addition, the classification accuracy of the proposed method is compared with several classical techniques, including fractal dimension (FD), sample entropy, differential entropy, and discrete wavelet transform (DWT). Experiment results on DEAP datasets demonstrate that our method can improve emotion recognition performance.

Analysis of the time structure of synchronization in multidimensional chaotic systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Makarenko, A. V., E-mail: avm.science@mail.ru

2015-05-15

A new approach is proposed to the integrated analysis of the time structure of synchronization of multidimensional chaotic systems. The method allows one to diagnose and quantitatively evaluate the intermittency characteristics during synchronization of chaotic oscillations in the T-synchronization mode. A system of two identical logistic mappings with unidirectional coupling that operate in the developed chaos regime is analyzed. It is shown that the widely used approach, in which only synchronization patterns are subjected to analysis while desynchronization areas are considered as a background signal and removed from analysis, should be regarded as methodologically incomplete.

The pursuit of happiness measurement: a psychometric model based on psychophysiological correlates.

PubMed

Pietro, Cipresso; Silvia, Serino; Giuseppe, Riva

2014-01-01

Everyone is interested in the pursuit of happiness, but the real problem for the researchers is how to measure it. Our aim was to deeply investigate happiness measurement through biomedical signals, using psychophysiological methods to objectify the happiness experiences measurements. The classic valence-arousal model of affective states to study happiness has been extensively used in psychophysiology. However, really few studies considered a real combination of these two dimensions and no study further investigated multidimensional models. More, most studies focused mainly on self-report to measure happiness and a deeper psychophysiological investigation on the dimensions of such an experience is still missing. A multidimensional model of happiness is presented and both the dimensions and the measures extracted within each dimension are comprehensively explained. This multidimensional model aims at being a milestone for future systematic study on psychophysiology of happiness and affective states.

Noncollinear wave mixing of attosecond XUV and few-cycle optical laser pulses in gas-phase atoms: Toward multidimensional spectroscopy involving XUV excitations

NASA Astrophysics Data System (ADS)

Cao, Wei; Warrick, Erika R.; Fidler, Ashley; Neumark, Daniel M.; Leone, Stephen R.

2016-11-01

Ultrafast nonlinear spectroscopy, which records transient wave-mixing signals in a medium, is a powerful tool to access microscopic information using light sources in the radio-frequency and optical regimes. The extension of this technique towards the extreme ultraviolet (XUV) or even x-ray regimes holds the promise to uncover rich structural or dynamical information with even higher spatial or temporal resolution. Here, we demonstrate noncollinear wave mixing between weak XUV attosecond pulses and a strong near-infrared (NIR) few-cycle laser pulse in gas phase atoms (one photon of XUV and two photons of NIR). In the noncollinear geometry the attosecond and either one or two NIR pulses interact with argon atoms. Nonlinear XUV signals are generated in a spatially resolved fashion as required by phase matching. Different transition pathways can be identified from these background-free nonlinear signals according to the specific phase-matching conditions. Time-resolved measurements of the spatially gated XUV signals reveal electronic coherences of Rydberg wave packets prepared by a single XUV photon or XUV-NIR two-photon excitation, depending on the applied pulse sequences. These measurements open possible applications of tabletop multidimensional spectroscopy to the study of dynamics associated with valence or core excitation with XUV photons.

A New Time-varying Concept of Risk in a Changing Climate.

PubMed

Sarhadi, Ali; Ausín, María Concepción; Wiper, Michael P

2016-10-20

In a changing climate arising from anthropogenic global warming, the nature of extreme climatic events is changing over time. Existing analytical stationary-based risk methods, however, assume multi-dimensional extreme climate phenomena will not significantly vary over time. To strengthen the reliability of infrastructure designs and the management of water systems in the changing environment, multidimensional stationary risk studies should be replaced with a new adaptive perspective. The results of a comparison indicate that current multi-dimensional stationary risk frameworks are no longer applicable to projecting the changing behaviour of multi-dimensional extreme climate processes. Using static stationary-based multivariate risk methods may lead to undesirable consequences in designing water system infrastructures. The static stationary concept should be replaced with a flexible multi-dimensional time-varying risk framework. The present study introduces a new multi-dimensional time-varying risk concept to be incorporated in updating infrastructure design strategies under changing environments arising from human-induced climate change. The proposed generalized time-varying risk concept can be applied for all stochastic multi-dimensional systems that are under the influence of changing environments.

Modeling Quantum Dynamics in Multidimensional Systems

NASA Astrophysics Data System (ADS)

Liss, Kyle; Weinacht, Thomas; Pearson, Brett

2017-04-01

Coupling between different degrees-of-freedom is an inherent aspect of dynamics in multidimensional quantum systems. As experiments and theory begin to tackle larger molecular structures and environments, models that account for vibrational and/or electronic couplings are essential for interpretation. Relevant processes include intramolecular vibrational relaxation, conical intersections, and system-bath coupling. We describe a set of simulations designed to model coupling processes in multidimensional molecular systems, focusing on models that provide insight and allow visualization of the dynamics. Undergraduates carried out much of the work as part of a senior research project. In addition to the pedagogical value, the simulations allow for comparison between both explicit and implicit treatments of a system's many degrees-of-freedom.

Ideal-observer analysis of lesion detectability in planar, conventional SPECT, and dedicated SPECT scintimammography using effective multi-dimensional smoothing

NASA Astrophysics Data System (ADS)

La Riviere, P. J.; Pan, X.; Penney, B. C.

1998-06-01

Scintimammography, a nuclear-medicine imaging technique that relies on the preferential uptake of Tc-99m-sestamibi and other radionuclides in breast malignancies, has the potential to provide differentiation of mammographically suspicious lesions, as well as outright detection of malignancies in women with radiographically dense breasts. In this work we use the ideal-observer framework to quantify the detectability of a 1-cm lesion using three different imaging geometries: the planar technique that is the current clinical standard, conventional single-photon emission computed tomography (SPECT), in which the scintillation cameras rotate around the entire torso, and dedicated breast SPECT, in which the cameras rotate around the breast alone. We also introduce an adaptive smoothing technique for the processing of planar images and of sinograms that exploits Fourier transforms to achieve effective multidimensional smoothing at a reasonable computational cost. For the detection of a 1-cm lesion with a clinically typical 6:1 tumor-background ratio, we find ideal-observer signal-to-noise ratios (SNR) that suggest that the dedicated breast SPECT geometry is the most effective of the three, and that the adaptive, two-dimensional smoothing technique should enhance lesion detectability in the tomographic reconstructions.

Network structure of multivariate time series.

PubMed

Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

2015-10-21

Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

Cross-Approximate Entropy parallel computation on GPUs for biomedical signal analysis. Application to MEG recordings.

PubMed

Martínez-Zarzuela, Mario; Gómez, Carlos; Díaz-Pernas, Francisco Javier; Fernández, Alberto; Hornero, Roberto

2013-10-01

Cross-Approximate Entropy (Cross-ApEn) is a useful measure to quantify the statistical dissimilarity of two time series. In spite of the advantage of Cross-ApEn over its one-dimensional counterpart (Approximate Entropy), only a few studies have applied it to biomedical signals, mainly due to its high computational cost. In this paper, we propose a fast GPU-based implementation of the Cross-ApEn that makes feasible its use over a large amount of multidimensional data. The scheme followed is fully scalable, thus maximizes the use of the GPU despite of the number of neural signals being processed. The approach consists in processing many trials or epochs simultaneously, with independence of its origin. In the case of MEG data, these trials can proceed from different input channels or subjects. The proposed implementation achieves an average speedup greater than 250× against a CPU parallel version running on a processor containing six cores. A dataset of 30 subjects containing 148 MEG channels (49 epochs of 1024 samples per channel) can be analyzed using our development in about 30min. The same processing takes 5 days on six cores and 15 days when running on a single core. The speedup is much larger if compared to a basic sequential Matlab(®) implementation, that would need 58 days per subject. To our knowledge, this is the first contribution of Cross-ApEn measure computation using GPUs. This study demonstrates that this hardware is, to the day, the best option for the signal processing of biomedical data with Cross-ApEn. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

NMRPipe: a multidimensional spectral processing system based on UNIX pipes.

PubMed

Delaglio, F; Grzesiek, S; Vuister, G W; Zhu, G; Pfeifer, J; Bax, A

1995-11-01

The NMRPipe system is a UNIX software environment of processing, graphics, and analysis tools designed to meet current routine and research-oriented multidimensional processing requirements, and to anticipate and accommodate future demands and developments. The system is based on UNIX pipes, which allow programs running simultaneously to exchange streams of data under user control. In an NMRPipe processing scheme, a stream of spectral data flows through a pipeline of processing programs, each of which performs one component of the overall scheme, such as Fourier transformation or linear prediction. Complete multidimensional processing schemes are constructed as simple UNIX shell scripts. The processing modules themselves maintain and exploit accurate records of data sizes, detection modes, and calibration information in all dimensions, so that schemes can be constructed without the need to explicitly define or anticipate data sizes or storage details of real and imaginary channels during processing. The asynchronous pipeline scheme provides other substantial advantages, including high flexibility, favorable processing speeds, choice of both all-in-memory and disk-bound processing, easy adaptation to different data formats, simpler software development and maintenance, and the ability to distribute processing tasks on multi-CPU computers and computer networks.

Off-Line Multidimensional Liquid Chromatography and Auto Sampling Result in Sample Loss in LC/LC–MS/MS

PubMed Central

2015-01-01

Large-scale proteomics often employs two orthogonal separation methods to fractionate complex peptide mixtures. Fractionation can involve ion exchange separation coupled to reversed-phase separation or, more recently, two reversed-phase separations performed at different pH values. When multidimensional separations are combined with tandem mass spectrometry for protein identification, the strategy is often referred to as multidimensional protein identification technology (MudPIT). MudPIT has been used in either an automated (online) or manual (offline) format. In this study, we evaluated the performance of different MudPIT strategies by both label-free and tandem mass tag (TMT) isobaric tagging. Our findings revealed that online MudPIT provided more peptide/protein identifications and higher sequence coverage than offline platforms. When employing an off-line fractionation method with direct loading of samples onto the column from an eppendorf tube via a high-pressure device, a 5.3% loss in protein identifications is observed. When off-line fractionated samples are loaded via an autosampler, a 44.5% loss in protein identifications is observed compared with direct loading of samples onto a triphasic capillary column. Moreover, peptide recovery was significantly lower after offline fractionation than in online fractionation. Signal-to-noise (S/N) ratio, however, was not significantly altered between experimental groups. It is likely that offline sample collection results in stochastic peptide loss due to noncovalent adsorption to solid surfaces. Therefore, the use of the offline approaches should be considered carefully when processing minute quantities of valuable samples. PMID:25040086

Computer object segmentation by nonlinear image enhancement, multidimensional clustering, and geometrically constrained contour optimization

NASA Astrophysics Data System (ADS)

Bruynooghe, Michel M.

1998-04-01

In this paper, we present a robust method for automatic object detection and delineation in noisy complex images. The proposed procedure is a three stage process that integrates image segmentation by multidimensional pixel clustering and geometrically constrained optimization of deformable contours. The first step is to enhance the original image by nonlinear unsharp masking. The second step is to segment the enhanced image by multidimensional pixel clustering, using our reducible neighborhoods clustering algorithm that has a very interesting theoretical maximal complexity. Then, candidate objects are extracted and initially delineated by an optimized region merging algorithm, that is based on ascendant hierarchical clustering with contiguity constraints and on the maximization of average contour gradients. The third step is to optimize the delineation of previously extracted and initially delineated objects. Deformable object contours have been modeled by cubic splines. An affine invariant has been used to control the undesired formation of cusps and loops. Non linear constrained optimization has been used to maximize the external energy. This avoids the difficult and non reproducible choice of regularization parameters, that are required by classical snake models. The proposed method has been applied successfully to the detection of fine and subtle microcalcifications in X-ray mammographic images, to defect detection by moire image analysis, and to the analysis of microrugosities of thin metallic films. The later implementation of the proposed method on a digital signal processor associated to a vector coprocessor would allow the design of a real-time object detection and delineation system for applications in medical imaging and in industrial computer vision.

Chemometric Strategies for Peak Detection and Profiling from Multidimensional Chromatography.

PubMed

Navarro-Reig, Meritxell; Bedia, Carmen; Tauler, Romà; Jaumot, Joaquim

2018-04-03

The increasing complexity of omics research has encouraged the development of new instrumental technologies able to deal with these challenging samples. In this way, the rise of multidimensional separations should be highlighted due to the massive amounts of information that provide with an enhanced analyte determination. Both proteomics and metabolomics benefit from this higher separation capacity achieved when different chromatographic dimensions are combined, either in LC or GC. However, this vast quantity of experimental information requires the application of chemometric data analysis strategies to retrieve this hidden knowledge, especially in the case of nontargeted studies. In this work, the most common chemometric tools and approaches for the analysis of this multidimensional chromatographic data are reviewed. First, different options for data preprocessing and enhancement of the instrumental signal are introduced. Next, the most used chemometric methods for the detection of chromatographic peaks and the resolution of chromatographic and spectral contributions (profiling) are presented. The description of these data analysis approaches is complemented with enlightening examples from omics fields that demonstrate the exceptional potential of the combination of multidimensional separation techniques and chemometric tools of data analysis. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mukamel, Shaul, E-mail: smukamel@uci.edu; Bakker, Huib J.

Multidimensional signals are generated by subjecting molecules to sequences of short optical pulses and recording correlation plots related to the various controlled delay periods. These techniques which span all the way from the THz to the x-ray regimes provide qualitatively new structural and dynamical molecular information not available from conventional one-dimensional techniques. This issue surveys the recent experimental and theoretical progresses in this rapidly developing 20 year old field which illustrates the novel insights provided by multidimensional techniques into electronic and nuclear motions. It should serve as a valuable source for experts in the field and help introduce newcomers tomore » this exciting and challenging branch of nonlinear spectroscopy.« less

Dynamic Cross-Entropy.

PubMed

Aur, Dorian; Vila-Rodriguez, Fidel

2017-01-01

Complexity measures for time series have been used in many applications to quantify the regularity of one dimensional time series, however many dynamical systems are spatially distributed multidimensional systems. We introduced Dynamic Cross-Entropy (DCE) a novel multidimensional complexity measure that quantifies the degree of regularity of EEG signals in selected frequency bands. Time series generated by discrete logistic equations with varying control parameter r are used to test DCE measures. Sliding window DCE analyses are able to reveal specific period doubling bifurcations that lead to chaos. A similar behavior can be observed in seizures triggered by electroconvulsive therapy (ECT). Sample entropy data show the level of signal complexity in different phases of the ictal ECT. The transition to irregular activity is preceded by the occurrence of cyclic regular behavior. A significant increase of DCE values in successive order from high frequencies in gamma to low frequencies in delta band reveals several phase transitions into less ordered states, possible chaos in the human brain. To our knowledge there are no reliable techniques able to reveal the transition to chaos in case of multidimensional times series. In addition, DCE based on sample entropy appears to be robust to EEG artifacts compared to DCE based on Shannon entropy. The applied technique may offer new approaches to better understand nonlinear brain activity. Copyright © 2016 Elsevier B.V. All rights reserved.

Multidimensional k-nearest neighbor model based on EEMD for financial time series forecasting

NASA Astrophysics Data System (ADS)

Zhang, Ningning; Lin, Aijing; Shang, Pengjian

2017-07-01

In this paper, we propose a new two-stage methodology that combines the ensemble empirical mode decomposition (EEMD) with multidimensional k-nearest neighbor model (MKNN) in order to forecast the closing price and high price of the stocks simultaneously. The modified algorithm of k-nearest neighbors (KNN) has an increasingly wide application in the prediction of all fields. Empirical mode decomposition (EMD) decomposes a nonlinear and non-stationary signal into a series of intrinsic mode functions (IMFs), however, it cannot reveal characteristic information of the signal with much accuracy as a result of mode mixing. So ensemble empirical mode decomposition (EEMD), an improved method of EMD, is presented to resolve the weaknesses of EMD by adding white noise to the original data. With EEMD, the components with true physical meaning can be extracted from the time series. Utilizing the advantage of EEMD and MKNN, the new proposed ensemble empirical mode decomposition combined with multidimensional k-nearest neighbor model (EEMD-MKNN) has high predictive precision for short-term forecasting. Moreover, we extend this methodology to the case of two-dimensions to forecast the closing price and high price of the four stocks (NAS, S&P500, DJI and STI stock indices) at the same time. The results indicate that the proposed EEMD-MKNN model has a higher forecast precision than EMD-KNN, KNN method and ARIMA.

Full data acquisition in Kelvin Probe Force Microscopy: Mapping dynamic electric phenomena in real space

DOE Office of Scientific and Technical Information (OSTI.GOV)

Balke, Nina; Kalinin, Sergei V.; Jesse, Stephen

Kelvin probe force microscopy (KPFM) has provided deep insights into the role local electronic, ionic and electrochemical processes play on the global functionality of materials and devices, even down to the atomic scale. Conventional KPFM utilizes heterodyne detection and bias feedback to measure the contact potential difference (CPD) between tip and sample. This measurement paradigm, however, permits only partial recovery of the information encoded in bias- and time-dependent electrostatic interactions between the tip and sample and effectively down-samples the cantilever response to a single measurement of CPD per pixel. This level of detail is insufficient for electroactive materials, devices, ormore » solid-liquid interfaces, where non-linear dielectrics are present or spurious electrostatic events are possible. Here, we simulate and experimentally validate a novel approach for spatially resolved KPFM capable of a full information transfer of the dynamic electric processes occurring between tip and sample. General acquisition mode, or G-Mode, adopts a big data approach utilising high speed detection, compression, and storage of the raw cantilever deflection signal in its entirety at high sampling rates (> 4 MHz), providing a permanent record of the tip trajectory. We develop a range of methodologies for analysing the resultant large multidimensional datasets involving classical, physics-based and information-based approaches. Physics-based analysis of G-Mode KPFM data recovers the parabolic bias dependence of the electrostatic force for each cycle of the excitation voltage, leading to a multidimensional dataset containing spatial and temporal dependence of the CPD and capacitance channels. We use multivariate statistical methods to reduce data volume and separate the complex multidimensional data sets into statistically significant components that can then be mapped onto separate physical mechanisms. Overall, G-Mode KPFM offers a new paradigm to study dynamic electric phenomena in electroactive interfaces as well as offer a promising approach to extend KPFM to solid-liquid interfaces.« less

Full data acquisition in Kelvin Probe Force Microscopy: Mapping dynamic electric phenomena in real space

DOE PAGES

Balke, Nina; Kalinin, Sergei V.; Jesse, Stephen; ...

2016-08-12

Kelvin probe force microscopy (KPFM) has provided deep insights into the role local electronic, ionic and electrochemical processes play on the global functionality of materials and devices, even down to the atomic scale. Conventional KPFM utilizes heterodyne detection and bias feedback to measure the contact potential difference (CPD) between tip and sample. This measurement paradigm, however, permits only partial recovery of the information encoded in bias- and time-dependent electrostatic interactions between the tip and sample and effectively down-samples the cantilever response to a single measurement of CPD per pixel. This level of detail is insufficient for electroactive materials, devices, ormore » solid-liquid interfaces, where non-linear dielectrics are present or spurious electrostatic events are possible. Here, we simulate and experimentally validate a novel approach for spatially resolved KPFM capable of a full information transfer of the dynamic electric processes occurring between tip and sample. General acquisition mode, or G-Mode, adopts a big data approach utilising high speed detection, compression, and storage of the raw cantilever deflection signal in its entirety at high sampling rates (> 4 MHz), providing a permanent record of the tip trajectory. We develop a range of methodologies for analysing the resultant large multidimensional datasets involving classical, physics-based and information-based approaches. Physics-based analysis of G-Mode KPFM data recovers the parabolic bias dependence of the electrostatic force for each cycle of the excitation voltage, leading to a multidimensional dataset containing spatial and temporal dependence of the CPD and capacitance channels. We use multivariate statistical methods to reduce data volume and separate the complex multidimensional data sets into statistically significant components that can then be mapped onto separate physical mechanisms. Overall, G-Mode KPFM offers a new paradigm to study dynamic electric phenomena in electroactive interfaces as well as offer a promising approach to extend KPFM to solid-liquid interfaces.« less

Delay differential analysis of time series.

PubMed

Lainscsek, Claudia; Sejnowski, Terrence J

2015-03-01

Nonlinear dynamical system analysis based on embedding theory has been used for modeling and prediction, but it also has applications to signal detection and classification of time series. An embedding creates a multidimensional geometrical object from a single time series. Traditionally either delay or derivative embeddings have been used. The delay embedding is composed of delayed versions of the signal, and the derivative embedding is composed of successive derivatives of the signal. The delay embedding has been extended to nonuniform embeddings to take multiple timescales into account. Both embeddings provide information on the underlying dynamical system without having direct access to all the system variables. Delay differential analysis is based on functional embeddings, a combination of the derivative embedding with nonuniform delay embeddings. Small delay differential equation (DDE) models that best represent relevant dynamic features of time series data are selected from a pool of candidate models for detection or classification. We show that the properties of DDEs support spectral analysis in the time domain where nonlinear correlation functions are used to detect frequencies, frequency and phase couplings, and bispectra. These can be efficiently computed with short time windows and are robust to noise. For frequency analysis, this framework is a multivariate extension of discrete Fourier transform (DFT), and for higher-order spectra, it is a linear and multivariate alternative to multidimensional fast Fourier transform of multidimensional correlations. This method can be applied to short or sparse time series and can be extended to cross-trial and cross-channel spectra if multiple short data segments of the same experiment are available. Together, this time-domain toolbox provides higher temporal resolution, increased frequency and phase coupling information, and it allows an easy and straightforward implementation of higher-order spectra across time compared with frequency-based methods such as the DFT and cross-spectral analysis.

Classification of Complex Nonspeech Sounds. Panel on Classification of Complex Nonspeech Sounds

DTIC Science & Technology

1989-04-14

learning of the discrimination task. Since reports on many of these studies have not yet been published, brief summaries of the studies are included below...tonal signal with a noise- producing auditory induction and introduced an intensity ramp that increased the intensity of the tone just before the onset... recorded hand clap signals . The physical properties of the hand claps can be altered (along the lines suggested by the multidimensional analysis

Multidimensional Programming Methods for Energy Facility Siting: Alternative Approaches

NASA Technical Reports Server (NTRS)

Solomon, B. D.; Haynes, K. E.

1982-01-01

The use of multidimensional optimization methods in solving power plant siting problems, which are characterized by several conflicting, noncommensurable objectives is addressed. After a discussion of data requirements and exclusionary site screening methods for bounding the decision space, classes of multiobjective and goal programming models are discussed in the context of finite site selection. Advantages and limitations of these approaches are highlighted and the linkage of multidimensional methods with the subjective, behavioral components of the power plant siting process is emphasized.

Application of the Allan Variance to Time Series Analysis in Astrometry and Geodesy: A Review.

PubMed

Malkin, Zinovy

2016-04-01

The Allan variance (AVAR) was introduced 50 years ago as a statistical tool for assessing the frequency standards deviations. For the past decades, AVAR has increasingly been used in geodesy and astrometry to assess the noise characteristics in geodetic and astrometric time series. A specific feature of astrometric and geodetic measurements, as compared with clock measurements, is that they are generally associated with uncertainties; thus, an appropriate weighting should be applied during data analysis. In addition, some physically connected scalar time series naturally form series of multidimensional vectors. For example, three station coordinates time series X, Y, and Z can be combined to analyze 3-D station position variations. The classical AVAR is not intended for processing unevenly weighted and/or multidimensional data. Therefore, AVAR modifications, namely weighted AVAR (WAVAR), multidimensional AVAR (MAVAR), and weighted multidimensional AVAR (WMAVAR), were introduced to overcome these deficiencies. In this paper, a brief review is given of the experience of using AVAR and its modifications in processing astrogeodetic time series.

Maximum-Likelihood Estimation With a Contracting-Grid Search Algorithm

PubMed Central

Hesterman, Jacob Y.; Caucci, Luca; Kupinski, Matthew A.; Barrett, Harrison H.; Furenlid, Lars R.

2010-01-01

A fast search algorithm capable of operating in multi-dimensional spaces is introduced. As a sample application, we demonstrate its utility in the 2D and 3D maximum-likelihood position-estimation problem that arises in the processing of PMT signals to derive interaction locations in compact gamma cameras. We demonstrate that the algorithm can be parallelized in pipelines, and thereby efficiently implemented in specialized hardware, such as field-programmable gate arrays (FPGAs). A 2D implementation of the algorithm is achieved in Cell/BE processors, resulting in processing speeds above one million events per second, which is a 20× increase in speed over a conventional desktop machine. Graphics processing units (GPUs) are used for a 3D application of the algorithm, resulting in processing speeds of nearly 250,000 events per second which is a 250× increase in speed over a conventional desktop machine. These implementations indicate the viability of the algorithm for use in real-time imaging applications. PMID:20824155

Hyperspectral chemical plume detection algorithms based on multidimensional iterative filtering decomposition.

PubMed

Cicone, A; Liu, J; Zhou, H

2016-04-13

Chemicals released in the air can be extremely dangerous for human beings and the environment. Hyperspectral images can be used to identify chemical plumes, however the task can be extremely challenging. Assuming we know a priori that some chemical plume, with a known frequency spectrum, has been photographed using a hyperspectral sensor, we can use standard techniques such as the so-called matched filter or adaptive cosine estimator, plus a properly chosen threshold value, to identify the position of the chemical plume. However, due to noise and inadequate sensing, the accurate identification of chemical pixels is not easy even in this apparently simple situation. In this paper, we present a post-processing tool that, in a completely adaptive and data-driven fashion, allows us to improve the performance of any classification methods in identifying the boundaries of a plume. This is done using the multidimensional iterative filtering (MIF) algorithm (Cicone et al. 2014 (http://arxiv.org/abs/1411.6051); Cicone & Zhou 2015 (http://arxiv.org/abs/1507.07173)), which is a non-stationary signal decomposition method like the pioneering empirical mode decomposition method (Huang et al. 1998 Proc. R. Soc. Lond. A 454, 903. (doi:10.1098/rspa.1998.0193)). Moreover, based on the MIF technique, we propose also a pre-processing method that allows us to decorrelate and mean-centre a hyperspectral dataset. The cosine similarity measure, which often fails in practice, appears to become a successful and outperforming classifier when equipped with such a pre-processing method. We show some examples of the proposed methods when applied to real-life problems. © 2016 The Author(s).

Two-stage optical recording: photoinduced birefringence and surface-mediated bits storage in bisazo-containing copolymers towards ultrahigh data memory.

PubMed

Hu, Yanlei; Wu, Dong; Li, Jiawen; Huang, Wenhao; Chu, Jiaru

2016-10-03

Ultrahigh density data storage is in high demand in the current age of big data and thus motivates many innovative storage technologies. Femtosecond laser induced multi-dimensional optical data storage is an appealing method to fulfill the demand of ultrahigh storage capacity. Here we report a femtosecond laser induced two-stage optical storage in bisazobenzene copolymer films by manipulating the recording energies. Different mechanisms can be selected for specified memory use: two-photon isomerization (TPI) and laser induced surface deformation. Giant birefringence can be generated by TPI and brings about high signal-to-noise ratio (>20 dB) multi-dimensional reversible storage. Polarization-dependent surface deformation arises when increasing the recording energy, which not only facilitates the multi-level storage by black bits (dots), but also enhances the bits' readout signal and storing stability. This facile bits recording method, which enables completely different recording mechanisms in an identical storage medium, paves the way for sustainable big data storage.

Multidimensional Model of Trauma and Correlated Antisocial Personality Disorder

ERIC Educational Resources Information Center

Martens, Willem H. J.

2005-01-01

Many studies have revealed an important relationship between psychosocial trauma and antisocial personality disorder. A multidimensional model is presented which describes the psychopathological route from trauma to antisocial development. A case report is also included that can illustrate the etiological process from trauma to severe antisocial…

Linear and Nonlinear Thinking: A Multidimensional Model and Measure

ERIC Educational Resources Information Center

Groves, Kevin S.; Vance, Charles M.

2015-01-01

Building upon previously developed and more general dual-process models, this paper provides empirical support for a multidimensional thinking style construct comprised of linear thinking and multiple dimensions of nonlinear thinking. A self-report assessment instrument (Linear/Nonlinear Thinking Style Profile; LNTSP) is presented and…

Coherent (photon) vs incoherent (current) detection of multidimensional optical signals from single molecules in open junctions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Agarwalla, Bijay Kumar; Hua, Weijie; Zhang, Yu

2015-06-07

The nonlinear optical response of a current-carrying single molecule coupled to two metal leads and driven by a sequence of impulsive optical pulses with controllable phases and time delays is calculated. Coherent (stimulated, heterodyne) detection of photons and incoherent detection of the optically induced current are compared. Using a diagrammatic Liouville space superoperator formalism, the signals are recast in terms of molecular correlation functions which are then expanded in the many-body molecular states. Two dimensional signals in benzene-1,4-dithiol molecule show cross peaks involving charged states. The correlation between optical and charge current signal is also observed.

High-frequency stock linkage and multi-dimensional stationary processes

NASA Astrophysics Data System (ADS)

Wang, Xi; Bao, Si; Chen, Jingchao

2017-02-01

In recent years, China's stock market has experienced dramatic fluctuations; in particular, in the second half of 2014 and 2015, the market rose sharply and fell quickly. Many classical financial phenomena, such as stock plate linkage, appeared repeatedly during this period. In general, these phenomena have usually been studied using daily-level data or minute-level data. Our paper focuses on the linkage phenomenon in Chinese stock 5-second-level data during this extremely volatile period. The method used to select the linkage points and the arbitrage strategy are both based on multi-dimensional stationary processes. A new program method for testing the multi-dimensional stationary process is proposed in our paper, and the detailed program is presented in the paper's appendix. Because of the existence of the stationary process, the strategy's logarithmic cumulative average return will converge under the condition of the strong ergodic theorem, and this ensures the effectiveness of the stocks' linkage points and the more stable statistical arbitrage strategy.

Multidimensional Treatment of Fear of Death.

ERIC Educational Resources Information Center

Hoelter, Jon W.

1979-01-01

Presents a multidimensional conception of fear of death and provides subscales for measuring suggested dimensions (fear of the dying process, of the dead, of being destroyed, for significant others, of the unknown, of conscious death, for body after death, and of premature death). Evidence for construct validity is provided. (Author/BEF)

Modelling Mathematics Problem Solving Item Responses Using a Multidimensional IRT Model

ERIC Educational Resources Information Center

Wu, Margaret; Adams, Raymond

2006-01-01

This research examined students' responses to mathematics problem-solving tasks and applied a general multidimensional IRT model at the response category level. In doing so, cognitive processes were identified and modelled through item response modelling to extract more information than would be provided using conventional practices in scoring…

Career Success: Constructing a Multidimensional Model

ERIC Educational Resources Information Center

Dries, Nicky; Pepermans, Roland; Carlier, Olivier

2008-01-01

A multidimensional model of career success was developed aiming to be more inclusive than existing models. In a first study, 22 managers were asked to tell the story of their careers. At the end of each interview, idiosyncratic career success "construct ladders" were constructed for each interviewee through an interactive process with the…

Multidimensional simulations of core-collapse supernovae with CHIMERA

NASA Astrophysics Data System (ADS)

Lentz, Eric J.; Bruenn, S. W.; Yakunin, K.; Endeve, E.; Blondin, J. M.; Harris, J. A.; Hix, W. R.; Marronetti, P.; Messer, O. B.; Mezzacappa, A.

2014-01-01

Core-collapse supernovae are driven by a multidimensional neutrino radiation hydrodynamic (RHD) engine, and full simulation requires at least axisymmetric (2D) and ultimately symmetry-free 3D RHD simulation. We present recent and ongoing work with our multidimensional RHD supernova code CHIMERA to understand the nature of the core-collapse explosion mechanism and its consequences. Recently completed simulations of 12-25 solar mass progenitors(Woosley & Heger 2007) in well resolved (0.7 degrees in latitude) 2D simulations exhibit robust explosions meeting the observationally expected explosion energy. We examine the role of hydrodynamic instabilities (standing accretion shock instability, neutrino driven convection, etc.) on the explosion dynamics and the development of the explosion energy. Ongoing 3D and 2D simulations examine the role that simulation resolution and the removal of the imposed axisymmetry have in the triggering and development of an explosion from stellar core collapse. Companion posters will explore the gravitational wave signals (Yakunin et al.) and nucleosynthesis (Harris et al.) of our simulations.

Multi-dimensional Fokker-Planck equation analysis using the modified finite element method

NASA Astrophysics Data System (ADS)

Náprstek, J.; Král, R.

2016-09-01

The Fokker-Planck equation (FPE) is a frequently used tool for the solution of cross probability density function (PDF) of a dynamic system response excited by a vector of random processes. FEM represents a very effective solution possibility, particularly when transition processes are investigated or a more detailed solution is needed. Actual papers deal with single degree of freedom (SDOF) systems only. So the respective FPE includes two independent space variables only. Stepping over this limit into MDOF systems a number of specific problems related to a true multi-dimensionality must be overcome. Unlike earlier studies, multi-dimensional simplex elements in any arbitrary dimension should be deployed and rectangular (multi-brick) elements abandoned. Simple closed formulae of integration in multi-dimension domain have been derived. Another specific problem represents the generation of multi-dimensional finite element mesh. Assembling of system global matrices should be subjected to newly composed algorithms due to multi-dimensionality. The system matrices are quite full and no advantages following from their sparse character can be profited from, as is commonly used in conventional FEM applications in 2D/3D problems. After verification of partial algorithms, an illustrative example dealing with a 2DOF non-linear aeroelastic system in combination with random and deterministic excitations is discussed.

The Extraction of One-Dimensional Flow Properties from Multi-Dimensional Data Sets

NASA Technical Reports Server (NTRS)

Baurle, Robert A.; Gaffney, Richard L., Jr.

2007-01-01

The engineering design and analysis of air-breathing propulsion systems relies heavily on zero- or one-dimensional properties (e.g. thrust, total pressure recovery, mixing and combustion efficiency, etc.) for figures of merit. The extraction of these parameters from experimental data sets and/or multi-dimensional computational data sets is therefore an important aspect of the design process. A variety of methods exist for extracting performance measures from multi-dimensional data sets. Some of the information contained in the multi-dimensional flow is inevitably lost when any one-dimensionalization technique is applied. Hence, the unique assumptions associated with a given approach may result in one-dimensional properties that are significantly different than those extracted using alternative approaches. The purpose of this effort is to examine some of the more popular methods used for the extraction of performance measures from multi-dimensional data sets, reveal the strengths and weaknesses of each approach, and highlight various numerical issues that result when mapping data from a multi-dimensional space to a space of one dimension.

The Art of Extracting One-Dimensional Flow Properties from Multi-Dimensional Data Sets

NASA Technical Reports Server (NTRS)

Baurle, R. A.; Gaffney, R. L.

2007-01-01

The engineering design and analysis of air-breathing propulsion systems relies heavily on zero- or one-dimensional properties (e:g: thrust, total pressure recovery, mixing and combustion efficiency, etc.) for figures of merit. The extraction of these parameters from experimental data sets and/or multi-dimensional computational data sets is therefore an important aspect of the design process. A variety of methods exist for extracting performance measures from multi-dimensional data sets. Some of the information contained in the multi-dimensional flow is inevitably lost when any one-dimensionalization technique is applied. Hence, the unique assumptions associated with a given approach may result in one-dimensional properties that are significantly different than those extracted using alternative approaches. The purpose of this effort is to examine some of the more popular methods used for the extraction of performance measures from multi-dimensional data sets, reveal the strengths and weaknesses of each approach, and highlight various numerical issues that result when mapping data from a multi-dimensional space to a space of one dimension.

The Cognitive Visualization System with the Dynamic Projection of Multidimensional Data

NASA Astrophysics Data System (ADS)

Gorohov, V.; Vitkovskiy, V.

2008-08-01

The phenomenon of cognitive machine drawing consists in the generation on the screen the special graphic representations, which create in the brain of human operator entertainment means. These means seem man by aesthetically attractive and, thus, they stimulate its descriptive imagination, closely related to the intuitive mechanisms of thinking. The essence of cognitive effect lies in the fact that man receives the moving projection as pseudo-three-dimensional object characterizing multidimensional means in the multidimensional space. After the thorough qualitative study of the visual aspects of multidimensional means with the aid of the enumerated algorithms appears the possibility, using algorithms of standard machine drawing to paint the interesting user separate objects or the groups of objects. Then it is possible to again return to the dynamic behavior of the rotation of means for the purpose of checking the intuitive ideas of user about the clusters and the connections in multidimensional data. Is possible the development of the methods of cognitive machine drawing in combination with other information technologies, first of all with the packets of digital processing of images and multidimensional statistical analysis.

Denoising of chaotic signal using independent component analysis and empirical mode decomposition with circulate translating

NASA Astrophysics Data System (ADS)

Wen-Bo, Wang; Xiao-Dong, Zhang; Yuchan, Chang; Xiang-Li, Wang; Zhao, Wang; Xi, Chen; Lei, Zheng

2016-01-01

In this paper, a new method to reduce noises within chaotic signals based on ICA (independent component analysis) and EMD (empirical mode decomposition) is proposed. The basic idea is decomposing chaotic signals and constructing multidimensional input vectors, firstly, on the base of EMD and its translation invariance. Secondly, it makes the independent component analysis on the input vectors, which means that a self adapting denoising is carried out for the intrinsic mode functions (IMFs) of chaotic signals. Finally, all IMFs compose the new denoised chaotic signal. Experiments on the Lorenz chaotic signal composed of different Gaussian noises and the monthly observed chaotic sequence on sunspots were put into practice. The results proved that the method proposed in this paper is effective in denoising of chaotic signals. Moreover, it can correct the center point in the phase space effectively, which makes it approach the real track of the chaotic attractor. Project supported by the National Science and Technology, China (Grant No. 2012BAJ15B04), the National Natural Science Foundation of China (Grant Nos. 41071270 and 61473213), the Natural Science Foundation of Hubei Province, China (Grant No. 2015CFB424), the State Key Laboratory Foundation of Satellite Ocean Environment Dynamics, China (Grant No. SOED1405), the Hubei Provincial Key Laboratory Foundation of Metallurgical Industry Process System Science, China (Grant No. Z201303), and the Hubei Key Laboratory Foundation of Transportation Internet of Things, Wuhan University of Technology, China (Grant No.2015III015-B02).

Random Matrix Theory in molecular dynamics analysis.

PubMed

Palese, Luigi Leonardo

2015-01-01

It is well known that, in some situations, principal component analysis (PCA) carried out on molecular dynamics data results in the appearance of cosine-shaped low index projections. Because this is reminiscent of the results obtained by performing PCA on a multidimensional Brownian dynamics, it has been suggested that short-time protein dynamics is essentially nothing more than a noisy signal. Here we use Random Matrix Theory to analyze a series of short-time molecular dynamics experiments which are specifically designed to be simulations with high cosine content. We use as a model system the protein apoCox17, a mitochondrial copper chaperone. Spectral analysis on correlation matrices allows to easily differentiate random correlations, simply deriving from the finite length of the process, from non-random signals reflecting the intrinsic system properties. Our results clearly show that protein dynamics is not really Brownian also in presence of the cosine-shaped low index projections on principal axes. Copyright © 2014 Elsevier B.V. All rights reserved.

Onboard image compression schemes for modular airborne imaging spectrometer (MAIS) based on wavelet transform

NASA Astrophysics Data System (ADS)

Zhu, Zhenyu; Wang, Jianyu

1996-11-01

In this paper, two compression schemes are presented to meet the urgent needs of compressing the huge volume and high data rate of imaging spectrometer images. According to the multidimensional feature of the images and the high fidelity requirement of the reconstruction, both schemes were devised to exploit the high redundancy in both spatial and spectral dimension based on the mature wavelet transform technology. Wavelet transform was applied here in two ways: First, with the spatial wavelet transform and the spectral DPCM decorrelation, a ratio up to 84.3 with PSNR > 48db's near-lossless result was attained. This is based ont he fact that the edge structure among all the spectral bands are similar while WT has higher resolution in high frequency components. Secondly, with the wavelet's high efficiency in processing the 'wideband transient' signals, it was used to transform the raw nonstationary signals in the spectral dimension. A good result was also attained.

Theory and simulations of covariance mapping in multiple dimensions for data analysis in high-event-rate experiments

NASA Astrophysics Data System (ADS)

Zhaunerchyk, V.; Frasinski, L. J.; Eland, J. H. D.; Feifel, R.

2014-05-01

Multidimensional covariance analysis and its validity for correlation of processes leading to multiple products are investigated from a theoretical point of view. The need to correct for false correlations induced by experimental parameters which fluctuate from shot to shot, such as the intensity of self-amplified spontaneous emission x-ray free-electron laser pulses, is emphasized. Threefold covariance analysis based on simple extension of the two-variable formulation is shown to be valid for variables exhibiting Poisson statistics. In this case, false correlations arising from fluctuations in an unstable experimental parameter that scale linearly with signals can be eliminated by threefold partial covariance analysis, as defined here. Fourfold covariance based on the same simple extension is found to be invalid in general. Where fluctuations in an unstable parameter induce nonlinear signal variations, a technique of contingent covariance analysis is proposed here to suppress false correlations. In this paper we also show a method to eliminate false correlations associated with fluctuations of several unstable experimental parameters.

A dynamic nuclear polarization strategy for multi-dimensional Earth's field NMR spectroscopy.

PubMed

Halse, Meghan E; Callaghan, Paul T

2008-12-01

Dynamic nuclear polarization (DNP) is introduced as a powerful tool for polarization enhancement in multi-dimensional Earth's field NMR spectroscopy. Maximum polarization enhancements, relative to thermal equilibrium in the Earth's magnetic field, are calculated theoretically and compared to the more traditional prepolarization approach for NMR sensitivity enhancement at ultra-low fields. Signal enhancement factors on the order of 3000 are demonstrated experimentally using DNP with a nitroxide free radical, TEMPO, which contains an unpaired electron which is strongly coupled to a neighboring (14)N nucleus via the hyperfine interaction. A high-quality 2D (19)F-(1)H COSY spectrum acquired in the Earth's magnetic field with DNP enhancement is presented and compared to simulation.

Diagnostics for insufficiencies of posterior calculations in Bayesian signal inference.

PubMed

Dorn, Sebastian; Oppermann, Niels; Ensslin, Torsten A

2013-11-01

We present an error-diagnostic validation method for posterior distributions in Bayesian signal inference, an advancement of a previous work. It transfers deviations from the correct posterior into characteristic deviations from a uniform distribution of a quantity constructed for this purpose. We show that this method is able to reveal and discriminate several kinds of numerical and approximation errors, as well as their impact on the posterior distribution. For this we present four typical analytical examples of posteriors with incorrect variance, skewness, position of the maximum, or normalization. We show further how this test can be applied to multidimensional signals.

Systems-level mechanisms of action of Panax ginseng: a network pharmacological approach.

PubMed

Park, Sa-Yoon; Park, Ji-Hun; Kim, Hyo-Su; Lee, Choong-Yeol; Lee, Hae-Jeung; Kang, Ki Sung; Kim, Chang-Eop

2018-01-01

Panax ginseng has been used since ancient times based on the traditional Asian medicine theory and clinical experiences, and currently, is one of the most popular herbs in the world. To date, most of the studies concerning P. ginseng have focused on specific mechanisms of action of individual constituents. However, in spite of many studies on the molecular mechanisms of P. ginseng , it still remains unclear how multiple active ingredients of P. ginseng interact with multiple targets simultaneously, giving the multidimensional effects on various conditions and diseases. In order to decipher the systems-level mechanism of multiple ingredients of P. ginseng , a novel approach is needed beyond conventional reductive analysis. We aim to review the systems-level mechanism of P. ginseng by adopting novel analytical framework-network pharmacology. Here, we constructed a compound-target network of P. ginseng using experimentally validated and machine learning-based prediction results. The targets of the network were analyzed in terms of related biological process, pathways, and diseases. The majority of targets were found to be related with primary metabolic process, signal transduction, nitrogen compound metabolic process, blood circulation, immune system process, cell-cell signaling, biosynthetic process, and neurological system process. In pathway enrichment analysis of targets, mainly the terms related with neural activity showed significant enrichment and formed a cluster. Finally, relative degrees analysis for the target-disease association of P. ginseng revealed several categories of related diseases, including respiratory, psychiatric, and cardiovascular diseases.

Object-based media and stream-based computing

NASA Astrophysics Data System (ADS)

Bove, V. Michael, Jr.

1998-03-01

Object-based media refers to the representation of audiovisual information as a collection of objects - the result of scene-analysis algorithms - and a script describing how they are to be rendered for display. Such multimedia presentations can adapt to viewing circumstances as well as to viewer preferences and behavior, and can provide a richer link between content creator and consumer. With faster networks and processors, such ideas become applicable to live interpersonal communications as well, creating a more natural and productive alternative to traditional videoconferencing. In this paper is outlined an example of object-based media algorithms and applications developed by my group, and present new hardware architectures and software methods that we have developed to enable meeting the computational requirements of object- based and other advanced media representations. In particular we describe stream-based processing, which enables automatic run-time parallelization of multidimensional signal processing tasks even given heterogenous computational resources.

Relations between inductive reasoning and deductive reasoning.

PubMed

Heit, Evan; Rotello, Caren M

2010-05-01

One of the most important open questions in reasoning research is how inductive reasoning and deductive reasoning are related. In an effort to address this question, we applied methods and concepts from memory research. We used 2 experiments to examine the effects of logical validity and premise-conclusion similarity on evaluation of arguments. Experiment 1 showed 2 dissociations: For a common set of arguments, deduction judgments were more affected by validity, and induction judgments were more affected by similarity. Moreover, Experiment 2 showed that fast deduction judgments were like induction judgments-in terms of being more influenced by similarity and less influenced by validity, compared with slow deduction judgments. These novel results pose challenges for a 1-process account of reasoning and are interpreted in terms of a 2-process account of reasoning, which was implemented as a multidimensional signal detection model and applied to receiver operating characteristic data. PsycINFO Database Record (c) 2010 APA, all rights reserved.

Toll-like receptors in inflammatory bowel diseases: a decade later.

PubMed

Cario, Elke

2010-09-01

Differential alteration of Toll-like receptor (TLR) expression in inflammatory bowel disease (IBD) was first described 10 years ago. Since then, studies from many groups have led to the current concept that TLRs represent key mediators of innate host defense in the intestine, involved in maintaining mucosal as well as commensal homeostasis. Recent findings in diverse murine models of colitis have helped to reveal the mechanistic importance of TLR dysfunction in IBD pathogenesis. It has become evident that environment, genetics, and host immunity form a multidimensional and highly interactive regulatory triad that controls TLR function in the intestinal mucosa. Imbalanced relationships within this triad may promote aberrant TLR signaling, critically contributing to acute and chronic intestinal inflammatory processes in IBD colitis and associated cancer.

Distributed delays in a hybrid model of tumor-immune system interplay.

PubMed

Caravagna, Giulio; Graudenzi, Alex; d'Onofrio, Alberto

2013-02-01

A tumor is kinetically characterized by the presence of multiple spatio-temporal scales in which its cells interplay with, for instance, endothelial cells or Immune system effectors, exchanging various chemical signals. By its nature, tumor growth is an ideal object of hybrid modeling where discrete stochastic processes model low-numbers entities, and mean-field equations model abundant chemical signals. Thus, we follow this approach to model tumor cells, effector cells and Interleukin-2, in order to capture the Immune surveillance effect. We here present a hybrid model with a generic delay kernel accounting that, due to many complex phenomena such as chemical transportation and cellular differentiation, the tumor-induced recruitment of effectors exhibits a lag period. This model is a Stochastic Hybrid Automata and its semantics is a Piecewise Deterministic Markov process where a two-dimensional stochastic process is interlinked to a multi-dimensional mean-field system. We instantiate the model with two well-known weak and strong delay kernels and perform simulations by using an algorithm to generate trajectories of this process. Via simulations and parametric sensitivity analysis techniques we (i) relate tumor mass growth with the two kernels, we (ii) measure the strength of the Immune surveillance in terms of probability distribution of the eradication times, and (iii) we prove, in the oscillatory regime, the existence of a stochastic bifurcation resulting in delay-induced tumor eradication.

A Multi-Method Multi-Analytic Approach to Establishing Internal Construct Validity Evidence: The Sport Multidimensional Perfectionism Scale 2

ERIC Educational Resources Information Center

Gotwals, John K.; Dunn, John G. H.

2009-01-01

This article presents a chronology of three empirical studies that outline the measurement process by which two new subscales ("Doubts about Actions" and "Organization") were developed and integrated into a revised version of Dunn, Causgrove Dunn, and Syrotuik's (2002) "Sport Multidimensional Perfectionism Scale"…

Scientific Visualization Tools for Enhancement of Undergraduate Research

NASA Astrophysics Data System (ADS)

Rodriguez, W. J.; Chaudhury, S. R.

2001-05-01

Undergraduate research projects that utilize remote sensing satellite instrument data to investigate atmospheric phenomena pose many challenges. A significant challenge is processing large amounts of multi-dimensional data. Remote sensing data initially requires mining; filtering of undesirable spectral, instrumental, or environmental features; and subsequently sorting and reformatting to files for easy and quick access. The data must then be transformed according to the needs of the investigation(s) and displayed for interpretation. These multidimensional datasets require views that can range from two-dimensional plots to multivariable-multidimensional scientific visualizations with animations. Science undergraduate students generally find these data processing tasks daunting. Generally, researchers are required to fully understand the intricacies of the dataset and write computer programs or rely on commercially available software, which may not be trivial to use. In the time that undergraduate researchers have available for their research projects, learning the data formats, programming languages, and/or visualization packages is impractical. When dealing with large multi-dimensional data sets appropriate Scientific Visualization tools are imperative in allowing students to have a meaningful and pleasant research experience, while producing valuable scientific research results. The BEST Lab at Norfolk State University has been creating tools for multivariable-multidimensional analysis of Earth Science data. EzSAGE and SAGE4D have been developed to sort, analyze and visualize SAGE II (Stratospheric Aerosol and Gas Experiment) data with ease. Three- and four-dimensional visualizations in interactive environments can be produced. EzSAGE provides atmospheric slices in three-dimensions where the researcher can change the scales in the three-dimensions, color tables and degree of smoothing interactively to focus on particular phenomena. SAGE4D provides a navigable four-dimensional interactive environment. These tools allow students to make higher order decisions based on large multidimensional sets of data while diminishing the level of frustration that results from dealing with the details of processing large data sets.

Peak picking multidimensional NMR spectra with the contour geometry based algorithm CYPICK.

PubMed

Würz, Julia M; Güntert, Peter

2017-01-01

The automated identification of signals in multidimensional NMR spectra is a challenging task, complicated by signal overlap, noise, and spectral artifacts, for which no universally accepted method is available. Here, we present a new peak picking algorithm, CYPICK, that follows, as far as possible, the manual approach taken by a spectroscopist who analyzes peak patterns in contour plots of the spectrum, but is fully automated. Human visual inspection is replaced by the evaluation of geometric criteria applied to contour lines, such as local extremality, approximate circularity (after appropriate scaling of the spectrum axes), and convexity. The performance of CYPICK was evaluated for a variety of spectra from different proteins by systematic comparison with peak lists obtained by other, manual or automated, peak picking methods, as well as by analyzing the results of automated chemical shift assignment and structure calculation based on input peak lists from CYPICK. The results show that CYPICK yielded peak lists that compare in most cases favorably to those obtained by other automated peak pickers with respect to the criteria of finding a maximal number of real signals, a minimal number of artifact peaks, and maximal correctness of the chemical shift assignments and the three-dimensional structure obtained by fully automated assignment and structure calculation.

An INS/WiFi Indoor Localization System Based on the Weighted Least Squares.

PubMed

Chen, Jian; Ou, Gang; Peng, Ao; Zheng, Lingxiang; Shi, Jianghong

2018-05-07

For smartphone indoor localization, an INS/WiFi hybrid localization system is proposed in this paper. Acceleration and angular velocity are used to estimate step lengths and headings. The problem with INS is that positioning errors grow with time. Using radio signal strength as a fingerprint is a widely used technology. The main problem with fingerprint matching is mismatching due to noise. Taking into account the different shortcomings and advantages, inertial sensors and WiFi from smartphones are integrated into indoor positioning. For a hybrid localization system, pre-processing techniques are used to enhance the WiFi signal quality. An inertial navigation system limits the range of WiFi matching. A Multi-dimensional Dynamic Time Warping (MDTW) is proposed to calculate the distance between the measured signals and the fingerprint in the database. A MDTW-based weighted least squares (WLS) is proposed for fusing multiple fingerprint localization results to improve positioning accuracy and robustness. Using four modes (calling, dangling, handheld and pocket), we carried out walking experiments in a corridor, a study room and a library stack room. Experimental results show that average localization accuracy for the hybrid system is about 2.03 m.

An INS/WiFi Indoor Localization System Based on the Weighted Least Squares

PubMed Central

Chen, Jian; Ou, Gang; Zheng, Lingxiang; Shi, Jianghong

2018-01-01

For smartphone indoor localization, an INS/WiFi hybrid localization system is proposed in this paper. Acceleration and angular velocity are used to estimate step lengths and headings. The problem with INS is that positioning errors grow with time. Using radio signal strength as a fingerprint is a widely used technology. The main problem with fingerprint matching is mismatching due to noise. Taking into account the different shortcomings and advantages, inertial sensors and WiFi from smartphones are integrated into indoor positioning. For a hybrid localization system, pre-processing techniques are used to enhance the WiFi signal quality. An inertial navigation system limits the range of WiFi matching. A Multi-dimensional Dynamic Time Warping (MDTW) is proposed to calculate the distance between the measured signals and the fingerprint in the database. A MDTW-based weighted least squares (WLS) is proposed for fusing multiple fingerprint localization results to improve positioning accuracy and robustness. Using four modes (calling, dangling, handheld and pocket), we carried out walking experiments in a corridor, a study room and a library stack room. Experimental results show that average localization accuracy for the hybrid system is about 2.03 m. PMID:29735960

Correlation analysis of respiratory signals by using parallel coordinate plots.

PubMed

Saatci, Esra

2018-01-01

The understanding of the bonds and the relationships between the respiratory signals, i.e. the airflow, the mouth pressure, the relative temperature and the relative humidity during breathing may provide the improvement on the measurement methods of respiratory mechanics and sensor designs or the exploration of the several possible applications in the analysis of respiratory disorders. Therefore, the main objective of this study was to propose a new combination of methods in order to determine the relationship between respiratory signals as a multidimensional data. In order to reveal the coupling between the processes two very different methods were used: the well-known statistical correlation analysis (i.e. Pearson's correlation and cross-correlation coefficient) and parallel coordinate plots (PCPs). Curve bundling with the number intersections for the correlation analysis, Least Mean Square Time Delay Estimator (LMS-TDE) for the point delay detection and visual metrics for the recognition of the visual structures were proposed and utilized in PCP. The number of intersections was increased when the correlation coefficient changed from high positive to high negative correlation between the respiratory signals, especially if whole breath was processed. LMS-TDE coefficients plotted in PCP indicated well-matched point delay results to the findings in the correlation analysis. Visual inspection of PCB by visual metrics showed range, dispersions, entropy comparisons and linear and sinusoidal-like relationships between the respiratory signals. It is demonstrated that the basic correlation analysis together with the parallel coordinate plots perceptually motivates the visual metrics in the display and thus can be considered as an aid to the user analysis by providing meaningful views of the data. Copyright © 2017 Elsevier B.V. All rights reserved.

Multidimensional incremental parsing for universal source coding.

PubMed

Bae, Soo Hyun; Juang, Biing-Hwang

2008-10-01

A multidimensional incremental parsing algorithm (MDIP) for multidimensional discrete sources, as a generalization of the Lempel-Ziv coding algorithm, is investigated. It consists of three essential component schemes, maximum decimation matching, hierarchical structure of multidimensional source coding, and dictionary augmentation. As a counterpart of the longest match search in the Lempel-Ziv algorithm, two classes of maximum decimation matching are studied. Also, an underlying behavior of the dictionary augmentation scheme for estimating the source statistics is examined. For an m-dimensional source, m augmentative patches are appended into the dictionary at each coding epoch, thus requiring the transmission of a substantial amount of information to the decoder. The property of the hierarchical structure of the source coding algorithm resolves this issue by successively incorporating lower dimensional coding procedures in the scheme. In regard to universal lossy source coders, we propose two distortion functions, the local average distortion and the local minimax distortion with a set of threshold levels for each source symbol. For performance evaluation, we implemented three image compression algorithms based upon the MDIP; one is lossless and the others are lossy. The lossless image compression algorithm does not perform better than the Lempel-Ziv-Welch coding, but experimentally shows efficiency in capturing the source structure. The two lossy image compression algorithms are implemented using the two distortion functions, respectively. The algorithm based on the local average distortion is efficient at minimizing the signal distortion, but the images by the one with the local minimax distortion have a good perceptual fidelity among other compression algorithms. Our insights inspire future research on feature extraction of multidimensional discrete sources.

Co-Creating Curriculum in Higher Education: Promoting Democratic Values and a Multidimensional View on Learning

ERIC Educational Resources Information Center

Bergmark, Ulrika; Westman, Susanne

2016-01-01

This paper discusses a case study in teacher education in Sweden, focusing on creating spaces for student engagement through co-creating curriculum. It highlights democratic values and a multidimensional learning view as underpinning such endeavors. The main findings are that co-creating curriculum is an ambiguous process entailing unpredictable,…

Points of View Analysis Revisited: Fitting Multidimensional Structures to Optimal Distance Components with Cluster Restrictions on the Variables.

ERIC Educational Resources Information Center

Meulman, Jacqueline J.; Verboon, Peter

1993-01-01

Points of view analysis, as a way to deal with individual differences in multidimensional scaling, was largely supplanted by the weighted Euclidean model. It is argued that the approach deserves new attention, especially as a technique to analyze group differences. A streamlined and integrated process is proposed. (SLD)

A Randomized Evaluation of Multidimensional Treatment Foster Care: Effects on School Attendance and Homework Completion in Juvenile Justice Girls

ERIC Educational Resources Information Center

Leve, Leslie D.; Chamberlain, Patricia

2007-01-01

Despite growing evidence that child welfare youth are at increased risk for juvenile delinquency, little is known about gender-specific processes and effective treatment programs for girls. Multidimensional Treatment Foster Care (MTFC), an empirically validated intervention for child welfare and juvenile justice populations, has demonstrated…

Behavioral modeling and digital compensation of nonlinearity in DFB lasers for multi-band directly modulated radio-over-fiber systems

NASA Astrophysics Data System (ADS)

Li, Jianqiang; Yin, Chunjing; Chen, Hao; Yin, Feifei; Dai, Yitang; Xu, Kun

2014-11-01

The envisioned C-RAN concept in wireless communication sector replies on distributed antenna systems (DAS) which consist of a central unit (CU), multiple remote antenna units (RAUs) and the fronthaul links between them. As the legacy and emerging wireless communication standards will coexist for a long time, the fronthaul links are preferred to carry multi-band multi-standard wireless signals. Directly-modulated radio-over-fiber (ROF) links can serve as a lowcost option to make fronthaul connections conveying multi-band wireless signals. However, directly-modulated radioover- fiber (ROF) systems often suffer from inherent nonlinearities from directly-modulated lasers. Unlike ROF systems working at the single-band mode, the modulation nonlinearities in multi-band ROF systems can result in both in-band and cross-band nonlinear distortions. In order to address this issue, we have recently investigated the multi-band nonlinear behavior of directly-modulated DFB lasers based on multi-dimensional memory polynomial model. Based on this model, an efficient multi-dimensional baseband digital predistortion technique was developed and experimentally demonstrated for linearization of multi-band directly-modulated ROF systems.

Multidimensional Trellis Coded Phase Modulation Using a Multilevel Concatenation Approach. Part 1; Code Design

NASA Technical Reports Server (NTRS)

Rajpal, Sandeep; Rhee, Do Jun; Lin, Shu

1997-01-01

The first part of this paper presents a simple and systematic technique for constructing multidimensional M-ary phase shift keying (MMK) trellis coded modulation (TCM) codes. The construction is based on a multilevel concatenation approach in which binary convolutional codes with good free branch distances are used as the outer codes and block MPSK modulation codes are used as the inner codes (or the signal spaces). Conditions on phase invariance of these codes are derived and a multistage decoding scheme for these codes is proposed. The proposed technique can be used to construct good codes for both the additive white Gaussian noise (AWGN) and fading channels as is shown in the second part of this paper.

Multidimensional Separation of Natural Products Using Liquid Chromatography Coupled to Hadamard Transform Ion Mobility Mass Spectrometry

NASA Astrophysics Data System (ADS)

Liu, Wenjie; Zhang, Xing; Knochenmuss, Richard; Siems, William F.; Hill, Herbert H.

2016-05-01

A high performance liquid chromatograph (HPLC)was interfaced to an atmospheric drift tube ion mobility time of flight mass spectrometry. The power of multidimensional separation was demonstrated using chili pepper extracts. The ambient pressure drift tube ion mobility provided high resolving powers up to 166 for the HPLC eluent. With implementation of Hadamard transform (HT), the duty cycle for the ion mobility drift tube was increased from less than 1% to 50%, and the ion transmission efficiency was improved by over 200 times compared with pulsed mode, improving signal to noise ratio 10 times. HT ion mobility and TOF mass spectrometry provide an additional dimension of separation for complex samples without increasing the analysis time compared with conventional HPLC.

Improving Multidimensional Wireless Sensor Network Lifetime Using Pearson Correlation and Fractal Clustering

PubMed Central

Almeida, Fernando R.; Brayner, Angelo; Rodrigues, Joel J. P. C.; Maia, Jose E. Bessa

2017-01-01

An efficient strategy for reducing message transmission in a wireless sensor network (WSN) is to group sensors by means of an abstraction denoted cluster. The key idea behind the cluster formation process is to identify a set of sensors whose sensed values present some data correlation. Nowadays, sensors are able to simultaneously sense multiple different physical phenomena, yielding in this way multidimensional data. This paper presents three methods for clustering sensors in WSNs whose sensors collect multidimensional data. The proposed approaches implement the concept of multidimensional behavioral clustering. To show the benefits introduced by the proposed methods, a prototype has been implemented and experiments have been carried out on real data. The results prove that the proposed methods decrease the amount of data flowing in the network and present low root-mean-square error (RMSE). PMID:28590450

Improving Multidimensional Wireless Sensor Network Lifetime Using Pearson Correlation and Fractal Clustering.

PubMed

Almeida, Fernando R; Brayner, Angelo; Rodrigues, Joel J P C; Maia, Jose E Bessa

2017-06-07

An efficient strategy for reducing message transmission in a wireless sensor network (WSN) is to group sensors by means of an abstraction denoted cluster. The key idea behind the cluster formation process is to identify a set of sensors whose sensed values present some data correlation. Nowadays, sensors are able to simultaneously sense multiple different physical phenomena, yielding in this way multidimensional data. This paper presents three methods for clustering sensors in WSNs whose sensors collect multidimensional data. The proposed approaches implement the concept of multidimensional behavioral clustering . To show the benefits introduced by the proposed methods, a prototype has been implemented and experiments have been carried out on real data. The results prove that the proposed methods decrease the amount of data flowing in the network and present low root-mean-square error (RMSE).

Membership determination of open clusters based on a spectral clustering method

NASA Astrophysics Data System (ADS)

Gao, Xin-Hua

2018-06-01

We present a spectral clustering (SC) method aimed at segregating reliable members of open clusters in multi-dimensional space. The SC method is a non-parametric clustering technique that performs cluster division using eigenvectors of the similarity matrix; no prior knowledge of the clusters is required. This method is more flexible in dealing with multi-dimensional data compared to other methods of membership determination. We use this method to segregate the cluster members of five open clusters (Hyades, Coma Ber, Pleiades, Praesepe, and NGC 188) in five-dimensional space; fairly clean cluster members are obtained. We find that the SC method can capture a small number of cluster members (weak signal) from a large number of field stars (heavy noise). Based on these cluster members, we compute the mean proper motions and distances for the Hyades, Coma Ber, Pleiades, and Praesepe clusters, and our results are in general quite consistent with the results derived by other authors. The test results indicate that the SC method is highly suitable for segregating cluster members of open clusters based on high-precision multi-dimensional astrometric data such as Gaia data.

The Pursuit of Happiness Measurement: A Psychometric Model Based on Psychophysiological Correlates

PubMed Central

Pietro, Cipresso; Silvia, Serino; Giuseppe, Riva

2014-01-01

Everyone is interested in the pursuit of happiness, but the real problem for the researchers is how to measure it. Our aim was to deeply investigate happiness measurement through biomedical signals, using psychophysiological methods to objectify the happiness experiences measurements. The classic valence-arousal model of affective states to study happiness has been extensively used in psychophysiology. However, really few studies considered a real combination of these two dimensions and no study further investigated multidimensional models. More, most studies focused mainly on self-report to measure happiness and a deeper psychophysiological investigation on the dimensions of such an experience is still missing. A multidimensional model of happiness is presented and both the dimensions and the measures extracted within each dimension are comprehensively explained. This multidimensional model aims at being a milestone for future systematic study on psychophysiology of happiness and affective states. It seems everyone has a view on happiness. Joan Collins, theDalai Lama and over 100 others have released new titles on the subject since the beginning of 2001 Richard Tooth “The Psychology of Happiness (2nd Edition)”Michael Argyle, Routledge PMID:24955383

Statistical Projections for Multi-resolution, Multi-dimensional Visual Data Exploration and Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hoa T. Nguyen; Stone, Daithi; E. Wes Bethel

2016-01-01

An ongoing challenge in visual exploration and analysis of large, multi-dimensional datasets is how to present useful, concise information to a user for some specific visualization tasks. Typical approaches to this problem have proposed either reduced-resolution versions of data, or projections of data, or both. These approaches still have some limitations such as consuming high computation or suffering from errors. In this work, we explore the use of a statistical metric as the basis for both projections and reduced-resolution versions of data, with a particular focus on preserving one key trait in data, namely variation. We use two different casemore » studies to explore this idea, one that uses a synthetic dataset, and another that uses a large ensemble collection produced by an atmospheric modeling code to study long-term changes in global precipitation. The primary findings of our work are that in terms of preserving the variation signal inherent in data, that using a statistical measure more faithfully preserves this key characteristic across both multi-dimensional projections and multi-resolution representations than a methodology based upon averaging.« less

What Is More Important for Fourth-Grade Primary School Students for Transforming Their Potential into Achievement: The Individual or the Environmental Box in Multidimensional Conceptions of Giftedness?

ERIC Educational Resources Information Center

Stoeger, Heidrun; Steinbach, Julia; Obergriesser, Stefanie; Matthes, Benjamin

2014-01-01

Multidimensional models of giftedness specify individual and environmental moderators or catalysts that help transform potential into achievement. However, these models do not state whether the importance of the "individual boxes" and the "environmental boxes" changes during this process. The present study examines whether,…

Electrochemical force microscopy

DOEpatents

Kalinin, Sergei V.; Jesse, Stephen; Collins, Liam F.; Rodriguez, Brian J.

2017-01-10

A system and method for electrochemical force microscopy are provided. The system and method are based on a multidimensional detection scheme that is sensitive to forces experienced by a biased electrode in a solution. The multidimensional approach allows separation of fast processes, such as double layer charging, and charge relaxation, and slow processes, such as diffusion and faradaic reactions, as well as capturing the bias dependence of the response. The time-resolved and bias measurements can also allow probing both linear (small bias range) and non-linear (large bias range) electrochemical regimes and potentially the de-convolution of charge dynamics and diffusion processes from steric effects and electrochemical reactivity.

Neurobehavioral and self-awareness changes after traumatic brain injury: Towards new multidimensional approaches.

PubMed

Arnould, A; Dromer, E; Rochat, L; Van der Linden, M; Azouvi, P

2016-02-01

Neurobehavioral and self-awareness changes are frequently observed following traumatic brain injury (TBI). These disturbances have been related to negative consequences on functional outcomes, caregiver distress and social reintegration, representing therefore a challenge for clinical research. Some studies have recently been conducted to specifically explore apathetic and impulsive manifestations, as well as self-awareness impairments in patients with TBI. These findings underlined the heterogeneity of clinical manifestations for each behavioral disturbance and the diversity of psychological processes involved. In this context, new multidimensional approaches taking into account the various processes at play have been proposed to better understand and apprehend the complexity and dynamic nature of these problematic behaviors. In addition, the involvement of social and environmental factors as well as premorbid personality traits have increasingly been addressed. These new multidimensional frameworks have the potential to ensure targeted and effective rehabilitation by allowing a better identification and therefore consideration of the various mechanisms involved in the onset of problematic behaviors. In this context, the main objective of this position paper was to demonstrate the interest of multidimensional approaches in the understanding and rehabilitation of problematic behaviors in patients with TBI. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Integrating Scientific Array Processing into Standard SQL

NASA Astrophysics Data System (ADS)

Misev, Dimitar; Bachhuber, Johannes; Baumann, Peter

2014-05-01

We live in a time that is dominated by data. Data storage is cheap and more applications than ever accrue vast amounts of data. Storing the emerging multidimensional data sets efficiently, however, and allowing them to be queried by their inherent structure, is a challenge many databases have to face today. Despite the fact that multidimensional array data is almost always linked to additional, non-array information, array databases have mostly developed separately from relational systems, resulting in a disparity between the two database categories. The current SQL standard and SQL DBMS supports arrays - and in an extension also multidimensional arrays - but does so in a very rudimentary and inefficient way. This poster demonstrates the practicality of an SQL extension for array processing, implemented in a proof-of-concept multi-faceted system that manages a federation of array and relational database systems, providing transparent, efficient and scalable access to the heterogeneous data in them.

Toll-like receptors in inflammatory bowel diseases: A decade later

PubMed Central

Cario, Elke

2010-01-01

Differential alteration of Toll-like receptor (TLR) expression in inflammatory bowel disease (IBD) was first described 10 years ago. Since then, studies from many groups have led to the current concept that TLRs represent key mediators of innate host defense in the intestine, involved in maintaining mucosal as well as commensal homeostasis. Recent findings in diverse murine models of colitis have helped to reveal the mechanistic importance of TLR dysfunction in IBD pathogenesis. It has become evident that environment, genetics, and host immunity form a multidimensional and highly interactive regulatory triad that controls TLR function in the intestinal mucosa. Imbalanced relationships within this triad may promote aberrant TLR signaling, critically contributing to acute and chronic intestinal inflammatory processes in IBD colitis and associated cancer. (Inflamm Bowel Dis 2010) PMID:20803699

Phase retrieval using regularization method in intensity correlation imaging

NASA Astrophysics Data System (ADS)

Li, Xiyu; Gao, Xin; Tang, Jia; Lu, Changming; Wang, Jianli; Wang, Bin

2014-11-01

Intensity correlation imaging(ICI) method can obtain high resolution image with ground-based low precision mirrors, in the imaging process, phase retrieval algorithm should be used to reconstituted the object's image. But the algorithm now used(such as hybrid input-output algorithm) is sensitive to noise and easy to stagnate. However the signal-to-noise ratio of intensity interferometry is low especially in imaging astronomical objects. In this paper, we build the mathematical model of phase retrieval and simplified it into a constrained optimization problem of a multi-dimensional function. New error function was designed by noise distribution and prior information using regularization method. The simulation results show that the regularization method can improve the performance of phase retrieval algorithm and get better image especially in low SNR condition

Simulation of a Multidimensional Input Quantum Perceptron

NASA Astrophysics Data System (ADS)

Yamamoto, Alexandre Y.; Sundqvist, Kyle M.; Li, Peng; Harris, H. Rusty

2018-06-01

In this work, we demonstrate the improved data separation capabilities of the Multidimensional Input Quantum Perceptron (MDIQP), a fundamental cell for the construction of more complex Quantum Artificial Neural Networks (QANNs). This is done by using input controlled alterations of ancillary qubits in combination with phase estimation and learning algorithms. The MDIQP is capable of processing quantum information and classifying multidimensional data that may not be linearly separable, extending the capabilities of the classical perceptron. With this powerful component, we get much closer to the achievement of a feedforward multilayer QANN, which would be able to represent and classify arbitrary sets of data (both quantum and classical).

Receptive fields and the theory of discriminant operators

NASA Astrophysics Data System (ADS)

Gupta, Madan M.; Hungenahally, Suresh K.

1991-02-01

Biological basis for machine vision is a notion which is being used extensively for the development of machine vision systems for various applications. In this paper we have made an attempt to emulate the receptive fields that exist in the biological visual channels. In particular we have exploited the notion of receptive fields for developing the mathematical functions named as discriminantfunctions for the extraction of transition information from signals and multi-dimensional signals and images. These functions are found to be useful for the development of artificial receptive fields for neuro-vision systems. 1.

Support-vector-machines-based multidimensional signal classification for fetal activity characterization

NASA Astrophysics Data System (ADS)

Ribes, S.; Voicu, I.; Girault, J. M.; Fournier, M.; Perrotin, F.; Tranquart, F.; Kouamé, D.

2011-03-01

Electronic fetal monitoring may be required during the whole pregnancy to closely monitor specific fetal and maternal disorders. Currently used methods suffer from many limitations and are not sufficient to evaluate fetal asphyxia. Fetal activity parameters such as movements, heart rate and associated parameters are essential indicators of the fetus well being, and no current device gives a simultaneous and sufficient estimation of all these parameters to evaluate the fetus well-being. We built for this purpose, a multi-transducer-multi-gate Doppler system and developed dedicated signal processing techniques for fetal activity parameter extraction in order to investigate fetus's asphyxia or well-being through fetal activity parameters. To reach this goal, this paper shows preliminary feasibility of separating normal and compromised fetuses using our system. To do so, data set consisting of two groups of fetal signals (normal and compromised) has been established and provided by physicians. From estimated parameters an instantaneous Manning-like score, referred to as ultrasonic score was introduced and was used together with movements, heart rate and associated parameters in a classification process using Support Vector Machines (SVM) method. The influence of the fetal activity parameters and the performance of the SVM were evaluated using the computation of sensibility, specificity, percentage of support vectors and total classification accuracy. We showed our ability to separate the data into two sets : normal fetuses and compromised fetuses and obtained an excellent matching with the clinical classification performed by physician.

GPS Technologies as a Tool to Detect the Pre-Earthquake Signals Associated with Strong Earthquakes

NASA Astrophysics Data System (ADS)

Pulinets, S. A.; Krankowski, A.; Hernandez-Pajares, M.; Liu, J. Y. G.; Hattori, K.; Davidenko, D.; Ouzounov, D.

2015-12-01

The existence of ionospheric anomalies before earthquakes is now widely accepted. These phenomena started to be considered by GPS community to mitigate the GPS signal degradation over the territories of the earthquake preparation. The question is still open if they could be useful for seismology and for short-term earthquake forecast. More than decade of intensive studies proved that ionospheric anomalies registered before earthquakes are initiated by processes in the boundary layer of atmosphere over earthquake preparation zone and are induced in the ionosphere by electromagnetic coupling through the Global Electric Circuit. Multiparameter approach based on the Lithosphere-Atmosphere-Ionosphere Coupling model demonstrated that earthquake forecast is possible only if we consider the final stage of earthquake preparation in the multidimensional space where every dimension is one from many precursors in ensemble, and they are synergistically connected. We demonstrate approaches developed in different countries (Russia, Taiwan, Japan, Spain, and Poland) within the framework of the ISSI and ESA projects) to identify the ionospheric precursors. They are also useful to determine the all three parameters necessary for the earthquake forecast: impending earthquake epicenter position, expectation time and magnitude. These parameters are calculated using different technologies of GPS signal processing: time series, correlation, spectral analysis, ionospheric tomography, wave propagation, etc. Obtained results from different teams demonstrate the high level of statistical significance and physical justification what gives us reason to suggest these methodologies for practical validation.

Investigation of Rho Signaling Pathways in 3-D Collagen Matrices with Multidimensional Microscopy and Visualization Techniques

DTIC Science & Technology

2008-03-01

most prevalent cancer among women .1 Therefore, tech- ologies to detect, classify, study, and combat breast cancer re of great significance. Among these...M. Sidani , J. Wyckoff, C. Xue, J. E. Segall, and J. Condeelis, “Prob- ing the microenvironment of mammary tumors using multiphoton microscopy,” J

Visual modeling in an analysis of multidimensional data

NASA Astrophysics Data System (ADS)

Zakharova, A. A.; Vekhter, E. V.; Shklyar, A. V.; Pak, A. J.

2018-01-01

The article proposes an approach to solve visualization problems and the subsequent analysis of multidimensional data. Requirements to the properties of visual models, which were created to solve analysis problems, are described. As a perspective direction for the development of visual analysis tools for multidimensional and voluminous data, there was suggested an active use of factors of subjective perception and dynamic visualization. Practical results of solving the problem of multidimensional data analysis are shown using the example of a visual model of empirical data on the current state of studying processes of obtaining silicon carbide by an electric arc method. There are several results of solving this problem. At first, an idea of possibilities of determining the strategy for the development of the domain, secondly, the reliability of the published data on this subject, and changes in the areas of attention of researchers over time.

Analysis of a municipal wastewater treatment plant using a neural network-based pattern analysis

USGS Publications Warehouse

Hong, Y.-S.T.; Rosen, Michael R.; Bhamidimarri, R.

2003-01-01

This paper addresses the problem of how to capture the complex relationships that exist between process variables and to diagnose the dynamic behaviour of a municipal wastewater treatment plant (WTP). Due to the complex biological reaction mechanisms, the highly time-varying, and multivariable aspects of the real WTP, the diagnosis of the WTP are still difficult in practice. The application of intelligent techniques, which can analyse the multi-dimensional process data using a sophisticated visualisation technique, can be useful for analysing and diagnosing the activated-sludge WTP. In this paper, the Kohonen Self-Organising Feature Maps (KSOFM) neural network is applied to analyse the multi-dimensional process data, and to diagnose the inter-relationship of the process variables in a real activated-sludge WTP. By using component planes, some detailed local relationships between the process variables, e.g., responses of the process variables under different operating conditions, as well as the global information is discovered. The operating condition and the inter-relationship among the process variables in the WTP have been diagnosed and extracted by the information obtained from the clustering analysis of the maps. It is concluded that the KSOFM technique provides an effective analysing and diagnosing tool to understand the system behaviour and to extract knowledge contained in multi-dimensional data of a large-scale WTP. ?? 2003 Elsevier Science Ltd. All rights reserved.

SSL: Signal Similarity-Based Localization for Ocean Sensor Networks.

PubMed

Chen, Pengpeng; Ma, Honglu; Gao, Shouwan; Huang, Yan

2015-11-24

Nowadays, wireless sensor networks are often deployed on the sea surface for ocean scientific monitoring. One of the important challenges is to localize the nodes' positions. Existing localization schemes can be roughly divided into two types: range-based and range-free. The range-based localization approaches heavily depend on extra hardware capabilities, while range-free ones often suffer from poor accuracy and low scalability, far from the practical ocean monitoring applications. In response to the above limitations, this paper proposes a novel signal similarity-based localization (SSL) technology, which localizes the nodes' positions by fully utilizing the similarity of received signal strength and the open-air characteristics of the sea surface. In the localization process, we first estimate the relative distance between neighboring nodes through comparing the similarity of received signal strength and then calculate the relative distance for non-neighboring nodes with the shortest path algorithm. After that, the nodes' relative relation map of the whole network can be obtained. Given at least three anchors, the physical locations of nodes can be finally determined based on the multi-dimensional scaling (MDS) technology. The design is evaluated by two types of ocean experiments: a zonal network and a non-regular network using 28 nodes. Results show that the proposed design improves the localization accuracy compared to typical connectivity-based approaches and also confirm its effectiveness for large-scale ocean sensor networks.

Multidimensional bioseparation with modular microfluidics

DOEpatents

Chirica, Gabriela S.; Renzi, Ronald F.

2013-08-27

A multidimensional chemical separation and analysis system is described including a prototyping platform and modular microfluidic components capable of rapid and convenient assembly, alteration and disassembly of numerous candidate separation systems. Partial or total computer control of the separation system is possible. Single or multiple alternative processing trains can be tested, optimized and/or run in parallel. Examples related to the separation and analysis of human bodily fluids are given.

Detecting Shielded Special Nuclear Materials Using Multi-Dimensional Neutron Source and Detector Geometries

NASA Astrophysics Data System (ADS)

Santarius, John; Navarro, Marcos; Michalak, Matthew; Fancher, Aaron; Kulcinski, Gerald; Bonomo, Richard

2016-10-01

A newly initiated research project will be described that investigates methods for detecting shielded special nuclear materials by combining multi-dimensional neutron sources, forward/adjoint calculations modeling neutron and gamma transport, and sparse data analysis of detector signals. The key tasks for this project are: (1) developing a radiation transport capability for use in optimizing adaptive-geometry, inertial-electrostatic confinement (IEC) neutron source/detector configurations for neutron pulses distributed in space and/or phased in time; (2) creating distributed-geometry, gas-target, IEC fusion neutron sources; (3) applying sparse data and noise reduction algorithms, such as principal component analysis (PCA) and wavelet transform analysis, to enhance detection fidelity; and (4) educating graduate and undergraduate students. Funded by DHS DNDO Project 2015-DN-077-ARI095.

Spider-web inspired multi-resolution graphene tactile sensor.

PubMed

Liu, Lu; Huang, Yu; Li, Fengyu; Ma, Ying; Li, Wenbo; Su, Meng; Qian, Xin; Ren, Wanjie; Tang, Kanglai; Song, Yanlin

2018-05-08

Multi-dimensional accurate response and smooth signal transmission are critical challenges in the advancement of multi-resolution recognition and complex environment analysis. Inspired by the structure-activity relationship between discrepant microstructures of the spiral and radial threads in a spider web, we designed and printed graphene with porous and densely-packed microstructures to integrate into a multi-resolution graphene tactile sensor. The three-dimensional (3D) porous graphene structure performs multi-dimensional deformation responses. The laminar densely-packed graphene structure contributes excellent conductivity with flexible stability. The spider-web inspired printed pattern inherits orientational and locational kinesis tracking. The multi-structure construction with homo-graphene material can integrate discrepant electronic properties with remarkable flexibility, which will attract enormous attention for electronic skin, wearable devices and human-machine interactions.

Speeding up NMR by in Situ Photo-Induced Reversible Acceleration of T1 -Relaxation (PIRAT).

PubMed

Stadler, Eduard; Dommaschk, Marcel; Frühwirt, Philipp; Herges, Rainer; Gescheidt, Georg

2018-03-05

Increasing the signal-to-noise ratio is one of the major goals in the field of NMR spectroscopy. In this proof of concept, we accelerate relaxation during an NMR pulse sequence using photo-generated paramagnetic states of an inert sensitizer. For the follow-up acquisition period, the system is converted to a diamagnetic state. The reversibility of the photo-induced switching allows extensive repetition required for multidimensional NMR. We thus eliminate the obstacle of line-broadening by the presence of paramagnetic species. In this contribution, we show how cycling of synchronized light/pulse sequences leads to an enhanced efficiency in multidimensional NMR. Our approach utilizes a molecular spin switch reversibly altering between a paramagnetic and diamagnetic state. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ibrahim, Yehia M.; Garimella, Sandilya V. B.; Prost, Spencer A.

Complex samples benefit from multidimensional measurements where higher resolution enables more complete characterization of biological and environmental systems. To address this challenge, we developed a drift tube-based ion mobility spectrometry-Orbitrap mass spectrometer (IMS-Orbitrap MS) platform. To circumvent the time scale disparity between the fast IMS separation and the much slower Orbitrap MS acquisition, we utilized a dual gate and pseudorandom sequences to multiplexed injection of ions and allowing operation in signal averaging (SA), single multiplexing (SM) and double multiplexing (DM) IMS modes to optimize the signal-to-noise ratio of the measurements. For the SM measurements, a previously developed algorithm was usedmore » to reconstruct the IMS data. A new algorithm was developed for the DM analyses involving a two-step process that first recovers the SM data and then decodes the SM data. The algorithm also performs multiple refining procedures in order to minimize demultiplexing artifacts. The new IMS-Orbitrap MS platform was demonstrated by the analysis of proteomic and petroleum samples, where the integration of IMS and high mass resolution proved essential for accurate assignment of molecular formulae.« less

Numeric invariants from multidimensional persistence

DOE PAGES

Skryzalin, Jacek; Carlsson, Gunnar

2017-05-19

Topological data analysis is the study of data using techniques from algebraic topology. Often, one begins with a finite set of points representing data and a “filter” function which assigns a real number to each datum. Using both the data and the filter function, one can construct a filtered complex for further analysis. For example, applying the homology functor to the filtered complex produces an algebraic object known as a “one-dimensional persistence module”, which can often be interpreted as a finite set of intervals representing various geometric features in the data. If one runs the above process incorporating multiple filtermore » functions simultaneously, one instead obtains a multidimensional persistence module. Unfortunately, these are much more difficult to interpret. In this article, we analyze the space of multidimensional persistence modules from the perspective of algebraic geometry. First we build a moduli space of a certain subclass of easily analyzed multidimensional persistence modules, which we construct specifically to capture much of the information which can be gained by using multidimensional persistence instead of one-dimensional persistence. Fruthermore, we argue that the global sections of this space provide interesting numeric invariants when evaluated against our subclass of multidimensional persistence modules. Finally, we extend these global sections to the space of all multidimensional persistence modules and discuss how the resulting numeric invariants might be used to study data. This paper extends the results of Adcock et al. (Homol Homotopy Appl 18(1), 381–402, 2016) by constructing numeric invariants from the computation of a multidimensional persistence module as given by Carlsson et al. (J Comput Geom 1(1), 72–100, 2010).« less

Signal enhancement in protein NMR using the spin-noise tuning optimum

PubMed Central

Nausner, Martin; Goger, Michael; Bendet-Taicher, Eli; Schlagnitweit, Judith

2010-01-01

We have assessed the potential of an alternative probe tuning strategy based on the spin-noise response for application in common high-resolution multi-dimensional biomolecular NMR experiments with water signal suppression on aqueous and salty samples. The method requires the adjustment of the optimal tuning condition, which may be offset by several 100 kHz from the conventional tuning settings using the noise response of the water protons as an indicator. Although the radio frequency-pulse durations are typically longer under such conditions, signal-to-noise gains of up to 22% were achieved. At salt concentrations up to 100 mM a substantial sensitivity gain was observed. PMID:20924647

Localization from near-source quasi-static electromagnetic fields

NASA Astrophysics Data System (ADS)

Mosher, J. C.

1993-09-01

A wide range of research has been published on the problem of estimating the parameters of electromagnetic and acoustical sources from measurements of signals measured at an array of sensors. In the quasi-static electromagnetic cases examined here, the signal variation from a point source is relatively slow with respect to the signal propagation and the spacing of the array of sensors. As such, the location of the point sources can only be determined from the spatial diversity of the received signal across the array. The inverse source localization problem is complicated by unknown model order and strong local minima. The nonlinear optimization problem is posed for solving for the parameters of the quasi-static source model. The transient nature of the sources can be exploited to allow subspace approaches to separate out the signal portion of the spatial correlation matrix. Decomposition techniques are examined for improved processing, and an adaptation of MUltiple SIgnal Characterization (MUSIC) is presented for solving the source localization problem. Recent results on calculating the Cramer-Rao error lower bounds are extended to the multidimensional problem here. This thesis focuses on the problem of source localization in magnetoencephalography (MEG), with a secondary application to thunderstorm source localization. Comparisons are also made between MEG and its electrical equivalent, electroencephalography (EEG). The error lower bounds are examined in detail for several MEG and EEG configurations, as well as localizing thunderstorm cells over Cape Canaveral and Kennedy Space Center. Time-eigenspectrum is introduced as a parsing technique for improving the performance of the optimization problem.

Localization from near-source quasi-static electromagnetic fields

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mosher, John Compton

1993-09-01

A wide range of research has been published on the problem of estimating the parameters of electromagnetic and acoustical sources from measurements of signals measured at an array of sensors. In the quasi-static electromagnetic cases examined here, the signal variation from a point source is relatively slow with respect to the signal propagation and the spacing of the array of sensors. As such, the location of the point sources can only be determined from the spatial diversity of the received signal across the array. The inverse source localization problem is complicated by unknown model order and strong local minima. Themore » nonlinear optimization problem is posed for solving for the parameters of the quasi-static source model. The transient nature of the sources can be exploited to allow subspace approaches to separate out the signal portion of the spatial correlation matrix. Decomposition techniques are examined for improved processing, and an adaptation of MUtiple SIgnal Characterization (MUSIC) is presented for solving the source localization problem. Recent results on calculating the Cramer-Rao error lower bounds are extended to the multidimensional problem here. This thesis focuses on the problem of source localization in magnetoencephalography (MEG), with a secondary application to thunderstorm source localization. Comparisons are also made between MEG and its electrical equivalent, electroencephalography (EEG). The error lower bounds are examined in detail for several MEG and EEG configurations, as well as localizing thunderstorm cells over Cape Canaveral and Kennedy Space Center. Time-eigenspectrum is introduced as a parsing technique for improving the performance of the optimization problem.« less

A systemic identification approach for primary transcription start site of Arabidopsis miRNAs from multidimensional omics data.

PubMed

You, Qi; Yan, Hengyu; Liu, Yue; Yi, Xin; Zhang, Kang; Xu, Wenying; Su, Zhen

2017-05-01

The 22-nucleotide non-coding microRNAs (miRNAs) are mostly transcribed by RNA polymerase II and are similar to protein-coding genes. Unlike the clear process from stem-loop precursors to mature miRNAs, the primary transcriptional regulation of miRNA, especially in plants, still needs to be further clarified, including the original transcription start site, functional cis-elements and primary transcript structures. Due to several well-characterized transcription signals in the promoter region, we proposed a systemic approach integrating multidimensional "omics" (including genomics, transcriptomics, and epigenomics) data to improve the genome-wide identification of primary miRNA transcripts. Here, we used the model plant Arabidopsis thaliana to improve the ability to identify candidate promoter locations in intergenic miRNAs and to determine rules for identifying primary transcription start sites of miRNAs by integrating high-throughput omics data, such as the DNase I hypersensitive sites, chromatin immunoprecipitation-sequencing of polymerase II and H3K4me3, as well as high throughput transcriptomic data. As a result, 93% of refined primary transcripts could be confirmed by the primer pairs from a previous study. Cis-element and secondary structure analyses also supported the feasibility of our results. This work will contribute to the primary transcriptional regulatory analysis of miRNAs, and the conserved regulatory pattern may be a suitable miRNA characteristic in other plant species.

Out of the blue: the evolution of horizontally polarized signals in Haptosquilla (Crustacea, Stomatopoda, Protosquillidae).

PubMed

How, Martin J; Porter, Megan L; Radford, Andrew N; Feller, Kathryn D; Temple, Shelby E; Caldwell, Roy L; Marshall, N Justin; Cronin, Thomas W; Roberts, Nicholas W

2014-10-01

The polarization of light provides information that is used by many animals for a number of different visually guided behaviours. Several marine species, such as stomatopod crustaceans and cephalopod molluscs, communicate using visual signals that contain polarized information, content that is often part of a more complex multi-dimensional visual signal. In this work, we investigate the evolution of polarized signals in species of Haptosquilla, a widespread genus of stomatopod, as well as related protosquillids. We present evidence for a pre-existing bias towards horizontally polarized signal content and demonstrate that the properties of the polarization vision system in these animals increase the signal-to-noise ratio of the signal. Combining these results with the increase in efficacy that polarization provides over intensity and hue in a shallow marine environment, we propose a joint framework for the evolution of the polarized form of these complex signals based on both efficacy-driven (proximate) and content-driven (ultimate) selection pressures. © 2014. Published by The Company of Biologists Ltd.

Medical image registration based on normalized multidimensional mutual information

NASA Astrophysics Data System (ADS)

Li, Qi; Ji, Hongbing; Tong, Ming

2009-10-01

Registration of medical images is an essential research topic in medical image processing and applications, and especially a preliminary and key step for multimodality image fusion. This paper offers a solution to medical image registration based on normalized multi-dimensional mutual information. Firstly, affine transformation with translational and rotational parameters is applied to the floating image. Then ordinal features are extracted by ordinal filters with different orientations to represent spatial information in medical images. Integrating ordinal features with pixel intensities, the normalized multi-dimensional mutual information is defined as similarity criterion to register multimodality images. Finally the immune algorithm is used to search registration parameters. The experimental results demonstrate the effectiveness of the proposed registration scheme.

Raman and Autofluorescence Spectrum Dynamics along the HRG-Induced Differentiation Pathway of MCF-7 Cells

PubMed Central

Morita, Shin-ichi; Takanezawa, Sota; Hiroshima, Michio; Mitsui, Toshiyuki; Ozaki, Yukihiro; Sako, Yasushi

2014-01-01

Cellular differentiation proceeds along complicated pathways, even when it is induced by extracellular signaling molecules. One of the major reasons for this complexity is the highly multidimensional internal dynamics of cells, which sometimes causes apparently stochastic responses in individual cells to extracellular stimuli. Therefore, to understand cell differentiation, it is necessary to monitor the internal dynamics of cells at single-cell resolution. Here, we used a Raman and autofluorescence spectrum analysis of single cells to detect dynamic changes in intracellular molecular components. MCF-7 cells are a human cancer-derived cell line that can be induced to differentiate into mammary-gland-like cells with the addition of heregulin (HRG) to the culture medium. We measured the spectra in the cytoplasm of MCF-7 cells during 12 days of HRG stimulation. The Raman scattering spectrum, which was the major component of the signal, changed with time. A multicomponent analysis of the Raman spectrum revealed that the dynamics of the major components of the intracellular molecules, including proteins and lipids, changed cyclically along the differentiation pathway. The background autofluorescence signals of Raman scattering also provided information about the differentiation process. Using the total information from the Raman and autofluorescence spectra, we were able to visualize the pathway of cell differentiation in the multicomponent phase space. PMID:25418290

Spectrally efficient digitized radio-over-fiber system with k-means clustering-based multidimensional quantization.

PubMed

Zhang, Lu; Pang, Xiaodan; Ozolins, Oskars; Udalcovs, Aleksejs; Popov, Sergei; Xiao, Shilin; Hu, Weisheng; Chen, Jiajia

2018-04-01

We propose a spectrally efficient digitized radio-over-fiber (D-RoF) system by grouping highly correlated neighboring samples of the analog signals into multidimensional vectors, where the k-means clustering algorithm is adopted for adaptive quantization. A 30  Gbit/s D-RoF system is experimentally demonstrated to validate the proposed scheme, reporting a carrier aggregation of up to 40 100 MHz orthogonal frequency division multiplexing (OFDM) channels with quadrate amplitude modulation (QAM) order of 4 and an aggregation of 10 100 MHz OFDM channels with a QAM order of 16384. The equivalent common public radio interface rates from 37 to 150  Gbit/s are supported. Besides, the error vector magnitude (EVM) of 8% is achieved with the number of quantization bits of 4, and the EVM can be further reduced to 1% by increasing the number of quantization bits to 7. Compared with conventional pulse coding modulation-based D-RoF systems, the proposed D-RoF system improves the signal-to-noise-ratio up to ∼9  dB and greatly reduces the EVM, given the same number of quantization bits.

Mode-Selective Photon Counting Via Quantum Frequency Conversion Using Spectrally-Engineered Pump Pulses

NASA Astrophysics Data System (ADS)

Manurkar, Paritosh

Most of the existing protocols for quantum communication operate in a two-dimensional Hilbert space where their manipulation and measurement have been routinely investigated. Moving to higher-dimensional Hilbert spaces is desirable because of advantages in terms of longer distance communication capabilities, higher channel capacity and better information security. We can exploit the spatio-temporal degrees of freedom for the quantum optical signals to provide the higher-dimensional signals. But this necessitates the need for measurement and manipulation of multidimensional quantum states. To that end, there have been significant theoretical studies based on quantum frequency conversion (QFC) in recent years even though the experimental progress has been limited. QFC is a process that allows preservation of the quantum information while changing the frequency of the input quantum state. It has deservedly garnered a lot of attention because it serves as the connecting bridge between the communications band (C-band near 1550 nm) where the fiber-optic infrastructure is already established and the visible spectrum where high efficiency single-photon detectors and optical memories have been demonstrated. In this experimental work, we demonstrate mode-selective frequency conversion as a means to measure and manipulate photonic signals occupying d -dimensional Hilbert spaces where d=2 and 4. In the d=2 case, we demonstrate mode contrast between two temporal modes (TMs) which serves as the proof-of-concept demonstration. In the d=4 version, we employ six different TMs for our detailed experimental study. These TMs also include superposition modes which are a crucial component in many quantum key distribution protocols. Our method is based on producing pump pulses which allow us to upconvert the TM of interest while ideally preserving the other modes. We use MATLAB simulations to determine the pump pulse shapes which are subsequently produced by controlling the amplitude and phase of each spectral frequency from an optical frequency comb. The latter is generated using a cascaded configuration of phase and amplitude modulators. We characterize the mode selectivity using classical signals by arranging the six TMs into two orthogonal signal sets. Furthermore, we also demonstrate that mode selectivity is preserved if we use sub-photon signals (weak coherent light). Thus, this work supports the idea that QFC has the basic properties needed for advanced multi-dimensional quantum measurements given that we have demonstrated for the first time the ability to move to high dimensions (d=4), measure coherent superposition modes, and measure sub-photon signal levels. In addition to mode-selective photon counting, we also experimentally demonstrate a method of reshaping optical pulses based on QFC. Such a method has the potential to serve as the interface between quantum memories and the existing fiber infrastructure. At the same time, it can be employed in all-optical systems for optical signal regeneration.

ComVisMD - compact visualization of multidimensional data: experimenting with cricket players data

NASA Astrophysics Data System (ADS)

Dandin, Shridhar B.; Ducassé, Mireille

2018-03-01

Database information is multidimensional and often displayed in tabular format (row/column display). Presented in aggregated form, multidimensional data can be used to analyze the records or objects. Online Analytical database Processing (OLAP) proposes mechanisms to display multidimensional data in aggregated forms. A choropleth map is a thematic map in which areas are colored in proportion to the measurement of a statistical variable being displayed, such as population density. They are used mostly for compact graphical representation of geographical information. We propose a system, ComVisMD inspired by choropleth map and the OLAP cube to visualize multidimensional data in a compact way. ComVisMD displays multidimensional data like OLAP Cube, where we are mapping an attribute a (first dimension, e.g. year started playing cricket) in vertical direction, object coloring based on b (second dimension, e.g. batting average), mapping varying-size circles based on attribute c (third dimension, e.g. highest score), mapping numbers based on attribute d (fourth dimension, e.g. matches played). We illustrate our approach on cricket players data, namely on two tables Country and Player. They have a large number of rows and columns: 246 rows and 17 columns for players of one country. ComVisMD’s visualization reduces the size of the tabular display by a factor of about 4, allowing users to grasp more information at a time than the bare table display.

Recognition and source memory as multivariate decision processes.

PubMed

Banks, W P

2000-07-01

Recognition memory, source memory, and exclusion performance are three important domains of study in memory, each with its own findings, it specific theoretical developments, and its separate research literature. It is proposed here that results from all three domains can be treated with a single analytic model. This article shows how to generate a comprehensive memory representation based on multidimensional signal detection theory and how to make predictions for each of these paradigms using decision axes drawn through the space. The detection model is simpler than the comparable multinomial model, it is more easily generalizable, and it does not make threshold assumptions. An experiment using the same memory set for all three tasks demonstrates the analysis and tests the model. The results show that some seemingly complex relations between the paradigms derive from an underlying simplicity of structure.

State Space Methods in Multidimensional Digital Signal Processing

DTIC Science & Technology

1991-01-01

2-D finite difference equation with quarter-plane support is given by [1]. Li L-2 Ll L2 g (nln2) =E E Zb(jl,j2)f(n,-j, n 2 -j 2 ) - E a(jl,j2) g (n, - j...B2 [ g (n , n2)] = [C1 C2 1 Sq’(n nl2) ]+ D [f (ni, n 2 )] (2.2) Roesser’s state space model is based upon assigning state variables to the output of...QH(n - 1,n2) + [ B1 [f(nl,n2)]Qv(ni, n2) I A3 A411 Qv(nl, n2 -1 1 B2 [ g (n 1 ,n 2 )] = [C1 C 2] Q(n - n) + D[f(nin 2 )] (2.5) I Qv(ni,n2- 1) 1 In this

Application of Multi-Parameter Data Visualization by Means of Multidimensional Scaling to Evaluate Possibility of Coal Gasification

NASA Astrophysics Data System (ADS)

Jamróz, Dariusz; Niedoba, Tomasz; Surowiak, Agnieszka; Tumidajski, Tadeusz; Szostek, Roman; Gajer, Mirosław

2017-09-01

The application of methods drawing upon multi-parameter visualization of data by transformation of multidimensional space into two-dimensional one allow to show multi-parameter data on computer screen. Thanks to that, it is possible to conduct a qualitative analysis of this data in the most natural way for human being, i.e. by the sense of sight. An example of such method of multi-parameter visualization is multidimensional scaling. This method was used in this paper to present and analyze a set of seven-dimensional data obtained from Janina Mining Plant and Wieczorek Coal Mine. It was decided to examine whether the method of multi-parameter data visualization allows to divide the samples space into areas of various applicability to fluidal gasification process. The "Technological applicability card for coals" was used for this purpose [Sobolewski et al., 2012; 2017], in which the key parameters, important and additional ones affecting the gasification process were described.

Robustness of multidimensional Brownian ratchets as directed transport mechanisms.

PubMed

González-Candela, Ernesto; Romero-Rochín, Víctor; Del Río, Fernando

2011-08-07

Brownian ratchets have recently been considered as models to describe the ability of certain systems to locate very specific states in multidimensional configuration spaces. This directional process has particularly been proposed as an alternative explanation for the protein folding problem, in which the polypeptide is driven toward the native state by a multidimensional Brownian ratchet. Recognizing the relevance of robustness in biological systems, in this work we analyze such a property of Brownian ratchets by pushing to the limits all the properties considered essential to produce directed transport. Based on the results presented here, we can state that Brownian ratchets are able to deliver current and locate funnel structures under a wide range of conditions. As a result, they represent a simple model that solves the Levinthal's paradox with great robustness and flexibility and without requiring any ad hoc biased transition probability. The behavior of Brownian ratchets shown in this article considerably enhances the plausibility of the model for at least part of the structural mechanism behind protein folding process.

The Designing of CALM (Computer Anxiety and Learning Measure): Validation of a Multidimensional Measure of Anxiety and Cognitions Relating to Adult Learning of Computing Skills Using Structural Equation Modeling.

ERIC Educational Resources Information Center

McInerney, Valentina; Marsh, Herbert W.; McInerney, Dennis M.

This paper discusses the process through which a powerful multidimensional measure of affect and cognition in relation to adult learning of computing skills was derived from its early theoretical stages to its validation using structural equation modeling. The discussion emphasizes the importance of ensuring a strong substantive base from which to…

Taxation of oil and gas revenues: Norway

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stauffer, T.R.

1982-04-01

Fiscalization of petroleum in Norway is a multidimensional process, which includes the conventional components of explicit taxation but also involves implicit nontax economic burdens. The latter are often even more important than the taxes themselves. The multidimensional fiscal structure reflects the multiple purposes of petroleum taxation in Norway, of which revenue collection appears to be but one. Given the multiple objectives, it is therefore not surprising that the components are partly inconsistent and contradictory.

6D Visualization of Multidimensional Data by Means of Cognitive Technology

NASA Astrophysics Data System (ADS)

Vitkovskiy, V.; Gorohov, V.; Komarinskiy, S.

2010-12-01

On the basis of the cognitive graphics concept, we worked out the SW-system for visualization and analysis. It allows to train and to aggravate intuition of researcher, to raise his interest and motivation to the creative, scientific cognition, to realize process of dialogue with the very problems simultaneously. The Space Hedgehog system is the next step in the cognitive means of the multidimensional data analyze. The technique and technology cognitive 6D visualization of the multidimensional data is developed on the basis of the cognitive visualization research and technology development. The Space Hedgehog system allows direct dynamic visualization of 6D objects. It is developed with use of experience of the program Space Walker creation and its applications.

Chapter 3. A multidimensional model for narrative analysis of substance use-related dependency.

PubMed

Larsson, Sam; von Braun, Therese; Lilja, John

2013-11-01

This chapter examines the possibilities and limitations of using a narrative method as a framework within a multidimensional model for exploring and analyzing the use and misuse of alcohol and drugs. It is posited that a multidimensional model, based on narrative reasoning, can give a more detailed and specific understanding of substance users, who represent a heterogeneous population of people, and of substance use-related dependency problems. Such a model describes and analyses the drug-use related problems in a manner that provides holistic and important information and knowledge about the person by contextual and situation interaction processes which are involved in the use/misuse of alcohol and drugs. Tentative conclusions and unresolved critical issues are considered.

Improving the signal subtle feature extraction performance based on dual improved fractal box dimension eigenvectors

NASA Astrophysics Data System (ADS)

Chen, Xiang; Li, Jingchao; Han, Hui; Ying, Yulong

2018-05-01

Because of the limitations of the traditional fractal box-counting dimension algorithm in subtle feature extraction of radiation source signals, a dual improved generalized fractal box-counting dimension eigenvector algorithm is proposed. First, the radiation source signal was preprocessed, and a Hilbert transform was performed to obtain the instantaneous amplitude of the signal. Then, the improved fractal box-counting dimension of the signal instantaneous amplitude was extracted as the first eigenvector. At the same time, the improved fractal box-counting dimension of the signal without the Hilbert transform was extracted as the second eigenvector. Finally, the dual improved fractal box-counting dimension eigenvectors formed the multi-dimensional eigenvectors as signal subtle features, which were used for radiation source signal recognition by the grey relation algorithm. The experimental results show that, compared with the traditional fractal box-counting dimension algorithm and the single improved fractal box-counting dimension algorithm, the proposed dual improved fractal box-counting dimension algorithm can better extract the signal subtle distribution characteristics under different reconstruction phase space, and has a better recognition effect with good real-time performance.

Phase stabilization of multidimensional amplification architectures for ultrashort pulses

NASA Astrophysics Data System (ADS)

Müller, M.; Kienel, M.; Klenke, A.; Eidam, T.; Limpert, J.; Tünnermann, A.

2015-03-01

The active phase stabilization of spatially and temporally combined ultrashort pulses is investigated theoretically and experimentally. Particularly, considering a combining scheme applying 2 amplifier channels and 4 divided-pulse replicas a bistable behavior is observed. The reason is mutual influence of the optical error signals that is intrinsic to temporal polarization beam combining. A successful mitigation strategy is proposed and is analyzed theoretically and experimentally.

Adaptive Detection and Parameter Estimation for Multidimensional Signal Models

DTIC Science & Technology

1989-04-19

first of Equations (3-3), it follows that H = fH (3-12) p BpP Moreover, with the help of Equations (Al-8) of Appendix I and Equation (3-6). we find that...7-29) 127 Substituting these results, we find that II + ZBSBBZB +Y T- YJ =+ Zi~t ÷ B SBR ZBI By introducing the definitions -t +BS1 ZB V E Y Ct

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shcheslavskiy, V. I.; Institute of Biomedical Technologies, Nizhny Novgorod State Medical Academy, Minin and Pozharsky Square, 10/1, Nizhny Novgorod 603005; Neubauer, A.

We present a lifetime imaging technique that simultaneously records the fluorescence and phosphorescence lifetime images in confocal laser scanning systems. It is based on modulating a high-frequency pulsed laser synchronously with the pixel clock of the scanner, and recording the fluorescence and phosphorescence signals by multidimensional time-correlated single photon counting board. We demonstrate our technique on the recording of the fluorescence/phosphorescence lifetime images of human embryonic kidney cells at different environmental conditions.

Multiuser Transmit Beamforming for Maximum Sum Capacity in Tactical Wireless Multicast Networks

DTIC Science & Technology

2006-08-01

commonly used extended Kalman filter . See [2, 5, 6] for recent tutorial overviews. In particle filtering , continuous distributions are approximated by...signals (using and developing associated particle filtering tools). Our work on these topics has been reported in seven (IEEE, SIAM) journal papers and...multidimensional scaling, tracking, intercept, particle filters . 16. PRICE CODE 17. SECURITY CLASSIFICATION OF REPORT 18. SECURITY CLASSIFICATION OF

The Contributions of Orthographic Processing Factors to the Spelling Achievement of Middle-Elementary Students

ERIC Educational Resources Information Center

Radaj, Jane M.

2013-01-01

The study examined the contributions of "orthographic processing" factors to the spelling achievement of typically developing middle-elementary students. The researcher framed orthographic processing as a multilinguistic, multidimensional construct involving process factors related to procedural orthographic operations and product…

Taking the Test Taker's Perspective: Response Process and Test Motivation in Multidimensional Forced-Choice Versus Rating Scale Instruments.

PubMed

Sass, Rachelle; Frick, Susanne; Reips, Ulf-Dietrich; Wetzel, Eunike

2018-03-01

The multidimensional forced-choice (MFC) format has been proposed as an alternative to the rating scale (RS) response format. However, it is unclear how changing the response format may affect the response process and test motivation of participants. In Study 1, we investigated the MFC response process using the think-aloud technique. In Study 2, we compared test motivation between the RS format and different versions of the MFC format (presenting 2, 3, 4, and 5 items simultaneously). The response process to MFC item blocks was similar to the RS response process but involved an additional step of weighing the items within a block against each other. The RS and MFC response format groups did not differ in their test motivation. Thus, from the test taker's perspective, the MFC format is somewhat more demanding to respond to, but this does not appear to decrease test motivation.

Supportive Listening

ERIC Educational Resources Information Center

Jones, Susanne M.

2011-01-01

"Listening" is a multidimensional construct that consists of complex (a) cognitive processes, such as attending to, understanding, receiving, and interpreting messages; (b) affective processes, such as being motivated and stimulated to attend to another person's messages; and (c) behavioral processes, such as responding with verbal and nonverbal…

A Stationary Wavelet Entropy-Based Clustering Approach Accurately Predicts Gene Expression

PubMed Central

Nguyen, Nha; Vo, An; Choi, Inchan

2015-01-01

Abstract Studying epigenetic landscapes is important to understand the condition for gene regulation. Clustering is a useful approach to study epigenetic landscapes by grouping genes based on their epigenetic conditions. However, classical clustering approaches that often use a representative value of the signals in a fixed-sized window do not fully use the information written in the epigenetic landscapes. Clustering approaches to maximize the information of the epigenetic signals are necessary for better understanding gene regulatory environments. For effective clustering of multidimensional epigenetic signals, we developed a method called Dewer, which uses the entropy of stationary wavelet of epigenetic signals inside enriched regions for gene clustering. Interestingly, the gene expression levels were highly correlated with the entropy levels of epigenetic signals. Dewer separates genes better than a window-based approach in the assessment using gene expression and achieved a correlation coefficient above 0.9 without using any training procedure. Our results show that the changes of the epigenetic signals are useful to study gene regulation. PMID:25383910

Distinct conflict resolution deficits related to different facets of Schizophrenia.

PubMed

Kerns, John G

2009-11-01

An important issue in understanding the nature of conflict processing is whether it is a unitary or multidimensional construct. One way to examine this is to study whether people with impaired conflict processing exhibit a general pattern of deficits or whether they exhibit impairments in distinct aspects of conflict processing. One group who might exhibit conflict deficits are people with schizophrenia. Schizophrenia is a heterogeneous disorder, with one way to break down the heterogeneity of schizophrenia is to examine specific symptoms. Previous research has found that specific symptoms of schizophrenia are associated with specific deficits in conflict processing. In particular, disorganization is associated with increased response conflict, alogia is associated with increased retrieval conflict, and anhedonia is associated with increased emotional conflict. Moreover, there is evidence that different types of conflict processing are unassociated with each other. This evidence suggests that conflict processing is a multidimensional construct and that different aspects of schizophrenia are associated with impairments in processing different types of conflict.

3D printing of nano- and micro-structures

NASA Astrophysics Data System (ADS)

Ramasamy, Mouli; Varadan, Vijay K.

2016-04-01

Additive manufacturing or 3D printing techniques are being vigorously investigated as a replacement to the traditional and conventional methods in fabrication to bring forth cost and time effective approaches. Introduction of 3D printing has led to printing micro and nanoscale structures including tissues and organelles, bioelectric sensors and devices, artificial bones and transplants, microfluidic devices, batteries and various other biomaterials. Various microfabrication processes have been developed to fabricate micro components and assemblies at lab scale. 3D Fabrication processes that can accommodate the functional and geometrical requirements to realize complicated structures are becoming feasible through advances in additive manufacturing. This advancement could lead to simpler development mechanisms of novel components and devices exhibiting complex features. For instance, development of microstructure electrodes that can penetrate the epidermis of the skin to collect the bio potential signal may prove very effective than the electrodes that measure signal from the skin's surface. The micro and nanostructures will have to possess extraordinary material and mechanical properties for its dexterity in the applications. A substantial amount of research being pursued on stretchable and flexible devices based on PDMA, textiles, and organic electronics. Despite the numerous advantages these substrates and techniques could solely offer, 3D printing enables a multi-dimensional approach towards finer and complex applications. This review emphasizes the use of 3D printing to fabricate micro and nanostructures for that can be applied for human healthcare.

Automated processing of label-free Raman microscope images of macrophage cells with standardized regression for high-throughput analysis.

PubMed

Milewski, Robert J; Kumagai, Yutaro; Fujita, Katsumasa; Standley, Daron M; Smith, Nicholas I

2010-11-19

Macrophages represent the front lines of our immune system; they recognize and engulf pathogens or foreign particles thus initiating the immune response. Imaging macrophages presents unique challenges, as most optical techniques require labeling or staining of the cellular compartments in order to resolve organelles, and such stains or labels have the potential to perturb the cell, particularly in cases where incomplete information exists regarding the precise cellular reaction under observation. Label-free imaging techniques such as Raman microscopy are thus valuable tools for studying the transformations that occur in immune cells upon activation, both on the molecular and organelle levels. Due to extremely low signal levels, however, Raman microscopy requires sophisticated image processing techniques for noise reduction and signal extraction. To date, efficient, automated algorithms for resolving sub-cellular features in noisy, multi-dimensional image sets have not been explored extensively. We show that hybrid z-score normalization and standard regression (Z-LSR) can highlight the spectral differences within the cell and provide image contrast dependent on spectral content. In contrast to typical Raman imaging processing methods using multivariate analysis, such as single value decomposition (SVD), our implementation of the Z-LSR method can operate nearly in real-time. In spite of its computational simplicity, Z-LSR can automatically remove background and bias in the signal, improve the resolution of spatially distributed spectral differences and enable sub-cellular features to be resolved in Raman microscopy images of mouse macrophage cells. Significantly, the Z-LSR processed images automatically exhibited subcellular architectures whereas SVD, in general, requires human assistance in selecting the components of interest. The computational efficiency of Z-LSR enables automated resolution of sub-cellular features in large Raman microscopy data sets without compromise in image quality or information loss in associated spectra. These results motivate further use of label free microscopy techniques in real-time imaging of live immune cells.

Assessment of health surveys: fitting a multidimensional graded response model.

PubMed

Depaoli, Sarah; Tiemensma, Jitske; Felt, John M

The multidimensional graded response model, an item response theory (IRT) model, can be used to improve the assessment of surveys, even when sample sizes are restricted. Typically, health-based survey development utilizes classical statistical techniques (e.g. reliability and factor analysis). In a review of four prominent journals within the field of Health Psychology, we found that IRT-based models were used in less than 10% of the studies examining scale development or assessment. However, implementing IRT-based methods can provide more details about individual survey items, which is useful when determining the final item content of surveys. An example using a quality of life survey for Cushing's syndrome (CushingQoL) highlights the main components for implementing the multidimensional graded response model. Patients with Cushing's syndrome (n = 397) completed the CushingQoL. Results from the multidimensional graded response model supported a 2-subscale scoring process for the survey. All items were deemed as worthy contributors to the survey. The graded response model can accommodate unidimensional or multidimensional scales, be used with relatively lower sample sizes, and is implemented in free software (example code provided in online Appendix). Use of this model can help to improve the quality of health-based scales being developed within the Health Sciences.

An efficient sampling algorithm for uncertain abnormal data detection in biomedical image processing and disease prediction.

PubMed

Liu, Fei; Zhang, Xi; Jia, Yan

2015-01-01

In this paper, we propose a computer information processing algorithm that can be used for biomedical image processing and disease prediction. A biomedical image is considered a data object in a multi-dimensional space. Each dimension is a feature that can be used for disease diagnosis. We introduce a new concept of the top (k1,k2) outlier. It can be used to detect abnormal data objects in the multi-dimensional space. This technique focuses on uncertain space, where each data object has several possible instances with distinct probabilities. We design an efficient sampling algorithm for the top (k1,k2) outlier in uncertain space. Some improvement techniques are used for acceleration. Experiments show our methods' high accuracy and high efficiency.

Multi-dimensional coherent optical spectroscopy of semiconductor nanostructures: Collinear and non-collinear approaches

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nardin, Gaël; Li, Hebin; Autry, Travis M.

2015-03-21

We review our recent work on multi-dimensional coherent optical spectroscopy (MDCS) of semiconductor nanostructures. Two approaches, appropriate for the study of semiconductor materials, are presented and compared. A first method is based on a non-collinear geometry, where the Four-Wave-Mixing (FWM) signal is detected in the form of a radiated optical field. This approach works for samples with translational symmetry, such as Quantum Wells (QWs) or large and dense ensembles of Quantum Dots (QDs). A second method detects the FWM in the form of a photocurrent in a collinear geometry. This second approach extends the horizon of MDCS to sub-diffraction nanostructures,more » such as single QDs, nanowires, or nanotubes, and small ensembles thereof. Examples of experimental results obtained on semiconductor QW structures are given for each method. In particular, it is shown how MDCS can assess coupling between excitons confined in separated QWs.« less

Fractal and topological sustainable methods of overcoming expected uncertainty in the radiolocation of low-contrast targets and in the processing of weak multi-dimensional signals on the background of high-intensity noise: A new direction in the statistical decision theory

NASA Astrophysics Data System (ADS)

Potapov, A. A.

2017-11-01

The main purpose of this work is to interpret the main directions of radio physics, radio engineering and radio location in “fractal” language that makes new ways and generalizations on future promising radio systems. We introduce a new kind and approach of up-to-date radiolocation: fractal-scaling or scale-invariant radiolocation. The new topologic signs and methods of detecting the low-contrast objects against the high-intensity noise background are presented. It leads to basic changes in the theoretical radiolocation structure itself and also in its mathematical apparatus. The fractal radio systems conception, sampling topology, global fractal-scaling approach and the fractal paradigm underlie the scientific direction established by the author in Russia and all over the world for the first time ever.

Testing Separability and Independence of Perceptual Dimensions with General Recognition Theory: A Tutorial and New R Package (grtools).

PubMed

Soto, Fabian A; Zheng, Emily; Fonseca, Johnny; Ashby, F Gregory

2017-01-01

Determining whether perceptual properties are processed independently is an important goal in perceptual science, and tools to test independence should be widely available to experimental researchers. The best analytical tools to test for perceptual independence are provided by General Recognition Theory (GRT), a multidimensional extension of signal detection theory. Unfortunately, there is currently a lack of software implementing GRT analyses that is ready-to-use by experimental psychologists and neuroscientists with little training in computational modeling. This paper presents grtools , an R package developed with the explicit aim of providing experimentalists with the ability to perform full GRT analyses using only a couple of command lines. We describe the software and provide a practical tutorial on how to perform each of the analyses available in grtools . We also provide advice to researchers on best practices for experimental design and interpretation of results when applying GRT and grtools .

Laboratory tools and e-learning elements in training of acousto-optics

NASA Astrophysics Data System (ADS)

Barócsi, Attila; Lenk, Sándor; Ujhelyi, Ferenc; Majoros, Tamás.; Maák, Paál.

2015-10-01

Due to the acousto-optic (AO) effect, the refractive index of an optical interaction medium is perturbed by an acoustic wave induced in the medium that builds up a phase grating that will diffract the incident light beam if the condition of constructive interference is satisfied. All parameters, such as magnitude, period or phase of the grating can be controlled that allows the construction of useful devices (modulators, switches, one or multi-dimensional deflectors, spectrum analyzers, tunable filters, frequency shifters, etc.) The research and training of acousto-optics have a long-term tradition at our department. In this presentation, we introduce the related laboratory exercises fitted into an e-learning frame. The BSc level exercise utilizes a laser source and an AO cell to demonstrate the effect and principal AO functions explaining signal processing terms such as amplitude or frequency modulation, modulation depth and Fourier transformation ending up in building a free space sound transmitting and demodulation system. The setup for MSc level utilizes an AO filter with mono- and polychromatic light sources to learn about spectral analysis and synthesis. Smart phones can be used to generate signal inputs or outputs for both setups as well as to help students' preparation and reporting.

Nmrglue: an open source Python package for the analysis of multidimensional NMR data.

PubMed

Helmus, Jonathan J; Jaroniec, Christopher P

2013-04-01

Nmrglue, an open source Python package for working with multidimensional NMR data, is described. When used in combination with other Python scientific libraries, nmrglue provides a highly flexible and robust environment for spectral processing, analysis and visualization and includes a number of common utilities such as linear prediction, peak picking and lineshape fitting. The package also enables existing NMR software programs to be readily tied together, currently facilitating the reading, writing and conversion of data stored in Bruker, Agilent/Varian, NMRPipe, Sparky, SIMPSON, and Rowland NMR Toolkit file formats. In addition to standard applications, the versatility offered by nmrglue makes the package particularly suitable for tasks that include manipulating raw spectrometer data files, automated quantitative analysis of multidimensional NMR spectra with irregular lineshapes such as those frequently encountered in the context of biomacromolecular solid-state NMR, and rapid implementation and development of unconventional data processing methods such as covariance NMR and other non-Fourier approaches. Detailed documentation, install files and source code for nmrglue are freely available at http://nmrglue.com. The source code can be redistributed and modified under the New BSD license.

Nmrglue: An Open Source Python Package for the Analysis of Multidimensional NMR Data

PubMed Central

Helmus, Jonathan J.; Jaroniec, Christopher P.

2013-01-01

Nmrglue, an open source Python package for working with multidimensional NMR data, is described. When used in combination with other Python scientific libraries, nmrglue provides a highly flexible and robust environment for spectral processing, analysis and visualization and includes a number of common utilities such as linear prediction, peak picking and lineshape fitting. The package also enables existing NMR software programs to be readily tied together, currently facilitating the reading, writing and conversion of data stored in Bruker, Agilent/Varian, NMRPipe, Sparky, SIMPSON, and Rowland NMR Toolkit file formats. In addition to standard applications, the versatility offered by nmrglue makes the package particularly suitable for tasks that include manipulating raw spectrometer data files, automated quantitative analysis of multidimensional NMR spectra with irregular lineshapes such as those frequently encountered in the context of biomacromolecular solid-state NMR, and rapid implementation and development of unconventional data processing methods such as covariance NMR and other non-Fourier approaches. Detailed documentation, install files and source code for nmrglue are freely available at http://nmrglue.com. The source code can be redistributed and modified under the New BSD license. PMID:23456039

Minimal disease detection of B-cell lymphoproliferative disorders by flow cytometry: multidimensional cluster analysis.

PubMed

Duque, Ricardo E

2012-04-01

Flow cytometric analysis of cell suspensions involves the sequential 'registration' of intrinsic and extrinsic parameters of thousands of cells in list mode files. Thus, it is almost irresistible to describe phenomena in numerical terms or by 'ratios' that have the appearance of 'accuracy' due to the presence of numbers obtained from thousands of cells. The concepts involved in the detection and characterization of B cell lymphoproliferative processes are revisited in this paper by identifying parameters that, when analyzed appropriately, are both necessary and sufficient. The neoplastic process (cluster) can be visualized easily because the parameters that distinguish it form a cluster in multidimensional space that is unique and distinguishable from neighboring clusters that are not of diagnostic interest but serve to provide a background. For B cell neoplasia it is operationally necessary to identify the multidimensional space occupied by a cluster whose kappa:lambda ratio is 100:0 or 0:100. Thus, the concept of kappa:lambda ratio is without meaning and would not detect B cell neoplasia in an unacceptably high number of cases.

Seismic interferometry by multidimensional deconvolution as a means to compensate for anisotropic illumination

NASA Astrophysics Data System (ADS)

Wapenaar, K.; van der Neut, J.; Ruigrok, E.; Draganov, D.; Hunziker, J.; Slob, E.; Thorbecke, J.; Snieder, R.

2008-12-01

It is well-known that under specific conditions the crosscorrelation of wavefields observed at two receivers yields the impulse response between these receivers. This principle is known as 'Green's function retrieval' or 'seismic interferometry'. Recently it has been recognized that in many situations it can be advantageous to replace the correlation process by deconvolution. One of the advantages is that deconvolution compensates for the waveform emitted by the source; another advantage is that it is not necessary to assume that the medium is lossless. The approaches that have been developed to date employ a 1D deconvolution process. We propose a method for seismic interferometry by multidimensional deconvolution and show that under specific circumstances the method compensates for irregularities in the source distribution. This is an important difference with crosscorrelation methods, which rely on the condition that waves are equipartitioned. This condition is for example fulfilled when the sources are regularly distributed along a closed surface and the power spectra of the sources are identical. The proposed multidimensional deconvolution method compensates for anisotropic illumination, without requiring knowledge about the positions and the spectra of the sources.

High-Maneuverability Airframe: Initial Investigation of Configuration’s Aft End for Increased Stability, Range, and Maneuverability

DTIC Science & Technology

2013-09-01

including the interaction effects between the fins and canards. 2. Solution Technique 2.1 Computational Aerodynamics The double-precision solver of a...and overset grids (unified-grid). • Total variation diminishing discretization based on a new multidimensional interpolation framework. • Riemann ... solvers to provide proper signal propagation physics including versions for preconditioned forms of the governing equations. • Consistent and

THE DYNAMIC REGIME CONCEPT FOR ECOSYSTEM MANAGEMENT AND RESTORATION

EPA Science Inventory

Dynamic regimes of ecosystems are multidimensional basis of attraction, characterized by particular species communities and ecosystems processes. Ecosystem patterns and processes rarely respond linerarly to disturbances, and the nonlinear cynamic regime concept offers a more real...

[Study on "multi-dimensional structure and process dynamics quality control system" of Danshen infusion solution based on component structure theory].

PubMed

Feng, Liang; Zhang, Ming-Hua; Gu, Jun-Fei; Wang, Gui-You; Zhao, Zi-Yu; Jia, Xiao-Bin

2013-11-01

As traditional Chinese medicine (TCM) preparation products feature complex compounds and multiple preparation processes, the implementation of quality control in line with the characteristics of TCM preparation products provides a firm guarantee for the clinical efficacy and safety of TCM preparation products. Danshen infusion solution is a preparation commonly used in clinic, but its quality control is restricted to indexes of finished products, which can not guarantee its inherent quality. Our study group has proposed "multi-dimensional structure and process dynamics quality control system" on the basis of "component structure theory", for the purpose of controlling the quality of Danshen infusion solution at multiple levels and in multiple links from the efficacy-related material basis, the safety-related material basis, the characteristics of dosage form to the preparation process. This article, we bring forth new ideas and models to the quality control of TCM preparation products.

A search for narrow band signals with SERENDIP II: a progress report

NASA Technical Reports Server (NTRS)

Werthimer, D.; Brady, R.; Berezin, A.; Bowyer, S.

1988-01-01

Commensal programs for the Search for Extraterrestrial Intelligence (SETI), carried out concurrently with conventional radio astronomical observing programs, can be an attractive and cost-effective means of exploring the large multidimensional search space intrinsic to this effort. Our automated commensal system, SERENDIP II, is a high resolution 131,072 channel spectrometer. It searches for 0.49 Hz signals in sequential 64,700 Hz bands of the IF signal from a radio telescope being used for an astronomical observation. Upon detection of a narrow band signal with power above a preset threshold, the frequency, power, time, and telescope direction are recorded for later study. The system has been tested at the Hat Creek Radio Astronomy Observatory 85 ft telescope and the NASA-JPL Deep Space Station (DSS 14) 64 m telescope. It is currently collecting data at the National Radio Astronomy Observatory 300 ft telescope.

A search for narrow band signals with SERENDIP II: a progress report.

PubMed

Werthimer, D; Brady, R; Berezin, A; Bowyer, S

1988-01-01

Commensal programs for the Search for Extraterrestrial Intelligence (SETI), carried out concurrently with conventional radio astronomical observing programs, can be an attractive and cost-effective means of exploring the large multidimensional search space intrinsic to this effort. Our automated commensal system, SERENDIP II, is a high resolution 131,072 channel spectrometer. It searches for 0.49 Hz signals in sequential 64,700 Hz bands of the IF signal from a radio telescope being used for an astronomical observation. Upon detection of a narrow band signal with power above a preset threshold, the frequency, power, time, and telescope direction are recorded for later study. The system has been tested at the Hat Creek Radio Astronomy Observatory 85 ft telescope and the NASA-JPL Deep Space Station (DSS 14) 64 m telescope. It is currently collecting data at the National Radio Astronomy Observatory 300 ft telescope.

Method of transmission of dynamic multibit digital images from micro-unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Petrov, E. P.; Kharina, N. L.

2018-01-01

In connection with successful usage of nanotechnologies in remote sensing great attention is paid to the systems in micro-unmanned aerial vehicles (MUAVs) capable to provide high spatial resolution of dynamic multibit digital images (MDI). Limited energy resources on board the MUAV do not allow transferring a large amount of video information in the shortest possible time. It keeps back the broad development of MUAV. The search for methods to shorten the transmission time of dynamic MDIs from MUAV over the radio channel leads to the methods of MDI compression without computational operations onboard the MUAV. The known compression codecs of video information can not be applied because of the limited energy resources. In this paper we propose a method for reducing the transmission time of dynamic MDIs without computational operations and distortions onboard the MUAV. To develop the method a mathematical apparatus of the theory of conditional Markov processes with discrete arguments was used. On its basis a mathematical model for the transformation of the MDI represented by binary images (BI) in the MDI, consisting of groups of neighboring BIs (GBI) transmitted by multiphase (MP) signals, is constructed. The algorithm for multidimensional nonlinear filtering of MP signals is synthesized, realizing the statistical redundancy of the MDI to compensate for the noise stability losses caused by the use of MP signals.

Multi-dimensional roles of ketone bodies in fuel metabolism, signaling, and therapeutics

PubMed Central

Puchalska, Patrycja; Crawford, Peter A.

2017-01-01

Ketone body metabolism is a central node in physiological homeostasis. In this review, we discuss how ketones serve discrete fine-tuning metabolic roles that optimize organ and organism performance in varying nutrient states, and protect from inflammation and injury in multiple organ systems. Traditionally viewed as metabolic substrates enlisted only in carbohydrate restriction, recent observations underscore the importance of ketone bodies as vital metabolic and signaling mediators when carbohydrates are abundant. Complementing a repertoire of known therapeutic options for diseases of the nervous system, prospective roles for ketone bodies in cancer have arisen, as have intriguing protective roles in heart and liver, opening therapeutic options in obesity-related and cardiovascular disease. Controversies in ketone metabolism and signaling are discussed to reconcile classical dogma with contemporary observations. PMID:28178565

Features of the use of time-frequency distributions for controlling the mixture-producing aggregate

NASA Astrophysics Data System (ADS)

Fedosenkov, D. B.; Simikova, A. A.; Fedosenkov, B. A.

2018-05-01

The paper submits and argues the information on filtering properties of the mixing unit as a part of the mixture-producing aggregate. Relevant theoretical data concerning a channel transfer function of the mixing unit and multidimensional material flow signals are adduced here. Note that ordinary one-dimensional material flow signals are defined in terms of time-frequency distributions of Cohen’s class representations operating with Gabor wavelet functions. Two time-frequencies signal representations are written about in the paper to show how one can solve controlling problems as applied to mixture-producing systems: they are the so-called Rihaczek and Wigner-Ville distributions. In particular, the latter illustrates low-pass filtering properties that are practically available in any of low-pass elements of a physical system.

Modeling stock prices in a portfolio using multidimensional geometric brownian motion

NASA Astrophysics Data System (ADS)

Maruddani, Di Asih I.; Trimono

2018-05-01

Modeling and forecasting stock prices of public corporates are important studies in financial analysis, due to their stock price characteristics. Stocks investments give a wide variety of risks. Taking a portfolio of several stocks is one way to minimize risk. Stochastic process of single stock price movements model can be formulated in Geometric Brownian Motion (GBM) model. But for a portfolio that consist more than one corporate stock, we need an expansion of GBM Model. In this paper, we use multidimensional Geometric Brownian Motion model. This paper aims to model and forecast two stock prices in a portfolio. These are PT. Matahari Department Store Tbk and PT. Telekomunikasi Indonesia Tbk on period January 4, 2016 until April 21, 2017. The goodness of stock price forecast value is based on Mean Absolute Percentage Error (MAPE). As the results, we conclude that forecast two stock prices in a portfolio using multidimensional GBM give less MAPE than using GBM for single stock price respectively. We conclude that multidimensional GBM is more appropriate for modeling stock prices, because the price of each stock affects each other.

Subtypes and comorbidity in mathematical learning disabilities: Multidimensional study of verbal and visual memory processes is key to understanding.

PubMed

Szűcs, D

2016-01-01

A large body of research suggests that mathematical learning disability (MLD) is related to working memory impairment. Here, I organize part of this literature through a meta-analysis of 36 studies with 665 MLD and 1049 control participants. I demonstrate that one subtype of MLD is associated with reading problems and weak verbal short-term and working memory. Another subtype of MLD does not have associated reading problems and is linked to weak visuospatial short-term and working memory. In order to better understand MLD we need to precisely define potentially modality-specific memory subprocesses and supporting executive functions, relevant for mathematical learning. This can be achieved by taking a multidimensional parametric approach systematically probing an extended network of cognitive functions. Rather than creating arbitrary subgroups and/or focus on a single factor, highly powered studies need to position individuals in a multidimensional parametric space. This will allow us to understand the multidimensional structure of cognitive functions and their relationship to mathematical performance. © 2016 Elsevier B.V. All rights reserved.

Simultaneous estimation of multiple phases in digital holographic interferometry using state space analysis

NASA Astrophysics Data System (ADS)

Kulkarni, Rishikesh; Rastogi, Pramod

2018-05-01

A new approach is proposed for the multiple phase estimation from a multicomponent exponential phase signal recorded in multi-beam digital holographic interferometry. It is capable of providing multidimensional measurements in a simultaneous manner from a single recording of the exponential phase signal encoding multiple phases. Each phase within a small window around each pixel is appproximated with a first order polynomial function of spatial coordinates. The problem of accurate estimation of polynomial coefficients, and in turn the unwrapped phases, is formulated as a state space analysis wherein the coefficients and signal amplitudes are set as the elements of a state vector. The state estimation is performed using the extended Kalman filter. An amplitude discrimination criterion is utilized in order to unambiguously estimate the coefficients associated with the individual signal components. The performance of proposed method is stable over a wide range of the ratio of signal amplitudes. The pixelwise phase estimation approach of the proposed method allows it to handle the fringe patterns that may contain invalid regions.

Role of Inflammation in Human Fatigue: Relevance of Multidimensional Assessments and Potential Neuronal Mechanisms

PubMed Central

Karshikoff, Bianka; Sundelin, Tina; Lasselin, Julie

2017-01-01

Fatigue is a highly disabling symptom in various medical conditions. While inflammation has been suggested as a potential contributor to the development of fatigue, underlying mechanisms remain poorly understood. In this review, we propose that a better assessment of central fatigue, taking into account its multidimensional features, could help elucidate the role and mechanisms of inflammation in fatigue development. A description of the features of central fatigue is provided, and the current evidence describing the association between inflammation and fatigue in various medical conditions is reviewed. Additionally, the effect of inflammation on specific neuronal processes that may be involved in distinct fatigue dimensions is described. We suggest that the multidimensional aspects of fatigue should be assessed in future studies of inflammation-induced fatigue and that this would benefit the development of effective therapeutic interventions. PMID:28163706

A nonlocal electron conduction model for multidimensional radiation hydrodynamics codes

NASA Astrophysics Data System (ADS)

Schurtz, G. P.; Nicolaï, Ph. D.; Busquet, M.

2000-10-01

Numerical simulation of laser driven Inertial Confinement Fusion (ICF) related experiments require the use of large multidimensional hydro codes. Though these codes include detailed physics for numerous phenomena, they deal poorly with electron conduction, which is the leading energy transport mechanism of these systems. Electron heat flow is known, since the work of Luciani, Mora, and Virmont (LMV) [Phys. Rev. Lett. 51, 1664 (1983)], to be a nonlocal process, which the local Spitzer-Harm theory, even flux limited, is unable to account for. The present work aims at extending the original formula of LMV to two or three dimensions of space. This multidimensional extension leads to an equivalent transport equation suitable for easy implementation in a two-dimensional radiation-hydrodynamic code. Simulations are presented and compared to Fokker-Planck simulations in one and two dimensions of space.

Multidimensional human dynamics in mobile phone communications.

PubMed

Quadri, Christian; Zignani, Matteo; Capra, Lorenzo; Gaito, Sabrina; Rossi, Gian Paolo

2014-01-01

In today's technology-assisted society, social interactions may be expressed through a variety of techno-communication channels, including online social networks, email and mobile phones (calls, text messages). Consequently, a clear grasp of human behavior through the diverse communication media is considered a key factor in understanding the formation of the today's information society. So far, all previous research on user communication behavior has focused on a sole communication activity. In this paper we move forward another step on this research path by performing a multidimensional study of human sociality as an expression of the use of mobile phones. The paper focuses on user temporal communication behavior in the interplay between the two complementary communication media, text messages and phone calls, that represent the bi-dimensional scenario of analysis. Our study provides a theoretical framework for analyzing multidimensional bursts as the most general burst category, that includes one-dimensional bursts as the simplest case, and offers empirical evidence of their nature by following the combined phone call/text message communication patterns of approximately one million people over three-month period. This quantitative approach enables the design of a generative model rooted in the three most significant features of the multidimensional burst - the number of dimensions, prevalence and interleaving degree - able to reproduce the main media usage attitude. The other findings of the paper include a novel multidimensional burst detection algorithm and an insight analysis of the human media selection process.

SABRE hyperpolarization enables high-sensitivity 1H and 13C benchtop NMR spectroscopy.

PubMed

Richardson, Peter M; Parrott, Andrew J; Semenova, Olga; Nordon, Alison; Duckett, Simon B; Halse, Meghan E

2018-06-19

Benchtop NMR spectrometers operating with low magnetic fields of 1-2 T at sub-ppm resolution show great promise as analytical platforms that can be used outside the traditional laboratory environment for industrial process monitoring. One current limitation that reduces the uptake of benchtop NMR is associated with the detection fields' reduced sensitivity. Here we demonstrate how para-hydrogen (p-H2) based signal amplification by reversible exchange (SABRE), a simple to achieve hyperpolarization technique, enhances agent detectability within the environment of a benchtop (1 T) NMR spectrometer so that informative 1H and 13C NMR spectra can be readily recorded for low-concentration analytes. SABRE-derived 1H NMR signal enhancements of up to 17 000-fold, corresponding to 1H polarization levels of P = 5.9%, were achieved for 26 mM pyridine in d4-methanol in a matter of seconds. Comparable enhancement levels can be achieved in both deuterated and protio solvents but now the SABRE-enhanced analyte signals dominate due to the comparatively weak thermally-polarized solvent response. The SABRE approach also enables the acquisition of 13C NMR spectra of analytes at natural isotopic abundance in a single scan as evidenced by hyperpolarized 13C NMR spectra of tens of millimolar concentrations of 4-methylpyridine. Now the associated signal enhancement factors are up to 45 500 fold (P = 4.0%) and achieved in just 15 s. Integration of an automated SABRE polarization system with the benchtop NMR spectrometer framework produces renewable and reproducible NMR signal enhancements that can be exploited for the collection of multi-dimensional NMR spectra, exemplified here by a SABRE-enhanced 2D COSY NMR spectrum.

Stochastic wave-function unravelling of the generalized Lindblad equation

NASA Astrophysics Data System (ADS)

Semin, V.; Semina, I.; Petruccione, F.

2017-12-01

We investigate generalized non-Markovian stochastic Schrödinger equations (SSEs), driven by a multidimensional counting process and multidimensional Brownian motion introduced by A. Barchielli and C. Pellegrini [J. Math. Phys. 51, 112104 (2010), 10.1063/1.3514539]. We show that these SSEs can be translated in a nonlinear form, which can be efficiently simulated. The simulation is illustrated by the model of a two-level system in a structured bath, and the results of the simulations are compared with the exact solution of the generalized master equation.

3D digital image processing for biofilm quantification from confocal laser scanning microscopy: Multidimensional statistical analysis of biofilm modeling

NASA Astrophysics Data System (ADS)

Zielinski, Jerzy S.

The dramatic increase in number and volume of digital images produced in medical diagnostics, and the escalating demand for rapid access to these relevant medical data, along with the need for interpretation and retrieval has become of paramount importance to a modern healthcare system. Therefore, there is an ever growing need for processed, interpreted and saved images of various types. Due to the high cost and unreliability of human-dependent image analysis, it is necessary to develop an automated method for feature extraction, using sophisticated mathematical algorithms and reasoning. This work is focused on digital image signal processing of biological and biomedical data in one- two- and three-dimensional space. Methods and algorithms presented in this work were used to acquire data from genomic sequences, breast cancer, and biofilm images. One-dimensional analysis was applied to DNA sequences which were presented as a non-stationary sequence and modeled by a time-dependent autoregressive moving average (TD-ARMA) model. Two-dimensional analyses used 2D-ARMA model and applied it to detect breast cancer from x-ray mammograms or ultrasound images. Three-dimensional detection and classification techniques were applied to biofilm images acquired using confocal laser scanning microscopy. Modern medical images are geometrically arranged arrays of data. The broadening scope of imaging as a way to organize our observations of the biophysical world has led to a dramatic increase in our ability to apply new processing techniques and to combine multiple channels of data into sophisticated and complex mathematical models of physiological function and dysfunction. With explosion of the amount of data produced in a field of biomedicine, it is crucial to be able to construct accurate mathematical models of the data at hand. Two main purposes of signal modeling are: data size conservation and parameter extraction. Specifically, in biomedical imaging we have four key problems that were addressed in this work: (i) registration, i.e. automated methods of data acquisition and the ability to align multiple data sets with each other; (ii) visualization and reconstruction, i.e. the environment in which registered data sets can be displayed on a plane or in multidimensional space; (iii) segmentation, i.e. automated and semi-automated methods to create models of relevant anatomy from images; (iv) simulation and prediction, i.e. techniques that can be used to simulate growth end evolution of researched phenomenon. Mathematical models can not only be used to verify experimental findings, but also to make qualitative and quantitative predictions, that might serve as guidelines for the future development of technology and/or treatment.

High resolution 4-D spectroscopy with sparse concentric shell sampling and FFT-CLEAN.

PubMed

Coggins, Brian E; Zhou, Pei

2008-12-01

Recent efforts to reduce the measurement time for multidimensional NMR experiments have fostered the development of a variety of new procedures for sampling and data processing. We recently described concentric ring sampling for 3-D NMR experiments, which is superior to radial sampling as input for processing by a multidimensional discrete Fourier transform. Here, we report the extension of this approach to 4-D spectroscopy as Randomized Concentric Shell Sampling (RCSS), where sampling points for the indirect dimensions are positioned on concentric shells, and where random rotations in the angular space are used to avoid coherent artifacts. With simulations, we show that RCSS produces a very low level of artifacts, even with a very limited number of sampling points. The RCSS sampling patterns can be adapted to fine rectangular grids to permit use of the Fast Fourier Transform in data processing, without an apparent increase in the artifact level. These artifacts can be further reduced to the noise level using the iterative CLEAN algorithm developed in radioastronomy. We demonstrate these methods on the high resolution 4-D HCCH-TOCSY spectrum of protein G's B1 domain, using only 1.2% of the sampling that would be needed conventionally for this resolution. The use of a multidimensional FFT instead of the slow DFT for initial data processing and for subsequent CLEAN significantly reduces the calculation time, yielding an artifact level that is on par with the level of the true spectral noise.

High Resolution 4-D Spectroscopy with Sparse Concentric Shell Sampling and FFT-CLEAN

PubMed Central

Coggins, Brian E.; Zhou, Pei

2009-01-01

SUMMARY Recent efforts to reduce the measurement time for multidimensional NMR experiments have fostered the development of a variety of new procedures for sampling and data processing. We recently described concentric ring sampling for 3-D NMR experiments, which is superior to radial sampling as input for processing by a multidimensional discrete Fourier transform. Here, we report the extension of this approach to 4-D spectroscopy as Randomized Concentric Shell Sampling (RCSS), where sampling points for the indirect dimensions are positioned on concentric shells, and where random rotations in the angular space are used to avoid coherent artifacts. With simulations, we show that RCSS produces a very low level of artifacts, even with a very limited number of sampling points. The RCSS sampling patterns can be adapted to fine rectangular grids to permit use of the Fast Fourier Transform in data processing, without an apparent increase in the artifact level. These artifacts can be further reduced to the noise level using the iterative CLEAN algorithm developed in radioastronomy. We demonstrate these methods on the high resolution 4-D HCCH-TOCSY spectrum of protein G's B1 domain, using only 1.2% of the sampling that would be needed conventionally for this resolution. The use of a multidimensional FFT instead of the slow DFT for initial data processing and for subsequent CLEAN significantly reduces the calculation time, yielding an artifact level that is on par with the level of the true spectral noise. PMID:18853260

Impact of targeting insulin-like growth factor signaling in head and neck cancers.

PubMed

Limesand, Kirsten H; Chibly, Alejandro Martinez; Fribley, Andrew

2013-10-01

The IGF system has been shown to have either negative or negligible impact on clinical outcomes of tumor development depending on specific tumor sites or stages. This review focuses on the clinical impact of IGF signaling in head and neck cancer, the effects of IGF targeted therapies, and the multi-dimensional role of IRS 1/2 signaling as a potential mechanism in resistance to targeted therapies. Similar to other tumor sites, both negative and positive correlations between levels of IGF-1/IGF-1-R and clinical outcomes in head and neck cancer have been reported. In addition, utilization of IGF targeted therapies has not demonstrated significant clinical benefit; therefore the prognostic impact of the IGF system on head and neck cancer remains uncertain. Copyright © 2013 Elsevier Ltd. All rights reserved.

Multidimensional Optimization of Signal Space Distance Parameters in WLAN Positioning

PubMed Central

Brković, Milenko; Simić, Mirjana

2014-01-01

Accurate indoor localization of mobile users is one of the challenging problems of the last decade. Besides delivering high speed Internet, Wireless Local Area Network (WLAN) can be used as an effective indoor positioning system, being competitive both in terms of accuracy and cost. Among the localization algorithms, nearest neighbor fingerprinting algorithms based on Received Signal Strength (RSS) parameter have been extensively studied as an inexpensive solution for delivering indoor Location Based Services (LBS). In this paper, we propose the optimization of the signal space distance parameters in order to improve precision of WLAN indoor positioning, based on nearest neighbor fingerprinting algorithms. Experiments in a real WLAN environment indicate that proposed optimization leads to substantial improvements of the localization accuracy. Our approach is conceptually simple, is easy to implement, and does not require any additional hardware. PMID:24757443

Multidimensional Organizational Communication as a Vehicle for Successful Schools?

ERIC Educational Resources Information Center

Arlestig, Helene

2007-01-01

This article explores how principals and teachers view their organizational communication processes, in successful and less successful schools. By dividing the organizational communication process into three dimensions--information, affirmation, and interpretation--different actions and expressions are visualized. To meet organizational needs, all…

Assessing the multidimensionality of coastal erosion risks: public participation and multicriteria analysis in a Mediterranean coastal system.

PubMed

Roca, Elisabet; Gamboa, Gonzalo; Tàbara, J David

2008-04-01

The complex and multidimensional nature of coastal erosion risks makes it necessary to move away from single-perspective assessment and management methods that have conventionally predominated in coastal management. This article explores the suitability of participatory multicriteria analysis (MCA) for improving the integration of diverse expertises and values and enhancing the social-ecological robustness of the processes that lead to the definition of relevant policy options to deal with those risks. We test this approach in the Mediterranean coastal locality of Lido de Sète in France. Results show that the more adaptive alternatives such as "retreating the shoreline" were preferred by our selected stakeholders to those corresponding to "protecting the shoreline" and the business as usual proposals traditionally put forward by experts and policymakers on these matters. Participative MCA contributed to represent coastal multidimensionality, elicit and integrate different views and preferences, facilitated knowledge exchange, and allowed highlighting existing uncertainties.

Connecting the dots across time: reconstruction of single-cell signalling trajectories using time-stamped data

NASA Astrophysics Data System (ADS)

Mukherjee, Sayak; Stewart, David; Stewart, William; Lanier, Lewis L.; Das, Jayajit

2017-08-01

Single-cell responses are shaped by the geometry of signalling kinetic trajectories carved in a multidimensional space spanned by signalling protein abundances. It is, however, challenging to assay a large number (more than 3) of signalling species in live-cell imaging, which makes it difficult to probe single-cell signalling kinetic trajectories in large dimensions. Flow and mass cytometry techniques can measure a large number (4 to more than 40) of signalling species but are unable to track single cells. Thus, cytometry experiments provide detailed time-stamped snapshots of single-cell signalling kinetics. Is it possible to use the time-stamped cytometry data to reconstruct single-cell signalling trajectories? Borrowing concepts of conserved and slow variables from non-equilibrium statistical physics we develop an approach to reconstruct signalling trajectories using snapshot data by creating new variables that remain invariant or vary slowly during the signalling kinetics. We apply this approach to reconstruct trajectories using snapshot data obtained from in silico simulations, live-cell imaging measurements, and, synthetic flow cytometry datasets. The application of invariants and slow variables to reconstruct trajectories provides a radically different way to track objects using snapshot data. The approach is likely to have implications for solving matching problems in a wide range of disciplines.

Measuring Healthcare Providers' Performances Within Managed Competition Using Multidimensional Quality and Cost Indicators.

PubMed

Portrait, France R M; van der Galiën, Onno; Van den Berg, Bernard

2016-04-01

The Dutch healthcare system is in transition towards managed competition. In theory, a system of managed competition involves incentives for quality and efficiency of provided care. This is mainly because health insurers contract on behalf of their clients with healthcare providers on, potentially, quality and costs. The paper develops a strategy to comprehensively analyse available multidimensional data on quality and costs to assess and report on the relative performance of healthcare providers within managed competition. We had access to individual information on 2409 clients of 19 Dutch diabetes care groups on a broad range of (outcome and process related) quality and cost indicators. We carried out a cost-consequences analysis and corrected for differences in case mix to reduce incentives for risk selection by healthcare providers. There is substantial heterogeneity between diabetes care groups' performances as measured using multidimensional indicators on quality and costs. Better quality diabetes care can be achieved with lower or higher costs. Routine monitoring using multidimensional data on quality and costs merged at the individual level would allow a systematic and comprehensive analysis of healthcare providers' performances within managed competition. Copyright © 2015 John Wiley & Sons, Ltd.

Toward a Dynamic, Multidimensional Research Framework for Strategic Processing

ERIC Educational Resources Information Center

Dinsmore, Daniel L.

2017-01-01

While the empirical literature on strategic processing is vast, understanding how and why certain strategies work for certain learners is far from clear. The purpose of this review is to systematically examine the theoretical and empirical literature on strategic process to parse out current conceptual and methodological progress to inform new…

The Racial and Ethnic Identity Formation Process of Second-Generation Asian Indian Americans: A Phenomenological Study

ERIC Educational Resources Information Center

Iwamoto, Derek Kenji; Negi, Nalini Junko; Partiali, Rachel Negar; Creswell, John W.

2013-01-01

This phenomenological study elucidates the identity development processes of 12 second-generation adult Asian Indian Americans. The results identify salient sociocultural factors and multidimensional processes of racial and ethnic identity development. Discrimination, parental, and community factors seemed to play a salient role in influencing…

rasdaman Array Database: current status

NASA Astrophysics Data System (ADS)

Merticariu, George; Toader, Alexandru

2015-04-01

rasdaman (Raster Data Manager) is a Free Open Source Array Database Management System which provides functionality for storing and processing massive amounts of raster data in the form of multidimensional arrays. The user can access, process and delete the data using SQL. The key features of rasdaman are: flexibility (datasets of any dimensionality can be processed with the help of SQL queries), scalability (rasdaman's distributed architecture enables it to seamlessly run on cloud infrastructures while offering an increase in performance with the increase of computation resources), performance (real-time access, processing, mixing and filtering of arrays of any dimensionality) and reliability (legacy communication protocol replaced with a new one based on cutting edge technology - Google Protocol Buffers and ZeroMQ). Among the data with which the system works, we can count 1D time series, 2D remote sensing imagery, 3D image time series, 3D geophysical data, and 4D atmospheric and climate data. Most of these representations cannot be stored only in the form of raw arrays, as the location information of the contents is also important for having a correct geoposition on Earth. This is defined by ISO 19123 as coverage data. rasdaman provides coverage data support through the Petascope service. Extensions were added on top of rasdaman in order to provide support for the Geoscience community. The following OGC standards are currently supported: Web Map Service (WMS), Web Coverage Service (WCS), and Web Coverage Processing Service (WCPS). The Web Map Service is an extension which provides zoom and pan navigation over images provided by a map server. Starting with version 9.1, rasdaman supports WMS version 1.3. The Web Coverage Service provides capabilities for downloading multi-dimensional coverage data. Support is also provided for several extensions of this service: Subsetting Extension, Scaling Extension, and, starting with version 9.1, Transaction Extension, which defines request types for inserting, updating and deleting coverages. A web client, designed for both novice and experienced users, is also available for the service and its extensions. The client offers an intuitive interface that allows users to work with multi-dimensional coverages by abstracting the specifics of the standard definitions of the requests. The Web Coverage Processing Service defines a language for on-the-fly processing and filtering multi-dimensional raster coverages. rasdaman exposes this service through the WCS processing extension. Demonstrations are provided online via the Earthlook website (earthlook.org) which presents use-cases from a wide variety of application domains, using the rasdaman system as processing engine.

Working Memory Deficit as a Risk Factor for Severe Apathy in Schizophrenia: A 1-Year Longitudinal Study.

PubMed

Raffard, Stéphane; Gutierrez, Laure-Anne; Yazbek, Hanan; Larue, Aurore; Boulenger, Jean-Philippe; Lançon, Christophe; Benoit, Michel; Faget, Catherine; Norton, Joanna; Capdevielle, Delphine

2016-05-01

Apathy, described as impaired motivation and goal-directed behavior, is a common yet often overlooked multidimensional psychopathological state in schizophrenia. Its underlying cognitive processes remain largely unexplored. Data was drawn from a longitudinal hospital study of patients with a DSM-IV diagnosis of schizophrenia; 137 (82.5%) participated at the 1-month follow-up and 81 (59.1%) at the 1-year follow-up. Apathy was assessed with the Lille Apathy Rating Scale, validated in French and in schizophrenia. Severe apathy, overall (total score > -13) and on 4 previously identified distinct dimensions, was considered. Episodic verbal learning was assessed with the California Verbal Learning Test, executive functioning with the Trail Making Test, the Six Element Test and the Stop Signal Paradigm and working memory with the Letter-Number Sequencing Test. After controlling for confounding variables, only episodic verbal learning was associated with severe overall apathy in the cross-sectional study. At 1 year, working memory was associated with an increased risk of severe overall apathy, adjusting for baseline apathy. Using a dimensional approach to apathy, specific types of cognition were found to be associated with specific dimensions of apathy. Our findings confirm the need for a multidimensional approach of negative symptoms in schizophrenia. Moreover, cognitive functioning could be a risk factor for developing severe apathy. Cognitive remediation may thus be a useful non-pharmacological intervention for treating apathy in schizophrenia patients. © The Author 2016. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Understanding neurodynamical systems via Fuzzy Symbolic Dynamics.

PubMed

Dobosz, Krzysztof; Duch, Włodzisław

2010-05-01

Neurodynamical systems are characterized by a large number of signal streams, measuring activity of individual neurons, local field potentials, aggregated electrical (EEG) or magnetic potentials (MEG), oxygen use (fMRI) or activity of simulated neurons. Various basis set decomposition techniques are used to analyze such signals, trying to discover components that carry meaningful information, but these techniques tell us little about the global activity of the whole system. A novel technique called Fuzzy Symbolic Dynamics (FSD) is introduced to help in understanding of the multidimensional dynamical system's behavior. It is based on a fuzzy partitioning of the signal space that defines a non-linear mapping of the system's trajectory to the low-dimensional space of membership function activations. This allows for visualization of the trajectory showing various aspects of observed signals that may be difficult to discover looking at individual components, or to notice otherwise. FSD mapping can be applied to raw signals, transformed signals (for example, ICA components), or to signals defined in the time-frequency domain. To illustrate the method two FSD visualizations are presented: a model system with artificial radial oscillatory sources, and the output layer (50 neurons) of Respiratory Rhythm Generator (RRG) composed of 300 spiking neurons. 2009 Elsevier Ltd. All rights reserved.

Implicitly causality enforced solution of multidimensional transient photon transport equation.

PubMed

Handapangoda, Chintha C; Premaratne, Malin

2009-12-21

A novel method for solving the multidimensional transient photon transport equation for laser pulse propagation in biological tissue is presented. A Laguerre expansion is used to represent the time dependency of the incident short pulse. Owing to the intrinsic causal nature of Laguerre functions, our technique automatically always preserve the causality constrains of the transient signal. This expansion of the radiance using a Laguerre basis transforms the transient photon transport equation to the steady state version. The resulting equations are solved using the discrete ordinates method, using a finite volume approach. Therefore, our method enables one to handle general anisotropic, inhomogeneous media using a single formulation but with an added degree of flexibility owing to the ability to invoke higher-order approximations of discrete ordinate quadrature sets. Therefore, compared with existing strategies, this method offers the advantage of representing the intensity with a high accuracy thus minimizing numerical dispersion and false propagation errors. The application of the method to one, two and three dimensional geometries is provided.

Imaging nanoscale lattice variations by machine learning of x-ray diffraction microscopy data

DOE PAGES

Laanait, Nouamane; Zhang, Zhan; Schlepütz, Christian M.

2016-08-09

In this paper, we present a novel methodology based on machine learning to extract lattice variations in crystalline materials, at the nanoscale, from an x-ray Bragg diffraction-based imaging technique. By employing a full-field microscopy setup, we capture real space images of materials, with imaging contrast determined solely by the x-ray diffracted signal. The data sets that emanate from this imaging technique are a hybrid of real space information (image spatial support) and reciprocal lattice space information (image contrast), and are intrinsically multidimensional (5D). By a judicious application of established unsupervised machine learning techniques and multivariate analysis to this multidimensional datamore » cube, we show how to extract features that can be ascribed physical interpretations in terms of common structural distortions, such as lattice tilts and dislocation arrays. Finally, we demonstrate this 'big data' approach to x-ray diffraction microscopy by identifying structural defects present in an epitaxial ferroelectric thin-film of lead zirconate titanate.« less

Imaging nanoscale lattice variations by machine learning of x-ray diffraction microscopy data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Laanait, Nouamane; Zhang, Zhan; Schlepütz, Christian M.

In this paper, we present a novel methodology based on machine learning to extract lattice variations in crystalline materials, at the nanoscale, from an x-ray Bragg diffraction-based imaging technique. By employing a full-field microscopy setup, we capture real space images of materials, with imaging contrast determined solely by the x-ray diffracted signal. The data sets that emanate from this imaging technique are a hybrid of real space information (image spatial support) and reciprocal lattice space information (image contrast), and are intrinsically multidimensional (5D). By a judicious application of established unsupervised machine learning techniques and multivariate analysis to this multidimensional datamore » cube, we show how to extract features that can be ascribed physical interpretations in terms of common structural distortions, such as lattice tilts and dislocation arrays. Finally, we demonstrate this 'big data' approach to x-ray diffraction microscopy by identifying structural defects present in an epitaxial ferroelectric thin-film of lead zirconate titanate.« less

Numerical simulation of multi-dimensional NMR response in tight sandstone

NASA Astrophysics Data System (ADS)

Guo, Jiangfeng; Xie, Ranhong; Zou, Youlong; Ding, Yejiao

2016-06-01

Conventional logging methods have limitations in the evaluation of tight sandstone reservoirs. The multi-dimensional nuclear magnetic resonance (NMR) logging method has the advantage that it can simultaneously measure transverse relaxation time (T 2), longitudinal relaxation time (T 1) and diffusion coefficient (D). In this paper, we simulate NMR measurements of tight sandstone with different wettability and saturations by the random walk method and obtain the magnetization decays of Carr-Purcell-Meiboom-Gill pulse sequences with different wait times (TW) and echo spacings (TE) under a magnetic field gradient, resulting in D-T 2-T 1 maps by the multiple echo trains joint inversion method. We also study the effects of wettability, saturation, signal-to-noise ratio (SNR) of data and restricted diffusion on the D-T 2-T 1 maps in tight sandstone. The results show that with decreasing wetting fluid saturation, the surface relaxation rate of the wetting fluid gradually increases and the restricted diffusion phenomenon becomes more and more obvious, which leads to the wetting fluid signal moving along the direction of short relaxation and the direction of the diffusion coefficient decreasing in D-T 2-T 1 maps. Meanwhile, the non-wetting fluid position in D-T 2-T 1 maps does not change with saturation variation. With decreasing SNR, the ability to identify water and oil signals based on NMR maps gradually decreases. The wetting fluid D-T 1 and D-T 2 correlations in NMR diffusion-relaxation maps of tight sandstone are obtained through expanding the wetting fluid restricted diffusion models, and are further applied to recognize the wetting fluid in simulated D-T 2 maps and D-T 1 maps.

Coherent Multidimensional Core Spectroscopy of Molecules with Multiple X-ray pulses

NASA Astrophysics Data System (ADS)

Mukamel, Shaul

2017-04-01

Multidimensional spectroscopy uses sequences of optical pulses to study dynamical processes in complex molecules through correlation plots involving several time delay periods. Extensions of these techniques to the x-ray regime will be discussed. Ultrafast nonlinear x-ray spectroscopy is made possible by newly developed free electron laser and high harmonic generation sources. The attosecond duration of X-ray pulses and the atomic selectivity of core X-ray excitations offer a uniquely high spatial and temporal resolution. We demonstrate how stimulated Raman detection of an X-ray probe may be used to monitor the phase and dynamics of the nonequilibrium valence electronic state wavepacket created by e.g. photoexcitation, photoionization and Auger processes. Spectroscopy of multiplecore excitations provides a new window into electron correlations. Applications will be presented to long-range charge transfer in proteins and to excitation energy transfer in porphyrin arrays. Conical intersections (CoIn) dominate the pathways and outcomes of virtually all photophysical and photochemical molecular processes. Despite extensive experimental and theoretical effort CoIns have not been directly observed yet and the experimental evidence is being inferred from fast reaction rates and some vibrational signatures. Novel ultrafast X ray probes for these processes will be presented. Short X-ray pulses can directly detect the passage through a CoIn with the adequate temporal and spectral sensitivity. The technique is based on a coherent Raman process that employs a composite femtosecond/attosecond X-ray pulse to directly detect the electronic coherences (rather than populations) that are generated as the system passes through the CoIn. Streaking of time-resolved photoelectron spectroscopy (TRPES) signals offers another powerful window into the joint electronic/vibrational dynamics at concial intersections. Strong coupling of molecules to the vacuum field of micro cavities can modify the potential energy surfaces thereby manipulating the photophysical and photochemical reaction pathways. The photonic vacuum state of a localized cavity mode can be strongly mixed with the molecular degrees of freedom to create hybrid field-matter states known as polaritons. Simulations of the avoided crossing of sodium iodide in a cavity which incorporate the quantized cavity field into the nuclear wave packet dynamics will be presented. Numerical results show how the branching ratio between the covalent and ionic dissociation channels can be strongly manipulated by the optical cavity.

Error control techniques for satellite and space communications

NASA Technical Reports Server (NTRS)

Costello, Daniel J., Jr.

1991-01-01

Shannon's capacity bound shows that coding can achieve large reductions in the required signal to noise ratio per information bit (E sub b/N sub 0 where E sub b is the energy per bit and (N sub 0)/2 is the double sided noise density) in comparison to uncoded schemes. For bandwidth efficiencies of 2 bit/sym or greater, these improvements were obtained through the use of Trellis Coded Modulation and Block Coded Modulation. A method of obtaining these high efficiencies using multidimensional Multiple Phase Shift Keying (MPSK) and Quadrature Amplitude Modulation (QAM) signal sets with trellis coding is described. These schemes have advantages in decoding speed, phase transparency, and coding gain in comparison to other trellis coding schemes. Finally, a general parity check equation for rotationally invariant trellis codes is introduced from which non-linear codes for two dimensional MPSK and QAM signal sets are found. These codes are fully transparent to all rotations of the signal set.

Application of artificial neural network to search for gravitational-wave signals associated with short gamma-ray bursts

NASA Astrophysics Data System (ADS)

Kim, Kyungmin; Harry, Ian W.; Hodge, Kari A.; Kim, Young-Min; Lee, Chang-Hwan; Lee, Hyun Kyu; Oh, John J.; Oh, Sang Hoon; Son, Edwin J.

2015-12-01

We apply a machine learning algorithm, the artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts (GRBs). The multi-dimensional samples consisting of data corresponding to the statistical and physical quantities from the coherent search pipeline are fed into the artificial neural network to distinguish simulated gravitational-wave signals from background noise artifacts. Our result shows that the data classification efficiency at a fixed false alarm probability (FAP) is improved by the artificial neural network in comparison to the conventional detection statistic. Specifically, the distance at 50% detection probability at a fixed false positive rate is increased about 8%-14% for the considered waveform models. We also evaluate a few seconds of the gravitational-wave data segment using the trained networks and obtain the FAP. We suggest that the artificial neural network can be a complementary method to the conventional detection statistic for identifying gravitational-wave signals related to the short GRBs.

Impact of Profiling Technologies in the Understanding of Recombinant Protein Production

NASA Astrophysics Data System (ADS)

Vijayendran, Chandran; Flaschel, Erwin

Since expression profiling methods have been available in a high throughput fashion, the implication of these technologies in the field of biotechnology has increased dramatically. Microarray technology is one such unique and efficient methodology for simultaneous exploration of expression levels of numerous genes. Likewise, two-dimensional gel electrophoresis or multidimensional liquid chromatography coupled with mass spectrometry are extensively utilised for studying expression levels of numerous proteins. In the field of biotechnology these highly parallel analytical methods have paved the way to study and understand various biological phenomena depending on expression patterns. The next phenomenological level is represented by the metabolome and the (metabolic) fluxome. However, this chapter reviews gene and protein profiling and their impact on understanding recombinant protein production. We focus on the computational methods utilised for the analyses of data obtained from these profiling technologies as well as prominent results focusing on recombinant protein expression with Escherichia coli. Owing to the knowledge accumulated with respect to cellular signals triggered during recombinant protein production, this field is on the way to design strategies for developing improved processes. Both gene and protein profiling have exhibited a handful of functional categories to concentrate on in order to identify target genes and proteins, respectively, involved in the signalling network with major impact on recombinant protein production.

Mid-late Holocene climate, demography, and cultural dynamics in Iberia: A multi-proxy approach

NASA Astrophysics Data System (ADS)

Lillios, Katina T.; Blanco-González, Antonio; Drake, Brandon Lee; López-Sáez, José Antonio

2016-03-01

Despite increasing interest in the relationship between culture transformation and abrupt climate change, their complexities are poorly understood. The local impact of global environmental fluctuations depends on multiple factors, and their effects on societal collapse are often assumed rather than demonstrated. One of the major changes in west European later prehistory was the Copper to Bronze Age transition, contemporaneous with the 4.2 ky cal. BP event. This article offers a multi-dimensional insight into this historical process in the Iberian Peninsula from a multi-proxy and comparative perspective. Three study areas, representative of diverse ecological settings and historical trajectories, are compared. Using radiocarbon dates, 13C discrimination (Δ13C) values on C3 plants, and high-resolution palynological records as palaeoclimatic and palaeodemographic proxies, this study tracks the uneven signals of Holocene climate. The wettest Northwest region features the most stable trend lines, whereas the Southwest exhibits an abrupt decrease in its demographic signals c. 4500 cal. BP, which is then followed by a subsequent rise in the neighbouring Southeast. These lines of evidence suggest the possibility, never previously noted, of demic migration from the Southwest to the Southeast in the Early Bronze Age as a contributing factor to the cultural dynamics of southern Iberia.

Development of a new ion mobility time-of-flight mass spectrometer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ibrahim, Yehia M.; Baker, Erin S.; Danielson, William F.

2015-02-01

Complex samples require multidimensional measurements with high resolution for full characterization of biological and environmental systems. To address this challenge, we developed a drift tube-based ion mobility spectrometry-Orbitrap mass spectrometry (IMS-Orbitrap MS) platform. To circumvent the timing difference between the fast IMS separation and the slow Orbitrap MS acquisition, we utilized a dual gate and pseudorandom sequence to multiplex ions into the drift tube and Orbitrap. The instrument was designed to operate in signal averaging (SA), single multiplexing (SM) and double multiplexing (DM) IMS modes to fully optimize the signal-to-ratio of the measurements. For the SM measurements, a previously developedmore » algorithm was used to reconstruct the IMS data, while a new algorithm was developed for the DM analyses. The new algorithm is a two-step process that first recovers the SM data from the encoded DM data and then decoded the SM data. The algorithm also performs multiple refining procedures in order to minimize the demultiplexing artifacts traditionally observed in such scheme. The new IMS-Orbitrap MS platform was demonstrated for the analysis of proteomic and petroleum samples, where the integration of IMS and high mass resolution proved essential for accurate assignment of molecular formulae.« less

The relation between categorical perception of speech stimuli and reading skills in children

NASA Astrophysics Data System (ADS)

Breier, Joshua; Fletcher, Jack; Klaas, Patricia; Gray, Lincoln

2005-09-01

Children ages 7 to 14 years listened to seven tokens, /ga/ to /ka/ synthesized in equal steps from 0 to 60 ms along the voice onset time (VOT) continuum, played in continuous rhythm. All possible changes (21) between the seven tokens were presented seven times at random intervals, maintaining the rhythm. Children were asked to press a button as soon as they detected a change. Maps of the seven tokens, constructed from multidimensional scaling of reaction times, indicated two salient dimensions: one phonological and the other acoustic/phonetic. Better reading, spelling, and phonological processing skills were associated with greater relative weighting of the phonological as compared to the acoustic dimension, suggesting that children with reading difficulty and associated deficits may underweight the phonological and/or overweight the acoustic information in speech signals. This task required no training and only momentary memory of the tokens. That an analysis of a simple task coincides with more complex reading tests suggests a low-level deficit (or shift in listening strategy). Compared to control children, children with reading disabilities may pay more attention to subtle details in these signals and less attention to the global pattern or attribute. [Supported by NIH Grant 1 RO1 HD35938 to JIB.

Multidimensional proteomics for cell biology.

PubMed

Larance, Mark; Lamond, Angus I

2015-05-01

The proteome is a dynamic system in which each protein has interconnected properties - dimensions - that together contribute to the phenotype of a cell. Measuring these properties has proved challenging owing to their diversity and dynamic nature. Advances in mass spectrometry-based proteomics now enable the measurement of multiple properties for thousands of proteins, including their abundance, isoform expression, turnover rate, subcellular localization, post-translational modifications and interactions. Complementing these experimental developments are new data analysis, integration and visualization tools as well as data-sharing resources. Together, these advances in the multidimensional analysis of the proteome are transforming our understanding of various cellular and physiological processes.

Assessing the Suitability of Process and Information Technology in Supporting Tacit Knowledge Transfer

ERIC Educational Resources Information Center

Wu, Chien-Hsing; Kao, Shu-Chen; Shih, Lan-Hsin

2010-01-01

The transfer of tacit knowledge, one of the most important issues in the knowledge sharing context, needs a multi-dimensional perception in its process. Information technology's (IT) supporting role has already been addressed in the process of tacit knowledge transfer. However, IT has its own characteristics, and in turn, may have dissimilar…

When Content Matters: The Role of Processing Code in Tactile Display Design.

PubMed

Ferris, Thomas K; Sarter, Nadine

2010-01-01

The distribution of tasks and stimuli across multiple modalities has been proposed as a means to support multitasking in data-rich environments. Recently, the tactile channel and, more specifically, communication via the use of tactile/haptic icons have received considerable interest. Past research has examined primarily the impact of concurrent task modality on the effectiveness of tactile information presentation. However, it is not well known to what extent the interpretation of iconic tactile patterns is affected by another attribute of information: the information processing codes of concurrent tasks. In two driving simulation studies (n = 25 for each), participants decoded icons composed of either spatial or nonspatial patterns of vibrations (engaging spatial and nonspatial processing code resources, respectively) while concurrently interpreting spatial or nonspatial visual task stimuli. As predicted by Multiple Resource Theory, performance was significantly worse (approximately 5-10 percent worse) when the tactile icons and visual tasks engaged the same processing code, with the overall worst performance in the spatial-spatial task pairing. The findings from these studies contribute to an improved understanding of information processing and can serve as input to multidimensional quantitative models of timesharing performance. From an applied perspective, the results suggest that competition for processing code resources warrants consideration, alongside other factors such as the naturalness of signal-message mapping, when designing iconic tactile displays. Nonspatially encoded tactile icons may be preferable in environments which already rely heavily on spatial processing, such as car cockpits.

Job Enlargement: A Multidimensional Process

ERIC Educational Resources Information Center

Donaldson, Lex

1975-01-01

An evaluation study into the effects of a job enlargement exercise indicates that the expected increases in satisfaction associated with greater work variety, novelty, and felt use of abilities were achieved. (Author/MLF)

Effectiveness of electronic guideline-based implementation systems in ambulatory care settings - a systematic review

PubMed Central

2009-01-01

Background Electronic guideline-based decision support systems have been suggested to successfully deliver the knowledge embedded in clinical practice guidelines. A number of studies have already shown positive findings for decision support systems such as drug-dosing systems and computer-generated reminder systems for preventive care services. Methods A systematic literature search (1990 to December 2008) of the English literature indexed in the Medline database, Embase, the Cochrane Central Register of Controlled Trials, and CRD (DARE, HTA and NHS EED databases) was conducted to identify evaluation studies of electronic multi-step guideline implementation systems in ambulatory care settings. Important inclusion criterions were the multidimensionality of the guideline (the guideline needed to consist of several aspects or steps) and real-time interaction with the system during consultation. Clinical decision support systems such as one-time reminders for preventive care for which positive findings were shown in earlier reviews were excluded. Two comparisons were considered: electronic multidimensional guidelines versus usual care (comparison one) and electronic multidimensional guidelines versus other guideline implementation methods (comparison two). Results Twenty-seven publications were selected for analysis in this systematic review. Most designs were cluster randomized controlled trials investigating process outcomes more than patient outcomes. With success defined as at least 50% of the outcome variables being significant, none of the studies were successful in improving patient outcomes. Only seven of seventeen studies that investigated process outcomes showed improvements in process of care variables compared with the usual care group (comparison one). No incremental effect of the electronic implementation over the distribution of paper versions of the guideline was found, neither for the patient outcomes nor for the process outcomes (comparison two). Conclusions There is little evidence at the moment for the effectiveness of an increasingly used and commercialised instrument such as electronic multidimensional guidelines. After more than a decade of development of numerous electronic systems, research on the most effective implementation strategy for this kind of guideline-based decision support systems is still lacking. This conclusion implies a considerable risk towards inappropriate investments in ineffective implementation interventions and in suboptimal care. PMID:20042070

Python Winding Itself Around Datacubes: How to Access Massive Multi-Dimensional Arrays in a Pythonic Way

NASA Astrophysics Data System (ADS)

Merticariu, Vlad; Misev, Dimitar; Baumann, Peter

2017-04-01

While python has developed into the lingua franca in Data Science there is often a paradigm break when accessing specialized tools. In particular for one of the core data categories in science and engineering, massive multi-dimensional arrays, out-of-memory solutions typically employ their own, different models. We discuss this situation on the example of the scalable open-source array engine, rasdaman ("raster data manager") which offers access to and processing of Petascale multi-dimensional arrays through an SQL-style array query language, rasql. Such queries are executed in the server on a storage engine utilizing adaptive array partitioning and based on a processing engine implementing a "tile streaming" paradigm to allow processing of arrays massively larger than server RAM. The rasdaman QL has acted as blueprint for forthcoming ISO Array SQL and the Open Geospatial Consortium (OGC) geo analytics language, Web Coverage Processing Service, adopted in 2008. Not surprisingly, rasdaman is OGC and INSPIRE Reference Implementation for their "Big Earth Data" standards suite. Recently, rasdaman has been augmented with a python interface which allows to transparently interact with the database (credits go to Siddharth Shukla's Master Thesis at Jacobs University). Programmers do not need to know the rasdaman query language, as the operators are silently transformed, through lazy evaluation, into queries. Arrays delivered are likewise automatically transformed into their python representation. In the talk, the rasdaman concept will be illustrated with the help of large-scale real-life examples of operational satellite image and weather data services, and sample python code.

A structural and a functional aspect of stable information processing by the brain

PubMed Central

2007-01-01

Brain is an expert in producing the same output from a particular set of inputs, even from a very noisy environment. In this article a model of neural circuit in the brain has been proposed which is composed of cyclic sub-circuits. A big loop has been defined to be consisting of a feed forward path from the sensory neurons to the highest processing area of the brain and feed back paths from that region back up to close to the same sensory neurons. It has been mathematically shown how some smaller cycles can amplify signal. A big loop processes information by contrast and amplify principle. How a pair of presynaptic and postsynaptic neurons can be identified by an exact synchronization detection method has also been mentioned. It has been assumed that the spike train coming out of a firing neuron encodes all the information produced by it as output. It is possible to extract this information over a period of time by Fourier transforms. The Fourier coefficients arranged in a vector form will uniquely represent the neural spike train over a period of time. The information emanating out of all the neurons in a given neural circuit over a period of time can be represented by a collection of points in a multidimensional vector space. This cluster of points represents the functional or behavioral form of the neural circuit. It has been proposed that a particular cluster of vectors as the representation of a new behavior is chosen by the brain interactively with respect to the memory stored in that circuit and the amount of emotion involved. It has been proposed that in this situation a Coulomb force like expression governs the dynamics of functioning of the circuit and stability of the system is reached at the minimum of all the minima of a potential function derived from the force like expression. The calculations have been done with respect to a pseudometric defined in a multidimensional vector space. PMID:19003500

A Demonstration of Optimal Apodization Determination for Proper Lateral Modulation

NASA Astrophysics Data System (ADS)

Sumi, Chikayoshi; Komiya, Yuichi; Uga, Shinya

2009-07-01

We have realized effective ultrasound (US) beamformings by the steering of plural beams and apodizations for B-mode imaging with a high lateral resolution and accurate measurement of tissue or blood displacement vector and/or strain tensor using the multidimensional cross-spectrum phase gradient method (MCSPGM), or multidimensional autocorrelation or Doppler methods (MAM and MDM) using multidimensional analytic signals. For instance, the coherent superposition of the steered beams performed in the lateral cosine modulation method (LCM) has a higher potential for realizing a more accurate measurement of a displacement vector than the synthesis of the displacement vector using the accurately measured axial displacements obtained by the multidimensional synthetic aperture method (MDSAM), multidirectional transmission method (MTM) or the use of plural US transducers. Originally, the apodization function to be used for realizing a designed point spread function (PSF) was obtained by the Fraunhofer approximation (FA). However, to obtain the best approximated, designed PSF in the least-squares sense, we proposed a linear optimization (LO) method. Furthermore, on the basis of the knowledge about the losts of US energy during the propagation, we have recently developed a nonlinear optimization (NLO) method, in which the feet of the main lobes in apodization function are properly truncated. Thus, NLO also allows the decrease in the number of channels or the confinement of the effective aperture. In this study, to gain insight into the ideal shape of the PSF, the accuracies of the two-dimensional (2D) displacement vector measurements were compared for typical PSFs with distinct lateral envelope shapes, particularly, in terms of full width at half maximum (FWHM) and the length of the feet, i.e., the Gaussian function, Hanning window and parabolic function. It was confirmed that a PSF having a wide FWHM and short feet was ideal. Such a PSF yielded an echo with a high signal-to-noise ratio (SNR), a large bandwidth and a large maximum spectrum of the center frequency. Moreover, for the three PSFs used, by calculating the PSFs using a typical transducer model and the apodization functions obtained by the respective LO and NLO methods and FA, we compare the approximation accuracies of the realized PSFs. NLO was effective for realizing such an ideal PSF. In addition, NLO allowed the significant decrease in the number of channels or the confinement of the effective aperture. Thus, in the comparisons of the three distinct PSFs, we obtain an appropriate apodization function. This study will assist the realization of the best lateral modulation.

Ion photon emission microscope

DOEpatents

Doyle, Barney L.

2003-04-22

An ion beam analysis system that creates microscopic multidimensional image maps of the effects of high energy ions from an unfocussed source upon a sample by correlating the exact entry point of an ion into a sample by projection imaging of the ion-induced photons emitted at that point with a signal from a detector that measures the interaction of that ion within the sample. The emitted photons are collected in the lens system of a conventional optical microscope, and projected on the image plane of a high resolution single photon position sensitive detector. Position signals from this photon detector are then correlated in time with electrical effects, including the malfunction of digital circuits, detected within the sample that were caused by the individual ion that created these photons initially.

Assessment of Language and Literacy: A Process of Hypothesis Testing for Individual Differences

ERIC Educational Resources Information Center

Scott, Cheryl M.

2011-01-01

Purpose: Older school-aged children and adolescents with persistent language and literacy impairments vary in their individual profiles of linguistic strengths and weaknesses. Given the multidimensional nature and complexity of language, designing an assessment protocol capable of uncovering linguistic variation is challenging. A process of…

3D printed bionic ears.

PubMed

Mannoor, Manu S; Jiang, Ziwen; James, Teena; Kong, Yong Lin; Malatesta, Karen A; Soboyejo, Winston O; Verma, Naveen; Gracias, David H; McAlpine, Michael C

2013-06-12

The ability to three-dimensionally interweave biological tissue with functional electronics could enable the creation of bionic organs possessing enhanced functionalities over their human counterparts. Conventional electronic devices are inherently two-dimensional, preventing seamless multidimensional integration with synthetic biology, as the processes and materials are very different. Here, we present a novel strategy for overcoming these difficulties via additive manufacturing of biological cells with structural and nanoparticle derived electronic elements. As a proof of concept, we generated a bionic ear via 3D printing of a cell-seeded hydrogel matrix in the anatomic geometry of a human ear, along with an intertwined conducting polymer consisting of infused silver nanoparticles. This allowed for in vitro culturing of cartilage tissue around an inductive coil antenna in the ear, which subsequently enables readout of inductively-coupled signals from cochlea-shaped electrodes. The printed ear exhibits enhanced auditory sensing for radio frequency reception, and complementary left and right ears can listen to stereo audio music. Overall, our approach suggests a means to intricately merge biologic and nanoelectronic functionalities via 3D printing.

Solar and chemical reaction-induced heating in the terrestrial mesosphere and lower thermosphere

NASA Technical Reports Server (NTRS)

Mlynczak, Martin G.

1992-01-01

Airglow and chemical processes in the terrestrial mesosphere and lower thermosphere are reviewed, and initial parameterizations of the processes applicable to multidimensional models are presented. The basic processes by which absorbed solar energy participates in middle atmosphere energetics for absorption events in which photolysis occurs are illustrated. An approach that permits the heating processes to be incorporated in numerical models is presented.

An integrated approach for the knowledge discovery in computer simulation models with a multi-dimensional parameter space

DOE Office of Scientific and Technical Information (OSTI.GOV)

Khawli, Toufik Al; Eppelt, Urs; Hermanns, Torsten

2016-06-08

In production industries, parameter identification, sensitivity analysis and multi-dimensional visualization are vital steps in the planning process for achieving optimal designs and gaining valuable information. Sensitivity analysis and visualization can help in identifying the most-influential parameters and quantify their contribution to the model output, reduce the model complexity, and enhance the understanding of the model behavior. Typically, this requires a large number of simulations, which can be both very expensive and time consuming when the simulation models are numerically complex and the number of parameter inputs increases. There are three main constituent parts in this work. The first part ismore » to substitute the numerical, physical model by an accurate surrogate model, the so-called metamodel. The second part includes a multi-dimensional visualization approach for the visual exploration of metamodels. In the third part, the metamodel is used to provide the two global sensitivity measures: i) the Elementary Effect for screening the parameters, and ii) the variance decomposition method for calculating the Sobol indices that quantify both the main and interaction effects. The application of the proposed approach is illustrated with an industrial application with the goal of optimizing a drilling process using a Gaussian laser beam.« less

An integrated approach for the knowledge discovery in computer simulation models with a multi-dimensional parameter space

NASA Astrophysics Data System (ADS)

Khawli, Toufik Al; Gebhardt, Sascha; Eppelt, Urs; Hermanns, Torsten; Kuhlen, Torsten; Schulz, Wolfgang

2016-06-01

In production industries, parameter identification, sensitivity analysis and multi-dimensional visualization are vital steps in the planning process for achieving optimal designs and gaining valuable information. Sensitivity analysis and visualization can help in identifying the most-influential parameters and quantify their contribution to the model output, reduce the model complexity, and enhance the understanding of the model behavior. Typically, this requires a large number of simulations, which can be both very expensive and time consuming when the simulation models are numerically complex and the number of parameter inputs increases. There are three main constituent parts in this work. The first part is to substitute the numerical, physical model by an accurate surrogate model, the so-called metamodel. The second part includes a multi-dimensional visualization approach for the visual exploration of metamodels. In the third part, the metamodel is used to provide the two global sensitivity measures: i) the Elementary Effect for screening the parameters, and ii) the variance decomposition method for calculating the Sobol indices that quantify both the main and interaction effects. The application of the proposed approach is illustrated with an industrial application with the goal of optimizing a drilling process using a Gaussian laser beam.

Multi-Dimensional, Mesoscopic Monte Carlo Simulations of Inhomogeneous Reaction-Drift-Diffusion Systems on Graphics-Processing Units

PubMed Central

Vigelius, Matthias; Meyer, Bernd

2012-01-01

For many biological applications, a macroscopic (deterministic) treatment of reaction-drift-diffusion systems is insufficient. Instead, one has to properly handle the stochastic nature of the problem and generate true sample paths of the underlying probability distribution. Unfortunately, stochastic algorithms are computationally expensive and, in most cases, the large number of participating particles renders the relevant parameter regimes inaccessible. In an attempt to address this problem we present a genuine stochastic, multi-dimensional algorithm that solves the inhomogeneous, non-linear, drift-diffusion problem on a mesoscopic level. Our method improves on existing implementations in being multi-dimensional and handling inhomogeneous drift and diffusion. The algorithm is well suited for an implementation on data-parallel hardware architectures such as general-purpose graphics processing units (GPUs). We integrate the method into an operator-splitting approach that decouples chemical reactions from the spatial evolution. We demonstrate the validity and applicability of our algorithm with a comprehensive suite of standard test problems that also serve to quantify the numerical accuracy of the method. We provide a freely available, fully functional GPU implementation. Integration into Inchman, a user-friendly web service, that allows researchers to perform parallel simulations of reaction-drift-diffusion systems on GPU clusters is underway. PMID:22506001

Building a symbolic computer algebra toolbox to compute 2D Fourier transforms in polar coordinates.

PubMed

Dovlo, Edem; Baddour, Natalie

2015-01-01

The development of a symbolic computer algebra toolbox for the computation of two dimensional (2D) Fourier transforms in polar coordinates is presented. Multidimensional Fourier transforms are widely used in image processing, tomographic reconstructions and in fact any application that requires a multidimensional convolution. By examining a function in the frequency domain, additional information and insights may be obtained. The advantages of our method include: •The implementation of the 2D Fourier transform in polar coordinates within the toolbox via the combination of two significantly simpler transforms.•The modular approach along with the idea of lookup tables implemented help avoid the issue of indeterminate results which may occur when attempting to directly evaluate the transform.•The concept also helps prevent unnecessary computation of already known transforms thereby saving memory and processing time.

Brain's tumor image processing using shearlet transform

NASA Astrophysics Data System (ADS)

Cadena, Luis; Espinosa, Nikolai; Cadena, Franklin; Korneeva, Anna; Kruglyakov, Alexey; Legalov, Alexander; Romanenko, Alexey; Zotin, Alexander

2017-09-01

Brain tumor detection is well known research area for medical and computer scientists. In last decades there has been much research done on tumor detection, segmentation, and classification. Medical imaging plays a central role in the diagnosis of brain tumors and nowadays uses methods non-invasive, high-resolution techniques, especially magnetic resonance imaging and computed tomography scans. Edge detection is a fundamental tool in image processing, particularly in the areas of feature detection and feature extraction, which aim at identifying points in a digital image at which the image has discontinuities. Shearlets is the most successful frameworks for the efficient representation of multidimensional data, capturing edges and other anisotropic features which frequently dominate multidimensional phenomena. The paper proposes an improved brain tumor detection method by automatically detecting tumor location in MR images, its features are extracted by new shearlet transform.

Combining collective, MSW, and turbulence effects in supernova neutrino flavor evolution

NASA Astrophysics Data System (ADS)

Lund, Tina; Kneller, James P.

2013-07-01

In order to decode the neutrino burst signal from a Galactic core-collapse supernova (ccSN) and reveal the complicated inner workings of the explosion we need a thorough understanding of the neutrino flavor evolution from the proto-neutron star outwards. The flavor content of the signal evolves due to both neutrino collective effects and matter effects which can lead to a highly interesting interplay and distinctive spectral features. In this paper we investigate the supernova neutrino flavor evolution in three different progenitors and include collective flavor effects, the evolution of the Mikheyev, Smirnov & Wolfenstein (MSW) conversion due to the shock wave passage through the star, and the impact of turbulence. We consider both normal and inverted neutrino mass hierarchies and a value of θ13 close to the current experimental measurements. In the Oxygen-Neon-Magnesium (ONeMg) supernova we find that the impact of turbulence is both brief and slight during a window of 1-2 seconds post bounce. This is because the shock races through the star extremely quickly and the turbulence amplitude is expected to be small, less than 10%, since these stars do not require multidimensional physics to explode. Thus the spectral features of collective and shock effects in the neutrino signals from Oxygen-Neon-Magnesium supernovae may be almost turbulence free making them the easiest to interpret. For the more massive progenitors we again find that small amplitude turbulence, up to 10%, leads to a minimal modification of the signal, and the emerging neutrino spectra retain both collective and MSW features. However, when larger amounts of turbulence is added, 30% and 50%, which is justified by the requirement of multidimensional physics in order to make these stars explode, the features of collective and shock wave effects in the high (H) density resonance channel are almost completely obscured at late times. Yet at the same time we find the other mixing channels—the low (L) density resonance channel and the nonresonant channels—begin to develop turbulence signatures. Large amplitude turbulent motions in the outer layers of more massive, iron core-collapse supernovae may obscure the most obvious fingerprints of collective and shock wave effects in the neutrino signal but cannot remove them completely, and additionally bring about new features in the signal.

Studying Relational Spaces in Secondary School: Applying a Spatial Framework for the Study of Borderlands and Relational Work in School Improvement Processes

ERIC Educational Resources Information Center

Frelin, Anneli; Grannäs, Jan

2014-01-01

This article introduces a theoretical framework for studying school improvement processes such as making school environments safer. Using concepts from spatial theory, in which distinctions between mental, social and physical space are applied makes for a multidimensional analysis of processes of change. In a multilevel case study, these were…

The Inclusive Educational Process of Students with Visual Impairments in Spain: An Analysis from the Perspective of Organizations

ERIC Educational Resources Information Center

Simon, Cecilia; Echeita, Gerardo; Sandoval, Marta; Lopez, Mauricio

2010-01-01

Inclusive education is a complex and multidimensional process that, among other aspirations, tries to foster the rights of every student to obtain a high-quality education. This process focuses on the diversity of needs of all students by increasing participation in learning, cultures, and communities and reducing exclusion within and from…

magHD: a new approach to multi-dimensional data storage, analysis, display and exploitation

NASA Astrophysics Data System (ADS)

Angleraud, Christophe

2014-06-01

The ever increasing amount of data and processing capabilities - following the well- known Moore's law - is challenging the way scientists and engineers are currently exploiting large datasets. The scientific visualization tools, although quite powerful, are often too generic and provide abstract views of phenomena, thus preventing cross disciplines fertilization. On the other end, Geographic information Systems allow nice and visually appealing maps to be built but they often get very confused as more layers are added. Moreover, the introduction of time as a fourth analysis dimension to allow analysis of time dependent phenomena such as meteorological or climate models, is encouraging real-time data exploration techniques that allow spatial-temporal points of interests to be detected by integration of moving images by the human brain. Magellium is involved in high performance image processing chains for satellite image processing as well as scientific signal analysis and geographic information management since its creation (2003). We believe that recent work on big data, GPU and peer-to-peer collaborative processing can open a new breakthrough in data analysis and display that will serve many new applications in collaborative scientific computing, environment mapping and understanding. The magHD (for Magellium Hyper-Dimension) project aims at developing software solutions that will bring highly interactive tools for complex datasets analysis and exploration commodity hardware, targeting small to medium scale clusters with expansion capabilities to large cloud based clusters.

Necessary Contributions of Human Frontal Lobe Subregions to Reward Learning in a Dynamic, Multidimensional Environment.

PubMed

Vaidya, Avinash R; Fellows, Lesley K

2016-09-21

Real-world decisions are typically made between options that vary along multiple dimensions, requiring prioritization of the important dimensions to support optimal choice. Learning in this setting depends on attributing decision outcomes to the dimensions with predictive relevance rather than to dimensions that are irrelevant and nonpredictive. This attribution problem is computationally challenging, and likely requires an interplay between selective attention and reward learning. Both these processes have been separately linked to the prefrontal cortex, but little is known about how they combine to support learning the reward value of multidimensional stimuli. Here, we examined the necessary contributions of frontal lobe subregions in attributing feedback to relevant and irrelevant dimensions on a trial-by-trial basis in humans. Patients with focal frontal lobe damage completed a demanding reward learning task where options varied on three dimensions, only one of which predicted reward. Participants with left lateral frontal lobe damage attributed rewards to irrelevant dimensions, rather than the relevant dimension. Damage to the ventromedial frontal lobe also impaired learning about the relevant dimension, but did not increase reward attribution to irrelevant dimensions. The results argue for distinct roles for these two regions in learning the value of multidimensional decision options under dynamic conditions, with the lateral frontal lobe required for selecting the relevant dimension to associate with reward, and the ventromedial frontal lobe required to learn the reward association itself. The real world is complex and multidimensional; how do we attribute rewards to predictive features when surrounded by competing cues? Here, we tested the critical involvement of human frontal lobe subregions in a probabilistic, multidimensional learning environment, asking whether focal lesions affected trial-by-trial attribution of feedback to relevant and irrelevant dimensions. The left lateral frontal lobe was required for filtering option dimensions to allow appropriate feedback attribution, while the ventromedial frontal lobe was necessary for learning the value of features in the relevant dimension. These findings argue that selective attention and associative learning processes mediated by anatomically distinct frontal lobe subregions are both critical for adaptive choice in more complex, ecologically valid settings. Copyright © 2016 the authors 0270-6474/16/369843-16$15.00/0.

Dynamic analysis, transformation, dissemination and applications of scientific multidimensional data in ArcGIS Platform

NASA Astrophysics Data System (ADS)

Shrestha, S. R.; Collow, T. W.; Rose, B.

2016-12-01

Scientific datasets are generated from various sources and platforms but they are typically produced either by earth observation systems or by modelling systems. These are widely used for monitoring, simulating, or analyzing measurements that are associated with physical, chemical, and biological phenomena over the ocean, atmosphere, or land. A significant subset of scientific datasets stores values directly as rasters or in a form that can be rasterized. This is where a value exists at every cell in a regular grid spanning the spatial extent of the dataset. Government agencies like NOAA, NASA, EPA, USGS produces large volumes of near real-time, forecast, and historical data that drives climatological and meteorological studies, and underpins operations ranging from weather prediction to sea ice loss. Modern science is computationally intensive because of the availability of an enormous amount of scientific data, the adoption of data-driven analysis, and the need to share these dataset and research results with the public. ArcGIS as a platform is sophisticated and capable of handling such complex domain. We'll discuss constructs and capabilities applicable to multidimensional gridded data that can be conceptualized as a multivariate space-time cube. Building on the concept of a two-dimensional raster, a typical multidimensional raster dataset could contain several "slices" within the same spatial extent. We will share a case from the NOAA Climate Forecast Systems Reanalysis (CFSR) multidimensional data as an example of how large collections of rasters can be efficiently organized and managed through a data model within a geodatabase called "Mosaic dataset" and dynamically transformed and analyzed using raster functions. A raster function is a lightweight, raster-valued transformation defined over a mixed set of raster and scalar input. That means, just like any tool, you can provide a raster function with input parameters. It enables dynamic processing of only the data that's being displayed on the screen or requested by an application. We will present the dynamic processing and analysis of CFSR data using the chains of raster function and share it as dynamic multidimensional image service. This workflow and capabilities can be easily applied to any scientific data formats that are supported in mosaic dataset.

Multidimensional Learner Model In Intelligent Learning System

NASA Astrophysics Data System (ADS)

Deliyska, B.; Rozeva, A.

2009-11-01

The learner model in an intelligent learning system (ILS) has to ensure the personalization (individualization) and the adaptability of e-learning in an online learner-centered environment. ILS is a distributed e-learning system whose modules can be independent and located in different nodes (servers) on the Web. This kind of e-learning is achieved through the resources of the Semantic Web and is designed and developed around a course, group of courses or specialty. An essential part of ILS is learner model database which contains structured data about learner profile and temporal status in the learning process of one or more courses. In the paper a learner model position in ILS is considered and a relational database is designed from learner's domain ontology. Multidimensional modeling agent for the source database is designed and resultant learner data cube is presented. Agent's modules are proposed with corresponding algorithms and procedures. Multidimensional (OLAP) analysis guidelines on the resultant learner module for designing dynamic learning strategy have been highlighted.

The scheme and research of TV series multidimensional comprehensive evaluation on cross-platform

NASA Astrophysics Data System (ADS)

Chai, Jianping; Bai, Xuesong; Zhou, Hongjun; Yin, Fulian

2016-10-01

As for shortcomings of the comprehensive evaluation system on traditional TV programs such as single data source, ignorance of new media as well as the high time cost and difficulty of making surveys, a new evaluation of TV series is proposed in this paper, which has a perspective in cross-platform multidimensional evaluation after broadcasting. This scheme considers the data directly collected from cable television and the Internet as research objects. It's based on TOPSIS principle, after preprocessing and calculation of the data, they become primary indicators that reflect different profiles of the viewing of TV series. Then after the process of reasonable empowerment and summation by the six methods(PCA, AHP, etc.), the primary indicators form the composite indices on different channels or websites. The scheme avoids the inefficiency and difficulty of survey and marking; At the same time, it not only reflects different dimensions of viewing, but also combines TV media and new media, completing the multidimensional comprehensive evaluation of TV series on cross-platform.

Numerical study of a stochastic particle algorithm solving a multidimensional population balance model for high shear granulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Braumann, Andreas; Kraft, Markus, E-mail: mk306@cam.ac.u; Wagner, Wolfgang

2010-10-01

This paper is concerned with computational aspects of a multidimensional population balance model of a wet granulation process. Wet granulation is a manufacturing method to form composite particles, granules, from small particles and binders. A detailed numerical study of a stochastic particle algorithm for the solution of a five-dimensional population balance model for wet granulation is presented. Each particle consists of two types of solids (containing pores) and of external and internal liquid (located in the pores). Several transformations of particles are considered, including coalescence, compaction and breakage. A convergence study is performed with respect to the parameter that determinesmore » the number of numerical particles. Averaged properties of the system are computed. In addition, the ensemble is subdivided into practically relevant size classes and analysed with respect to the amount of mass and the particle porosity in each class. These results illustrate the importance of the multidimensional approach. Finally, the kinetic equation corresponding to the stochastic model is discussed.« less

Findings of the International Nosocomial Infection Control Consortium (INICC), Part I: Effectiveness of a multidimensional infection control approach on catheter-associated urinary tract infection rates in pediatric intensive care units of 6 developing countries.

PubMed

Rosenthal, Victor D; Ramachandran, Bala; Dueñas, Lourdes; Alvarez-Moreno, Carlos; Navoa-Ng, J A; Armas-Ruiz, Alberto; Ersoz, Gulden; Matta-Cortés, Lorena; Pawar, Mandakini; Nevzat-Yalcin, Ata; Rodríguez-Ferrer, Marena; Bran de Casares, Ana Concepción; Linares, Claudia; Villanueva, Victoria D; Campuzano, Roberto; Kaya, Ali; Rendon-Campo, Luis Fernando; Gupta, Amit; Turhan, Ozge; Barahona-Guzmán, Nayide; de Jesús-Machuca, Lilian; Tolentino, María Corazon V; Mena-Brito, Jorge; Kuyucu, Necdet; Astudillo, Yamileth; Saini, Narinder; Gunay, Nurgul; Sarmiento-Villa, Guillermo; Gumus, Eylul; Lagares-Guzmán, Alfredo; Dursun, Oguz

2012-07-01

A before-after prospective surveillance study to assess the impact of a multidimensional infection control approach for the reduction of catheter-associated urinary tract infection (CAUTI) rates. Pediatric intensive care units (PICUs) of hospital members of the International Nosocomial Infection Control Consortium (INICC) from 10 cities of the following 6 developing countries: Colombia, El Salvador, India, Mexico, Philippines, and Turkey. PICU inpatients. We performed a prospective active surveillance to determine rates of CAUTI among 3,877 patients hospitalized in 10 PICUs for a total of 27,345 bed-days. The study was divided into a baseline period (phase 1) and an intervention period (phase 2). In phase 1, surveillance was performed without the implementation of the multidimensional approach. In phase 2, we implemented a multidimensional infection control approach that included outcome surveillance, process surveillance, feedback on CAUTI rates, feedback on performance, education, and a bundle of preventive measures. The rates of CAUTI obtained in phase 1 were compared with the rates obtained in phase 2, after interventions were implemented. During the study period, we recorded 8,513 urinary catheter (UC) days, including 1,513 UC-days in phase 1 and 7,000 UC-days in phase 2. In phase 1, the CAUTI rate was 5.9 cases per 1,000 UC-days, and in phase 2, after implementing the multidimensional infection control approach for CAUTI prevention, the rate of CAUTI decreased to 2.6 cases per 1,000 UC-days (relative risk, 0.43 [95% confidence interval, 0.21-1.0]), indicating a rate reduction of 57%. Our findings demonstrated that implementing a multidimensional infection control approach is associated with a significant reduction in the CAUTI rate of PICUs in developing countries.

Empirical modeling of an alcohol expectancy memory network using multidimensional scaling.

PubMed

Rather, B C; Goldman, M S; Roehrich, L; Brannick, M

1992-02-01

Risk-related antecedent variables can be linked to later alcohol consumption by memory processes, and alcohol expectancies may be one relevant memory content. To advance research in this area, it would be useful to apply current memory models such as semantic network theory to explain drinking decision processes. We used multidimensional scaling (MDS) to empirically model a preliminary alcohol expectancy semantic network, from which a theoretical account of drinking decision making was generated. Subanalyses (PREFMAP) showed how individuals with differing alcohol consumption histories may have had different association pathways within the expectancy network. These pathways may have, in turn influenced future drinking levels and behaviors while the person was under the influence of alcohol. All individuals associated positive/prosocial effects with drinking, but heavier drinkers indicated arousing effects as their highest probability associates, whereas light drinkers expected sedation. An important early step in this MDS modeling process is the determination of iso-meaning expectancy adjective groups, which correspond to theoretical network nodes.

Multidimensional poverty, household environment and short-term morbidity in India.

PubMed

Dehury, Bidyadhar; Mohanty, Sanjay K

2017-01-01

Using the unit data from the second round of the Indian Human Development Survey (IHDS-II), 2011-2012, which covered 42,152 households, this paper examines the association between multidimensional poverty, household environmental deprivation and short-term morbidities (fever, cough and diarrhoea) in India. Poverty is measured in a multidimensional framework that includes the dimensions of education, health and income, while household environmental deprivation is defined as lack of access to improved sanitation, drinking water and cooking fuel. A composite index combining multidimensional poverty and household environmental deprivation has been computed, and households are classified as follows: multidimensional poor and living in a poor household environment, multidimensional non-poor and living in a poor household environment, multidimensional poor and living in a good household environment and multidimensional non-poor and living in a good household environment. Results suggest that about 23% of the population belonging to multidimensional poor households and living in a poor household environment had experienced short-term morbidities in a reference period of 30 days compared to 20% of the population belonging to multidimensional non-poor households and living in a poor household environment, 19% of the population belonging to multidimensional poor households and living in a good household environment and 15% of the population belonging to multidimensional non-poor households and living in a good household environment. Controlling for socioeconomic covariates, the odds of short-term morbidity was 1.47 [CI 1.40-1.53] among the multidimensional poor and living in a poor household environment, 1.28 [CI 1.21-1.37] among the multidimensional non-poor and living in a poor household environment and 1.21 [CI 1.64-1.28] among the multidimensional poor and living in a good household environment compared to the multidimensional non-poor and living in a good household environment. Results are robust across states and hold good for each of the three morbidities: fever, cough and diarrhoea. This establishes that along with poverty, household environmental conditions have a significant bearing on short-term morbidities in India. Public investment in sanitation, drinking water and cooking fuel can reduce the morbidity and improve the health of the population.

A Multidimensional Investigation of Deep-Level and Surface-Level Processing

ERIC Educational Resources Information Center

Dinsmore, Daniel L.; Alexander, Patricia A.

2016-01-01

This study examines the moderating effects of a situational factor (i.e., text type) and an individual factor (i.e., subject-matter knowledge) on the relation between depth of processing and performance. One-hundred and fifty-one undergraduates completed measures of subject-matter knowledge, read either an expository or persuasive text about the…

Inhibition or Compensation? A Multidimensional Comparison of Reading Processes in Dutch and English

ERIC Educational Resources Information Center

Stevenson, Marie; Schoonen, Rob; de Glopper, Kees

2007-01-01

This study examined two competing hypotheses about second language reading processes: the inhibition hypothesis and the compensation hypothesis. Although the ideas expressed in these hypotheses have been reiterated in the literature, previous to this study, they had seldom been investigated systematically. The inhibition hypothesis states that in…

Designing a Digital Story Assignment for Basic Writers Using the TPCK Framework

ERIC Educational Resources Information Center

Bandi-Rao, Shoba; Sepp, Mary

2014-01-01

The process of digital storytelling allows basic writers to take a personal narrative and translate it into a multimodal and multidimensional experience, motivating a diverse group of writers with different learning styles to engage more creatively and meaningfully in the writing process. Digital storytelling has the capacity to contextualize…

From Career Decision-Making Styles to Career Decision-Making Profiles: A Multidimensional Approach

ERIC Educational Resources Information Center

Gati, Itamar; Landman, Shiri; Davidovitch, Shlomit; Asulin-Peretz, Lisa; Gadassi, Reuma

2010-01-01

Previous research on individual differences in career decision-making processes has often focused on classifying individuals into a few types of decision-making "styles" based on the most dominant trait or characteristic of their approach to the decision process (e.g., rational, intuitive, dependent; Harren, 1979). In this research, an…

"Competing Conceptions of Globalization" Revisited: Relocating the Tension between World-Systems Analysis and Globalization Analysis

ERIC Educational Resources Information Center

Clayton, Thomas

2004-01-01

In recent years, many scholars have become fascinated by a contemporary, multidimensional process that has come to be known as "globalization." Globalization originally described economic developments at the world level. More specifically, scholars invoked the concept in reference to the process of global economic integration and the seemingly…

Multidimensional pair-instability supernova simulations and their multi-messenger signals

NASA Astrophysics Data System (ADS)

Gilmer, Matthew; Kozyreva, Alexandra; Hirschi, Raphael; Fröhlich, Carla; Wright, Warren; Kneller, James P.; Yusof, Norhasliza

2018-01-01

Pair-Instability supernovae (PISNe) are an exotic class of supernovae which, in addition to being fascinating in its own right (its very existence is a topic of debate), may be important for many areas of astrophysics (early stellar populations, galaxy/chemical evolution, cosmic reionization, etc.). At present, PISNe are one of the three proposed mechanisms for explaining superluminous supernovae, though one major drawback is that PISN models predict longer rise times to peak luminosity than seen in observations of superluminous supernovae. Model rise times can be reduced by having shallower progenitor envelopes and/or outward mixing of radioactive material during the explosions. Here, we present explosions and light curves for four progenitor models, with relatively shallow envelopes, that span the PISN mass range. Our light curves exhibit significantly shorter rise times than other PISNe light curves. In addition, we investigate the effects of a multidimensional treatment during the explosive burning phase of PISNe, including the first such treatment in 3D. We find a small amount of outward mixing of radioactive Ni-56 that increases with the number of dimensions, however this mixing is insufficient to significantly alter the light curve rise time. We find significant mixing between the silicon and oxygen rich layers, especially in 3D, that may affect model spectra and should be investigated in the future. Finally, we present the neutrino signals expected from our most massive and least massive PISN models. Accounting for neutrino oscillations, we compute the expected event rates for current and future neutrino detectors.

Multidimensional competences of supply chain managers: an empirical study

NASA Astrophysics Data System (ADS)

Shou, Yongyi; Wang, Weijiao

2017-01-01

Supply chain manager competences have attracted increasing attention from both practitioners and scholars in recent years. This paper conducted an explorative study to understand the dimensionality of supply chain manager competences. Online job advertisements for supply chain managers were collected as secondary data, since these advertisements reflect employers' real job requirements. We adopted the multidimensional scaling (MDS) technique to process and analyse the data. Five dimensions of supply chain manager competences are identified: generic skills, functional skills, supply chain management (SCM) qualifications and leadership, SCM expertise, and industry-specific and senior management skills. Statistic tests indicate that supply chain manager competence saliences vary in different industries and regions.

Analysis of World Economic Variables Using Multidimensional Scaling

PubMed Central

Machado, J.A. Tenreiro; Mata, Maria Eugénia

2015-01-01

Waves of globalization reflect the historical technical progress and modern economic growth. The dynamics of this process are here approached using the multidimensional scaling (MDS) methodology to analyze the evolution of GDP per capita, international trade openness, life expectancy, and education tertiary enrollment in 14 countries. MDS provides the appropriate theoretical concepts and the exact mathematical tools to describe the joint evolution of these indicators of economic growth, globalization, welfare and human development of the world economy from 1977 up to 2012. The polarization dance of countries enlightens the convergence paths, potential warfare and present-day rivalries in the global geopolitical scene. PMID:25811177

Variational transition state theory for multidimensional activated rate processes in the presence of anisotropic friction

NASA Astrophysics Data System (ADS)

Berezhkovskii, Alexander M.; Frishman, Anatoli M.; Pollak, Eli

1994-09-01

Variational transition state theory (VTST) is applied to the study of the activated escape of a particle trapped in a multidimensional potential well and coupled to a heat bath. Special attention is given to the dependence of the rate constant on the friction coefficients in the case of anisotropic friction. It is demonstrated explicitly that both the traditional as well as the nontraditional scenarios for the particle escape are recovered uniformly within the framework of VTST. Effects such as saddle point avoidance and friction dependence of the activation energy are derived from VTST using optimized planar dividing surfaces.

Multidimensional chromatography in food analysis.

PubMed

Herrero, Miguel; Ibáñez, Elena; Cifuentes, Alejandro; Bernal, Jose

2009-10-23

In this work, the main developments and applications of multidimensional chromatographic techniques in food analysis are reviewed. Different aspects related to the existing couplings involving chromatographic techniques are examined. These couplings include multidimensional GC, multidimensional LC, multidimensional SFC as well as all their possible combinations. Main advantages and drawbacks of each coupling are critically discussed and their key applications in food analysis described.

An optimal modeling of multidimensional wave digital filtering network for free vibration analysis of symmetrically laminated composite FSDT plates

NASA Astrophysics Data System (ADS)

Tseng, Chien-Hsun

2015-02-01

The technique of multidimensional wave digital filtering (MDWDF) that builds on traveling wave formulation of lumped electrical elements, is successfully implemented on the study of dynamic responses of symmetrically laminated composite plate based on the first order shear deformation theory. The philosophy applied for the first time in this laminate mechanics relies on integration of certain principles involving modeling and simulation, circuit theory, and MD digital signal processing to provide a great variety of outstanding features. Especially benefited by the conservation of passivity gives rise to a nonlinear programming problem (NLP) for the issue of numerical stability of a MD discrete system. Adopting the augmented Lagrangian genetic algorithm, an effective optimization technique for rapidly achieving solution spaces of NLP models, numerical stability of the MDWDF network is well received at all time by the satisfaction of the Courant-Friedrichs-Levy stability criterion with the least restriction. In particular, optimum of the NLP has led to the optimality of the network in terms of effectively and accurately predicting the desired fundamental frequency, and thus to give an insight into the robustness of the network by looking at the distribution of system energies. To further explore the application of the optimum network, more numerical examples are engaged in efforts to achieve a qualitative understanding of the behavior of the laminar system. These are carried out by investigating various effects based on different stacking sequences, stiffness and span-to-thickness ratios, mode shapes and boundary conditions. Results are scrupulously validated by cross referencing with early published works, which show that the present method is in excellent agreement with other numerical and analytical methods.

Multidimensional Riemann problem with self-similar internal structure - part III - a multidimensional analogue of the HLLI Riemann solver for conservative hyperbolic systems

NASA Astrophysics Data System (ADS)

Balsara, Dinshaw S.; Nkonga, Boniface

2017-10-01

Just as the quality of a one-dimensional approximate Riemann solver is improved by the inclusion of internal sub-structure, the quality of a multidimensional Riemann solver is also similarly improved. Such multidimensional Riemann problems arise when multiple states come together at the vertex of a mesh. The interaction of the resulting one-dimensional Riemann problems gives rise to a strongly-interacting state. We wish to endow this strongly-interacting state with physically-motivated sub-structure. The fastest way of endowing such sub-structure consists of making a multidimensional extension of the HLLI Riemann solver for hyperbolic conservation laws. Presenting such a multidimensional analogue of the HLLI Riemann solver with linear sub-structure for use on structured meshes is the goal of this work. The multidimensional MuSIC Riemann solver documented here is universal in the sense that it can be applied to any hyperbolic conservation law. The multidimensional Riemann solver is made to be consistent with constraints that emerge naturally from the Galerkin projection of the self-similar states within the wave model. When the full eigenstructure in both directions is used in the present Riemann solver, it becomes a complete Riemann solver in a multidimensional sense. I.e., all the intermediate waves are represented in the multidimensional wave model. The work also presents, for the very first time, an important analysis of the dissipation characteristics of multidimensional Riemann solvers. The present Riemann solver results in the most efficient implementation of a multidimensional Riemann solver with sub-structure. Because it preserves stationary linearly degenerate waves, it might also help with well-balancing. Implementation-related details are presented in pointwise fashion for the one-dimensional HLLI Riemann solver as well as the multidimensional MuSIC Riemann solver.

Textile composite processing science

NASA Technical Reports Server (NTRS)

Loos, Alfred C.; Hammond, Vincent H.; Kranbuehl, David E.; Hasko, Gregory H.

1993-01-01

A multi-dimensional model of the Resin Transfer Molding (RTM) process was developed for the prediction of the infiltration behavior of a resin into an anisotropic fiber preform. Frequency dependent electromagnetic sensing (FDEMS) was developed for in-situ monitoring of the RTM process. Flow visualization and mold filling experiments were conducted to verify sensor measurements and model predictions. Test results indicated good agreement between model predictions, sensor readings, and experimental data.

Social identity and cooperation in cultural evolution.

PubMed

Smaldino, Paul E

2017-12-06

I discuss the function of social identity signaling in facilitating cooperative group formation, and how the nature of that function changes with the structure of social organization. I propose that signals of social identity facilitate assortment for successful coordination in large-scale societies, and that the multidimensional, context-dependent nature of social identity is crucial for successful coordination when individuals have to cooperate in different contexts. Furthermore, the structure of social identity is tied to the structure of society, so that as societies grow larger and more interconnected, the landscape of social identities grows more heterogeneous. This discussion bears directly on the need to articulate the dynamics of emergent, ephemeral groups as a major factor in human cultural evolution. Copyright © 2017 The Author. Published by Elsevier B.V. All rights reserved.

Multivariate Analysis of the Visual Information Processing of Numbers

ERIC Educational Resources Information Center

Levine, David M.

1977-01-01

Nonmetric multidimensional scaling and hierarchical clustering procedures are applied to a confusion matrix of numerals. Two dimensions were interpreted: straight versus curved, and locus of curvature. Four major clusters of numerals were developed. (Author/JKS)

Multidimensional Interactive Radiology Report and Analysis: standardization of workflow and reporting for renal mass tracking and quantification

NASA Astrophysics Data System (ADS)

Hwang, Darryl H.; Ma, Kevin; Yepes, Fernando; Nadamuni, Mridula; Nayyar, Megha; Liu, Brent; Duddalwar, Vinay; Lepore, Natasha

2015-12-01

A conventional radiology report primarily consists of a large amount of unstructured text, and lacks clear, concise, consistent and content-rich information. Hence, an area of unmet clinical need consists of developing better ways to communicate radiology findings and information specific to each patient. Here, we design a new workflow and reporting system that combines and integrates advances in engineering technology with those from the medical sciences, the Multidimensional Interactive Radiology Report and Analysis (MIRRA). Until recently, clinical standards have primarily relied on 2D images for the purpose of measurement, but with the advent of 3D processing, many of the manually measured metrics can be automated, leading to better reproducibility and less subjective measurement placement. Hence, we make use this newly available 3D processing in our workflow. Our pipeline is used here to standardize the labeling, tracking, and quantifying of metrics for renal masses.

Computer systems and methods for the query and visualization multidimensional databases

DOEpatents

Stolte, Chris; Tang, Diane L.; Hanrahan, Patrick

2017-04-25

A method of generating a data visualization is performed at a computer having a display, one or more processors, and memory. The memory stores one or more programs for execution by the one or more processors. The process receives user specification of a plurality of characteristics of a data visualization. The data visualization is based on data from a multidimensional database. The characteristics specify at least x-position and y-position of data marks corresponding to tuples of data retrieved from the database. The process generates a data visualization according to the specified plurality of characteristics. The data visualization has an x-axis defined based on data for one or more first fields from the database that specify x-position of the data marks and the data visualization has a y-axis defined based on data for one or more second fields from the database that specify y-position of the data marks.

Multidimensional heuristic process for high-yield production of astaxanthin and fragrance molecules in Escherichia coli.

PubMed

Zhang, Congqiang; Seow, Vui Yin; Chen, Xixian; Too, Heng-Phon

2018-05-11

Optimization of metabolic pathways consisting of large number of genes is challenging. Multivariate modular methods (MMMs) are currently available solutions, in which reduced regulatory complexities are achieved by grouping multiple genes into modules. However, these methods work well for balancing the inter-modules but not intra-modules. In addition, application of MMMs to the 15-step heterologous route of astaxanthin biosynthesis has met with limited success. Here, we expand the solution space of MMMs and develop a multidimensional heuristic process (MHP). MHP can simultaneously balance different modules by varying promoter strength and coordinating intra-module activities by using ribosome binding sites (RBSs) and enzyme variants. Consequently, MHP increases enantiopure 3S,3'S-astaxanthin production to 184 mg l -1 day -1 or 320 mg l -1 . Similarly, MHP improves the yields of nerolidol and linalool. MHP may be useful for optimizing other complex biochemical pathways.

Building a symbolic computer algebra toolbox to compute 2D Fourier transforms in polar coordinates

PubMed Central

Dovlo, Edem; Baddour, Natalie

2015-01-01

The development of a symbolic computer algebra toolbox for the computation of two dimensional (2D) Fourier transforms in polar coordinates is presented. Multidimensional Fourier transforms are widely used in image processing, tomographic reconstructions and in fact any application that requires a multidimensional convolution. By examining a function in the frequency domain, additional information and insights may be obtained. The advantages of our method include: • The implementation of the 2D Fourier transform in polar coordinates within the toolbox via the combination of two significantly simpler transforms. • The modular approach along with the idea of lookup tables implemented help avoid the issue of indeterminate results which may occur when attempting to directly evaluate the transform. • The concept also helps prevent unnecessary computation of already known transforms thereby saving memory and processing time. PMID:26150988

Influence of heat transfer rates on pressurization of liquid/slush hydrogen propellant tanks

NASA Technical Reports Server (NTRS)

Sasmal, G. P.; Hochstein, J. I.; Hardy, T. L.

1993-01-01

A multi-dimensional computational model of the pressurization process in liquid/slush hydrogen tank is developed and used to study the influence of heat flux rates at the ullage boundaries on the process. The new model computes these rates and performs an energy balance for the tank wall whereas previous multi-dimensional models required a priori specification of the boundary heat flux rates. Analyses of both liquid hydrogen and slush hydrogen pressurization were performed to expose differences between the two processes. Graphical displays are presented to establish the dependence of pressurization time, pressurant mass required, and other parameters of interest on ullage boundary heat flux rates and pressurant mass flow rate. Detailed velocity fields and temperature distributions are presented for selected cases to further illuminate the details of the pressurization process. It is demonstrated that ullage boundary heat flux rates do significantly effect the pressurization process and that minimizing heat loss from the ullage and maximizing pressurant flow rate minimizes the mass of pressurant gas required to pressurize the tank. It is further demonstrated that proper dimensionless scaling of pressure and time permit all the pressure histories examined during this study to be displayed as a single curve.

Multidimensional spectrometer

DOEpatents

Zanni, Martin Thomas; Damrauer, Niels H.

2010-07-20

A multidimensional spectrometer for the infrared, visible, and ultraviolet regions of the electromagnetic spectrum, and a method for making multidimensional spectroscopic measurements in the infrared, visible, and ultraviolet regions of the electromagnetic spectrum. The multidimensional spectrometer facilitates measurements of inter- and intra-molecular interactions.

Discrete decoding based ultrafast multidimensional nuclear magnetic resonance spectroscopy

NASA Astrophysics Data System (ADS)

Wei, Zhiliang; Lin, Liangjie; Ye, Qimiao; Li, Jing; Cai, Shuhui; Chen, Zhong

2015-07-01

The three-dimensional (3D) nuclear magnetic resonance (NMR) spectroscopy constitutes an important and powerful tool in analyzing chemical and biological systems. However, the abundant 3D information arrives at the expense of long acquisition times lasting hours or even days. Therefore, there has been a continuous interest in developing techniques to accelerate recordings of 3D NMR spectra, among which the ultrafast spatiotemporal encoding technique supplies impressive acquisition speed by compressing a multidimensional spectrum in a single scan. However, it tends to suffer from tradeoffs among spectral widths in different dimensions, which deteriorates in cases of NMR spectroscopy with more dimensions. In this study, the discrete decoding is proposed to liberate the ultrafast technique from tradeoffs among spectral widths in different dimensions by focusing decoding on signal-bearing sites. For verifying its feasibility and effectiveness, we utilized the method to generate two different types of 3D spectra. The proposed method is also applicable to cases with more than three dimensions, which, based on the experimental results, may widen applications of the ultrafast technique.

Attention - Control in the Frequentistic Processing of Multidimensional Event Streams.

DTIC Science & Technology

1980-07-01

Human memory. Annual Review of Psychology, 1979, 30, 63-702. Craik , F. I. M., & Lockhart , R. S. Levels of processing : A framework for memory research...1979; Jacoby & Craik , 1979). Thus, the notions of memora- bility (or retrievability) and levels of processing are tied closely in the sense that the...differing levels and degrees of elaborateness (Jacoby & Craik , 1979). Decisions as to which attributes receive elaborated processing and how they are

Embedding of multidimensional time-dependent observations.

PubMed

Barnard, J P; Aldrich, C; Gerber, M

2001-10-01

A method is proposed to reconstruct dynamic attractors by embedding of multivariate observations of dynamic nonlinear processes. The Takens embedding theory is combined with independent component analysis to transform the embedding into a vector space of linearly independent vectors (phase variables). The method is successfully tested against prediction of the unembedded state vector in two case studies of simulated chaotic processes.

Embedding of multidimensional time-dependent observations

NASA Astrophysics Data System (ADS)

Barnard, Jakobus P.; Aldrich, Chris; Gerber, Marius

2001-10-01

A method is proposed to reconstruct dynamic attractors by embedding of multivariate observations of dynamic nonlinear processes. The Takens embedding theory is combined with independent component analysis to transform the embedding into a vector space of linearly independent vectors (phase variables). The method is successfully tested against prediction of the unembedded state vector in two case studies of simulated chaotic processes.

Understanding Children's Emotional Processes and Behavioral Strategies in the Context of Marital Conflict

ERIC Educational Resources Information Center

Koss, Kalsea J.; George, Melissa R. W.; Bergman, Kathleen N.; Cummings, E. M.; Davies, Patrick T.; Cicchetti, Dante

2011-01-01

Marital conflict is a distressing context in which children must regulate their emotion and behavior; however, the associations between the multidimensionality of conflict and children's regulatory processes need to be examined. The current study examined differences in children's (N=207, mean age=8.02 years) emotions (mad, sad, scared, and happy)…

Word-Level and Sentence-Level Automaticity in English as a Foreign Language (EFL) Learners: a Comparative Study

ERIC Educational Resources Information Center

Ma, Dongmei; Yu, Xiaoru; Zhang, Haomin

2017-01-01

The present study aimed to investigate second language (L2) word-level and sentence-level automatic processing among English as a foreign language students through a comparative analysis of students with different proficiency levels. As a multidimensional and dynamic construct, automaticity is conceptualized as processing speed, stability, and…

Change Theory. Training for Change. International Programme for Teacher Training Institutions. Draft.

ERIC Educational Resources Information Center

Dalin, Per

Educational change is a process occurring through time, a systemic and dynamic phenomenon in which every action leads to reactions in related areas of the system, and a multidimensional phenomenon requiring examination from the perspective of several disciplines. The success of an innovation depends on how the change process is managed, how the…

Examining High-Performing Education Systems in Terms of Teacher Training: Lessons Learnt for Low-Performers

ERIC Educational Resources Information Center

Çer, Erkan; Solak, Ekrem

2018-01-01

The quality of a teacher plays one of the most important roles in the achievement of an education system. Teacher training is a multi-dimensional process which comprises the selection of teacher candidates, pre-service training, appointment, in-service training and teaching practices. Therefore, this study focuses on teacher training processes in…

ICM: a web server for integrated clustering of multi-dimensional biomedical data.

PubMed

He, Song; He, Haochen; Xu, Wenjian; Huang, Xin; Jiang, Shuai; Li, Fei; He, Fuchu; Bo, Xiaochen

2016-07-08

Large-scale efforts for parallel acquisition of multi-omics profiling continue to generate extensive amounts of multi-dimensional biomedical data. Thus, integrated clustering of multiple types of omics data is essential for developing individual-based treatments and precision medicine. However, while rapid progress has been made, methods for integrated clustering are lacking an intuitive web interface that facilitates the biomedical researchers without sufficient programming skills. Here, we present a web tool, named Integrated Clustering of Multi-dimensional biomedical data (ICM), that provides an interface from which to fuse, cluster and visualize multi-dimensional biomedical data and knowledge. With ICM, users can explore the heterogeneity of a disease or a biological process by identifying subgroups of patients. The results obtained can then be interactively modified by using an intuitive user interface. Researchers can also exchange the results from ICM with collaborators via a web link containing a Project ID number that will directly pull up the analysis results being shared. ICM also support incremental clustering that allows users to add new sample data into the data of a previous study to obtain a clustering result. Currently, the ICM web server is available with no login requirement and at no cost at http://biotech.bmi.ac.cn/icm/. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

A Conceptual Modeling Approach for OLAP Personalization

NASA Astrophysics Data System (ADS)

Garrigós, Irene; Pardillo, Jesús; Mazón, Jose-Norberto; Trujillo, Juan

Data warehouses rely on multidimensional models in order to provide decision makers with appropriate structures to intuitively analyze data with OLAP technologies. However, data warehouses may be potentially large and multidimensional structures become increasingly complex to be understood at a glance. Even if a departmental data warehouse (also known as data mart) is used, these structures would be also too complex. As a consequence, acquiring the required information is more costly than expected and decision makers using OLAP tools may get frustrated. In this context, current approaches for data warehouse design are focused on deriving a unique OLAP schema for all analysts from their previously stated information requirements, which is not enough to lighten the complexity of the decision making process. To overcome this drawback, we argue for personalizing multidimensional models for OLAP technologies according to the continuously changing user characteristics, context, requirements and behaviour. In this paper, we present a novel approach to personalizing OLAP systems at the conceptual level based on the underlying multidimensional model of the data warehouse, a user model and a set of personalization rules. The great advantage of our approach is that a personalized OLAP schema is provided for each decision maker contributing to better satisfy their specific analysis needs. Finally, we show the applicability of our approach through a sample scenario based on our CASE tool for data warehouse development.

Ion yields in UV-MALDI mass spectrometry as a function of excitation laser wavelength and optical and physico-chemical properties of classical and halogen-substituted MALDI matrixes.

PubMed

Soltwisch, Jens; Jaskolla, Thorsten W; Hillenkamp, Franz; Karas, Michael; Dreisewerd, Klaus

2012-08-07

The laser wavelength constitutes a key parameter in ultraviolet-matrix-assisted laser desorption ionization-mass spectrometry (UV-MALDI-MS). Optimal analytical results are only achieved at laser wavelengths that correspond to a high optical absorption of the matrix. In the presented work, the wavelength dependence and the contribution of matrix proton affinity to the MALDI process were investigated. A tunable dye laser was used to examine the wavelength range between 280 and 355 nm. The peptide and matrix ion signals recorded as a function of these irradiation parameters are displayed in the form of heat maps, a data representation that furnishes multidimensional data interpretation. Matrixes with a range of proton affinities from 809 to 866 kJ/mol were investigated. Among those selected are the standard matrixes 2,5-dihydroxybenzoic acid (DHB) and α-cyano-4-hydroxycinnamic acid (HCCA) as well as five halogen-substituted cinnamic acid derivatives, including the recently introduced 4-chloro-α-cyanocinnamic acid (ClCCA) and α-cyano-2,4-difluorocinnamic acid (DiFCCA) matrixes. With the exception of DHB, the highest analyte ion signals were obtained toward the red side of the peak optical absorption in the solid state. A stronger decline of the molecular analyte ion signals generated from the matrixes was consistently observed at the low wavelength side of the peak absorption. This effect is mainly the result of increased fragmentation of both analyte and matrix ions. Optimal use of multiply halogenated matrixes requires adjustment of the excitation wavelength to values below that of the standard MALDI lasers emitting at 355 (Nd:YAG) or 337 nm (N(2) laser). The combined data provide new insights into the UV-MALDI desorption/ionization processes and indicate ways to improve the analytical sensitivity.

DataFed: A Federated Data System for Visualization and Analysis of Spatio-Temporal Air Quality Data

NASA Astrophysics Data System (ADS)

Husar, R. B.; Hoijarvi, K.

2017-12-01

DataFed is a distributed web-services-based computing environment for accessing, processing, and visualizing atmospheric data in support of air quality science and management. The flexible, adaptive environment facilitates the access and flow of atmospheric data from provider to users by enabling the creation of user-driven data processing/visualization applications. DataFed `wrapper' components, non-intrusively wrap heterogeneous, distributed datasets for access by standards-based GIS web services. The mediator components (also web services) map the heterogeneous data into a spatio-temporal data model. Chained web services provide homogeneous data views (e.g., geospatial, time views) using a global multi-dimensional data model. In addition to data access and rendering, the data processing component services can be programmed for filtering, aggregation, and fusion of multidimensional data. A complete application software is written in a custom made data flow language. Currently, the federated data pool consists of over 50 datasets originating from globally distributed data providers delivering surface-based air quality measurements, satellite observations, emissions data as well as regional and global-scale air quality models. The web browser-based user interface allows point and click navigation and browsing the XYZT multi-dimensional data space. The key applications of DataFed are for exploring spatial pattern of pollutants, seasonal, weekly, diurnal cycles and frequency distributions for exploratory air quality research. Since 2008, DataFed has been used to support EPA in the implementation of the Exceptional Event Rule. The data system is also used at universities in the US, Europe and Asia.

A quantitative study on magnesium alloy stent biodegradation.

PubMed

Gao, Yuanming; Wang, Lizhen; Gu, Xuenan; Chu, Zhaowei; Guo, Meng; Fan, Yubo

2018-06-06

Insufficient scaffolding time in the process of rapid corrosion is the main problem of magnesium alloy stent (MAS). Finite element method had been used to investigate corrosion of MAS. However, related researches mostly described all elements suffered corrosion in view of one-dimensional corrosion. Multi-dimensional corrosions significantly influence mechanical integrity of MAS structures such as edges and corners. In this study, the effects of multi-dimensional corrosion were studied using experiment quantitatively, then a phenomenological corrosion model was developed to consider these effects. We implemented immersion test with magnesium alloy (AZ31B) cubes, which had different numbers of exposed surfaces to analyze differences of dimension. It was indicated that corrosion rates of cubes are almost proportional to their exposed-surface numbers, especially when pitting corrosions are not marked. The cubes also represented the hexahedron elements in simulation. In conclusion, corrosion rate of every element accelerates by increasing corrosion-surface numbers in multi-dimensional corrosion. The damage ratios among elements with the same size are proportional to the ratios of corrosion-surface numbers under uniform corrosion. The finite element simulation using proposed model provided more details of changes of morphology and mechanics in scaffolding time by removing 25.7% of elements of MAS. The proposed corrosion model reflected the effects of multi-dimension on corrosions. It would be used to predict degradation process of MAS quantitatively. Copyright © 2018 Elsevier Ltd. All rights reserved.

The Relationship Between Acoustic Signal Typing and Perceptual Evaluation of Tracheoesophageal Voice Quality for Sustained Vowels.

PubMed

Clapham, Renee P; van As-Brooks, Corina J; van Son, Rob J J H; Hilgers, Frans J M; van den Brekel, Michiel W M

2015-07-01

To investigate the relationship between acoustic signal typing and perceptual evaluation of sustained vowels produced by tracheoesophageal (TE) speakers and the use of signal typing in the clinical setting. Two evaluators independently categorized 1.75-second segments of narrow-band spectrograms according to acoustic signal typing and independently evaluated the recording of the same segments on a visual analog scale according to overall perceptual acoustic voice quality. The relationship between acoustic signal typing and overall voice quality (as a continuous scale and as a four-point ordinal scale) was investigated and the proportion of inter-rater agreement as well as the reliability between the two measures is reported. The agreement between signal type (I-IV) and ordinal voice quality (four-point scale) was low but significant, and there was a significant linear relationship between the variables. Signal type correctly predicted less than half of the voice quality data. There was a significant main effect of signal type on continuous voice quality scores with significant differences in median quality scores between signal types I-IV, I-III, and I-II. Signal typing can be used as an adjunct to perceptual and acoustic evaluation of the same stimuli for TE speech as part of a multidimensional evaluation protocol. Signal typing in its current form provides limited predictive information on voice quality, and there is significant overlap between signal types II and III and perceptual categories. Future work should consider whether the current four signal types could be refined. Copyright © 2015 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Cytobank: providing an analytics platform for community cytometry data analysis and collaboration.

PubMed

Chen, Tiffany J; Kotecha, Nikesh

2014-01-01

Cytometry is used extensively in clinical and laboratory settings to diagnose and track cell subsets in blood and tissue. High-throughput, single-cell approaches leveraging cytometry are developed and applied in the computational and systems biology communities by researchers, who seek to improve the diagnosis of human diseases, map the structures of cell signaling networks, and identify new cell types. Data analysis and management present a bottleneck in the flow of knowledge from bench to clinic. Multi-parameter flow and mass cytometry enable identification of signaling profiles of patient cell samples. Currently, this process is manual, requiring hours of work to summarize multi-dimensional data and translate these data for input into other analysis programs. In addition, the increase in the number and size of collaborative cytometry studies as well as the computational complexity of analytical tools require the ability to assemble sufficient and appropriately configured computing capacity on demand. There is a critical need for platforms that can be used by both clinical and basic researchers who routinely rely on cytometry. Recent advances provide a unique opportunity to facilitate collaboration and analysis and management of cytometry data. Specifically, advances in cloud computing and virtualization are enabling efficient use of large computing resources for analysis and backup. An example is Cytobank, a platform that allows researchers to annotate, analyze, and share results along with the underlying single-cell data.

Numeric invariants from multidimensional persistence

DOE Office of Scientific and Technical Information (OSTI.GOV)

Skryzalin, Jacek; Carlsson, Gunnar

2017-05-19

In this paper, we analyze the space of multidimensional persistence modules from the perspectives of algebraic geometry. We first build a moduli space of a certain subclass of easily analyzed multidimensional persistence modules, which we construct specifically to capture much of the information which can be gained by using multidimensional persistence over one-dimensional persistence. We argue that the global sections of this space provide interesting numeric invariants when evaluated against our subclass of multidimensional persistence modules. Lastly, we extend these global sections to the space of all multidimensional persistence modules and discuss how the resulting numeric invariants might be usedmore » to study data.« less

Flame-Generated Vorticity Production in Premixed Flame-Vortex Interactions

NASA Technical Reports Server (NTRS)

Patnaik, G.; Kailasanath, K.

2003-01-01

In this study, we use detailed time-dependent, multi-dimensional numerical simulations to investigate the relative importance of the processes leading to FGV in flame-vortex interactions in normal gravity and microgravity and to determine if the production of vorticity in flames in gravity is the same as that in zero gravity except for the contribution of the gravity term. The numerical simulations will be performed using the computational model developed at NRL, FLAME3D. FLAME3D is a parallel, multi-dimensional (either two- or three-dimensional) flame model based on FLIC2D, which has been used extensively to study the structure and stability of premixed hydrogen and methane flames.

Multidimensional treatment foster care as a preventive intervention to promote resiliency among youth in the child welfare system.

PubMed

Leve, Leslie D; Fisher, Philip A; Chamberlain, Patricia

2009-12-01

Demographic trends indicate that a growing segment of families is exposed to adversity such as poverty, drug use problems, caregiver transitions, and domestic violence. Although these risk processes and the accompanying poor outcomes for children have been well studied, little is known about why some children develop resilience in the face of such adversity, particularly when it is severe enough to invoke child welfare involvement. This paper describes a program of research involving families in the child welfare system. Using a resiliency framework, evidence from 4 randomized clinical trials that included components of the Multidimensional Treatment Foster Care program is presented. Future directions and next steps are proposed.

Digital signaling decouples activation probability and population heterogeneity.

PubMed

Kellogg, Ryan A; Tian, Chengzhe; Lipniacki, Tomasz; Quake, Stephen R; Tay, Savaş

2015-10-21

Digital signaling enhances robustness of cellular decisions in noisy environments, but it is unclear how digital systems transmit temporal information about a stimulus. To understand how temporal input information is encoded and decoded by the NF-κB system, we studied transcription factor dynamics and gene regulation under dose- and duration-modulated inflammatory inputs. Mathematical modeling predicted and microfluidic single-cell experiments confirmed that integral of the stimulus (or area, concentration × duration) controls the fraction of cells that activate NF-κB in the population. However, stimulus temporal profile determined NF-κB dynamics, cell-to-cell variability, and gene expression phenotype. A sustained, weak stimulation lead to heterogeneous activation and delayed timing that is transmitted to gene expression. In contrast, a transient, strong stimulus with the same area caused rapid and uniform dynamics. These results show that digital NF-κB signaling enables multidimensional control of cellular phenotype via input profile, allowing parallel and independent control of single-cell activation probability and population heterogeneity.

Single-Scan Multidimensional NMR Analysis of Mixtures at Sub-Millimolar Concentrations by using SABRE Hyperpolarization.

PubMed

Daniele, Valeria; Legrand, François-Xavier; Berthault, Patrick; Dumez, Jean-Nicolas; Huber, Gaspard

2015-11-16

Signal amplification by reversible exchange (SABRE) is a promising method to increase the sensitivity of nuclear magnetic resonance (NMR) experiments. However, SABRE-enhanced (1)H NMR signals are short lived, and SABRE is often used to record 1D NMR spectra only. When the sample of interest is a complex mixture, this results in severe overlaps for (1)H spectra. In addition, the use of a co-substrate, whose signals may obscure the (1) H spectra, is currently the most efficient way to lower the detection limit of SABRE experiments. Here, we describe an approach to obtain clean, SABRE-hyperpolarized 2D (1)H NMR spectra of mixtures of small molecules at sub-millimolar concentrations in a single scan. The method relies on the use of para-hydrogen together with a deuterated co-substrate for hyperpolarization and ultrafast 2D NMR for acquisition. It is applicable to all substrates that can be polarized with SABRE. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Oceans 2.0: Interactive tools for the Visualization of Multi-dimensional Ocean Sensor Data

NASA Astrophysics Data System (ADS)

Biffard, B.; Valenzuela, M.; Conley, P.; MacArthur, M.; Tredger, S.; Guillemot, E.; Pirenne, B.

2016-12-01

Ocean Networks Canada (ONC) operates ocean observatories on all three of Canada's coasts. The instruments produce 280 gigabytes of data per day with 1/2 petabyte archived so far. In 2015, 13 terabytes were downloaded by over 500 users from across the world. ONC's data management system is referred to as "Oceans 2.0" owing to its interactive, participative features. A key element of Oceans 2.0 is real time data acquisition and processing: custom device drivers implement the input-output protocol of each instrument. Automatic parsing and calibration takes place on the fly, followed by event detection and quality control. All raw data are stored in a file archive, while the processed data are copied to fast databases. Interactive access to processed data is provided through data download and visualization/quick look features that are adapted to diverse data types (scalar, acoustic, video, multi-dimensional, etc). Data may be post or re-processed to add features, analysis or correct errors, update calibrations, etc. A robust storage structure has been developed consisting of an extensive file system and a no-SQL database (Cassandra). Cassandra is a node-based open source distributed database management system. It is scalable and offers improved performance for big data. A key feature is data summarization. The system has also been integrated with web services and an ERDDAP OPeNDAP server, capable of serving scalar and multidimensional data from Cassandra for fixed or mobile devices.A complex data viewer has been developed making use of the big data capability to interactively display live or historic echo sounder and acoustic Doppler current profiler data, where users can scroll, apply processing filters and zoom through gigabytes of data with simple interactions. This new technology brings scientists one step closer to a comprehensive, web-based data analysis environment in which visual assessment, filtering, event detection and annotation can be integrated.

A Comparative Study of Online Item Calibration Methods in Multidimensional Computerized Adaptive Testing

ERIC Educational Resources Information Center

Chen, Ping

2017-01-01

Calibration of new items online has been an important topic in item replenishment for multidimensional computerized adaptive testing (MCAT). Several online calibration methods have been proposed for MCAT, such as multidimensional "one expectation-maximization (EM) cycle" (M-OEM) and multidimensional "multiple EM cycles"…

Best Design for Multidimensional Computerized Adaptive Testing with the Bifactor Model

ERIC Educational Resources Information Center

Seo, Dong Gi; Weiss, David J.

2015-01-01

Most computerized adaptive tests (CATs) have been studied using the framework of unidimensional item response theory. However, many psychological variables are multidimensional and might benefit from using a multidimensional approach to CATs. This study investigated the accuracy, fidelity, and efficiency of a fully multidimensional CAT algorithm…

Multidimensional Measurement of Poverty among Women in Sub-Saharan Africa

ERIC Educational Resources Information Center

Batana, Yele Maweki

2013-01-01

Since the seminal work of Sen, poverty has been recognized as a multidimensional phenomenon. The recent availability of relevant databases renewed the interest in this approach. This paper estimates multidimensional poverty among women in fourteen Sub-Saharan African countries using the Alkire and Foster multidimensional poverty measures, whose…

The Efficacy of Multidimensional Constraint Keys in Database Query Performance

ERIC Educational Resources Information Center

Cardwell, Leslie K.

2012-01-01

This work is intended to introduce a database design method to resolve the two-dimensional complexities inherent in the relational data model and its resulting performance challenges through abstract multidimensional constructs. A multidimensional constraint is derived and utilized to implement an indexed Multidimensional Key (MK) to abstract a…

Converging evidence for an impact of a functional NOS gene variation on anxiety-related processes

PubMed Central

Haaker, Jan; Glotzbach-Schoon, Evelyn; Schümann, Dirk; Andreatta, Marta; Mechias, Marie-Luise; Raczka, Karolina; Gartmann, Nina; Büchel, Christian; Mühlberger, Andreas; Pauli, Paul; Reif, Andreas; Kalisch, Raffael; Lonsdorf, Tina B.

2016-01-01

Abstract Being a complex phenotype with substantial heritability, anxiety and related phenotypes are characterized by a complex polygenic basis. Thereby, one candidate pathway is neuronal nitric oxide (NO) signaling, and accordingly, rodent studies have identified NO synthase (NOS-I), encoded by NOS1, as a strong molecular candidate for modulating anxiety and hippocampus-dependent learning processes. Using a multi-dimensional and -methodological replication approach, we investigated the impact of a functional promoter polymorphism (NOS1-ex1f-VNTR) on human anxiety-related phenotypes in a total of 1019 healthy controls in five different studies. Homozygous carriers of the NOS1-ex1f short-allele displayed enhanced trait anxiety, worrying and depression scores. Furthermore, short-allele carriers were characterized by increased anxious apprehension during contextual fear conditioning. While autonomous measures (fear-potentiated startle) provided only suggestive evidence for a modulatory role of NOS1-ex1f-VNTR on (contextual) fear conditioning processes, neural activation at the amygdala/anterior hippocampus junction was significantly increased in short-allele carriers during context conditioning. Notably, this could not be attributed to morphological differences. In accordance with data from a plethora of rodent studies, we here provide converging evidence from behavioral, subjective, psychophysiological and neuroimaging studies in large human cohorts that NOS-I plays an important role in anxious apprehension but provide only limited evidence for a role in (contextual) fear conditioning. PMID:26746182

Neural Prediction of Multidimensional Decisions in Monkey Superior Colliculus

NASA Astrophysics Data System (ADS)

Hasegawa, Ryohei P.; Hasegawa, Yukako T.; Segraves, Mark A.

To examine the function of the superior colliculus (SC) in decision-making processes and the application of its single trial activity for “neural mind reading,” we recorded from SC deep layers while two monkeys performed oculomotor go/no-go tasks. We have recently focused on monitoring single trial activities in single SC neurons, and designed a virtual decision function (VDF) to provide a good estimation of single-dimensional decisions (go/no-go decisions for a cue presented at a specific visual field, a response field of each neuron). In this study, we used two VDFs for multidimensional decisions (go/no-go decisions at two cue locations) with the ensemble activity which was simultaneously recorded from a small group (4 to 6) of neurons at both sides of the SC. VDFs predicted cue locations as well as go/no-go decisions. These results suggest that monitoring of ensemble SC activity had sufficient capacity to predict multidimensional decisions on a trial-by-trial basis, which is an ideal candidate to serve for cognitive brain-machine interfaces (BMI) such as two-dimensional word spellers.

Self-Learning Adaptive Umbrella Sampling Method for the Determination of Free Energy Landscapes in Multiple Dimensions

PubMed Central

Wojtas-Niziurski, Wojciech; Meng, Yilin; Roux, Benoit; Bernèche, Simon

2013-01-01

The potential of mean force describing conformational changes of biomolecules is a central quantity that determines the function of biomolecular systems. Calculating an energy landscape of a process that depends on three or more reaction coordinates might require a lot of computational power, making some of multidimensional calculations practically impossible. Here, we present an efficient automatized umbrella sampling strategy for calculating multidimensional potential of mean force. The method progressively learns by itself, through a feedback mechanism, which regions of a multidimensional space are worth exploring and automatically generates a set of umbrella sampling windows that is adapted to the system. The self-learning adaptive umbrella sampling method is first explained with illustrative examples based on simplified reduced model systems, and then applied to two non-trivial situations: the conformational equilibrium of the pentapeptide Met-enkephalin in solution and ion permeation in the KcsA potassium channel. With this method, it is demonstrated that a significant smaller number of umbrella windows needs to be employed to characterize the free energy landscape over the most relevant regions without any loss in accuracy. PMID:23814508

Situation exploration in a persistent surveillance system with multidimensional data

NASA Astrophysics Data System (ADS)

Habibi, Mohammad S.

2013-03-01

There is an emerging need for fusing hard and soft sensor data in an efficient surveillance system to provide accurate estimation of situation awareness. These mostly abstract, multi-dimensional and multi-sensor data pose a great challenge to the user in performing analysis of multi-threaded events efficiently and cohesively. To address this concern an interactive Visual Analytics (VA) application is developed for rapid assessment and evaluation of different hypotheses based on context-sensitive ontology spawn from taxonomies describing human/human and human/vehicle/object interactions. A methodology is described here for generating relevant ontology in a Persistent Surveillance System (PSS) and demonstrates how they can be utilized in the context of PSS to track and identify group activities pertaining to potential threats. The proposed VA system allows for visual analysis of raw data as well as metadata that have spatiotemporal representation and content-based implications. Additionally in this paper, a technique for rapid search of tagged information contingent to ranking and confidence is explained for analysis of multi-dimensional data. Lastly the issue of uncertainty associated with processing and interpretation of heterogeneous data is also addressed.

Effectiveness, efficiency and efficacy in the multidimensional treatment of schizophrenia: Rethinking project.

PubMed

Crespo-Facorro, Benedicto; Bernardo, Miguel; Argimon, Josep Maria; Arrojo, Manuel; Bravo-Ortiz, Maria Fe; Cabrera-Cifuentes, Ana; Carretero-Román, Julián; Franco-Martín, Manuel A; García-Portilla, Paz; Haro, Josep Maria; Olivares, José Manuel; Penadés, Rafael; Del Pino-Montes, Javier; Sanjuán, Julio; Arango, Celso

Schizophrenia is a clinically heterogeneous syndrome affecting multiple dimensions of patients' life. Therefore, its treatment might require a multidimensional approach that should take into account the efficacy (the ability of an intervention to get the desired result under ideal conditions), the effectiveness (the degree to which the intended effect is obtained under routine clinical practice conditions or settings) and the efficiency (value of the intervention as relative to its cost to the individual or society) of any therapeutic intervention. In a first step of the process, a group of 90 national experts from different areas of health-care and with a multidimensional and multidisciplinary perspective of the disease, defined the concepts of efficacy, effectiveness and efficiency of established therapeutic interventions within 7 key dimensions of the illness: symptomatology; comorbidity; relapse and adherence; insight and subjective experience; cognition; quality of life, autonomy and functional capacity; and social inclusion and associated factors. The main conclusions and recommendations of this stage of the work are presented herein. Copyright © 2016 SEP y SEPB. Publicado por Elsevier España, S.L.U. All rights reserved.

Mega-analysis of Odds Ratio: A Convergent Method for a Deep Understanding of the Genetic Evidence in Schizophrenia.

PubMed

Jia, Peilin; Chen, Xiangning; Xie, Wei; Kendler, Kenneth S; Zhao, Zhongming

2018-06-20

Numerous high-throughput omics studies have been conducted in schizophrenia, providing an accumulated catalog of susceptible variants and genes. The results from these studies, however, are highly heterogeneous. The variants and genes nominated by different omics studies often have limited overlap with each other. There is thus a pressing need for integrative analysis to unify the different types of data and provide a convergent view of schizophrenia candidate genes (SZgenes). In this study, we collected a comprehensive, multidimensional dataset, including 7819 brain-expressed genes. The data hosted genome-wide association evidence in genetics (eg, genotyping data, copy number variations, de novo mutations), epigenetics, transcriptomics, and literature mining. We developed a method named mega-analysis of odds ratio (MegaOR) to prioritize SZgenes. Application of MegaOR in the multidimensional data resulted in consensus sets of SZgenes (up to 530), each enriched with dense, multidimensional evidence. We proved that these SZgenes had highly tissue-specific expression in brain and nerve and had intensive interactions that were significantly stronger than chance expectation. Furthermore, we found these SZgenes were involved in human brain development by showing strong spatiotemporal expression patterns; these characteristics were replicated in independent brain expression datasets. Finally, we found the SZgenes were enriched in critical functional gene sets involved in neuronal activities, ligand gated ion signaling, and fragile X mental retardation protein targets. In summary, MegaOR analysis reported consensus sets of SZgenes with enriched association evidence to schizophrenia, providing insights into the pathophysiology underlying schizophrenia.

Knowledge-based nonuniform sampling in multidimensional NMR.

PubMed

Schuyler, Adam D; Maciejewski, Mark W; Arthanari, Haribabu; Hoch, Jeffrey C

2011-07-01

The full resolution afforded by high-field magnets is rarely realized in the indirect dimensions of multidimensional NMR experiments because of the time cost of uniformly sampling to long evolution times. Emerging methods utilizing nonuniform sampling (NUS) enable high resolution along indirect dimensions by sampling long evolution times without sampling at every multiple of the Nyquist sampling interval. While the earliest NUS approaches matched the decay of sampling density to the decay of the signal envelope, recent approaches based on coupled evolution times attempt to optimize sampling by choosing projection angles that increase the likelihood of resolving closely-spaced resonances. These approaches employ knowledge about chemical shifts to predict optimal projection angles, whereas prior applications of tailored sampling employed only knowledge of the decay rate. In this work we adapt the matched filter approach as a general strategy for knowledge-based nonuniform sampling that can exploit prior knowledge about chemical shifts and is not restricted to sampling projections. Based on several measures of performance, we find that exponentially weighted random sampling (envelope matched sampling) performs better than shift-based sampling (beat matched sampling). While shift-based sampling can yield small advantages in sensitivity, the gains are generally outweighed by diminished robustness. Our observation that more robust sampling schemes are only slightly less sensitive than schemes highly optimized using prior knowledge about chemical shifts has broad implications for any multidimensional NMR study employing NUS. The results derived from simulated data are demonstrated with a sample application to PfPMT, the phosphoethanolamine methyltransferase of the human malaria parasite Plasmodium falciparum.

Signature neural networks: definition and application to multidimensional sorting problems.

PubMed

Latorre, Roberto; de Borja Rodriguez, Francisco; Varona, Pablo

2011-01-01

In this paper we present a self-organizing neural network paradigm that is able to discriminate information locally using a strategy for information coding and processing inspired in recent findings in living neural systems. The proposed neural network uses: 1) neural signatures to identify each unit in the network; 2) local discrimination of input information during the processing; and 3) a multicoding mechanism for information propagation regarding the who and the what of the information. The local discrimination implies a distinct processing as a function of the neural signature recognition and a local transient memory. In the context of artificial neural networks none of these mechanisms has been analyzed in detail, and our goal is to demonstrate that they can be used to efficiently solve some specific problems. To illustrate the proposed paradigm, we apply it to the problem of multidimensional sorting, which can take advantage of the local information discrimination. In particular, we compare the results of this new approach with traditional methods to solve jigsaw puzzles and we analyze the situations where the new paradigm improves the performance.

Rules Mothers and Sons Use to Integrate Intent and Damage Information in Their Moral Judgments.

ERIC Educational Resources Information Center

Leon, Manuel

1984-01-01

The similarity between rules used by mothers and those used by sons was extensive. Results suggest that research should emphasize the process by which children come to employ multidimensional rules and the role of parental models in this process. Current research in moral judgments largely ignores the rule-governed nature of children's judgments.…

[Establishment and application of "multi-dimensional structure and process dynamic quality control technology system" in preparation products of traditional Chinese medicine (I)].

PubMed

Gu, Jun-Fei; Feng, Liang; Zhang, Ming-Hua; Wu, Chan; Jia, Xiao-Bin

2013-11-01

Safety is an important component of the quality control of traditional Chinese medicine (TCM) preparation products, as well as an important guarantee for clinical application. Currently, the quality control of TCMs in Chinese Pharmacopoeia mostly focuses on indicative compounds for TCM efficacy. TCM preparations are associated with multiple links, from raw materials to products, and each procedure may have impacts on the safety of preparation. We make a summary and analysis on the factors impacting safety during the preparation of TCM products, and then expound the important role of the "multi-dimensional structure and process dynamic quality control technology system" in the quality safety of TCM preparations. Because the product quality of TCM preparation is closely related to the safety, the control over safety-related material basis is an important component of the product quality control of TCM preparations. The implementation of the quality control over the dynamic process of TCM preparations from raw materials to products, and the improvement of the TCM quality safety control at the microcosmic level help lay a firm foundation for the development of the modernization process of TCM preparations.

Error control techniques for satellite and space communications

NASA Technical Reports Server (NTRS)

Costello, Daniel J., Jr.

1989-01-01

Two aspects of the work for NASA are examined: the construction of multi-dimensional phase modulation trellis codes and a performance analysis of these codes. A complete list is contained of all the best trellis codes for use with phase modulation. LxMPSK signal constellations are included for M = 4, 8, and 16 and L = 1, 2, 3, and 4. Spectral efficiencies range from 1 bit/channel symbol (equivalent to rate 1/2 coded QPSK) to 3.75 bits/channel symbol (equivalent to 15/16 coded 16-PSK). The parity check polynomials, rotational invariance properties, free distance, path multiplicities, and coding gains are given for all codes. These codes are considered to be the best candidates for implementation of a high speed decoder for satellite transmission. The design of a hardware decoder for one of these codes, viz., the 16-state 3x8-PSK code with free distance 4.0 and coding gain 3.75 dB is discussed. An exhaustive simulation study of the multi-dimensional phase modulation trellis codes is contained. This study was motivated by the fact that coding gains quoted for almost all codes found in literature are in fact only asymptotic coding gains, i.e., the coding gain at very high signal to noise ratios (SNRs) or very low BER. These asymptotic coding gains can be obtained directly from a knowledge of the free distance of the code. On the other hand, real coding gains at BERs in the range of 10(exp -2) to 10(exp -6), where these codes are most likely to operate in a concatenated system, must be done by simulation.

Selective excitation enables assignment of proton resonances and (1)H-(1)H distance measurement in ultrafast magic angle spinning solid state NMR spectroscopy.

PubMed

Zhang, Rongchun; Ramamoorthy, Ayyalusamy

2015-07-21

Remarkable developments in ultrafast magic angle spinning (MAS) solid-state NMR spectroscopy enabled proton-based high-resolution multidimensional experiments on solids. To fully utilize the benefits rendered by proton-based ultrafast MAS experiments, assignment of (1)H resonances becomes absolutely necessary. Herein, we propose an approach to identify different proton peaks by using dipolar-coupled heteronuclei such as (13)C or (15)N. In this method, after the initial preparation of proton magnetization and cross-polarization to (13)C nuclei, transverse magnetization of desired (13)C nuclei is selectively prepared by using DANTE (Delays Alternating with Nutations for Tailored Excitation) sequence and then, it is transferred to bonded protons with a short-contact-time cross polarization. Our experimental results demonstrate that protons bonded to specific (13)C atoms can be identified and overlapping proton peaks can also be assigned. In contrast to the regular 2D HETCOR experiment, only a few 1D experiments are required for the complete assignment of peaks in the proton spectrum. Furthermore, the finite-pulse radio frequency driven recoupling sequence could be incorporated right after the selection of specific proton signals to monitor the intensity buildup for other proton signals. This enables the extraction of (1)H-(1)H distances between different pairs of protons. Therefore, we believe that the proposed method will greatly aid in fast assignment of peaks in proton spectra and will be useful in the development of proton-based multi-dimensional solid-state NMR experiments to study atomic-level resolution structure and dynamics of solids.

gpICA: A Novel Nonlinear ICA Algorithm Using Geometric Linearization

NASA Astrophysics Data System (ADS)

Nguyen, Thang Viet; Patra, Jagdish Chandra; Emmanuel, Sabu

2006-12-01

A new geometric approach for nonlinear independent component analysis (ICA) is presented in this paper. Nonlinear environment is modeled by the popular post nonlinear (PNL) scheme. To eliminate the nonlinearity in the observed signals, a novel linearizing method named as geometric post nonlinear ICA (gpICA) is introduced. Thereafter, a basic linear ICA is applied on these linearized signals to estimate the unknown sources. The proposed method is motivated by the fact that in a multidimensional space, a nonlinear mixture is represented by a nonlinear surface while a linear mixture is represented by a plane, a special form of the surface. Therefore, by geometrically transforming the surface representing a nonlinear mixture into a plane, the mixture can be linearized. Through simulations on different data sets, superior performance of gpICA algorithm has been shown with respect to other algorithms.

Objective measurement of complex multimodal and multidimensional display formats: a common metric for predicting format effectiveness

NASA Astrophysics Data System (ADS)

Marshak, William P.; Darkow, David J.; Wesler, Mary M.; Fix, Edward L.

2000-08-01

Computer-based display designers have more sensory modes and more dimensions within sensory modality with which to encode information in a user interface than ever before. This elaboration of information presentation has made measurement of display/format effectiveness and predicting display/format performance extremely difficult. A multivariate method has been devised which isolates critical information, physically measures its signal strength, and compares it with other elements of the display, which act like background noise. This common Metric relates signal-to-noise ratios (SNRs) within each stimulus dimension, then combines SNRs among display modes, dimensions and cognitive factors can predict display format effectiveness. Examples with their Common Metric assessment and validation in performance will be presented along with the derivation of the metric. Implications of the Common Metric in display design and evaluation will be discussed.

SciSpark's SRDD : A Scientific Resilient Distributed Dataset for Multidimensional Data

NASA Astrophysics Data System (ADS)

Palamuttam, R. S.; Wilson, B. D.; Mogrovejo, R. M.; Whitehall, K. D.; Mattmann, C. A.; McGibbney, L. J.; Ramirez, P.

2015-12-01

Remote sensing data and climate model output are multi-dimensional arrays of massive sizes locked away in heterogeneous file formats (HDF5/4, NetCDF 3/4) and metadata models (HDF-EOS, CF) making it difficult to perform multi-stage, iterative science processing since each stage requires writing and reading data to and from disk. We have developed SciSpark, a robust Big Data framework, that extends ApacheTM Spark for scaling scientific computations. Apache Spark improves the map-reduce implementation in ApacheTM Hadoop for parallel computing on a cluster, by emphasizing in-memory computation, "spilling" to disk only as needed, and relying on lazy evaluation. Central to Spark is the Resilient Distributed Dataset (RDD), an in-memory distributed data structure that extends the functional paradigm provided by the Scala programming language. However, RDDs are ideal for tabular or unstructured data, and not for highly dimensional data. The SciSpark project introduces the Scientific Resilient Distributed Dataset (sRDD), a distributed-computing array structure which supports iterative scientific algorithms for multidimensional data. SciSpark processes data stored in NetCDF and HDF files by partitioning them across time or space and distributing the partitions among a cluster of compute nodes. We show usability and extensibility of SciSpark by implementing distributed algorithms for geospatial operations on large collections of multi-dimensional grids. In particular we address the problem of scaling an automated method for finding Mesoscale Convective Complexes. SciSpark provides a tensor interface to support the pluggability of different matrix libraries. We evaluate performance of the various matrix libraries in distributed pipelines, such as Nd4jTM and BreezeTM. We detail the architecture and design of SciSpark, our efforts to integrate climate science algorithms, parallel ingest and partitioning (sharding) of A-Train satellite observations from model grids. These solutions are encompassed in SciSpark, an open-source software framework for distributed computing on scientific data.

Accessing Multi-Dimensional Images and Data Cubes in the Virtual Observatory

NASA Astrophysics Data System (ADS)

Tody, Douglas; Plante, R. L.; Berriman, G. B.; Cresitello-Dittmar, M.; Good, J.; Graham, M.; Greene, G.; Hanisch, R. J.; Jenness, T.; Lazio, J.; Norris, P.; Pevunova, O.; Rots, A. H.

2014-01-01

Telescopes across the spectrum are routinely producing multi-dimensional images and datasets, such as Doppler velocity cubes, polarization datasets, and time-resolved “movies.” Examples of current telescopes producing such multi-dimensional images include the JVLA, ALMA, and the IFU instruments on large optical and near-infrared wavelength telescopes. In the near future, both the LSST and JWST will also produce such multi-dimensional images routinely. High-energy instruments such as Chandra produce event datasets that are also a form of multi-dimensional data, in effect being a very sparse multi-dimensional image. Ensuring that the data sets produced by these telescopes can be both discovered and accessed by the community is essential and is part of the mission of the Virtual Observatory (VO). The Virtual Astronomical Observatory (VAO, http://www.usvao.org/), in conjunction with its international partners in the International Virtual Observatory Alliance (IVOA), has developed a protocol and an initial demonstration service designed for the publication, discovery, and access of arbitrarily large multi-dimensional images. The protocol describing multi-dimensional images is the Simple Image Access Protocol, version 2, which provides the minimal set of metadata required to characterize a multi-dimensional image for its discovery and access. A companion Image Data Model formally defines the semantics and structure of multi-dimensional images independently of how they are serialized, while providing capabilities such as support for sparse data that are essential to deal effectively with large cubes. A prototype data access service has been deployed and tested, using a suite of multi-dimensional images from a variety of telescopes. The prototype has demonstrated the capability to discover and remotely access multi-dimensional data via standard VO protocols. The prototype informs the specification of a protocol that will be submitted to the IVOA for approval, with an operational data cube service to be delivered in mid-2014. An associated user-installable VO data service framework will provide the capabilities required to publish VO-compatible multi-dimensional images or data cubes.

Developmental trends in the interaction between auditory and linguistic processing.

PubMed

Jerger, S; Pirozzolo, F; Jerger, J; Elizondo, R; Desai, S; Wright, E; Reynosa, R

1993-09-01

The developmental course of multidimensional speech processing was examined in 80 children between 3 and 6 years of age and in 60 adults between 20 and 86 years of age. Processing interactions were assessed with a speeded classification task (Garner, 1974a), which required the subjects to attend selectively to the voice dimension while ignoring the linguistic dimension, and vice versa. The children and adults exhibited both similarities and differences in the patterns of processing dependencies. For all ages, performance for each dimension was slower in the presence of variation in the irrelevant dimension; irrelevant variation in the voice dimension disrupted performance more than irrelevant variation in the linguistic dimension. Trends in the degree of interference, on the other hand, showed significant differences between dimensions as a function of age. Whereas the degree of interference for the voice-dimension-relevant did not show significant age-related change, the degree of interference for the word-dimension-relevant declined significantly with age in a linear as well as a quadratic manner. A major age-related change in the relation between dimensions was that word processing, relative to voice-gender processing, required significantly more time in the children than in the adults. Overall, the developmental course characterizing multidimensional speech processing evidenced more pronounced change when the linguistic dimension, rather than the voice dimension, was relevant.

Will the Conscious-Subconscious Pacing Quagmire Help Elucidate the Mechanisms of Self-Paced Exercise? New Opportunities in Dual Process Theory and Process Tracing Methods.

PubMed

Micklewright, Dominic; Kegerreis, Sue; Raglin, John; Hettinga, Florentina

2017-07-01

The extent to which athletic pacing decisions are made consciously or subconsciously is a prevailing issue. In this article we discuss why the one-dimensional conscious-subconscious debate that has reigned in the pacing literature has suppressed our understanding of the multidimensional processes that occur in pacing decisions. How do we make our decisions in real-life competitive situations? What information do we use and how do we respond to opponents? These are questions that need to be explored and better understood, using smartly designed experiments. The paper provides clarity about key conscious, preconscious, subconscious and unconscious concepts, terms that have previously been used in conflicting and confusing ways. The potential of dual process theory in articulating multidimensional aspects of intuitive and deliberative decision-making processes is discussed in the context of athletic pacing along with associated process-tracing research methods. In attempting to refine pacing models and improve training strategies and psychological skills for athletes, the dual-process framework could be used to gain a clearer understanding of (1) the situational conditions for which either intuitive or deliberative decisions are optimal; (2) how intuitive and deliberative decisions are biased by things such as perception, emotion and experience; and (3) the underlying cognitive mechanisms such as memory, attention allocation, problem solving and hypothetical thought.

High-Dimensional Brain: A Tool for Encoding and Rapid Learning of Memories by Single Neurons.

PubMed

Tyukin, Ivan; Gorban, Alexander N; Calvo, Carlos; Makarova, Julia; Makarov, Valeri A

2018-03-19

Codifying memories is one of the fundamental problems of modern Neuroscience. The functional mechanisms behind this phenomenon remain largely unknown. Experimental evidence suggests that some of the memory functions are performed by stratified brain structures such as the hippocampus. In this particular case, single neurons in the CA1 region receive a highly multidimensional input from the CA3 area, which is a hub for information processing. We thus assess the implication of the abundance of neuronal signalling routes converging onto single cells on the information processing. We show that single neurons can selectively detect and learn arbitrary information items, given that they operate in high dimensions. The argument is based on stochastic separation theorems and the concentration of measure phenomena. We demonstrate that a simple enough functional neuronal model is capable of explaining: (i) the extreme selectivity of single neurons to the information content, (ii) simultaneous separation of several uncorrelated stimuli or informational items from a large set, and (iii) dynamic learning of new items by associating them with already "known" ones. These results constitute a basis for organization of complex memories in ensembles of single neurons. Moreover, they show that no a priori assumptions on the structural organization of neuronal ensembles are necessary for explaining basic concepts of static and dynamic memories.

Multidimensional Unfolding by Nonmetric Multidimensional Scaling of Spearman Distances in the Extended Permutation Polytope

ERIC Educational Resources Information Center

Van Deun, Katrijn; Heiser, Willem J.; Delbeke, Luc

2007-01-01

A multidimensional unfolding technique that is not prone to degenerate solutions and is based on multidimensional scaling of a complete data matrix is proposed: distance information about the unfolding data and about the distances both among judges and among objects is included in the complete matrix. The latter information is derived from the…

Elucidation of chemical reactions by two-dimensional resonance Raman spectroscopy

NASA Astrophysics Data System (ADS)

Molesky, Brian Paul

It has been shown for many systems, including photosynthetic complexes, molecule-semiconductor interfaces, and bulk heterojunctions, that interaction between electronic and nuclear dynamics may heavily influence chemical mechanisms. Four-wave-mixing spectroscopies (i.e. transient absorption, two-dimensional spectroscopy) provide some insight into such non-equilibrium processes but are limited by the single "population time" available in these types of experiments. In this dissertation, two-dimensional resonance Raman spectroscopy (2DRR) is developed to obtain new information regarding chemical reactions that possess time coincident electronic and nuclear evolution. These new insights can only be acquired through higher-order techniques possessing two "population times". Specifically, the coherent reaction mechanism in triiodide photodissociation and structural heterogeneity in myoglobin are investigated. All multidimensional spectroscopies have roots in the off-resonant multidimensional Raman techniques developed from the late 1980's to the early 2000's. Throughout their development these experiments were plagued with technical challenges that eventually halted further use. In this dissertation it is shown through rigorous experimental tests that the technical challenges of the past are obviated for 2DRR, which is done under electronically resonant conditions. The key is that under electronic resonance the harmonic character of vibrational modes contributes to the signal. Under off-resonant conditions signal generation depends on much weaker effects. Upon absorption of light ranging from 250 to 500 nm triiodide photodissociates into diiodide and radical iodine on the same time scale as the period of triiodide's symmetric stretch, impulsively initiating coherence in the stretching coordinate of diiodide. In this dissertation, the sensitivity of 2DRR to coherent reaction mechanisms is shown by directly measuring, for the first time, how the nonequilibrium geometry of triiodide at the moment of photodissociation determines the stretching frequency of diiodide. The functions of heme proteins involve ligand binding and dissociation events, which are facilitated by the fast exchange of energy between the heme and aqueous solvent. It is known that the heme's propionic acid side chains act as an effective "gateway" for this fast energy exchange. In this dissertation it is shown that the propionic chains within myoglobin posses significant structural heterogeneity, suggesting that this may be an important factor in facilitating the functions of heme proteins.

Attention, in and Out: Scalp-Level and Intracranial EEG Correlates of Interoception and Exteroception

PubMed Central

García-Cordero, Indira; Esteves, Sol; Mikulan, Ezequiel P.; Hesse, Eugenia; Baglivo, Fabricio H.; Silva, Walter; García, María del Carmen; Vaucheret, Esteban; Ciraolo, Carlos; García, Hernando S.; Adolfi, Federico; Pietto, Marcos; Herrera, Eduar; Legaz, Agustina; Manes, Facundo; García, Adolfo M.; Sigman, Mariano; Bekinschtein, Tristán A.; Ibáñez, Agustín; Sedeño, Lucas

2017-01-01

Interoception, the monitoring of visceral signals, is often presumed to engage attentional mechanisms specifically devoted to inner bodily sensing. In fact, most standardized interoceptive tasks require directing attention to internal signals. However, most studies in the field have failed to compare attentional modulations between internally- and externally-driven processes, thus probing blind to the specificity of the former. Here we address this issue through a multidimensional approach combining behavioral measures, analyses of event-related potentials and functional connectivity via high-density electroencephalography, and intracranial recordings. In Study 1, 50 healthy volunteers performed a heartbeat detection task as we recorded modulations of the heartbeat-evoked potential (HEP) in three conditions: exteroception, basal interoception (also termed interoceptive accuracy), and post-feedback interoception (sometimes called interoceptive learning). In Study 2, to evaluate whether key interoceptive areas (posterior insula, inferior frontal gyrus, amygdala, and somatosensory cortex) were differentially modulated by externally- and internally-driven processes, we analyzed human intracranial recordings with depth electrodes in these regions. This unique technique provides a very fine grained spatio-temporal resolution compared to other techniques, such as EEG or fMRI. We found that both interoceptive conditions in Study 1 yielded greater HEP amplitudes than the exteroceptive one. In addition, connectivity analysis showed that post-feedback interoception, relative to basal interoception, involved enhanced long-distance connections linking frontal and posterior regions. Moreover, results from Study 2 showed a differentiation between oscillations during basal interoception (broadband: 35–110 Hz) and exteroception (1–35 Hz) in the insula, the amygdala, the somatosensory cortex, and the inferior frontal gyrus. In sum, this work provides convergent evidence for the specificity and dynamics of attentional mechanisms involved in interoception. PMID:28769749

Attention, in and Out: Scalp-Level and Intracranial EEG Correlates of Interoception and Exteroception.

PubMed

García-Cordero, Indira; Esteves, Sol; Mikulan, Ezequiel P; Hesse, Eugenia; Baglivo, Fabricio H; Silva, Walter; García, María Del Carmen; Vaucheret, Esteban; Ciraolo, Carlos; García, Hernando S; Adolfi, Federico; Pietto, Marcos; Herrera, Eduar; Legaz, Agustina; Manes, Facundo; García, Adolfo M; Sigman, Mariano; Bekinschtein, Tristán A; Ibáñez, Agustín; Sedeño, Lucas

2017-01-01

Interoception, the monitoring of visceral signals, is often presumed to engage attentional mechanisms specifically devoted to inner bodily sensing. In fact, most standardized interoceptive tasks require directing attention to internal signals. However, most studies in the field have failed to compare attentional modulations between internally- and externally-driven processes, thus probing blind to the specificity of the former. Here we address this issue through a multidimensional approach combining behavioral measures, analyses of event-related potentials and functional connectivity via high-density electroencephalography, and intracranial recordings. In Study 1, 50 healthy volunteers performed a heartbeat detection task as we recorded modulations of the heartbeat-evoked potential (HEP) in three conditions: exteroception, basal interoception (also termed interoceptive accuracy), and post-feedback interoception (sometimes called interoceptive learning). In Study 2, to evaluate whether key interoceptive areas (posterior insula, inferior frontal gyrus, amygdala, and somatosensory cortex) were differentially modulated by externally- and internally-driven processes, we analyzed human intracranial recordings with depth electrodes in these regions. This unique technique provides a very fine grained spatio-temporal resolution compared to other techniques, such as EEG or fMRI. We found that both interoceptive conditions in Study 1 yielded greater HEP amplitudes than the exteroceptive one. In addition, connectivity analysis showed that post-feedback interoception, relative to basal interoception, involved enhanced long-distance connections linking frontal and posterior regions. Moreover, results from Study 2 showed a differentiation between oscillations during basal interoception (broadband: 35-110 Hz) and exteroception (1-35 Hz) in the insula, the amygdala, the somatosensory cortex, and the inferior frontal gyrus. In sum, this work provides convergent evidence for the specificity and dynamics of attentional mechanisms involved in interoception.

InFlo: a novel systems biology framework identifies cAMP-CREB1 axis as a key modulator of platinum resistance in ovarian cancer.

PubMed

Dimitrova, N; Nagaraj, A B; Razi, A; Singh, S; Kamalakaran, S; Banerjee, N; Joseph, P; Mankovich, A; Mittal, P; DiFeo, A; Varadan, V

2017-04-27

Characterizing the complex interplay of cellular processes in cancer would enable the discovery of key mechanisms underlying its development and progression. Published approaches to decipher driver mechanisms do not explicitly model tissue-specific changes in pathway networks and the regulatory disruptions related to genomic aberrations in cancers. We therefore developed InFlo, a novel systems biology approach for characterizing complex biological processes using a unique multidimensional framework integrating transcriptomic, genomic and/or epigenomic profiles for any given cancer sample. We show that InFlo robustly characterizes tissue-specific differences in activities of signalling networks on a genome scale using unique probabilistic models of molecular interactions on a per-sample basis. Using large-scale multi-omics cancer datasets, we show that InFlo exhibits higher sensitivity and specificity in detecting pathway networks associated with specific disease states when compared to published pathway network modelling approaches. Furthermore, InFlo's ability to infer the activity of unmeasured signalling network components was also validated using orthogonal gene expression signatures. We then evaluated multi-omics profiles of primary high-grade serous ovarian cancer tumours (N=357) to delineate mechanisms underlying resistance to frontline platinum-based chemotherapy. InFlo was the only algorithm to identify hyperactivation of the cAMP-CREB1 axis as a key mechanism associated with resistance to platinum-based therapy, a finding that we subsequently experimentally validated. We confirmed that inhibition of CREB1 phosphorylation potently sensitized resistant cells to platinum therapy and was effective in killing ovarian cancer stem cells that contribute to both platinum-resistance and tumour recurrence. Thus, we propose InFlo to be a scalable and widely applicable and robust integrative network modelling framework for the discovery of evidence-based biomarkers and therapeutic targets.

Biologically-Inspired Spike-Based Automatic Speech Recognition of Isolated Digits Over a Reproducing Kernel Hilbert Space

PubMed Central

Li, Kan; Príncipe, José C.

2018-01-01

This paper presents a novel real-time dynamic framework for quantifying time-series structure in spoken words using spikes. Audio signals are converted into multi-channel spike trains using a biologically-inspired leaky integrate-and-fire (LIF) spike generator. These spike trains are mapped into a function space of infinite dimension, i.e., a Reproducing Kernel Hilbert Space (RKHS) using point-process kernels, where a state-space model learns the dynamics of the multidimensional spike input using gradient descent learning. This kernelized recurrent system is very parsimonious and achieves the necessary memory depth via feedback of its internal states when trained discriminatively, utilizing the full context of the phoneme sequence. A main advantage of modeling nonlinear dynamics using state-space trajectories in the RKHS is that it imposes no restriction on the relationship between the exogenous input and its internal state. We are free to choose the input representation with an appropriate kernel, and changing the kernel does not impact the system nor the learning algorithm. Moreover, we show that this novel framework can outperform both traditional hidden Markov model (HMM) speech processing as well as neuromorphic implementations based on spiking neural network (SNN), yielding accurate and ultra-low power word spotters. As a proof of concept, we demonstrate its capabilities using the benchmark TI-46 digit corpus for isolated-word automatic speech recognition (ASR) or keyword spotting. Compared to HMM using Mel-frequency cepstral coefficient (MFCC) front-end without time-derivatives, our MFCC-KAARMA offered improved performance. For spike-train front-end, spike-KAARMA also outperformed state-of-the-art SNN solutions. Furthermore, compared to MFCCs, spike trains provided enhanced noise robustness in certain low signal-to-noise ratio (SNR) regime. PMID:29666568

Biologically-Inspired Spike-Based Automatic Speech Recognition of Isolated Digits Over a Reproducing Kernel Hilbert Space.

PubMed

Li, Kan; Príncipe, José C

2018-01-01

This paper presents a novel real-time dynamic framework for quantifying time-series structure in spoken words using spikes. Audio signals are converted into multi-channel spike trains using a biologically-inspired leaky integrate-and-fire (LIF) spike generator. These spike trains are mapped into a function space of infinite dimension, i.e., a Reproducing Kernel Hilbert Space (RKHS) using point-process kernels, where a state-space model learns the dynamics of the multidimensional spike input using gradient descent learning. This kernelized recurrent system is very parsimonious and achieves the necessary memory depth via feedback of its internal states when trained discriminatively, utilizing the full context of the phoneme sequence. A main advantage of modeling nonlinear dynamics using state-space trajectories in the RKHS is that it imposes no restriction on the relationship between the exogenous input and its internal state. We are free to choose the input representation with an appropriate kernel, and changing the kernel does not impact the system nor the learning algorithm. Moreover, we show that this novel framework can outperform both traditional hidden Markov model (HMM) speech processing as well as neuromorphic implementations based on spiking neural network (SNN), yielding accurate and ultra-low power word spotters. As a proof of concept, we demonstrate its capabilities using the benchmark TI-46 digit corpus for isolated-word automatic speech recognition (ASR) or keyword spotting. Compared to HMM using Mel-frequency cepstral coefficient (MFCC) front-end without time-derivatives, our MFCC-KAARMA offered improved performance. For spike-train front-end, spike-KAARMA also outperformed state-of-the-art SNN solutions. Furthermore, compared to MFCCs, spike trains provided enhanced noise robustness in certain low signal-to-noise ratio (SNR) regime.

Trends in data processing of comprehensive two-dimensional chromatography: state of the art.

PubMed

Matos, João T V; Duarte, Regina M B O; Duarte, Armando C

2012-12-01

The operation of advanced chromatographic systems, namely comprehensive two-dimensional (2D) chromatography coupled to multidimensional detectors, allows achieving a great deal of data that need special care to be processed in order to characterize and quantify as much as possible the analytes under study. The aim of this review is to identify the main trends, research needs and gaps on the techniques for data processing of multidimensional data sets obtained from comprehensive 2D chromatography. The following topics have been identified as the most promising for new developments in the near future: data acquisition and handling, peak detection and quantification, measurement of overlapping of 2D peaks, and data analysis software for 2D chromatography. The rational supporting most of the data processing techniques is based on the generalization of one-dimensional (1D) chromatography although algorithms, such as the inverted watershed algorithm, use the 2D chromatographic data as such. However, for processing more complex N-way data there is a need for using more sophisticated techniques. Apart from using other concepts from 1D chromatography, which have not been tested for 2D chromatography, there is still room for new improvements and developments in algorithms and software for dealing with 2D comprehensive chromatographic data. Copyright © 2012 Elsevier B.V. All rights reserved.

3D Printed Bionic Ears

PubMed Central

Mannoor, Manu S.; Jiang, Ziwen; James, Teena; Kong, Yong Lin; Malatesta, Karen A.; Soboyejo, Winston O.; Verma, Naveen; Gracias, David H.; McAlpine, Michael C.

2013-01-01

The ability to three-dimensionally interweave biological tissue with functional electronics could enable the creation of bionic organs possessing enhanced functionalities over their human counterparts. Conventional electronic devices are inherently two-dimensional, preventing seamless multidimensional integration with synthetic biology, as the processes and materials are very different. Here, we present a novel strategy for overcoming these difficulties via additive manufacturing of biological cells with structural and nanoparticle derived electronic elements. As a proof of concept, we generated a bionic ear via 3D printing of a cell-seeded hydrogel matrix in the precise anatomic geometry of a human ear, along with an intertwined conducting polymer consisting of infused silver nanoparticles. This allowed for in vitro culturing of cartilage tissue around an inductive coil antenna in the ear, which subsequently enables readout of inductively-coupled signals from cochlea-shaped electrodes. The printed ear exhibits enhanced auditory sensing for radio frequency reception, and complementary left and right ears can listen to stereo audio music. Overall, our approach suggests a means to intricately merge biologic and nanoelectronic functionalities via 3D printing. PMID:23635097

The Racial and Ethnic Identity Formation Process of Second-Generation Asian Indian Americans: A Phenomenological Study.

PubMed

Iwamoto, Derek Kenji; Negi, Nalini Junko; Partiali, Rachel Negar; Creswell, John W

2013-10-01

This phenomenological study elucidates the identity development processes of 12 second-generation adult Asian Indian Americans. The results identify salient sociocultural factors and multidimensional processes of racial and ethnic identity development. Discrimination, parental, and community factors seemed to play a salient role in influencing participants' racial and ethnic identity development. The emergent Asian Indian American racial and ethnic identity model provides a contextualized overview of key developmental periods and turning points within the process of identity development.

The Racial and Ethnic Identity Formation Process of Second-Generation Asian Indian Americans: A Phenomenological Study

PubMed Central

Iwamoto, Derek Kenji; Negi, Nalini Junko; Partiali, Rachel Negar; Creswell, John W.

2014-01-01

This phenomenological study elucidates the identity development processes of 12 second-generation adult Asian Indian Americans. The results identify salient sociocultural factors and multidimensional processes of racial and ethnic identity development. Discrimination, parental, and community factors seemed to play a salient role in influencing participants’ racial and ethnic identity development. The emergent Asian Indian American racial and ethnic identity model provides a contextualized overview of key developmental periods and turning points within the process of identity development. PMID:25298617

Impact of the International Nosocomial Infection Control Consortium (INICC) Multidimensional Hand Hygiene Approach in five intensive care units in three cities of China.

PubMed

Su, D; Hu, B; Rosenthal, V D; Li, R; Hao, C; Pan, W; Tao, L; Gao, X; Liu, K

2015-07-01

To evaluate the impact of the International Nosocomial Infection Control Consortium (INICC) Multidimensional Hand Hygiene (HH) Approach in three hospitals in three cities of China, and analyze predictors of poor hand hygiene compliance. A prospective before-after study from May 2009 to December 2010 in five intensive care units members of the INICC in China. The study was divided into two periods: a 3-month baseline period and a follow-up period. A Multidimensional HH Approach was implemented, which included the following elements: 1- administrative support, 2- supplies availability, 3- education and training, 4- reminders in the workplace, 5- process surveillance and 6- performance feedback. Observations were done for HH compliance in each ICU, during randomly selected 30-min periods. A total of 2079 opportunities for HH were recorded. Overall HH compliance increased from 51.5% to 80.1% (95% CI 73.2-87.8; P = 0.004). Multivariate analysis indicated that several variables were significantly associated with poor HH compliance: females vs males (64% vs 55%; 95% CI 0.81-0.94; P = 0.0005), nurses vs physicians (64% vs 57%, P = 0.004), among others. Adherence to HH was increased significantly with the INICC multidimensional approach. Specific programs directed to improve HH in variables found to be predictors of poor HH compliance should be implemented. Copyright © 2015 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Changes in frontal plane dynamics and the loading response phase of the gait cycle are characteristic of severe knee osteoarthritis application of a multidimensional analysis technique.

PubMed

Astephen, J L; Deluzio, K J

2005-02-01

Osteoarthritis of the knee is related to many correlated mechanical factors that can be measured with gait analysis. Gait analysis results in large data sets. The analysis of these data is difficult due to the correlated, multidimensional nature of the measures. A multidimensional model that uses two multivariate statistical techniques, principal component analysis and discriminant analysis, was used to discriminate between the gait patterns of the normal subject group and the osteoarthritis subject group. Nine time varying gait measures and eight discrete measures were included in the analysis. All interrelationships between and within the measures were retained in the analysis. The multidimensional analysis technique successfully separated the gait patterns of normal and knee osteoarthritis subjects with a misclassification error rate of <6%. The most discriminatory feature described a static and dynamic alignment factor. The second most discriminatory feature described a gait pattern change during the loading response phase of the gait cycle. The interrelationships between gait measures and between the time instants of the gait cycle can provide insight into the mechanical mechanisms of pathologies such as knee osteoarthritis. These results suggest that changes in frontal plane loading and alignment and the loading response phase of the gait cycle are characteristic of severe knee osteoarthritis gait patterns. Subsequent investigations earlier in the disease process may suggest the importance of these factors to the progression of knee osteoarthritis.

SPECT3D - A multi-dimensional collisional-radiative code for generating diagnostic signatures based on hydrodynamics and PIC simulation output

NASA Astrophysics Data System (ADS)

MacFarlane, J. J.; Golovkin, I. E.; Wang, P.; Woodruff, P. R.; Pereyra, N. A.

2007-05-01

SPECT3D is a multi-dimensional collisional-radiative code used to post-process the output from radiation-hydrodynamics (RH) and particle-in-cell (PIC) codes to generate diagnostic signatures (e.g. images, spectra) that can be compared directly with experimental measurements. This ability to post-process simulation code output plays a pivotal role in assessing the reliability of RH and PIC simulation codes and their physics models. SPECT3D has the capability to operate on plasmas in 1D, 2D, and 3D geometries. It computes a variety of diagnostic signatures that can be compared with experimental measurements, including: time-resolved and time-integrated spectra, space-resolved spectra and streaked spectra; filtered and monochromatic images; and X-ray diode signals. Simulated images and spectra can include the effects of backlighters, as well as the effects of instrumental broadening and time-gating. SPECT3D also includes a drilldown capability that shows where frequency-dependent radiation is emitted and absorbed as it propagates through the plasma towards the detector, thereby providing insights on where the radiation seen by a detector originates within the plasma. SPECT3D has the capability to model a variety of complex atomic and radiative processes that affect the radiation seen by imaging and spectral detectors in high energy density physics (HEDP) experiments. LTE (local thermodynamic equilibrium) or non-LTE atomic level populations can be computed for plasmas. Photoabsorption rates can be computed using either escape probability models or, for selected 1D and 2D geometries, multi-angle radiative transfer models. The effects of non-thermal (i.e. non-Maxwellian) electron distributions can also be included. To study the influence of energetic particles on spectra and images recorded in intense short-pulse laser experiments, the effects of both relativistic electrons and energetic proton beams can be simulated. SPECT3D is a user-friendly software package that runs on Windows, Linux, and Mac platforms. A parallel version of SPECT3D is supported for Linux clusters for large-scale calculations. We will discuss the major features of SPECT3D, and present example results from simulations and comparisons with experimental data.

Lab-X-ray multidimensional imaging of processes inside porous media

NASA Astrophysics Data System (ADS)

Godinho, Jose

2017-04-01

Time-lapse and other multidimensional X-ray imaging techniques have mostly been applied using synchrotron radiation, which limits accessibility and complicates data analysis. Here, we present new time-lapse imaging approaches using laboratory X-ray computed microtomography (CT) to study transformations inside porous media. Specifically, three methods will be presented: 1) Quantitative time-lapse radiography to study sub-second processes. For example to study the penetration of particles into fractures and pores, which is essential to understand how proppants keep fractures opened during hydraulic fracturing and how filter cakes form during borehole drilling. 2) Combination of time-lapse CT with diffraction tomography to study the transformation between bio-inspired polymorphs in 6D, e.g. mineral phase transformation between ACC, Vaterite and Calcite - CaCO3, and between ACS, Anhydrite and Gypsum - CaSO4. Crystals can be resolved in nanopores down to 7 nm (over 100 times smaller than the resolution of CT), which allows studying the effect of confinement on phase stability and growth rates. 3) Fast iterative helical micro-CT scanning to study samples of high ratio height to width (e.g. long cores) with optimal resolution. Here we show how this can be useful to study the distribution of the products from fluid-mediated mineral reactions throughout longer reaction paths and more representative volumes. Using state of the art reconstruction algorithms allows reducing the scanning times from over ten hours to below two hours enabling time-lapse studies. It is expected that these new techniques will open new possibilities for time-lapse imaging of a wider range of geological processes using laboratory X-ray CT, thereby increasing the accessibility of multidimensional imaging to a larger number of users and applications in geology.

On the complex quantification of risk: systems-based perspective on terrorism.

PubMed

Haimes, Yacov Y

2011-08-01

This article highlights the complexity of the quantification of the multidimensional risk function, develops five systems-based premises on quantifying the risk of terrorism to a threatened system, and advocates the quantification of vulnerability and resilience through the states of the system. The five premises are: (i) There exists interdependence between a specific threat to a system by terrorist networks and the states of the targeted system, as represented through the system's vulnerability, resilience, and criticality-impact. (ii) A specific threat, its probability, its timing, the states of the targeted system, and the probability of consequences can be interdependent. (iii) The two questions in the risk assessment process: "What is the likelihood?" and "What are the consequences?" can be interdependent. (iv) Risk management policy options can reduce both the likelihood of a threat to a targeted system and the associated likelihood of consequences by changing the states (including both vulnerability and resilience) of the system. (v) The quantification of risk to a vulnerable system from a specific threat must be built on a systemic and repeatable modeling process, by recognizing that the states of the system constitute an essential step to construct quantitative metrics of the consequences based on intelligence gathering, expert evidence, and other qualitative information. The fact that the states of all systems are functions of time (among other variables) makes the time frame pivotal in each component of the process of risk assessment, management, and communication. Thus, risk to a system, caused by an initiating event (e.g., a threat) is a multidimensional function of the specific threat, its probability and time frame, the states of the system (representing vulnerability and resilience), and the probabilistic multidimensional consequences. © 2011 Society for Risk Analysis.

A cross-diffusion system derived from a Fokker-Planck equation with partial averaging

NASA Astrophysics Data System (ADS)

Jüngel, Ansgar; Zamponi, Nicola

2017-02-01

A cross-diffusion system for two components with a Laplacian structure is analyzed on the multi-dimensional torus. This system, which was recently suggested by P.-L. Lions, is formally derived from a Fokker-Planck equation for the probability density associated with a multi-dimensional Itō process, assuming that the diffusion coefficients depend on partial averages of the probability density with exponential weights. A main feature is that the diffusion matrix of the limiting cross-diffusion system is generally neither symmetric nor positive definite, but its structure allows for the use of entropy methods. The global-in-time existence of positive weak solutions is proved and, under a simplifying assumption, the large-time asymptotics is investigated.

Trajectory design for Saturnian Ocean Worlds orbiters using multidimensional Poincaré maps

NASA Astrophysics Data System (ADS)

Davis, Diane Craig; Phillips, Sean M.; McCarthy, Brian P.

2018-02-01

Missions based on low-energy orbits in the vicinity of planetary moons, such as Titan or Enceladus, involve significant end-to-end trajectory design challenges due to the gravitational effects of the distant larger primary. To address these challenges, the current investigation focuses on the visualization and use of multidimensional Poincaré maps to perform preliminary design of orbits with significant out-of-plane components, including orbits that provide polar coverage. Poincaré maps facilitate the identification of families of solutions to a given orbit problem and provide the ability to easily respond to changing inputs and requirements. A visual-based design process highlights a variety of trajectory options near Saturn's ocean worlds, including both moon-centered orbits and libration point orbits.

Multidimensional Treatment Foster Care as a Preventive Intervention to Promote Resiliency Among Youth in the Child Welfare System

PubMed Central

Leve, Leslie D.; Fisher, Philip A.; Chamberlain, Patricia

2009-01-01

Demographic trends indicate that a growing segment of families is exposed to adversity such as poverty, drug use problems, caregiver transitions, and domestic violence. Although these risk processes and the accompanying poor outcomes for children have been well-studied, little is known about why some children develop resilience in the face of such adversity, particularly when it is severe enough to invoke child welfare involvement. This paper describes a program of research involving families in the child welfare system. Using a resiliency framework, evidence from four randomized clinical trials that included components of the Multidimensional Treatment Foster Care program is presented. Future directions and next steps are proposed. PMID:19807861

A novel method for the line-of-response and time-of-flight reconstruction in TOF-PET detectors based on a library of synchronized model signals

NASA Astrophysics Data System (ADS)

Moskal, P.; Zoń, N.; Bednarski, T.; Białas, P.; Czerwiński, E.; Gajos, A.; Kamińska, D.; Kapłon, Ł.; Kochanowski, A.; Korcyl, G.; Kowal, J.; Kowalski, P.; Kozik, T.; Krzemień, W.; Kubicz, E.; Niedźwiecki, Sz.; Pałka, M.; Raczyński, L.; Rudy, Z.; Rundel, O.; Salabura, P.; Sharma, N. G.; Silarski, M.; Słomski, A.; Smyrski, J.; Strzelecki, A.; Wieczorek, A.; Wiślicki, W.; Zieliński, M.

2015-03-01

A novel method of hit time and hit position reconstruction in scintillator detectors is described. The method is based on comparison of detector signals with results stored in a library of synchronized model signals registered for a set of well-defined positions of scintillation points. The hit position is reconstructed as the one corresponding to the signal from the library which is most similar to the measurement signal. The time of the interaction is determined as a relative time between the measured signal and the most similar one in the library. A degree of similarity of measured and model signals is defined as the distance between points representing the measurement- and model-signal in the multi-dimensional measurement space. Novelty of the method lies also in the proposed way of synchronization of model signals enabling direct determination of the difference between time-of-flights (TOF) of annihilation quanta from the annihilation point to the detectors. The introduced method was validated using experimental data obtained by means of the double strip prototype of the J-PET detector and 22Na sodium isotope as a source of annihilation gamma quanta. The detector was built out from plastic scintillator strips with dimensions of 5 mm×19 mm×300 mm, optically connected at both sides to photomultipliers, from which signals were sampled by means of the Serial Data Analyzer. Using the introduced method, the spatial and TOF resolution of about 1.3 cm (σ) and 125 ps (σ) were established, respectively.

Rethinking the Concept of Acculturation: Implications for Theory and Research

ERIC Educational Resources Information Center

Schwartz, Seth J.; Unger, Jennifer B.; Zamboanga, Byron L.; Szapocznik, Jose

2010-01-01

This article presents an expanded model of acculturation among international migrants and their immediate descendants. Acculturation is proposed as a multidimensional process consisting of the confluence among heritage-cultural and receiving-cultural practices, values, and identifications. The implications of this reconceptualization for the…

A Flexible Modeling Framework For Hydraulic and Water Quality Performance Assessment of Stormwater Green Infrastructure

EPA Science Inventory

A flexible framework has been created for modeling multi-dimensional hydrological and water quality processes within stormwater green infrastructures (GIs). The framework models a GI system using a set of blocks (spatial features) and connectors (interfaces) representing differen...

Community Integration, Local Media Use, and Democratic Processes.

ERIC Educational Resources Information Center

McLeod, Jack M.; And Others

1996-01-01

Explicates the concept of community integration and its dimensions. Specifies structural and media antecedents and political consequences of these dimensions. Uses 15 indicators to test the hypothesis that integration is a multidimensional concept. Reveals that community integration has at least five dimensions: psychological attachment,…

The Diagonal Model of Job Satisfaction and Motivation: Extracted from the Logical Comparison of Content and Process Theories

ERIC Educational Resources Information Center

Sahito, Zafarullah; Vaisanen, Pertti

2017-01-01

The purpose of this study is to explore the strongest areas of all prime theories of job satisfaction and motivation to create a new multidimensional model. This model relies on all explored areas from the logical comparison of content and process theories to understand the phenomenon of job satisfaction and motivation of employees. The model…

Continuum and discrete approach in modeling biofilm development and structure: a review.

PubMed

Mattei, M R; Frunzo, L; D'Acunto, B; Pechaud, Y; Pirozzi, F; Esposito, G

2018-03-01

The scientific community has recognized that almost 99% of the microbial life on earth is represented by biofilms. Considering the impacts of their sessile lifestyle on both natural and human activities, extensive experimental activity has been carried out to understand how biofilms grow and interact with the environment. Many mathematical models have also been developed to simulate and elucidate the main processes characterizing the biofilm growth. Two main mathematical approaches for biomass representation can be distinguished: continuum and discrete. This review is aimed at exploring the main characteristics of each approach. Continuum models can simulate the biofilm processes in a quantitative and deterministic way. However, they require a multidimensional formulation to take into account the biofilm spatial heterogeneity, which makes the models quite complicated, requiring significant computational effort. Discrete models are more recent and can represent the typical multidimensional structural heterogeneity of biofilm reflecting the experimental expectations, but they generate computational results including elements of randomness and introduce stochastic effects into the solutions.

Development of Numerical Tools for the Investigation of Plasma Detachment from Magnetic Nozzles

NASA Technical Reports Server (NTRS)

Sankaran, Kamesh; Polzin, Kurt A.

2007-01-01

A multidimensional numerical simulation framework aimed at investigating the process of plasma detachment from a magnetic nozzle is introduced. An existing numerical code based on a magnetohydrodynamic formulation of the plasma flow equations that accounts for various dispersive and dissipative processes in plasmas was significantly enhanced to allow for the modeling of axisymmetric domains containing three.dimensiunai momentum and magnetic flux vectors. A separate magnetostatic solver was used to simulate the applied magnetic field topologies found in various nozzle experiments. Numerical results from a magnetic diffusion test problem in which all three components of the magnetic field were present exhibit excellent quantitative agreement with the analytical solution, and the lack of numerical instabilities due to fluctuations in the value of del(raised dot)B indicate that the conservative MHD framework with dissipative effects is well-suited for multi-dimensional analysis of magnetic nozzles. Further studies will focus on modeling literature experiments both for the purpose of code validation and to extract physical insight regarding the mechanisms driving detachment.

Neuroscience of drug craving for addiction medicine: From circuits to therapies.

PubMed

Ekhtiari, Hamed; Nasseri, Padideh; Yavari, Fatemeh; Mokri, Azarkhsh; Monterosso, John

2016-01-01

Drug craving is a dynamic neurocognitive emotional-motivational response to a wide range of cues, from internal to external environments and from drug-related to stressful or affective events. The subjective feeling of craving, as an appetitive or compulsive state, could be considered a part of this multidimensional process, with modules in different levels of consciousness and embodiment. The neural correspondence of this dynamic and complex phenomenon may be productively investigated in relation to regional, small-scale networks, large-scale networks, and brain states. Within cognitive neuroscience, this approach has provided a long list of neural and cognitive targets for craving modulations with different cognitive, electrical, or pharmacological interventions. There are new opportunities to integrate different approaches for carving management from environmental, behavioral, psychosocial, cognitive, and neural perspectives. By using cognitive neuroscience models that treat drug craving as a dynamic and multidimensional process, these approaches may yield more effective interventions for addiction medicine. © 2016 Elsevier B.V. All rights reserved.

High sensitive vectorial B-probe for low frequency plasma waves.

PubMed

Ullrich, Stefan; Grulke, Olaf; Klinger, Thomas; Rahbarnia, Kian

2013-11-01

A miniaturized multidimensional magnetic probe is developed for application in a low-temperature plasma environment. A very high sensitivity for low-frequency magnetic field fluctuations with constant phase run, a very good signal-to-noise ratio combined with an efficient electrostatic pickup rejection, renders the probe superior compared with any commercial solution. A two-step calibration allows for absolute measurement of amplitude and direction of magnetic field fluctuations. The excellent probe performance is demonstrated by measurements of the parallel current pattern of coherent electrostatic drift wave modes in the VINETA (versatile instrument for studies on nonlinearity, electromagnetism, turbulence, and applications) experiment.

Automatic construction of a recurrent neural network based classifier for vehicle passage detection

NASA Astrophysics Data System (ADS)

Burnaev, Evgeny; Koptelov, Ivan; Novikov, German; Khanipov, Timur

2017-03-01

Recurrent Neural Networks (RNNs) are extensively used for time-series modeling and prediction. We propose an approach for automatic construction of a binary classifier based on Long Short-Term Memory RNNs (LSTM-RNNs) for detection of a vehicle passage through a checkpoint. As an input to the classifier we use multidimensional signals of various sensors that are installed on the checkpoint. Obtained results demonstrate that the previous approach to handcrafting a classifier, consisting of a set of deterministic rules, can be successfully replaced by an automatic RNN training on an appropriately labelled data.

CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets

PubMed Central

Nowicka, Malgorzata; Krieg, Carsten; Weber, Lukas M.; Hartmann, Felix J.; Guglietta, Silvia; Becher, Burkhard; Levesque, Mitchell P.; Robinson, Mark D.

2017-01-01

High dimensional mass and flow cytometry (HDCyto) experiments have become a method of choice for high throughput interrogation and characterization of cell populations.Here, we present an R-based pipeline for differential analyses of HDCyto data, largely based on Bioconductor packages. We computationally define cell populations using FlowSOM clustering, and facilitate an optional but reproducible strategy for manual merging of algorithm-generated clusters. Our workflow offers different analysis paths, including association of cell type abundance with a phenotype or changes in signaling markers within specific subpopulations, or differential analyses of aggregated signals. Importantly, the differential analyses we show are based on regression frameworks where the HDCyto data is the response; thus, we are able to model arbitrary experimental designs, such as those with batch effects, paired designs and so on. In particular, we apply generalized linear mixed models to analyses of cell population abundance or cell-population-specific analyses of signaling markers, allowing overdispersion in cell count or aggregated signals across samples to be appropriately modeled. To support the formal statistical analyses, we encourage exploratory data analysis at every step, including quality control (e.g. multi-dimensional scaling plots), reporting of clustering results (dimensionality reduction, heatmaps with dendrograms) and differential analyses (e.g. plots of aggregated signals). PMID:28663787

High-resolution 3D laser imaging based on tunable fiber array link

NASA Astrophysics Data System (ADS)

Zhao, Sisi; Ruan, Ningjuan; Yang, Song

2017-10-01

Airborne photoelectric reconnaissance system with the bore sight down to the ground is an important battlefield situational awareness system, which can be used for reconnaissance and surveillance of complex ground scene. Airborne 3D imaging Lidar system is recognized as the most potential candidates for target detection under the complex background, and is progressing in the directions of high resolution, long distance detection, high sensitivity, low power consumption, high reliability, eye safe and multi-functional. However, the traditional 3D laser imaging system has the disadvantages of lower imaging resolutions because of the small size of the existing detector, and large volume. This paper proposes a high resolution laser 3D imaging technology based on the tunable optical fiber array link. The echo signal is modulated by a tunable optical fiber array link and then transmitted to the focal plane detector. The detector converts the optical signal into electrical signals which is given to the computer. Then, the computer accomplishes the signal calculation and image restoration based on modulation information, and then reconstructs the target image. This paper establishes the mathematical model of tunable optical fiber array signal receiving link, and proposes the simulation and analysis of the affect factors on high density multidimensional point cloud reconstruction.

Spatial Lattice Modulation for MIMO Systems

NASA Astrophysics Data System (ADS)

Choi, Jiwook; Nam, Yunseo; Lee, Namyoon

2018-06-01

This paper proposes spatial lattice modulation (SLM), a spatial modulation method for multipleinput-multiple-output (MIMO) systems. The key idea of SLM is to jointly exploit spatial, in-phase, and quadrature dimensions to modulate information bits into a multi-dimensional signal set that consists oflattice points. One major finding is that SLM achieves a higher spectral efficiency than the existing spatial modulation and spatial multiplexing methods for the MIMO channel under the constraint ofM-ary pulseamplitude-modulation (PAM) input signaling per dimension. In particular, it is shown that when the SLM signal set is constructed by using dense lattices, a significant signal-to-noise-ratio (SNR) gain, i.e., a nominal coding gain, is attainable compared to the existing methods. In addition, closed-form expressions for both the average mutual information and average symbol-vector-error-probability (ASVEP) of generic SLM are derived under Rayleigh-fading environments. To reduce detection complexity, a low-complexity detection method for SLM, which is referred to as lattice sphere decoding, is developed by exploiting lattice theory. Simulation results verify the accuracy of the conducted analysis and demonstrate that the proposed SLM techniques achieve higher average mutual information and lower ASVEP than do existing methods.

Near Real-time Scientific Data Analysis and Visualization with the ArcGIS Platform

NASA Astrophysics Data System (ADS)

Shrestha, S. R.; Viswambharan, V.; Doshi, A.

2017-12-01

Scientific multidimensional data are generated from a variety of sources and platforms. These datasets are mostly produced by earth observation and/or modeling systems. Agencies like NASA, NOAA, USGS, and ESA produce large volumes of near real-time observation, forecast, and historical data that drives fundamental research and its applications in larger aspects of humanity from basic decision making to disaster response. A common big data challenge for organizations working with multidimensional scientific data and imagery collections is the time and resources required to manage and process such large volumes and varieties of data. The challenge of adopting data driven real-time visualization and analysis, as well as the need to share these large datasets, workflows, and information products to wider and more diverse communities, brings an opportunity to use the ArcGIS platform to handle such demand. In recent years, a significant effort has put in expanding the capabilities of ArcGIS to support multidimensional scientific data across the platform. New capabilities in ArcGIS to support scientific data management, processing, and analysis as well as creating information products from large volumes of data using the image server technology are becoming widely used in earth science and across other domains. We will discuss and share the challenges associated with big data by the geospatial science community and how we have addressed these challenges in the ArcGIS platform. We will share few use cases, such as NOAA High Resolution Refresh Radar (HRRR) data, that demonstrate how we access large collections of near real-time data (that are stored on-premise or on the cloud), disseminate them dynamically, process and analyze them on-the-fly, and serve them to a variety of geospatial applications. We will also share how on-the-fly processing using raster functions capabilities, can be extended to create persisted data and information products using raster analytics capabilities that exploit distributed computing in an enterprise environment.

An advanced process-based distributed model for the investigation of rainfall-induced landslides: The effect of process representation and boundary conditions

NASA Astrophysics Data System (ADS)

Anagnostopoulos, Grigorios G.; Fatichi, Simone; Burlando, Paolo

2015-09-01

Extreme rainfall events are the major driver of shallow landslide occurrences in mountainous and steep terrain regions around the world. Subsurface hydrology has a dominant role on the initiation of rainfall-induced shallow landslides, since changes in the soil water content affect significantly the soil shear strength. Rainfall infiltration produces an increase of soil water potential, which is followed by a rapid drop in apparent cohesion. Especially on steep slopes of shallow soils, this loss of shear strength can lead to failure even in unsaturated conditions before positive water pressures are developed. We present HYDROlisthisis, a process-based model, fully distributed in space with fine time resolution, in order to investigate the interactions between surface and subsurface hydrology and shallow landslides initiation. Fundamental elements of the approach are the dependence of shear strength on the three-dimensional (3-D) field of soil water potential, as well as the temporal evolution of soil water potential during the wetting and drying phases. Specifically, 3-D variably saturated flow conditions, including soil hydraulic hysteresis and preferential flow phenomena, are simulated for the subsurface flow, coupled with a surface runoff routine based on the kinematic wave approximation. The geotechnical component of the model is based on a multidimensional limit equilibrium analysis, which takes into account the basic principles of unsaturated soil mechanics. A series of numerical simulations were carried out with various boundary conditions and using different hydrological and geotechnical components. Boundary conditions in terms of distributed soil depth were generated using both empirical and process-based models. The effect of including preferential flow and soil hydraulic hysteresis was tested together with the replacement of the infinite slope assumption with the multidimensional limit equilibrium analysis. The results show that boundary conditions play a crucial role in the model performance and that the introduced hydrological (preferential flow and soil hydraulic hysteresis) and geotechnical components (multidimensional limit equilibrium analysis) significantly improve predictive capabilities in the presented case study.

Multidimensional effects in nonadiabatic statistical theories of spin- forbidden kinetics. A case study of 3O + CO → CO 2

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jasper, Ahren

2015-04-14

The appropriateness of treating crossing seams of electronic states of different spins as nonadiabatic transition states in statistical calculations of spin-forbidden reaction rates is considered. We show that the spin-forbidden reaction coordinate, the nuclear coordinate perpendicular to the crossing seam, is coupled to the remaining nuclear degrees of freedom. We found that this coupling gives rise to multidimensional effects that are not typically included in statistical treatments of spin-forbidden kinetics. Three qualitative categories of multidimensional effects may be identified: static multidimensional effects due to the geometry-dependence of the local shape of the crossing seam and of the spin–orbit coupling, dynamicalmore » multidimensional effects due to energy exchange with the reaction coordinate during the seam crossing, and nonlocal(history-dependent) multidimensional effects due to interference of the electronic variables at second, third, and later seam crossings. Nonlocal multidimensional effects are intimately related to electronic decoherence, where electronic dephasing acts to erase the history of the system. A semiclassical model based on short-time full-dimensional trajectories that includes all three multidimensional effects as well as a model for electronic decoherence is presented. The results of this multidimensional nonadiabatic statistical theory (MNST) for the 3O + CO → CO 2 reaction are compared with the results of statistical theories employing one-dimensional (Landau–Zener and weak coupling) models for the transition probability and with those calculated previously using multistate trajectories. The MNST method is shown to accurately reproduce the multistate decay-of-mixing trajectory results, so long as consistent thresholds are used. Furthermore, the MNST approach has several advantages over multistate trajectory approaches and is more suitable in chemical kinetics calculations at low temperatures and for complex systems. The error in statistical calculations that neglect multidimensional effects is shown to be as large as a factor of 2 for this system, with static multidimensional effects identified as the largest source of error.« less

Investigating fast enzyme-DNA kinetics using multidimensional fluorescence imaging and microfluidics

NASA Astrophysics Data System (ADS)

Robinson, Tom; Manning, Hugh B.; Dunsby, Christopher; Neil, Mark A. A.; Baldwin, Geoff S.; de Mello, Andrew J.; French, Paul M. W.

2010-02-01

We have developed a rapid microfluidic mixing device to image fast kinetics. To verify the performance of the device it was simulated using computational fluid dynamics (CFD) and the results were directly compared to experimental fluorescence lifetime imaging (FLIM) measurements. The theoretical and measured mixing times of the device were found to be in agreement over a range of flow rates. This mixing device is being developed with the aim of analysing fast enzyme kinetics in the sub-millisecond time domain, which cannot be achieved with conventional macro-stopped flow devices. Here we have studied the binding of a DNA repair enzyme, uracil DNA glycosylase (UDG), to a fluorescently labelled DNA substrate. Bulk phase fluorescence measurements have been used to measure changes on binding: it was found that the fluorescence lifetime increased along with an increase in the polarisation anisotropy and rotational correlation time. Analysis of the same reaction in the microfluidic mixer by CFD enabled us to predict the mixing time of the device to be 46 μs, more than 20 times faster than current stopped-flow techniques. We also demonstrate that it is possible to image UDG-DNA interactions within the micromixer using the signal changes observed from the multidimensional spectrofluorometer.

Multidimensional protein identification technology (MudPIT): technical overview of a profiling method optimized for the comprehensive proteomic investigation of normal and diseased heart tissue.

PubMed

Kislinger, Thomas; Gramolini, Anthony O; MacLennan, David H; Emili, Andrew

2005-08-01

An optimized analytical expression profiling strategy based on gel-free multidimensional protein identification technology (MudPIT) is reported for the systematic investigation of biochemical (mal)-adaptations associated with healthy and diseased heart tissue. Enhanced shotgun proteomic detection coverage and improved biological inference is achieved by pre-fractionation of excised mouse cardiac muscle into subcellular components, with each organellar fraction investigated exhaustively using multiple repeat MudPIT analyses. Functional-enrichment, high-confidence identification, and relative quantification of hundreds of organelle- and tissue-specific proteins are achieved readily, including detection of low abundance transcriptional regulators, signaling factors, and proteins linked to cardiac disease. Important technical issues relating to data validation, including minimization of artifacts stemming from biased under-sampling and spurious false discovery, together with suggestions for further fine-tuning of sample preparation, are discussed. A framework for follow-up bioinformatic examination, pattern recognition, and data mining is also presented in the context of a stringent application of MudPIT for probing fundamental aspects of heart muscle physiology as well as the discovery of perturbations associated with heart failure.

Applications of wavelet-based compression to multidimensional Earth science data

NASA Technical Reports Server (NTRS)

Bradley, Jonathan N.; Brislawn, Christopher M.

1993-01-01

A data compression algorithm involving vector quantization (VQ) and the discrete wavelet transform (DWT) is applied to two different types of multidimensional digital earth-science data. The algorithms (WVQ) is optimized for each particular application through an optimization procedure that assigns VQ parameters to the wavelet transform subbands subject to constraints on compression ratio and encoding complexity. Preliminary results of compressing global ocean model data generated on a Thinking Machines CM-200 supercomputer are presented. The WVQ scheme is used in both a predictive and nonpredictive mode. Parameters generated by the optimization algorithm are reported, as are signal-to-noise (SNR) measurements of actual quantized data. The problem of extrapolating hydrodynamic variables across the continental landmasses in order to compute the DWT on a rectangular grid is discussed. Results are also presented for compressing Landsat TM 7-band data using the WVQ scheme. The formulation of the optimization problem is presented along with SNR measurements of actual quantized data. Postprocessing applications are considered in which the seven spectral bands are clustered into 256 clusters using a k-means algorithm and analyzed using the Los Alamos multispectral data analysis program, SPECTRUM, both before and after being compressed using the WVQ program.

On the Need for Multidimensional Stirling Simulations

NASA Technical Reports Server (NTRS)

Dyson, Rodger W.; Wilson, Scott D.; Tew, Roy C.; Demko, Rikako

2005-01-01

Given the cost and complication of simulating Stirling convertors, do we really need multidimensional modeling when one-dimensional capabilities exist? This paper provides a comprehensive description of when and why multidimensional simulation is needed.

Developing Gifted Programs in Science.

ERIC Educational Resources Information Center

Consuegra, Gerard F.

The paper explores the needs of gifted students with exceptional interests and talents in science. General characteristics of gifted students are listed, as are characteristics of the gifted in science (including questing, personal drive, and an enjoyment of numbers). A multidimensional gifted identification process is reviewed, and the lack of…

Multidimensional Scaling in the Poincare Disk

DTIC Science & Technology

2011-05-01

REPORT Multidimensional Scaling in the Poincare Dis 14. ABSTRACT 16. SECURITY CLASSIFICATION OF: Multidimensional scaling (MDS) is a class of projective...DATES COVERED (From - To) Standard Form 298 (Rev 8/98) Prescribed by ANSI Std. Z39.18 - Multidimensional Scaling in the Poincare Dis Report Title... plane . Our construction is based on an approximate hyperbolic line search and exempli?es some of the particulars that need to be addressed when

Scaling Laws for the Multidimensional Burgers Equation with Quadratic External Potential

NASA Astrophysics Data System (ADS)

Leonenko, N. N.; Ruiz-Medina, M. D.

2006-07-01

The reordering of the multidimensional exponential quadratic operator in coordinate-momentum space (see X. Wang, C.H. Oh and L.C. Kwek (1998). J. Phys. A.: Math. Gen. 31:4329-4336) is applied to derive an explicit formulation of the solution to the multidimensional heat equation with quadratic external potential and random initial conditions. The solution to the multidimensional Burgers equation with quadratic external potential under Gaussian strongly dependent scenarios is also obtained via the Hopf-Cole transformation. The limiting distributions of scaling solutions to the multidimensional heat and Burgers equations with quadratic external potential are then obtained under such scenarios.

Quantitative, equal carbon response HSQC experiment, QEC-HSQC

NASA Astrophysics Data System (ADS)

Mäkelä, Valtteri; Helminen, Jussi; Kilpeläinen, Ilkka; Heikkinen, Sami

2016-10-01

Quantitative NMR has become increasingly useful and popular in recent years, with many new and emerging applications in metabolomics, quality control, reaction monitoring and other types of mixture analysis. While sensitive and simple to acquire, the low resolving power of 1D 1H NMR spectra can be a limiting factor when analyzing complex mixtures. This drawback can be solved by observing a different type of nuclei offering improved resolution or with multidimensional experiments, such as HSQC. In this paper, we present a novel Quantitative, Equal Carbon HSQC (QEC-HSQC) experiment providing an equal response across different type of carbons regardless of the number of attached protons, in addition to an uniform response over a wide range of 1JCH couplings. This enables rapid quantification and integration over multiple signals without the need for complete resonance assignments and simplifies the integration of overlapping signals.

Multidimensional signaling via wavelet packets

NASA Astrophysics Data System (ADS)

Lindsey, Alan R.

1995-04-01

This work presents a generalized signaling strategy for orthogonally multiplexed communication. Wavelet packet modulation (WPM) employs the basis functions from an arbitrary pruning of a full dyadic tree structured filter bank as orthogonal pulse shapes for conventional QAM symbols. The multi-scale modulation (MSM) and M-band wavelet modulation (MWM) schemes which have been recently introduced are handled as special cases, with the added benefit of an entire library of potentially superior sets of basis functions. The figures of merit are derived and it is shown that the power spectral density is equivalent to that for QAM (in fact, QAM is another special case) and hence directly applicable in existing systems employing this standard modulation. Two key advantages of this method are increased flexibility in time-frequency partitioning and an efficient all-digital filter bank implementation, making the WPM scheme more robust to a larger set of interferences (both temporal and sinusoidal) and computationally attractive as well.

Practical aspects of NMR signal assignment in larger and challenging proteins

PubMed Central

Frueh, Dominique P.

2014-01-01

NMR has matured into a technique routinely employed for studying proteins in near physiological conditions. However, applications to larger proteins are impeded by the complexity of the various correlation maps necessary to assign NMR signals. This article reviews the data analysis techniques traditionally employed for resonance assignment and describes alternative protocols necessary for overcoming challenges in large protein spectra. In particular, simultaneous analysis of multiple spectra may help overcome ambiguities or may reveal correlations in an indirect manner. Similarly, visualization of orthogonal planes in a multidimensional spectrum can provide alternative assignment procedures. We describe examples of such strategies for assignment of backbone, methyl, and nOe resonances. We describe experimental aspects of data acquisition for the related experiments and provide guidelines for preliminary studies. Focus is placed on large folded monomeric proteins and examples are provided for 37, 48, 53, and 81 kDa proteins. PMID:24534088

EEG and MEG source localization using recursively applied (RAP) MUSIC

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mosher, J.C.; Leahy, R.M.

1996-12-31

The multiple signal characterization (MUSIC) algorithm locates multiple asynchronous dipolar sources from electroencephalography (EEG) and magnetoencephalography (MEG) data. A signal subspace is estimated from the data, then the algorithm scans a single dipole model through a three-dimensional head volume and computes projections onto this subspace. To locate the sources, the user must search the head volume for local peaks in the projection metric. Here we describe a novel extension of this approach which we refer to as RAP (Recursively APplied) MUSIC. This new procedure automatically extracts the locations of the sources through a recursive use of subspace projections, which usesmore » the metric of principal correlations as a multidimensional form of correlation analysis between the model subspace and the data subspace. The dipolar orientations, a form of `diverse polarization,` are easily extracted using the associated principal vectors.« less

Ion-induced electron emission microscopy

DOEpatents

Doyle, Barney L.; Vizkelethy, Gyorgy; Weller, Robert A.

2001-01-01

An ion beam analysis system that creates multidimensional maps of the effects of high energy ions from an unfocussed source upon a sample by correlating the exact entry point of an ion into a sample by projection imaging of the secondary electrons emitted at that point with a signal from a detector that measures the interaction of that ion within the sample. The emitted secondary electrons are collected in a strong electric field perpendicular to the sample surface and (optionally) projected and refocused by the electron lenses found in a photon emission electron microscope, amplified by microchannel plates and then their exact position is sensed by a very sensitive X Y position detector. Position signals from this secondary electron detector are then correlated in time with nuclear, atomic or electrical effects, including the malfunction of digital circuits, detected within the sample that were caused by the individual ion that created these secondary electrons in the fit place.

Models of multidimensional discrete distribution of probabilities of random variables in information systems

NASA Astrophysics Data System (ADS)

Gromov, Yu Yu; Minin, Yu V.; Ivanova, O. G.; Morozova, O. N.

2018-03-01

Multidimensional discrete distributions of probabilities of independent random values were received. Their one-dimensional distribution is widely used in probability theory. Producing functions of those multidimensional distributions were also received.

Use patterns of health information exchange through a multidimensional lens: conceptual framework and empirical validation.

PubMed

Politi, Liran; Codish, Shlomi; Sagy, Iftach; Fink, Lior

2014-12-01

Insights about patterns of system use are often gained through the analysis of system log files, which record the actual behavior of users. In a clinical context, however, few attempts have been made to typify system use through log file analysis. The present study offers a framework for identifying, describing, and discerning among patterns of use of a clinical information retrieval system. We use the session attributes of volume, diversity, granularity, duration, and content to define a multidimensional space in which each specific session can be positioned. We also describe an analytical method for identifying the common archetypes of system use in this multidimensional space. We demonstrate the value of the proposed framework with a log file of the use of a health information exchange (HIE) system by physicians in an emergency department (ED) of a large Israeli hospital. The analysis reveals five distinct patterns of system use, which have yet to be described in the relevant literature. The results of this study have the potential to inform the design of HIE systems for efficient and effective use, thus increasing their contribution to the clinical decision-making process. Copyright © 2014 Elsevier Inc. All rights reserved.

Analysis on the multi-dimensional spectrum of the thrust force for the linear motor feed drive system in machine tools

NASA Astrophysics Data System (ADS)

Yang, Xiaojun; Lu, Dun; Ma, Chengfang; Zhang, Jun; Zhao, Wanhua

2017-01-01

The motor thrust force has lots of harmonic components due to the nonlinearity of drive circuit and motor itself in the linear motor feed drive system. What is more, in the motion process, these thrust force harmonics may vary with the position, velocity, acceleration and load, which affects the displacement fluctuation of the feed drive system. Therefore, in this paper, on the basis of the thrust force spectrum obtained by the Maxwell equation and the electromagnetic energy method, the multi-dimensional variation of each thrust harmonic is analyzed under different motion parameters. Then the model of the servo system is established oriented to the dynamic precision. The influence of the variation of the thrust force spectrum on the displacement fluctuation is discussed. At last the experiments are carried out to verify the theoretical analysis above. It can be found that the thrust harmonics show multi-dimensional spectrum characteristics under different motion parameters and loads, which should be considered to choose the motion parameters and optimize the servo control parameters in the high-speed and high-precision machine tools equipped with the linear motor feed drive system.

Umyuangcaryaraq "Reflecting": multidimensional assessment of reflective processes on the consequences of alcohol use among rural Yup'ik Alaska Native youth.

PubMed

Allen, James; Fok, Carlotta Ching Ting; Henry, David; Skewes, Monica

2012-09-01

Concerns in some settings regarding the accuracy and ethics of employing direct questions about alcohol use suggest need for alternative assessment approaches with youth. Umyuangcaryaraq is a Yup'ik Alaska Native word meaning "Reflecting." The Reflective Processes Scale was developed as a youth measure tapping awareness and thinking over potential negative consequences of alcohol misuse as a protective factor that includes cultural elements often shared by many other Alaska Native and American Indian cultures. This study assessed multidimensional structure, item functioning, and validity. Responses from 284 rural Alaska Native youth allowed bifactor analysis to assess structure, estimates of location and discrimination parameters, and convergent and discriminant validity. A bifactor model of the scale items with three content factors provided excellent fit to observed data. Item response theory analysis suggested a binary response format as optimal. Evidence of convergent and discriminant validity was established. The measure provides an assessment of reflective processes about alcohol that Alaska Native youth engage in when thinking about reasons not to drink. The concept of reflective processes has potential to extend understandings of cultural variation in mindfulness, alcohol expectancies research, and culturally mediated protective factors in Alaska Native and American Indian youth.

Multidimensional Knowledge Structures.

ERIC Educational Resources Information Center

Schuh, Kathy L.

Multidimensional knowledge structures, described from a constructivist perspective and aligned with the "Mind as Rhizome" metaphor, provide support for constructivist learning strategies. This qualitative study was conducted to seek empirical support for a description of multidimensional knowledge structures, focusing on the…

Multidimensional quantum entanglement with large-scale integrated optics.

PubMed

Wang, Jianwei; Paesani, Stefano; Ding, Yunhong; Santagati, Raffaele; Skrzypczyk, Paul; Salavrakos, Alexia; Tura, Jordi; Augusiak, Remigiusz; Mančinska, Laura; Bacco, Davide; Bonneau, Damien; Silverstone, Joshua W; Gong, Qihuang; Acín, Antonio; Rottwitt, Karsten; Oxenløwe, Leif K; O'Brien, Jeremy L; Laing, Anthony; Thompson, Mark G

2018-04-20

The ability to control multidimensional quantum systems is central to the development of advanced quantum technologies. We demonstrate a multidimensional integrated quantum photonic platform able to generate, control, and analyze high-dimensional entanglement. A programmable bipartite entangled system is realized with dimensions up to 15 × 15 on a large-scale silicon photonics quantum circuit. The device integrates more than 550 photonic components on a single chip, including 16 identical photon-pair sources. We verify the high precision, generality, and controllability of our multidimensional technology, and further exploit these abilities to demonstrate previously unexplored quantum applications, such as quantum randomness expansion and self-testing on multidimensional states. Our work provides an experimental platform for the development of multidimensional quantum technologies. Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

Beyond the continuum: a multi-dimensional phase space for neutral-niche community assembly.

PubMed

Latombe, Guillaume; Hui, Cang; McGeoch, Melodie A

2015-12-22

Neutral and niche processes are generally considered to interact in natural communities along a continuum, exhibiting community patterns bounded by pure neutral and pure niche processes. The continuum concept uses niche separation, an attribute of the community, to test the hypothesis that communities are bounded by pure niche or pure neutral conditions. It does not accommodate interactions via feedback between processes and the environment. By contrast, we introduce the Community Assembly Phase Space (CAPS), a multi-dimensional space that uses community processes (such as dispersal and niche selection) to define the limiting neutral and niche conditions and to test the continuum hypothesis. We compare the outputs of modelled communities in a heterogeneous landscape, assembled by pure neutral, pure niche and composite processes. Differences in patterns under different combinations of processes in CAPS reveal hidden complexity in neutral-niche community dynamics. The neutral-niche continuum only holds for strong dispersal limitation and niche separation. For weaker dispersal limitation and niche separation, neutral and niche processes amplify each other via feedback with the environment. This generates patterns that lie well beyond those predicted by a continuum. Inferences drawn from patterns about community assembly processes can therefore be misguided when based on the continuum perspective. CAPS also demonstrates the complementary information value of different patterns for inferring community processes and captures the complexity of community assembly. It provides a general tool for studying the processes structuring communities and can be applied to address a range of questions in community and metacommunity ecology. © 2015 The Author(s).

Beyond the continuum: a multi-dimensional phase space for neutral–niche community assembly

PubMed Central

Latombe, Guillaume; McGeoch, Melodie A.

2015-01-01

Neutral and niche processes are generally considered to interact in natural communities along a continuum, exhibiting community patterns bounded by pure neutral and pure niche processes. The continuum concept uses niche separation, an attribute of the community, to test the hypothesis that communities are bounded by pure niche or pure neutral conditions. It does not accommodate interactions via feedback between processes and the environment. By contrast, we introduce the Community Assembly Phase Space (CAPS), a multi-dimensional space that uses community processes (such as dispersal and niche selection) to define the limiting neutral and niche conditions and to test the continuum hypothesis. We compare the outputs of modelled communities in a heterogeneous landscape, assembled by pure neutral, pure niche and composite processes. Differences in patterns under different combinations of processes in CAPS reveal hidden complexity in neutral–niche community dynamics. The neutral–niche continuum only holds for strong dispersal limitation and niche separation. For weaker dispersal limitation and niche separation, neutral and niche processes amplify each other via feedback with the environment. This generates patterns that lie well beyond those predicted by a continuum. Inferences drawn from patterns about community assembly processes can therefore be misguided when based on the continuum perspective. CAPS also demonstrates the complementary information value of different patterns for inferring community processes and captures the complexity of community assembly. It provides a general tool for studying the processes structuring communities and can be applied to address a range of questions in community and metacommunity ecology. PMID:26702047

An Augmented Two-Layer Model Captures Nonlinear Analog Spatial Integration Effects in Pyramidal Neuron Dendrites.

PubMed

Jadi, Monika P; Behabadi, Bardia F; Poleg-Polsky, Alon; Schiller, Jackie; Mel, Bartlett W

2014-05-01

In pursuit of the goal to understand and eventually reproduce the diverse functions of the brain, a key challenge lies in reverse engineering the peculiar biology-based "technology" that underlies the brain's remarkable ability to process and store information. The basic building block of the nervous system is the nerve cell, or "neuron," yet after more than 100 years of neurophysiological study and 60 years of modeling, the information processing functions of individual neurons, and the parameters that allow them to engage in so many different types of computation (sensory, motor, mnemonic, executive, etc.) remain poorly understood. In this paper, we review both historical and recent findings that have led to our current understanding of the analog spatial processing capabilities of dendrites, the major input structures of neurons, with a focus on the principal cell type of the neocortex and hippocampus, the pyramidal neuron (PN). We encapsulate our current understanding of PN dendritic integration in an abstract layered model whose spatially sensitive branch-subunits compute multidimensional sigmoidal functions. Unlike the 1-D sigmoids found in conventional neural network models, multidimensional sigmoids allow the cell to implement a rich spectrum of nonlinear modulation effects directly within their dendritic trees.

Theoretical description of quantum mechanical permeation of graphene membranes by charged hydrogen isotopes

NASA Astrophysics Data System (ADS)

Mazzuca, James W.; Haut, Nathaniel K.

2018-06-01

It has been recently shown that in the presence of an applied voltage, hydrogen and deuterium nuclei can be separated from one another using graphene membranes as a nuclear sieve, resulting in a 10-fold enhancement in the concentration of the lighter isotope. While previous studies, both experimental and theoretical, have attributed this effect mostly to differences in vibrational zero point energy (ZPE) of the various isotopes near the membrane surface, we propose that multi-dimensional quantum mechanical tunneling of nuclei through the graphene membrane influences this proton permeation process in a fundamental way. We perform ring polymer molecular dynamics calculations in which we include both ZPE and tunneling effects of various hydrogen isotopes as they permeate the graphene membrane and compute rate constants across a range of temperatures near 300 K. While capturing the experimentally observed separation factor, our calculations indicate that the transverse motion of the various isotopes across the surface of the graphene membrane is an essential part of this sieving mechanism. An understanding of the multi-dimensional quantum mechanical nature of this process could serve to guide the design of other such isotopic enrichment processes for a variety of atomic and molecular species of interest.

Theoretical description of quantum mechanical permeation of graphene membranes by charged hydrogen isotopes.

PubMed

Mazzuca, James W; Haut, Nathaniel K

2018-06-14

It has been recently shown that in the presence of an applied voltage, hydrogen and deuterium nuclei can be separated from one another using graphene membranes as a nuclear sieve, resulting in a 10-fold enhancement in the concentration of the lighter isotope. While previous studies, both experimental and theoretical, have attributed this effect mostly to differences in vibrational zero point energy (ZPE) of the various isotopes near the membrane surface, we propose that multi-dimensional quantum mechanical tunneling of nuclei through the graphene membrane influences this proton permeation process in a fundamental way. We perform ring polymer molecular dynamics calculations in which we include both ZPE and tunneling effects of various hydrogen isotopes as they permeate the graphene membrane and compute rate constants across a range of temperatures near 300 K. While capturing the experimentally observed separation factor, our calculations indicate that the transverse motion of the various isotopes across the surface of the graphene membrane is an essential part of this sieving mechanism. An understanding of the multi-dimensional quantum mechanical nature of this process could serve to guide the design of other such isotopic enrichment processes for a variety of atomic and molecular species of interest.

Selective excitation enables assignment of proton resonances and {sup 1}H-{sup 1}H distance measurement in ultrafast magic angle spinning solid state NMR spectroscopy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Rongchun; Ramamoorthy, Ayyalusamy, E-mail: ramamoor@umich.edu

2015-07-21

Remarkable developments in ultrafast magic angle spinning (MAS) solid-state NMR spectroscopy enabled proton-based high-resolution multidimensional experiments on solids. To fully utilize the benefits rendered by proton-based ultrafast MAS experiments, assignment of {sup 1}H resonances becomes absolutely necessary. Herein, we propose an approach to identify different proton peaks by using dipolar-coupled heteronuclei such as {sup 13}C or {sup 15}N. In this method, after the initial preparation of proton magnetization and cross-polarization to {sup 13}C nuclei, transverse magnetization of desired {sup 13}C nuclei is selectively prepared by using DANTE (Delays Alternating with Nutations for Tailored Excitation) sequence and then, it is transferredmore » to bonded protons with a short-contact-time cross polarization. Our experimental results demonstrate that protons bonded to specific {sup 13}C atoms can be identified and overlapping proton peaks can also be assigned. In contrast to the regular 2D HETCOR experiment, only a few 1D experiments are required for the complete assignment of peaks in the proton spectrum. Furthermore, the finite-pulse radio frequency driven recoupling sequence could be incorporated right after the selection of specific proton signals to monitor the intensity buildup for other proton signals. This enables the extraction of {sup 1}H-{sup 1}H distances between different pairs of protons. Therefore, we believe that the proposed method will greatly aid in fast assignment of peaks in proton spectra and will be useful in the development of proton-based multi-dimensional solid-state NMR experiments to study atomic-level resolution structure and dynamics of solids.« less

Environmental risk perception from visual cues: the psychophysics of tornado risk perception

NASA Astrophysics Data System (ADS)

Dewitt, Barry; Fischhoff, Baruch; Davis, Alexander; Broomell, Stephen B.

2015-12-01

Lay judgments of environmental risks are central to both immediate decisions (e.g., taking shelter from a storm) and long-term ones (e.g., building in locations subject to storm surges). Using methods from quantitative psychology, we provide a general approach to studying lay perceptions of environmental risks. As a first application of these methods, we investigate a setting where lay decisions have not taken full advantage of advances in natural science understanding: tornado forecasts in the US and Canada. Because official forecasts are imperfect, members of the public must often evaluate the risks on their own, by checking environmental cues (such as cloud formations) before deciding whether to take protective action. We study lay perceptions of cloud formations, demonstrating an approach that could be applied to other environmental judgments. We use signal detection theory to analyse how well people can distinguish tornadic from non-tornadic clouds, and multidimensional scaling to determine how people make these judgments. We find that participants (N = 400 recruited from Amazon Mechanical Turk) have heuristics that generally serve them well, helping participants to separate tornadic from non-tornadic clouds, but which also lead them to misjudge the tornado risk of certain cloud types. The signal detection task revealed confusion regarding shelf clouds, mammatus clouds, and clouds with upper- and mid-level tornadic features, which the multidimensional scaling task suggested was the result of participants focusing on the darkness of the weather scene and the ease of discerning its features. We recommend procedures for training (e.g., for storm spotters) and communications (e.g., tornado warnings) that will reduce systematic misclassifications of tornadicity arising from observers’ reliance on otherwise useful heuristics.

Dual-transduction-mode sensing approach for chemical detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Liang; Swensen, James S.

2012-11-01

Smart devices such as electronic nose have been developed for application in many fields like national security, defense, environmental regulation, health care, pipeline monitoring and food analysis. Despite a large array of individual sensors, these devices still lack the ability to identify a target at a very low concentration out of a mixture of odors, limited by a single type of transduction as the sensing response to distinguish one odor from another. Here, we propose a new sensor architecture empowering each individual sensor with multi-dimensional transduction signals. The resolving power of our proposed electronic nose is thereby multiplied by amore » set of different and independent variables which synergistically will provide a unique combined fingerprint for each analyte. We demonstrate this concept using a Light Emitting Organic Field-Effect Transistor (LEOFET). Sensing response has been observed on both electrical and optical output signals from a green LEOFET upon exposure to an explosive taggant, with optical signal exhibiting much higher sensitivity. This new sensor architecture opens a field of devices for smart detection of chemical and biological targets.« less

Modeling of cortical signals using echo state networks

NASA Astrophysics Data System (ADS)

Zhou, Hanying; Wang, Yongji; Huang, Jiangshuai

2009-10-01

Diverse modeling frameworks have been utilized with the ultimate goal of translating brain cortical signals into prediction of visible behavior. The inputs to these models are usually multidimensional neural recordings collected from relevant regions of a monkey's brain while the outputs are the associated behavior which is typically the 2-D or 3-D hand position of a primate. Here our task is to set up a proper model in order to figure out the move trajectories by input the neural signals which are simultaneously collected in the experiment. In this paper, we propose to use Echo State Networks (ESN) to map the neural firing activities into hand positions. ESN is a newly developed recurrent neural network(RNN) model. Besides its dynamic property and short term memory just as other recurrent neural networks have, it has a special echo state property which endows it with the ability to model nonlinear dynamic systems powerfully. What distinguished it from transitional recurrent neural networks most significantly is its special learning method. In this paper we train this net with a refined version of its typical training method and get a better model.

A Tensor-Based Subspace Approach for Bistatic MIMO Radar in Spatial Colored Noise

PubMed Central

Wang, Xianpeng; Wang, Wei; Li, Xin; Wang, Junxiang

2014-01-01

In this paper, a new tensor-based subspace approach is proposed to estimate the direction of departure (DOD) and the direction of arrival (DOA) for bistatic multiple-input multiple-output (MIMO) radar in the presence of spatial colored noise. Firstly, the received signals can be packed into a third-order measurement tensor by exploiting the inherent structure of the matched filter. Then, the measurement tensor can be divided into two sub-tensors, and a cross-covariance tensor is formulated to eliminate the spatial colored noise. Finally, the signal subspace is constructed by utilizing the higher-order singular value decomposition (HOSVD) of the cross-covariance tensor, and the DOD and DOA can be obtained through the estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm, which are paired automatically. Since the multidimensional inherent structure and the cross-covariance tensor technique are used, the proposed method provides better angle estimation performance than Chen's method, the ESPRIT algorithm and the multi-SVD method. Simulation results confirm the effectiveness and the advantage of the proposed method. PMID:24573313

A tensor-based subspace approach for bistatic MIMO radar in spatial colored noise.

PubMed

Wang, Xianpeng; Wang, Wei; Li, Xin; Wang, Junxiang

2014-02-25

In this paper, a new tensor-based subspace approach is proposed to estimate the direction of departure (DOD) and the direction of arrival (DOA) for bistatic multiple-input multiple-output (MIMO) radar in the presence of spatial colored noise. Firstly, the received signals can be packed into a third-order measurement tensor by exploiting the inherent structure of the matched filter. Then, the measurement tensor can be divided into two sub-tensors, and a cross-covariance tensor is formulated to eliminate the spatial colored noise. Finally, the signal subspace is constructed by utilizing the higher-order singular value decomposition (HOSVD) of the cross-covariance tensor, and the DOD and DOA can be obtained through the estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm, which are paired automatically. Since the multidimensional inherent structure and the cross-covariance tensor technique are used, the proposed method provides better angle estimation performance than Chen's method, the ESPRIT algorithm and the multi-SVD method. Simulation results confirm the effectiveness and the advantage of the proposed method.

Selective Data Acquisition in NMR. The Quantification of Anti-phase Scalar Couplings

NASA Astrophysics Data System (ADS)

Hodgkinson, P.; Holmes, K. J.; Hore, P. J.

Almost all time-domain NMR experiments employ "linear sampling," in which the NMR response is digitized at equally spaced times, with uniform signal averaging. Here, the possibilities of nonlinear sampling are explored using anti-phase doublets in the indirectly detected dimensions of multidimensional COSY-type experiments as an example. The Cramér-Rao lower bounds are used to evaluate and optimize experiments in which the sampling points, or the extent of signal averaging at each point, or both, are varied. The optimal nonlinear sampling for the estimation of the coupling constant J, by model fitting, turns out to involve just a few key time points, for example, at the first node ( t= 1/ J) of the sin(π Jt) modulation. Such sparse sampling patterns can be used to derive more practical strategies, in which the sampling or the signal averaging is distributed around the most significant time points. The improvements in the quantification of NMR parameters can be quite substantial especially when, as is often the case for indirectly detected dimensions, the total number of samples is limited by the time available.

Development and Psychometric Evaluation of the Personal Growth Initiative Scale-II

ERIC Educational Resources Information Center

Robitschek, Christine; Ashton, Matthew W.; Spering, Cynthia C.; Geiger, Nathaniel; Byers, Danielle; Schotts, G. Christian; Thoen, Megan A.

2012-01-01

The original Personal Growth Initiative Scale (PGIS; Robitschek, 1998) was unidimensional, despite theory identifying multiple components (e.g., cognition and behavior) of personal growth initiative (PGI). The present research developed a multidimensional measure of the complex process of PGI, while retaining the brief and psychometrically sound…

A Study of Early Childhood Teachers' Conceptions of Creativity in Hong Kong

ERIC Educational Resources Information Center

Cheung, Rebecca Hun Ping; Mok, Magdalena Mo Ching

2013-01-01

The study aimed to uncover the conceptions of creativity among early childhood teachers in Hong Kong. The sample comprised 563 early childhood teachers. Factor analysis supported the multidimensional hypothesis of teachers' conceptions of creativity. Five dimensions were found: novelty, product, problem solving, cognitive processes and personal…

A Multidimensional Model for the Identification of Dual-Exceptional Learners

ERIC Educational Resources Information Center

Al-Hroub, Anies

2013-01-01

This research takes mathematics as a model for investigating the definitions, identification, classification and characteristics of a group of gifted student related to the notion of "dual-exceptionality". An extensive process using qualitative and quantitative methods was conducted by a multidisciplinary team to develop and implement a…

Best Practices in Wraparound: A Multidimensional View of the Evidence

ERIC Educational Resources Information Center

Walter, Uta M.; Petr, Christopher G.

2011-01-01

This article presents a systematic review of the effectiveness of wraparound, a value-guided, widely used service planning process and philosophy of care originally developed for children with serious emotional disturbance and their families. In contrast to conventional systematic reviews, which concentrate on the empirical literature, this…

Definitions: Health, Fitness, and Physical Activity.

ERIC Educational Resources Information Center

Corbin, Charles B.; Pangrazi, Robert P.; Franks, B. Don

2000-01-01

This paper defines a variety of fitness components, using a simple multidimensional hierarchical model that is consistent with recent definitions in the literature. It groups the definitions into two broad categories: product and process. Products refer to states of being such as physical fitness, health, and wellness. They are commonly referred…

Educational Environment Risks: Problems of Identification and Classification

ERIC Educational Resources Information Center

Kayumova, Leysan R.; Zakirova, Venera G.

2016-01-01

The relevance of the research problem is determined by the multidimensionality of educational environment, that is the system of business and interpersonal relationships of educational process subjects. The maintenance of these relations defines quality and nature of risks for teachers and their pupils. The article aims to identify and justify the…

Theory of Test Translation Error

ERIC Educational Resources Information Center

Solano-Flores, Guillermo; Backhoff, Eduardo; Contreras-Nino, Luis Angel

2009-01-01

In this article, we present a theory of test translation whose intent is to provide the conceptual foundation for effective, systematic work in the process of test translation and test translation review. According to the theory, translation error is multidimensional; it is not simply the consequence of defective translation but an inevitable fact…

Multi-Dimensional Planning/Evaluation Schema for Community Education.

ERIC Educational Resources Information Center

Merkel-Keller, Claudia; Herr, Audrey

A model for planning and evaluating community education programs--Stufflebeam's context, input, process, product (CIPP) evaluation model--was described and field-tested with the community education programs in Lakewood, New Jersey. Community education was defined as a concern for everything that affects the well-being of all citizens within a…

TRACE-LEVEL MEASUREMENT OF COMPLEX COMBUSTION EFFLUENTS AND RESIDUES USING MULTIDIMENSIONAL GAS CHROMATOGRAPHY-MASS SPECTROMETRY (MDGC-MS). (R828190)

EPA Science Inventory

The identification and quantitation of non-method-specific target analytes have greater importance with respect to EPA's current combustion strategy. The risk associated with combustion process emissions must now be characterized. EPA has recently released draft guidance on pr...

Dimensions of Immigrant Integration and Civic Engagement: Issues and Exemplary Programs

ERIC Educational Resources Information Center

Wrigley, Heide Spruck

2012-01-01

Immigrant integration is a multidimensional process that involves both newcomers and the receiving community. Although the United States does not have a coherent policy of immigrant integration, several city- and state-wide efforts support immigrant integration, as do individual initiatives operating across states. In this article, the author…

A Multidimensional Needs Assessment of Social Emotional Learning Skill Areas

ERIC Educational Resources Information Center

Yopp, Ashley; McKim, Billy R.; Moore, Lori L.; Odom, Summer F.; Hanagriff, Roger

2017-01-01

Social and Emotional Learning (SEL) has often been an umbrella term for a wide range of competencies, including emotional processes, social and interpersonal skills, and cognitive regulation (Jones, Bouffard, & Weissbourd, 2013). We used the Borich (1980) needs assessment model to assess the professional development needs of Texas agricultural…

Unmasking the Capability of Strategic Learning: A Validation Study

ERIC Educational Resources Information Center

Siren, Charlotta A.

2012-01-01

Purpose: The strategic learning perspective has attracted increased interest among strategic management scholars, yet the operationalisation of this concept is still in its infancy. The aim of this study is to develop a multidimensional understanding of the strategic learning process and to build an instrument to measure this concept.…

A Process Approach to Assessing Children with Learning Difficulties: Merging Assessment and Intervention.

ERIC Educational Resources Information Center

Smith, Corinne Roth

A multidimensional approach to assessment of children with learning difficulties is examined. The approach explores factors along five dimensions: (1) learner characteristics (motivation, social-emotional maturity, cognitive abilities and styles); (2) task-based contributors (match of tasks to maturational levels and to cognitive style); (3)…

The Relation of Emotional Maltreatment to Early Adolescent Competence: Developmental Processes in a Prospective Study

ERIC Educational Resources Information Center

Shaffer, Anne; Yates, Tuppett M.; Egeland, Byron R.

2009-01-01

Objectives: This investigation examined developmental pathways between childhood emotional maltreatment and adaptational outcomes in early adolescence. This study utilized a developmental psychopathology perspective in adopting a multidimensional approach to the assessment of different forms of emotional maltreatment and later adjustment outcomes.…

Beyond Recruitment: Retention and Promotion Strategies To Ensure Diversity and Success.

ERIC Educational Resources Information Center

Howland, Joan

1999-01-01

Discusses the need for libraries not only to recruit, but also to retain, diverse professional staffs. Topics include diversity as a multi-dimensional concept; creating an environment conducive to retention; ensuring equity in regard to promotion, professional development, and success; the tenure process in academic libraries; and mentoring…

Mining a Web Citation Database for Author Co-Citation Analysis.

ERIC Educational Resources Information Center

He, Yulan; Hui, Siu Cheung

2002-01-01

Proposes a mining process to automate author co-citation analysis based on the Web Citation Database, a data warehouse for storing citation indices of Web publications. Describes the use of agglomerative hierarchical clustering for author clustering and multidimensional scaling for displaying author cluster maps, and explains PubSearch, a…

Impact of a multidimensional infection control approach on catheter-associated urinary tract infection rates in an adult intensive care unit in Lebanon: International Nosocomial Infection Control Consortium (INICC) findings.

PubMed

Kanj, Souha S; Zahreddine, Nada; Rosenthal, Victor Daniel; Alamuddin, Lamia; Kanafani, Zeina; Molaeb, Bassel

2013-09-01

The objective of this study was to assess the impact of a multidimensional infection control approach for the reduction of catheter-associated urinary tract infection (CAUTI) in an adult intensive care unit (ICU) of a hospital member of the International Nosocomial Infection Control Consortium (INICC) in Lebanon. A before-after prospective active surveillance study was carried out to determine rates of CAUTI in 1506 ICU patients, hospitalized during 10 291 bed-days. The study period was divided into two phases: phase 1 (baseline) and phase 2 (intervention). During phase 1, surveillance was performed applying the definitions of the US Centers for Disease Control and Prevention National Healthcare Safety Network (CDC/NHSN). In phase 2, we adopted a multidimensional approach that included: (1) a bundle of infection control interventions, (2) education, (3) surveillance of CAUTI rates, (4) feedback on CAUTI rates, (5) process surveillance, and (6) performance feedback. We used random effects Poisson regression to account for clustering of CAUTI rates across time-periods. We recorded a total of 9829 urinary catheter-days: 306 in phase 1 and 9523 in phase 2. The rate of CAUTI was 13.07 per 1000 urinary catheter-days in phase 1, and was decreased by 83% in phase 2 to 2.21 per 1000 urinary catheter-days (risk ratio 0.17; 95% confidence interval 0.06-0.5; p=0.0002). Our multidimensional approach was associated with a significant reduction in the CAUTI rate. Copyright © 2013. Published by Elsevier Ltd.

The Impact of Time Perspective Latent Profiles on College Drinking: A Multidimensional Approach

PubMed Central

Braitman, Abby L.; Henson, James M.

2015-01-01

Background Zimbardo and Boyd’s1 time perspective, or the temporal framework individuals use to process information, has been shown to predict health behaviors such as alcohol use. Previous studies supported the predictive validity of individual dimensions of time perspective, with some dimensions acting as protective factors and others as risk factors. However, some studies produced findings contrary to the general body of literature. In addition, time perspective is a multidimensional construct, and the combination of perspectives may be more predictive than individual dimensions in isolation; consequently, multidimensional profiles are a more accurate measure of individual differences and more appropriate for predicting health behaviors. Objectives The current study identified naturally occurring profiles of time perspective and examined their association with risky alcohol use. Methods Data were collected from a college student sample (n = 431, mean age = 20.41 years) using an online survey. Time perspective profiles were identified using latent profile analysis. Results Bootstrapped regression models identified a protective class that engaged in significantly less overall drinking (β = −0.254) as well as engaging in significantly less episodic high risk drinking (β = −0.274). There was also emerging evidence of a high risk time perspective profile that was linked to more overall drinking (β = 0.198) and engaging in more high risk drinking (β = 0.245), though these differences were not significant. Conclusions/Importance These findings support examining time perspective in a multidimensional framework rather than individual dimensions in isolation. Implications include identifying students most in need of interventions, and tailoring interventions to target temporal framing in decision-making. PMID:25607806

The impact of time perspective latent profiles on college drinking: a multidimensional approach.

PubMed

Braitman, Abby L; Henson, James M

2015-04-01

Zimbardo and Boyd's(1) time perspective, or the temporal framework individuals use to process information, has been shown to predict health behaviors such as alcohol use. Previous studies supported the predictive validity of individual dimensions of time perspective, with some dimensions acting as protective factors and others as risk factors. However, some studies produced findings contrary to the general body of literature. In addition, time perspective is a multidimensional construct, and the combination of perspectives may be more predictive than individual dimensions in isolation; consequently, multidimensional profiles are a more accurate measure of individual differences and more appropriate for predicting health behaviors. The current study identified naturally occurring profiles of time perspective and examined their association with risky alcohol use. Data were collected from a college student sample (n = 431, mean age = 20.41 years) using an online survey. Time perspective profiles were identified using latent profile analysis. Bootstrapped regression models identified a protective class that engaged in significantly less overall drinking (β = -0.254) as well as engaging in significantly less episodic high risk drinking (β = -0.274). There was also emerging evidence of a high risk time perspective profile that was linked to more overall drinking (β = 0.198) and engaging in more high risk drinking (β = 0.245), though these differences were not significant. CONCLUSIONS/IMPORTANCE: These findings support examining time perspective in a multidimensional framework rather than individual dimensions in isolation. Implications include identifying students most in need of interventions, and tailoring interventions to target temporal framing in decision-making.

Generalizing DTW to the multi-dimensional case requires an adaptive approach

PubMed Central

Hu, Bing; Jin, Hongxia; Wang, Jun; Keogh, Eamonn

2017-01-01

In recent years Dynamic Time Warping (DTW) has emerged as the distance measure of choice for virtually all time series data mining applications. For example, virtually all applications that process data from wearable devices use DTW as a core sub-routine. This is the result of significant progress in improving DTW’s efficiency, together with multiple empirical studies showing that DTW-based classifiers at least equal (and generally surpass) the accuracy of all their rivals across dozens of datasets. Thus far, most of the research has considered only the one-dimensional case, with practitioners generalizing to the multi-dimensional case in one of two ways, dependent or independent warping. In general, it appears the community believes either that the two ways are equivalent, or that the choice is irrelevant. In this work, we show that this is not the case. The two most commonly used multi-dimensional DTW methods can produce different classifications, and neither one dominates over the other. This seems to suggest that one should learn the best method for a particular application. However, we will show that this is not necessary; a simple, principled rule can be used on a case-by-case basis to predict which of the two methods we should trust at the time of classification. Our method allows us to ensure that classification results are at least as accurate as the better of the two rival methods, and, in many cases, our method is significantly more accurate. We demonstrate our ideas with the most extensive set of multi-dimensional time series classification experiments ever attempted. PMID:29104448

The Swedish version of the multidimensional scale of perceived social support (MSPSS)--a psychometric evaluation study in women with hirsutism and nursing students.

PubMed

Ekbäck, Maria; Benzein, Eva; Lindberg, Magnus; Arestedt, Kristofer

2013-10-10

The Multidimensional Scale of Perceived Social Support (MSPSS) is a short instrument, developed to assess perceived social support. The original English version has been widely used. The original scale has demonstrated satisfactory psychometric properties in different settings, but no validated Swedish version has been available. The aim was therefore to translate, adapt and psychometrically evaluate the Multidimensional Scale of Perceived Social Support for use in a Swedish context. In total 281 participants accepted to join the study, a main sample of 127 women with hirsutism and a reference sample of 154 nursing students. The MSPSS was translated and culturally adapted according to the rigorous official process approved by WHO. The psychometric evaluation included item analysis, evaluation of factor structure, known-group validity, internal consistency and reproducibility. The original three-factor structure was reproduced in the main sample of women with hirsutism. An equivalent factor structure was demonstrated in a cross-validation, based on the reference sample of nursing students. Known-group validity was supported and internal consistency was good for all scales (α = 0.91-0.95). The test-retest showed acceptable to very good reproducibility for the items (κw = 0.58-0.85) and the scales (ICC = 0.89-0.92; CCC = 0.89-0.92). The Swedish version of the MSPSS is a multidimensional scale with sound psychometric properties in the present study sample. The simple and short format makes it a useful tool for measuring perceived social support.

The Role of Science in Advising the Decision Making Process: A Pathway for Building Effective Climate Change Mitigation Policies in Mexico at the Local Level.

PubMed

Barraza, Roberto; Velazquez-Angulo, Gilberto; Flores-Tavizón, Edith; Romero-González, Jaime; Huertas-Cardozo, José Ignacio

2016-04-27

This study examines a pathway for building urban climate change mitigation policies by presenting a multi-dimensional and transdisciplinary approach in which technical, economic, environmental, social, and political dimensions interact. Now, more than ever, the gap between science and policymaking needs to be bridged; this will enable judicious choices to be made in regarding energy and climate change mitigation strategies, leading to positive social impacts, in particular for the populations at-risk at the local level. Through a case study in Juarez, Chihuahua, Mexico, we propose a multidimensional and transdisciplinary approach with the role of scientist as policy advisers to improve the role of science in decision-making on mitigation policies at the local level in Mexico.

Xarray: multi-dimensional data analysis in Python

NASA Astrophysics Data System (ADS)

Hoyer, Stephan; Hamman, Joe; Maussion, Fabien

2017-04-01

xarray (http://xarray.pydata.org) is an open source project and Python package that provides a toolkit and data structures for N-dimensional labeled arrays, which are the bread and butter of modern geoscientific data analysis. Key features of the package include label-based indexing and arithmetic, interoperability with the core scientific Python packages (e.g., pandas, NumPy, Matplotlib, Cartopy), out-of-core computation on datasets that don't fit into memory, a wide range of input/output options, and advanced multi-dimensional data manipulation tools such as group-by and resampling. In this contribution we will present the key features of the library and demonstrate its great potential for a wide range of applications, from (big-)data processing on super computers to data exploration in front of a classroom.

Visualizing Big Data Outliers through Distributed Aggregation.

PubMed

Wilkinson, Leland

2017-08-29

Visualizing outliers in massive datasets requires statistical pre-processing in order to reduce the scale of the problem to a size amenable to rendering systems like D3, Plotly or analytic systems like R or SAS. This paper presents a new algorithm, called hdoutliers, for detecting multidimensional outliers. It is unique for a) dealing with a mixture of categorical and continuous variables, b) dealing with big-p (many columns of data), c) dealing with big-n (many rows of data), d) dealing with outliers that mask other outliers, and e) dealing consistently with unidimensional and multidimensional datasets. Unlike ad hoc methods found in many machine learning papers, hdoutliers is based on a distributional model that allows outliers to be tagged with a probability. This critical feature reduces the likelihood of false discoveries.

Multidimensional Perfectionism and the Self

ERIC Educational Resources Information Center

Ward, Andrew M.; Ashby, Jeffrey S.

2008-01-01

This study examined multidimensional perfectionism and self-development. Two hundred seventy-one undergraduates completed a measure of multidimensional perfectionism and two Kohutian measures designed to measure aspects of self-development including social connectedness, social assurance, goal instability (idealization), and grandiosity. The…

Chemical space visualization: transforming multidimensional chemical spaces into similarity-based molecular networks.

PubMed

de la Vega de León, Antonio; Bajorath, Jürgen

2016-09-01

The concept of chemical space is of fundamental relevance for medicinal chemistry and chemical informatics. Multidimensional chemical space representations are coordinate-based. Chemical space networks (CSNs) have been introduced as a coordinate-free representation. A computational approach is presented for the transformation of multidimensional chemical space into CSNs. The design of transformation CSNs (TRANS-CSNs) is based upon a similarity function that directly reflects distance relationships in original multidimensional space. TRANS-CSNs provide an immediate visualization of coordinate-based chemical space and do not require the use of dimensionality reduction techniques. At low network density, TRANS-CSNs are readily interpretable and make it possible to evaluate structure-activity relationship information originating from multidimensional chemical space.

Converging evidence for an impact of a functional NOS gene variation on anxiety-related processes.

PubMed

Kuhn, Manuel; Haaker, Jan; Glotzbach-Schoon, Evelyn; Schümann, Dirk; Andreatta, Marta; Mechias, Marie-Luise; Raczka, Karolina; Gartmann, Nina; Büchel, Christian; Mühlberger, Andreas; Pauli, Paul; Reif, Andreas; Kalisch, Raffael; Lonsdorf, Tina B

2016-05-01

Being a complex phenotype with substantial heritability, anxiety and related phenotypes are characterized by a complex polygenic basis. Thereby, one candidate pathway is neuronal nitric oxide (NO) signaling, and accordingly, rodent studies have identified NO synthase (NOS-I), encoded by NOS1, as a strong molecular candidate for modulating anxiety and hippocampus-dependent learning processes. Using a multi-dimensional and -methodological replication approach, we investigated the impact of a functional promoter polymorphism (NOS1-ex1f-VNTR) on human anxiety-related phenotypes in a total of 1019 healthy controls in five different studies. Homozygous carriers of the NOS1-ex1f short-allele displayed enhanced trait anxiety, worrying and depression scores. Furthermore, short-allele carriers were characterized by increased anxious apprehension during contextual fear conditioning. While autonomous measures (fear-potentiated startle) provided only suggestive evidence for a modulatory role of NOS1-ex1f-VNTR on (contextual) fear conditioning processes, neural activation at the amygdala/anterior hippocampus junction was significantly increased in short-allele carriers during context conditioning. Notably, this could not be attributed to morphological differences. In accordance with data from a plethora of rodent studies, we here provide converging evidence from behavioral, subjective, psychophysiological and neuroimaging studies in large human cohorts that NOS-I plays an important role in anxious apprehension but provide only limited evidence for a role in (contextual) fear conditioning. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Statistical Analysis of the Processes Controlling Choline and Ethanolamine Glycerophospholipid Molecular Species Composition

PubMed Central

Kiebish, Michael A.; Yang, Kui; Han, Xianlin; Gross, Richard W.; Chuang, Jeffrey

2012-01-01

The regulation and maintenance of the cellular lipidome through biosynthetic, remodeling, and catabolic mechanisms are critical for biological homeostasis during development, health and disease. These complex mechanisms control the architectures of lipid molecular species, which have diverse yet highly regulated fatty acid chains at both the sn1 and sn2 positions. Phosphatidylcholine (PC) and phosphatidylethanolamine (PE) serve as the predominant biophysical scaffolds in membranes, acting as reservoirs for potent lipid signals and regulating numerous enzymatic processes. Here we report the first rigorous computational dissection of the mechanisms influencing PC and PE molecular architectures from high-throughput shotgun lipidomic data. Using novel statistical approaches, we have analyzed multidimensional mass spectrometry-based shotgun lipidomic data from developmental mouse heart and mature mouse heart, lung, brain, and liver tissues. We show that in PC and PE, sn1 and sn2 positions are largely independent, though for low abundance species regulatory processes may interact with both the sn1 and sn2 chain simultaneously, leading to cooperative effects. Chains with similar biochemical properties appear to be remodeled similarly. We also see that sn2 positions are more regulated than sn1, and that PC exhibits stronger cooperative effects than PE. A key aspect of our work is a novel statistically rigorous approach to determine cooperativity based on a modified Fisher's exact test using Markov Chain Monte Carlo sampling. This computational approach provides a novel tool for developing mechanistic insight into lipidomic regulation. PMID:22662143

Multidimensional poverty and catastrophic health spending in the mountainous regions of Myanmar, Nepal and India.

PubMed

Mohanty, Sanjay K; Agrawal, Nand Kishor; Mahapatra, Bidhubhusan; Choudhury, Dhrupad; Tuladhar, Sabarnee; Holmgren, E Valdemar

2017-01-18

Economic burden to households due to out-of-pocket expenditure (OOPE) is large in many Asian countries. Though studies suggest increasing household poverty due to high OOPE in developing countries, studies on association of multidimensional poverty and household health spending is limited. This paper tests the hypothesis that the multidimensionally poor are more likely to incur catastrophic health spending cutting across countries. Data from the Poverty and Vulnerability Assessment (PVA) Survey carried out by the International Center for Integrated Mountain Development (ICIMOD) has been used in the analyses. The PVA survey was a comprehensive household survey that covered the mountainous regions of India, Nepal and Myanmar. A total of 2647 households from India, 2310 households in Nepal and 4290 households in Myanmar covered under the PVA survey. Poverty is measured in a multidimensional framework by including the dimensions of education, income and energy, water and sanitation using the Alkire and Foster method. Health shock is measured using the frequency of illness, family sickness and death of any family member in a reference period of one year. Catastrophic health expenditure is defined as 40% above the household's capacity to pay. Results suggest that about three-fifths of the population in Myanmar, two-fifths of the population in Nepal and one-third of the population in India are multidimensionally poor. About 47% of the multidimensionally poor in India had incurred catastrophic health spending compared to 35% of the multidimensionally non-poor and the pattern was similar in both Nepal and Myanmar. The odds of incurring catastrophic health spending was 56% more among the multidimensionally poor than among the multidimensionally non-poor [95% CI: 1.35-1.76]. While health shocks to households are consistently significant predictors of catastrophic health spending cutting across country of residence, the educational attainment of the head of the household is not significant. The multidimensionally poor in the poorer regions are more likely to face health shocks and are less likely to afford professional health services. Increasing government spending on health and increasing households' access to health insurance can reduce catastrophic health spending and multidimensional poverty.

Quantifying multi-dimensional attributes of human activities at various geographic scales based on smartphone tracking.

PubMed

Zhou, Xiaolu; Li, Dongying

2018-05-09

Advancement in location-aware technologies, and information and communication technology in the past decades has furthered our knowledge of the interaction between human activities and the built environment. An increasing number of studies have collected data regarding individual activities to better understand how the environment shapes human behavior. Despite this growing interest, some challenges exist in collecting and processing individual's activity data, e.g., capturing people's precise environmental contexts and analyzing data at multiple spatial scales. In this study, we propose and implement an innovative system that integrates smartphone-based step tracking with an app and the sequential tile scan techniques to collect and process activity data. We apply the OpenStreetMap tile system to aggregate positioning points at various scales. We also propose duration, step and probability surfaces to quantify the multi-dimensional attributes of activities. Results show that, by running the app in the background, smartphones can measure multi-dimensional attributes of human activities, including space, duration, step, and location uncertainty at various spatial scales. By coordinating Global Positioning System (GPS) sensor with accelerometer sensor, this app can save battery which otherwise would be drained by GPS sensor quickly. Based on a test dataset, we were able to detect the recreational center and sports center as the space where the user was most active, among other places visited. The methods provide techniques to address key issues in analyzing human activity data. The system can support future studies on behavioral and health consequences related to individual's environmental exposure.

Multidimensional analysis of the frequencies and rates of cytokine secretion from single cells by quantitative microengraving.

PubMed

Han, Qing; Bradshaw, Elizabeth M; Nilsson, Björn; Hafler, David A; Love, J Christopher

2010-06-07

The large diversity of cells that comprise the human immune system requires methods that can resolve the individual contributions of specific subsets to an immunological response. Microengraving is process that uses a dense, elastomeric array of microwells to generate microarrays of proteins secreted from large numbers of individual live cells (approximately 10(4)-10(5) cells/assay). In this paper, we describe an approach based on this technology to quantify the rates of secretion from single immune cells. Numerical simulations of the microengraving process indicated an operating regime between 30 min-4 h that permits quantitative analysis of the rates of secretion. Through experimental validation, we demonstrate that microengraving can provide quantitative measurements of both the frequencies and the distribution in rates of secretion for up to four cytokines simultaneously released from individual viable primary immune cells. The experimental limits of detection ranged from 0.5 to 4 molecules/s for IL-6, IL-17, IFNgamma, IL-2, and TNFalpha. These multidimensional measures resolve the number and intensities of responses by cells exposed to stimuli with greater sensitivity than single-parameter assays for cytokine release. We show that cells from different donors exhibit distinct responses based on both the frequency and magnitude of cytokine secretion when stimulated under different activating conditions. Primary T cells with specific profiles of secretion can also be recovered after microengraving for subsequent expansion in vitro. These examples demonstrate the utility of quantitative, multidimensional profiles of single cells for analyzing the diversity and dynamics of immune responses in vitro and for identifying rare cells from clinical samples.

OsiriX: an open-source software for navigating in multidimensional DICOM images.

PubMed

Rosset, Antoine; Spadola, Luca; Ratib, Osman

2004-09-01

A multidimensional image navigation and display software was designed for display and interpretation of large sets of multidimensional and multimodality images such as combined PET-CT studies. The software is developed in Objective-C on a Macintosh platform under the MacOS X operating system using the GNUstep development environment. It also benefits from the extremely fast and optimized 3D graphic capabilities of the OpenGL graphic standard widely used for computer games optimized for taking advantage of any hardware graphic accelerator boards available. In the design of the software special attention was given to adapt the user interface to the specific and complex tasks of navigating through large sets of image data. An interactive jog-wheel device widely used in the video and movie industry was implemented to allow users to navigate in the different dimensions of an image set much faster than with a traditional mouse or on-screen cursors and sliders. The program can easily be adapted for very specific tasks that require a limited number of functions, by adding and removing tools from the program's toolbar and avoiding an overwhelming number of unnecessary tools and functions. The processing and image rendering tools of the software are based on the open-source libraries ITK and VTK. This ensures that all new developments in image processing that could emerge from other academic institutions using these libraries can be directly ported to the OsiriX program. OsiriX is provided free of charge under the GNU open-source licensing agreement at http://homepage.mac.com/rossetantoine/osirix.

An introduction to multidimensional measurement using Rasch models.

PubMed

Briggs, Derek C; Wilson, Mark

2003-01-01

The act of constructing a measure requires a number of important assumptions. Principle among these assumptions is that the construct is unidimensional. In practice there are many instances when the assumption of unidimensionality does not hold, and where the application of a multidimensional measurement model is both technically appropriate and substantively advantageous. In this paper we illustrate the usefulness of a multidimensional approach to measurement with the Multidimensional Random Coefficient Multinomial Logit (MRCML) model, an extension of the unidimensional Rasch model. An empirical example is taken from a collection of embedded assessments administered to 541 students enrolled in middle school science classes with a hands-on science curriculum. Student achievement on these assessments are multidimensional in nature, but can also be treated as consecutive unidimensional estimates, or as is most common, as a composite unidimensional estimate. Structural parameters are estimated for each model using ConQuest, and model fit is compared. Student achievement in science is also compared across models. The multidimensional approach has the best fit to the data, and provides more reliable estimates of student achievement than under the consecutive unidimensional approach. Finally, at an interpretational level, the multidimensional approach may well provide richer information to the classroom teacher about the nature of student achievement.

A Framework for Robust Multivariable Optimization of Integrated Circuits in Space Applications

NASA Technical Reports Server (NTRS)

DuMonthier, Jeffrey; Suarez, George

2013-01-01

Application Specific Integrated Circuit (ASIC) design for space applications involves multiple challenges of maximizing performance, minimizing power and ensuring reliable operation in extreme environments. This is a complex multidimensional optimization problem which must be solved early in the development cycle of a system due to the time required for testing and qualification severely limiting opportunities to modify and iterate. Manual design techniques which generally involve simulation at one or a small number of corners with a very limited set of simultaneously variable parameters in order to make the problem tractable are inefficient and not guaranteed to achieve the best possible results within the performance envelope defined by the process and environmental requirements. What is required is a means to automate design parameter variation, allow the designer to specify operational constraints and performance goals, and to analyze the results in a way which facilitates identifying the tradeoffs defining the performance envelope over the full set of process and environmental corner cases. The system developed by the Mixed Signal ASIC Group (MSAG) at the Goddard Space Flight Center is implemented as framework of software modules, templates and function libraries. It integrates CAD tools and a mathematical computing environment, and can be customized for new circuit designs with only a modest amount of effort as most common tasks are already encapsulated. Customization is required for simulation test benches to determine performance metrics and for cost function computation. Templates provide a starting point for both while toolbox functions minimize the code required. Once a test bench has been coded to optimize a particular circuit, it is also used to verify the final design. The combination of test bench and cost function can then serve as a template for similar circuits or be re-used to migrate the design to different processes by re-running it with the new process specific device models. The system has been used in the design of time to digital converters for laser ranging and time-of-flight mass spectrometry to optimize analog, mixed signal and digital circuits such as charge sensitive amplifiers, comparators, delay elements, radiation tolerant dual interlocked (DICE) flip-flops and two of three voter gates.

The Tunneling Method for Global Optimization in Multidimensional Scaling.

ERIC Educational Resources Information Center

Groenen, Patrick J. F.; Heiser, Willem J.

1996-01-01

A tunneling method for global minimization in multidimensional scaling is introduced and adjusted for multidimensional scaling with general Minkowski distances. The method alternates a local search step with a tunneling step in which a different configuration is sought with the same STRESS implementation. (SLD)

Multidimensional Poverty and Health Status as a Predictor of Chronic Income Poverty.

PubMed

Callander, Emily J; Schofield, Deborah J

2015-12-01

Longitudinal analysis of Wave 5 to 10 of the nationally representative Household, Income and Labour Dynamics in Australia dataset was undertaken to assess whether multidimensional poverty status can predict chronic income poverty. Of those who were multidimensionally poor (low income plus poor health or poor health and insufficient education attainment) in 2007, and those who were in income poverty only (no other forms of disadvantage) in 2007, a greater proportion of those in multidimensional poverty continued to be in income poverty for the subsequent 5 years through to 2012. People who were multidimensionally poor in 2007 had 2.17 times the odds of being in income poverty each year through to 2012 than those who were in income poverty only in 2005 (95% CI: 1.23-3.83). Multidimensional poverty measures are a useful tool for policymakers to identify target populations for policies aiming to improve equity and reduce chronic disadvantage. Copyright © 2014 John Wiley & Sons, Ltd.

The resolution capability of an irregularly sampled dataset: With application to Geosat altimeter data

NASA Technical Reports Server (NTRS)

Chelton, Dudley B.; Schlax, Michael G.

1994-01-01

A formalism is presented for determining the wavenumber-frequency transfer function associated with an irregularly sampled multidimensional dataset. This transfer function reveals the filtering characteristics and aliasing patterns inherent in the sample design. In combination with information about the spectral characteristics of the signal, the transfer function can be used to quantify the spatial and temporal resolution capability of the dataset. Application of the method to idealized Geosat altimeter data (i.e., neglecting measurement errors and data dropouts) concludes that the Geosat orbit configuration is capable of resolving scales of about 3 deg in latitude and longitude by about 30 days.

Enhancing Student Motivation and Engagement: The Effects of a Multidimensional Intervention

ERIC Educational Resources Information Center

Martin, Andrew J.

2008-01-01

The present study sought to investigate the effects of a multidimensional educational intervention on high school students' motivation and engagement. The intervention incorporated: (a) multidimensional targets of motivation and engagement, (b) empirically derived intervention methodology, (c) research-based risk and protective factors, (d)…

Linguistic and Literacy Predictors of Early Spelling in First and Second Language Learners

ERIC Educational Resources Information Center

Keilty, Megan; Harrison, Gina L.

2015-01-01

Error analyses using a multidimensional measure were conducted on the misspellings of Kindergarten children speaking English as a first (EL1) and English as a second language (ESL) in order to detect any differences in early spelling ability between language groups. Oral vocabulary, syntactic knowledge, phonological processing, letter/word…

Intensifying Innovation Adoption in Educational eHealth

ERIC Educational Resources Information Center

Rissanen, M. K.

2014-01-01

In demanding innovation areas such as eHealth, the primary emphasis is easily placed on the product and process quality aspects in the design phase. Customer quality may receive adequate attention when the target audience is well-defined. But if the multidimensional evaluative focus does not get enough space until the implementation phase, this…

Atypical Neural Self-Representation in Autism

ERIC Educational Resources Information Center

Lombardo, Michael V.; Chakrabarti, Bhismadev; Bullmore, Edward T.; Sadek, Susan A.; Pasco, Greg; Wheelwright, Sally J.; Suckling, John; Baron-Cohen, Simon

2010-01-01

The "self" is a complex multidimensional construct deeply embedded and in many ways defined by our relations with the social world. Individuals with autism are impaired in both self-referential and other-referential social cognitive processing. Atypical neural representation of the self may be a key to understanding the nature of such impairments.…

Teaching and Learning Forgiveness: A Multidimensional Approach

ERIC Educational Resources Information Center

Malcolm, Lois; Ramsey, Janet

2006-01-01

This essay seeks to illumine the teaching and learning of the practice of forgiveness by relating a range of theoretical perspectives (theological, psychological, and socio-cultural) to the process of cultivating the practical wisdom needed for forgiveness. We discuss how a Trinitarian "epistemology of the cross" might lead one to a new way of…

Negotiating Family-Centered Early Education: A Multi-Dimensional Assessment of Interests and Needs.

ERIC Educational Resources Information Center

Burton-Maxwell, Christine; Gullo, Dominic F.

1995-01-01

Examined the priorities in early childhood education program development from the perspectives of school staff and families. The results revealed important differences between the staff and family perspectives and indicated a need for greater staff training in the processes of delivering relationship-based, consumer-driven family services, and in…

A Futures Study of Internationalization of the Carlson School of Management: Diverse Perspectives of Key Stakeholders

ERIC Educational Resources Information Center

D'Angelo, Anne Marie

2010-01-01

Internationalization is a multi-faceted, multi-dimensional and complex concept described most notably as a higher educational process that integrates an international perspective into its organizational leadership, vision, and curricular goals. Success is dependent upon ongoing engagement of a multitude of internal and external stakeholders with…

Methods for evaluating information in managing the enterprise on the basis of a hybrid three-tier system

NASA Astrophysics Data System (ADS)

Vasil'ev, V. A.; Dobrynina, N. V.

2017-01-01

The article presents data on the influence of information upon the functioning of complex systems in the process of ensuring their effective management. Ways and methods for evaluating multidimensional information that reduce time and resources, improve the validity of the studied system management decisions, were proposed.

On the Four Types of Characteristics of Super Mathematically Gifted Students

ERIC Educational Resources Information Center

Leikin, Roza; Leikin, Mark; Paz-Baruch, Nurit; Waisman, Ilana; Lev, Miri

2017-01-01

In order to achieve the present study's goal--to understand better the phenomenon of mathematical giftedness--we performed a multidimensional examination of the mental processing in students who exhibited mathematical expertise (EM) at the secondary school level. The study included participants from the three groups: students who excelled in…

Interdisciplinary Teaching in Elementary Schools: Educating English Language Learner (ELL) Students with Multidimensional Practices

ERIC Educational Resources Information Center

Rodriguez-Valls, Fernando

2012-01-01

School accountability has funnelled educational practices into a path where teaching practices are heavily centred in Language Arts instruction. Focusing learning almost exclusively in the aforesaid area develops a one-dimensional process that could hold back certain students from a well-balanced education. This article presents a model of…

Theory of Mind and Empathy as Multidimensional Constructs: Neurological Foundations

ERIC Educational Resources Information Center

Dvash, Jonathan; Shamay-Tsoory, Simone G.

2014-01-01

Empathy describes an individual's ability to understand and feel the other. In this article, we review recent theoretical approaches to the study of empathy. Recent evidence supports 2 possible empathy systems: an emotional system and a cognitive system. These processes are served by separate, albeit interacting, brain networks. When a cognitive…

Multidimensional spectral load balancing

DOEpatents

Hendrickson, Bruce A.; Leland, Robert W.

1996-12-24

A method of and apparatus for graph partitioning involving the use of a plurality of eigenvectors of the Laplacian matrix of the graph of the problem for which load balancing is desired. The invention is particularly useful for optimizing parallel computer processing of a problem and for minimizing total pathway lengths of integrated circuits in the design stage.

The Most Frequent Metacognitive Strategies Used in Reading Comprehension among ESP Learners

ERIC Educational Resources Information Center

Khoshsima, Hooshang; Samani, Elham Amiri

2015-01-01

Reading strategies are plans for solving problems encountered during reading while learners are deeply engage with the text. So, comprehension is not a simple decoding of symbols, but a complex multidimensional process in which the leaner draws on previous schemata applying strategies consciously. In fact, metacognitive strategies are accessible…

Multidimensional Assessment of Criminal Recidivism: Problems, Pitfalls, and Proposed Solutions

ERIC Educational Resources Information Center

Vrieze, Scott I.; Grove, William M.

2010-01-01

All states have statutes in place to civilly commit individuals at high risk for violence. The authors address difficulties in assessing such risk but use as an example the task of predicting sexual violence recidivism; the principles espoused here generalize to predicting all violence. As part of the commitment process, mental health…

Real-Time Data Warehousing and On-Line Analytical Processing at Aberdeen Test Center’s Distributed Center

DTIC Science & Technology

2005-12-01

data collected via on-board instrumentation -VxWorks based computer. Each instrument produces a continuous time history record of up to 250...data in multidimensional hierarchies and views. UGC 2005 Institute a high performance data warehouse • PostgreSQL 7.4 installed on dedicated filesystem

Global Journal of Computer Science and Technology. Volume 9, Issue 5 (Ver. 2.0)

ERIC Educational Resources Information Center

Dixit, R. K.

2010-01-01

This is a special issue published in version 1.0 of "Global Journal of Computer Science and Technology." Articles in this issue include: (1) [Theta] Scheme (Orthogonal Milstein Scheme), a Better Numerical Approximation for Multi-dimensional SDEs (Klaus Schmitz Abe); (2) Input Data Processing Techniques in Intrusion Detection…

Providing Elementary Students Equitable Access to Notebook Computers by Empowering Three School Communities in Shared Decision Making.

ERIC Educational Resources Information Center

Despot, Paula C.

This practicum was designed to provide elementary students from low-socioeconomic school communities equitable opportunities to use notebook computer technology in the communication process. A multi-dimensional staff development program was designed and conducted to integrate computer technology in the classroom. Students and their families were…

Pain and emotion as predictive factors of interoception in fibromyalgia

PubMed Central

Borg, Céline; Chouchou, Florian; Dayot-Gorlero, Jenny; Zimmerman, Perrine; Maudoux, Delphine; Laurent, Bernard; Michael, George A

2018-01-01

Introduction This study investigated interoception in fibromyalgia (FM), a disorder characterized by chronic pain accompanied by mood deregulation. Based on observations on the relationship between somatosensory processing and pain in FM and considering the affective symptoms of this disorder, we tested in FM three dimensions of interoception: interoceptive accuracy (IA), interoceptive awareness (IAW) and interoceptive sensibility (IS). Materials and methods Twenty-one female FM patients (Mage = 50.3) and 21 female matched controls (Mage = 46.3) completed a heartbeat tracking task as an assessment of IA, rated confidence in their responses as a measure of IAW and completed the Multidimensional Assessment of Interoceptive Awareness as a measure of IS. Furthermore, they completed self-report scales that, according to a principal component analysis, targeted anxiety, emotional consciousness and pain-related affect and reactions. Results Multiple regression analyses showed that increased pain-related affect and reactions decrease IA in FM. When the results of each group were examined separately, such effect was found only in FM patients. On its turn, IS was predicted by emotional consciousness and pain-related affect and reactions, but these effects did not differ between FM and controls. Finally, none of the variables we used predicted IAW. Discussion Pain-related affect and reactions in FM patients can reduce their interoceptive ability. Our results help to better understand the integration between bodily signals and emotional processing in chronic pain. PMID:29719416

Robust Multivariable Optimization and Performance Simulation for ASIC Design

NASA Technical Reports Server (NTRS)

DuMonthier, Jeffrey; Suarez, George

2013-01-01

Application-specific-integrated-circuit (ASIC) design for space applications involves multiple challenges of maximizing performance, minimizing power, and ensuring reliable operation in extreme environments. This is a complex multidimensional optimization problem, which must be solved early in the development cycle of a system due to the time required for testing and qualification severely limiting opportunities to modify and iterate. Manual design techniques, which generally involve simulation at one or a small number of corners with a very limited set of simultaneously variable parameters in order to make the problem tractable, are inefficient and not guaranteed to achieve the best possible results within the performance envelope defined by the process and environmental requirements. What is required is a means to automate design parameter variation, allow the designer to specify operational constraints and performance goals, and to analyze the results in a way that facilitates identifying the tradeoffs defining the performance envelope over the full set of process and environmental corner cases. The system developed by the Mixed Signal ASIC Group (MSAG) at the Goddard Space Flight Center is implemented as a framework of software modules, templates, and function libraries. It integrates CAD tools and a mathematical computing environment, and can be customized for new circuit designs with only a modest amount of effort as most common tasks are already encapsulated. Customization is required for simulation test benches to determine performance metrics and for cost function computation.

Multi-Dimensional Scaling based grouping of known complexes and intelligent protein complex detection.

PubMed

Rehman, Zia Ur; Idris, Adnan; Khan, Asifullah

2018-06-01

Protein-Protein Interactions (PPI) play a vital role in cellular processes and are formed because of thousands of interactions among proteins. Advancements in proteomics technologies have resulted in huge PPI datasets that need to be systematically analyzed. Protein complexes are the locally dense regions in PPI networks, which extend important role in metabolic pathways and gene regulation. In this work, a novel two-phase protein complex detection and grouping mechanism is proposed. In the first phase, topological and biological features are extracted for each complex, and prediction performance is investigated using Bagging based Ensemble classifier (PCD-BEns). Performance evaluation through cross validation shows improvement in comparison to CDIP, MCode, CFinder and PLSMC methods Second phase employs Multi-Dimensional Scaling (MDS) for the grouping of known complexes by exploring inter complex relations. It is experimentally observed that the combination of topological and biological features in the proposed approach has greatly enhanced prediction performance for protein complex detection, which may help to understand various biological processes, whereas application of MDS based exploration may assist in grouping potentially similar complexes. Copyright © 2018 Elsevier Ltd. All rights reserved.

Metadynamics convergence law in a multidimensional system

NASA Astrophysics Data System (ADS)

Crespo, Yanier; Marinelli, Fabrizio; Pietrucci, Fabio; Laio, Alessandro

2010-05-01

Metadynamics is a powerful sampling technique that uses a nonequilibrium history-dependent process to reconstruct the free-energy surface as a function of the relevant collective variables s . In Bussi [Phys. Rev. Lett. 96, 090601 (2006)] it is proved that, in a Langevin process, metadynamics provides an unbiased estimate of the free energy F(s) . We here study the convergence properties of this approach in a multidimensional system, with a Hamiltonian depending on several variables. Specifically, we show that in a Monte Carlo metadynamics simulation of an Ising model the time average of the history-dependent potential converge to F(s) with the same law of an umbrella sampling performed in optimal conditions (i.e., with a bias exactly equal to the negative of the free energy). Remarkably, after a short transient, the error becomes approximately independent on the filling speed, showing that even in out-of-equilibrium conditions metadynamics allows recovering an accurate estimate of F(s) . These results have been obtained introducing a functional form of the history-dependent potential that avoids the onset of systematic errors near the boundaries of the free-energy landscape.

Metadynamics convergence law in a multidimensional system.

PubMed

Crespo, Yanier; Marinelli, Fabrizio; Pietrucci, Fabio; Laio, Alessandro

2010-05-01

Metadynamics is a powerful sampling technique that uses a nonequilibrium history-dependent process to reconstruct the free-energy surface as a function of the relevant collective variables s . In Bussi [Phys. Rev. Lett. 96, 090601 (2006)] it is proved that, in a Langevin process, metadynamics provides an unbiased estimate of the free energy F(s) . We here study the convergence properties of this approach in a multidimensional system, with a Hamiltonian depending on several variables. Specifically, we show that in a Monte Carlo metadynamics simulation of an Ising model the time average of the history-dependent potential converge to F(s) with the same law of an umbrella sampling performed in optimal conditions (i.e., with a bias exactly equal to the negative of the free energy). Remarkably, after a short transient, the error becomes approximately independent on the filling speed, showing that even in out-of-equilibrium conditions metadynamics allows recovering an accurate estimate of F(s) . These results have been obtained introducing a functional form of the history-dependent potential that avoids the onset of systematic errors near the boundaries of the free-energy landscape.

Bayesian nonparametric clustering in phylogenetics: modeling antigenic evolution in influenza.

PubMed

Cybis, Gabriela B; Sinsheimer, Janet S; Bedford, Trevor; Rambaut, Andrew; Lemey, Philippe; Suchard, Marc A

2018-01-30

Influenza is responsible for up to 500,000 deaths every year, and antigenic variability represents much of its epidemiological burden. To visualize antigenic differences across many viral strains, antigenic cartography methods use multidimensional scaling on binding assay data to map influenza antigenicity onto a low-dimensional space. Analysis of such assay data ideally leads to natural clustering of influenza strains of similar antigenicity that correlate with sequence evolution. To understand the dynamics of these antigenic groups, we present a framework that jointly models genetic and antigenic evolution by combining multidimensional scaling of binding assay data, Bayesian phylogenetic machinery and nonparametric clustering methods. We propose a phylogenetic Chinese restaurant process that extends the current process to incorporate the phylogenetic dependency structure between strains in the modeling of antigenic clusters. With this method, we are able to use the genetic information to better understand the evolution of antigenicity throughout epidemics, as shown in applications of this model to H1N1 influenza. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Emotional competencies in geriatric nursing: empirical evidence from a computer based large scale assessment calibration study.

PubMed

Kaspar, Roman; Hartig, Johannes

2016-03-01

The care of older people was described as involving substantial emotion-related affordances. Scholars in vocational training and nursing disagree whether emotion-related skills could be conceptualized and assessed as a professional competence. Studies on emotion work and empathy regularly neglect the multidimensionality of these phenomena and their relation to the care process, and are rarely conclusive with respect to nursing behavior in practice. To test the status of emotion-related skills as a facet of client-directed geriatric nursing competence, 402 final-year nursing students from 24 German schools responded to a 62-item computer-based test. 14 items were developed to represent emotion-related affordances. Multi-dimensional IRT modeling was employed to assess a potential subdomain structure. Emotion-related test items did not form a separate subdomain, and were found to be discriminating across the whole competence continuum. Tasks concerning emotion work and empathy are reliable indicators for various levels of client-directed nursing competence. Claims for a distinct emotion-related competence in geriatric nursing, however, appear excessive with a process-oriented perspective.

Compressed Continuous Computation v. 12/20/2016

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gorodetsky, Alex

2017-02-17

A library for performing numerical computation with low-rank functions. The (C3) library enables performing continuous linear and multilinear algebra with multidimensional functions. Common tasks include taking "matrix" decompositions of vector- or matrix-valued functions, approximating multidimensional functions in low-rank format, adding or multiplying functions together, integrating multidimensional functions.

The Discriminating Power of Items that Measure More than One Dimension.

ERIC Educational Resources Information Center

Reckase, Mark D.

The work presented in this paper defined conceptually the concepts of multidimensional discrimination and information, derived mathematical expressions for the concepts for a particular multidimensional item response theory (IRT) model, and applied the concepts to actual test data. Multidimensional discrimination was defined as a function of the…

Multidimensional Computerized Adaptive Testing for Indonesia Junior High School Biology

ERIC Educational Resources Information Center

Kuo, Bor-Chen; Daud, Muslem; Yang, Chih-Wei

2015-01-01

This paper describes a curriculum-based multidimensional computerized adaptive test that was developed for Indonesia junior high school Biology. In adherence to the Indonesian curriculum of different Biology dimensions, 300 items was constructed, and then tested to 2238 students. A multidimensional random coefficients multinomial logit model was…

Supervised and Unsupervised Learning of Multidimensional Acoustic Categories

ERIC Educational Resources Information Center

Goudbeek, Martijn; Swingley, Daniel; Smits, Roel

2009-01-01

Learning to recognize the contrasts of a language-specific phonemic repertoire can be viewed as forming categories in a multidimensional psychophysical space. Research on the learning of distributionally defined visual categories has shown that categories defined over 1 dimension are easy to learn and that learning multidimensional categories is…

Health, Wealth and Wisdom: Exploring Multidimensional Inequality in a Developing Country

ERIC Educational Resources Information Center

Nilsson, Therese

2010-01-01

Despite a broad theoretical literature on multidimensional inequality and a widespread belief that welfare is not synonymous to income--not the least in a developing context--empirical inequality examinations rarely includes several welfare attributes. We explore three techniques on how to evaluate multidimensional inequality using Zambian…

Multidimensional Physical Self-Concept of Athletes with Physical Disabilities

ERIC Educational Resources Information Center

Shapiro, Deborah R.; Martin, Jeffrey J.

2010-01-01

The purposes of this investigation were first to predict reported PA (physical activity) behavior and self-esteem using a multidimensional physical self-concept model and second to describe perceptions of multidimensional physical self-concept (e.g., strength, endurance, sport competence) among athletes with physical disabilities. Athletes (N =…

Method of data mining including determining multidimensional coordinates of each item using a predetermined scalar similarity value for each item pair

DOEpatents

Meyers, Charles E.; Davidson, George S.; Johnson, David K.; Hendrickson, Bruce A.; Wylie, Brian N.

1999-01-01

A method of data mining represents related items in a multidimensional space. Distance between items in the multidimensional space corresponds to the extent of relationship between the items. The user can select portions of the space to perceive. The user also can interact with and control the communication of the space, focusing attention on aspects of the space of most interest. The multidimensional spatial representation allows more ready comprehension of the structure of the relationships among the items.

A Conceptual Model for Multidimensional Analysis of Documents

NASA Astrophysics Data System (ADS)

Ravat, Franck; Teste, Olivier; Tournier, Ronan; Zurlfluh, Gilles

Data warehousing and OLAP are mainly used for the analysis of transactional data. Nowadays, with the evolution of Internet, and the development of semi-structured data exchange format (such as XML), it is possible to consider entire fragments of data such as documents as analysis sources. As a consequence, an adapted multidimensional analysis framework needs to be provided. In this paper, we introduce an OLAP multidimensional conceptual model without facts. This model is based on the unique concept of dimensions and is adapted for multidimensional document analysis. We also provide a set of manipulation operations.

A review of snapshot multidimensional optical imaging: measuring photon tags in parallel

PubMed Central

Gao, Liang; Wang, Lihong V.

2015-01-01

Multidimensional optical imaging has seen remarkable growth in the past decade. Rather than measuring only the two-dimensional spatial distribution of light, as in conventional photography, multidimensional optical imaging captures light in up to nine dimensions, providing unprecedented information about incident photons’ spatial coordinates, emittance angles, wavelength, time, and polarization. Multidimensional optical imaging can be accomplished either by scanning or parallel acquisition. Compared with scanning-based imagers, parallel acquisition—also dubbed snapshot imaging—has a prominent advantage in maximizing optical throughput, particularly when measuring a datacube of high dimensions. Here, we first categorize snapshot multidimensional imagers based on their acquisition and image reconstruction strategies, then highlight the snapshot advantage in the context of optical throughput, and finally we discuss their state-of-the-art implementations and applications. PMID:27134340

Progress in multi-dimensional upwind differencing

NASA Technical Reports Server (NTRS)

Vanleer, Bram

1992-01-01

Multi-dimensional upwind-differencing schemes for the Euler equations are reviewed. On the basis of the first-order upwind scheme for a one-dimensional convection equation, the two approaches to upwind differencing are discussed: the fluctuation approach and the finite-volume approach. The usual extension of the finite-volume method to the multi-dimensional Euler equations is not entirely satisfactory, because the direction of wave propagation is always assumed to be normal to the cell faces. This leads to smearing of shock and shear waves when these are not grid-aligned. Multi-directional methods, in which upwind-biased fluxes are computed in a frame aligned with a dominant wave, overcome this problem, but at the expense of robustness. The same is true for the schemes incorporating a multi-dimensional wave model not based on multi-dimensional data but on an 'educated guess' of what they could be. The fluctuation approach offers the best possibilities for the development of genuinely multi-dimensional upwind schemes. Three building blocks are needed for such schemes: a wave model, a way to achieve conservation, and a compact convection scheme. Recent advances in each of these components are discussed; putting them all together is the present focus of a worldwide research effort. Some numerical results are presented, illustrating the potential of the new multi-dimensional schemes.

The Role of Science in Advising the Decision Making Process: A Pathway for Building Effective Climate Change Mitigation Policies in Mexico at the Local Level

PubMed Central

Barraza, Roberto; Velazquez-Angulo, Gilberto; Flores-Tavizón, Edith; Romero-González, Jaime; Huertas-Cardozo, José Ignacio

2016-01-01

This study examines a pathway for building urban climate change mitigation policies by presenting a multi-dimensional and transdisciplinary approach in which technical, economic, environmental, social, and political dimensions interact. Now, more than ever, the gap between science and policymaking needs to be bridged; this will enable judicious choices to be made in regarding energy and climate change mitigation strategies, leading to positive social impacts, in particular for the populations at-risk at the local level. Through a case study in Juarez, Chihuahua, Mexico, we propose a multidimensional and transdisciplinary approach with the role of scientist as policy advisers to improve the role of science in decision-making on mitigation policies at the local level in Mexico. PMID:27128933

Impact of a multidimensional infection control strategy on catheter-associated urinary tract infection rates in the adult intensive care units of 15 developing countries: findings of the International Nosocomial Infection Control Consortium (INICC).

PubMed

Rosenthal, V D; Todi, S K; Álvarez-Moreno, C; Pawar, M; Karlekar, A; Zeggwagh, A A; Mitrev, Z; Udwadia, F E; Navoa-Ng, J A; Chakravarthy, M; Salomao, R; Sahu, S; Dilek, A; Kanj, S S; Guanche-Garcell, H; Cuéllar, L E; Ersoz, G; Nevzat-Yalcin, A; Jaggi, N; Medeiros, E A; Ye, G; Akan, Ö A; Mapp, T; Castañeda-Sabogal, A; Matta-Cortés, L; Sirmatel, F; Olarte, N; Torres-Hernández, H; Barahona-Guzmán, N; Fernández-Hidalgo, R; Villamil-Gómez, W; Sztokhamer, D; Forciniti, S; Berba, R; Turgut, H; Bin, C; Yang, Y; Pérez-Serrato, I; Lastra, C E; Singh, S; Ozdemir, D; Ulusoy, S

2012-10-01

We aimed to evaluate the impact of a multidimensional infection control strategy for the reduction of the incidence of catheter-associated urinary tract infection (CAUTI) in patients hospitalized in adult intensive care units (AICUs) of hospitals which are members of the International Nosocomial Infection Control Consortium (INICC), from 40 cities of 15 developing countries: Argentina, Brazil, China, Colombia, Costa Rica, Cuba, India, Lebanon, Macedonia, Mexico, Morocco, Panama, Peru, Philippines, and Turkey. We conducted a prospective before-after surveillance study of CAUTI rates on 56,429 patients hospitalized in 57 AICUs, during 360,667 bed-days. The study was divided into the baseline period (Phase 1) and the intervention period (Phase 2). In Phase 1, active surveillance was performed. In Phase 2, we implemented a multidimensional infection control approach that included: (1) a bundle of preventive measures, (2) education, (3) outcome surveillance, (4) process surveillance, (5) feedback of CAUTI rates, and (6) feedback of performance. The rates of CAUTI obtained in Phase 1 were compared with the rates obtained in Phase 2, after interventions were implemented. We recorded 253,122 urinary catheter (UC)-days: 30,390 in Phase 1 and 222,732 in Phase 2. In Phase 1, before the intervention, the CAUTI rate was 7.86 per 1,000 UC-days, and in Phase 2, after intervention, the rate of CAUTI decreased to 4.95 per 1,000 UC-days [relative risk (RR) 0.63 (95% confidence interval [CI] 0.55-0.72)], showing a 37% rate reduction. Our study showed that the implementation of a multidimensional infection control strategy is associated with a significant reduction in the CAUTI rate in AICUs from developing countries.

Impact of an International Nosocomial Infection Control Consortium multidimensional approach on catheter-associated urinary tract infections in adult intensive care units in the Philippines: International Nosocomial Infection Control Consortium (INICC) findings.

PubMed

Navoa-Ng, Josephine Anne; Berba, Regina; Rosenthal, Victor D; Villanueva, Victoria D; Tolentino, María Corazon V; Genuino, Glenn Angelo S; Consunji, Rafael J; Mantaring, Jacinto Blas V

2013-10-01

To assess the impact of a multidimensional infection control approach on the reduction of catheter-associated urinary tract infection (CAUTI) rates in adult intensive care units (AICUs) in two hospitals in the Philippines that are members of the International Nosocomial Infection Control Consortium. This was a before-after prospective active surveillance study to determine the rates of CAUTI in 3183 patients hospitalized in 4 ICUS over 14,426 bed-days. The study was divided into baseline and intervention periods. During baseline, surveillance was performed using the definitions of the US Centers for Disease Control and Prevention and the National Healthcare Safety Network (CDC/NHSN). During intervention, we implemented a multidimensional approach that included: (1) a bundle of infection control interventions, (2) education, (3) surveillance of CAUTI rates, (4) feedback on CAUTI rates, (5) process surveillance and (6) performance feedback. We used random effects Poisson regression to account for the clustering of CAUTI rates across time. We recorded 8720 urinary catheter (UC)-days: 819 at baseline and 7901 during intervention. The rate of CAUTI was 11.0 per 1000 UC-days at baseline and was decreased by 76% to 2.66 per 1000 UC-days during intervention [rate ratio [RR], 0.24; 95% confidence interval [CI], 0.11-0.53; P-value, 0.0001]. Our multidimensional approach was associated with a significant reduction in the CAUTI rates in the ICU setting of a limited-resource country. Copyright © 2013 King Saud Bin Abdulaziz University for Health Sciences. Published by Elsevier Ltd. All rights reserved.

The INTERGROWTH-21st Project Neurodevelopment Package: A Novel Method for the Multi-Dimensional Assessment of Neurodevelopment in Pre-School Age Children

PubMed Central

Fernandes, Michelle; Stein, Alan; Newton, Charles R.; Cheikh-Ismail, Leila; Kihara, Michael; Wulff, Katharina; de León Quintana, Enrique; Aranzeta, Luis; Soria-Frisch, Aureli; Acedo, Javier; Ibanez, David; Abubakar, Amina; Giuliani, Francesca; Lewis, Tamsin; Kennedy, Stephen; Villar, Jose

2014-01-01

Background The International Fetal and Newborn Growth Consortium for the 21st Century (INTERGROWTH-21st) Project is a population-based, longitudinal study describing early growth and development in an optimally healthy cohort of 4607 mothers and newborns. At 24 months, children are assessed for neurodevelopmental outcomes with the INTERGROWTH-21st Neurodevelopment Package. This paper describes neurodevelopment tools for preschoolers and the systematic approach leading to the development of the Package. Methods An advisory panel shortlisted project-specific criteria (such as multi-dimensional assessments and suitability for international populations) to be fulfilled by a neurodevelopment instrument. A literature review of well-established tools for preschoolers revealed 47 candidates, none of which fulfilled all the project's criteria. A multi-dimensional assessment was, therefore, compiled using a package-based approach by: (i) categorizing desired outcomes into domains, (ii) devising domain-specific criteria for tool selection, and (iii) selecting the most appropriate measure for each domain. Results The Package measures vision (Cardiff tests); cortical auditory processing (auditory evoked potentials to a novelty oddball paradigm); and cognition, language skills, behavior, motor skills and attention (the INTERGROWTH-21st Neurodevelopment Assessment) in 35–45 minutes. Sleep-wake patterns (actigraphy) are also assessed. Tablet-based applications with integrated quality checks and automated, wireless electroencephalography make the Package easy to administer in the field by non-specialist staff. The Package is in use in Brazil, India, Italy, Kenya and the United Kingdom. Conclusions The INTERGROWTH-21st Neurodevelopment Package is a multi-dimensional instrument measuring early child development (ECD). Its developmental approach may be useful to those involved in large-scale ECD research and surveillance efforts. PMID:25423589

The Comprehensive Geriatric Assessment and the multidimensional approach. A new look at the older patient with gastroenterological disorders.

PubMed

Pilotto, Alberto; Addante, Filomena; D'Onofrio, Grazia; Sancarlo, Daniele; Ferrucci, Luigi

2009-01-01

The Comprehensive Geriatric Assessment (CGA) is a multidimensional, usually interdisciplinary, diagnostic process intended to determine an elderly person's medical, psychosocial, and functional capacity and problems with the objective of developing an overall plan for treatment and short- and long-term follow-up. The potential usefulness of the CGA in evaluating treatment and follow-up of older patients with gastroenterological disorders is unknown. In the paper we reported the efficacy of a Multidimensional-Prognostic Index (MPI), calculated from information collected by a standardized CGA, in predicting mortality risk in older patients hospitalized with upper gastrointestinal bleeding and liver cirrhosis. Patients underwent a CGA that included six standardized scales, i.e. Activities of Daily Living (ADL), Instrumental Activities of Daily Living (IADL), Short-Portable Mental Status Questionnaire (SPMSQ), Mini-Nutritional Assessment (MNA), Exton-Smith Score (ESS) and Comorbity Index Rating Scale (CIRS), as well as information on medication history and cohabitation, for a total of 63 items. The MPI was calculated from the integrated total scores and expressed as MPI 1=low risk, MPI 2=moderate risk and MPI 3=severe risk of mortality. Higher MPI values were significantly associated with higher short- and long-term mortality in older patients with both upper gastrointestinal bleeding and liver cirrhosis. A close agreement was found between the estimated mortality by MPI and the observed mortality. Moreover, MPI seems to have a greater discriminatory power than organ-specific prognostic indices such as Rockall and Blatchford scores (in upper gastrointestinal bleeding patients) and Child-Plugh score (in liver cirrhosis patients). All these findings support the concept that a multidimensional approach may be appropriate for the evaluation of older patients with gastroenterological disorders, like it has been reported for patients with other pathological conditions.

The relation between cognitive and metacognitive strategic processing during a science simulation.

PubMed

Dinsmore, Daniel L; Zoellner, Brian P

2018-03-01

This investigation was designed to uncover the relations between students' cognitive and metacognitive strategies used during a complex climate simulation. While cognitive strategy use during science inquiry has been studied, the factors related to this strategy use, such as concurrent metacognition, prior knowledge, and prior interest, have not been investigated in a multidimensional fashion. This study addressed current issues in strategy research by examining not only how metacognitive, surface-level, and deep-level strategies influence performance, but also how these strategies related to each other during a contextually relevant science simulation. The sample for this study consisted of 70 undergraduates from a mid-sized Southeastern university in the United States. These participants were recruited from both physical and life science (e.g., biology) and education majors to obtain a sample with variance in terms of their prior knowledge, interest, and strategy use. Participants completed measures of prior knowledge and interest about global climate change. Then, they were asked to engage in an online climate simulator for up to 30 min while thinking aloud. Finally, participants were asked to answer three outcome questions about global climate change. Results indicated a poor fit for the statistical model of the frequency and level of processing predicting performance. However, a statistical model that independently examined the influence of metacognitive monitoring and control of cognitive strategies showed a very strong relation between the metacognitive and cognitive strategies. Finally, smallest space analysis results provided evidence that strategy use may be better captured in a multidimensional fashion, particularly with attention paid towards the combination of strategies employed. Conclusions drawn from the evidence point to the need for more dynamic, multidimensional models of strategic processing that account for the patterns of optimal and non-optimal strategy use. Additionally, analyses that can capture these complex patterns need to be further explored. © 2017 The British Psychological Society.

Modelling spatiotemporal change using multidimensional arrays Meng

NASA Astrophysics Data System (ADS)

Lu, Meng; Appel, Marius; Pebesma, Edzer

2017-04-01

The large variety of remote sensors, model simulations, and in-situ records provide great opportunities to model environmental change. The massive amount of high-dimensional data calls for methods to integrate data from various sources and to analyse spatiotemporal and thematic information jointly. An array is a collection of elements ordered and indexed in arbitrary dimensions, which naturally represent spatiotemporal phenomena that are identified by their geographic locations and recording time. In addition, array regridding (e.g., resampling, down-/up-scaling), dimension reduction, and spatiotemporal statistical algorithms are readily applicable to arrays. However, the role of arrays in big geoscientific data analysis has not been systematically studied: How can arrays discretise continuous spatiotemporal phenomena? How can arrays facilitate the extraction of multidimensional information? How can arrays provide a clean, scalable and reproducible change modelling process that is communicable between mathematicians, computer scientist, Earth system scientist and stakeholders? This study emphasises on detecting spatiotemporal change using satellite image time series. Current change detection methods using satellite image time series commonly analyse data in separate steps: 1) forming a vegetation index, 2) conducting time series analysis on each pixel, and 3) post-processing and mapping time series analysis results, which does not consider spatiotemporal correlations and ignores much of the spectral information. Multidimensional information can be better extracted by jointly considering spatial, spectral, and temporal information. To approach this goal, we use principal component analysis to extract multispectral information and spatial autoregressive models to account for spatial correlation in residual based time series structural change modelling. We also discuss the potential of multivariate non-parametric time series structural change methods, hierarchical modelling, and extreme event detection methods to model spatiotemporal change. We show how array operations can facilitate expressing these methods, and how the open-source array data management and analytics software SciDB and R can be used to scale the process and make it easily reproducible.

Airborne multidimensional integrated remote sensing system

NASA Astrophysics Data System (ADS)

Xu, Weiming; Wang, Jianyu; Shu, Rong; He, Zhiping; Ma, Yanhua

2006-12-01

In this paper, we present a kind of airborne multidimensional integrated remote sensing system that consists of an imaging spectrometer, a three-line scanner, a laser ranger, a position & orientation subsystem and a stabilizer PAV30. The imaging spectrometer is composed of two sets of identical push-broom high spectral imager with a field of view of 22°, which provides a field of view of 42°. The spectral range of the imaging spectrometer is from 420nm to 900nm, and its spectral resolution is 5nm. The three-line scanner is composed of two pieces of panchromatic CCD and a RGB CCD with 20° stereo angle and 10cm GSD(Ground Sample Distance) with 1000m flying height. The laser ranger can provide height data of three points every other four scanning lines of the spectral imager and those three points are calibrated to match the corresponding pixels of the spectral imager. The post-processing attitude accuracy of POS/AV 510 used as the position & orientation subsystem, which is the aerial special exterior parameters measuring product of Canadian Applanix Corporation, is 0.005° combined with base station data. The airborne multidimensional integrated remote sensing system was implemented successfully, performed the first flying experiment on April, 2005, and obtained satisfying data.

Modulation format identification aided hitless flexible coherent transceiver.

PubMed

Xiang, Meng; Zhuge, Qunbi; Qiu, Meng; Zhou, Xingyu; Zhang, Fangyuan; Tang, Ming; Liu, Deming; Fu, Songnian; Plant, David V

2016-07-11

We propose a hitless flexible coherent transceiver enabled by a novel modulation format identification (MFI) scheme for dynamic agile optical networks. The modulation format transparent digital signal processing (DSP) is realized by a block-wise decision-directed least-mean-square (DD-LMS) equalizer for channel tracking, and a pilot symbol aided superscalar phase locked loop (PLL) for carrier phase estimation (CPE). For the MFI, the modulation format information is encoded onto the pilot symbols initially used for CPE. Therefore, the proposed MFI method does not require extra overhead. Moreover, it can identify arbitrary modulation formats including multi-dimensional formats, and it enables tracking of the format change for short data blocks. The performance of the proposed hitless flexible coherent transceiver is successfully evaluated with five modulation formats including QPSK, 16QAM, 64QAM, Hybrid QPSK/8QAM and set-partitioning (SP)-512-QAM. We show that the proposed MFI method induces a negligible performance penalty. Moreover, we experimentally demonstrate that such a hitless transceiver can adapt to fast block-by-block modulation format switching. Finally, the performance improvement of the proposed MFI method is experimentally verified with respect to other commonly used MFI methods.

Modeling the Behavior of an Underwater Acoustic Relative Positioning System Based on Complementary Set of Sequences

PubMed Central

Aparicio, Joaquín; Jiménez, Ana; Álvarez, Fernando J.; Ureña, Jesús; De Marziani, Carlos; Diego, Cristina

2011-01-01

The great variability usually found in underwater media makes modeling a challenging task, but helpful for better understanding or predicting the performance of future deployed systems. In this work, an underwater acoustic propagation model is presented. This model obtains the multipath structure by means of the ray tracing technique. Using this model, the behavior of a relative positioning system is presented. One of the main advantages of relative positioning systems is that only the distances between all the buoys are needed to obtain their positions. In order to obtain the distances, the propagation times of acoustic signals coded by Complementary Set of Sequences (CSS) are used. In this case, the arrival instants are obtained by means of correlation processes. The distances are then used to obtain the position of the buoys by means of the Multidimensional Scaling Technique (MDS). As an early example of an application using this relative positioning system, a tracking of the position of the buoys at different times is performed. With this tracking, the surface current of a particular region could be studied. The performance of the system is evaluated in terms of the distance from the real position to the estimated one. PMID:22247661

High-Level Performance Modeling of SAR Systems

NASA Technical Reports Server (NTRS)

Chen, Curtis

2006-01-01

SAUSAGE (Still Another Utility for SAR Analysis that s General and Extensible) is a computer program for modeling (see figure) the performance of synthetic- aperture radar (SAR) or interferometric synthetic-aperture radar (InSAR or IFSAR) systems. The user is assumed to be familiar with the basic principles of SAR imaging and interferometry. Given design parameters (e.g., altitude, power, and bandwidth) that characterize a radar system, the software predicts various performance metrics (e.g., signal-to-noise ratio and resolution). SAUSAGE is intended to be a general software tool for quick, high-level evaluation of radar designs; it is not meant to capture all the subtleties, nuances, and particulars of specific systems. SAUSAGE was written to facilitate the exploration of engineering tradeoffs within the multidimensional space of design parameters. Typically, this space is examined through an iterative process of adjusting the values of the design parameters and examining the effects of the adjustments on the overall performance of the system at each iteration. The software is designed to be modular and extensible to enable consideration of a variety of operating modes and antenna beam patterns, including, for example, strip-map and spotlight SAR acquisitions, polarimetry, burst modes, and squinted geometries.

Optical image encryption method based on incoherent imaging and polarized light encoding

NASA Astrophysics Data System (ADS)

Wang, Q.; Xiong, D.; Alfalou, A.; Brosseau, C.

2018-05-01

We propose an incoherent encoding system for image encryption based on a polarized encoding method combined with an incoherent imaging. Incoherent imaging is the core component of this proposal, in which the incoherent point-spread function (PSF) of the imaging system serves as the main key to encode the input intensity distribution thanks to a convolution operation. An array of retarders and polarizers is placed on the input plane of the imaging structure to encrypt the polarized state of light based on Mueller polarization calculus. The proposal makes full use of randomness of polarization parameters and incoherent PSF so that a multidimensional key space is generated to deal with illegal attacks. Mueller polarization calculus and incoherent illumination of imaging structure ensure that only intensity information is manipulated. Another key advantage is that complicated processing and recording related to a complex-valued signal are avoided. The encoded information is just an intensity distribution, which is advantageous for data storage and transition because information expansion accompanying conventional encryption methods is also avoided. The decryption procedure can be performed digitally or using optoelectronic devices. Numerical simulation tests demonstrate the validity of the proposed scheme.

NeuroPhysics: Studying how neurons create the perception of space-time using Physics' tools and techniques

NASA Astrophysics Data System (ADS)

Dhingra, Shonali; Sandler, Roman; Rios, Rodrigo; Vuong, Cliff; Mehta, Mayank

All animals naturally perceive the abstract concept of space-time. A brain region called the Hippocampus is known to be important in creating these perceptions, but the underlying mechanisms are unknown. In our lab we employ several experimental and computational techniques from Physics to tackle this fundamental puzzle. Experimentally, we use ideas from Nanoscience and Materials Science to develop techniques to measure the activity of hippocampal neurons, in freely-behaving animals. Computationally, we develop models to study neuronal activity patterns, which are point processes that are highly stochastic and multidimensional. We then apply these techniques to collect and analyze neuronal signals from rodents while they're exploring space in Real World or Virtual Reality with various stimuli. Our findings show that under these conditions neuronal activity depends on various parameters, such as sensory cues including visual and auditory, and behavioral cues including, linear and angular, position and velocity. Further, neuronal networks create internally-generated rhythms, which influence perception of space and time. In totality, these results further our understanding of how the brain develops a cognitive map of our surrounding space, and keep track of time.

Multi-fluid CFD analysis in Process Engineering

NASA Astrophysics Data System (ADS)

Hjertager, B. H.

2017-12-01

An overview of modelling and simulation of flow processes in gas/particle and gas/liquid systems are presented. Particular emphasis is given to computational fluid dynamics (CFD) models that use the multi-dimensional multi-fluid techniques. Turbulence modelling strategies for gas/particle flows based on the kinetic theory for granular flows are given. Sub models for the interfacial transfer processes and chemical kinetics modelling are presented. Examples are shown for some gas/particle systems including flow and chemical reaction in risers as well as gas/liquid systems including bubble columns and stirred tanks.

Longitudinal and Transverse Instability of Ion Acoustic Waves

NASA Astrophysics Data System (ADS)

Chapman, T.; Berger, R. L.; Cohen, B. I.; Banks, J. W.; Brunner, S.

2017-08-01

Ion acoustic waves are found to be susceptible to at least two distinct decay processes. Which process dominates depends on the parameters. In the cases examined, the decay channel where daughter modes propagate parallel to the mother mode is found to dominate at larger amplitudes, while the decay channel where the daughter modes propagate at angles to the mother mode dominates at smaller amplitudes. Both decay processes may occur simultaneously and with onset thresholds below those suggested by fluid theory, resulting in the eventual multidimensional collapse of the mother mode to a turbulent state.