Intelligent feature selection techniques for pattern classification of Lamb wave signals

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

Hinders, Mark K.; Miller, Corey A.

2014-02-18

Lamb wave interaction with flaws is a complex, three-dimensional phenomenon, which often frustrates signal interpretation schemes based on mode arrival time shifts predicted by dispersion curves. As the flaw severity increases, scattering and mode conversion effects will often dominate the time-domain signals, obscuring available information about flaws because multiple modes may arrive on top of each other. Even for idealized flaw geometries the scattering and mode conversion behavior of Lamb waves is very complex. Here, multi-mode Lamb waves in a metal plate are propagated across a rectangular flat-bottom hole in a sequence of pitch-catch measurements corresponding to the double crossholemore » tomography geometry. The flaw is sequentially deepened, with the Lamb wave measurements repeated at each flaw depth. Lamb wave tomography reconstructions are used to identify which waveforms have interacted with the flaw and thereby carry information about its depth. Multiple features are extracted from each of the Lamb wave signals using wavelets, which are then fed to statistical pattern classification algorithms that identify flaw severity. In order to achieve the highest classification accuracy, an optimal feature space is required but it’s never known a priori which features are going to be best. For structural health monitoring we make use of the fact that physical flaws, such as corrosion, will only increase over time. This allows us to identify feature vectors which are topologically well-behaved by requiring that sequential classes “line up” in feature vector space. An intelligent feature selection routine is illustrated that identifies favorable class distributions in multi-dimensional feature spaces using computational homology theory. Betti numbers and formal classification accuracies are calculated for each feature space subset to establish a correlation between the topology of the class distribution and the corresponding classification accuracy.« less

Feature space analysis of MRI

NASA Astrophysics Data System (ADS)

Soltanian-Zadeh, Hamid; Windham, Joe P.; Peck, Donald J.

1997-04-01

This paper presents development and performance evaluation of an MRI feature space method. The method is useful for: identification of tissue types; segmentation of tissues; and quantitative measurements on tissues, to obtain information that can be used in decision making (diagnosis, treatment planning, and evaluation of treatment). The steps of the work accomplished are as follows: (1) Four T2-weighted and two T1-weighted images (before and after injection of Gadolinium) were acquired for ten tumor patients. (2) Images were analyed by two image analysts according to the following algorithm. The intracranial brain tissues were segmented from the scalp and background. The additive noise was suppressed using a multi-dimensional non-linear edge- preserving filter which preserves partial volume information on average. Image nonuniformities were corrected using a modified lowpass filtering approach. The resulting images were used to generate and visualize an optimal feature space. Cluster centers were identified on the feature space. Then images were segmented into normal tissues and different zones of the tumor. (3) Biopsy samples were extracted from each patient and were subsequently analyzed by the pathology laboratory. (4) Image analysis results were compared to each other and to the biopsy results. Pre- and post-surgery feature spaces were also compared. The proposed algorithm made it possible to visualize the MRI feature space and to segment the image. In all cases, the operators were able to find clusters for normal and abnormal tissues. Also, clusters for different zones of the tumor were found. Based on the clusters marked for each zone, the method successfully segmented the image into normal tissues (white matter, gray matter, and CSF) and different zones of the lesion (tumor, cyst, edema, radiation necrosis, necrotic core, and infiltrated tumor). The results agreed with those obtained from the biopsy samples. Comparison of pre- to post-surgery and radiation feature spaces confirmed that the tumor was not present in the second study but radiation necrosis was generated as a result of radiation.

Fast multi-dimensional NMR by minimal sampling

NASA Astrophysics Data System (ADS)

Kupče, Ēriks; Freeman, Ray

2008-03-01

A new scheme is proposed for very fast acquisition of three-dimensional NMR spectra based on minimal sampling, instead of the customary step-wise exploration of all of evolution space. The method relies on prior experiments to determine accurate values for the evolving frequencies and intensities from the two-dimensional 'first planes' recorded by setting t1 = 0 or t2 = 0. With this prior knowledge, the entire three-dimensional spectrum can be reconstructed by an additional measurement of the response at a single location (t1∗,t2∗) where t1∗ and t2∗ are fixed values of the evolution times. A key feature is the ability to resolve problems of overlap in the acquisition dimension. Applied to a small protein, agitoxin, the three-dimensional HNCO spectrum is obtained 35 times faster than systematic Cartesian sampling of the evolution domain. The extension to multi-dimensional spectroscopy is outlined.

A Multidimensional Diversity‐Oriented Synthesis Strategy for Structurally Diverse and Complex Macrocycles

PubMed Central

Nie, Feilin; Kunciw, Dominique L.; Wilcke, David; Stokes, Jamie E.; Galloway, Warren R. J. D.; Bartlett, Sean; Sore, Hannah F.

2016-01-01

Abstract Synthetic macrocycles are an attractive area in drug discovery. However, their use has been hindered by a lack of versatile platforms for the generation of structurally (and thus shape) diverse macrocycle libraries. Herein, we describe a new concept in library synthesis, termed multidimensional diversity‐oriented synthesis, and its application towards macrocycles. This enabled the step‐efficient generation of a library of 45 novel, structurally diverse, and highly‐functionalized macrocycles based around a broad range of scaffolds and incorporating a wide variety of biologically relevant structural motifs. The synthesis strategy exploited the diverse reactivity of aza‐ylides and imines, and featured eight different macrocyclization methods, two of which were novel. Computational analyses reveal a broad coverage of molecular shape space by the library and provides insight into how the various diversity‐generating steps of the synthesis strategy impact on molecular shape. PMID:27484830

A multidimensional representation model of geographic features

USGS Publications Warehouse

Usery, E. Lynn; Timson, George; Coletti, Mark

2016-01-28

A multidimensional model of geographic features has been developed and implemented with data from The National Map of the U.S. Geological Survey. The model, programmed in C++ and implemented as a feature library, was tested with data from the National Hydrography Dataset demonstrating the capability to handle changes in feature attributes, such as increases in chlorine concentration in a stream, and feature geometry, such as the changing shoreline of barrier islands over time. Data can be entered directly, from a comma separated file, or features with attributes and relationships can be automatically populated in the model from data in the Spatial Data Transfer Standard format.

Phase-space reaction network on a multisaddle energy landscape: HCN isomerization.

PubMed

Li, Chun-Biu; Matsunaga, Yasuhiro; Toda, Mikito; Komatsuzaki, Tamiki

2005-11-08

By using the HCN/CNH isomerization reaction as an illustrative vehicle of chemical reactions on multisaddle energy landscapes, we give explicit visualizations of molecular motions associated with a straight-through reaction tube in the phase space inside which all reactive trajectories pass from one basin to another, with eliminating recrossing trajectories in the configuration space. This visualization provides us with a chemical intuition of how chemical species "walk along" the reaction-rate slope in the multidimensional phase space compared with the intrinsic reaction path in the configuration space. The distinct nonergodic features in the two different HCN and CNH wells can be easily demonstrated by a section of Poincare surface of section in those potential minima, which predicts in a priori the pattern of trajectories residing in the potential well. We elucidate the global phase-space structure which gives rise to the non-Markovian dynamics or the dynamical correlation of sequential multisaddle chemical reactions. The phase-space structure relevant to the controllability of the product state in chemical reactions is also discussed.

Multidimensional brain activity dictated by winner-take-all mechanisms.

PubMed

Tozzi, Arturo; Peters, James F

2018-06-21

A novel demon-based architecture is introduced to elucidate brain functions such as pattern recognition during human perception and mental interpretation of visual scenes. Starting from the topological concepts of invariance and persistence, we introduce a Selfridge pandemonium variant of brain activity that takes into account a novel feature, namely, demons that recognize short straight-line segments, curved lines and scene shapes, such as shape interior, density and texture. Low-level representations of objects can be mapped to higher-level views (our mental interpretations): a series of transformations can be gradually applied to a pattern in a visual scene, without affecting its invariant properties. This makes it possible to construct a symbolic multi-dimensional representation of the environment. These representations can be projected continuously to an object that we have seen and continue to see, thanks to the mapping from shapes in our memory to shapes in Euclidean space. Although perceived shapes are 3-dimensional (plus time), the evaluation of shape features (volume, color, contour, closeness, texture, and so on) leads to n-dimensional brain landscapes. Here we discuss the advantages of our parallel, hierarchical model in pattern recognition, computer vision and biological nervous system's evolution. Copyright © 2018 Elsevier B.V. All rights reserved.

Comparison of the effectiveness of alternative feature sets in shape retrieval of multicomponent images

NASA Astrophysics Data System (ADS)

Eakins, John P.; Edwards, Jonathan D.; Riley, K. Jonathan; Rosin, Paul L.

2001-01-01

Many different kinds of features have been used as the basis for shape retrieval from image databases. This paper investigates the relative effectiveness of several types of global shape feature, both singly and in combination. The features compared include well-established descriptors such as Fourier coefficients and moment invariants, as well as recently-proposed measures of triangularity and ellipticity. Experiments were conducted within the framework of the ARTISAN shape retrieval system, and retrieval effectiveness assessed on a database of over 10,000 images, using 24 queries and associated ground truth supplied by the UK Patent Office . Our experiments revealed only minor differences in retrieval effectiveness between different measures, suggesting that a wide variety of shape feature combinations can provide adequate discriminating power for effective shape retrieval in multi-component image collections such as trademark registries. Marked differences between measures were observed for some individual queries, suggesting that there could be considerable scope for improving retrieval effectiveness by providing users with an improved framework for searching multi-dimensional feature space.

Comparison of the effectiveness of alternative feature sets in shape retrieval of multicomponent images

NASA Astrophysics Data System (ADS)

Eakins, John P.; Edwards, Jonathan D.; Riley, K. Jonathan; Rosin, Paul L.

2000-12-01

Many different kinds of features have been used as the basis for shape retrieval from image databases. This paper investigates the relative effectiveness of several types of global shape feature, both singly and in combination. The features compared include well-established descriptors such as Fourier coefficients and moment invariants, as well as recently-proposed measures of triangularity and ellipticity. Experiments were conducted within the framework of the ARTISAN shape retrieval system, and retrieval effectiveness assessed on a database of over 10,000 images, using 24 queries and associated ground truth supplied by the UK Patent Office . Our experiments revealed only minor differences in retrieval effectiveness between different measures, suggesting that a wide variety of shape feature combinations can provide adequate discriminating power for effective shape retrieval in multi-component image collections such as trademark registries. Marked differences between measures were observed for some individual queries, suggesting that there could be considerable scope for improving retrieval effectiveness by providing users with an improved framework for searching multi-dimensional feature space.

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…

A semantic model for multimodal data mining in healthcare information systems.

PubMed

Iakovidis, Dimitris; Smailis, Christos

2012-01-01

Electronic health records (EHRs) are representative examples of multimodal/multisource data collections; including measurements, images and free texts. The diversity of such information sources and the increasing amounts of medical data produced by healthcare institutes annually, pose significant challenges in data mining. In this paper we present a novel semantic model that describes knowledge extracted from the lowest-level of a data mining process, where information is represented by multiple features i.e. measurements or numerical descriptors extracted from measurements, images, texts or other medical data, forming multidimensional feature spaces. Knowledge collected by manual annotation or extracted by unsupervised data mining from one or more feature spaces is modeled through generalized qualitative spatial semantics. This model enables a unified representation of knowledge across multimodal data repositories. It contributes to bridging the semantic gap, by enabling direct links between low-level features and higher-level concepts e.g. describing body parts, anatomies and pathological findings. The proposed model has been developed in web ontology language based on description logics (OWL-DL) and can be applied to a variety of data mining tasks in medical informatics. It utility is demonstrated for automatic annotation of medical data.

Evolutionary multidimensional access architecture featuring cost-reduced components

NASA Astrophysics Data System (ADS)

Farjady, Farsheed; Parker, Michael C.; Walker, Stuart D.

1998-12-01

We describe a three-stage wavelength-routed optical access network, utilizing coarse passband-flattened arrayed- waveguide grating routers. An N-dimensional addressing strategy enables 6912 customers to be bi-directionally addressed with multi-Gb/s data using only 24 wavelengths spaced by 1.6 nm. Coarse wavelength separation allows use of increased tolerance WDM components at the exchange and customer premises. The architecture is designed to map onto standard access network topologies, allowing elegant upgradability from legacy PON infrastructures at low cost. Passband-flattening of the routers is achieved through phase apodization.

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.

Upscaling the Coupled Water and Heat Transport in the Shallow Subsurface

NASA Astrophysics Data System (ADS)

Sviercoski, R. F.; Efendiev, Y.; Mohanty, B. P.

2018-02-01

Predicting simultaneous movement of liquid water, water vapor, and heat in the shallow subsurface has many practical interests. The demand for multidimensional multiscale models for this region is important given: (a) the critical role that these processes play in the global water and energy balances, (b) that more data from air-borne and space-borne sensors are becoming available for parameterizations of modeling efforts. On the other hand, numerical models that consider spatial variations of the soil properties, termed here as multiscale, are prohibitively expensive. Thus, there is a need for upscaled models that take into consideration these features, and be computationally affordable. In this paper, a multidimensional multiscale model coupling the water flow and heat transfer and its respective upscaled version are proposed. The formulation is novel as it describes the multidimensional and multiscale tensorial versions of the hydraulic conductivity and the vapor diffusivity, taking into account the tortuosity and porosity properties of the medium. It also includes the coupling with the energy balance equation as a boundary describing atmospheric influences at the shallow subsurface. To demonstrate the accuracy of both models, comparisons were made between simulation and field experiments for soil moisture and temperature at 2, 7, and 12 cm deep, during 11 days. The root-mean-square errors showed that the upscaled version of the system captured the multiscale features with similar accuracy. Given the good matching between simulated and field data for near-surface soil temperature, the results suggest that it can be regarded as a 1-D variable.

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.

Self-organizing neural networks--an alternative way of cluster analysis in clinical chemistry.

PubMed

Reibnegger, G; Wachter, H

1996-04-15

Supervised learning schemes have been employed by several workers for training neural networks designed to solve clinical problems. We demonstrate that unsupervised techniques can also produce interesting and meaningful results. Using a data set on the chemical composition of milk from 22 different mammals, we demonstrate that self-organizing feature maps (Kohonen networks) as well as a modified version of error backpropagation technique yield results mimicking conventional cluster analysis. Both techniques are able to project a potentially multi-dimensional input vector onto a two-dimensional space whereby neighborhood relationships remain conserved. Thus, these techniques can be used for reducing dimensionality of complicated data sets and for enhancing comprehensibility of features hidden in the data matrix.

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.

Multidimensional display controller for displaying to a user an aspect of a multidimensional space visible from a base viewing location along a desired viewing orientation

DOEpatents

Davidson, George S.; Anderson, Thomas G.

2001-01-01

A display controller allows a user to control a base viewing location, a base viewing orientation, and a relative viewing orientation. The base viewing orientation and relative viewing orientation are combined to determine a desired viewing orientation. An aspect of a multidimensional space visible from the base viewing location along the desired viewing orientation is displayed to the user. The user can change the base viewing location, base viewing orientation, and relative viewing orientation by changing the location or other properties of input objects.

LC-IMS-MS Feature Finder. Detecting Multidimensional Liquid Chromatography, Ion Mobility, and Mass Spectrometry Features in Complex Datasets

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

Crowell, Kevin L.; Slysz, Gordon W.; Baker, Erin Shammel

2013-09-05

We introduce a command line software application LC-IMS-MS Feature Finder that searches for molecular ion signatures in multidimensional liquid chromatography-ion mobility spectrometry-mass spectrometry (LC-IMS-MS) data by clustering deisotoped peaks with similar monoisotopic mass, charge state, LC elution time, and ion mobility drift time values. The software application includes an algorithm for detecting and quantifying co-eluting chemical species, including species that exist in multiple conformations that may have been separated in the IMS dimension.

Assessing Construct Validity Using Multidimensional Item Response Theory.

ERIC Educational Resources Information Center

Ackerman, Terry A.

The concept of a user-specified validity sector is discussed. The idea of the validity sector combines the work of M. D. Reckase (1986) and R. Shealy and W. Stout (1991). Reckase developed a methodology to represent an item in a multidimensional latent space as a vector. Item vectors are computed using multidimensional item response theory item…

A cross-sectional analysis of green space prevalence and mental wellbeing in England.

PubMed

Houlden, Victoria; Weich, Scott; Jarvis, Stephen

2017-05-17

With urbanisation increasing, it is important to understand how to design changing environments to promote mental wellbeing. Evidence suggests that local-area proportions of green space may be associated with happiness and life satisfaction; however, the available evidence on such associations with more broadly defined mental wellbeing in still very scarce. This study aimed to establish whether the amount of neighbourhood green space was associated with mental wellbeing. Data were drawn from Understanding Society, a national survey of 30,900 individuals across 11,096 Census Lower-Layer Super Output Areas (LSOAs) in England, over the period 2009-2010. Measures included the multi-dimensional Warwick-Edinburgh Mental Well-Being Scale (SWEMWBS) and LSOA proportion of green space, which was derived from the General Land Use Database (GLUD), and were analysed using linear regression, while controlling for individual, household and area-level factors. Those living in areas with greater proportions of green space had significantly higher mental wellbeing scores in unadjusted analyses (an expected increase of 0.17 points (95% CI 0.11, 0.23) in the SWEMWBS score for a standard deviation increase of green space). However, after adjustment for confounding by respondent sociodemographic characteristics and urban/rural location, the association was attenuated to the null (regression coefficient B = - 0.01, 95% CI -0.08, 0.05, p = 0.712). While the green space in an individual's local area has been shown through other research to be related to aspects of mental health such as happiness and life satisfaction, the association with multidimensional mental wellbeing is much less clear from our results. While we did not find a statistically significant association between the amount of green space in residents' local areas and mental wellbeing, further research is needed to understand whether other features of green space, such as accessibility, aesthetics or use, are important for mental wellbeing.

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.

Low-discrepancy sampling of parametric surface using adaptive space-filling curves (SFC)

NASA Astrophysics Data System (ADS)

Hsu, Charles; Szu, Harold

2014-05-01

Space-Filling Curves (SFCs) are encountered in different fields of engineering and computer science, especially where it is important to linearize multidimensional data for effective and robust interpretation of the information. Examples of multidimensional data are matrices, images, tables, computational grids, and Electroencephalography (EEG) sensor data resulting from the discretization of partial differential equations (PDEs). Data operations like matrix multiplications, load/store operations and updating and partitioning of data sets can be simplified when we choose an efficient way of going through the data. In many applications SFCs present just this optimal manner of mapping multidimensional data onto a one dimensional sequence. In this report, we begin with an example of a space-filling curve and demonstrate how it can be used to find the most similarity using Fast Fourier transform (FFT) through a set of points. Next we give a general introduction to space-filling curves and discuss properties of them. Finally, we consider a discrete version of space-filling curves and present experimental results on discrete space-filling curves optimized for special tasks.

Unidimensional Vertical Scaling in Multidimensional Space. Research Report. ETS RR-17-29

ERIC Educational Resources Information Center

Carlson, James E.

2017-01-01

In this paper, I consider a set of test items that are located in a multidimensional space, S[subscript M], but are located along a curved line in S[subscript M] and can be scaled unidimensionally. Furthermore, I am demonstrating a case in which the test items are administered across 6 levels, such as occurs in K-12 assessment across 6 grade…

Lost in space: design of experiments and scientific exploration in a Hogarth Universe.

PubMed

Lendrem, Dennis W; Lendrem, B Clare; Woods, David; Rowland-Jones, Ruth; Burke, Matthew; Chatfield, Marion; Isaacs, John D; Owen, Martin R

2015-11-01

A Hogarth, or 'wicked', universe is an irregular environment generating data to support erroneous beliefs. Here, we argue that development scientists often work in such a universe. We demonstrate that exploring these multidimensional spaces using small experiments guided by scientific intuition alone, gives rise to an illusion of validity and a misplaced confidence in that scientific intuition. By contrast, design of experiments (DOE) permits the efficient mapping of such complex, multidimensional spaces. We describe simulation tools that enable research scientists to explore these spaces in relative safety. Copyright © 2015 Elsevier Ltd. All rights reserved.

Implicit face prototype learning from geometric information.

PubMed

Or, Charles C-F; Wilson, Hugh R

2013-04-19

There is evidence that humans implicitly learn an average or prototype of previously studied faces, as the unseen face prototype is falsely recognized as having been learned (Solso & McCarthy, 1981). Here we investigated the extent and nature of face prototype formation where observers' memory was tested after they studied synthetic faces defined purely in geometric terms in a multidimensional face space. We found a strong prototype effect: The basic results showed that the unseen prototype averaged from the studied faces was falsely identified as learned at a rate of 86.3%, whereas individual studied faces were identified correctly 66.3% of the time and the distractors were incorrectly identified as having been learned only 32.4% of the time. This prototype learning lasted at least 1 week. Face prototype learning occurred even when the studied faces were further from the unseen prototype than the median variation in the population. Prototype memory formation was evident in addition to memory formation of studied face exemplars as demonstrated in our models. Additional studies showed that the prototype effect can be generalized across viewpoints, and head shape and internal features separately contribute to prototype formation. Thus, implicit face prototype extraction in a multidimensional space is a very general aspect of geometric face learning. Copyright © 2013 Elsevier Ltd. All rights reserved.

Time-space and cognition-space transformations for transportation network analysis based on multidimensional scaling and self-organizing map

NASA Astrophysics Data System (ADS)

Hong, Zixuan; Bian, Fuling

2008-10-01

Geographic space, time space and cognition space are three fundamental and interrelated spaces in geographic information systems for transportation. However, the cognition space and its relationships to the time space and geographic space are often neglected. This paper studies the relationships of these three spaces in urban transportation system from a new perspective and proposes a novel MDS-SOM transformation method which takes the advantages of the techniques of multidimensional scaling (MDS) and self-organizing map (SOM). The MDS-SOM transformation framework includes three kinds of mapping: the geographic-time transformation, the cognition-time transformation and the time-cognition transformation. The transformations in our research provide a better understanding of the interactions of these three spaces and beneficial knowledge is discovered to help the transportation analysis and decision supports.

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.

A theoretical framework for the associations between identity and psychopathology.

PubMed

Klimstra, Theo A; Denissen, Jaap J A

2017-11-01

Identity research largely emerged from clinical observations. Decades of empirical work advanced the field in refining existing approaches and adding new approaches. Furthermore, the existence of linkages of identity with psychopathology is now well established. Unfortunately, both the directionality of effects between identity aspects and psychopathology symptoms, and the mechanisms underlying associations are unclear. In the present paper, we present a new framework to inspire hypothesis-driven empirical research to overcome this limitation. The framework has a basic resemblance to theoretical models for the study of personality and psychopathology, so we provide examples of how these might apply to the study of identity. Next, we explain that unique features of identity may come into play in individuals suffering from psychopathology that are mostly related to the content of one's identity. These include pros and cons of identifying with one's diagnostic label. Finally, inspired by Hermans' dialogical self theory and principles derived from Piaget's, Swann's and Kelly's work, we delineate a framework with identity at the core of an individual multidimensional space. In this space, psychopathology symptoms have a known distance (representing relevance) to one's identity, and individual multidimensional spaces are connected to those of other individuals in one's social network. We discuss methodological (quantitative and qualitative, idiographic and nomothetic) and statistical procedures (multilevel models and network models) to test the framework. Resulting evidence can boost the field of identity research in demonstrating its high practical relevance for the emergence and conservation of psychopathology. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Quantiprot - a Python package for quantitative analysis of protein sequences.

PubMed

Konopka, Bogumił M; Marciniak, Marta; Dyrka, Witold

2017-07-17

The field of protein sequence analysis is dominated by tools rooted in substitution matrices and alignments. A complementary approach is provided by methods of quantitative characterization. A major advantage of the approach is that quantitative properties defines a multidimensional solution space, where sequences can be related to each other and differences can be meaningfully interpreted. Quantiprot is a software package in Python, which provides a simple and consistent interface to multiple methods for quantitative characterization of protein sequences. The package can be used to calculate dozens of characteristics directly from sequences or using physico-chemical properties of amino acids. Besides basic measures, Quantiprot performs quantitative analysis of recurrence and determinism in the sequence, calculates distribution of n-grams and computes the Zipf's law coefficient. We propose three main fields of application of the Quantiprot package. First, quantitative characteristics can be used in alignment-free similarity searches, and in clustering of large and/or divergent sequence sets. Second, a feature space defined by quantitative properties can be used in comparative studies of protein families and organisms. Third, the feature space can be used for evaluating generative models, where large number of sequences generated by the model can be compared to actually observed sequences.

PCA feature extraction for change detection in multidimensional unlabeled data.

PubMed

Kuncheva, Ludmila I; Faithfull, William J

2014-01-01

When classifiers are deployed in real-world applications, it is assumed that the distribution of the incoming data matches the distribution of the data used to train the classifier. This assumption is often incorrect, which necessitates some form of change detection or adaptive classification. While there has been a lot of work on change detection based on the classification error monitored over the course of the operation of the classifier, finding changes in multidimensional unlabeled data is still a challenge. Here, we propose to apply principal component analysis (PCA) for feature extraction prior to the change detection. Supported by a theoretical example, we argue that the components with the lowest variance should be retained as the extracted features because they are more likely to be affected by a change. We chose a recently proposed semiparametric log-likelihood change detection criterion that is sensitive to changes in both mean and variance of the multidimensional distribution. An experiment with 35 datasets and an illustration with a simple video segmentation demonstrate the advantage of using extracted features compared to raw data. Further analysis shows that feature extraction through PCA is beneficial, specifically for data with multiple balanced classes.

Hidden multidimensional social structure modeling applied to biased social perception

NASA Astrophysics Data System (ADS)

Maletić, Slobodan; Zhao, Yi

2018-02-01

Intricacies of the structure of social relations are realized by representing a collection of overlapping opinions as a simplicial complex, thus building latent multidimensional structures, through which agents are, virtually, moving as they exchange opinions. The influence of opinion space structure on the distribution of opinions is demonstrated by modeling consensus phenomena when the opinion exchange between individuals may be affected by the false consensus effect. The results indicate that in the cases with and without bias, the road toward consensus is influenced by the structure of multidimensional space of opinions, and in the biased case, complete consensus is achieved. The applications of proposed modeling framework can easily be generalized, as they transcend opinion formation modeling.

Igloo-Plot: a tool for visualization of multidimensional datasets.

PubMed

Kuntal, Bhusan K; Ghosh, Tarini Shankar; Mande, Sharmila S

2014-01-01

Advances in science and technology have resulted in an exponential growth of multivariate (or multi-dimensional) datasets which are being generated from various research areas especially in the domain of biological sciences. Visualization and analysis of such data (with the objective of uncovering the hidden patterns therein) is an important and challenging task. We present a tool, called Igloo-Plot, for efficient visualization of multidimensional datasets. The tool addresses some of the key limitations of contemporary multivariate visualization and analysis tools. The visualization layout, not only facilitates an easy identification of clusters of data-points having similar feature compositions, but also the 'marker features' specific to each of these clusters. The applicability of the various functionalities implemented herein is demonstrated using several well studied multi-dimensional datasets. Igloo-Plot is expected to be a valuable resource for researchers working in multivariate data mining studies. Igloo-Plot is available for download from: http://metagenomics.atc.tcs.com/IglooPlot/. Copyright © 2014 Elsevier Inc. All rights reserved.

A Query Expansion Framework in Image Retrieval Domain Based on Local and Global Analysis

PubMed Central

Rahman, M. M.; Antani, S. K.; Thoma, G. R.

2011-01-01

We present an image retrieval framework based on automatic query expansion in a concept feature space by generalizing the vector space model of information retrieval. In this framework, images are represented by vectors of weighted concepts similar to the keyword-based representation used in text retrieval. To generate the concept vocabularies, a statistical model is built by utilizing Support Vector Machine (SVM)-based classification techniques. The images are represented as “bag of concepts” that comprise perceptually and/or semantically distinguishable color and texture patches from local image regions in a multi-dimensional feature space. To explore the correlation between the concepts and overcome the assumption of feature independence in this model, we propose query expansion techniques in the image domain from a new perspective based on both local and global analysis. For the local analysis, the correlations between the concepts based on the co-occurrence pattern, and the metrical constraints based on the neighborhood proximity between the concepts in encoded images, are analyzed by considering local feedback information. We also analyze the concept similarities in the collection as a whole in the form of a similarity thesaurus and propose an efficient query expansion based on the global analysis. The experimental results on a photographic collection of natural scenes and a biomedical database of different imaging modalities demonstrate the effectiveness of the proposed framework in terms of precision and recall. PMID:21822350

Item Selection in Multidimensional Computerized Adaptive Testing--Gaining Information from Different Angles

ERIC Educational Resources Information Center

Wang, Chun; Chang, Hua-Hua

2011-01-01

Over the past thirty years, obtaining diagnostic information from examinees' item responses has become an increasingly important feature of educational and psychological testing. The objective can be achieved by sequentially selecting multidimensional items to fit the class of latent traits being assessed, and therefore Multidimensional…

The Gamma-Ray Burst ToolSHED is Open for Business

NASA Astrophysics Data System (ADS)

Giblin, Timothy W.; Hakkila, Jon; Haglin, David J.; Roiger, Richard J.

2004-09-01

The GRB ToolSHED, a Gamma-Ray Burst SHell for Expeditions in Data-Mining, is now online and available via a web browser to all in the scientific community. The ToolSHED is an online web utility that contains pre-processed burst attributes of the BATSE catalog and a suite of induction-based machine learning and statistical tools for classification and cluster analysis. Users create their own login account and study burst properties within user-defined multi-dimensional parameter spaces. Although new GRB attributes are periodically added to the database for user selection, the ToolSHED has a feature that allows users to upload their own burst attributes (e.g. spectral parameters, etc.) so that additional parameter spaces can be explored. A data visualization feature using GNUplot and web-based IDL has also been implemented to provide interactive plotting of user-selected session output. In an era in which GRB observations and attributes are becoming increasingly more complex, a utility such as the GRB ToolSHED may play an important role in deciphering GRB classes and understanding intrinsic burst properties.

"Just another pretty face": a multidimensional scaling approach to face attractiveness and variability.

PubMed

Potter, Timothy; Corneille, Olivier; Ruys, Kirsten I; Rhodes, Ginwan

2007-04-01

Findings on both attractiveness and memory for faces suggest that people should perceive more similarity among attractive than among unattractive faces. A multidimensional scaling approach was used to test this hypothesis in two studies. In Study 1, we derived a psychological face space from similarity ratings of attractive and unattractive Caucasian female faces. In Study 2, we derived a face space for attractive and unattractive male faces of Caucasians and non-Caucasians. Both studies confirm that attractive faces are indeed more tightly clustered than unattractive faces in people's psychological face spaces. These studies provide direct and original support for theoretical assumptions previously made in the face space and face memory literatures.

Clustering, hierarchical organization, and the topography of abstract and concrete nouns.

PubMed

Troche, Joshua; Crutch, Sebastian; Reilly, Jamie

2014-01-01

The empirical study of language has historically relied heavily upon concrete word stimuli. By definition, concrete words evoke salient perceptual associations that fit well within feature-based, sensorimotor models of word meaning. In contrast, many theorists argue that abstract words are "disembodied" in that their meaning is mediated through language. We investigated word meaning as distributed in multidimensional space using hierarchical cluster analysis. Participants (N = 365) rated target words (n = 400 English nouns) across 12 cognitive dimensions (e.g., polarity, ease of teaching, emotional valence). Factor reduction revealed three latent factors, corresponding roughly to perceptual salience, affective association, and magnitude. We plotted the original 400 words for the three latent factors. Abstract and concrete words showed overlap in their topography but also differentiated themselves in semantic space. This topographic approach to word meaning offers a unique perspective to word concreteness.

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.

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

Lexical Sophistication as a Multidimensional Phenomenon: Relations to Second Language Lexical Proficiency, Development, and Writing Quality

ERIC Educational Resources Information Center

Kim, Minkyung; Crossley, Scott A.; Kyle, Kristopher

2018-01-01

This study conceptualizes lexical sophistication as a multidimensional phenomenon by reducing numerous lexical features of lexical sophistication into 12 aggregated components (i.e., dimensions) via a principal component analysis approach. These components were then used to predict second language (L2) writing proficiency levels, holistic lexical…

A New Heterogeneous Multidimensional Unfolding Procedure

ERIC Educational Resources Information Center

Park, Joonwook; Rajagopal, Priyali; DeSarbo, Wayne S.

2012-01-01

A variety of joint space multidimensional scaling (MDS) methods have been utilized for the spatial analysis of two- or three-way dominance data involving subjects' preferences, choices, considerations, intentions, etc. so as to provide a parsimonious spatial depiction of the underlying relevant dimensions, attributes, stimuli, and/or subjects'…

Some applications of the multi-dimensional fractional order for the Riemann-Liouville derivative

NASA Astrophysics Data System (ADS)

Ahmood, Wasan Ajeel; Kiliçman, Adem

2017-01-01

In this paper, the aim of this work is to study theorem for the one-dimensional space-time fractional deriative, generalize some function for the one-dimensional fractional by table represents the fractional Laplace transforms of some elementary functions to be valid for the multi-dimensional fractional Laplace transform and give the definition of the multi-dimensional fractional Laplace transform. This study includes that, dedicate the one-dimensional fractional Laplace transform for functions of only one independent variable and develop of the one-dimensional fractional Laplace transform to multi-dimensional fractional Laplace transform based on the modified Riemann-Liouville derivative.

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.

Measuring the Perceived Quality of an AR-Based Learning Application: A Multidimensional Model

ERIC Educational Resources Information Center

Pribeanu, Costin; Balog, Alexandru; Iordache, Dragos Daniel

2017-01-01

Augmented reality (AR) technologies could enhance learning in several ways. The quality of an AR-based educational platform is a combination of key features that manifests in usability, usefulness, and enjoyment for the learner. In this paper, we present a multidimensional model to measure the quality of an AR-based application as perceived by…

Psychometric Characteristics of the Persian Version of the Multidimensional School Anger Inventory-Revised

ERIC Educational Resources Information Center

Aryadoust, Vahid; Akbarzadeh, Sanaz; Akbarzedeh, Sara

2011-01-01

The Multidimensional School Anger Inventory-Revised (MSAI-R) is a measurement tool to evaluate high school students' anger. Its psychometric features have been tested in the USA, Australia, Japan, Guatemala, and Italy. This study investigates the factor structure and psychometric quality of the Persian version of the MSAI-R using data from an…

Collaborative Sharing of Multidimensional Space-time Data Using HydroShare

NASA Astrophysics Data System (ADS)

Gan, T.; Tarboton, D. G.; Horsburgh, J. S.; Dash, P. K.; Idaszak, R.; Yi, H.; Blanton, B.

2015-12-01

HydroShare is a collaborative environment being developed for sharing hydrological data and models. It includes capability to upload data in many formats as resources that can be shared. The HydroShare data model for resources uses a specific format for the representation of each type of data and specifies metadata common to all resource types as well as metadata unique to specific resource types. The Network Common Data Form (NetCDF) was chosen as the format for multidimensional space-time data in HydroShare. NetCDF is widely used in hydrological and other geoscience modeling because it contains self-describing metadata and supports the creation of array-oriented datasets that may include three spatial dimensions, a time dimension and other user defined dimensions. For example, NetCDF may be used to represent precipitation or surface air temperature fields that have two dimensions in space and one dimension in time. This presentation will illustrate how NetCDF files are used in HydroShare. When a NetCDF file is loaded into HydroShare, header information is extracted using the "ncdump" utility. Python functions developed for the Django web framework on which HydroShare is based, extract science metadata present in the NetCDF file, saving the user from having to enter it. Where the file follows Climate Forecast (CF) convention and Attribute Convention for Dataset Discovery (ACDD) standards, metadata is thus automatically populated. Users also have the ability to add metadata to the resource that may not have been present in the original NetCDF file. HydroShare's metadata editing functionality then writes this science metadata back into the NetCDF file to maintain consistency between the science metadata in HydroShare and the metadata in the NetCDF file. This further helps researchers easily add metadata information following the CF and ACDD conventions. Additional data inspection and subsetting functions were developed, taking advantage of Python and command line libraries for working with NetCDF files. We describe the design and implementation of these features and illustrate how NetCDF files from a modeling application may be curated in HydroShare and thus enhance reproducibility of the associated research. We also discuss future development planned for multidimensional space-time data in HydroShare.

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.

Psychogenic and organic amnesia: a multidimensional assessment of clinical, neuroradiological, neuropsychological and psychopathological features.

PubMed

Serra, Laura; Fadda, Lucia; Buccione, Ivana; Caltagirone, Carlo; Carlesimo, Giovanni A

2007-01-01

Psychogenic amnesia is a complex disorder characterised by a wide variety of symptoms. Consequently, in a number of cases it is difficult distinguish it from organic memory impairment. The present study reports a new case of global psychogenic amnesia compared with two patients with amnesia underlain by organic brain damage. Our aim was to identify features useful for distinguishing between psychogenic and organic forms of memory impairment. The findings show the usefulness of a multidimensional evaluation of clinical, neuroradiological, neuropsychological and psychopathological aspects, to provide convergent findings useful for differentiating the two forms of memory disorder.

A General Multidimensional Model for the Measurement of Cultural Differences.

ERIC Educational Resources Information Center

Olmedo, Esteban L.; Martinez, Sergio R.

A multidimensional model for measuring cultural differences (MCD) based on factor analytic theory and techniques is proposed. The model assumes that a cultural space may be defined by means of a relatively small number of orthogonal dimensions which are linear combinations of a much larger number of cultural variables. Once a suitable,…

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.

A new method for mapping multidimensional data to lower dimensions

NASA Technical Reports Server (NTRS)

Gowda, K. C.

1983-01-01

A multispectral mapping method is proposed which is based on the new concept of BEND (Bidimensional Effective Normalised Difference). The method, which involves taking one sample point at a time and finding the interrelationships between its features, is found very economical from the point of view of storage and processing time. It has good dimensionality reduction and clustering properties, and is highly suitable for computer analysis of large amounts of data. The transformed values obtained by this procedure are suitable for either a planar 2-space mapping of geological sample points or for making grayscale and color images of geo-terrains. A few examples are given to justify the efficacy of the proposed procedure.

Stargate GTM: Bridging Descriptor and Activity Spaces.

PubMed

Gaspar, Héléna A; Baskin, Igor I; Marcou, Gilles; Horvath, Dragos; Varnek, Alexandre

2015-11-23

Predicting the activity profile of a molecule or discovering structures possessing a specific activity profile are two important goals in chemoinformatics, which could be achieved by bridging activity and molecular descriptor spaces. In this paper, we introduce the "Stargate" version of the Generative Topographic Mapping approach (S-GTM) in which two different multidimensional spaces (e.g., structural descriptor space and activity space) are linked through a common 2D latent space. In the S-GTM algorithm, the manifolds are trained simultaneously in two initial spaces using the probabilities in the 2D latent space calculated as a weighted geometric mean of probability distributions in both spaces. S-GTM has the following interesting features: (1) activities are involved during the training procedure; therefore, the method is supervised, unlike conventional GTM; (2) using molecular descriptors of a given compound as input, the model predicts a whole activity profile, and (3) using an activity profile as input, areas populated by relevant chemical structures can be detected. To assess the performance of S-GTM prediction models, a descriptor space (ISIDA descriptors) of a set of 1325 GPCR ligands was related to a B-dimensional (B = 1 or 8) activity space corresponding to pKi values for eight different targets. S-GTM outperforms conventional GTM for individual activities and performs similarly to the Lasso multitask learning algorithm, although it is still slightly less accurate than the Random Forest method.

Acoustic structure of the five perceptual dimensions of timbre in orchestral instrument tones

PubMed Central

Elliott, Taffeta M.; Hamilton, Liberty S.; Theunissen, Frédéric E.

2013-01-01

Attempts to relate the perceptual dimensions of timbre to quantitative acoustical dimensions have been tenuous, leading to claims that timbre is an emergent property, if measurable at all. Here, a three-pronged analysis shows that the timbre space of sustained instrument tones occupies 5 dimensions and that a specific combination of acoustic properties uniquely determines gestalt perception of timbre. Firstly, multidimensional scaling (MDS) of dissimilarity judgments generated a perceptual timbre space in which 5 dimensions were cross-validated and selected by traditional model comparisons. Secondly, subjects rated tones on semantic scales. A discriminant function analysis (DFA) accounting for variance of these semantic ratings across instruments and between subjects also yielded 5 significant dimensions with similar stimulus ordination. The dimensions of timbre space were then interpreted semantically by rotational and reflectional projection of the MDS solution into two DFA dimensions. Thirdly, to relate this final space to acoustical structure, the perceptual MDS coordinates of each sound were regressed with its joint spectrotemporal modulation power spectrum. Sound structures correlated significantly with distances in perceptual timbre space. Contrary to previous studies, most perceptual timbre dimensions are not the result of purely temporal or spectral features but instead depend on signature spectrotemporal patterns. PMID:23297911

Devaney chaos, Li-Yorke chaos, and multi-dimensional Li-Yorke chaos for topological dynamics

NASA Astrophysics Data System (ADS)

Dai, Xiongping; Tang, Xinjia

2017-11-01

Let π : T × X → X, written T↷π X, be a topological semiflow/flow on a uniform space X with T a multiplicative topological semigroup/group not necessarily discrete. We then prove: If T↷π X is non-minimal topologically transitive with dense almost periodic points, then it is sensitive to initial conditions. As a result of this, Devaney chaos ⇒ Sensitivity to initial conditions, for this very general setting. Let R+↷π X be a C0-semiflow on a Polish space; then we show: If R+↷π X is topologically transitive with at least one periodic point p and there is a dense orbit with no nonempty interior, then it is multi-dimensional Li-Yorke chaotic; that is, there is a uncountable set Θ ⊆ X such that for any k ≥ 2 and any distinct points x1 , … ,xk ∈ Θ, one can find two time sequences sn → ∞ ,tn → ∞ with Moreover, let X be a non-singleton Polish space; then we prove: Any weakly-mixing C0-semiflow R+↷π X is densely multi-dimensional Li-Yorke chaotic. Any minimal weakly-mixing topological flow T↷π X with T abelian is densely multi-dimensional Li-Yorke chaotic. Any weakly-mixing topological flow T↷π X is densely Li-Yorke chaotic. We in addition construct a completely Li-Yorke chaotic minimal SL (2 , R)-acting flow on the compact metric space R ∪ { ∞ }. Our various chaotic dynamics are sensitive to the choices of the topology of the phase semigroup/group T.

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,…

Speeded Old-New Recognition of Multidimensional Perceptual Stimuli: Modeling Performance at the Individual-Participant and Individual-Item Levels

ERIC Educational Resources Information Center

Nosofsky, Robert M.; Stanton, Roger D.

2006-01-01

Observers made speeded old-new recognition judgments of color stimuli embedded in a multidimensional similarity space. The paradigm used multiple lists but with the underlying similarity structures repeated across lists, to allow for quantitative modeling of the data at the individual-participant and individual-item levels. Correct rejection…

Exploring children's face-space: a multidimensional scaling analysis of the mental representation of facial identity.

PubMed

Nishimura, Mayu; Maurer, Daphne; Gao, Xiaoqing

2009-07-01

We explored differences in the mental representation of facial identity between 8-year-olds and adults. The 8-year-olds and adults made similarity judgments of a homogeneous set of faces (individual hair cues removed) using an "odd-man-out" paradigm. Multidimensional scaling (MDS) analyses were performed to represent perceived similarity of faces in a multidimensional space. Five dimensions accounted optimally for the judgments of both children and adults, with similar local clustering of faces. However, the fit of the MDS solutions was better for adults, in part because children's responses were more variable. More children relied predominantly on a single dimension, namely eye color, whereas adults appeared to use multiple dimensions for each judgment. The pattern of findings suggests that children's mental representation of faces has a structure similar to that of adults but that children's judgments are influenced less consistently by that overall structure.

Multidimensional Analysis of Nuclear Detonations

DTIC Science & Technology

2015-09-17

Features on the nuclear weapons testing films because of the expanding and emissive nature of the nuclear fireball. The use of these techniques to produce...Treaty (New Start Treaty) have reduced the acceptable margins of error. Multidimensional analysis provides the modern approach to nuclear weapon ...scientific community access to the information necessary to expand upon the knowledge of nuclear weapon effects. This data set has the potential to provide

Assessing efficiency of spatial sampling using combined coverage analysis in geographical and feature spaces

NASA Astrophysics Data System (ADS)

Hengl, Tomislav

2015-04-01

Efficiency of spatial sampling largely determines success of model building. This is especially important for geostatistical mapping where an initial sampling plan should provide a good representation or coverage of both geographical (defined by the study area mask map) and feature space (defined by the multi-dimensional covariates). Otherwise the model will need to extrapolate and, hence, the overall uncertainty of the predictions will be high. In many cases, geostatisticians use point data sets which are produced using unknown or inconsistent sampling algorithms. Many point data sets in environmental sciences suffer from spatial clustering and systematic omission of feature space. But how to quantify these 'representation' problems and how to incorporate this knowledge into model building? The author has developed a generic function called 'spsample.prob' (Global Soil Information Facilities package for R) and which simultaneously determines (effective) inclusion probabilities as an average between the kernel density estimation (geographical spreading of points; analysed using the spatstat package in R) and MaxEnt analysis (feature space spreading of points; analysed using the MaxEnt software used primarily for species distribution modelling). The output 'iprob' map indicates whether the sampling plan has systematically missed some important locations and/or features, and can also be used as an input for geostatistical modelling e.g. as a weight map for geostatistical model fitting. The spsample.prob function can also be used in combination with the accessibility analysis (cost of field survey are usually function of distance from the road network, slope and land cover) to allow for simultaneous maximization of average inclusion probabilities and minimization of total survey costs. The author postulates that, by estimating effective inclusion probabilities using combined geographical and feature space analysis, and by comparing survey costs to representation efficiency, an optimal initial sampling plan can be produced which satisfies both criteria: (a) good representation (i.e. within a tolerance threshold), and (b) minimized survey costs. This sampling analysis framework could become especially interesting for generating sampling plans in new areas e.g. for which no previous spatial prediction model exists. The presentation includes data processing demos with standard soil sampling data sets Ebergotzen (Germany) and Edgeroi (Australia), also available via the GSIF package.

High-Order Central WENO Schemes for Multi-Dimensional Hamilton-Jacobi Equations

NASA Technical Reports Server (NTRS)

Bryson, Steve; Levy, Doron; Biegel, Bryan (Technical Monitor)

2002-01-01

We present new third- and fifth-order Godunov-type central schemes for approximating solutions of the Hamilton-Jacobi (HJ) equation in an arbitrary number of space dimensions. These are the first central schemes for approximating solutions of the HJ equations with an order of accuracy that is greater than two. In two space dimensions we present two versions for the third-order scheme: one scheme that is based on a genuinely two-dimensional Central WENO reconstruction, and another scheme that is based on a simpler dimension-by-dimension reconstruction. The simpler dimension-by-dimension variant is then extended to a multi-dimensional fifth-order scheme. Our numerical examples in one, two and three space dimensions verify the expected order of accuracy of the schemes.

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.

Lip-reading aids word recognition most in moderate noise: a Bayesian explanation using high-dimensional feature space.

PubMed

Ma, Wei Ji; Zhou, Xiang; Ross, Lars A; Foxe, John J; Parra, Lucas C

2009-01-01

Watching a speaker's facial movements can dramatically enhance our ability to comprehend words, especially in noisy environments. From a general doctrine of combining information from different sensory modalities (the principle of inverse effectiveness), one would expect that the visual signals would be most effective at the highest levels of auditory noise. In contrast, we find, in accord with a recent paper, that visual information improves performance more at intermediate levels of auditory noise than at the highest levels, and we show that a novel visual stimulus containing only temporal information does the same. We present a Bayesian model of optimal cue integration that can explain these conflicts. In this model, words are regarded as points in a multidimensional space and word recognition is a probabilistic inference process. When the dimensionality of the feature space is low, the Bayesian model predicts inverse effectiveness; when the dimensionality is high, the enhancement is maximal at intermediate auditory noise levels. When the auditory and visual stimuli differ slightly in high noise, the model makes a counterintuitive prediction: as sound quality increases, the proportion of reported words corresponding to the visual stimulus should first increase and then decrease. We confirm this prediction in a behavioral experiment. We conclude that auditory-visual speech perception obeys the same notion of optimality previously observed only for simple multisensory stimuli.

Defect-Repairable Latent Feature Extraction of Driving Behavior via a Deep Sparse Autoencoder

PubMed Central

Taniguchi, Tadahiro; Takenaka, Kazuhito; Bando, Takashi

2018-01-01

Data representing driving behavior, as measured by various sensors installed in a vehicle, are collected as multi-dimensional sensor time-series data. These data often include redundant information, e.g., both the speed of wheels and the engine speed represent the velocity of the vehicle. Redundant information can be expected to complicate the data analysis, e.g., more factors need to be analyzed; even varying the levels of redundancy can influence the results of the analysis. We assume that the measured multi-dimensional sensor time-series data of driving behavior are generated from low-dimensional data shared by the many types of one-dimensional data of which multi-dimensional time-series data are composed. Meanwhile, sensor time-series data may be defective because of sensor failure. Therefore, another important function is to reduce the negative effect of defective data when extracting low-dimensional time-series data. This study proposes a defect-repairable feature extraction method based on a deep sparse autoencoder (DSAE) to extract low-dimensional time-series data. In the experiments, we show that DSAE provides high-performance latent feature extraction for driving behavior, even for defective sensor time-series data. In addition, we show that the negative effect of defects on the driving behavior segmentation task could be reduced using the latent features extracted by DSAE. PMID:29462931

Rethinking language in autism.

PubMed

Sterponi, Laura; de Kirby, Kenton; Shankey, Jennifer

2015-07-01

In this article, we invite a rethinking of traditional perspectives of language in autism. We advocate a theoretical reappraisal that offers a corrective to the dominant and largely tacitly held view that language, in its essence, is a referential system and a reflection of the individual's cognition. Drawing on scholarship in Conversation Analysis and linguistic anthropology, we present a multidimensional view of language, showing how it also functions as interactional accomplishment, social action, and mode of experience. From such a multidimensional perspective, we revisit data presented by other researchers that include instances of prototypical features of autistic speech, giving them a somewhat different-at times complementary, at times alternative-interpretation. In doing so, we demonstrate that there is much at stake in the view of language that we as researchers bring to our analysis of autistic speech. Ultimately, we argue that adopting a multidimensional view of language has wide ranging implications, deepening our understanding of autism's core features and developmental trajectory. © The Author(s) 2014.

Similarity of the Multidimensional Space Defined by Parallel Forms of a Mathematics Test.

ERIC Educational Resources Information Center

Reckase, Mark D.; And Others

The purpose of the paper is to determine whether test forms of the Mathematics Usage Test (AAP Math) of the American College Testing Program are parallel in a multidimensional sense. The AAP Math is an achievement test of mathematics concepts acquired by high school students by the end of their third year. To determine the dimensionality of the…

Exploring Children's Face-Space: A Multidimensional Scaling Analysis of the Mental Representation of Facial Identity

ERIC Educational Resources Information Center

Nishimura, Mayu; Maurer, Daphne; Gao, Xiaoqing

2009-01-01

We explored differences in the mental representation of facial identity between 8-year-olds and adults. The 8-year-olds and adults made similarity judgments of a homogeneous set of faces (individual hair cues removed) using an "odd-man-out" paradigm. Multidimensional scaling (MDS) analyses were performed to represent perceived similarity of faces…

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…

Self-consistent pseudopotential calculation of the bulk properties of Mo and W

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

Zunger, A.; Cohen, M.L.

The bulk properties of Mo and W are calculated using the recently developed momentum-space approach for calculating total energy via a nonlocal pseudopotential. This approach avoids any shape approximation to the variational charge density (e.g., muffin tins), is fully self-consistent, and replaces the multidimensional and multicenter integrals akin to real-space representations by simple and readily convergent reciprocal-space lattice sums. We use first-principles atomic pseudopotentials which have been previously demonstrated to yield band structures and charge densities for both semiconductors and transition metals in good agreement with experiment and all-electron calculations. Using a mixed-basis representation for the crystalline wave function, wemore » are able to accurately reproduce both the localized and itinerant features of the electronic states in these systems. These first-principles pseudopotentials, together with the self-consistent density-functional representation for both the exchange and the correlation screening, yields agreement with experiment of 0.2% in the lattice parameters, 2% and 11% for the binding energies of Mo and W, respectively, and 12% and 7% for the bulk moduli of Mo and W, respectively.« less

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.

Hypergraph Based Feature Selection Technique for Medical Diagnosis.

PubMed

Somu, Nivethitha; Raman, M R Gauthama; Kirthivasan, Kannan; Sriram, V S Shankar

2016-11-01

The impact of internet and information systems across various domains have resulted in substantial generation of multidimensional datasets. The use of data mining and knowledge discovery techniques to extract the original information contained in the multidimensional datasets play a significant role in the exploitation of complete benefit provided by them. The presence of large number of features in the high dimensional datasets incurs high computational cost in terms of computing power and time. Hence, feature selection technique has been commonly used to build robust machine learning models to select a subset of relevant features which projects the maximal information content of the original dataset. In this paper, a novel Rough Set based K - Helly feature selection technique (RSKHT) which hybridize Rough Set Theory (RST) and K - Helly property of hypergraph representation had been designed to identify the optimal feature subset or reduct for medical diagnostic applications. Experiments carried out using the medical datasets from the UCI repository proves the dominance of the RSKHT over other feature selection techniques with respect to the reduct size, classification accuracy and time complexity. The performance of the RSKHT had been validated using WEKA tool, which shows that RSKHT had been computationally attractive and flexible over massive datasets.

Framework for analyzing ecological trait-based models in multidimensional niche spaces

NASA Astrophysics Data System (ADS)

Biancalani, Tommaso; DeVille, Lee; Goldenfeld, Nigel

2015-05-01

We develop a theoretical framework for analyzing ecological models with a multidimensional niche space. Our approach relies on the fact that ecological niches are described by sequences of symbols, which allows us to include multiple phenotypic traits. Ecological drivers, such as competitive exclusion, are modeled by introducing the Hamming distance between two sequences. We show that a suitable transform diagonalizes the community interaction matrix of these models, making it possible to predict the conditions for niche differentiation and, close to the instability onset, the asymptotically long time population distributions of niches. We exemplify our method using the Lotka-Volterra equations with an exponential competition kernel.

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.

Interactions across Multiple Stimulus Dimensions in Primary Auditory Cortex.

PubMed

Sloas, David C; Zhuo, Ran; Xue, Hongbo; Chambers, Anna R; Kolaczyk, Eric; Polley, Daniel B; Sen, Kamal

2016-01-01

Although sensory cortex is thought to be important for the perception of complex objects, its specific role in representing complex stimuli remains unknown. Complex objects are rich in information along multiple stimulus dimensions. The position of cortex in the sensory hierarchy suggests that cortical neurons may integrate across these dimensions to form a more gestalt representation of auditory objects. Yet, studies of cortical neurons typically explore single or few dimensions due to the difficulty of determining optimal stimuli in a high dimensional stimulus space. Evolutionary algorithms (EAs) provide a potentially powerful approach for exploring multidimensional stimulus spaces based on real-time spike feedback, but two important issues arise in their application. First, it is unclear whether it is necessary to characterize cortical responses to multidimensional stimuli or whether it suffices to characterize cortical responses to a single dimension at a time. Second, quantitative methods for analyzing complex multidimensional data from an EA are lacking. Here, we apply a statistical method for nonlinear regression, the generalized additive model (GAM), to address these issues. The GAM quantitatively describes the dependence between neural response and all stimulus dimensions. We find that auditory cortical neurons in mice are sensitive to interactions across dimensions. These interactions are diverse across the population, indicating significant integration across stimulus dimensions in auditory cortex. This result strongly motivates using multidimensional stimuli in auditory cortex. Together, the EA and the GAM provide a novel quantitative paradigm for investigating neural coding of complex multidimensional stimuli in auditory and other sensory cortices.

Effect of finite sample size on feature selection and classification: a simulation study.

PubMed

Way, Ted W; Sahiner, Berkman; Hadjiiski, Lubomir M; Chan, Heang-Ping

2010-02-01

The small number of samples available for training and testing is often the limiting factor in finding the most effective features and designing an optimal computer-aided diagnosis (CAD) system. Training on a limited set of samples introduces bias and variance in the performance of a CAD system relative to that trained with an infinite sample size. In this work, the authors conducted a simulation study to evaluate the performances of various combinations of classifiers and feature selection techniques and their dependence on the class distribution, dimensionality, and the training sample size. The understanding of these relationships will facilitate development of effective CAD systems under the constraint of limited available samples. Three feature selection techniques, the stepwise feature selection (SFS), sequential floating forward search (SFFS), and principal component analysis (PCA), and two commonly used classifiers, Fisher's linear discriminant analysis (LDA) and support vector machine (SVM), were investigated. Samples were drawn from multidimensional feature spaces of multivariate Gaussian distributions with equal or unequal covariance matrices and unequal means, and with equal covariance matrices and unequal means estimated from a clinical data set. Classifier performance was quantified by the area under the receiver operating characteristic curve Az. The mean Az values obtained by resubstitution and hold-out methods were evaluated for training sample sizes ranging from 15 to 100 per class. The number of simulated features available for selection was chosen to be 50, 100, and 200. It was found that the relative performance of the different combinations of classifier and feature selection method depends on the feature space distributions, the dimensionality, and the available training sample sizes. The LDA and SVM with radial kernel performed similarly for most of the conditions evaluated in this study, although the SVM classifier showed a slightly higher hold-out performance than LDA for some conditions and vice versa for other conditions. PCA was comparable to or better than SFS and SFFS for LDA at small samples sizes, but inferior for SVM with polynomial kernel. For the class distributions simulated from clinical data, PCA did not show advantages over the other two feature selection methods. Under this condition, the SVM with radial kernel performed better than the LDA when few training samples were available, while LDA performed better when a large number of training samples were available. None of the investigated feature selection-classifier combinations provided consistently superior performance under the studied conditions for different sample sizes and feature space distributions. In general, the SFFS method was comparable to the SFS method while PCA may have an advantage for Gaussian feature spaces with unequal covariance matrices. The performance of the SVM with radial kernel was better than, or comparable to, that of the SVM with polynomial kernel under most conditions studied.

LC-IMS-MS Feature Finder: detecting multidimensional liquid chromatography, ion mobility and mass spectrometry features in complex datasets.

PubMed

Crowell, Kevin L; Slysz, Gordon W; Baker, Erin S; LaMarche, Brian L; Monroe, Matthew E; Ibrahim, Yehia M; Payne, Samuel H; Anderson, Gordon A; Smith, Richard D

2013-11-01

The addition of ion mobility spectrometry to liquid chromatography-mass spectrometry experiments requires new, or updated, software tools to facilitate data processing. We introduce a command line software application LC-IMS-MS Feature Finder that searches for molecular ion signatures in multidimensional liquid chromatography-ion mobility spectrometry-mass spectrometry (LC-IMS-MS) data by clustering deisotoped peaks with similar monoisotopic mass, charge state, LC elution time and ion mobility drift time values. The software application includes an algorithm for detecting and quantifying co-eluting chemical species, including species that exist in multiple conformations that may have been separated in the IMS dimension. LC-IMS-MS Feature Finder is available as a command-line tool for download at http://omics.pnl.gov/software/LC-IMS-MS_Feature_Finder.php. The Microsoft.NET Framework 4.0 is required to run the software. All other dependencies are included with the software package. Usage of this software is limited to non-profit research to use (see README). rds@pnnl.gov. Supplementary data are available at Bioinformatics online.

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.

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.

Assessing semantic similarity of texts - Methods and algorithms

NASA Astrophysics Data System (ADS)

Rozeva, Anna; Zerkova, Silvia

2017-12-01

Assessing the semantic similarity of texts is an important part of different text-related applications like educational systems, information retrieval, text summarization, etc. This task is performed by sophisticated analysis, which implements text-mining techniques. Text mining involves several pre-processing steps, which provide for obtaining structured representative model of the documents in a corpus by means of extracting and selecting the features, characterizing their content. Generally the model is vector-based and enables further analysis with knowledge discovery approaches. Algorithms and measures are used for assessing texts at syntactical and semantic level. An important text-mining method and similarity measure is latent semantic analysis (LSA). It provides for reducing the dimensionality of the document vector space and better capturing the text semantics. The mathematical background of LSA for deriving the meaning of the words in a given text by exploring their co-occurrence is examined. The algorithm for obtaining the vector representation of words and their corresponding latent concepts in a reduced multidimensional space as well as similarity calculation are presented.

Data-adaptive harmonic spectra and multilayer Stuart-Landau models

NASA Astrophysics Data System (ADS)

Chekroun, Mickaël D.; Kondrashov, Dmitri

2017-09-01

Harmonic decompositions of multivariate time series are considered for which we adopt an integral operator approach with periodic semigroup kernels. Spectral decomposition theorems are derived that cover the important cases of two-time statistics drawn from a mixing invariant measure. The corresponding eigenvalues can be grouped per Fourier frequency and are actually given, at each frequency, as the singular values of a cross-spectral matrix depending on the data. These eigenvalues obey, furthermore, a variational principle that allows us to define naturally a multidimensional power spectrum. The eigenmodes, as far as they are concerned, exhibit a data-adaptive character manifested in their phase which allows us in turn to define a multidimensional phase spectrum. The resulting data-adaptive harmonic (DAH) modes allow for reducing the data-driven modeling effort to elemental models stacked per frequency, only coupled at different frequencies by the same noise realization. In particular, the DAH decomposition extracts time-dependent coefficients stacked by Fourier frequency which can be efficiently modeled—provided the decay of temporal correlations is sufficiently well-resolved—within a class of multilayer stochastic models (MSMs) tailored here on stochastic Stuart-Landau oscillators. Applications to the Lorenz 96 model and to a stochastic heat equation driven by a space-time white noise are considered. In both cases, the DAH decomposition allows for an extraction of spatio-temporal modes revealing key features of the dynamics in the embedded phase space. The multilayer Stuart-Landau models (MSLMs) are shown to successfully model the typical patterns of the corresponding time-evolving fields, as well as their statistics of occurrence.

Time-Accurate Local Time Stepping and High-Order Time CESE Methods for Multi-Dimensional Flows Using Unstructured Meshes

NASA Technical Reports Server (NTRS)

Chang, Chau-Lyan; Venkatachari, Balaji Shankar; Cheng, Gary

2013-01-01

With the wide availability of affordable multiple-core parallel supercomputers, next generation numerical simulations of flow physics are being focused on unsteady computations for problems involving multiple time scales and multiple physics. These simulations require higher solution accuracy than most algorithms and computational fluid dynamics codes currently available. This paper focuses on the developmental effort for high-fidelity multi-dimensional, unstructured-mesh flow solvers using the space-time conservation element, solution element (CESE) framework. Two approaches have been investigated in this research in order to provide high-accuracy, cross-cutting numerical simulations for a variety of flow regimes: 1) time-accurate local time stepping and 2) highorder CESE method. The first approach utilizes consistent numerical formulations in the space-time flux integration to preserve temporal conservation across the cells with different marching time steps. Such approach relieves the stringent time step constraint associated with the smallest time step in the computational domain while preserving temporal accuracy for all the cells. For flows involving multiple scales, both numerical accuracy and efficiency can be significantly enhanced. The second approach extends the current CESE solver to higher-order accuracy. Unlike other existing explicit high-order methods for unstructured meshes, the CESE framework maintains a CFL condition of one for arbitrarily high-order formulations while retaining the same compact stencil as its second-order counterpart. For large-scale unsteady computations, this feature substantially enhances numerical efficiency. Numerical formulations and validations using benchmark problems are discussed in this paper along with realistic examples.

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.

Direct Manipulation in Virtual Reality

NASA Technical Reports Server (NTRS)

Bryson, Steve

2003-01-01

Virtual Reality interfaces offer several advantages for scientific visualization such as the ability to perceive three-dimensional data structures in a natural way. The focus of this chapter is direct manipulation, the ability for a user in virtual reality to control objects in the virtual environment in a direct and natural way, much as objects are manipulated in the real world. Direct manipulation provides many advantages for the exploration of complex, multi-dimensional data sets, by allowing the investigator the ability to intuitively explore the data environment. Because direct manipulation is essentially a control interface, it is better suited for the exploration and analysis of a data set than for the publishing or communication of features found in that data set. Thus direct manipulation is most relevant to the analysis of complex data that fills a volume of three-dimensional space, such as a fluid flow data set. Direct manipulation allows the intuitive exploration of that data, which facilitates the discovery of data features that would be difficult to find using more conventional visualization methods. Using a direct manipulation interface in virtual reality, an investigator can, for example, move a data probe about in space, watching the results and getting a sense of how the data varies within its spatial volume.

Fluorescence Intrinsic Characterization of Excitation-Emission Matrix Using Multi-Dimensional Ensemble Empirical Mode Decomposition

PubMed Central

Chang, Chi-Ying; Chang, Chia-Chi; Hsiao, Tzu-Chien

2013-01-01

Excitation-emission matrix (EEM) fluorescence spectroscopy is a noninvasive method for tissue diagnosis and has become important in clinical use. However, the intrinsic characterization of EEM fluorescence remains unclear. Photobleaching and the complexity of the chemical compounds make it difficult to distinguish individual compounds due to overlapping features. Conventional studies use principal component analysis (PCA) for EEM fluorescence analysis, and the relationship between the EEM features extracted by PCA and diseases has been examined. The spectral features of different tissue constituents are not fully separable or clearly defined. Recently, a non-stationary method called multi-dimensional ensemble empirical mode decomposition (MEEMD) was introduced; this method can extract the intrinsic oscillations on multiple spatial scales without loss of information. The aim of this study was to propose a fluorescence spectroscopy system for EEM measurements and to describe a method for extracting the intrinsic characteristics of EEM by MEEMD. The results indicate that, although PCA provides the principal factor for the spectral features associated with chemical compounds, MEEMD can provide additional intrinsic features with more reliable mapping of the chemical compounds. MEEMD has the potential to extract intrinsic fluorescence features and improve the detection of biochemical changes. PMID:24240806

Classification of holter registers by dynamic clustering using multi-dimensional particle swarm optimization.

PubMed

Kiranyaz, Serkan; Ince, Turker; Pulkkinen, Jenni; Gabbouj, Moncef

2010-01-01

In this paper, we address dynamic clustering in high dimensional data or feature spaces as an optimization problem where multi-dimensional particle swarm optimization (MD PSO) is used to find out the true number of clusters, while fractional global best formation (FGBF) is applied to avoid local optima. Based on these techniques we then present a novel and personalized long-term ECG classification system, which addresses the problem of labeling the beats within a long-term ECG signal, known as Holter register, recorded from an individual patient. Due to the massive amount of ECG beats in a Holter register, visual inspection is quite difficult and cumbersome, if not impossible. Therefore the proposed system helps professionals to quickly and accurately diagnose any latent heart disease by examining only the representative beats (the so called master key-beats) each of which is representing a cluster of homogeneous (similar) beats. We tested the system on a benchmark database where the beats of each Holter register have been manually labeled by cardiologists. The selection of the right master key-beats is the key factor for achieving a highly accurate classification and the proposed systematic approach produced results that were consistent with the manual labels with 99.5% average accuracy, which basically shows the efficiency of the system.

Coherent concepts are computed in the anterior temporal lobes.

PubMed

Lambon Ralph, Matthew A; Sage, Karen; Jones, Roy W; Mayberry, Emily J

2010-02-09

In his Philosophical Investigations, Wittgenstein famously noted that the formation of semantic representations requires more than a simple combination of verbal and nonverbal features to generate conceptually based similarities and differences. Classical and contemporary neuroscience has tended to focus upon how different neocortical regions contribute to conceptualization through the summation of modality-specific information. The additional yet critical step of computing coherent concepts has received little attention. Some computational models of semantic memory are able to generate such concepts by the addition of modality-invariant information coded in a multidimensional semantic space. By studying patients with semantic dementia, we demonstrate that this aspect of semantic memory becomes compromised following atrophy of the anterior temporal lobes and, as a result, the patients become increasingly influenced by superficial rather than conceptual similarities.

An extended Lagrangian method

NASA Technical Reports Server (NTRS)

Liou, Meng-Sing

1993-01-01

A unique formulation of describing fluid motion is presented. The method, referred to as 'extended Lagrangian method', is interesting from both theoretical and numerical points of view. The formulation offers accuracy in numerical solution by avoiding numerical diffusion resulting from mixing of fluxes in the Eulerian description. Meanwhile, it also avoids the inaccuracy incurred due to geometry and variable interpolations used by the previous Lagrangian methods. The present method is general and capable of treating subsonic flows as well as supersonic flows. The method proposed in this paper is robust and stable. It automatically adapts to flow features without resorting to clustering, thereby maintaining rather uniform grid spacing throughout and large time step. Moreover, the method is shown to resolve multidimensional discontinuities with a high level of accuracy, similar to that found in 1D problems.

A New Time-Space Accurate Scheme for Hyperbolic Problems. 1; Quasi-Explicit Case

NASA Technical Reports Server (NTRS)

Sidilkover, David

1998-01-01

This paper presents a new discretization scheme for hyperbolic systems of conservations laws. It satisfies the TVD property and relies on the new high-resolution mechanism which is compatible with the genuinely multidimensional approach proposed recently. This work can be regarded as a first step towards extending the genuinely multidimensional approach to unsteady problems. Discontinuity capturing capabilities and accuracy of the scheme are verified by a set of numerical tests.

Influence of Multidimensionality on Convergence of Sampling in Protein Simulation

NASA Astrophysics Data System (ADS)

Metsugi, Shoichi

2005-06-01

We study the problem of convergence of sampling in protein simulation originating in the multidimensionality of protein’s conformational space. Since several important physical quantities are given by second moments of dynamical variables, we attempt to obtain the time of simulation necessary for their sufficient convergence. We perform a molecular dynamics simulation of a protein and the subsequent principal component (PC) analysis as a function of simulation time T. As T increases, PC vectors with smaller amplitude of variations are identified and their amplitudes are equilibrated before identifying and equilibrating vectors with larger amplitude of variations. This sequential identification and equilibration mechanism makes protein simulation a useful method although it has an intrinsic multidimensional nature.

The multidimensional behavioural hypervolumes of two interacting species predict their space use and survival

PubMed Central

Lichtenstein, James L. L.; Wright, Colin M; McEwen, Brendan; Pinter-Wollman, Noa; Pruitt, Jonathan N.

2018-01-01

Individual animals differ consistently in their behaviour, thus impacting a wide variety of ecological outcomes. Recent advances in animal personality research have established the ecological importance of the multidimensional behavioural volume occupied by individuals and by multispecies communities. Here, we examine the degree to which the multidimensional behavioural volume of a group predicts the outcome of both intra- and interspecific interactions. In particular, we test the hypothesis that a population of conspecifics will experience low intraspecific competition when the population occupies a large volume in behavioural space. We further hypothesize that populations of interacting species will exhibit greater interspecific competition when one or both species occupy large volumes in behavioural space. We evaluate these hypotheses by studying groups of katydids (Scudderia nymphs) and froghoppers (Philaenus spumarius), which compete for food and space on their shared host plant, Solidago canadensis. We found that individuals in single-species groups of katydids positioned themselves closer to one another, suggesting reduced competition, when groups occupied a large behavioural volume. When both species were placed together, we found that the survival of froghoppers was greatest when both froghoppers and katydids occupied a small volume in behavioural space, particularly at high froghopper densities. These results suggest that groups that occupy large behavioural volumes can have low intraspecific competition but high interspecific competition. Thus, behavioural hypervolumes appear to have ecological consequences at both the level of the population and the community and may help to predict the intensity of competition both within and across species. PMID:29681647

Multidimensional flamelet-generated manifolds for partially premixed combustion

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

Nguyen, Phuc-Danh; Vervisch, Luc; Subramanian, Vallinayagam

2010-01-15

Flamelet-generated manifolds have been restricted so far to premixed or diffusion flame archetypes, even though the resulting tables have been applied to nonpremixed and partially premixed flame simulations. By using a projection of the full set of mass conservation species balance equations into a restricted subset of the composition space, unsteady multidimensional flamelet governing equations are derived from first principles, under given hypotheses. During the projection, as in usual one-dimensional flamelets, the tangential strain rate of scalar isosurfaces is expressed in the form of the scalar dissipation rates of the control parameters of the multidimensional flamelet-generated manifold (MFM), which ismore » tested in its five-dimensional form for partially premixed combustion, with two composition space directions and three scalar dissipation rates. It is shown that strain-rate-induced effects can hardly be fully neglected in chemistry tabulation of partially premixed combustion, because of fluxes across iso-equivalence-ratio and iso-progress-of-reaction surfaces. This is illustrated by comparing the 5D flamelet-generated manifold with one-dimensional premixed flame and unsteady strained diffusion flame composition space trajectories. The formal links between the asymptotic behavior of MFM and stratified flame, weakly varying partially premixed front, triple-flame, premixed and nonpremixed edge flames are also evidenced. (author)« less

Multidimensional nanomaterials for the control of stem cell fate

NASA Astrophysics Data System (ADS)

Chueng, Sy-Tsong Dean; Yang, Letao; Zhang, Yixiao; Lee, Ki-Bum

2016-09-01

Current stem cell therapy suffers low efficiency in giving rise to differentiated cell lineages, which can replace the original damaged cells. Nanomaterials, on the other hand, provide unique physical size, surface chemistry, conductivity, and topographical microenvironment to regulate stem cell differentiation through multidimensional approaches to facilitate gene delivery, cell-cell, and cell-ECM interactions. In this review, nanomaterials are demonstrated to work both alone and synergistically to guide selective stem cell differentiation. From three different nanotechnology families, three approaches are shown: (1) soluble microenvironmental factors; (2) insoluble physical microenvironment; and (3) nano-topographical features. As regenerative medicine is heavily invested in effective stem cell therapy, this review is inspired to generate discussions in the potential clinical applications of multi-dimensional nanomaterials.

LC-IMS-MS Feature Finder

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

2013-03-07

LC-IMS-MS Feature Finder is a command line software application which searches for possible molecular ion signatures in multidimensional liquid chromatography, ion mobility spectrometry, and mass spectrometry data by clustering deisotoped peaks with similar monoisotopic mass values, charge states, elution times, and drift times. The software application includes an algorithm for detecting multiple conformations and co-eluting species in the ion mobility dimension. LC-IMS-MS Feature Finder is designed to create an output file with detected features that includes associated information about the detected features.

Evidence of Second-Order Factor Structure in a Diagnostic Problem Space: Implications for Medical Education.

ERIC Educational Resources Information Center

Papa, Frank J.; And Others

1997-01-01

Chest pain was identified as a specific medical problem space, and disease classes were modeled to define it. Results from a test taken by 628 medical residents indicate a second-order factor structure that suggests that chest pain is a multidimensional problem space. Implications for medical education are discussed. (SLD)

Multidimensional poverty and child survival in India.

PubMed

Mohanty, Sanjay K

2011-01-01

Though the concept of multidimensional poverty has been acknowledged cutting across the disciplines (among economists, public health professionals, development thinkers, social scientists, policy makers and international organizations) and included in the development agenda, its measurement and application are still limited. OBJECTIVES AND METHODOLOGY: Using unit data from the National Family and Health Survey 3, India, this paper measures poverty in multidimensional space and examine the linkages of multidimensional poverty with child survival. The multidimensional poverty is measured in the dimension of knowledge, health and wealth and the child survival is measured with respect to infant mortality and under-five mortality. Descriptive statistics, principal component analyses and the life table methods are used in the analyses. The estimates of multidimensional poverty are robust and the inter-state differentials are large. While infant mortality rate and under-five mortality rate are disproportionately higher among the abject poor compared to the non-poor, there are no significant differences in child survival among educationally, economically and health poor at the national level. State pattern in child survival among the education, economical and health poor are mixed. Use of multidimensional poverty measures help to identify abject poor who are unlikely to come out of poverty trap. The child survival is significantly lower among abject poor compared to moderate poor and non-poor. We urge to popularize the concept of multiple deprivations in research and program so as to reduce poverty and inequality in the population.

Data Fusion and Visualization with the OpenEarth Framework (OEF)

NASA Astrophysics Data System (ADS)

Nadeau, D. R.; Baru, C.; Fouch, M. J.; Crosby, C. J.

2010-12-01

Data fusion is an increasingly important problem to solve as we strive to integrate data from multiple sources and build better models of the complex processes operating at the Earth’s surface and its interior. These data are often large, multi-dimensional, and subject to differing conventions for file formats, data structures, coordinate spaces, units of measure, and metadata organization. When visualized, these data require differing, and often conflicting, conventions for visual representations, dimensionality, icons, color schemes, labeling, and interaction. These issues make the visualization of fused Earth science data particularly difficult. The OpenEarth Framework (OEF) is an open-source data fusion and visualization suite of software being developed at the Supercomputer Center at the University of California, San Diego. Funded by the NSF, the project is leveraging virtual globe technology from NASA’s WorldWind to create interactive 3D visualization tools that combine layered data from a variety of sources to create a holistic view of features at, above, and beneath the Earth’s surface. The OEF architecture is cross-platform, multi-threaded, modular, and based upon Java. The OEF’s modular approach yields a collection of compatible mix-and-match components for assembling custom applications. Available modules support file format handling, web service communications, data management, data filtering, user interaction, and 3D visualization. File parsers handle a variety of formal and de facto standard file formats. Each one imports data into a general-purpose data representation that supports multidimensional grids, topography, points, lines, polygons, images, and more. From there these data then may be manipulated, merged, filtered, reprojected, and visualized. Visualization features support conventional and new visualization techniques for looking at topography, tomography, maps, and feature geometry. 3D grid data such as seismic tomography may be sliced by multiple oriented cutting planes and isosurfaced to create 3D skins that trace feature boundaries within the data. Topography may be overlaid with satellite imagery along with data such as gravity and magnetics measurements. Multiple data sets may be visualized simultaneously using overlapping layers and a common 3D+time coordinate space. Data management within the OEF handles and hides the quirks of differing file formats, web protocols, storage structures, coordinate spaces, and metadata representations. Derived data are computed automatically to support interaction and visualization while the original data is left unchanged in its original form. Data is cached for better memory and network efficiency, and all visualization is accelerated by 3D graphics hardware found on today’s computers. The OpenEarth Framework project is currently prototyping the software for use in the visualization, and integration of continental scale geophysical data being produced by EarthScope-related research in the Western US. The OEF is providing researchers with new ways to display and interrogate their data and is anticipated to be a valuable tool for future EarthScope-related research.

Disrupted white matter connectivity underlying developmental dyslexia: A machine learning approach.

PubMed

Cui, Zaixu; Xia, Zhichao; Su, Mengmeng; Shu, Hua; Gong, Gaolang

2016-04-01

Developmental dyslexia has been hypothesized to result from multiple causes and exhibit multiple manifestations, implying a distributed multidimensional effect on human brain. The disruption of specific white-matter (WM) tracts/regions has been observed in dyslexic children. However, it remains unknown if developmental dyslexia affects the human brain WM in a multidimensional manner. Being a natural tool for evaluating this hypothesis, the multivariate machine learning approach was applied in this study to compare 28 school-aged dyslexic children with 33 age-matched controls. Structural magnetic resonance imaging (MRI) and diffusion tensor imaging were acquired to extract five multitype WM features at a regional level: white matter volume, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity. A linear support vector machine (LSVM) classifier achieved an accuracy of 83.61% using these MRI features to distinguish dyslexic children from controls. Notably, the most discriminative features that contributed to the classification were primarily associated with WM regions within the putative reading network/system (e.g., the superior longitudinal fasciculus, inferior fronto-occipital fasciculus, thalamocortical projections, and corpus callosum), the limbic system (e.g., the cingulum and fornix), and the motor system (e.g., the cerebellar peduncle, corona radiata, and corticospinal tract). These results were well replicated using a logistic regression classifier. These findings provided direct evidence supporting a multidimensional effect of developmental dyslexia on WM connectivity of human brain, and highlighted the involvement of WM tracts/regions beyond the well-recognized reading system in dyslexia. Finally, the discriminating results demonstrated a potential of WM neuroimaging features as imaging markers for identifying dyslexic individuals. © 2016 Wiley Periodicals, Inc.

The OpenEarth Framework (OEF) for the 3D Visualization of Integrated Earth Science Data

NASA Astrophysics Data System (ADS)

Nadeau, David; Moreland, John; Baru, Chaitan; Crosby, Chris

2010-05-01

Data integration is increasingly important as we strive to combine data from disparate sources and assemble better models of the complex processes operating at the Earth's surface and within its interior. These data are often large, multi-dimensional, and subject to differing conventions for data structures, file formats, coordinate spaces, and units of measure. When visualized, these data require differing, and sometimes conflicting, conventions for visual representations, dimensionality, symbology, and interaction. All of this makes the visualization of integrated Earth science data particularly difficult. The OpenEarth Framework (OEF) is an open-source data integration and visualization suite of applications and libraries being developed by the GEON project at the University of California, San Diego, USA. Funded by the NSF, the project is leveraging virtual globe technology from NASA's WorldWind to create interactive 3D visualization tools that combine and layer data from a wide variety of sources to create a holistic view of features at, above, and beneath the Earth's surface. The OEF architecture is open, cross-platform, modular, and based upon Java. The OEF's modular approach to software architecture yields an array of mix-and-match software components for assembling custom applications. Available modules support file format handling, web service communications, data management, user interaction, and 3D visualization. File parsers handle a variety of formal and de facto standard file formats used in the field. Each one imports data into a general-purpose common data model supporting multidimensional regular and irregular grids, topography, feature geometry, and more. Data within these data models may be manipulated, combined, reprojected, and visualized. The OEF's visualization features support a variety of conventional and new visualization techniques for looking at topography, tomography, point clouds, imagery, maps, and feature geometry. 3D data such as seismic tomography may be sliced by multiple oriented cutting planes and isosurfaced to create 3D skins that trace feature boundaries within the data. Topography may be overlaid with satellite imagery, maps, and data such as gravity and magnetics measurements. Multiple data sets may be visualized simultaneously using overlapping layers within a common 3D coordinate space. Data management within the OEF handles and hides the inevitable quirks of differing file formats, web protocols, storage structures, coordinate spaces, and metadata representations. Heuristics are used to extract necessary metadata used to guide data and visual operations. Derived data representations are computed to better support fluid interaction and visualization while the original data is left unchanged in its original form. Data is cached for better memory and network efficiency, and all visualization makes use of 3D graphics hardware support found on today's computers. The OpenEarth Framework project is currently prototyping the software for use in the visualization, and integration of continental scale geophysical data being produced by EarthScope-related research in the Western US. The OEF is providing researchers with new ways to display and interrogate their data and is anticipated to be a valuable tool for future EarthScope-related research.

Multi-attribute subjective evaluations of manual tracking tasks vs. objective performance of the human operator

NASA Technical Reports Server (NTRS)

Siapkaras, A.

1977-01-01

A computational method to deal with the multidimensional nature of tracking and/or monitoring tasks is developed. Operator centered variables, including the operator's perception of the task, are considered. Matrix ratings are defined based on multidimensional scaling techniques and multivariate analysis. The method consists of two distinct steps: (1) to determine the mathematical space of subjective judgements of a certain individual (or group of evaluators) for a given set of tasks and experimental conditionings; and (2) to relate this space with respect to both the task variables and the objective performance criteria used. Results for a variety of second-order trackings with smoothed noise-driven inputs indicate that: (1) many of the internally perceived task variables form a nonorthogonal set; and (2) the structure of the subjective space varies among groups of individuals according to the degree of familiarity they have with such tasks.

Density-based clustering: A 'landscape view' of multi-channel neural data for inference and dynamic complexity analysis.

PubMed

Baglietto, Gabriel; Gigante, Guido; Del Giudice, Paolo

2017-01-01

Two, partially interwoven, hot topics in the analysis and statistical modeling of neural data, are the development of efficient and informative representations of the time series derived from multiple neural recordings, and the extraction of information about the connectivity structure of the underlying neural network from the recorded neural activities. In the present paper we show that state-space clustering can provide an easy and effective option for reducing the dimensionality of multiple neural time series, that it can improve inference of synaptic couplings from neural activities, and that it can also allow the construction of a compact representation of the multi-dimensional dynamics, that easily lends itself to complexity measures. We apply a variant of the 'mean-shift' algorithm to perform state-space clustering, and validate it on an Hopfield network in the glassy phase, in which metastable states are largely uncorrelated from memories embedded in the synaptic matrix. In this context, we show that the neural states identified as clusters' centroids offer a parsimonious parametrization of the synaptic matrix, which allows a significant improvement in inferring the synaptic couplings from the neural activities. Moving to the more realistic case of a multi-modular spiking network, with spike-frequency adaptation inducing history-dependent effects, we propose a procedure inspired by Boltzmann learning, but extending its domain of application, to learn inter-module synaptic couplings so that the spiking network reproduces a prescribed pattern of spatial correlations; we then illustrate, in the spiking network, how clustering is effective in extracting relevant features of the network's state-space landscape. Finally, we show that the knowledge of the cluster structure allows casting the multi-dimensional neural dynamics in the form of a symbolic dynamics of transitions between clusters; as an illustration of the potential of such reduction, we define and analyze a measure of complexity of the neural time series.

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.

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.

Multidimensional generalized-ensemble algorithms for complex systems.

PubMed

Mitsutake, Ayori; Okamoto, Yuko

2009-06-07

We give general formulations of the multidimensional multicanonical algorithm, simulated tempering, and replica-exchange method. We generalize the original potential energy function E(0) by adding any physical quantity V of interest as a new energy term. These multidimensional generalized-ensemble algorithms then perform a random walk not only in E(0) space but also in V space. Among the three algorithms, the replica-exchange method is the easiest to perform because the weight factor is just a product of regular Boltzmann-like factors, while the weight factors for the multicanonical algorithm and simulated tempering are not a priori known. We give a simple procedure for obtaining the weight factors for these two latter algorithms, which uses a short replica-exchange simulation and the multiple-histogram reweighting techniques. As an example of applications of these algorithms, we have performed a two-dimensional replica-exchange simulation and a two-dimensional simulated-tempering simulation using an alpha-helical peptide system. From these simulations, we study the helix-coil transitions of the peptide in gas phase and in aqueous solution.

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.

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.

Improved surface-wave retrieval from ambient seismic noise by multi-dimensional deconvolution

NASA Astrophysics Data System (ADS)

Wapenaar, Kees; Ruigrok, Elmer; van der Neut, Joost; Draganov, Deyan

2011-01-01

The methodology of surface-wave retrieval from ambient seismic noise by crosscorrelation relies on the assumption that the noise field is equipartitioned. Deviations from equipartitioning degrade the accuracy of the retrieved surface-wave Green's function. A point-spread function, derived from the same ambient noise field, quantifies the smearing in space and time of the virtual source of the Green's function. By multidimensionally deconvolving the retrieved Green's function by the point-spread function, the virtual source becomes better focussed in space and time and hence the accuracy of the retrieved surface-wave Green's function may improve significantly. We illustrate this at the hand of a numerical example and discuss the advantages and limitations of this new methodology.

Feature theory and the two-step hypothesis of Müllerian mimicry evolution.

PubMed

Balogh, Alexandra Catherine Victoria; Gamberale-Stille, Gabriella; Tullberg, Birgitta Sillén; Leimar, Olof

2010-03-01

The two-step hypothesis of Müllerian mimicry evolution states that mimicry starts with a major mutational leap between adaptive peaks, followed by gradual fine-tuning. The hypothesis was suggested to solve the problem of apostatic selection producing a valley between adaptive peaks, and appears reasonable for a one-dimensional phenotype. Extending the hypothesis to the realistic scenario of multidimensional phenotypes controlled by multiple genetic loci can be problematic, because it is unlikely that major mutational leaps occur simultaneously in several traits. Here we consider the implications of predator psychology on the evolutionary process. According to feature theory, single prey traits may be used by predators as features to classify prey into discrete categories. A mutational leap in such a trait could initiate mimicry evolution. We conducted individual-based evolutionary simulations in which virtual predators both categorize prey according to features and generalize over total appearances. We found that an initial mutational leap toward feature similarity in one dimension facilitates mimicry evolution of multidimensional traits. We suggest that feature-based predator categorization together with predator generalization over total appearances solves the problem of applying the two-step hypothesis to complex phenotypes, and provides a basis for a theory of the evolution of mimicry rings.

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.

The use of multidimensional scaling for facilities layout - An application to the design of the Space Station

NASA Technical Reports Server (NTRS)

Tullis, Thomas S.; Bied Sperling, Barbra; Steinberg, A. L.

1986-01-01

Before an optimum layout of the facilities for the proposed Space Station can be designed, it is necessary to understand the functions that will be performed by the Space Station crew and the relationships among those functions. Five criteria for assessing functional relationships were identified. For each of these criteria, a matrix representing the degree of association of all pairs of functions was developed. The key to making inferences about the layout of the Space Station from these matrices was the use of multidimensional scaling (MDS). Applying MDS to these matrices resulted in spatial configurations of the crew functions in which smaller distances in the MDS configuration reflected closer associations. An MDS analysis of a composite matrix formed by combining the five individual matrices resulted in two dimensions that describe the configuration: a 'private-public' dimension and a 'group-individual' dimension. Seven specific recommendations for Space Station layout were derived from analyses of the MDS configurations. Although these techniques have been applied to the design of the Space Station, they can be applied to the design of any facility where people live or work.

Multidimensional stability of traveling fronts in combustion and non-KPP monostable equations

NASA Astrophysics Data System (ADS)

Bu, Zhen-Hui; Wang, Zhi-Cheng

2018-02-01

This paper is concerned with the multidimensional stability of traveling fronts for the combustion and non-KPP monostable equations. Our study contains two parts: in the first part, we first show that the two-dimensional V-shaped traveling fronts are asymptotically stable in R^{n+2} with n≥1 under any (possibly large) initial perturbations that decay at space infinity, and then, we prove that there exists a solution that oscillates permanently between two V-shaped traveling fronts, which implies that even very small perturbations to the V-shaped traveling front can lead to permanent oscillation. In the second part, we establish the multidimensional stability of planar traveling front in R^{n+1} with n≥1.

Knowledge Space: A Conceptual Basis for the Organization of Knowledge

ERIC Educational Resources Information Center

Meincke, Peter P. M.; Atherton, Pauline

1976-01-01

Proposes a new conceptual basis for visualizing the organization of information, or knowledge, which differentiates between the concept "vectors" for a field of knowledge represented in a multidimensional space, and the state "vectors" for a person based on his understanding of these concepts, and the representational…

Coastal Seabed Mapping with Hyperspectral and Lidar data

NASA Astrophysics Data System (ADS)

Taramelli, A.; Valentini, E.; Filipponi, F.; Cappucci, S.

2017-12-01

A synoptic view of the coastal seascape and its dynamics needs a quantitative ability to dissect different components over the complexity of the seafloor where a mixture of geo - biological facies determines geomorphological features and their coverage. The present study uses an analytical approach that takes advantage of a multidimensional model to integrate different data sources from airborne Hyperspectral and LiDAR remote sensing and in situ measurements to detect antropogenic features and ecological `tipping points' in coastal seafloors. The proposed approach has the ability to generate coastal seabed maps using: 1) a multidimensional dataset to account for radiometric and morphological properties of waters and the seafloor; 2) a field spectral library to assimilate the high environmental variability into the multidimensional model; 3) a final classification scheme to represent the spatial gradients in the seafloor. The spatial pattern of the response to anthropogenic forcing may be indistinguishable from patterns of natural variability. It is argued that this novel approach to define tipping points following anthropogenic impacts could be most valuable in the management of natural resources and the economic development of coastal areas worldwide. Examples are reported from different sites of the Mediterranean Sea, both from Marine Protected and un-Protected Areas.

Shallow Strategy Development in a Teachable Agent Environment Designed to Support Self-Regulated Learning

ERIC Educational Resources Information Center

Roscoe, Rod D.; Segedy, James R.; Sulcer, Brian; Jeong, Hogyeong; Biswas, Gautam

2013-01-01

To support self-regulated learning (SRL), computer-based learning environments (CBLEs) are often designed to be open-ended and multidimensional. These systems incorporate diverse features that allow students to enact and reveal their SRL strategies via the choices they make. However, research shows that students' use of such features is limited;…

Multidimensional Poverty and Child Survival in India

PubMed Central

Mohanty, Sanjay K.

2011-01-01

Background Though the concept of multidimensional poverty has been acknowledged cutting across the disciplines (among economists, public health professionals, development thinkers, social scientists, policy makers and international organizations) and included in the development agenda, its measurement and application are still limited. Objectives and Methodology Using unit data from the National Family and Health Survey 3, India, this paper measures poverty in multidimensional space and examine the linkages of multidimensional poverty with child survival. The multidimensional poverty is measured in the dimension of knowledge, health and wealth and the child survival is measured with respect to infant mortality and under-five mortality. Descriptive statistics, principal component analyses and the life table methods are used in the analyses. Results The estimates of multidimensional poverty are robust and the inter-state differentials are large. While infant mortality rate and under-five mortality rate are disproportionately higher among the abject poor compared to the non-poor, there are no significant differences in child survival among educationally, economically and health poor at the national level. State pattern in child survival among the education, economical and health poor are mixed. Conclusion Use of multidimensional poverty measures help to identify abject poor who are unlikely to come out of poverty trap. The child survival is significantly lower among abject poor compared to moderate poor and non-poor. We urge to popularize the concept of multiple deprivations in research and program so as to reduce poverty and inequality in the population. PMID:22046384

Using partially labeled data for normal mixture identification with application to class definition

NASA Technical Reports Server (NTRS)

Shahshahani, Behzad M.; Landgrebe, David A.

1992-01-01

The problem of estimating the parameters of a normal mixture density when, in addition to the unlabeled samples, sets of partially labeled samples are available is addressed. The density of the multidimensional feature space is modeled with a normal mixture. It is assumed that the set of components of the mixture can be partitioned into several classes and that training samples are available from each class. Since for any training sample the class of origin is known but the exact component of origin within the corresponding class is unknown, the training samples as considered to be partially labeled. The EM iterative equations are derived for estimating the parameters of the normal mixture in the presence of partially labeled samples. These equations can be used to combine the supervised and nonsupervised learning processes.

Transformation to equivalent dimensions—a new methodology to study earthquake clustering

NASA Astrophysics Data System (ADS)

Lasocki, Stanislaw

2014-05-01

A seismic event is represented by a point in a parameter space, quantified by the vector of parameter values. Studies of earthquake clustering involve considering distances between such points in multidimensional spaces. However, the metrics of earthquake parameters are different, hence the metric in a multidimensional parameter space cannot be readily defined. The present paper proposes a solution of this metric problem based on a concept of probabilistic equivalence of earthquake parameters. Under this concept the lengths of parameter intervals are equivalent if the probability for earthquakes to take values from either interval is the same. Earthquake clustering is studied in an equivalent rather than the original dimensions space, where the equivalent dimension (ED) of a parameter is its cumulative distribution function. All transformed parameters are of linear scale in [0, 1] interval and the distance between earthquakes represented by vectors in any ED space is Euclidean. The unknown, in general, cumulative distributions of earthquake parameters are estimated from earthquake catalogues by means of the model-free non-parametric kernel estimation method. Potential of the transformation to EDs is illustrated by two examples of use: to find hierarchically closest neighbours in time-space and to assess temporal variations of earthquake clustering in a specific 4-D phase space.

Development and Application of Methods for Estimating Operating Characteristics of Discrete Test Item Responses without Assuming any Mathematical Form.

ERIC Educational Resources Information Center

Samejima, Fumiko

In latent trait theory the latent space, or space of the hypothetical construct, is usually represented by some unidimensional or multi-dimensional continuum of real numbers. Like the latent space, the item response can either be treated as a discrete variable or as a continuous variable. Latent trait theory relates the item response to the latent…

Subjective evaluation with FAA criteria: A multidimensional scaling approach. [ground track control management

NASA Technical Reports Server (NTRS)

Kreifeldt, J. G.; Parkin, L.; Wempe, T. E.; Huff, E. F.

1975-01-01

Perceived orderliness in the ground tracks of five A/C during their simulated flights was studied. Dynamically developing ground tracks for five A/C from 21 separate runs were reproduced from computer storage and displayed on CRTS to professional pilots and controllers for their evaluations and preferences under several criteria. The ground tracks were developed in 20 seconds as opposed to the 5 minutes of simulated flight using speedup techniques for display. Metric and nonmetric multidimensional scaling techniques are being used to analyze the subjective responses in an effort to: (1) determine the meaningfulness of basing decisions on such complex subjective criteria; (2) compare pilot/controller perceptual spaces; (3) determine the dimensionality of the subjects' perceptual spaces; and thereby (4) determine objective measures suitable for comparing alternative traffic management simulations.

Trajectories of Smooth: The Multidimensionality of Spatial Relations and Autism Spectrum

ERIC Educational Resources Information Center

Reddington, Sarah; Price, Deborah

2017-01-01

This paper examines how two men with autism spectrum (AS) experience educational spaces having attended public school in Nova Scotia, Canada. Smooth and striated space is mobilised as the main conceptual framework to account for the men's affectivities when experiencing the educational terrain. The central aim when applying smooth and striated…

Invisible and Hypervisible Academics: The Experiences of Black and Minority Ethnic Teacher Educators

ERIC Educational Resources Information Center

Lander, Vini; Santoro, Ninetta

2017-01-01

This qualitative study investigated the experiences of Black and Minority Ethnic (BME) teacher educators in England and Australia working within the predominantly white space of the academy. Data analysis was informed by a multidimensional theoretical framework drawing on Critical Race Theory, whiteness and Puwar's concept of the Space Invader.…

The effects of context on multidimensional spatial cognitive models. Ph.D. Thesis - Arizona Univ.

NASA Technical Reports Server (NTRS)

Dupnick, E. G.

1979-01-01

Spatial cognitive models obtained by multidimensional scaling represent cognitive structure by defining alternatives as points in a coordinate space based on relevant dimensions such that interstimulus dissimilarities perceived by the individual correspond to distances between the respective alternatives. The dependence of spatial models on the context of the judgments required of the individual was investigated. Context, which is defined as a perceptual interpretation and cognitive understanding of a judgment situation, was analyzed and classified with respect to five characteristics: physical environment, social environment, task definition, individual perspective, and temporal setting. Four experiments designed to produce changes in the characteristics of context and to test the effects of these changes upon individual cognitive spaces are described with focus on experiment design, objectives, statistical analysis, results, and conclusions. The hypothesis is advanced that an individual can be characterized as having a master cognitive space for a set of alternatives. When the context changes, the individual appears to change the dimension weights to give a new spatial configuration. Factor analysis was used in the interpretation and labeling of cognitive space dimensions.

Multidimensional quantitative analysis of mRNA expression within intact vertebrate embryos.

PubMed

Trivedi, Vikas; Choi, Harry M T; Fraser, Scott E; Pierce, Niles A

2018-01-08

For decades, in situ hybridization methods have been essential tools for studies of vertebrate development and disease, as they enable qualitative analyses of mRNA expression in an anatomical context. Quantitative mRNA analyses typically sacrifice the anatomy, relying on embryo microdissection, dissociation, cell sorting and/or homogenization. Here, we eliminate the trade-off between quantitation and anatomical context, using quantitative in situ hybridization chain reaction (qHCR) to perform accurate and precise relative quantitation of mRNA expression with subcellular resolution within whole-mount vertebrate embryos. Gene expression can be queried in two directions: read-out from anatomical space to expression space reveals co-expression relationships in selected regions of the specimen; conversely, read-in from multidimensional expression space to anatomical space reveals those anatomical locations in which selected gene co-expression relationships occur. As we demonstrate by examining gene circuits underlying somitogenesis, quantitative read-out and read-in analyses provide the strengths of flow cytometry expression analyses, but by preserving subcellular anatomical context, they enable bi-directional queries that open a new era for in situ hybridization. © 2018. Published by The Company of Biologists Ltd.

A novel framework for change detection in bi-temporal polarimetric SAR images

NASA Astrophysics Data System (ADS)

Pirrone, Davide; Bovolo, Francesca; Bruzzone, Lorenzo

2016-10-01

Last years have seen relevant increase of polarimetric Synthetic Aperture Radar (SAR) data availability, thanks to satellite sensors like Sentinel-1 or ALOS-2 PALSAR-2. The augmented information lying in the additional polarimetric channels represents a possibility for better discriminate different classes of changes in change detection (CD) applications. This work aims at proposing a framework for CD in multi-temporal multi-polarization SAR data. The framework includes both a tool for an effective visual representation of the change information and a method for extracting the multiple-change information. Both components are designed to effectively handle the multi-dimensionality of polarimetric data. In the novel representation, multi-temporal intensity SAR data are employed to compute a polarimetric log-ratio. The multitemporal information of the polarimetric log-ratio image is represented in a multi-dimensional features space, where changes are highlighted in terms of magnitude and direction. This representation is employed to design a novel unsupervised multi-class CD approach. This approach considers a sequential two-step analysis of the magnitude and the direction information for separating non-changed and changed samples. The proposed approach has been validated on a pair of Sentinel-1 data acquired before and after the flood in Tamil-Nadu in 2015. Preliminary results demonstrate that the representation tool is effective and that the use of polarimetric SAR data is promising in multi-class change detection applications.

Phenomenology of COMPASS data: Multiplicities and phenomenology - part II

DOE PAGES

Anselmino, M.; Boglione, M.; Gonzalez H., J. O.; ...

2015-01-23

In this study, we present some of the main features of the multidimensional COMPASS multiplicities, via our analysis using the simple Gaussian model. We briefly discuss these results in connection with azimuthal asymmetries.

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.

Perceptual representation and effectiveness of local figure–ground cues in natural contours

PubMed Central

Sakai, Ko; Matsuoka, Shouhei; Kurematsu, Ken; Hatori, Yasuhiro

2015-01-01

A contour shape strongly influences the perceptual segregation of a figure from the ground. We investigated the contribution of local contour shape to figure–ground segregation. Although previous studies have reported local contour features that evoke figure–ground perception, they were often image features and not necessarily perceptual features. First, we examined whether contour features, specifically, convexity, closure, and symmetry, underlie the perceptual representation of natural contour shapes. We performed similarity tests between local contours, and examined the contribution of the contour features to the perceptual similarities between the contours. The local contours were sampled from natural contours so that their distribution was uniform in the space composed of the three contour features. This sampling ensured the equal appearance frequency of the factors and a wide variety of contour shapes including those comprised of contradictory factors that induce figure in the opposite directions. This sampling from natural contours is advantageous in order to randomly pickup a variety of contours that satisfy a wide range of cue combinations. Multidimensional scaling analyses showed that the combinations of convexity, closure, and symmetry contribute to perceptual similarity, thus they are perceptual quantities. Second, we examined whether the three features contribute to local figure–ground perception. We performed psychophysical experiments to judge the direction of the figure along the local contours, and examined the contribution of the features to the figure–ground judgment. Multiple linear regression analyses showed that closure was a significant factor, but that convexity and symmetry were not. These results indicate that closure is dominant in the local figure–ground perception with natural contours when the other cues coexist with equal probability including contradictory cases. PMID:26579057

Perceptual representation and effectiveness of local figure-ground cues in natural contours.

PubMed

Sakai, Ko; Matsuoka, Shouhei; Kurematsu, Ken; Hatori, Yasuhiro

2015-01-01

A contour shape strongly influences the perceptual segregation of a figure from the ground. We investigated the contribution of local contour shape to figure-ground segregation. Although previous studies have reported local contour features that evoke figure-ground perception, they were often image features and not necessarily perceptual features. First, we examined whether contour features, specifically, convexity, closure, and symmetry, underlie the perceptual representation of natural contour shapes. We performed similarity tests between local contours, and examined the contribution of the contour features to the perceptual similarities between the contours. The local contours were sampled from natural contours so that their distribution was uniform in the space composed of the three contour features. This sampling ensured the equal appearance frequency of the factors and a wide variety of contour shapes including those comprised of contradictory factors that induce figure in the opposite directions. This sampling from natural contours is advantageous in order to randomly pickup a variety of contours that satisfy a wide range of cue combinations. Multidimensional scaling analyses showed that the combinations of convexity, closure, and symmetry contribute to perceptual similarity, thus they are perceptual quantities. Second, we examined whether the three features contribute to local figure-ground perception. We performed psychophysical experiments to judge the direction of the figure along the local contours, and examined the contribution of the features to the figure-ground judgment. Multiple linear regression analyses showed that closure was a significant factor, but that convexity and symmetry were not. These results indicate that closure is dominant in the local figure-ground perception with natural contours when the other cues coexist with equal probability including contradictory cases.

Multidimensional Space-Time Methodology for Development of Planetary and Space Sciences, S-T Data Management and S-T Computational Tomography

NASA Astrophysics Data System (ADS)

Andonov, Zdravko

This R&D represent innovative multidimensional 6D-N(6n)D Space-Time (S-T) Methodology, 6D-6nD Coordinate Systems, 6D Equations, new 6D strategy and technology for development of Planetary Space Sciences, S-T Data Management and S-T Computational To-mography. . . The Methodology is actual for brain new RS Microwaves' Satellites and Compu-tational Tomography Systems development, aimed to defense sustainable Earth, Moon, & Sun System evolution. Especially, extremely important are innovations for monitoring and protec-tion of strategic threelateral system H-OH-H2O Hydrogen, Hydroxyl and Water), correspond-ing to RS VHRS (Very High Resolution Systems) of 1.420-1.657-22.089GHz microwaves. . . One of the Greatest Paradox and Challenge of World Science is the "transformation" of J. L. Lagrange 4D Space-Time (S-T) System to H. Minkovski 4D S-T System (O-X,Y,Z,icT) for Einstein's "Theory of Relativity". As a global result: -In contemporary Advanced Space Sciences there is not real adequate 4D-6D Space-Time Coordinate System and 6D Advanced Cosmos Strategy & Methodology for Multidimensional and Multitemporal Space-Time Data Management and Tomography. . . That's one of the top actual S-T Problems. Simple and optimal nD S-T Methodology discovery is extremely important for all Universities' Space Sci-ences' Education Programs, for advances in space research and especially -for all young Space Scientists R&D!... The top ten 21-Century Challenges ahead of Planetary and Space Sciences, Space Data Management and Computational Space Tomography, important for successfully de-velopment of Young Scientist Generations, are following: 1. R&D of W. R. Hamilton General Idea for transformation all Space Sciences to Time Sciences, beginning with 6D Eukonal for 6D anisotropic mediums & velocities. Development of IERS Earth & Space Systems (VLBI; LLR; GPS; SLR; DORIS Etc.) for Planetary-Space Data Management & Computational Planetary & Space Tomography. 2. R&D of S. W. Hawking Paradigm for 2D Complex Time and Quan-tum Wave Cosmology Paradigm for Decision of the Main Problem of Contemporary Physics. 3. R&D of Einstein-Minkowski Geodesies' Paradigm in the 4D-Space-Time Continuum to 6D-6nD Space-Time Continuum Paradigms and 6D S-T Equations. . . 4. R&D of Erwin Schrüdinger 4D S-T Universe' Evolutional Equation; It's David Bohm 4D generalization for anisotropic mediums and innovative 6D -for instantaneously quantum measurement -Bohm-Schrüdinger 6D S-T Universe' Evolutional Equation. 5. R&D of brain new 6D Planning of S-T Experi-ments, brain new 6D Space Technicks and Space Technology Generalizations, especially for 6D RS VHRS Research, Monitoring and 6D Computational Tomography. 6. R&D of "6D Euler-Poisson Equations" and "6D Kolmogorov Turbulence Theory" for GeoDynamics and for Space Dynamics as evolution of Gauss-Riemann Paradigms. 7. R&D of N. Boneff NASA RD for Asteroid "Eros" & Space Science' Laws Evolution. 8. R&D of H. Poincare Paradigm for Nature and Cosmos as 6D Group of Transferences. 9. R&D of K. Popoff N-Body General Problem & General Thermodynamic S-T Theory as Einstein-Prigogine-Landau' Paradigms Development. ü 10. R&D of 1st GUT since 1958 by N. S. Kalitzin (Kalitzin N. S., 1958: Uber eine einheitliche Feldtheorie. ZAHeidelberg-ARI, WZHUmnR-B., 7 (2), 207-215) and "Multitemporal Theory of Relativity" -With special applications to Photon Rockets and all Space-Time R&D. GENERAL CONCLUSION: Multidimensional Space-Time Methodology is advance in space research, corresponding to the IAF-IAA-COSPAR Innovative Strategy and R&D Programs -UNEP, UNDP, GEOSS, GMES, Etc.

Using Virtual Worlds to Identify Multidimensional Student Engagement in High School Foreign Language Learning Classrooms

ERIC Educational Resources Information Center

Jacob, Laura Beth

2012-01-01

Virtual world environments have evolved from object-oriented, text-based online games to complex three-dimensional immersive social spaces where the lines between reality and computer-generated begin to blur. Educators use virtual worlds to create engaging three-dimensional learning spaces for students, but the impact of virtual worlds in…

n-SIFT: n-dimensional scale invariant feature transform.

PubMed

Cheung, Warren; Hamarneh, Ghassan

2009-09-01

We propose the n-dimensional scale invariant feature transform (n-SIFT) method for extracting and matching salient features from scalar images of arbitrary dimensionality, and compare this method's performance to other related features. The proposed features extend the concepts used for 2-D scalar images in the computer vision SIFT technique for extracting and matching distinctive scale invariant features. We apply the features to images of arbitrary dimensionality through the use of hyperspherical coordinates for gradients and multidimensional histograms to create the feature vectors. We analyze the performance of a fully automated multimodal medical image matching technique based on these features, and successfully apply the technique to determine accurate feature point correspondence between pairs of 3-D MRI images and dynamic 3D + time CT data.

A Machine Learns to Predict the Stability of Tightly Packed Planetary Systems

NASA Astrophysics Data System (ADS)

Tamayo, Daniel; Silburt, Ari; Valencia, Diana; Menou, Kristen; Ali-Dib, Mohamad; Petrovich, Cristobal; Huang, Chelsea X.; Rein, Hanno; van Laerhoven, Christa; Paradise, Adiv; Obertas, Alysa; Murray, Norman

2016-12-01

The requirement that planetary systems be dynamically stable is often used to vet new discoveries or set limits on unconstrained masses or orbital elements. This is typically carried out via computationally expensive N-body simulations. We show that characterizing the complicated and multi-dimensional stability boundary of tightly packed systems is amenable to machine-learning methods. We find that training an XGBoost machine-learning algorithm on physically motivated features yields an accurate classifier of stability in packed systems. On the stability timescale investigated (107 orbits), it is three orders of magnitude faster than direct N-body simulations. Optimized machine-learning classifiers for dynamical stability may thus prove useful across the discipline, e.g., to characterize the exoplanet sample discovered by the upcoming Transiting Exoplanet Survey Satellite. This proof of concept motivates investing computational resources to train algorithms capable of predicting stability over longer timescales and over broader regions of phase space.

Local/non-local regularized image segmentation using graph-cuts: application to dynamic and multispectral MRI.

PubMed

Hanson, Erik A; Lundervold, Arvid

2013-11-01

Multispectral, multichannel, or time series image segmentation is important for image analysis in a wide range of applications. Regularization of the segmentation is commonly performed using local image information causing the segmented image to be locally smooth or piecewise constant. A new spatial regularization method, incorporating non-local information, was developed and tested. Our spatial regularization method applies to feature space classification in multichannel images such as color images and MR image sequences. The spatial regularization involves local edge properties, region boundary minimization, as well as non-local similarities. The method is implemented in a discrete graph-cut setting allowing fast computations. The method was tested on multidimensional MRI recordings from human kidney and brain in addition to simulated MRI volumes. The proposed method successfully segment regions with both smooth and complex non-smooth shapes with a minimum of user interaction.

An extended Lagrangian method

NASA Technical Reports Server (NTRS)

Liou, Meng-Sing

1992-01-01

A unique formulation of describing fluid motion is presented. The method, referred to as 'extended Lagrangian method', is interesting from both theoretical and numerical points of view. The formulation offers accuracy in numerical solution by avoiding numerical diffusion resulting from mixing of fluxes in the Eulerian description. Meanwhile, it also avoids the inaccuracy incurred due to geometry and variable interpolations used by the previous Lagrangian methods. Unlike the Lagrangian method previously imposed which is valid only for supersonic flows, the present method is general and capable of treating subsonic flows as well as supersonic flows. The method proposed in this paper is robust and stable. It automatically adapts to flow features without resorting to clustering, thereby maintaining rather uniform grid spacing throughout and large time step. Moreover, the method is shown to resolve multi-dimensional discontinuities with a high level of accuracy, similar to that found in one-dimensional problems.

A second-order cell-centered Lagrangian ADER-MOOD finite volume scheme on multidimensional unstructured meshes for hydrodynamics

NASA Astrophysics Data System (ADS)

Boscheri, Walter; Dumbser, Michael; Loubère, Raphaël; Maire, Pierre-Henri

2018-04-01

In this paper we develop a conservative cell-centered Lagrangian finite volume scheme for the solution of the hydrodynamics equations on unstructured multidimensional grids. The method is derived from the Eucclhyd scheme discussed in [47,43,45]. It is second-order accurate in space and is combined with the a posteriori Multidimensional Optimal Order Detection (MOOD) limiting strategy to ensure robustness and stability at shock waves. Second-order of accuracy in time is achieved via the ADER (Arbitrary high order schemes using DERivatives) approach. A large set of numerical test cases is proposed to assess the ability of the method to achieve effective second order of accuracy on smooth flows, maintaining an essentially non-oscillatory behavior on discontinuous profiles, general robustness ensuring physical admissibility of the numerical solution, and precision where appropriate.

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.

Deterministic multidimensional nonuniform gap sampling.

PubMed

Worley, Bradley; Powers, Robert

2015-12-01

Born from empirical observations in nonuniformly sampled multidimensional NMR data relating to gaps between sampled points, the Poisson-gap sampling method has enjoyed widespread use in biomolecular NMR. While the majority of nonuniform sampling schemes are fully randomly drawn from probability densities that vary over a Nyquist grid, the Poisson-gap scheme employs constrained random deviates to minimize the gaps between sampled grid points. We describe a deterministic gap sampling method, based on the average behavior of Poisson-gap sampling, which performs comparably to its random counterpart with the additional benefit of completely deterministic behavior. We also introduce a general algorithm for multidimensional nonuniform sampling based on a gap equation, and apply it to yield a deterministic sampling scheme that combines burst-mode sampling features with those of Poisson-gap schemes. Finally, we derive a relationship between stochastic gap equations and the expectation value of their sampling probability densities. Copyright © 2015 Elsevier Inc. All rights reserved.

Multidimensionality and scale in a landscape ethnoecological partitioning of a mountainous landscape (Gyimes, Eastern Carpathians, Romania)

PubMed Central

2013-01-01

Background Traditional habitat knowledge is an understudied part of traditional knowledge. Though the number of studies increased world-wide in the last decade, this knowledge is still rarely studied in Europe. We document the habitat vocabulary used by Csángó people, and determine features they used to name and describe these categories. Study area and methods Csángó people live in Gyimes (Carpathians, Romania). The area is dominated by coniferous forests, hay meadows and pastures. Animal husbandry is the main source of living. Data on the knowledge of habitat preference of 135 salient wild plant species were collected (2908 records, 44 interviewees). Data collected indoors were counterchecked during outdoor interviews and participatory field work. Results Csángós used a rich and sophisticated vocabulary to name and describe habitat categories. They distinguished altogether at least 142–148 habitat types, and named them by 242 habitat terms. We argue that the method applied and the questions asked (‘what kind of place does species X like?’) helped the often implicit knowledge of habitats to be verbalized more efficiently than usual in an interview. Habitat names were highly lexicalized and most of them were widely shared. The main features were biotic or abiotic, like land-use, dominant plant species, vegetation structure, successional stage, disturbance, soil characteristics, hydrological, and geomorphological features. Csángós often used indicator species (28, mainly herbaceous taxa) in describing habitats of species. To prevent reduction in the quantity and/or quality of hay, unnecessary disturbance of grasslands was avoided by the Csángós. This could explain the high number of habitats (35) distinguished dominantly by the type and severity of disturbance. Based on the spatial scale and topological inclusiveness of habitat categories we distinguished macro-, meso-, and microhabitats. Conclusions Csángó habitat categories were not organized into a single hierarchy, and the partitioning was multidimensional. Multidimensional description of habitats, made the nuanced characterization of plant species’ habitats possible by providing innumerable possibilities to combine the most salient habitat features. We conclude that multidimensionality of landscape partitioning and the number of dimensions applied in a landscape seem to depend on the number of key habitat gradients in the given landscape. PMID:23388111

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.

Two-dimensional Manifold with Point-like Defects

NASA Astrophysics Data System (ADS)

Gani, V. A.; Dmitriev, A. E.; Rubin, S. G.

We study a class of two-dimensional compact extra spaces isomorphic to the sphere S 2 in the framework of multidimensional gravitation. We show that there exists a family of stationary metrics that depend on the initial (boundary) conditions. All these geometries have a singular point. We also discuss the possibility for these deformed extra spaces to be considered as dark matter candidates.

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…

Multidimensionally encoded magnetic resonance imaging.

PubMed

Lin, Fa-Hsuan

2013-07-01

Magnetic resonance imaging (MRI) typically achieves spatial encoding by measuring the projection of a q-dimensional object over q-dimensional spatial bases created by linear spatial encoding magnetic fields (SEMs). Recently, imaging strategies using nonlinear SEMs have demonstrated potential advantages for reconstructing images with higher spatiotemporal resolution and reducing peripheral nerve stimulation. In practice, nonlinear SEMs and linear SEMs can be used jointly to further improve the image reconstruction performance. Here, we propose the multidimensionally encoded (MDE) MRI to map a q-dimensional object onto a p-dimensional encoding space where p > q. MDE MRI is a theoretical framework linking imaging strategies using linear and nonlinear SEMs. Using a system of eight surface SEM coils with an eight-channel radiofrequency coil array, we demonstrate the five-dimensional MDE MRI for a two-dimensional object as a further generalization of PatLoc imaging and O-space imaging. We also present a method of optimizing spatial bases in MDE MRI. Results show that MDE MRI with a higher dimensional encoding space can reconstruct images more efficiently and with a smaller reconstruction error when the k-space sampling distribution and the number of samples are controlled. Copyright © 2012 Wiley Periodicals, Inc.

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.

Application of Fourier analysis to multispectral/spatial recognition

NASA Technical Reports Server (NTRS)

Hornung, R. J.; Smith, J. A.

1973-01-01

One approach for investigating spectral response from materials is to consider spatial features of the response. This might be accomplished by considering the Fourier spectrum of the spatial response. The Fourier Transform may be used in a one-dimensional to multidimensional analysis of more than one channel of data. The two-dimensional transform represents the Fraunhofer diffraction pattern of the image in optics and has certain invariant features. Physically the diffraction pattern contains spatial features which are possibly unique to a given configuration or classification type. Different sampling strategies may be used to either enhance geometrical differences or extract additional features.

MM-MDS: a multidimensional scaling database with similarity ratings for 240 object categories from the Massive Memory picture database.

PubMed

Hout, Michael C; Goldinger, Stephen D; Brady, Kyle J

2014-01-01

Cognitive theories in visual attention and perception, categorization, and memory often critically rely on concepts of similarity among objects, and empirically require measures of "sameness" among their stimuli. For instance, a researcher may require similarity estimates among multiple exemplars of a target category in visual search, or targets and lures in recognition memory. Quantifying similarity, however, is challenging when everyday items are the desired stimulus set, particularly when researchers require several different pictures from the same category. In this article, we document a new multidimensional scaling database with similarity ratings for 240 categories, each containing color photographs of 16-17 exemplar objects. We collected similarity ratings using the spatial arrangement method. Reports include: the multidimensional scaling solutions for each category, up to five dimensions, stress and fit measures, coordinate locations for each stimulus, and two new classifications. For each picture, we categorized the item's prototypicality, indexed by its proximity to other items in the space. We also classified pairs of images along a continuum of similarity, by assessing the overall arrangement of each MDS space. These similarity ratings will be useful to any researcher that wishes to control the similarity of experimental stimuli according to an objective quantification of "sameness."

Structural diversity: a multi-dimensional approach to assess recreational services in urban parks.

PubMed

Voigt, Annette; Kabisch, Nadja; Wurster, Daniel; Haase, Dagmar; Breuste, Jürgen

2014-05-01

Urban green spaces provide important recreational services for urban residents. In general, when park visitors enjoy "the green," they are in actuality appreciating a mix of biotic, abiotic, and man-made park infrastructure elements and qualities. We argue that these three dimensions of structural diversity have an influence on how people use and value urban parks. We present a straightforward approach for assessing urban parks that combines multi-dimensional landscape mapping and questionnaire surveys. We discuss the method as well the results from its application to differently sized parks in Berlin and Salzburg.

On Multi-Dimensional Unstructured Mesh Adaption

NASA Technical Reports Server (NTRS)

Wood, William A.; Kleb, William L.

1999-01-01

Anisotropic unstructured mesh adaption is developed for a truly multi-dimensional upwind fluctuation splitting scheme, as applied to scalar advection-diffusion. The adaption is performed locally using edge swapping, point insertion/deletion, and nodal displacements. Comparisons are made versus the current state of the art for aggressive anisotropic unstructured adaption, which is based on a posteriori error estimates. Demonstration of both schemes to model problems, with features representative of compressible gas dynamics, show the present method to be superior to the a posteriori adaption for linear advection. The performance of the two methods is more similar when applied to nonlinear advection, with a difference in the treatment of shocks. The a posteriori adaption can excessively cluster points to a shock, while the present multi-dimensional scheme tends to merely align with a shock, using fewer nodes. As a consequence of this alignment tendency, an implementation of eigenvalue limiting for the suppression of expansion shocks is developed for the multi-dimensional distribution scheme. The differences in the treatment of shocks by the adaption schemes, along with the inherently low levels of artificial dissipation in the fluctuation splitting solver, suggest the present method is a strong candidate for applications to compressible gas dynamics.

Systems and Methods for Data Visualization Using Three-Dimensional Displays

NASA Technical Reports Server (NTRS)

Davidoff, Scott (Inventor); Djorgovski, Stanislav G. (Inventor); Estrada, Vicente (Inventor); Donalek, Ciro (Inventor)

2017-01-01

Data visualization systems and methods for generating 3D visualizations of a multidimensional data space are described. In one embodiment a 3D data visualization application directs a processing system to: load a set of multidimensional data points into a visualization table; create representations of a set of 3D objects corresponding to the set of data points; receive mappings of data dimensions to visualization attributes; determine the visualization attributes of the set of 3D objects based upon the selected mappings of data dimensions to 3D object attributes; update a visibility dimension in the visualization table for each of the plurality of 3D object to reflect the visibility of each 3D object based upon the selected mappings of data dimensions to visualization attributes; and interactively render 3D data visualizations of the 3D objects within the virtual space from viewpoints determined based upon received user input.

3D variational brain tumor segmentation on a clustered feature set

NASA Astrophysics Data System (ADS)

Popuri, Karteek; Cobzas, Dana; Jagersand, Martin; Shah, Sirish L.; Murtha, Albert

2009-02-01

Tumor segmentation from MRI data is a particularly challenging and time consuming task. Tumors have a large diversity in shape and appearance with intensities overlapping the normal brain tissues. In addition, an expanding tumor can also deflect and deform nearby tissue. Our work addresses these last two difficult problems. We use the available MRI modalities (T1, T1c, T2) and their texture characteristics to construct a multi-dimensional feature set. Further, we extract clusters which provide a compact representation of the essential information in these features. The main idea in this paper is to incorporate these clustered features into the 3D variational segmentation framework. In contrast to the previous variational approaches, we propose a segmentation method that evolves the contour in a supervised fashion. The segmentation boundary is driven by the learned inside and outside region voxel probabilities in the cluster space. We incorporate prior knowledge about the normal brain tissue appearance, during the estimation of these region statistics. In particular, we use a Dirichlet prior that discourages the clusters in the ventricles to be in the tumor and hence better disambiguate the tumor from brain tissue. We show the performance of our method on real MRI scans. The experimental dataset includes MRI scans, from patients with difficult instances, with tumors that are inhomogeneous in appearance, small in size and in proximity to the major structures in the brain. Our method shows good results on these test cases.

Data analytics and parallel-coordinate materials property charts

NASA Astrophysics Data System (ADS)

Rickman, Jeffrey M.

2018-01-01

It is often advantageous to display material properties relationships in the form of charts that highlight important correlations and thereby enhance our understanding of materials behavior and facilitate materials selection. Unfortunately, in many cases, these correlations are highly multidimensional in nature, and one typically employs low-dimensional cross-sections of the property space to convey some aspects of these relationships. To overcome some of these difficulties, in this work we employ methods of data analytics in conjunction with a visualization strategy, known as parallel coordinates, to represent better multidimensional materials data and to extract useful relationships among properties. We illustrate the utility of this approach by the construction and systematic analysis of multidimensional materials properties charts for metallic and ceramic systems. These charts simplify the description of high-dimensional geometry, enable dimensional reduction and the identification of significant property correlations and underline distinctions among different materials classes.

Variational calculation of macrostate transition rates

NASA Astrophysics Data System (ADS)

Ulitsky, Alex; Shalloway, David

1998-08-01

We develop the macrostate variational method (MVM) for computing reaction rates of diffusive conformational transitions in multidimensional systems by a variational coarse-grained "macrostate" decomposition of the Smoluchowski equation. MVM uses multidimensional Gaussian packets to identify and focus computational effort on the "transition region," a localized, self-consistently determined region in conformational space positioned roughly between the macrostates. It also determines the "transition direction" which optimally specifies the projected potential of mean force for mean first-passage time calculations. MVM is complementary to variational transition state theory in that it can efficiently solve multidimensional problems but does not accommodate memory-friction effects. It has been tested on model 1- and 2-dimensional potentials and on the 12-dimensional conformational transition between the isoforms of a microcluster of six-atoms having only van der Waals interactions. Comparison with Brownian dynamics calculations shows that MVM obtains equivalent results at a fraction of the computational cost.

Behavioural hypervolumes of spider communities predict community performance and disbandment

PubMed Central

Sih, Andrew; DiRienzo, Nicholas; Pinter-Wollman, Noa

2016-01-01

Trait-based ecology argues that an understanding of the traits of interactors can enhance the predictability of ecological outcomes. We examine here whether the multidimensional behavioural-trait diversity of communities influences community performance and stability in situ. We created experimental communities of web-building spiders, each with an identical species composition. Communities contained one individual of each of five different species. Prior to establishing these communities in the field, we examined three behavioural traits for each individual spider. These behavioural measures allowed us to estimate community-wide behavioural diversity, as inferred by the multidimensional behavioural volume occupied by the entire community. Communities that occupied a larger region of behavioural-trait space (i.e. where spiders differed more from each other behaviourally) gained more mass and were less likely to disband. Thus, there is a community-wide benefit to multidimensional behavioural diversity in this system that might translate to other multispecies assemblages. PMID:27974515

Performance analysis of deciduous morphology for detecting biological siblings.

PubMed

Paul, Kathleen S; Stojanowski, Christopher M

2015-08-01

Family-centered burial practices influence cemetery structure and can represent social group composition in both modern and ancient contexts. In ancient sites dental phenotypic data are often used as proxies for underlying genotypes to identify potential biological relatives. Here, we test the performance of deciduous dental morphological traits for differentiating sibling pairs from unrelated individuals from the same population. We collected 46 deciduous morphological traits for 69 sibling pairs from the Burlington Growth Centre's long term Family Study. Deciduous crown features were recorded following published standards. After variable winnowing, inter-individual Euclidean distances were generated using 20 morphological traits. To determine whether sibling pairs are more phenotypically similar than expected by chance we used bootstrap resampling of distances to generate P values. Multidimensional scaling (MDS) plots were used to evaluate the degree of clustering among sibling pairs. Results indicate an average distance between siblings of 0.252, which is significantly less than 9,999 replicated averages of 69 resampled pseudo-distances generated from: 1) a sample of non-relative pairs (P < 0.001), and 2) a sample of relative and non-relative pairs (P < 0.001). MDS plots indicate moderate to strong clustering among siblings; families occupied 3.83% of the multidimensional space on average (versus 63.10% for the total sample). Deciduous crown morphology performed well in identifying related sibling pairs. However, there was considerable variation in the extent to which different families exhibited similarly low levels of phenotypic divergence. © 2015 Wiley Periodicals, Inc.

Structural Optimization of a Force Balance Using a Computational Experiment Design

NASA Technical Reports Server (NTRS)

Parker, P. A.; DeLoach, R.

2002-01-01

This paper proposes a new approach to force balance structural optimization featuring a computational experiment design. Currently, this multi-dimensional design process requires the designer to perform a simplification by executing parameter studies on a small subset of design variables. This one-factor-at-a-time approach varies a single variable while holding all others at a constant level. Consequently, subtle interactions among the design variables, which can be exploited to achieve the design objectives, are undetected. The proposed method combines Modern Design of Experiments techniques to direct the exploration of the multi-dimensional design space, and a finite element analysis code to generate the experimental data. To efficiently search for an optimum combination of design variables and minimize the computational resources, a sequential design strategy was employed. Experimental results from the optimization of a non-traditional force balance measurement section are presented. An approach to overcome the unique problems associated with the simultaneous optimization of multiple response criteria is described. A quantitative single-point design procedure that reflects the designer's subjective impression of the relative importance of various design objectives, and a graphical multi-response optimization procedure that provides further insights into available tradeoffs among competing design objectives are illustrated. The proposed method enhances the intuition and experience of the designer by providing new perspectives on the relationships between the design variables and the competing design objectives providing a systematic foundation for advancements in structural design.

Study of multi-dimensional radiative energy transfer in molecular gases

NASA Technical Reports Server (NTRS)

Liu, Jiwen; Tiwari, S. N.

1993-01-01

The Monte Carlo method (MCM) is applied to analyze radiative heat transfer in nongray gases. The nongray model employed is based on the statistical arrow band model with an exponential-tailed inverse intensity distribution. Consideration of spectral correlation results in some distinguishing features of the Monte Carlo formulations. Validation of the Monte Carlo formulations has been conducted by comparing results of this method with other solutions. Extension of a one-dimensional problem to a multi-dimensional problem requires some special treatments in the Monte Carlo analysis. Use of different assumptions results in different sets of Monte Carlo formulations. The nongray narrow band formulations provide the most accurate results.

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.

Making Schools Work for Every Child.

ERIC Educational Resources Information Center

Thorson, Annette, Ed.

2000-01-01

This issue of "ENC Focus" explores educational equity. Featured articles provide many perspectives on gender equity, multidimensional classrooms, technology, and special needs students. Articles include: (1) "Editorial: Educational Equity--A Moving Target" (Annette Thorson); (2) "Thinking of Each and Every One" (Francena Cummings); (3) "Teacher…

Turning Interoperability Operational with GST

NASA Astrophysics Data System (ADS)

Schaeben, Helmut; Gabriel, Paul; Gietzel, Jan; Le, Hai Ha

2013-04-01

GST - Geosciences in space and time is being developed and implemented as hub to facilitate the exchange of spatially and temporally indexed multi-dimensional geoscience data and corresponding geomodels amongst partners. It originates from TUBAF's contribution to the EU project "ProMine" and its perspective extensions are TUBAF's contribution to the actual EU project "GeoMol". As of today, it provides basic components of a geodata infrastructure as required to establish interoperability with respect to geosciences. Generally, interoperability means the facilitation of cross-border and cross-sector information exchange, taking into account legal, organisational, semantic and technical aspects, cf. Interoperability Solutions for European Public Administrations (ISA), cf. http://ec.europa.eu/isa/. Practical interoperability for partners of a joint geoscience project, say European Geological Surveys acting in a border region, means in particular provision of IT technology to exchange spatially and maybe additionally temporally indexed multi-dimensional geoscience data and corresponding models, i.e. the objects composing geomodels capturing the geometry, topology, and various geoscience contents. Geodata Infrastructure (GDI) and interoperability are objectives of several inititatives, e.g. INSPIRE, OneGeology-Europe, and most recently EGDI-SCOPE to name just the most prominent ones. Then there are quite a few markup languages (ML) related to geographical or geological information like GeoSciML, EarthResourceML, BoreholeML, ResqML for reservoir characterization, earth and reservoir models, and many others featuring geoscience information. Several Web Services are focused on geographical or geoscience information. The Open Geospatial Consortium (OGC) promotes specifications of a Web Feature Service (WFS), a Web Map Service (WMS), a Web Coverage Serverice (WCS), a Web 3D Service (W3DS), and many more. It will be clarified how GST is related to these initiatives, especially how it complies with existing or developing standards or quasi-standards and how it applies and extents services towards interoperability in the Earth sciences.

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

Similarity metrics, kernels and similarity-based algorithms have gained much attention due to their increasing applications in information retrieval, data mining, pattern recognition and machine learning. Similarity Graphs are often adopted as the underlying representation of similarity matrices and are at the origin of known clustering algorithms such as spectral clustering. Similarity matrices offer the advantage of working in object-object (two-dimensional) space where visualization of clusters similarities is available instead of object-features (multi-dimensional) space. In this paper, sparse ɛ-similarity graphs are constructed and decomposed into strong components using appropriate methods such as Dulmage-Mendelsohn permutation (DMperm) and/or Reverse Cuthill-McKee (RCM) algorithms. The obtained strong components correspond to groups (clusters) in the input (feature) space. Parameter ɛi is estimated locally, at each data point i from a corresponding narrow range of the number of nearest neighbors. Although more advanced clustering techniques are available, our method has the advantages of simplicity, better complexity and direct visualization of the clusters similarities in a two-dimensional space. Also, no prior information about the number of clusters is needed. We conducted our experiments on two and three dimensional, low and high-sized synthetic datasets as well as on an astronomical real-dataset. The results are verified graphically and analyzed using gap statistics over a range of neighbors to verify the robustness of the algorithm and the stability of the results. Combining the proposed algorithm with gap statistics provides a promising tool for solving clustering problems. An astronomical application is conducted for confirming the existence of 45 galaxy clusters around the X-ray positions of galaxy clusters in the redshift range [0.1..0.8]. We re-estimate the photometric redshifts of the identified galaxy clusters and obtain acceptable values compared to published spectroscopic redshifts with a 0.029 standard deviation of their differences.

Mediterranean space-time extremes of wind wave sea states

NASA Astrophysics Data System (ADS)

Barbariol, Francesco; Carniel, Sandro; Sclavo, Mauro; Marcello Falcieri, Francesco; Bonaldo, Davide; Bergamasco, Andrea; Benetazzo, Alvise

2014-05-01

Traditionally, wind wave sea states during storms have been observed, modeled, and predicted mostly in the time domain, i.e. at a fixed point. In fact, the standard statistical models used in ocean waves analysis rely on the implicit assumption of long-crested waves. Nevertheless, waves in storms are mainly short-crested. Hence, spatio-temporal features of the wave field are crucial to accurately model the sea state characteristics and to provide reliable predictions, particurly of wave extremes. Indeed, the experimental evidence provided by novel instrumentations, e.g. WASS (Wave Acquisition Stereo System), showed that the maximum sea surface elevation gathered in time over an area, i.e. the space-time extreme, is larger than that one measured in time at a point, i.e. the time extreme. Recently, stochastic models used to estimate maxima of multidimensional Gaussian random fields have been applied to ocean waves statistics. These models are based either on Piterbarg's theorem or Adler and Taylor's Euler Characteristics approach. Besides a probability of exceedance of a certain threshold, they can provide the expected space-time extreme of a sea state, as long as space-time wave features (i.e. some parameters of the directional variance density spectrum) are known. These models have been recently validated against WASS observation from fixed and moving platforms. In this context, our focus was modeling and predicting extremes of wind waves during storms. Thus, to intensively gather space-time extremes data over the Mediterranean region, we used directional spectra provided by the numerical wave model SWAN (Simulating WAves Nearshore). Therefore, we set up a 6x6 km2 resolution grid entailing most of the Mediterranean Sea and we forced it with COSMO-I7 high resolution (7x7 km2) hourly wind fields, within 2007-2013 period. To obtain the space-time features, i.e. the spectral parameters, at each grid node and over the 6 simulated years, we developed a modified version of the SWAN model, the SWAN Space-Time (SWAN-ST). SWAN-ST results were post-processed to obtain the expected space-time extremes over the model domain. To this end, we applied the stochastic model of Fedele, developed starting from Adler and Taylor's approach, which we found to be more accurate and versatile with respect to Piterbarg's theorem. Results we obtained provide an alternative sight on Mediterranean extreme wave climate, which could represent the first step towards operationl forecasting of space-time wave extremes, on the one hand, and the basis for a novel statistical standard wave model, on the other. These results may benefit marine designers, seafarers and other subjects operating at sea and exposed to the frequent and severe hazard represented by extreme wave conditions.

Using a multidimensional unfolding approach to assess multiple sclerosis patient preferences for disease-modifying therapy: a pilot study

PubMed Central

Sempere, Angel Perez; Vera-Lopez, Vanesa; Gimenez-Martinez, Juana; Ruiz-Beato, Elena; Cuervo, Jesús; Maurino, Jorge

2017-01-01

Purpose Multidimensional unfolding is a multivariate method to assess preferences using a small sample size, a geometric model locating individuals and alternatives as points in a joint space. The objective was to evaluate relapsing–remitting multiple sclerosis (RRMS) patient preferences toward key disease-modifying therapy (DMT) attributes using multidimensional unfolding. Patients and methods A cross-sectional pilot study in RRMS patients was conducted. Drug attributes included relapse prevention, disease progression prevention, side-effect risk and route and schedule of administration. Assessment of preferences was performed through a five-card game. Patients were asked to value attributes from 1 (most preferred) to 5 (least preferred). Results A total of 37 patients were included; the mean age was 38.6 years, and 78.4% were female. Disease progression prevention was the most important factor (51.4%), followed by relapse prevention (40.5%). The frequency of administration had the lowest preference rating for 56.8% of patients. Finally, 19.6% valued the side-effect risk attribute as having low/very low importance. Conclusion Patients’ perspective for DMT attributes may provide valuable information to facilitate shared decision-making. Efficacy attributes were the most important drug characteristics for RRMS patients. Multidimensional unfolding seems to be a feasible approach to assess preferences in multiple sclerosis patients. Further elicitation studies using multidimensional unfolding with other stated choice methods are necessary to confirm these findings. PMID:28615928

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.

SVM Based Descriptor Selection and Classification of Neurodegenerative Disease Drugs for Pharmacological Modeling.

PubMed

Shahid, Mohammad; Shahzad Cheema, Muhammad; Klenner, Alexander; Younesi, Erfan; Hofmann-Apitius, Martin

2013-03-01

Systems pharmacological modeling of drug mode of action for the next generation of multitarget drugs may open new routes for drug design and discovery. Computational methods are widely used in this context amongst which support vector machines (SVM) have proven successful in addressing the challenge of classifying drugs with similar features. We have applied a variety of such SVM-based approaches, namely SVM-based recursive feature elimination (SVM-RFE). We use the approach to predict the pharmacological properties of drugs widely used against complex neurodegenerative disorders (NDD) and to build an in-silico computational model for the binary classification of NDD drugs from other drugs. Application of an SVM-RFE model to a set of drugs successfully classified NDD drugs from non-NDD drugs and resulted in overall accuracy of ∼80 % with 10 fold cross validation using 40 top ranked molecular descriptors selected out of total 314 descriptors. Moreover, SVM-RFE method outperformed linear discriminant analysis (LDA) based feature selection and classification. The model reduced the multidimensional descriptors space of drugs dramatically and predicted NDD drugs with high accuracy, while avoiding over fitting. Based on these results, NDD-specific focused libraries of drug-like compounds can be designed and existing NDD-specific drugs can be characterized by a well-characterized set of molecular descriptors. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Black-hole production at LHC: Special features, problems, and expectations

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

Savina, M. V., E-mail: savina@cern.ch

2011-03-15

A brief survey of the present-day status of the problem of multidimensional-black-hole production at accelerators according to models featuring large extra dimensions is given. The respective production cross section and the Hawking temperature and decay rate are estimated versus model parameters. Possible flaws and assumptions whose accurate inclusion can reduce significantly the probability of blackhole production at accelerators in relation to earlier optimistic estimates are also discussed.

Scientific Visualization of Radio Astronomy Data using Gesture Interaction

NASA Astrophysics Data System (ADS)

Mulumba, P.; Gain, J.; Marais, P.; Woudt, P.

2015-09-01

MeerKAT in South Africa (Meer = More Karoo Array Telescope) will require software to help visualize, interpret and interact with multidimensional data. While visualization of multi-dimensional data is a well explored topic, little work has been published on the design of intuitive interfaces to such systems. More specifically, the use of non-traditional interfaces (such as motion tracking and multi-touch) has not been widely investigated within the context of visualizing astronomy data. We hypothesize that a natural user interface would allow for easier data exploration which would in turn lead to certain kinds of visualizations (volumetric, multidimensional). To this end, we have developed a multi-platform scientific visualization system for FITS spectral data cubes using VTK (Visualization Toolkit) and a natural user interface to explore the interaction between a gesture input device and multidimensional data space. Our system supports visual transformations (translation, rotation and scaling) as well as sub-volume extraction and arbitrary slicing of 3D volumetric data. These tasks were implemented across three prototypes aimed at exploring different interaction strategies: standard (mouse/keyboard) interaction, volumetric gesture tracking (Leap Motion controller) and multi-touch interaction (multi-touch monitor). A Heuristic Evaluation revealed that the volumetric gesture tracking prototype shows great promise for interfacing with the depth component (z-axis) of 3D volumetric space across multiple transformations. However, this is limited by users needing to remember the required gestures. In comparison, the touch-based gesture navigation is typically more familiar to users as these gestures were engineered from standard multi-touch actions. Future work will address a complete usability test to evaluate and compare the different interaction modalities against the different visualization tasks.

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...

A comparative analysis of predictors of sense of place dimensions: attachment to, dependence on, and identification with lakeshore properties.

PubMed

Jorgensen, Bradley S; Stedman, Richard C

2006-05-01

Sense of place can be conceived as a multidimensional construct representing beliefs, emotions and behavioural commitments concerning a particular geographic setting. This view, grounded in attitude theory, can better reveal complex relationships between the experience of a place and attributes of that place than approaches that do not differentiate cognitive, affective and conative domains. Shoreline property owners (N=290) in northern Wisconsin were surveyed about their sense of place for their lakeshore properties. A predictive model comprising owners' age, length of ownership, participation in recreational activities, days spent on the property, extent of property development, and perceptions of environmental features, was employed to explain the variation in dimensions of sense of place. In general, the results supported a multidimensional approach to sense of place in a context where there were moderate to high correlations among the three place dimensions. Perceptions of environmental features were the biggest predictors of place dimensions, with owners' perceptions of lake importance varying in explanatory power across place dimensions.

Multidimensional Modeling of Coronal Rain Dynamics

NASA Astrophysics Data System (ADS)

Fang, X.; Xia, C.; Keppens, R.

2013-07-01

We present the first multidimensional, magnetohydrodynamic simulations that capture the initial formation and long-term sustainment of the enigmatic coronal rain phenomenon. We demonstrate how thermal instability can induce a spectacular display of in situ forming blob-like condensations which then start their intimate ballet on top of initially linear force-free arcades. Our magnetic arcades host a chromospheric, transition region, and coronal plasma. Following coronal rain dynamics for over 80 minutes of physical time, we collect enough statistics to quantify blob widths, lengths, velocity distributions, and other characteristics which directly match modern observational knowledge. Our virtual coronal rain displays the deformation of blobs into V-shaped features, interactions of blobs due to mostly pressure-mediated levitations, and gives the first views of blobs that evaporate in situ or are siphoned over the apex of the background arcade. Our simulations pave the way for systematic surveys of coronal rain showers in true multidimensional settings to connect parameterized heating prescriptions with rain statistics, ultimately allowing us to quantify the coronal heating input.

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.

Entropy production due to gravitational-wave viscosity in a Kaluza-Klein inflationary universe

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

Tomita, K.; Ishihara, H.

1985-10-15

The role of viscosity due to the transport of gravitational radiation in a Kaluza-Klein multidimensional universe is considered, and it is shown that vast entropy may be produced by it owing to inflation of the external (ordinary) space and collapse of the internal (compact) space. The inflation and collapse factors necessary for increasing the total entropy by a factor approx.10/sup 88/ are derived.

Assessment of Resources and Needs for Water Development

ERIC Educational Resources Information Center

United Nations and Water, 1977

1977-01-01

Presents a brief history of water resource utilization, the present availability and uses of water, and strategies for water management. Three characteristic features of water demand management are explained: (1) emphasis on non-structural measures; (2) multi-dimensional organization and policies; (3) emphasis on research. (MA)

An Environmentally Sensitive Fluorescent Dye as a Multidimensional Probe of Amyloid Formation

PubMed Central

2016-01-01

We have explored amyloid formation using poly(amino acid) model systems in which differences in peptide secondary structure and hydrophobicity can be introduced in a controlled manner. We show that an environmentally sensitive fluorescent dye, dapoxyl, is able to identify β-sheet structure and hydrophobic surfaces, structural features likely to be related to toxicity, as a result of changes in its excitation and emission profiles and its relative quantum yield. These results show that dapoxyl is a multidimensional probe of the time dependence of amyloid aggregation, which provides information about the presence and nature of metastable aggregation intermediates that is inaccessible to the conventional probes that rely on changes in quantum yield alone. PMID:26865546

Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets.

PubMed

Demartines, P; Herault, J

1997-01-01

We present a new strategy called "curvilinear component analysis" (CCA) for dimensionality reduction and representation of multidimensional data sets. The principle of CCA is a self-organized neural network performing two tasks: vector quantization (VQ) of the submanifold in the data set (input space); and nonlinear projection (P) of these quantizing vectors toward an output space, providing a revealing unfolding of the submanifold. After learning, the network has the ability to continuously map any new point from one space into another: forward mapping of new points in the input space, or backward mapping of an arbitrary position in the output space.

Identification of symptom and functional domains that fibromyalgia patients would like to see improved: a cluster analysis.

PubMed

Bennett, Robert M; Russell, Jon; Cappelleri, Joseph C; Bushmakin, Andrew G; Zlateva, Gergana; Sadosky, Alesia

2010-06-28

The purpose of this study was to determine whether some of the clinical features of fibromyalgia (FM) that patients would like to see improved aggregate into definable clusters. Seven hundred and eighty-eight patients with clinically confirmed FM and baseline pain > or =40 mm on a 100 mm visual analogue scale ranked 5 FM clinical features that the subjects would most like to see improved after treatment (one for each priority quintile) from a list of 20 developed during focus groups. For each subject, clinical features were transformed into vectors with rankings assigned values 1-5 (lowest to highest ranking). Logistic analysis was used to create a distance matrix and hierarchical cluster analysis was applied to identify cluster structure. The frequency of cluster selection was determined, and cluster importance was ranked using cluster scores derived from rankings of the clinical features. Multidimensional scaling was used to visualize and conceptualize cluster relationships. Six clinical features clusters were identified and named based on their key characteristics. In order of selection frequency, the clusters were Pain (90%; 4 clinical features), Fatigue (89%; 4 clinical features), Domestic (42%; 4 clinical features), Impairment (29%; 3 functions), Affective (21%; 3 clinical features), and Social (9%; 2 functional). The "Pain Cluster" was ranked of greatest importance by 54% of subjects, followed by Fatigue, which was given the highest ranking by 28% of subjects. Multidimensional scaling mapped these clusters to two dimensions: Status (bounded by Physical and Emotional domains), and Setting (bounded by Individual and Group interactions). Common clinical features of FM could be grouped into 6 clusters (Pain, Fatigue, Domestic, Impairment, Affective, and Social) based on patient perception of relevance to treatment. Furthermore, these 6 clusters could be charted in the 2 dimensions of Status and Setting, thus providing a unique perspective for interpretation of FM symptomatology.

Mission Analysis and Design for Space Based Inter-Satellite Laser Power Beaming

DTIC Science & Technology

2010-03-01

56 4.3.1 Darwin Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.4 Obscuration Analysis...81 Appendix B. Additional Multi-Dimensional Darwin Plots from ModelCenter . 86 Appendix C. STK Access Report for... Darwin Data Explorer Window Showing Optimized Results in Tabular Form

A Spatial View of the Interpersonal Structure of Family Interviews: Similarities and Differences Across Counselors.

ERIC Educational Resources Information Center

Friedlander, Myrna L.; Highlen, Pamela S.

1984-01-01

Examined the interpersonal structures of interviews by Ackerman, Bowen, Jackson, and Whitaker with the same family to identify common features across counselors. Multidimensional scaling provided a spatial representation of the hidden structure in the communication patterns of these interviews. Correlations indicated counselors' interactions were…

Authenticity and TV Shows: A Multidimensional Analysis Perspective

ERIC Educational Resources Information Center

Al-Surmi, Mansoor

2012-01-01

Television shows, especially soap operas and sitcoms, are usually considered by English as a second language practitioners as a source of authentic spoken conversational materials presumably because they reflect the linguistic features of natural conversation. However, practitioners are faced with the dilemma of how to assess whether such…

Contexts in a Paper Recommendation System with Collaborative Filtering

ERIC Educational Resources Information Center

Winoto, Pinata; Tang, Tiffany Ya; McCalla, Gordon

2012-01-01

Making personalized paper recommendations to users in an educational domain is not a trivial task of simply matching users' interests with a paper topic. Therefore, we proposed a context-aware multidimensional paper recommendation system that considers additional user and paper features. Earlier experiments on experienced graduate students…

Mapping of High Value Crops Through AN Object-Based Svm Model Using LIDAR Data and Orthophoto in Agusan del Norte Philippines

NASA Astrophysics Data System (ADS)

Candare, Rudolph Joshua; Japitana, Michelle; Cubillas, James Earl; Ramirez, Cherry Bryan

2016-06-01

This research describes the methods involved in the mapping of different high value crops in Agusan del Norte Philippines using LiDAR. This project is part of the Phil-LiDAR 2 Program which aims to conduct a nationwide resource assessment using LiDAR. Because of the high resolution data involved, the methodology described here utilizes object-based image analysis and the use of optimal features from LiDAR data and Orthophoto. Object-based classification was primarily done by developing rule-sets in eCognition. Several features from the LiDAR data and Orthophotos were used in the development of rule-sets for classification. Generally, classes of objects can't be separated by simple thresholds from different features making it difficult to develop a rule-set. To resolve this problem, the image-objects were subjected to Support Vector Machine learning. SVMs have gained popularity because of their ability to generalize well given a limited number of training samples. However, SVMs also suffer from parameter assignment issues that can significantly affect the classification results. More specifically, the regularization parameter C in linear SVM has to be optimized through cross validation to increase the overall accuracy. After performing the segmentation in eCognition, the optimization procedure as well as the extraction of the equations of the hyper-planes was done in Matlab. The learned hyper-planes separating one class from another in the multi-dimensional feature-space can be thought of as super-features which were then used in developing the classifier rule set in eCognition. In this study, we report an overall classification accuracy of greater than 90% in different areas.

Multidimensional analysis of the abnormal neural oscillations associated with lexical processing in schizophrenia.

PubMed

Xu, Tingting; Stephane, Massoud; Parhi, Keshab K

2013-04-01

The neural mechanisms of language abnormalities, the core symptoms in schizophrenia, remain unclear. In this study, a new experimental paradigm, combining magnetoencephalography (MEG) techniques and machine intelligence methodologies, was designed to gain knowledge about the frequency, brain location, and time of occurrence of the neural oscillations that are associated with lexical processing in schizophrenia. The 248-channel MEG recordings were obtained from 12 patients with schizophrenia and 10 healthy controls, during a lexical processing task, where the patients discriminated correct from incorrect lexical stimuli that were visually presented. Event-related desynchronization/synchronization (ERD/ERS) was computed along the frequency, time, and space dimensions combined, that resulted in a large spectral-spatial-temporal ERD/ERS feature set. Machine intelligence techniques were then applied to select a small subset of oscillation patterns that are abnormal in patients with schizophrenia, according to their discriminating power in patient and control classification. Patients with schizophrenia showed abnormal ERD/ERS patterns during both lexical encoding and post-encoding periods. The top-ranked features were located at the occipital and left frontal-temporal areas, and covered a wide frequency range, including δ (1-4 Hz), α (8-12 Hz), β (12-32 Hz), and γ (32-48 Hz) bands. These top features could discriminate the patient group from the control group with 90.91% high accuracy, which demonstrates significant brain oscillation abnormalities in patients with schizophrenia at the specific frequency, time, and brain location indicated by these top features. As neural oscillation abnormality may be due to the mechanisms of the disease, the spectral, spatial, and temporal content of the discriminating features can offer useful information for helping understand the physiological basis of the language disorder in schizophrenia, as well as the pathology of the disease itself.

The Application of Support Vector Machine (svm) Using Cielab Color Model, Color Intensity and Color Constancy as Features for Ortho Image Classification of Benthic Habitats in Hinatuan, Surigao del Sur, Philippines

NASA Astrophysics Data System (ADS)

Cubillas, J. E.; Japitana, M.

2016-06-01

This study demonstrates the application of CIELAB, Color intensity, and One Dimensional Scalar Constancy as features for image recognition and classifying benthic habitats in an image with the coastal areas of Hinatuan, Surigao Del Sur, Philippines as the study area. The study area is composed of four datasets, namely: (a) Blk66L005, (b) Blk66L021, (c) Blk66L024, and (d) Blk66L0114. SVM optimization was performed in Matlab® software with the help of Parallel Computing Toolbox to hasten the SVM computing speed. The image used for collecting samples for SVM procedure was Blk66L0114 in which a total of 134,516 sample objects of mangrove, possible coral existence with rocks, sand, sea, fish pens and sea grasses were collected and processed. The collected samples were then used as training sets for the supervised learning algorithm and for the creation of class definitions. The learned hyper-planes separating one class from another in the multi-dimensional feature space can be thought of as a super feature which will then be used in developing the C (classifier) rule set in eCognition® software. The classification results of the sampling site yielded an accuracy of 98.85% which confirms the reliability of remote sensing techniques and analysis employed to orthophotos like the CIELAB, Color Intensity and One dimensional scalar constancy and the use of SVM classification algorithm in classifying benthic habitats.

Tensor-Train Split-Operator Fourier Transform (TT-SOFT) Method: Multidimensional Nonadiabatic Quantum Dynamics.

PubMed

Greene, Samuel M; Batista, Victor S

2017-09-12

We introduce the "tensor-train split-operator Fourier transform" (TT-SOFT) method for simulations of multidimensional nonadiabatic quantum dynamics. TT-SOFT is essentially the grid-based SOFT method implemented in dynamically adaptive tensor-train representations. In the same spirit of all matrix product states, the tensor-train format enables the representation, propagation, and computation of observables of multidimensional wave functions in terms of the grid-based wavepacket tensor components, bypassing the need of actually computing the wave function in its full-rank tensor product grid space. We demonstrate the accuracy and efficiency of the TT-SOFT method as applied to propagation of 24-dimensional wave packets, describing the S 1 /S 2 interconversion dynamics of pyrazine after UV photoexcitation to the S 2 state. Our results show that the TT-SOFT method is a powerful computational approach for simulations of quantum dynamics of polyatomic systems since it avoids the exponential scaling problem of full-rank grid-based representations.

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.

Multistability inspired by the oblique, pennate architectures of skeletal muscle

NASA Astrophysics Data System (ADS)

Kidambi, Narayanan; Harne, Ryan L.; Wang, K. W.

2017-04-01

Skeletal muscle mechanics exhibit a range of noteworthy characteristics, providing great inspiration for the development of advanced structural and material systems. These characteristics arise from the synergies demonstrated between muscle's constituents across the various length scales. From the macroscale oblique orientation of muscle fibers to the microscale lattice spacing of sarcomeres, muscle takes advantage of geometries and multidimensionality for force generation or length change along a desired axis. Inspired by these behaviors, this research investigates how the incorporation of multidimensionality afforded by oblique, pennate architectures can uncover novel mechanics in structures exhibiting multistability. Experimental investigation of these mechanics is undertaken using specimens of molded silicone rubber with patterned voids, and results reveal tailorable mono-, bi-, and multi-stability under axial displacements by modulation of transverse confinement. If the specimen is considered as an architected material, these results show its ability to generate intriguing, non-monotonic shear stresses. The outcomes would foster the development of novel, advanced mechanical metamaterials that exploit pennation and multidimensionality.

Experimental Study on the Perception Characteristics of Haptic Texture by Multidimensional Scaling.

PubMed

Wu, Juan; Li, Na; Liu, Wei; Song, Guangming; Zhang, Jun

2015-01-01

Recent works regarding real texture perception demonstrate that physical factors such as stiffness and spatial period play a fundamental role in texture perception. This research used a multidimensional scaling (MDS) analysis to further characterize and quantify the effects of the simulation parameters on haptic texture rendering and perception. In a pilot experiment, 12 haptic texture samples were generated by using a 3-degrees-of-freedom (3-DOF) force-feedback device with varying spatial period, height, and stiffness coefficient parameter values. The subjects' perceptions of the virtual textures indicate that roughness, denseness, flatness and hardness are distinguishing characteristics of texture. In the main experiment, 19 participants rated the dissimilarities of the textures and estimated the magnitudes of their characteristics. The MDS method was used to recover the underlying perceptual space and reveal the significance of the space from the recorded data. The physical parameters and their combinations have significant effects on the perceptual characteristics. A regression model was used to quantitatively analyze the parameters and their effects on the perceptual characteristics. This paper is to illustrate that haptic texture perception based on force feedback can be modeled in two- or three-dimensional space and provide suggestions on improving perception-based haptic texture rendering.

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.

Data Visualization in Information Retrieval and Data Mining (SIG VIS).

ERIC Educational Resources Information Center

Efthimiadis, Efthimis

2000-01-01

Presents abstracts that discuss using data visualization for information retrieval and data mining, including immersive information space and spatial metaphors; spatial data using multi-dimensional matrices with maps; TREC (Text Retrieval Conference) experiments; users' information needs in cartographic information retrieval; and users' relevance…

Defining and Differentiating the Makerspace

ERIC Educational Resources Information Center

Dousay, Tonia A.

2017-01-01

Many resources now punctuate the maker movement landscape. However, some schools and communities still struggle to understand this burgeoning movement. How do we define these spaces and differentiate them from previous labs and shops? Through a multidimensional framework, stakeholders should consider how the structure, access, staffing, and tools…

A Computational Model of Multidimensional Shape

PubMed Central

Liu, Xiuwen; Shi, Yonggang; Dinov, Ivo

2010-01-01

We develop a computational model of shape that extends existing Riemannian models of curves to multidimensional objects of general topological type. We construct shape spaces equipped with geodesic metrics that measure how costly it is to interpolate two shapes through elastic deformations. The model employs a representation of shape based on the discrete exterior derivative of parametrizations over a finite simplicial complex. We develop algorithms to calculate geodesics and geodesic distances, as well as tools to quantify local shape similarities and contrasts, thus obtaining a formulation that accounts for regional differences and integrates them into a global measure of dissimilarity. The Riemannian shape spaces provide a common framework to treat numerous problems such as the statistical modeling of shapes, the comparison of shapes associated with different individuals or groups, and modeling and simulation of shape dynamics. We give multiple examples of geodesic interpolations and illustrations of the use of the models in brain mapping, particularly, the analysis of anatomical variation based on neuroimaging data. PMID:21057668

Escher in color space: individual-differences multidimensional scaling of color dissimilarities collected with a gestalt formation task.

PubMed

Bimler, David; Kirkland, John; Pichler, Shaun

2004-02-01

The structure of color perception can be examined by collecting judgments about color dissimilarities. In the procedure used here, stimuli are presented three at a time on a computer monitor and the spontaneous grouping of most-similar stimuli into gestalts provides the dissimilarity comparisons. Analysis with multidimensional scaling allows such judgments to be pooled from a number of observers without obscuring the variations among them. The anomalous perceptions of color-deficient observers produce comparisons that are represented well by a geometric model of compressed individual color spaces, with different forms of deficiency distinguished by different directions of compression. The geometrical model is also capable of accommodating the normal spectrum of variation, so that there is greater variation in compression parameters between tests on normal subjects than in those between repeated tests on individual subjects. The method is sufficiently sensitive and the variations sufficiently large that they are not obscured by the use of a range of monitors, even under somewhat loosely controlled conditions.

A multidimensional analysis of the epistemic origins of nursing theories, models, and frameworks.

PubMed

Beckstead, Jason W; Beckstead, Laura Grace

2006-01-01

The purpose of this article is to introduce our notion of epistemic space and to demonstrate its utility for understanding the origins and trajectories of nursing theory in the 20th century using multidimensional scaling (MDS). A literature review was conducted on primary and secondary sources written by and about 20 nurse theorists to investigate whether or not they cited 129 different scholars in the fields of anthropology, biology, nursing, philosophy, psychology, and sociology. Seventy-four scholars were identified as having been cited by at least two nurse theorists (319 citations total). Proximity scores, quantifying the similarity among nurse theorists based on proportions of shared citations, were calculated and analyzed using MDS. The emergent model of epistemic space that accommodated these similarities among nurse theorists revealed the systematic influence of scholars from various fields, notably psychology, biology, and philosophy. We believe that this schema and resulting taxonomy will prove useful for furthering our understanding of the relationships among nursing theories and theories in other fields of science.

Technical support for creating an artificial intelligence system for feature extraction and experimental design

NASA Technical Reports Server (NTRS)

Glick, B. J.

1985-01-01

Techniques for classifying objects into groups or clases go under many different names including, most commonly, cluster analysis. Mathematically, the general problem is to find a best mapping of objects into an index set consisting of class identifiers. When an a priori grouping of objects exists, the process of deriving the classification rules from samples of classified objects is known as discrimination. When such rules are applied to objects of unknown class, the process is denoted classification. The specific problem addressed involves the group classification of a set of objects that are each associated with a series of measurements (ratio, interval, ordinal, or nominal levels of measurement). Each measurement produces one variable in a multidimensional variable space. Cluster analysis techniques are reviewed and methods for incuding geographic location, distance measures, and spatial pattern (distribution) as parameters in clustering are examined. For the case of patterning, measures of spatial autocorrelation are discussed in terms of the kind of data (nominal, ordinal, or interval scaled) to which they may be applied.

Influence of implantable hearing aids and neuroprosthesison music perception.

PubMed

Rahne, Torsten; Böhme, Lars; Götze, Gerrit

2012-01-01

The identification and discrimination of timbre are essential features of music perception. One dominating parameter within the multidimensional timbre space is the spectral shape of complex sounds. As hearing loss interferes with the perception and enjoyment of music, we approach the individual timbre discrimination skills in individuals with severe to profound hearing loss using a cochlear implant (CI) and normal hearing individuals using a bone-anchored hearing aid (Baha). With a recent developed behavioral test relying on synthetically sounds forming a spectral continuum, the timbre difference was changed adaptively to measure the individual just noticeable difference (JND) in a forced-choice paradigm. To explore the differences in timbre perception abilities caused by the hearing mode, the sound stimuli were varied in their fundamental frequency, thus generating different spectra which are not completely covered by a CI or Baha system. The resulting JNDs demonstrate differences in timbre perception between normal hearing individuals, Baha users, and CI users. Beside the physiological reasons, also technical limitations appear as the main contributing factors.

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.

Multidimensional data analysis in immunophenotyping.

PubMed

Loken, M R

2001-05-01

The complexity of cell populations requires careful selection of reagents to detect cells of interest and distinguish them from other types. Additional reagents are frequently used to provide independent criteria for cell identification. Two or three monoclonal antibodies in combination with forward and right-angle light scatter generate a data set that is difficult to visualize because the data must be represented in four- or five-dimensional space. The separation between cell populations provided by the multiple characteristics is best visualized by multidimensional analysis using all parameters simultaneously to identify populations within the resulting hyperspace. Groups of cells are distinguished based on a combination of characteristics not apparent in any usual two-dimensional representation of the data.

The Structure of Integral Dimensions: Contrasting Topological and Cartesian Representations

ERIC Educational Resources Information Center

Jones, Matt; Goldstone, Robert L.

2013-01-01

Diverse evidence shows that perceptually integral dimensions, such as those composing color, are represented holistically. However, the nature of these holistic representations is poorly understood. Extant theories, such as those founded on multidimensional scaling or general recognition theory, model integral stimulus spaces using a Cartesian…

A Re-Unification of Two Competing Models for Document Retrieval.

ERIC Educational Resources Information Center

Bodoff, David

1999-01-01

Examines query-oriented versus document-oriented information retrieval and feedback learning. Highlights include a reunification of the two approaches for probabilistic document retrieval and for vector space model (VSM) retrieval; learning in VSM and in probabilistic models; multi-dimensional scaling; and ongoing field studies. (LRW)

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.

The Multidimensional Structure of University Absenteeism: An Exploratory Study

ERIC Educational Resources Information Center

López-Bonilla, Jesús Manuel; López-Bonilla, Luis Miguel

2015-01-01

Absenteeism has been a common and very extended problem in university spheres for several years. This problem has become a permanent feature in academic studies in general, yet it has received scarce empirical research attention. This work is focused on the analysis of the factors that determine university absenteeism. It evaluates a series of…

Assessment of Student Engagement: An Analysis of Trends

ERIC Educational Resources Information Center

Nauffal, Diane I.

2012-01-01

Quality is a multi-dimensional concept and embraces all functions and activities of higher education (academic programs, research, and community services) in all their features and components. Traditionally quality was a measure of resources and reputation. In recent years there has been a shift in emphasis to institutional best practices such as…

Multidimensionality and Gravity in Global Trade, 1950-2000

ERIC Educational Resources Information Center

Zhou, Min

2010-01-01

The expansion of global trade in the post-war period is subject to various interpretations. Some stress the trade-promoting role of the novel features in the world economy; some insist on the role of traditional factors, such as geographic distance, political difference and cultural dissimilarity, in continuously depressing trade flows; others…

The Robustness of IRT-Based Vertical Scaling Methods to Violation of Unidimensionality

ERIC Educational Resources Information Center

Yin, Liqun

2013-01-01

In recent years, many states have adopted Item Response Theory (IRT) based vertically scaled tests due to their compelling features in a growth-based accountability context. However, selection of a practical and effective calibration/scaling method and proper understanding of issues with possible multidimensionality in the test data is critical to…

A Multidimensional Reappraisal of Language in Autism: Insights from a Discourse Analytic Study

ERIC Educational Resources Information Center

Sterponi, Laura; de Kirby, Kenton

2016-01-01

In this article, we leverage theoretical insights and methodological guidelines of discourse analytic scholarship to re-examine language phenomena typically associated with autism. Through empirical analysis of the verbal behavior of three children with autism, we engage the question of how prototypical features of autistic language--notably…

Assessing Student Orientation to School to Address Low Achievement and Dropping Out

ERIC Educational Resources Information Center

Nadirova, Anna; Burger, John Michael

2014-01-01

This study contributes to applied and theoretical research for schools and districts by helping inform programs and policies directed at school improvement, raising student achievement, and high school completion. The paper features recent results of ongoing research on student orientation to school that was assessed via a multidimensional Student…

Robustness of Ability Estimation to Multidimensionality in CAST with Implications to Test Assembly

ERIC Educational Resources Information Center

Zhang, Yanwei; Nandakumar, Ratna

2006-01-01

Computer Adaptive Sequential Testing (CAST) is a test delivery model that combines features of the traditional conventional paper-and-pencil testing and item-based computerized adaptive testing (CAT). The basic structure of CAST is a panel composed of multiple testlets adaptively administered to examinees at different stages. Current applications…

Structured Constructs Models Based on Change-Point Analysis

ERIC Educational Resources Information Center

Shin, Hyo Jeong; Wilson, Mark; Choi, In-Hee

2017-01-01

This study proposes a structured constructs model (SCM) to examine measurement in the context of a multidimensional learning progression (LP). The LP is assumed to have features that go beyond a typical multidimentional IRT model, in that there are hypothesized to be certain cross-dimensional linkages that correspond to requirements between the…

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.

Multidimensional assessment of strongly irregular voices such as in substitution voicing and spasmodic dysphonia: a compilation of own research.

PubMed

Moerman, Mieke; Martens, Jean-Pierre; Dejonckere, Philippe

2015-04-01

This article is a compilation of own research performed during the European COoperation in Science and Technology (COST) action 2103: 'Advance Voice Function Assessment', an initiative of voice and speech processing teams consisting of physicists, engineers, and clinicians. This manuscript concerns analyzing largely irregular voicing types, namely substitution voicing (SV) and adductor spasmodic dysphonia (AdSD). A specific perceptual rating scale (IINFVo) was developed, and the Auditory Model Based Pitch Extractor (AMPEX), a piece of software that automatically analyses running speech and generates pitch values in background noise, was applied. The IINFVo perceptual rating scale has been shown to be useful in evaluating SV. The analysis of strongly irregular voices stimulated a modification of the European Laryngological Society's assessment protocol which was originally designed for the common types of (less severe) dysphonia. Acoustic analysis with AMPEX demonstrates that the most informative features are, for SV, the voicing-related acoustic features and, for AdSD, the perturbation measures. Poor correlations between self-assessment and acoustic and perceptual dimensions in the assessment of highly irregular voices argue for a multidimensional approach.

Condenser: a statistical aggregation tool for multi-sample quantitative proteomic data from Matrix Science Mascot Distiller™.

PubMed

Knudsen, Anders Dahl; Bennike, Tue; Kjeldal, Henrik; Birkelund, Svend; Otzen, Daniel Erik; Stensballe, Allan

2014-05-30

We describe Condenser, a freely available, comprehensive open-source tool for merging multidimensional quantitative proteomics data from the Matrix Science Mascot Distiller Quantitation Toolbox into a common format ready for subsequent bioinformatic analysis. A number of different relative quantitation technologies, such as metabolic (15)N and amino acid stable isotope incorporation, label-free and chemical-label quantitation are supported. The program features multiple options for curative filtering of the quantified peptides, allowing the user to choose data quality thresholds appropriate for the current dataset, and ensure the quality of the calculated relative protein abundances. Condenser also features optional global normalization, peptide outlier removal, multiple testing and calculation of t-test statistics for highlighting and evaluating proteins with significantly altered relative protein abundances. Condenser provides an attractive addition to the gold-standard quantitative workflow of Mascot Distiller, allowing easy handling of larger multi-dimensional experiments. Source code, binaries, test data set and documentation are available at http://condenser.googlecode.com/. Copyright © 2014 Elsevier B.V. All rights reserved.

A new bidirectional heteroassociative memory encompassing correlational, competitive and topological properties.

PubMed

Chartier, Sylvain; Giguère, Gyslain; Langlois, Dominic

2009-01-01

In this paper, we present a new recurrent bidirectional model that encompasses correlational, competitive and topological model properties. The simultaneous use of many classes of network behaviors allows for the unsupervised learning/categorization of perceptual patterns (through input compression) and the concurrent encoding of proximities in a multidimensional space. All of these operations are achieved within a common learning operation, and using a single set of defining properties. It is shown that the model can learn categories by developing prototype representations strictly from exposition to specific exemplars. Moreover, because the model is recurrent, it can reconstruct perfect outputs from incomplete and noisy patterns. Empirical exploration of the model's properties and performance shows that its ability for adequate clustering stems from: (1) properly distributing connection weights, and (2) producing a weight space with a low dispersion level (or higher density). In addition, since the model uses a sparse representation (k-winners), the size of topological neighborhood can be fixed, and no longer requires a decrease through time as was the case with classic self-organizing feature maps. Since the model's learning and transmission parameters are independent from learning trials, the model can develop stable fixed points in a constrained topological architecture, while being flexible enough to learn novel patterns.

An intrinsic algorithm for parallel Poisson disk sampling on arbitrary surfaces.

PubMed

Ying, Xiang; Xin, Shi-Qing; Sun, Qian; He, Ying

2013-09-01

Poisson disk sampling has excellent spatial and spectral properties, and plays an important role in a variety of visual computing. Although many promising algorithms have been proposed for multidimensional sampling in euclidean space, very few studies have been reported with regard to the problem of generating Poisson disks on surfaces due to the complicated nature of the surface. This paper presents an intrinsic algorithm for parallel Poisson disk sampling on arbitrary surfaces. In sharp contrast to the conventional parallel approaches, our method neither partitions the given surface into small patches nor uses any spatial data structure to maintain the voids in the sampling domain. Instead, our approach assigns each sample candidate a random and unique priority that is unbiased with regard to the distribution. Hence, multiple threads can process the candidates simultaneously and resolve conflicts by checking the given priority values. Our algorithm guarantees that the generated Poisson disks are uniformly and randomly distributed without bias. It is worth noting that our method is intrinsic and independent of the embedding space. This intrinsic feature allows us to generate Poisson disk patterns on arbitrary surfaces in IR(n). To our knowledge, this is the first intrinsic, parallel, and accurate algorithm for surface Poisson disk sampling. Furthermore, by manipulating the spatially varying density function, we can obtain adaptive sampling easily.

Learning what matters: A neural explanation for the sparsity bias.

PubMed

Hassall, Cameron D; Connor, Patrick C; Trappenberg, Thomas P; McDonald, John J; Krigolson, Olave E

2018-05-01

The visual environment is filled with complex, multi-dimensional objects that vary in their value to an observer's current goals. When faced with multi-dimensional stimuli, humans may rely on biases to learn to select those objects that are most valuable to the task at hand. Here, we show that decision making in a complex task is guided by the sparsity bias: the focusing of attention on a subset of available features. Participants completed a gambling task in which they selected complex stimuli that varied randomly along three dimensions: shape, color, and texture. Each dimension comprised three features (e.g., color: red, green, yellow). Only one dimension was relevant in each block (e.g., color), and a randomly-chosen value ranking determined outcome probabilities (e.g., green > yellow > red). Participants were faster to respond to infrequent probe stimuli that appeared unexpectedly within stimuli that possessed a more valuable feature than to probes appearing within stimuli possessing a less valuable feature. Event-related brain potentials recorded during the task provided a neurophysiological explanation for sparsity as a learning-dependent increase in optimal attentional performance (as measured by the N2pc component of the human event-related potential) and a concomitant learning-dependent decrease in prediction errors (as measured by the feedback-elicited reward positivity). Together, our results suggest that the sparsity bias guides human reinforcement learning in complex environments. Copyright © 2018 Elsevier B.V. All rights reserved.

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.

Modeling Age-Related Differences in Immediate Memory Using SIMPLE

ERIC Educational Resources Information Center

Surprenant, Aimee M.; Neath, Ian; Brown, Gordon D. A.

2006-01-01

In the SIMPLE model (Scale Invariant Memory and Perceptual Learning), performance on memory tasks is determined by the locations of items in multidimensional space, and better performance is associated with having fewer close neighbors. Unlike most previous simulations with SIMPLE, the ones reported here used measured, rather than assumed,…

Multi-Dimensional Optimization for Cloud Based Multi-Tier Applications

ERIC Educational Resources Information Center

Jung, Gueyoung

2010-01-01

Emerging trends toward cloud computing and virtualization have been opening new avenues to meet enormous demands of space, resource utilization, and energy efficiency in modern data centers. By being allowed to host many multi-tier applications in consolidated environments, cloud infrastructure providers enable resources to be shared among these…

Discovering Middle Space: Distinctions of Sex and Gender in Resilient Leadership

ERIC Educational Resources Information Center

Christman, Dana E.; McClellan, Rhonda L.

2012-01-01

This study contrasts findings from two Delphi studies that investigated how women and men who are higher education academic administrators in educational leadership programs and colleges define and describe resiliency in their leadership. Using gender theories, both studies revealed a multidimensional gendering of leadership, a gendering more…

Visual reconciliation of alternative similarity spaces in climate modeling

Treesearch

J Poco; A Dasgupta; Y Wei; William Hargrove; C.R. Schwalm; D.N. Huntzinger; R Cook; E Bertini; C.T. Silva

2015-01-01

Visual data analysis often requires grouping of data objects based on their similarity. In many application domains researchers use algorithms and techniques like clustering and multidimensional scaling to extract groupings from data. While extracting these groups using a single similarity criteria is relatively straightforward, comparing alternative criteria poses...

Multi-Dimensional Perception of Parental Involvement

ERIC Educational Resources Information Center

Fisher, Yael

2016-01-01

The main purpose of this study was to define and conceptualize the term parental involvement. A questionnaire was administrated to parents (140), teachers (145), students (120) and high ranking civil servants in the Ministry of Education (30). Responses were analyzed through Smallest Space Analysis (SSA). The SSA solution among all groups rendered…

Using Visual Information to Determine the Subjective Valuation of Public Space for Transportation : Application to Subway Crowding Costs in NYC

DOT National Transportation Integrated Search

2017-11-30

The objective of this project is to explore the role of visual information in determining the users subjective valuation of multidimensional trip attributes that are relevant in decision-making, but are neglected in standard travel demand models. ...

Error control techniques for satellite and space communications

NASA Technical Reports Server (NTRS)

Costello, Daniel J., Jr.

1988-01-01

During the period December 1, 1987 through May 31, 1988, progress was made in the following areas: construction of Multi-Dimensional Bandwidth Efficient Trellis Codes with MPSK modulation; performance analysis of Bandwidth Efficient Trellis Coded Modulation schemes; and performance analysis of Bandwidth Efficient Trellis Codes on Fading Channels.

The "Why" of Learning

ERIC Educational Resources Information Center

Geary, David C.

2009-01-01

Alexander, Schallert, and Reynolds's (2009/this issue) "what," "where," "who," and "when" framework situates different perspectives on learning in different places in this multidimensional space and by doing so helps us to better understand seemingly disparate approaches to learning. The framework is in need of a fifth, "why" dimension. The "why"…

"Pedagogy of Third Space": A Multidimensional Early Childhood Curriculum

ERIC Educational Resources Information Center

Gupta, Amita

2015-01-01

This paper will illustrate how philosophical and pedagogical boundaries that are defined by diverse cultures and ideologies might be navigated in the practical implementation of an early childhood curriculum in postcolonial urban India. Findings from a qualitative naturalistic inquiry indicated that a complex, multifaceted curriculum shaped by…

Knowledge and Vision in Teaching

ERIC Educational Resources Information Center

Kennedy, Mary M.

2006-01-01

The author challenges the role of knowledge in teaching by pointing out the variety of issues and concerns teachers must simultaneously address. Teachers use two strategies to manage their multidimensional space: They develop integrated habits and rules of thumb for handling situations as they arise, and they plan their lessons by envisioning them…

Measuring the Effectiveness of Educational Technology: What Are We Attempting to Measure?

ERIC Educational Resources Information Center

Jenkinson, Jodie

2009-01-01

In many academic areas, students' success depends upon their ability to envision and manipulate complex multidimensional information spaces. Fields in which students struggle with mastering these types of representations include (but are by no means limited to) mathematics, science, medicine, and engineering. There has been some educational…

Nonlinear Aeroacoustics Computations by the Space-Time CE/SE Method

NASA Technical Reports Server (NTRS)

Loh, Ching Y.

2003-01-01

The Space-Time Conservation Element and Solution Element Method, or CE/SE Method for short, is a recently developed numerical method for conservation laws. Despite its second order accuracy in space and time, it possesses low dispersion errors and low dissipation. The method is robust enough to cover a wide range of compressible flows: from weak linear acoustic waves to strong discontinuous waves (shocks). An outstanding feature of the CE/SE scheme is its truly multi-dimensional, simple but effective non-reflecting boundary condition (NRBC), which is particularly valuable for computational aeroacoustics (CAA). In nature, the method may be categorized as a finite volume method, where the conservation element (CE) is equivalent to a finite control volume (or cell) and the solution element (SE) can be understood as the cell interface. However, due to its careful treatment of the surface fluxes and geometry, it is different from the existing schemes. Currently, the CE/SE scheme has been developed to a matured stage that a 3-D unstructured CE/SE Navier-Stokes solver is already available. However, in the present review paper, as a general introduction to the CE/SE method, only the 2-D unstructured Euler CE/SE solver is chosen and sketched in section 2. Then applications of the 2-D and 3-D CE/SE schemes to linear, and in particular, nonlinear aeroacoustics are depicted in sections 3, 4, and 5 to demonstrate its robustness and capability.

Gravitational wave-Gauge field oscillations

NASA Astrophysics Data System (ADS)

Caldwell, R. R.; Devulder, C.; Maksimova, N. A.

2016-09-01

Gravitational waves propagating through a stationary gauge field transform into gauge field waves and back again. When multiple families of flavor-space locked gauge fields are present, the gravitational and gauge field waves exhibit novel dynamics. At high frequencies, the system behaves like coupled oscillators in which the gravitational wave is the central pacemaker. Due to energy conservation and exchange among the oscillators, the wave amplitudes lie on a multidimensional sphere, reminiscent of neutrino flavor oscillations. This phenomenon has implications for cosmological scenarios based on flavor-space locked gauge fields.

Direct laser writing of polymeric nanostructures via optically induced local thermal effect

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

Tong, Quang Cong; Institute of Materials Science, Vietnam Academy of Science and Technology, 18 Hoang Quoc Viet, Cau Giay, 10000 Hanoi; Nguyen, Dam Thuy Trang

We demonstrate the fabrication of desired structures with feature size below the diffraction limit by use of a positive photoresist. The direct laser writing technique employing a continuous-wave laser was used to optically induce a local thermal effect in a positive photoresist, which then allowed the formation of solid nanostructures. This technique enabled us to realize multi-dimensional sub-microstructures by use of a positive photoresist, with a feature size down to 57 nm. This mechanism acting on positive photoresists opens a simple and low-cost way for nanofabrication.

Feature combinations and the divergence criterion

NASA Technical Reports Server (NTRS)

Decell, H. P., Jr.; Mayekar, S. M.

1976-01-01

Classifying large quantities of multidimensional remotely sensed agricultural data requires efficient and effective classification techniques and the construction of certain transformations of a dimension reducing, information preserving nature. The construction of transformations that minimally degrade information (i.e., class separability) is described. Linear dimension reducing transformations for multivariate normal populations are presented. Information content is measured by divergence.

Tensor Toolbox for MATLAB v. 3.0

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

Kola, Tamara; Bader, Brett W.; Acar Ataman, Evrim NMN

Tensors (also known as multidimensional arrays or N-way arrays) are used in a variety of applications ranging from chemometrics to network analysis. The Tensor Toolbox provides classes for manipulating dense, sparse, and structured tensors using MATLAB's object-oriented features. It also provides algorithms for tensor decomposition and factorization, algorithms for computing tensor eigenvalues, and methods for visualization of results.

Visual Analytics for Exploration of a High-Dimensional Structure

DTIC Science & Technology

2013-04-01

5 Figure 3. Comparison of Euclidean vs. geodesic distance. LDRs use...manifold, whereas an LDR fails. ...........................6 Figure 4. WEKA GUI for data mining HDD using FRFS-ACO...multidimensional scaling (CMDS)— are a linear DR ( LDR ). An LDR is based on a linear combination of the feature data. LDRs keep similar data points close together

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.

A Structure-Based Distance Metric for High-Dimensional Space Exploration with Multi-Dimensional Scaling

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

Lee, Hyun Jung; McDonnell, Kevin T.; Zelenyuk, Alla

2014-03-01

Although the Euclidean distance does well in measuring data distances within high-dimensional clusters, it does poorly when it comes to gauging inter-cluster distances. This significantly impacts the quality of global, low-dimensional space embedding procedures such as the popular multi-dimensional scaling (MDS) where one can often observe non-intuitive layouts. We were inspired by the perceptual processes evoked in the method of parallel coordinates which enables users to visually aggregate the data by the patterns the polylines exhibit across the dimension axes. We call the path of such a polyline its structure and suggest a metric that captures this structure directly inmore » high-dimensional space. This allows us to better gauge the distances of spatially distant data constellations and so achieve data aggregations in MDS plots that are more cognizant of existing high-dimensional structure similarities. Our MDS plots also exhibit similar visual relationships as the method of parallel coordinates which is often used alongside to visualize the high-dimensional data in raw form. We then cast our metric into a bi-scale framework which distinguishes far-distances from near-distances. The coarser scale uses the structural similarity metric to separate data aggregates obtained by prior classification or clustering, while the finer scale employs the appropriate Euclidean distance.« less

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

Utilization of the Discrete Differential Evolution for Optimization in Multidimensional Point Clouds.

PubMed

Uher, Vojtěch; Gajdoš, Petr; Radecký, Michal; Snášel, Václav

2016-01-01

The Differential Evolution (DE) is a widely used bioinspired optimization algorithm developed by Storn and Price. It is popular for its simplicity and robustness. This algorithm was primarily designed for real-valued problems and continuous functions, but several modified versions optimizing both integer and discrete-valued problems have been developed. The discrete-coded DE has been mostly used for combinatorial problems in a set of enumerative variants. However, the DE has a great potential in the spatial data analysis and pattern recognition. This paper formulates the problem as a search of a combination of distinct vertices which meet the specified conditions. It proposes a novel approach called the Multidimensional Discrete Differential Evolution (MDDE) applying the principle of the discrete-coded DE in discrete point clouds (PCs). The paper examines the local searching abilities of the MDDE and its convergence to the global optimum in the PCs. The multidimensional discrete vertices cannot be simply ordered to get a convenient course of the discrete data, which is crucial for good convergence of a population. A novel mutation operator utilizing linear ordering of spatial data based on the space filling curves is introduced. The algorithm is tested on several spatial datasets and optimization problems. The experiments show that the MDDE is an efficient and fast method for discrete optimizations in the multidimensional point clouds.

Utilization of the Discrete Differential Evolution for Optimization in Multidimensional Point Clouds

PubMed Central

Radecký, Michal; Snášel, Václav

2016-01-01

The Differential Evolution (DE) is a widely used bioinspired optimization algorithm developed by Storn and Price. It is popular for its simplicity and robustness. This algorithm was primarily designed for real-valued problems and continuous functions, but several modified versions optimizing both integer and discrete-valued problems have been developed. The discrete-coded DE has been mostly used for combinatorial problems in a set of enumerative variants. However, the DE has a great potential in the spatial data analysis and pattern recognition. This paper formulates the problem as a search of a combination of distinct vertices which meet the specified conditions. It proposes a novel approach called the Multidimensional Discrete Differential Evolution (MDDE) applying the principle of the discrete-coded DE in discrete point clouds (PCs). The paper examines the local searching abilities of the MDDE and its convergence to the global optimum in the PCs. The multidimensional discrete vertices cannot be simply ordered to get a convenient course of the discrete data, which is crucial for good convergence of a population. A novel mutation operator utilizing linear ordering of spatial data based on the space filling curves is introduced. The algorithm is tested on several spatial datasets and optimization problems. The experiments show that the MDDE is an efficient and fast method for discrete optimizations in the multidimensional point clouds. PMID:27974884

Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty.

PubMed

Mihaljević, Bojan; Bielza, Concha; Benavides-Piccione, Ruth; DeFelipe, Javier; Larrañaga, Pedro

2014-01-01

Interneuron classification is an important and long-debated topic in neuroscience. A recent study provided a data set of digitally reconstructed interneurons classified by 42 leading neuroscientists according to a pragmatic classification scheme composed of five categorical variables, namely, of the interneuron type and four features of axonal morphology. From this data set we now learned a model which can classify interneurons, on the basis of their axonal morphometric parameters, into these five descriptive variables simultaneously. Because of differences in opinion among the neuroscientists, especially regarding neuronal type, for many interneurons we lacked a unique, agreed-upon classification, which we could use to guide model learning. Instead, we guided model learning with a probability distribution over the neuronal type and the axonal features, obtained, for each interneuron, from the neuroscientists' classification choices. We conveniently encoded such probability distributions with Bayesian networks, calling them label Bayesian networks (LBNs), and developed a method to predict them. This method predicts an LBN by forming a probabilistic consensus among the LBNs of the interneurons most similar to the one being classified. We used 18 axonal morphometric parameters as predictor variables, 13 of which we introduce in this paper as quantitative counterparts to the categorical axonal features. We were able to accurately predict interneuronal LBNs. Furthermore, when extracting crisp (i.e., non-probabilistic) predictions from the predicted LBNs, our method outperformed related work on interneuron classification. Our results indicate that our method is adequate for multi-dimensional classification of interneurons with probabilistic labels. Moreover, the introduced morphometric parameters are good predictors of interneuron type and the four features of axonal morphology and thus may serve as objective counterparts to the subjective, categorical axonal features.

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.

New multidimensional functional diversity indices for a multifaceted framework in functional ecology.

PubMed

Villéger, Sébastien; Mason, Norman W H; Mouillot, David

2008-08-01

Functional diversity is increasingly identified as an important driver of ecosystem functioning. Various indices have been proposed to measure the functional diversity of a community, but there is still no consensus on which are most suitable. Indeed, none of the existing indices meets all the criteria required for general use. The main criteria are that they must be designed to deal with several traits, take into account abundances, and measure all the facets of functional diversity. Here we propose three indices to quantify each facet of functional diversity for a community with species distributed in a multidimensional functional space: functional richness (volume of the functional space occupied by the community), functional evenness (regularity of the distribution of abundance in this volume), and functional divergence (divergence in the distribution of abundance in this volume). Functional richness is estimated using the existing convex hull volume index. The new functional evenness index is based on the minimum spanning tree which links all the species in the multidimensional functional space. Then this new index quantifies the regularity with which species abundances are distributed along the spanning tree. Functional divergence is measured using a novel index which quantifies how species diverge in their distances (weighted by their abundance) from the center of gravity in the functional space. We show that none of the indices meets all the criteria required for a functional diversity index, but instead we show that the set of three complementary indices meets these criteria. Through simulations of artificial data sets, we demonstrate that functional divergence and functional evenness are independent of species richness and that the three functional diversity indices are independent of each other. Overall, our study suggests that decomposition of functional diversity into its three primary components provides a meaningful framework for its quantification and for the classification of existing functional diversity indices. This decomposition has the potential to shed light on the role of biodiversity on ecosystem functioning and on the influence of biotic and abiotic filters on the structure of species communities. Finally, we propose a general framework for applying these three functional diversity indices.

The Development of Voiceless Sibilant Fricatives in Putonghua-Speaking Children

ERIC Educational Resources Information Center

Li, Fangfang; Munson, Benjamin

2016-01-01

Purpose The aims of the present study are (a) to quantify the developmental sequence of fricative mastery in Putonghua-speaking children and discuss the observed pattern in relation to existing theoretical positions, and (b) to describe the acquisition of the fine-articulatory/acoustic details of fricatives in the multidimensional acoustic space.…

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…

Chlorofluoromethanes and the Stratosphere

NASA Technical Reports Server (NTRS)

Hudson, R. D. (Editor)

1977-01-01

The conclusions of a workshop held by the National Aeronautics and Space Administration to assess the current knowledge of the impact of chlorofluoromethane release in the troposphere on stratospheric ozone concentrations. The following topics are discussed; (1) Laboratory measurements; (2) Ozone measurements and trends; (3) Minor species and aerosol measurements; (4) One dimensional modeling; and (5) Multidimensional modeling.

Diverse applications of advanced man-telerobot interfaces

NASA Technical Reports Server (NTRS)

Mcaffee, Douglas A.

1991-01-01

Advancements in man-machine interfaces and control technologies used in space telerobotics and teleoperators have potential application wherever human operators need to manipulate multi-dimensional spatial relationships. Bilateral six degree-of-freedom position and force cues exchanged between the user and a complex system can broaden and improve the effectiveness of several diverse man-machine interfaces.

Dynamic State Estimation of Terrestrial and Solar Plasmas

NASA Astrophysics Data System (ADS)

Kamalabadi, Farzad

A pervasive problem in virtually all branches of space science is the estimation of multi-dimensional state parameters of a dynamical system from a collection of indirect, often incomplete, and imprecise measurements. Subsequent scientific inference is predicated on rigorous analysis, interpretation, and understanding of physical observations and on the reliability of the associated quantitative statistical bounds and performance characteristics of the algorithms used. In this work, we focus on these dynamic state estimation problems and illustrate their importance in the context of two timely activities in space remote sensing. First, we discuss the estimation of multi-dimensional ionospheric state parameters from UV spectral imaging measurements anticipated to be acquired the recently selected NASA Heliophysics mission, Ionospheric Connection Explorer (ICON). Next, we illustrate that similar state-space formulations provide the means for the estimation of 3D, time-dependent densities and temperatures in the solar corona from a series of white-light and EUV measurements. We demonstrate that, while a general framework for the stochastic formulation of the state estimation problem is suited for systematic inference of the parameters of a hidden Markov process, several challenges must be addressed in the assimilation of an increasing volume and diversity of space observations. These challenges are: (1) the computational tractability when faced with voluminous and multimodal data, (2) the inherent limitations of the underlying models which assume, often incorrectly, linear dynamics and Gaussian noise, and (3) the unavailability or inaccuracy of transition probabilities and noise statistics. We argue that pursuing answers to these questions necessitates cross-disciplinary research that enables progress toward systematically reconciling observational and theoretical understanding of the space environment.

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.

Multiple tests for wind turbine fault detection and score fusion using two- level multidimensional scaling (MDS)

NASA Astrophysics Data System (ADS)

Ye, Xiang; Gao, Weihua; Yan, Yanjun; Osadciw, Lisa A.

2010-04-01

Wind is an important renewable energy source. The energy and economic return from building wind farms justify the expensive investments in doing so. However, without an effective monitoring system, underperforming or faulty turbines will cause a huge loss in revenue. Early detection of such failures help prevent these undesired working conditions. We develop three tests on power curve, rotor speed curve, pitch angle curve of individual turbine. In each test, multiple states are defined to distinguish different working conditions, including complete shut-downs, under-performing states, abnormally frequent default states, as well as normal working states. These three tests are combined to reach a final conclusion, which is more effective than any single test. Through extensive data mining of historical data and verification from farm operators, some state combinations are discovered to be strong indicators of spindle failures, lightning strikes, anemometer faults, etc, for fault detection. In each individual test, and in the score fusion of these tests, we apply multidimensional scaling (MDS) to reduce the high dimensional feature space into a 3-dimensional visualization, from which it is easier to discover turbine working information. This approach gains a qualitative understanding of turbine performance status to detect faults, and also provides explanations on what has happened for detailed diagnostics. The state-of-the-art SCADA (Supervisory Control And Data Acquisition) system in industry can only answer the question whether there are abnormal working states, and our evaluation of multiple states in multiple tests is also promising for diagnostics. In the future, these tests can be readily incorporated in a Bayesian network for intelligent analysis and decision support.

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.

A support vector machine based test for incongruence between sets of trees in tree space

PubMed Central

2012-01-01

Background The increased use of multi-locus data sets for phylogenetic reconstruction has increased the need to determine whether a set of gene trees significantly deviate from the phylogenetic patterns of other genes. Such unusual gene trees may have been influenced by other evolutionary processes such as selection, gene duplication, or horizontal gene transfer. Results Motivated by this problem we propose a nonparametric goodness-of-fit test for two empirical distributions of gene trees, and we developed the software GeneOut to estimate a p-value for the test. Our approach maps trees into a multi-dimensional vector space and then applies support vector machines (SVMs) to measure the separation between two sets of pre-defined trees. We use a permutation test to assess the significance of the SVM separation. To demonstrate the performance of GeneOut, we applied it to the comparison of gene trees simulated within different species trees across a range of species tree depths. Applied directly to sets of simulated gene trees with large sample sizes, GeneOut was able to detect very small differences between two set of gene trees generated under different species trees. Our statistical test can also include tree reconstruction into its test framework through a variety of phylogenetic optimality criteria. When applied to DNA sequence data simulated from different sets of gene trees, results in the form of receiver operating characteristic (ROC) curves indicated that GeneOut performed well in the detection of differences between sets of trees with different distributions in a multi-dimensional space. Furthermore, it controlled false positive and false negative rates very well, indicating a high degree of accuracy. Conclusions The non-parametric nature of our statistical test provides fast and efficient analyses, and makes it an applicable test for any scenario where evolutionary or other factors can lead to trees with different multi-dimensional distributions. The software GeneOut is freely available under the GNU public license. PMID:22909268

New functionalities of potassium tantalate niobate deflectors enabled by the coexistence of pre-injected space charge and composition gradient

NASA Astrophysics Data System (ADS)

Zhu, Wenbin; Chao, Ju-Hung; Chen, Chang-Jiang; Campbell, Adrian L.; Henry, Michael G.; Yin, Stuart Shizhuo; Hoffman, Robert C.

2017-10-01

In most beam steering applications such as 3D printing and in vivo imaging, one of the essential challenges has been high-resolution high-speed multi-dimensional optical beam scanning. Although the pre-injected space charge controlled potassium tantalate niobate (KTN) deflectors can achieve speeds in the nanosecond regime, they deflect in only one dimension. In order to develop a high-resolution high-speed multi-dimensional KTN deflector, we studied the deflection behavior of KTN deflectors in the case of coexisting pre-injected space charge and composition gradient. We find that such coexistence can enable new functionalities of KTN crystal based electro-optic deflectors. When the direction of the composition gradient is parallel to the direction of the external electric field, the zero-deflection position can be shifted, which can reduce the internal electric field induced beam distortion, and thus enhance the resolution. When the direction of the composition gradient is perpendicular to the direction of the external electric field, two-dimensional beam scanning can be achieved by harnessing only one single piece of KTN crystal, which can result in a compact, high-speed two-dimensional deflector. Both theoretical analyses and experiments are conducted, which are consistent with each other. These new functionalities can expedite the usage of KTN deflection in many applications such as high-speed 3D printing, high-speed, high-resolution imaging, and free space broadband optical communication.

Multidimensional Coherent Spectroscopy of GaAs Excitons and Quantum Microcavity Polaritons

NASA Astrophysics Data System (ADS)

Wilmer, Brian L.

Light-matter interactions associated with excitons and exciton related complexes are explored in bulk GaAs and semiconductor microcavities using multidimensional coherent spectroscopy (MDCS). This approach provides rich spectra determining quantum excitation pathways, structural influences on the excitons, and coherence times. Polarization, excitation density, and temperature-dependent MDCS is performed on excitons in strained bulk GaAs layers, probing the coherent response for differing amounts of strain. Biaxial tensile strain lifts the degeneracy of heavy-hole and light-hole valence states, leading to an observed splitting of the associated excitons at low temperature. Increasing the strain increases the magnitude of the heavy-/light- hole exciton peak splitting, induces an asymmetry in the off-diagonal interaction coherences, increases the difference in the heavy- and light- hole exciton homogenous linewidths, and increases the inhomogeneous broadening of both exciton species. All results arise from strain-induced variations in the local electronic environment, which is not uniform along the growth direction of the thin layers. For cross-linear polarized excitation, wherein excitonic signals give way to biexcitonic signals, the high-strain sample shows evidence of bound light-, heavy- and mixed- hole biexcitons. 2DCS maps the anticrossing associated with normal mode splitting in a semiconductor microcavity. For a detuning range near zero, it is observed that there are two diagonal features related to the intra-action of exciton-polariton branches and two off-diagonal features related to coherent interaction between the polaritons. At negative detuning, the line shape properties of the diagonal intra-action features are distinguishable and can be associated with cavity-like and exciton-like modes. A biexcitonic companion feature is observed, shifted from the exciton feature by the biexciton binding energy. Closer to zero detuning, all features are enhanced and the diagonal intra-action features become nearly equal in amplitude and linewidth. At positive detuning the exciton-like and cavity-like characteristics return to the diagonal intra-action features. Off-diagonal interaction features exhibit asymmetry in their amplitudes throughout the detuning range. The amplitudes are strongly modulated as the lower polariton branch crosses the bound biexciton energy determined from negatively detuned spectra.

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.

A Computational Methodology for Simulating Thermal Loss Testing of the Advanced Stirling Convertor

NASA Technical Reports Server (NTRS)

Reid, Terry V.; Wilson, Scott D.; Schifer, Nicholas A.; Briggs, Maxwell H.

2012-01-01

The U.S. Department of Energy (DOE) and Lockheed Martin Space Systems Company (LMSSC) have been developing the Advanced Stirling Radioisotope Generator (ASRG) for use as a power system for space science missions. This generator would use two highefficiency Advanced Stirling Convertors (ASCs), developed by Sunpower Inc. and NASA Glenn Research Center (GRC). The ASCs convert thermal energy from a radioisotope heat source into electricity. As part of ground testing of these ASCs, different operating conditions are used to simulate expected mission conditions. These conditions require achieving a particular operating frequency, hot end and cold end temperatures, and specified electrical power output for a given net heat input. In an effort to improve net heat input predictions, numerous tasks have been performed which provided a more accurate value for net heat input into the ASCs, including the use of multidimensional numerical models. Validation test hardware has also been used to provide a direct comparison of numerical results and validate the multi-dimensional numerical models used to predict convertor net heat input and efficiency. These validation tests were designed to simulate the temperature profile of an operating Stirling convertor and resulted in a measured net heat input of 244.4 W. The methodology was applied to the multi-dimensional numerical model which resulted in a net heat input of 240.3 W. The computational methodology resulted in a value of net heat input that was 1.7 percent less than that measured during laboratory testing. The resulting computational methodology and results are discussed.

Rapid Computation of Thermodynamic Properties over Multidimensional Nonbonded Parameter Spaces Using Adaptive Multistate Reweighting.

PubMed

Naden, Levi N; Shirts, Michael R

2016-04-12

We show how thermodynamic properties of molecular models can be computed over a large, multidimensional parameter space by combining multistate reweighting analysis with a linear basis function approach. This approach reduces the computational cost to estimate thermodynamic properties from molecular simulations for over 130,000 tested parameter combinations from over 1000 CPU years to tens of CPU days. This speed increase is achieved primarily by computing the potential energy as a linear combination of basis functions, computed from either modified simulation code or as the difference of energy between two reference states, which can be done without any simulation code modification. The thermodynamic properties are then estimated with the Multistate Bennett Acceptance Ratio (MBAR) as a function of multiple model parameters without the need to define a priori how the states are connected by a pathway. Instead, we adaptively sample a set of points in parameter space to create mutual configuration space overlap. The existence of regions of poor configuration space overlap are detected by analyzing the eigenvalues of the sampled states' overlap matrix. The configuration space overlap to sampled states is monitored alongside the mean and maximum uncertainty to determine convergence, as neither the uncertainty or the configuration space overlap alone is a sufficient metric of convergence. This adaptive sampling scheme is demonstrated by estimating with high precision the solvation free energies of charged particles of Lennard-Jones plus Coulomb functional form with charges between -2 and +2 and generally physical values of σij and ϵij in TIP3P water. We also compute entropy, enthalpy, and radial distribution functions of arbitrary unsampled parameter combinations using only the data from these sampled states and use the estimates of free energies over the entire space to examine the deviation of atomistic simulations from the Born approximation to the solvation free energy.

Molecular quantum control landscapes in von Neumann time-frequency phase space

NASA Astrophysics Data System (ADS)

Ruetzel, Stefan; Stolzenberger, Christoph; Fechner, Susanne; Dimler, Frank; Brixner, Tobias; Tannor, David J.

2010-10-01

Recently we introduced the von Neumann representation as a joint time-frequency description for femtosecond laser pulses and suggested its use as a basis for pulse shaping experiments. Here we use the von Neumann basis to represent multidimensional molecular control landscapes, providing insight into the molecular dynamics. We present three kinds of time-frequency phase space scanning procedures based on the von Neumann formalism: variation of intensity, time-frequency phase space position, and/or the relative phase of single subpulses. The shaped pulses produced are characterized via Fourier-transform spectral interferometry. Quantum control is demonstrated on the laser dye IR140 elucidating a time-frequency pump-dump mechanism.

Molecular quantum control landscapes in von Neumann time-frequency phase space.

PubMed

Ruetzel, Stefan; Stolzenberger, Christoph; Fechner, Susanne; Dimler, Frank; Brixner, Tobias; Tannor, David J

2010-10-28

Recently we introduced the von Neumann representation as a joint time-frequency description for femtosecond laser pulses and suggested its use as a basis for pulse shaping experiments. Here we use the von Neumann basis to represent multidimensional molecular control landscapes, providing insight into the molecular dynamics. We present three kinds of time-frequency phase space scanning procedures based on the von Neumann formalism: variation of intensity, time-frequency phase space position, and/or the relative phase of single subpulses. The shaped pulses produced are characterized via Fourier-transform spectral interferometry. Quantum control is demonstrated on the laser dye IR140 elucidating a time-frequency pump-dump mechanism.

Asymptotical AdS space from nonlinear gravitational models with stabilized extra dimensions

NASA Astrophysics Data System (ADS)

Günther, U.; Moniz, P.; Zhuk, A.

2002-08-01

We consider nonlinear gravitational models with a multidimensional warped product geometry. Particular attention is payed to models with quadratic scalar curvature terms. It is shown that for certain parameter ranges, the extra dimensions are stabilized if the internal spaces have a negative constant curvature. In this case, the four-dimensional effective cosmological constant as well as the bulk cosmological constant become negative. As a consequence, the homogeneous and isotropic external space is asymptotically AdS4. The connection between the D-dimensional and the four-dimensional fundamental mass scales sets a restriction on the parameters of the considered nonlinear models.

Feature Extraction of High-Dimensional Structures for Exploratory Analytics

DTIC Science & Technology

2013-04-01

Comparison of Euclidean vs. geodesic distance. LDRs use metric based on the Euclidean distance between two points, while the NLDRs are based on...geodesic distance. An NLDR successfully unrolls the curved manifold, whereas an LDR fails. ...........................3 1 1. Introduction An...and classical metric multidimensional scaling, are a linear DR ( LDR ). An LDR is based on a linear combination of

A Reduced-Order Model for Efficient Simulation of Synthetic Jet Actuators

NASA Technical Reports Server (NTRS)

Yamaleev, Nail K.; Carpenter, Mark H.

2003-01-01

A new reduced-order model of multidimensional synthetic jet actuators that combines the accuracy and conservation properties of full numerical simulation methods with the efficiency of simplified zero-order models is proposed. The multidimensional actuator is simulated by solving the time-dependent compressible quasi-1-D Euler equations, while the diaphragm is modeled as a moving boundary. The governing equations are approximated with a fourth-order finite difference scheme on a moving mesh such that one of the mesh boundaries coincides with the diaphragm. The reduced-order model of the actuator has several advantages. In contrast to the 3-D models, this approach provides conservation of mass, momentum, and energy. Furthermore, the new method is computationally much more efficient than the multidimensional Navier-Stokes simulation of the actuator cavity flow, while providing practically the same accuracy in the exterior flowfield. The most distinctive feature of the present model is its ability to predict the resonance characteristics of synthetic jet actuators; this is not practical when using the 3-D models because of the computational cost involved. Numerical results demonstrating the accuracy of the new reduced-order model and its limitations are presented.

Integrating multiple fitting regression and Bayes decision for cancer diagnosis with transcriptomic data from tumor-educated blood platelets.

PubMed

Huang, Guangzao; Yuan, Mingshun; Chen, Moliang; Li, Lei; You, Wenjie; Li, Hanjie; Cai, James J; Ji, Guoli

2017-10-07

The application of machine learning in cancer diagnostics has shown great promise and is of importance in clinic settings. Here we consider applying machine learning methods to transcriptomic data derived from tumor-educated platelets (TEPs) from individuals with different types of cancer. We aim to define a reliability measure for diagnostic purposes to increase the potential for facilitating personalized treatments. To this end, we present a novel classification method called MFRB (for Multiple Fitting Regression and Bayes decision), which integrates the process of multiple fitting regression (MFR) with Bayes decision theory. MFR is first used to map multidimensional features of the transcriptomic data into a one-dimensional feature. The probability density function of each class in the mapped space is then adjusted using the Gaussian probability density function. Finally, the Bayes decision theory is used to build a probabilistic classifier with the estimated probability density functions. The output of MFRB can be used to determine which class a sample belongs to, as well as to assign a reliability measure for a given class. The classical support vector machine (SVM) and probabilistic SVM (PSVM) are used to evaluate the performance of the proposed method with simulated and real TEP datasets. Our results indicate that the proposed MFRB method achieves the best performance compared to SVM and PSVM, mainly due to its strong generalization ability for limited, imbalanced, and noisy data.

Exploration of the Medicinal Peptide Space.

PubMed

Gevaert, Bert; Stalmans, Sofie; Wynendaele, Evelien; Taevernier, Lien; Bracke, Nathalie; D'Hondt, Matthias; De Spiegeleer, Bart

2016-01-01

The chemical properties of peptide medicines, known as the 'medicinal peptide space' is considered a multi-dimensional subset of the global peptide space, where each dimension represents a chemical descriptor. These descriptors can be linked to biofunctional, medicinal properties to varying degrees. Knowledge of this space can increase the efficiency of the peptide-drug discovery and development process, as well as advance our understanding and classification of peptide medicines. For 245 peptide drugs, already available on the market or in clinical development, multivariate dataexploration was performed using peptide relevant physicochemical descriptors, their specific peptidedrug target and their clinical use. Our retrospective analysis indicates that clusters in the medicinal peptide space are located in a relatively narrow range of the physicochemical space: dense and empty regions were found, which can be explored for the discovery of novel peptide drugs.

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.

Amyloid PET in clinical practice: Its place in the multidimensional space of Alzheimer's disease☆

PubMed Central

Vandenberghe, Rik; Adamczuk, Katarzyna; Dupont, Patrick; Laere, Koen Van; Chételat, Gaël

2013-01-01

Amyloid imaging is currently introduced to the market for clinical use. We will review the evidence demonstrating that the different amyloid PET ligands that are currently available are valid biomarkers for Alzheimer-related β amyloidosis. Based on recent findings from cross-sectional and longitudinal imaging studies using different modalities, we will incorporate amyloid imaging into a multidimensional model of Alzheimer's disease. Aside from the critical role in improving clinical trial design for amyloid-lowering drugs, we will also propose a tentative algorithm for when it may be useful in a memory clinic environment. Gaps in our evidence-based knowledge of the added value of amyloid imaging in a clinical context will be identified and will need to be addressed by dedicated studies of clinical utility. PMID:24179802

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.

Validity Study in Multidimensional Latent Space and Efficient Computerized Adaptive Testing. Final Report.

ERIC Educational Resources Information Center

Samejima, Fumiko

This paper is the final report of a multi-year project sponsored by the Office of Naval Research (ONR) in 1987 through 1990. The main objectives of the research summarized were to: investigate the non-parametric approach to the estimation of the operating characteristics of discrete item responses; revise and strengthen the package computer…

The National Mapping of Teacher Professional Learning Project: A Multi-Dimensional Space?

ERIC Educational Resources Information Center

Doecke, Brenton; Parr, Graham

2011-01-01

This essay focuses on the "National Mapping of Teacher Professional Learning" (2008), a report that we co-authored along with a number of other researchers on the basis of extensive surveys and interviews relating to the policies and practices of teacher professional learning in Australia. The report is an update of an earlier survey…

Sequential memory: Binding dynamics

NASA Astrophysics Data System (ADS)

Afraimovich, Valentin; Gong, Xue; Rabinovich, Mikhail

2015-10-01

Temporal order memories are critical for everyday animal and human functioning. Experiments and our own experience show that the binding or association of various features of an event together and the maintaining of multimodality events in sequential order are the key components of any sequential memories—episodic, semantic, working, etc. We study a robustness of binding sequential dynamics based on our previously introduced model in the form of generalized Lotka-Volterra equations. In the phase space of the model, there exists a multi-dimensional binding heteroclinic network consisting of saddle equilibrium points and heteroclinic trajectories joining them. We prove here the robustness of the binding sequential dynamics, i.e., the feasibility phenomenon for coupled heteroclinic networks: for each collection of successive heteroclinic trajectories inside the unified networks, there is an open set of initial points such that the trajectory going through each of them follows the prescribed collection staying in a small neighborhood of it. We show also that the symbolic complexity function of the system restricted to this neighborhood is a polynomial of degree L - 1, where L is the number of modalities.

Evaluation of space SAR as a land-cover classification

NASA Technical Reports Server (NTRS)

Brisco, B.; Ulaby, F. T.; Williams, T. H. L.

1985-01-01

The multidimensional approach to the mapping of land cover, crops, and forests is reported. Dimensionality is achieved by using data from sensors such as LANDSAT to augment Seasat and Shuttle Image Radar (SIR) data, using different image features such as tone and texture, and acquiring multidate data. Seasat, Shuttle Imaging Radar (SIR-A), and LANDSAT data are used both individually and in combination to map land cover in Oklahoma. The results indicates that radar is the best single sensor (72% accuracy) and produces the best sensor combination (97.5% accuracy) for discriminating among five land cover categories. Multidate Seasat data and a single data of LANDSAT coverage are then used in a crop classification study of western Kansas. The highest accuracy for a single channel is achieved using a Seasat scene, which produces a classification accuracy of 67%. Classification accuracy increases to approximately 75% when either a multidate Seasat combination or LANDSAT data in a multisensor combination is used. The tonal and textural elements of SIR-A data are then used both alone and in combination to classify forests into five categories.

A component-based system for agricultural drought monitoring by remote sensing.

PubMed

Dong, Heng; Li, Jun; Yuan, Yanbin; You, Lin; Chen, Chao

2017-01-01

In recent decades, various kinds of remote sensing-based drought indexes have been proposed and widely used in the field of drought monitoring. However, the drought-related software and platform development lag behind the theoretical research. The current drought monitoring systems focus mainly on information management and publishing, and cannot implement professional drought monitoring or parameter inversion modelling, especially the models based on multi-dimensional feature space. In view of the above problems, this paper aims at fixing this gap with a component-based system named RSDMS to facilitate the application of drought monitoring by remote sensing. The system is designed and developed based on Component Object Model (COM) to ensure the flexibility and extendibility of modules. RSDMS realizes general image-related functions such as data management, image display, spatial reference management, image processing and analysis, and further provides drought monitoring and evaluation functions based on internal and external models. Finally, China's Ningxia region is selected as the study area to validate the performance of RSDMS. The experimental results show that RSDMS provide an efficient and scalable support to agricultural drought monitoring.

A component-based system for agricultural drought monitoring by remote sensing

PubMed Central

Yuan, Yanbin; You, Lin; Chen, Chao

2017-01-01

In recent decades, various kinds of remote sensing-based drought indexes have been proposed and widely used in the field of drought monitoring. However, the drought-related software and platform development lag behind the theoretical research. The current drought monitoring systems focus mainly on information management and publishing, and cannot implement professional drought monitoring or parameter inversion modelling, especially the models based on multi-dimensional feature space. In view of the above problems, this paper aims at fixing this gap with a component-based system named RSDMS to facilitate the application of drought monitoring by remote sensing. The system is designed and developed based on Component Object Model (COM) to ensure the flexibility and extendibility of modules. RSDMS realizes general image-related functions such as data management, image display, spatial reference management, image processing and analysis, and further provides drought monitoring and evaluation functions based on internal and external models. Finally, China’s Ningxia region is selected as the study area to validate the performance of RSDMS. The experimental results show that RSDMS provide an efficient and scalable support to agricultural drought monitoring. PMID:29236700

Sequential memory: Binding dynamics.

PubMed

Afraimovich, Valentin; Gong, Xue; Rabinovich, Mikhail

2015-10-01

Temporal order memories are critical for everyday animal and human functioning. Experiments and our own experience show that the binding or association of various features of an event together and the maintaining of multimodality events in sequential order are the key components of any sequential memories-episodic, semantic, working, etc. We study a robustness of binding sequential dynamics based on our previously introduced model in the form of generalized Lotka-Volterra equations. In the phase space of the model, there exists a multi-dimensional binding heteroclinic network consisting of saddle equilibrium points and heteroclinic trajectories joining them. We prove here the robustness of the binding sequential dynamics, i.e., the feasibility phenomenon for coupled heteroclinic networks: for each collection of successive heteroclinic trajectories inside the unified networks, there is an open set of initial points such that the trajectory going through each of them follows the prescribed collection staying in a small neighborhood of it. We show also that the symbolic complexity function of the system restricted to this neighborhood is a polynomial of degree L - 1, where L is the number of modalities.

Dynamic analysis and pattern visualization of forest fires.

PubMed

Lopes, António M; Tenreiro Machado, J A

2014-01-01

This paper analyses forest fires in the perspective of dynamical systems. Forest fires exhibit complex correlations in size, space and time, revealing features often present in complex systems, such as the absence of a characteristic length-scale, or the emergence of long range correlations and persistent memory. This study addresses a public domain forest fires catalogue, containing information of events for Portugal, during the period from 1980 up to 2012. The data is analysed in an annual basis, modelling the occurrences as sequences of Dirac impulses with amplitude proportional to the burnt area. First, we consider mutual information to correlate annual patterns. We use visualization trees, generated by hierarchical clustering algorithms, in order to compare and to extract relationships among the data. Second, we adopt the Multidimensional Scaling (MDS) visualization tool. MDS generates maps where each object corresponds to a point. Objects that are perceived to be similar to each other are placed on the map forming clusters. The results are analysed in order to extract relationships among the data and to identify forest fire patterns.

Dynamic Analysis and Pattern Visualization of Forest Fires

PubMed Central

Lopes, António M.; Tenreiro Machado, J. A.

2014-01-01

This paper analyses forest fires in the perspective of dynamical systems. Forest fires exhibit complex correlations in size, space and time, revealing features often present in complex systems, such as the absence of a characteristic length-scale, or the emergence of long range correlations and persistent memory. This study addresses a public domain forest fires catalogue, containing information of events for Portugal, during the period from 1980 up to 2012. The data is analysed in an annual basis, modelling the occurrences as sequences of Dirac impulses with amplitude proportional to the burnt area. First, we consider mutual information to correlate annual patterns. We use visualization trees, generated by hierarchical clustering algorithms, in order to compare and to extract relationships among the data. Second, we adopt the Multidimensional Scaling (MDS) visualization tool. MDS generates maps where each object corresponds to a point. Objects that are perceived to be similar to each other are placed on the map forming clusters. The results are analysed in order to extract relationships among the data and to identify forest fire patterns. PMID:25137393

Visualizing Gyrokinetic Turbulence in a Tokamak

NASA Astrophysics Data System (ADS)

Stantchev, George

2005-10-01

Multi-dimensional data output from gyrokinetic microturbulence codes are often difficult to visualize, in part due to the non-trivial geometry of the underlying grids, in part due to high irregularity of the relevant scalar field structures in turbulent regions. For instance, traditional isosurface extraction methods are likely to fail for the electrostatic potential field whose level sets may exhibit various geometric pathologies. To address these issues we develop an advanced interactive 3D gyrokinetic turbulence visualization framework which we apply in the study of microtearing instabilities calculated with GS2 in the MAST and NSTX geometries. In these simulations GS2 uses field-line-following coordinates such that the computational domain maps in physical space to a long, twisting flux tube with strong cross-sectional shear. Using statistical wavelet analysis we create a sparse multiple-scale volumetric representation of the relevant scalar fields, which we visualize via a variation of the so called splatting technique. To handle the problem of highly anisotropic flux tube configurations we adapt a geometry-driven surface illumination algorithm that places local light sources for effective feature-enhanced visualization.

Systematization of actinides using cluster analysis

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

Kopyrin, A.A.; Terent`eva, T.N.; Khramov, N.N.

1994-11-01

A representation of the actinides in multidimensional property space is proposed for systematization of these elements using cluster analysis. Literature data for their atomic properties are used. Owing to the wide variation of published ionization potentials, medians are used to estimate them. Vertical dendograms are used for classification on the basis of distances between the actinides in atomic-property space. The properties of actinium and lawrencium are furthest removed from the main group. Thorium and mendelevium exhibit individualized properties. A cluster based on the einsteinium-fermium pair is joined by californium.

On the enhanced sampling over energy barriers in molecular dynamics simulations.

PubMed

Gao, Yi Qin; Yang, Lijiang

2006-09-21

We present here calculations of free energies of multidimensional systems using an efficient sampling method. The method uses a transformed potential energy surface, which allows an efficient sampling of both low and high energy spaces and accelerates transitions over barriers. It allows efficient sampling of the configuration space over and only over the desired energy range(s). It does not require predetermined or selected reaction coordinate(s). We apply this method to study the dynamics of slow barrier crossing processes in a disaccharide and a dipeptide system.

The COSMIC-DANCE project: Unravelling the origin of the mass function

NASA Astrophysics Data System (ADS)

Bouy, H.; Bertin, E.; Sarro, L. M.; Barrado, D.; Berihuete, A.; Olivares, J.; Moraux, E.; Bouvier, J.; Tamura, M.; Cuillandre, J.-C.; Beletsky, Y.; Wright, N.; Huelamo, N.; Allen, L.; Solano, E.; Brandner, B.

2017-03-01

The COSMIC-DANCE project is an observational program aiming at understanding the origin and evolution of ultracool objects by measuring the mass function and internal dynamics of young nearby associations down to the fragmentation limit. The least massive members of young nearby associations are identified using modern statistical methods in a multi-dimensional space made of optical and infrared luminosities and colors and proper motions. The photometry and astrometry are obtained by combining ground and in some case space based archival observations with new observations, covering between one and two decades.

DD-HDS: A method for visualization and exploration of high-dimensional data.

PubMed

Lespinats, Sylvain; Verleysen, Michel; Giron, Alain; Fertil, Bernard

2007-09-01

Mapping high-dimensional data in a low-dimensional space, for example, for visualization, is a problem of increasingly major concern in data analysis. This paper presents data-driven high-dimensional scaling (DD-HDS), a nonlinear mapping method that follows the line of multidimensional scaling (MDS) approach, based on the preservation of distances between pairs of data. It improves the performance of existing competitors with respect to the representation of high-dimensional data, in two ways. It introduces (1) a specific weighting of distances between data taking into account the concentration of measure phenomenon and (2) a symmetric handling of short distances in the original and output spaces, avoiding false neighbor representations while still allowing some necessary tears in the original distribution. More precisely, the weighting is set according to the effective distribution of distances in the data set, with the exception of a single user-defined parameter setting the tradeoff between local neighborhood preservation and global mapping. The optimization of the stress criterion designed for the mapping is realized by "force-directed placement" (FDP). The mappings of low- and high-dimensional data sets are presented as illustrations of the features and advantages of the proposed algorithm. The weighting function specific to high-dimensional data and the symmetric handling of short distances can be easily incorporated in most distance preservation-based nonlinear dimensionality reduction methods.

An explicit dissipation-preserving method for Riesz space-fractional nonlinear wave equations in multiple dimensions

NASA Astrophysics Data System (ADS)

Macías-Díaz, J. E.

2018-06-01

In this work, we investigate numerically a model governed by a multidimensional nonlinear wave equation with damping and fractional diffusion. The governing partial differential equation considers the presence of Riesz space-fractional derivatives of orders in (1, 2], and homogeneous Dirichlet boundary data are imposed on a closed and bounded spatial domain. The model under investigation possesses an energy function which is preserved in the undamped regime. In the damped case, we establish the property of energy dissipation of the model using arguments from functional analysis. Motivated by these results, we propose an explicit finite-difference discretization of our fractional model based on the use of fractional centered differences. Associated to our discrete model, we also propose discretizations of the energy quantities. We establish that the discrete energy is conserved in the undamped regime, and that it dissipates in the damped scenario. Among the most important numerical features of our scheme, we show that the method has a consistency of second order, that it is stable and that it has a quadratic order of convergence. Some one- and two-dimensional simulations are shown in this work to illustrate the fact that the technique is capable of preserving the discrete energy in the undamped regime. For the sake of convenience, we provide a Matlab implementation of our method for the one-dimensional scenario.

Computerized lung cancer malignancy level analysis using 3D texture features

NASA Astrophysics Data System (ADS)

Sun, Wenqing; Huang, Xia; Tseng, Tzu-Liang; Zhang, Jianying; Qian, Wei

2016-03-01

Based on the likelihood of malignancy, the nodules are classified into five different levels in Lung Image Database Consortium (LIDC) database. In this study, we tested the possibility of using threedimensional (3D) texture features to identify the malignancy level of each nodule. Five groups of features were implemented and tested on 172 nodules with confident malignancy levels from four radiologists. These five feature groups are: grey level co-occurrence matrix (GLCM) features, local binary pattern (LBP) features, scale-invariant feature transform (SIFT) features, steerable features, and wavelet features. Because of the high dimensionality of our proposed features, multidimensional scaling (MDS) was used for dimension reduction. RUSBoost was applied for our extracted features for classification, due to its advantages in handling imbalanced dataset. Each group of features and the final combined features were used to classify nodules highly suspicious for cancer (level 5) and moderately suspicious (level 4). The results showed that the area under the curve (AUC) and accuracy are 0.7659 and 0.8365 when using the finalized features. These features were also tested on differentiating benign and malignant cases, and the reported AUC and accuracy were 0.8901 and 0.9353.

GENESIS: a hybrid-parallel and multi-scale molecular dynamics simulator with enhanced sampling algorithms for biomolecular and cellular simulations.

PubMed

Jung, Jaewoon; Mori, Takaharu; Kobayashi, Chigusa; Matsunaga, Yasuhiro; Yoda, Takao; Feig, Michael; Sugita, Yuji

2015-07-01

GENESIS (Generalized-Ensemble Simulation System) is a new software package for molecular dynamics (MD) simulations of macromolecules. It has two MD simulators, called ATDYN and SPDYN. ATDYN is parallelized based on an atomic decomposition algorithm for the simulations of all-atom force-field models as well as coarse-grained Go-like models. SPDYN is highly parallelized based on a domain decomposition scheme, allowing large-scale MD simulations on supercomputers. Hybrid schemes combining OpenMP and MPI are used in both simulators to target modern multicore computer architectures. Key advantages of GENESIS are (1) the highly parallel performance of SPDYN for very large biological systems consisting of more than one million atoms and (2) the availability of various REMD algorithms (T-REMD, REUS, multi-dimensional REMD for both all-atom and Go-like models under the NVT, NPT, NPAT, and NPγT ensembles). The former is achieved by a combination of the midpoint cell method and the efficient three-dimensional Fast Fourier Transform algorithm, where the domain decomposition space is shared in real-space and reciprocal-space calculations. Other features in SPDYN, such as avoiding concurrent memory access, reducing communication times, and usage of parallel input/output files, also contribute to the performance. We show the REMD simulation results of a mixed (POPC/DMPC) lipid bilayer as a real application using GENESIS. GENESIS is released as free software under the GPLv2 licence and can be easily modified for the development of new algorithms and molecular models. WIREs Comput Mol Sci 2015, 5:310-323. doi: 10.1002/wcms.1220.

Computational Aeroacoustics by the Space-time CE/SE Method

NASA Technical Reports Server (NTRS)

Loh, Ching Y.

2001-01-01

In recent years, a new numerical methodology for conservation laws-the Space-Time Conservation Element and Solution Element Method (CE/SE), was developed by Dr. Chang of NASA Glenn Research Center and collaborators. In nature, the new method may be categorized as a finite volume method, where the conservation element (CE) is equivalent to a finite control volume (or cell) and the solution element (SE) can be understood as the cell interface. However, due to its rigorous treatment of the fluxes and geometry, it is different from the existing schemes. The CE/SE scheme features: (1) space and time treated on the same footing, the integral equations of conservation laws are solve( for with second order accuracy, (2) high resolution, low dispersion and low dissipation, (3) novel, truly multi-dimensional, simple but effective non-reflecting boundary condition, (4) effortless implementation of computation, no numerical fix or parameter choice is needed, an( (5) robust enough to cover a wide spectrum of compressible flow: from weak linear acoustic waves to strong, discontinuous waves (shocks) appropriate for linear and nonlinear aeroacoustics. Currently, the CE/SE scheme has been developed to such a stage that a 3-13 unstructured CE/SE Navier-Stokes solver is already available. However, in the present paper, as a general introduction to the CE/SE method, only the 2-D unstructured Euler CE/SE solver is chosen as a prototype and is sketched in Section 2. Then applications of the CE/SE scheme to linear, nonlinear aeroacoustics and airframe noise are depicted in Sections 3, 4, and 5 respectively to demonstrate its robustness and capability.

Angular description for 3D scattering centers

NASA Astrophysics Data System (ADS)

Bhalla, Rajan; Raynal, Ann Marie; Ling, Hao; Moore, John; Velten, Vincent J.

2006-05-01

The electromagnetic scattered field from an electrically large target can often be well modeled as if it is emanating from a discrete set of scattering centers (see Fig. 1). In the scattering center extraction tool we developed previously based on the shooting and bouncing ray technique, no correspondence is maintained amongst the 3D scattering center extracted at adjacent angles. In this paper we present a multi-dimensional clustering algorithm to track the angular and spatial behaviors of 3D scattering centers and group them into features. The extracted features for the Slicy and backhoe targets are presented. We also describe two metrics for measuring the angular persistence and spatial mobility of the 3D scattering centers that make up these features in order to gather insights into target physics and feature stability. We find that features that are most persistent are also the most mobile and discuss implications for optimal SAR imaging.

Identification of key regulators of pancreatic cancer progression through multidimensional systems-level analysis.

PubMed

Rajamani, Deepa; Bhasin, Manoj K

2016-05-03

Pancreatic cancer is an aggressive cancer with dismal prognosis, urgently necessitating better biomarkers to improve therapeutic options and early diagnosis. Traditional approaches of biomarker detection that consider only one aspect of the biological continuum like gene expression alone are limited in their scope and lack robustness in identifying the key regulators of the disease. We have adopted a multidimensional approach involving the cross-talk between the omics spaces to identify key regulators of disease progression. Multidimensional domain-specific disease signatures were obtained using rank-based meta-analysis of individual omics profiles (mRNA, miRNA, DNA methylation) related to pancreatic ductal adenocarcinoma (PDAC). These domain-specific PDAC signatures were integrated to identify genes that were affected across multiple dimensions of omics space in PDAC (genes under multiple regulatory controls, GMCs). To further pin down the regulators of PDAC pathophysiology, a systems-level network was generated from knowledge-based interaction information applied to the above identified GMCs. Key regulators were identified from the GMC network based on network statistics and their functional importance was validated using gene set enrichment analysis and survival analysis. Rank-based meta-analysis identified 5391 genes, 109 miRNAs and 2081 methylation-sites significantly differentially expressed in PDAC (false discovery rate ≤ 0.05). Bimodal integration of meta-analysis signatures revealed 1150 and 715 genes regulated by miRNAs and methylation, respectively. Further analysis identified 189 altered genes that are commonly regulated by miRNA and methylation, hence considered GMCs. Systems-level analysis of the scale-free GMCs network identified eight potential key regulator hubs, namely E2F3, HMGA2, RASA1, IRS1, NUAK1, ACTN1, SKI and DLL1, associated with important pathways driving cancer progression. Survival analysis on individual key regulators revealed that higher expression of IRS1 and DLL1 and lower expression of HMGA2, ACTN1 and SKI were associated with better survival probabilities. It is evident from the results that our hierarchical systems-level multidimensional analysis approach has been successful in isolating the converging regulatory modules and associated key regulatory molecules that are potential biomarkers for pancreatic cancer progression.

Progress Toward a Multidimensional Representation of Mortar Interior Ballistics

DTIC Science & Technology

2009-06-01

reached, act as rigid bodies within the chamber. Using computational particles to represent the propellant charge permits a host of modeling features...walls are represented by special Lagrange particles, which remain impermeable (hence the charges act as rigid bodies ) until a specified wall...composition, and table 2 provides the thermochemical calculations done using Cheetah (14), the basis of which is discussed in Schmidt and Nusca (12

Manycore Performance-Portability: Kokkos Multidimensional Array Library

DOE PAGES

Edwards, H. Carter; Sunderland, Daniel; Porter, Vicki; ...

2012-01-01

Large, complex scientific and engineering application code have a significant investment in computational kernels to implement their mathematical models. Porting these computational kernels to the collection of modern manycore accelerator devices is a major challenge in that these devices have diverse programming models, application programming interfaces (APIs), and performance requirements. The Kokkos Array programming model provides library-based approach to implement computational kernels that are performance-portable to CPU-multicore and GPGPU accelerator devices. This programming model is based upon three fundamental concepts: (1) manycore compute devices each with its own memory space, (2) data parallel kernels and (3) multidimensional arrays. Kernel executionmore » performance is, especially for NVIDIA® devices, extremely dependent on data access patterns. Optimal data access pattern can be different for different manycore devices – potentially leading to different implementations of computational kernels specialized for different devices. The Kokkos Array programming model supports performance-portable kernels by (1) separating data access patterns from computational kernels through a multidimensional array API and (2) introduce device-specific data access mappings when a kernel is compiled. An implementation of Kokkos Array is available through Trilinos [Trilinos website, http://trilinos.sandia.gov/, August 2011].« less

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

Parekh, V; Jacobs, MA

Purpose: Multiparametric radiological imaging is used for diagnosis in patients. Potentially extracting useful features specific to a patient’s pathology would be crucial step towards personalized medicine and assessing treatment options. In order to automatically extract features directly from multiparametric radiological imaging datasets, we developed an advanced unsupervised machine learning algorithm called the multidimensional imaging radiomics-geodesics(MIRaGe). Methods: Seventy-six breast tumor patients underwent 3T MRI breast imaging were used for this study. We tested the MIRaGe algorithm to extract features for classification of breast tumors into benign or malignant. The MRI parameters used were T1-weighted, T2-weighted, dynamic contrast enhanced MR imaging (DCE-MRI)more » and diffusion weighted imaging(DWI). The MIRaGe algorithm extracted the radiomics-geodesics features (RGFs) from multiparametric MRI datasets. This enable our method to learn the intrinsic manifold representations corresponding to the patients. To determine the informative RGF, a modified Isomap algorithm(t-Isomap) was created for a radiomics-geodesics feature space(tRGFS) to avoid overfitting. Final classification was performed using SVM. The predictive power of the RGFs was tested and validated using k-fold cross validation. Results: The RGFs extracted by the MIRaGe algorithm successfully classified malignant lesions from benign lesions with a sensitivity of 93% and a specificity of 91%. The top 50 RGFs identified as the most predictive by the t-Isomap procedure were consistent with the radiological parameters known to be associated with breast cancer diagnosis and were categorized as kinetic curve characterizing RGFs, wash-in rate characterizing RGFs, wash-out rate characterizing RGFs and morphology characterizing RGFs. Conclusion: In this paper, we developed a novel feature extraction algorithm for multiparametric radiological imaging. The results demonstrated the power of the MIRaGe algorithm at automatically discovering useful feature representations directly from the raw multiparametric MRI data. In conclusion, the MIRaGe informatics model provides a powerful tool with applicability in cancer diagnosis and a possibility of extension to other kinds of pathologies. NIH (P50CA103175, 5P30CA006973 (IRAT), R01CA190299, U01CA140204), Siemens Medical Systems (JHU-2012-MR-86-01) and Nivida Graphics Corporation.« less

Plasticity of Arabidopsis root gravitropism throughout a multidimensional condition space quantified by automated image analysis.

PubMed

Brooks, Tessa L Durham; Miller, Nathan D; Spalding, Edgar P

2010-01-01

Plant development is genetically determined but it is also plastic, a fundamental duality that can be investigated provided large number of measurements can be made in various conditions. Plasticity of gravitropism in wild-type Arabidopsis (Arabidopsis thaliana) seedling roots was investigated using automated image acquisition and analysis. A bank of computer-controlled charge-coupled device cameras acquired images with high spatiotemporal resolution. Custom image analysis algorithms extracted time course measurements of tip angle and growth rate. Twenty-two discrete conditions defined by seedling age (2, 3, or 4 d), seed size (extra small, small, medium, or large), and growth medium composition (simple or rich) formed the condition space sampled with 1,216 trials. Computational analyses including dimension reduction by principal components analysis, classification by k-means clustering, and differentiation by wavelet convolution showed distinct response patterns within the condition space, i.e. response plasticity. For example, 2-d-old roots (regardless of seed size) displayed a response time course similar to those of roots from large seeds (regardless of age). Enriching the growth medium with nutrients suppressed response plasticity along the seed size and age axes, possibly by ameliorating a mineral deficiency, although analysis of seeds did not identify any elements with low levels on a per weight basis. Characterizing relationships between growth rate and tip swing rate as a function of condition cast gravitropism in a multidimensional response space that provides new mechanistic insights as well as conceptually setting the stage for mutational analysis of plasticity in general and root gravitropism in particular.

Plasticity of Arabidopsis Root Gravitropism throughout a Multidimensional Condition Space Quantified by Automated Image Analysis1[W][OA

PubMed Central

Durham Brooks, Tessa L.; Miller, Nathan D.; Spalding, Edgar P.

2010-01-01

Plant development is genetically determined but it is also plastic, a fundamental duality that can be investigated provided large number of measurements can be made in various conditions. Plasticity of gravitropism in wild-type Arabidopsis (Arabidopsis thaliana) seedling roots was investigated using automated image acquisition and analysis. A bank of computer-controlled charge-coupled device cameras acquired images with high spatiotemporal resolution. Custom image analysis algorithms extracted time course measurements of tip angle and growth rate. Twenty-two discrete conditions defined by seedling age (2, 3, or 4 d), seed size (extra small, small, medium, or large), and growth medium composition (simple or rich) formed the condition space sampled with 1,216 trials. Computational analyses including dimension reduction by principal components analysis, classification by k-means clustering, and differentiation by wavelet convolution showed distinct response patterns within the condition space, i.e. response plasticity. For example, 2-d-old roots (regardless of seed size) displayed a response time course similar to those of roots from large seeds (regardless of age). Enriching the growth medium with nutrients suppressed response plasticity along the seed size and age axes, possibly by ameliorating a mineral deficiency, although analysis of seeds did not identify any elements with low levels on a per weight basis. Characterizing relationships between growth rate and tip swing rate as a function of condition cast gravitropism in a multidimensional response space that provides new mechanistic insights as well as conceptually setting the stage for mutational analysis of plasticity in general and root gravitropism in particular. PMID:19923240

Visualizing Structure and Dynamics of Disaccharide Simulations

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

Matthews, J. F.; Beckham, G. T.; Himmel, M. E.

2012-01-01

We examine the effect of several solvent models on the conformational properties and dynamics of disaccharides such as cellobiose and lactose. Significant variation in timescale for large scale conformational transformations are observed. Molecular dynamics simulation provides enough detail to enable insight through visualization of multidimensional data sets. We present a new way to visualize conformational space for disaccharides with Ramachandran plots.

The Circumplex Model of the Questionnaire on Teacher Interaction among Hong Kong Students: A Multidimensional Scaling Solution

ERIC Educational Resources Information Center

Sivan, Atara; Cohen, Arie; Chan, Dennis W.; Kwan, Yee Wan

2017-01-01

The Questionnaire on Teacher Interaction (QTI) is a teacher--student relationship measure whose underlying two-dimensional structure is represented in a circumplex model with eight sectors. Using Smallest Space Analysis (SSA), this study examined the circumplex structure of the Chinese version of the QTI among a convenience sample of 731…

The Earth Is Flat when Personally Significant Experiences with the Sphericity of the Earth Are Absent

ERIC Educational Resources Information Center

Carbon, Claus-Christian

2010-01-01

Participants with personal and without personal experiences with the Earth as a sphere estimated large-scale distances between six cities located on different continents. Cognitive distances were submitted to a specific multidimensional scaling algorithm in the 3D Euclidean space with the constraint that all cities had to lie on the same sphere. A…

A Latent Class Multidimensional Scaling Model for Two-Way One-Mode Continuous Rating Dissimilarity Data

ERIC Educational Resources Information Center

Vera, J. Fernando; Macias, Rodrigo; Heiser, Willem J.

2009-01-01

In this paper, we propose a cluster-MDS model for two-way one-mode continuous rating dissimilarity data. The model aims at partitioning the objects into classes and simultaneously representing the cluster centers in a low-dimensional space. Under the normal distribution assumption, a latent class model is developed in terms of the set of…

The Integration of Delta Prime (f)in a Multidimensional Space

NASA Technical Reports Server (NTRS)

Farassat, F.

1999-01-01

Consideration is given to the thickness noise term of the Ffowcs Williams-Hawkings equation when the time derivative is taken explicitly. An interpretation is presented of the integral I = function phi(x)delta-prime(f) dx, where it is initially assumed that the absolute value of Del-f is not equal to 1 on the surface f = 0.

The Role of Student Loan Programs in Higher Education Policy in the United States

ERIC Educational Resources Information Center

Amatya, Sachi

2009-01-01

The increasing cost of higher education, coupled with the inability of federal and state governments to sustain parallel increases in levels of funding for student financial aid, has led to significant growth of student loans. This project analyzes the multidimensional student loans space in the US. This project also compares and contrasts some of…

Male/Female Preferences for Masculine-Feminine Trait Clusters Derived from a Multidimensional Scaling of Hypothesized Masculine-Feminine and Creative-Noncreative Trait Terms.

ERIC Educational Resources Information Center

Hyman, Ruth Bernstein

There still remains in our social institutions and individual lives a considerable splitting between feminine and masculine gender distinctions. The present study determined the dimensionality of the space of 53 admirable personality traits hypothesized to relate to femininity-masculinity and creativity, and assessed preferences of females versus…

Fast and high-order numerical algorithms for the solution of multidimensional nonlinear fractional Ginzburg-Landau equation

NASA Astrophysics Data System (ADS)

Mohebbi, Akbar

2018-02-01

In this paper we propose two fast and accurate numerical methods for the solution of multidimensional space fractional Ginzburg-Landau equation (FGLE). In the presented methods, to avoid solving a nonlinear system of algebraic equations and to increase the accuracy and efficiency of method, we split the complex problem into simpler sub-problems using the split-step idea. For a homogeneous FGLE, we propose a method which has fourth-order of accuracy in time component and spectral accuracy in space variable and for nonhomogeneous one, we introduce another scheme based on the Crank-Nicolson approach which has second-order of accuracy in time variable. Due to using the Fourier spectral method for fractional Laplacian operator, the resulting schemes are fully diagonal and easy to code. Numerical results are reported in terms of accuracy, computational order and CPU time to demonstrate the accuracy and efficiency of the proposed methods and to compare the results with the analytical solutions. The results show that the present methods are accurate and require low CPU time. It is illustrated that the numerical results are in good agreement with the theoretical ones.

Shape determination and control for large space structures

NASA Technical Reports Server (NTRS)

Weeks, C. J.

1981-01-01

An integral operator approach is used to derive solutions to static shape determination and control problems associated with large space structures. Problem assumptions include a linear self-adjoint system model, observations and control forces at discrete points, and performance criteria for the comparison of estimates or control forms. Results are illustrated by simulations in the one dimensional case with a flexible beam model, and in the multidimensional case with a finite model of a large space antenna. Modal expansions for terms in the solution algorithms are presented, using modes from the static or associated dynamic mode. These expansions provide approximated solutions in the event that a used form analytical solution to the system boundary value problem is not available.

Dissociation of performance and subjective measures of workload

NASA Technical Reports Server (NTRS)

Yeh, Yei-Yu; Wickens, Christopher D.

1988-01-01

A theory is presented to identify sources that produce dissociations between performance and subjective measures of workload. The theory states that performance is determined by (1) amount of resources invested, (2) resource efficiency, and (3) degree of competition for common resources in a multidimensional space described in the multiple-resources model. Subjective perception of workload, multidimensional in nature, increases with greater amounts of resource investment and with greater demands on working memory. Performance and subjective workload measures dissociate when greater resources are invested to improve performance of a resource-limited task; when demands on working memory are increased by time-sharing between concurrent tasks or between display elements; and when performance is sensitive to resource competition and subjective measures are more sensitive to total investment. These dissociation findings and their implications are discussed and directions for future research are suggested.

A Robust Absorbing Boundary Condition for Compressible Flows

NASA Technical Reports Server (NTRS)

Loh, Ching Y.; orgenson, Philip C. E.

2005-01-01

An absorbing non-reflecting boundary condition (NRBC) for practical computations in fluid dynamics and aeroacoustics is presented with theoretical proof. This paper is a continuation and improvement of a previous paper by the author. The absorbing NRBC technique is based on a first principle of non reflecting, which contains the essential physics that a plane wave solution of the Euler equations remains intact across the boundary. The technique is theoretically shown to work for a large class of finite volume approaches. When combined with the hyperbolic conservation laws, the NRBC is simple, robust and truly multi-dimensional; no additional implementation is needed except the prescribed physical boundary conditions. Several numerical examples in multi-dimensional spaces using two different finite volume schemes are illustrated to demonstrate its robustness in practical computations. Limitations and remedies of the technique are also discussed.

High-Order Semi-Discrete Central-Upwind Schemes for Multi-Dimensional Hamilton-Jacobi Equations

NASA Technical Reports Server (NTRS)

Bryson, Steve; Levy, Doron; Biegel, Bryan (Technical Monitor)

2002-01-01

We present the first fifth order, semi-discrete central upwind method for approximating solutions of multi-dimensional Hamilton-Jacobi equations. Unlike most of the commonly used high order upwind schemes, our scheme is formulated as a Godunov-type scheme. The scheme is based on the fluxes of Kurganov-Tadmor and Kurganov-Tadmor-Petrova, and is derived for an arbitrary number of space dimensions. A theorem establishing the monotonicity of these fluxes is provided. The spacial discretization is based on a weighted essentially non-oscillatory reconstruction of the derivative. The accuracy and stability properties of our scheme are demonstrated in a variety of examples. A comparison between our method and other fifth-order schemes for Hamilton-Jacobi equations shows that our method exhibits smaller errors without any increase in the complexity of the computations.

Multidimensional fractional Schrödinger equation

NASA Astrophysics Data System (ADS)

Rodrigues, M. M.; Vieira, N.

2012-11-01

This work is intended to investigate the multi-dimensional space-time fractional Schrödinger equation of the form (CDt0+αu)(t,x) = iħ/2m(C∇βu)(t,x), with ħ the Planck's constant divided by 2π, m is the mass and u(t,x) is a wave function of the particle. Here (CDt0+α,C∇β are operators of the Caputo fractional derivatives, where α ∈]0,1] and β ∈]1,2]. The wave function is obtained using Laplace and Fourier transforms methods and a symbolic operational form of solutions in terms of the Mittag-Leffler functions is exhibited. It is presented an expression for the wave function and for the quantum mechanical probability density. Using Banach fixed point theorem, the existence and uniqueness of solutions is studied for this kind of fractional differential equations.

Q-space trajectory imaging for multidimensional diffusion MRI of the human brain

PubMed Central

Westin, Carl-Fredrik; Knutsson, Hans; Pasternak, Ofer; Szczepankiewicz, Filip; Özarslan, Evren; van Westen, Danielle; Mattisson, Cecilia; Bogren, Mats; O’Donnell, Lauren; Kubicki, Marek; Topgaard, Daniel; Nilsson, Markus

2016-01-01

This work describes a new diffusion MR framework for imaging and modeling of microstructure that we call q-space trajectory imaging (QTI). The QTI framework consists of two parts: encoding and modeling. First we propose q-space trajectory encoding, which uses time-varying gradients to probe a trajectory in q-space, in contrast to traditional pulsed field gradient sequences that attempt to probe a point in q-space. Then we propose a microstructure model, the diffusion tensor distribution (DTD) model, which takes advantage of additional information provided by QTI to estimate a distributional model over diffusion tensors. We show that the QTI framework enables microstructure modeling that is not possible with the traditional pulsed gradient encoding as introduced by Stejskal and Tanner. In our analysis of QTI, we find that the well-known scalar b-value naturally extends to a tensor-valued entity, i.e., a diffusion measurement tensor, which we call the b-tensor. We show that b-tensors of rank 2 or 3 enable estimation of the mean and covariance of the DTD model in terms of a second order tensor (the diffusion tensor) and a fourth order tensor. The QTI framework has been designed to improve discrimination of the sizes, shapes, and orientations of diffusion microenvironments within tissue. We derive rotationally invariant scalar quantities describing intuitive microstructural features including size, shape, and orientation coherence measures. To demonstrate the feasibility of QTI on a clinical scanner, we performed a small pilot study comparing a group of five healthy controls with five patients with schizophrenia. The parameter maps derived from QTI were compared between the groups, and 9 out of the 14 parameters investigated showed differences between groups. The ability to measure and model the distribution of diffusion tensors, rather than a quantity that has already been averaged within a voxel, has the potential to provide a powerful paradigm for the study of complex tissue architecture. PMID:26923372

A signal detection model predicts the effects of set size on visual search accuracy for feature, conjunction, triple conjunction, and disjunction displays

NASA Technical Reports Server (NTRS)

Eckstein, M. P.; Thomas, J. P.; Palmer, J.; Shimozaki, S. S.

2000-01-01

Recently, quantitative models based on signal detection theory have been successfully applied to the prediction of human accuracy in visual search for a target that differs from distractors along a single attribute (feature search). The present paper extends these models for visual search accuracy to multidimensional search displays in which the target differs from the distractors along more than one feature dimension (conjunction, disjunction, and triple conjunction displays). The model assumes that each element in the display elicits a noisy representation for each of the relevant feature dimensions. The observer combines the representations across feature dimensions to obtain a single decision variable, and the stimulus with the maximum value determines the response. The model accurately predicts human experimental data on visual search accuracy in conjunctions and disjunctions of contrast and orientation. The model accounts for performance degradation without resorting to a limited-capacity spatially localized and temporally serial mechanism by which to bind information across feature dimensions.

A multidimensional approach to examine student interdisciplinary learning in science and engineering in higher education

NASA Astrophysics Data System (ADS)

Spelt, Elisabeth Jacoba Hendrika; Luning, Pieternelleke Arianne; van Boekel, Martinus A. J. S.; Mulder, Martin

2017-11-01

Preparing science and engineering students to work in interdisciplinary teams necessitates research on teaching and learning of interdisciplinary thinking. A multidimensional approach was taken to examine student interdisciplinary learning in a master course on food quality management. The collected 615 student experiences were analysed for the cognitive, emotional, and social learning dimensions using the learning theory of Illeris. Of these 615 experiences, the analysis showed that students reported 214, 194, and 207 times on, respectively, the emotional, the cognitive, and the social dimension. Per learning dimension, key learning experiences featuring interdisciplinary learning were identified such as 'frustrations in selecting and matching disciplinary knowledge to complex problems' (emotional), 'understanding how to apply theoretical models or concepts to real-world situations' (cognitive), and 'socially engaging with peers to recognise similarities in perceptions and experiences' (social). Furthermore, the results showed that students appreciated the cognitive dimension relatively more than the emotional and social dimensions.

Multiphysics Simulations of Hot-Spot Initiation in Shocked Insensitive High-Explosive

NASA Astrophysics Data System (ADS)

Najjar, Fady; Howard, W. M.; Fried, L. E.

2010-11-01

Solid plastic-bonded high-explosive materials consist of crystals with micron-sized pores embedded. Under mechanical or thermal insults, these voids increase the ease of shock initiation by generating high-temperature regions during their collapse that might lead to ignition. Understanding the mechanisms of hot-spot initiation has significant research interest due to safety, reliability and development of new insensitive munitions. Multi-dimensional high-resolution meso-scale simulations are performed using the multiphysics software, ALE3D, to understand the hot-spot initiation. The Cheetah code is coupled to ALE3D, creating multi-dimensional sparse tables for the HE properties. The reaction rates were obtained from MD Quantum computations. Our current predictions showcase several interesting features regarding hot spot dynamics including the formation of a "secondary" jet. We will discuss the results obtained with hydro-thermo-chemical processes leading to ignition growth for various pore sizes and different shock pressures.

A practical nonlocal model for heat transport in magnetized laser plasmas

NASA Astrophysics Data System (ADS)

Nicolaï, Ph. D.; Feugeas, J.-L. A.; Schurtz, G. P.

2006-03-01

A model of nonlocal transport for multidimensional radiation magnetohydrodynamics codes is presented. In laser produced plasmas, it is now believed that the heat transport can be strongly modified by the nonlocal nature of the electron conduction. Other mechanisms, such as self-generated magnetic fields, may also affect the heat transport. The model described in this work, based on simplified Fokker-Planck equations aims at extending the model of G. Schurtz, Ph. Nicolaï, and M. Busquet [Phys. Plasmas 7, 4238 (2000)] to magnetized plasmas. A complete system of nonlocal equations is derived from kinetic equations with self-consistent electric and magnetic fields. These equations are analyzed and simplified in order to be implemented into large laser fusion codes and coupled to other relevant physics. The model is applied to two laser configurations that demonstrate the main features of the model and point out the nonlocal Righi-Leduc effect in a multidimensional case.

Highly Transparent Water-Repelling Surfaces based on Biomimetic Hierarchical Structure

NASA Astrophysics Data System (ADS)

Wooh, Sanghyuk; Koh, Jai; Yoon, Hyunsik; Char, Kookheon

2013-03-01

Nature is a great source of inspiration for creating unique structures with special functions. The representative examples of water-repelling surfaces in nature, such as lotus leaves, rose petals, and insect wings, consist of an array of bumps (or long hairs) and nanoscale surface features with different dimension scales. Herein, we introduced a method of realizing multi-dimensional hierarchical structures and water-repellancy of the surfaces with different drop impact scenarios. The multi-dimensional hierarchical structures were fabricated by soft imprinting method with TiO2 nanoparticle pastes. In order to achieve the enhanced hydrophobicity, fluorinated moieties were attached to the etched surfaces to lower the surface energy. As a result, super-hydrophobic surfaces with high transparency were realized (over 176° water contact angle), and for further investigation, these hierarchical surfaces with different drop impact scenarios were characterized by varying the impact speed, drop size, and the geometry of the surfaces.

Multidimensional Scaling Analysis of the Dynamics of a Country Economy

PubMed Central

Mata, Maria Eugénia

2013-01-01

This paper analyzes the Portuguese short-run business cycles over the last 150 years and presents the multidimensional scaling (MDS) for visualizing the results. The analytical and numerical assessment of this long-run perspective reveals periods with close connections between the macroeconomic variables related to government accounts equilibrium, balance of payments equilibrium, and economic growth. The MDS method is adopted for a quantitative statistical analysis. In this way, similarity clusters of several historical periods emerge in the MDS maps, namely, in identifying similarities and dissimilarities that identify periods of prosperity and crises, growth, and stagnation. Such features are major aspects of collective national achievement, to which can be associated the impact of international problems such as the World Wars, the Great Depression, or the current global financial crisis, as well as national events in the context of broad political blueprints for the Portuguese society in the rising globalization process. PMID:24294132

[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.

Analysis of crew functions as an aid in Space Station interior layout

NASA Technical Reports Server (NTRS)

Steinberg, A. L.; Tullis, Thomas S.; Bied, Barbra

1986-01-01

The Space Station must be designed to facilitate all of the functions that its crew will perform, both on-duty and off-duty, as efficiently and comfortably as possible. This paper examines the functions to be performed by the Space Station crew in order to make inferences about the design of an interior layout that optimizes crew productivity. Twenty-seven crew functions were defined, as well as five criteria for assessing relationships among all pairs of those functions. Hierarchical clustering and multidimensional scaling techniques were used to visually summarize the relationships. A key result was the identification of two dimensions for describing the configuration of crew functions: 'Private-Public' and 'Group-Individual'. Seven specific recommendations for Space Station interior layout were derived from the analyses.

Studying relationships between environment and malaria incidence in Camopi (French Guiana) through the objective selection of buffer-based landscape characterisations

PubMed Central

2011-01-01

Background Malaria remains a major health problem in French Guiana, with a mean of 3800 cases each year. A previous study in Camopi, an Amerindian village on the Oyapock River, highlighted the major contribution of environmental features to the incidence of malaria attacks. We propose a method for the objective selection of the best multivariate peridomestic landscape characterisation that maximises the chances of identifying relationships between environmental features and malaria incidence, statistically significant and meaningful from an epidemiological point of view. Methods A land-cover map, the hydrological network and the geolocalised inhabited houses were used to characterise the peridomestic landscape in eleven discoid buffers with radii of 50, 100, 200, 300, 400, 500, 600, 700, 800, 900 and 1000 metres. Buffer-based landscape characterisations were first compared in terms of their capacity to discriminate between sites within the geographic space and of their effective multidimensionality in variable space. The Akaike information criterion (AIC) was then used to select the landscape model best explaining the incidences of P. vivax and P. falciparum malaria. Finally, we calculated Pearson correlation coefficients for the relationships between environmental variables and malaria incidence, by species, for the more relevant buffers. Results The optimal buffers for environmental characterisation had radii of 100 m around houses for P. vivax and 400 m around houses for P. falciparum. The incidence of P. falciparum malaria seemed to be more strongly linked to environmental features than that of P. vivax malaria, within these buffers. The incidence of P. falciparum malaria in children was strongly correlated with proportions of bare soil (r = -0.69), land under high vegetation (r = 0.68) and primary forest (r = 0.54), landscape division (r = 0.48) and the number of inhabited houses (r = -0.60). The incidence of P. vivax malaria was associated only with landscape division (r = 0.49). Conclusions The proposed methodology provides a simple and general framework for objective characterisation of the landscape to account for field observations. The use of this method enabled us to identify different optimal observation horizons around houses, depending on the Plasmodium species considered, and to demonstrate significant correlations between environmental features and the incidence of malaria. PMID:22151738

Studying relationships between environment and malaria incidence in Camopi (French Guiana) through the objective selection of buffer-based landscape characterisations.

PubMed

Stefani, Aurélia; Roux, Emmanuel; Fotsing, Jean-Marie; Carme, Bernard

2011-12-13

Malaria remains a major health problem in French Guiana, with a mean of 3800 cases each year. A previous study in Camopi, an Amerindian village on the Oyapock River, highlighted the major contribution of environmental features to the incidence of malaria attacks. We propose a method for the objective selection of the best multivariate peridomestic landscape characterisation that maximises the chances of identifying relationships between environmental features and malaria incidence, statistically significant and meaningful from an epidemiological point of view. A land-cover map, the hydrological network and the geolocalised inhabited houses were used to characterise the peridomestic landscape in eleven discoid buffers with radii of 50, 100, 200, 300, 400, 500, 600, 700, 800, 900 and 1000 metres. Buffer-based landscape characterisations were first compared in terms of their capacity to discriminate between sites within the geographic space and of their effective multidimensionality in variable space. The Akaike information criterion (AIC) was then used to select the landscape model best explaining the incidences of P. vivax and P. falciparum malaria. Finally, we calculated Pearson correlation coefficients for the relationships between environmental variables and malaria incidence, by species, for the more relevant buffers. The optimal buffers for environmental characterisation had radii of 100 m around houses for P. vivax and 400 m around houses for P. falciparum. The incidence of P. falciparum malaria seemed to be more strongly linked to environmental features than that of P. vivax malaria, within these buffers. The incidence of P. falciparum malaria in children was strongly correlated with proportions of bare soil (r = -0.69), land under high vegetation (r = 0.68) and primary forest (r = 0.54), landscape division (r = 0.48) and the number of inhabited houses (r = -0.60). The incidence of P. vivax malaria was associated only with landscape division (r = 0.49). The proposed methodology provides a simple and general framework for objective characterisation of the landscape to account for field observations. The use of this method enabled us to identify different optimal observation horizons around houses, depending on the Plasmodium species considered, and to demonstrate significant correlations between environmental features and the incidence of malaria. © 2011 Stefani et al; licensee BioMed Central Ltd.

Face-space: A unifying concept in face recognition research.

PubMed

Valentine, Tim; Lewis, Michael B; Hills, Peter J

2016-10-01

The concept of a multidimensional psychological space, in which faces can be represented according to their perceived properties, is fundamental to the modern theorist in face processing. Yet the idea was not clearly expressed until 1991. The background that led to the development of face-space is explained, and its continuing influence on theories of face processing is discussed. Research that has explored the properties of the face-space and sought to understand caricature, including facial adaptation paradigms, is reviewed. Face-space as a theoretical framework for understanding the effect of ethnicity and the development of face recognition is evaluated. Finally, two applications of face-space in the forensic setting are discussed. From initially being presented as a model to explain distinctiveness, inversion, and the effect of ethnicity, face-space has become a central pillar in many aspects of face processing. It is currently being developed to help us understand adaptation effects with faces. While being in principle a simple concept, face-space has shaped, and continues to shape, our understanding of face perception.

A novel framework to alleviate the sparsity problem in context-aware recommender systems

NASA Astrophysics Data System (ADS)

Yu, Penghua; Lin, Lanfen; Wang, Jing

2017-04-01

Recommender systems have become indispensable for services in the era of big data. To improve accuracy and satisfaction, context-aware recommender systems (CARSs) attempt to incorporate contextual information into recommendations. Typically, valid and influential contexts are determined in advance by domain experts or feature selection approaches. Most studies have focused on utilizing the unitary context due to the differences between various contexts. Meanwhile, multi-dimensional contexts will aggravate the sparsity problem, which means that the user preference matrix would become extremely sparse. Consequently, there are not enough or even no preferences in most multi-dimensional conditions. In this paper, we propose a novel framework to alleviate the sparsity issue for CARSs, especially when multi-dimensional contextual variables are adopted. Motivated by the intuition that the overall preferences tend to show similarities among specific groups of users and conditions, we first explore to construct one contextual profile for each contextual condition. In order to further identify those user and context subgroups automatically and simultaneously, we apply a co-clustering algorithm. Furthermore, we expand user preferences in a given contextual condition with the identified user and context clusters. Finally, we perform recommendations based on expanded preferences. Extensive experiments demonstrate the effectiveness of the proposed framework.

Multi-dimensional Upwind Fluctuation Splitting Scheme with Mesh Adaption for Hypersonic Viscous Flow. Degree awarded by Virginia Polytechnic Inst. and State Univ., 9 Nov. 2001

NASA Technical Reports Server (NTRS)

Wood, William A., III

2002-01-01

A multi-dimensional upwind fluctuation splitting scheme is developed and implemented for two-dimensional and axisymmetric formulations of the Navier-Stokes equations on unstructured meshes. Key features of the scheme are the compact stencil, full upwinding, and non-linear discretization which allow for second-order accuracy with enforced positivity. Throughout, the fluctuation splitting scheme is compared to a current state-of-the-art finite volume approach, a second-order, dual mesh upwind flux difference splitting scheme (DMFDSFV), and is shown to produce more accurate results using fewer computer resources for a wide range of test cases. A Blasius flat plate viscous validation case reveals a more accurate upsilon-velocity profile for fluctuation splitting, and the reduced artificial dissipation production is shown relative to DMFDSFV. Remarkably, the fluctuation splitting scheme shows grid converged skin friction coefficients with only five points in the boundary layer for this case. The second half of the report develops a local, compact, anisotropic unstructured mesh adaptation scheme in conjunction with the multi-dimensional upwind solver, exhibiting a characteristic alignment behavior for scalar problems. The adaptation strategy is extended to the two-dimensional and axisymmetric Navier-Stokes equations of motion through the concept of fluctuation minimization.

Multi-Autonomous Ground-robotic International Challenge (MAGIC) 2010

DTIC Science & Technology

2010-12-14

SLAM technique since this setup, having a LIDAR with long-range high-accuracy measurement capability, allows accurate localization and mapping more...achieve the accuracy of 25cm due to the use of multi-dimensional information. OGM is, similarly to SLAM , carried out by using LIDAR data. The OGM...a result of the development and implementation of the hybrid feature-based/scan-matching Simultaneous Localization and Mapping ( SLAM ) technique, the

Binary Multidimensional Scaling for Hashing.

PubMed

Huang, Yameng; Lin, Zhouchen

2017-10-04

Hashing is a useful technique for fast nearest neighbor search due to its low storage cost and fast query speed. Unsupervised hashing aims at learning binary hash codes for the original features so that the pairwise distances can be best preserved. While several works have targeted on this task, the results are not satisfactory mainly due to the oversimplified model. In this paper, we propose a unified and concise unsupervised hashing framework, called Binary Multidimensional Scaling (BMDS), which is able to learn the hash code for distance preservation in both batch and online mode. In the batch mode, unlike most existing hashing methods, we do not need to simplify the model by predefining the form of hash map. Instead, we learn the binary codes directly based on the pairwise distances among the normalized original features by Alternating Minimization. This enables a stronger expressive power of the hash map. In the online mode, we consider the holistic distance relationship between current query example and those we have already learned, rather than only focusing on current data chunk. It is useful when the data come in a streaming fashion. Empirical results show that while being efficient for training, our algorithm outperforms state-of-the-art methods by a large margin in terms of distance preservation, which is practical for real-world applications.

Predicting and interpreting identification errors in military vehicle training using multidimensional scaling.

PubMed

Bohil, Corey J; Higgins, Nicholas A; Keebler, Joseph R

2014-01-01

We compared methods for predicting and understanding the source of confusion errors during military vehicle identification training. Participants completed training to identify main battle tanks. They also completed card-sorting and similarity-rating tasks to express their mental representation of resemblance across the set of training items. We expected participants to selectively attend to a subset of vehicle features during these tasks, and we hypothesised that we could predict identification confusion errors based on the outcomes of the card-sort and similarity-rating tasks. Based on card-sorting results, we were able to predict about 45% of observed identification confusions. Based on multidimensional scaling of the similarity-rating data, we could predict more than 80% of identification confusions. These methods also enabled us to infer the dimensions receiving significant attention from each participant. This understanding of mental representation may be crucial in creating personalised training that directs attention to features that are critical for accurate identification. Participants completed military vehicle identification training and testing, along with card-sorting and similarity-rating tasks. The data enabled us to predict up to 84% of identification confusion errors and to understand the mental representation underlying these errors. These methods have potential to improve training and reduce identification errors leading to fratricide.

Assessment of urban green space structures and their quality from a multidimensional perspective.

PubMed

Daniels, Benjamin; Zaunbrecher, Barbara S; Paas, Bastian; Ottermanns, Richard; Ziefle, Martina; Roß-Nickoll, Martina

2018-02-15

Facing the growing amount of people living in cities and, at the same time, the need for a compact and sustainable urban development to mitigate urban sprawl, it becomes increasingly important that green spaces in compact cities are designed to meet the various needs within an urban environment. Urban green spaces have a multitude of functions: Maintaining ecological processes and resulting services, e.g. providing habitat for animals and plants, providing a beneficial city microclimate as well as recreational space for citizens. Regarding these requirements, currently existing assessment procedures for green spaces have some major shortcomings, which are discussed in this paper. It is argued why a more detailed spatial level as well as a distinction between natural and artificial varieties of structural elements is justified and needed and how the assessment of urban green spaces benefits from the multidimensional perspective that is applied. By analyzing a selection of structural elements from an ecological, microclimatic and social perspective, indicator values are derived and a new, holistic metrics 1 is proposed. The results of the integrated analysis led to two major findings: first, that for some elements, the evaluation differs to a great extent between the different perspectives (disciplines) and second, that natural and artificial varieties are, in most cases, evaluated considerably different from each other. The differences between the perspectives call for an integrative planning policy which acknowledges the varying contribution of a structural element to different purposes (ecological, microclimatic, social) as well as a discussion about the prioritization of those purposes. The differences in the evaluation of natural vs. artificial elements verify the assumption that indicators which consider only generic elements fail to account for those refinements and are thus less suitable for planning and assessment purposes. Implications, challenges and scenarios for the application of such a metrics are finally discussed. Copyright © 2017 Elsevier B.V. All rights reserved.

What History Tells Us about the Distinct Nature of Chemistry.

PubMed

Chang, Hasok

2017-11-01

Attention to the history of chemistry can help us recognise the characteristics of chemistry that have helped to maintain it as a separate scientific discipline with a unique identity. Three such features are highlighted in this paper. First, chemistry has maintained a distinct type of theoretical thinking, independent from that of physics even in the era of quantum chemistry. Second, chemical research has always been shaped by its ineliminable practical relevance and usefulness. Third, the lived experience of chemistry, spanning the laboratory, the classroom and everyday life, is distinctive in its multidimensional sensuousness. Furthermore, I argue that the combination of these three features makes chemistry an exemplary science.

The cancellous bone multiscale morphology-elasticity relationship.

PubMed

Agić, Ante; Nikolić, Vasilije; Mijović, Budimir

2006-06-01

The cancellous bone effective properties relations are analysed on multiscale across two aspects; properties of representative volume element on micro scale and statistical measure of trabecular trajectory orientation on mesoscale. Anisotropy of the microstructure is described across fabric tensor measure with trajectory orientation tensor as bridging scale connection. The scatter measured data (elastic modulus, trajectory orientation, apparent density) from compression test are fitted by stochastic interpolation procedure. The engineering constants of the elasticity tensor are estimated by last square fitt procedure in multidimensional space by Nelder-Mead simplex. The multiaxial failure surface in strain space is constructed and interpolated by modified super-ellipsoid.

Bridging the gap between Hydrologic and Atmospheric communities through a standard based framework

NASA Astrophysics Data System (ADS)

Boldrini, E.; Salas, F.; Maidment, D. R.; Mazzetti, P.; Santoro, M.; Nativi, S.; Domenico, B.

2012-04-01

Data interoperability in the study of Earth sciences is essential to performing interdisciplinary multi-scale multi-dimensional analyses (e.g. hydrologic impacts of global warming, regional urbanization, global population growth etc.). This research aims to bridge the existing gap between hydrologic and atmospheric communities both at semantic and technological levels. Within the context of hydrology, scientists are usually concerned with data organized as time series: a time series can be seen as a variable measured at a particular point in space over a period of time (e.g. the stream flow values as periodically measured by a buoy sensor in a river); atmospheric scientists instead usually organize their data as coverages: a coverage can be seen as a multidimensional data array (e.g. satellite images acquired through time). These differences make non-trivial the set up of a common framework to perform data discovery and access. A set of web services specifications and implementations is already in place in both the scientific communities to allow data discovery and access in the different domains. The CUAHSI-Hydrologic Information System (HIS) service stack lists different services types and implementations: - a metacatalog (implemented as a CSW) used to discover metadata services by distributing the query to a set of catalogs - time series catalogs (implemented as CSW) used to discover datasets published by the feature services - feature services (implemented as WFS) containing features with data access link - sensor observation services (implemented as SOS) enabling access to the stream of acquisitions Within the Unidata framework, there lies a similar service stack for atmospheric data: - the broker service (implemented as a CSW) distributes a user query to a set of heterogeneous services (i.e. catalogs services, but also inventory and access services) - the catalog service (implemented as a CSW) is able to harvest the available metadata offered by THREDDS services, and executes complex queries against the available metadata. - inventory service (implemented as a THREDDS) being able to hierarchically organize and publish a local collection of multi-dimensional arrays (e.g. NetCDF, GRIB files), as well as publish auxiliary standard services to realize the actual data access and visualization (e.g. WCS, OPeNDAP, WMS). The approach followed in this research is to build on top of the existing standards and implementations, by setting up a standard-aware interoperable framework, able to deal with the existing heterogeneity in an organic way. As a methodology, interoperability tests against real services were performed; existing problems were thus highlighted and possibly solved. The use of flexible tools, able to deal in a smart way with heterogeneity has proven to be successful, in particular experiments were carried on with both GI-cat broker and ESRI GeoPortal frameworks. GI-cat discovery broker was proven successful at implementing the CSW interface, as well as federating heterogeneous resources, such as THREDDS and WCS services published by Unidata, HydroServer, WFS and SOS services published by CUAHSI. Experiments with ESRI GeoPortal were also successful: the GeoPortal was used to deploy a web interface able to distribute searches amongst catalog implementations from both the hydrologic and the atmospheric communities, including HydroServers and GI-cat, combining results from both the domains in a seamless way.

A method of using cluster analysis to study statistical dependence in multivariate data

NASA Technical Reports Server (NTRS)

Borucki, W. J.; Card, D. H.; Lyle, G. C.

1975-01-01

A technique is presented that uses both cluster analysis and a Monte Carlo significance test of clusters to discover associations between variables in multidimensional data. The method is applied to an example of a noisy function in three-dimensional space, to a sample from a mixture of three bivariate normal distributions, and to the well-known Fisher's Iris data.

Associative memory model for searching an image database by image snippet

NASA Astrophysics Data System (ADS)

Khan, Javed I.; Yun, David Y.

1994-09-01

This paper presents an associative memory called an multidimensional holographic associative computing (MHAC), which can be potentially used to perform feature based image database query using image snippet. MHAC has the unique capability to selectively focus on specific segments of a query frame during associative retrieval. As a result, this model can perform search on the basis of featural significance described by a subset of the snippet pixels. This capability is critical for visual query in image database because quite often the cognitive index features in the snippet are statistically weak. Unlike, the conventional artificial associative memories, MHAC uses a two level representation and incorporates additional meta-knowledge about the reliability status of segments of information it receives and forwards. In this paper we present the analysis of focus characteristics of MHAC.

Multiple mechanisms in the perception of face gender: Effect of sex-irrelevant features.

PubMed

Komori, Masashi; Kawamura, Satoru; Ishihara, Shigekazu

2011-06-01

Effects of sex-relevant and sex-irrelevant facial features on the evaluation of facial gender were investigated. Participants rated masculinity of 48 male facial photographs and femininity of 48 female facial photographs. Eighty feature points were measured on each of the facial photographs. Using a generalized Procrustes analysis, facial shapes were converted into multidimensional vectors, with the average face as a starting point. Each vector was decomposed into a sex-relevant subvector and a sex-irrelevant subvector which were, respectively, parallel and orthogonal to the main male-female axis. Principal components analysis (PCA) was performed on the sex-irrelevant subvectors. One principal component was negatively correlated with both perceived masculinity and femininity, and another was correlated only with femininity, though both components were orthogonal to the male-female dimension (and thus by definition sex-irrelevant). These results indicate that evaluation of facial gender depends on sex-irrelevant as well as sex-relevant facial features.

Recognizing upper limb movements with wrist worn inertial sensors using k-means clustering classification.

PubMed

Biswas, Dwaipayan; Cranny, Andy; Gupta, Nayaab; Maharatna, Koushik; Achner, Josy; Klemke, Jasmin; Jöbges, Michael; Ortmann, Steffen

2015-04-01

In this paper we present a methodology for recognizing three fundamental movements of the human forearm (extension, flexion and rotation) using pattern recognition applied to the data from a single wrist-worn, inertial sensor. We propose that this technique could be used as a clinical tool to assess rehabilitation progress in neurodegenerative pathologies such as stroke or cerebral palsy by tracking the number of times a patient performs specific arm movements (e.g. prescribed exercises) with their paretic arm throughout the day. We demonstrate this with healthy subjects and stroke patients in a simple proof of concept study in which these arm movements are detected during an archetypal activity of daily-living (ADL) - 'making-a-cup-of-tea'. Data is collected from a tri-axial accelerometer and a tri-axial gyroscope located proximal to the wrist. In a training phase, movements are initially performed in a controlled environment which are represented by a ranked set of 30 time-domain features. Using a sequential forward selection technique, for each set of feature combinations three clusters are formed using k-means clustering followed by 10 runs of 10-fold cross validation on the training data to determine the best feature combinations. For the testing phase, movements performed during the ADL are associated with each cluster label using a minimum distance classifier in a multi-dimensional feature space, comprised of the best ranked features, using Euclidean or Mahalanobis distance as the metric. Experiments were performed with four healthy subjects and four stroke survivors and our results show that the proposed methodology can detect the three movements performed during the ADL with an overall average accuracy of 88% using the accelerometer data and 83% using the gyroscope data across all healthy subjects and arm movement types. The average accuracy across all stroke survivors was 70% using accelerometer data and 66% using gyroscope data. We also use a Linear Discriminant Analysis (LDA) classifier and a Support Vector Machine (SVM) classifier in association with the same set of features to detect the three arm movements and compare the results to demonstrate the effectiveness of our proposed methodology. Copyright © 2014 Elsevier B.V. All rights reserved.

Random forest classification of large volume structures for visuo-haptic rendering in CT images

NASA Astrophysics Data System (ADS)

Mastmeyer, Andre; Fortmeier, Dirk; Handels, Heinz

2016-03-01

For patient-specific voxel-based visuo-haptic rendering of CT scans of the liver area, the fully automatic segmentation of large volume structures such as skin, soft tissue, lungs and intestine (risk structures) is important. Using a machine learning based approach, several existing segmentations from 10 segmented gold-standard patients are learned by random decision forests individually and collectively. The core of this paper is feature selection and the application of the learned classifiers to a new patient data set. In a leave-some-out cross-validation, the obtained full volume segmentations are compared to the gold-standard segmentations of the untrained patients. The proposed classifiers use a multi-dimensional feature space to estimate the hidden truth, instead of relying on clinical standard threshold and connectivity based methods. The result of our efficient whole-body section classification are multi-label maps with the considered tissues. For visuo-haptic simulation, other small volume structures would have to be segmented additionally. We also take a look into these structures (liver vessels). For an experimental leave-some-out study consisting of 10 patients, the proposed method performs much more efficiently compared to state of the art methods. In two variants of leave-some-out experiments we obtain best mean DICE ratios of 0.79, 0.97, 0.63 and 0.83 for skin, soft tissue, hard bone and risk structures. Liver structures are segmented with DICE 0.93 for the liver, 0.43 for blood vessels and 0.39 for bile vessels.

Nonaxial hexadecapole deformation effects on the fission barrier

NASA Astrophysics Data System (ADS)

Kardan, A.; Nejati, S.

2016-06-01

Fission barrier of the heavy nucleus 250Cf is analyzed in a multi-dimensional deformation space. This space includes two quadrupole (ɛ2,γ) and three hexadecapole deformation (ɛ40,ɛ42,ɛ44) parameters. The analysis is performed within an unpaired macroscopic-microscopic approach. Special attention is given to the effects of the axial and non-axial hexadecapole deformation shapes. It is found that the inclusion of the nonaxial hexadecapole shapes does not change the fission barrier heights, so it should be sufficient to minimize the energy in only one degree of freedom in the hexadecapole space ɛ4. The role of hexadecapole deformation parameters is also discussed on the Lublin-Strasbourg drop (LSD) macroscopic and the Strutinsky shell energies.

SCORE-EVET: a computer code for the multidimensional transient thermal-hydraulic analysis of nuclear fuel rod arrays. [BWR; PWR

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

Benedetti, R. L.; Lords, L. V.; Kiser, D. M.

1978-02-01

The SCORE-EVET code was developed to study multidimensional transient fluid flow in nuclear reactor fuel rod arrays. The conservation equations used were derived by volume averaging the transient compressible three-dimensional local continuum equations in Cartesian coordinates. No assumptions associated with subchannel flow have been incorporated into the derivation of the conservation equations. In addition to the three-dimensional fluid flow equations, the SCORE-EVET code ocntains: (a) a one-dimensional steady state solution scheme to initialize the flow field, (b) steady state and transient fuel rod conduction models, and (c) comprehensive correlation packages to describe fluid-to-fuel rod interfacial energy and momentum exchange. Velocitymore » and pressure boundary conditions can be specified as a function of time and space to model reactor transient conditions such as a hypothesized loss-of-coolant accident (LOCA) or flow blockage.« less

Nonlinear multidimensional cosmological models with form fields: Stabilization of extra dimensions and the cosmological constant problem

NASA Astrophysics Data System (ADS)

Günther, U.; Moniz, P.; Zhuk, A.

2003-08-01

We consider multidimensional gravitational models with a nonlinear scalar curvature term and form fields in the action functional. In our scenario it is assumed that the higher dimensional spacetime undergoes a spontaneous compactification to a warped product manifold. Particular attention is paid to models with quadratic scalar curvature terms and a Freund-Rubin-like ansatz for solitonic form fields. It is shown that for certain parameter ranges the extra dimensions are stabilized. In particular, stabilization is possible for any sign of the internal space curvature, the bulk cosmological constant, and of the effective four-dimensional cosmological constant. Moreover, the effective cosmological constant can satisfy the observable limit on the dark energy density. Finally, we discuss the restrictions on the parameters of the considered nonlinear models and how they follow from the connection between the D-dimensional and the four-dimensional fundamental mass scales.

New generic indexing technology

NASA Technical Reports Server (NTRS)

Freeston, Michael

1996-01-01

There has been no fundamental change in the dynamic indexing methods supporting database systems since the invention of the B-tree twenty-five years ago. And yet the whole classical approach to dynamic database indexing has long since become inappropriate and increasingly inadequate. We are moving rapidly from the conventional one-dimensional world of fixed-structure text and numbers to a multi-dimensional world of variable structures, objects and images, in space and time. But, even before leaving the confines of conventional database indexing, the situation is highly unsatisfactory. In fact, our research has led us to question the basic assumptions of conventional database indexing. We have spent the past ten years studying the properties of multi-dimensional indexing methods, and in this paper we draw the strands of a number of developments together - some quite old, some very new, to show how we now have the basis for a new generic indexing technology for the next generation of database systems.

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.

Multidimensional Processing and Visual Rendering of Complex 3D Biomedical Images

NASA Technical Reports Server (NTRS)

Sams, Clarence F.

2016-01-01

The proposed technology uses advanced image analysis techniques to maximize the resolution and utility of medical imaging methods being used during spaceflight. We utilize COTS technology for medical imaging, but our applications require higher resolution assessment of the medical images than is routinely applied with nominal system software. By leveraging advanced data reduction and multidimensional imaging techniques utilized in analysis of Planetary Sciences and Cell Biology imaging, it is possible to significantly increase the information extracted from the onboard biomedical imaging systems. Year 1 focused on application of these techniques to the ocular images collected on ground test subjects and ISS crewmembers. Focus was on the choroidal vasculature and the structure of the optic disc. Methods allowed for increased resolution and quantitation of structural changes enabling detailed assessment of progression over time. These techniques enhance the monitoring and evaluation of crew vision issues during space flight.

Mergeomics: a web server for identifying pathological pathways, networks, and key regulators via multidimensional data integration.

PubMed

Arneson, Douglas; Bhattacharya, Anindya; Shu, Le; Mäkinen, Ville-Petteri; Yang, Xia

2016-09-09

Human diseases are commonly the result of multidimensional changes at molecular, cellular, and systemic levels. Recent advances in genomic technologies have enabled an outpour of omics datasets that capture these changes. However, separate analyses of these various data only provide fragmented understanding and do not capture the holistic view of disease mechanisms. To meet the urgent needs for tools that effectively integrate multiple types of omics data to derive biological insights, we have developed Mergeomics, a computational pipeline that integrates multidimensional disease association data with functional genomics and molecular networks to retrieve biological pathways, gene networks, and central regulators critical for disease development. To make the Mergeomics pipeline available to a wider research community, we have implemented an online, user-friendly web server ( http://mergeomics. idre.ucla.edu/ ). The web server features a modular implementation of the Mergeomics pipeline with detailed tutorials. Additionally, it provides curated genomic resources including tissue-specific expression quantitative trait loci, ENCODE functional annotations, biological pathways, and molecular networks, and offers interactive visualization of analytical results. Multiple computational tools including Marker Dependency Filtering (MDF), Marker Set Enrichment Analysis (MSEA), Meta-MSEA, and Weighted Key Driver Analysis (wKDA) can be used separately or in flexible combinations. User-defined summary-level genomic association datasets (e.g., genetic, transcriptomic, epigenomic) related to a particular disease or phenotype can be uploaded and computed real-time to yield biologically interpretable results, which can be viewed online and downloaded for later use. Our Mergeomics web server offers researchers flexible and user-friendly tools to facilitate integration of multidimensional data into holistic views of disease mechanisms in the form of tissue-specific key regulators, biological pathways, and gene networks.

GENESIS: a hybrid-parallel and multi-scale molecular dynamics simulator with enhanced sampling algorithms for biomolecular and cellular simulations

PubMed Central

Jung, Jaewoon; Mori, Takaharu; Kobayashi, Chigusa; Matsunaga, Yasuhiro; Yoda, Takao; Feig, Michael; Sugita, Yuji

2015-01-01

GENESIS (Generalized-Ensemble Simulation System) is a new software package for molecular dynamics (MD) simulations of macromolecules. It has two MD simulators, called ATDYN and SPDYN. ATDYN is parallelized based on an atomic decomposition algorithm for the simulations of all-atom force-field models as well as coarse-grained Go-like models. SPDYN is highly parallelized based on a domain decomposition scheme, allowing large-scale MD simulations on supercomputers. Hybrid schemes combining OpenMP and MPI are used in both simulators to target modern multicore computer architectures. Key advantages of GENESIS are (1) the highly parallel performance of SPDYN for very large biological systems consisting of more than one million atoms and (2) the availability of various REMD algorithms (T-REMD, REUS, multi-dimensional REMD for both all-atom and Go-like models under the NVT, NPT, NPAT, and NPγT ensembles). The former is achieved by a combination of the midpoint cell method and the efficient three-dimensional Fast Fourier Transform algorithm, where the domain decomposition space is shared in real-space and reciprocal-space calculations. Other features in SPDYN, such as avoiding concurrent memory access, reducing communication times, and usage of parallel input/output files, also contribute to the performance. We show the REMD simulation results of a mixed (POPC/DMPC) lipid bilayer as a real application using GENESIS. GENESIS is released as free software under the GPLv2 licence and can be easily modified for the development of new algorithms and molecular models. WIREs Comput Mol Sci 2015, 5:310–323. doi: 10.1002/wcms.1220 PMID:26753008

AdS and stabilized extra dimensions in multi-dimensional gravitational models with nonlinear scalar curvature terms R-1 and R4

NASA Astrophysics Data System (ADS)

Günther, Uwe; Zhuk, Alexander; Bezerra, Valdir B.; Romero, Carlos

2005-08-01

We study multi-dimensional gravitational models with scalar curvature nonlinearities of types R-1 and R4. It is assumed that the corresponding higher dimensional spacetime manifolds undergo a spontaneous compactification to manifolds with a warped product structure. Special attention has been paid to the stability of the extra-dimensional factor spaces. It is shown that for certain parameter regions the systems allow for a freezing stabilization of these spaces. In particular, we find for the R-1 model that configurations with stabilized extra dimensions do not provide a late-time acceleration (they are AdS), whereas the solution branch which allows for accelerated expansion (the dS branch) is incompatible with stabilized factor spaces. In the case of the R4 model, we obtain that the stability region in parameter space depends on the total dimension D = dim(M) of the higher dimensional spacetime M. For D > 8 the stability region consists of a single (absolutely stable) sector which is shielded from a conformal singularity (and an antigravity sector beyond it) by a potential barrier of infinite height and width. This sector is smoothly connected with the stability region of a curvature-linear model. For D < 8 an additional (metastable) sector exists which is separated from the conformal singularity by a potential barrier of finite height and width so that systems in this sector are prone to collapse into the conformal singularity. This second sector is not smoothly connected with the first (absolutely stable) one. Several limiting cases and the possibility of inflation are discussed for the R4 model.

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

Multidimensional, mapping-based complex wavelet transforms.

PubMed

Fernandes, Felix C A; van Spaendonck, Rutger L C; Burrus, C Sidney

2005-01-01

Although the discrete wavelet transform (DWT) is a powerful tool for signal and image processing, it has three serious disadvantages: shift sensitivity, poor directionality, and lack of phase information. To overcome these disadvantages, we introduce multidimensional, mapping-based, complex wavelet transforms that consist of a mapping onto a complex function space followed by a DWT of the complex mapping. Unlike other popular transforms that also mitigate DWT shortcomings, the decoupled implementation of our transforms has two important advantages. First, the controllable redundancy of the mapping stage offers a balance between degree of shift sensitivity and transform redundancy. This allows us to create a directional, nonredundant, complex wavelet transform with potential benefits for image coding systems. To the best of our knowledge, no other complex wavelet transform is simultaneously directional and nonredundant. The second advantage of our approach is the flexibility to use any DWT in the transform implementation. As an example, we exploit this flexibility to create the complex double-density DWT: a shift-insensitive, directional, complex wavelet transform with a low redundancy of (3M - 1)/(2M - 1) in M dimensions. No other transform achieves all these properties at a lower redundancy, to the best of our knowledge. By exploiting the advantages of our multidimensional, mapping-based complex wavelet transforms in seismic signal-processing applications, we have demonstrated state-of-the-art results.

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.

Interferometric and nonlinear-optical spectral-imaging techniques for outer space and live cells

NASA Astrophysics Data System (ADS)

Itoh, Kazuyoshi

2015-12-01

Multidimensional signals such as the spectral images allow us to have deeper insights into the natures of objects. In this paper the spectral imaging techniques that are based on optical interferometry and nonlinear optics are presented. The interferometric imaging technique is based on the unified theory of Van Cittert-Zernike and Wiener-Khintchine theorems and allows us to retrieve a spectral image of an object in the far zone from the 3D spatial coherence function. The retrieval principle is explained using a very simple object. The promising applications to space interferometers for astronomy that are currently in progress will also be briefly touched on. An interesting extension of interferometric spectral imaging is a 3D and spectral imaging technique that records 4D information of objects where the 3D and spectral information is retrieved from the cross-spectral density function of optical field. The 3D imaging is realized via the numerical inverse propagation of the cross-spectral density. A few techniques suggested recently are introduced. The nonlinear optical technique that utilizes stimulated Raman scattering (SRS) for spectral imaging of biomedical targets is presented lastly. The strong signals of SRS permit us to get vibrational information of molecules in the live cell or tissue in real time. The vibrational information of unstained or unlabeled molecules is crucial especially for medical applications. The 3D information due to the optical nonlinearity is also the attractive feature of SRS spectral microscopy.

3D variational brain tumor segmentation using Dirichlet priors on a clustered feature set.

PubMed

Popuri, Karteek; Cobzas, Dana; Murtha, Albert; Jägersand, Martin

2012-07-01

Brain tumor segmentation is a required step before any radiation treatment or surgery. When performed manually, segmentation is time consuming and prone to human errors. Therefore, there have been significant efforts to automate the process. But, automatic tumor segmentation from MRI data is a particularly challenging task. Tumors have a large diversity in shape and appearance with intensities overlapping the normal brain tissues. In addition, an expanding tumor can also deflect and deform nearby tissue. In our work, we propose an automatic brain tumor segmentation method that addresses these last two difficult problems. We use the available MRI modalities (T1, T1c, T2) and their texture characteristics to construct a multidimensional feature set. Then, we extract clusters which provide a compact representation of the essential information in these features. The main idea in this work is to incorporate these clustered features into the 3D variational segmentation framework. In contrast to previous variational approaches, we propose a segmentation method that evolves the contour in a supervised fashion. The segmentation boundary is driven by the learned region statistics in the cluster space. We incorporate prior knowledge about the normal brain tissue appearance during the estimation of these region statistics. In particular, we use a Dirichlet prior that discourages the clusters from the normal brain region to be in the tumor region. This leads to a better disambiguation of the tumor from brain tissue. We evaluated the performance of our automatic segmentation method on 15 real MRI scans of brain tumor patients, with tumors that are inhomogeneous in appearance, small in size and in proximity to the major structures in the brain. Validation with the expert segmentation labels yielded encouraging results: Jaccard (58%), Precision (81%), Recall (67%), Hausdorff distance (24 mm). Using priors on the brain/tumor appearance, our proposed automatic 3D variational segmentation method was able to better disambiguate the tumor from the surrounding tissue.

Probabilistic eruption forecasting at short and long time scales

NASA Astrophysics Data System (ADS)

Marzocchi, Warner; Bebbington, Mark S.

2012-10-01

Any effective volcanic risk mitigation strategy requires a scientific assessment of the future evolution of a volcanic system and its eruptive behavior. Some consider the onus should be on volcanologists to provide simple but emphatic deterministic forecasts. This traditional way of thinking, however, does not deal with the implications of inherent uncertainties, both aleatoric and epistemic, that are inevitably present in observations, monitoring data, and interpretation of any natural system. In contrast to deterministic predictions, probabilistic eruption forecasting attempts to quantify these inherent uncertainties utilizing all available information to the extent that it can be relied upon and is informative. As with many other natural hazards, probabilistic eruption forecasting is becoming established as the primary scientific basis for planning rational risk mitigation actions: at short-term (hours to weeks or months), it allows decision-makers to prioritize actions in a crisis; and at long-term (years to decades), it is the basic component for land use and emergency planning. Probabilistic eruption forecasting consists of estimating the probability of an eruption event and where it sits in a complex multidimensional time-space-magnitude framework. In this review, we discuss the key developments and features of models that have been used to address the problem.

The landscape of fear conceptual framework: definition and review of current applications and misuses.

PubMed

Bleicher, Sonny S

2017-01-01

Landscapes of Fear (LOF), the spatially explicit distribution of perceived predation risk as seen by a population, is increasingly cited in ecological literature and has become a frequently used "buzz-word". With the increase in popularity, it became necessary to clarify the definition for the term, suggest boundaries and propose a common framework for its use. The LOF, as a progeny of the "ecology of fear" conceptual framework, defines fear as the strategic manifestation of the cost-benefit analysis of food and safety tradeoffs. In addition to direct predation risk, the LOF is affected by individuals' energetic-state, inter- and intra-specific competition and is constrained by the evolutionary history of each species. Herein, based on current applications of the LOF conceptual framework, I suggest the future research in this framework will be directed towards: (1) finding applied management uses as a trait defining a population's habitat-use and habitat-suitability; (2) studying multi-dimensional distribution of risk-assessment through time and space; (3) studying variability between individuals within a population; (4) measuring eco-neurological implications of risk as a feature of environmental heterogeneity and (5) expanding temporal and spatial scales of empirical studies.

Chiroptical methods in a wide wavelength range for obtaining Ln3+ complexes with circularly polarized luminescence of practical interest.

PubMed

Górecki, Marcin; Carpita, Luca; Arrico, Lorenzo; Zinna, Francesco; Di Bari, Lorenzo

2018-05-29

We studied enantiopure chiral trivalent lanthanide (Ln3+ = La3+, Sm3+, Eu3+, Gd3+, Tm3+, and Yb3+) complexes with two fluorinated achiral tris(β-diketonate) ligands (HFA = hexafluoroacetylacetonate and TTA = 2-thenoyltrifluoroacetonate), incorporating a chiral bis(oxazolinyl)pyridine (PyBox) unit as a neutral ancillary ligand, by the combined use of optical and chiroptical methods, ranging from UV to IR both in absorption and circular dichroism (CD), and including circularly polarized luminescence (CPL). Ultimately, all the spectroscopic information is integrated into a total and a chiroptical super-spectrum, which allows one to characterize a multidimensional chemical space, spanned by the different Ln3+ ions, the acidity and steric demand of the diketone and the chirality of the PyBox ligand. In all cases, the Ln3+ ions endow the systems with peculiar chiroptical properties, either allied to f-f transitions or induced by the metal onto the ligand. In more detail, we found that Sm3+ complexes display interesting CPL features, which partly superimpose and partly integrate the more common Eu3+ properties. Especially, in the context of security tags, the pair Sm/Eu may be a winning choice for chiroptical barcoding.

Damage Detection in Composite Structures with Wavenumber Array Data Processing

NASA Technical Reports Server (NTRS)

Tian, Zhenhua; Leckey, Cara; Yu, Lingyu

2013-01-01

Guided ultrasonic waves (GUW) have the potential to be an efficient and cost-effective method for rapid damage detection and quantification of large structures. Attractive features include sensitivity to a variety of damage types and the capability of traveling relatively long distances. They have proven to be an efficient approach for crack detection and localization in isotropic materials. However, techniques must be pushed beyond isotropic materials in order to be valid for composite aircraft components. This paper presents our study on GUW propagation and interaction with delamination damage in composite structures using wavenumber array data processing, together with advanced wave propagation simulations. Parallel elastodynamic finite integration technique (EFIT) is used for the example simulations. Multi-dimensional Fourier transform is used to convert time-space wavefield data into frequency-wavenumber domain. Wave propagation in the wavenumber-frequency domain shows clear distinction among the guided wave modes that are present. This allows for extracting a guided wave mode through filtering and reconstruction techniques. Presence of delamination causes spectral change accordingly. Results from 3D CFRP guided wave simulations with delamination damage in flat-plate specimens are used for wave interaction with structural defect study.

Recapitulation of Ayurveda constitution types by machine learning of phenotypic traits.

PubMed

Tiwari, Pradeep; Kutum, Rintu; Sethi, Tavpritesh; Shrivastava, Ankita; Girase, Bhushan; Aggarwal, Shilpi; Patil, Rutuja; Agarwal, Dhiraj; Gautam, Pramod; Agrawal, Anurag; Dash, Debasis; Ghosh, Saurabh; Juvekar, Sanjay; Mukerji, Mitali; Prasher, Bhavana

2017-01-01

In Ayurveda system of medicine individuals are classified into seven constitution types, "Prakriti", for assessing disease susceptibility and drug responsiveness. Prakriti evaluation involves clinical examination including questions about physiological and behavioural traits. A need was felt to develop models for accurately predicting Prakriti classes that have been shown to exhibit molecular differences. The present study was carried out on data of phenotypic attributes in 147 healthy individuals of three extreme Prakriti types, from a genetically homogeneous population of Western India. Unsupervised and supervised machine learning approaches were used to infer inherent structure of the data, and for feature selection and building classification models for Prakriti respectively. These models were validated in a North Indian population. Unsupervised clustering led to emergence of three natural clusters corresponding to three extreme Prakriti classes. The supervised modelling approaches could classify individuals, with distinct Prakriti types, in the training and validation sets. This study is the first to demonstrate that Prakriti types are distinct verifiable clusters within a multidimensional space of multiple interrelated phenotypic traits. It also provides a computational framework for predicting Prakriti classes from phenotypic attributes. This approach may be useful in precision medicine for stratification of endophenotypes in healthy and diseased populations.

PREMER: a Tool to Infer Biological Networks.

PubMed

Villaverde, Alejandro F; Becker, Kolja; Banga, Julio R

2017-10-04

Inferring the structure of unknown cellular networks is a main challenge in computational biology. Data-driven approaches based on information theory can determine the existence of interactions among network nodes automatically. However, the elucidation of certain features - such as distinguishing between direct and indirect interactions or determining the direction of a causal link - requires estimating information-theoretic quantities in a multidimensional space. This can be a computationally demanding task, which acts as a bottleneck for the application of elaborate algorithms to large-scale network inference problems. The computational cost of such calculations can be alleviated by the use of compiled programs and parallelization. To this end we have developed PREMER (Parallel Reverse Engineering with Mutual information & Entropy Reduction), a software toolbox that can run in parallel and sequential environments. It uses information theoretic criteria to recover network topology and determine the strength and causality of interactions, and allows incorporating prior knowledge, imputing missing data, and correcting outliers. PREMER is a free, open source software tool that does not require any commercial software. Its core algorithms are programmed in FORTRAN 90 and implement OpenMP directives. It has user interfaces in Python and MATLAB/Octave, and runs on Windows, Linux and OSX (https://sites.google.com/site/premertoolbox/).

The landscape of fear conceptual framework: definition and review of current applications and misuses

PubMed Central

2017-01-01

Landscapes of Fear (LOF), the spatially explicit distribution of perceived predation risk as seen by a population, is increasingly cited in ecological literature and has become a frequently used “buzz-word”. With the increase in popularity, it became necessary to clarify the definition for the term, suggest boundaries and propose a common framework for its use. The LOF, as a progeny of the “ecology of fear” conceptual framework, defines fear as the strategic manifestation of the cost-benefit analysis of food and safety tradeoffs. In addition to direct predation risk, the LOF is affected by individuals’ energetic-state, inter- and intra-specific competition and is constrained by the evolutionary history of each species. Herein, based on current applications of the LOF conceptual framework, I suggest the future research in this framework will be directed towards: (1) finding applied management uses as a trait defining a population’s habitat-use and habitat-suitability; (2) studying multi-dimensional distribution of risk-assessment through time and space; (3) studying variability between individuals within a population; (4) measuring eco-neurological implications of risk as a feature of environmental heterogeneity and (5) expanding temporal and spatial scales of empirical studies. PMID:28929015

III Potsdam-V Kiev International Workshop on Nonlinear Processes in Physics. Held in Potsdam, New York on August 1-11, 1991

DTIC Science & Technology

1991-08-01

Bona, Burke, Grundbaum, Hasagawa, Horton, Krichever, Kruskal, Kuznetsov , Lax, McLaughlin, Mikhailov., Rubenchik, Sabatier, Tabor, Zabusky) for their...British Telecom Lab., GB Fibers Oleg Bogoyavlenskij Breaking Solitons Steklov Mathematical Institute USSR Marco Boiti Real and Virtual Multidimensional...Beyond Rutgers University, USA Boris Kupershmidt Relativistic Analogs of Lax Equations Tennessee Space Institute, USA E.A. Kuznetsov Weak MHD Turbulence

Compressed Semi-Discrete Central-Upwind Schemes for Hamilton-Jacobi Equations

NASA Technical Reports Server (NTRS)

Bryson, Steve; Kurganov, Alexander; Levy, Doron; Petrova, Guergana

2003-01-01

We introduce a new family of Godunov-type semi-discrete central schemes for multidimensional Hamilton-Jacobi equations. These schemes are a less dissipative generalization of the central-upwind schemes that have been recently proposed in series of works. We provide the details of the new family of methods in one, two, and three space dimensions, and then verify their expected low-dissipative property in a variety of examples.

Multivariate analysis of light scattering spectra of liquid dairy products

NASA Astrophysics Data System (ADS)

Khodasevich, M. A.

2010-05-01

Visible light scattering spectra from the surface layer of samples of commercial liquid dairy products are recorded with a colorimeter. The principal component method is used to analyze these spectra. Vectors representing the samples of dairy products in a multidimensional space of spectral counts are projected onto a three-dimensional subspace of principal components. The magnitudes of these projections are found to depend on the type of dairy product.

A software package for the data-independent management of multidimensional data

NASA Technical Reports Server (NTRS)

Treinish, Lloyd A.; Gough, Michel L.

1987-01-01

The Common Data Format (CDF), a structure which provides true data independence for applications software and has been developed at the National Space Science Data Center, is discussed. The background to the CDF is reviewed, and the CDF is described. The conceptual organization of the CDF is discussed, and a sample CDF structure is shown and described. The implementation of CDF, its status, and its applications are examined.

CALIBRATION OF SEMI-ANALYTIC MODELS OF GALAXY FORMATION USING PARTICLE SWARM OPTIMIZATION

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

Ruiz, Andrés N.; Domínguez, Mariano J.; Yaryura, Yamila

2015-03-10

We present a fast and accurate method to select an optimal set of parameters in semi-analytic models of galaxy formation and evolution (SAMs). Our approach compares the results of a model against a set of observables applying a stochastic technique called Particle Swarm Optimization (PSO), a self-learning algorithm for localizing regions of maximum likelihood in multidimensional spaces that outperforms traditional sampling methods in terms of computational cost. We apply the PSO technique to the SAG semi-analytic model combined with merger trees extracted from a standard Lambda Cold Dark Matter N-body simulation. The calibration is performed using a combination of observedmore » galaxy properties as constraints, including the local stellar mass function and the black hole to bulge mass relation. We test the ability of the PSO algorithm to find the best set of free parameters of the model by comparing the results with those obtained using a MCMC exploration. Both methods find the same maximum likelihood region, however, the PSO method requires one order of magnitude fewer evaluations. This new approach allows a fast estimation of the best-fitting parameter set in multidimensional spaces, providing a practical tool to test the consequences of including other astrophysical processes in SAMs.« less

Convergence in the Bilingual Lexicon: A Pre-registered Replication of Previous Studies.

PubMed

White, Anne; Malt, Barbara C; Storms, Gert

2016-01-01

Naming patterns of bilinguals have been found to converge and form a new intermediate language system from elements of both the bilinguals' languages. This converged naming pattern differs from the monolingual naming patterns of both a bilingual's languages. We conducted a pre-registered replication study of experiments addressing the question whether there is a convergence between a bilingual's both lexicons. The replication used an enlarged set of stimuli of common household containers, providing generalizability, and more reliable representations of the semantic domain. Both an analysis at the group-level and at the individual level of the correlations between naming patterns reject the two-pattern hypothesis that poses that bilinguals use two monolingual-like naming patterns, one for each of their two languages. However, the results of the original study and the replication comply with the one-pattern hypothesis, which poses that bilinguals converge the naming patterns of their two languages and form a compromise. Since this convergence is only partial the naming pattern in bilinguals corresponds to a moderate version of the one-pattern hypothesis. These findings are further confirmed by a representation of the semantic domain in a multidimensional space and the finding of shorter distances between bilingual category centers than monolingual category centers in this multidimensional space both in the original and in the replication study.

Investigating Pharmacological Similarity by Charting Chemical Space.

PubMed

Buonfiglio, Rosa; Engkvist, Ola; Várkonyi, Péter; Henz, Astrid; Vikeved, Elisabet; Backlund, Anders; Kogej, Thierry

2015-11-23

In this study, biologically relevant areas of the chemical space were analyzed using ChemGPS-NP. This application enables comparing groups of ligands within a multidimensional space based on principle components derived from physicochemical descriptors. Also, 3D visualization of the ChemGPS-NP global map can be used to conveniently evaluate bioactive compound similarity and visually distinguish between different types or groups of compounds. To further establish ChemGPS-NP as a method to accurately represent the chemical space, a comparison with structure-based fingerprint has been performed. Interesting complementarities between the two descriptions of molecules were observed. It has been shown that the accuracy of describing molecules with physicochemical descriptors like in ChemGPS-NP is similar to the accuracy of structural fingerprints in retrieving bioactive molecules. Lastly, pharmacological similarity of structurally diverse compounds has been investigated in ChemGPS-NP space. These results further strengthen the case of using ChemGPS-NP as a tool to explore and visualize chemical space.

Face-infringement space: the frame of reference of the ventral intraparietal area.

PubMed

McCollum, Gin; Klam, François; Graf, Werner

2012-07-01

Experimental studies have shown that responses of ventral intraparietal area (VIP) neurons specialize in head movements and the environment near the head. VIP neurons respond to visual, auditory, and tactile stimuli, smooth pursuit eye movements, and passive and active movements of the head. This study demonstrates mathematical structure on a higher organizational level created within VIP by the integration of a complete set of variables covering face-infringement. Rather than positing dynamics in an a priori defined coordinate system such as those of physical space, we assemble neuronal receptive fields to find out what space of variables VIP neurons together cover. Section 1 presents a view of neurons as multidimensional mathematical objects. Each VIP neuron occupies or is responsive to a region in a sensorimotor phase space, thus unifying variables relevant to the disparate sensory modalities and movements. Convergence on one neuron joins variables functionally, as space and time are joined in relativistic physics to form a unified spacetime. The space of position and motion together forms a neuronal phase space, bridging neurophysiology and the physics of face-infringement. After a brief review of the experimental literature, the neuronal phase space natural to VIP is sequentially characterized, based on experimental data. Responses of neurons indicate variables that may serve as axes of neural reference frames, and neuronal responses have been so used in this study. The space of sensory and movement variables covered by VIP receptive fields joins visual and auditory space to body-bound sensory modalities: somatosensation and the inertial senses. This joining of allocentric and egocentric modalities is in keeping with the known relationship of the parietal lobe to the sense of self in space and to hemineglect, in both humans and monkeys. Following this inductive step, variables are formalized in terms of the mathematics of graph theory to deduce which combinations are complete as a multidimensional neural structure that provides the organism with a complete set of options regarding objects impacting the face, such as acceptance, pursuit, and avoidance. We consider four basic variable types: position and motion of the face and of an external object. Formalizing the four types of variables allows us to generalize to any sensory system and to determine the necessary and sufficient conditions for a neural center (for example, a cortical region) to provide a face-infringement space. We demonstrate that VIP includes at least one such face-infringement space.

A spatially informative optic flow model of bee colony with saccadic flight strategy for global optimization.

PubMed

Das, Swagatam; Biswas, Subhodip; Panigrahi, Bijaya K; Kundu, Souvik; Basu, Debabrota

2014-10-01

This paper presents a novel search metaheuristic inspired from the physical interpretation of the optic flow of information in honeybees about the spatial surroundings that help them orient themselves and navigate through search space while foraging. The interpreted behavior combined with the minimal foraging is simulated by the artificial bee colony algorithm to develop a robust search technique that exhibits elevated performance in multidimensional objective space. Through detailed experimental study and rigorous analysis, we highlight the statistical superiority enjoyed by our algorithm over a wide variety of functions as compared to some highly competitive state-of-the-art methods.

Quality by Design: Multidimensional exploration of the design space in high performance liquid chromatography method development for better robustness before validation.

PubMed

Monks, K; Molnár, I; Rieger, H-J; Bogáti, B; Szabó, E

2012-04-06

Robust HPLC separations lead to fewer analysis failures and better method transfer as well as providing an assurance of quality. This work presents the systematic development of an optimal, robust, fast UHPLC method for the simultaneous assay of two APIs of an eye drop sample and their impurities, in accordance with Quality by Design principles. Chromatography software is employed to effectively generate design spaces (Method Operable Design Regions), which are subsequently employed to determine the final method conditions and to evaluate robustness prior to validation. Copyright © 2011 Elsevier B.V. All rights reserved.

GazeAppraise v. 0.1

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

Wilson, Andrew; Haass, Michael; Rintoul, Mark Daniel

GazeAppraise advances the state of the art of gaze pattern analysis using methods that simultaneously analyze spatial and temporal characteristics of gaze patterns. GazeAppraise enables novel research in visual perception and cognition; for example, using shape features as distinguishing elements to assess individual differences in visual search strategy. Given a set of point-to-point gaze sequences, hereafter referred to as scanpaths, the method constructs multiple descriptive features for each scanpath. Once the scanpath features have been calculated, they are used to form a multidimensional vector representing each scanpath and cluster analysis is performed on the set of vectors from all scanpaths.more » An additional benefit of this method is the identification of causal or correlated characteristics of the stimuli, subjects, and visual task through statistical analysis of descriptive metadata distributions within and across clusters.« less

Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: an application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39).

PubMed

Borchani, Hanen; Bielza, Concha; Martı Nez-Martı N, Pablo; Larrañaga, Pedro

2012-12-01

Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models recently proposed to deal with multi-dimensional classification problems, where each instance in the data set has to be assigned to more than one class variable. In this paper, we propose a Markov blanket-based approach for learning MBCs from data. Basically, it consists of determining the Markov blanket around each class variable using the HITON algorithm, then specifying the directionality over the MBC subgraphs. Our approach is applied to the prediction problem of the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39) in order to estimate the health-related quality of life of Parkinson's patients. Fivefold cross-validation experiments were carried out on randomly generated synthetic data sets, Yeast data set, as well as on a real-world Parkinson's disease data set containing 488 patients. The experimental study, including comparison with additional Bayesian network-based approaches, back propagation for multi-label learning, multi-label k-nearest neighbor, multinomial logistic regression, ordinary least squares, and censored least absolute deviations, shows encouraging results in terms of predictive accuracy as well as the identification of dependence relationships among class and feature variables. Copyright © 2012 Elsevier Inc. All rights reserved.

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.

Quantification and Visualization of Variation in Anatomical Trees

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

Amenta, Nina; Datar, Manasi; Dirksen, Asger

This paper presents two approaches to quantifying and visualizing variation in datasets of trees. The first approach localizes subtrees in which significant population differences are found through hypothesis testing and sparse classifiers on subtree features. The second approach visualizes the global metric structure of datasets through low-distortion embedding into hyperbolic planes in the style of multidimensional scaling. A case study is made on a dataset of airway trees in relation to Chronic Obstructive Pulmonary Disease.

Built spaces and features associated with user satisfaction in maternity waiting homes in Malawi.

PubMed

McIntosh, Nathalie; Gruits, Patricia; Oppel, Eva; Shao, Amie

2018-07-01

To assess satisfaction with maternity waiting home built spaces and features in women who are at risk for underutilizing maternity waiting homes (i.e. residential facilities that temporarily house near-term pregnant mothers close to healthcare facilities that provide obstetrical care). Specifically we wanted to answer the questions: (1) Are built spaces and features associated with maternity waiting home user satisfaction? (2) Can built spaces and features designed to improve hygiene, comfort, privacy and function improve maternity waiting home user satisfaction? And (3) Which built spaces and features are most important for maternity waiting home user satisfaction? A cross-sectional study comparing satisfaction with standard and non-standard maternity waiting home designs. Between December 2016 and February 2017 we surveyed expectant mothers at two maternity waiting homes that differed in their design of built spaces and features. We used bivariate analyses to assess if built spaces and features were associated with satisfaction. We compared ratings of built spaces and features between the two maternity waiting homes using chi-squares and t-tests to assess if design features to improve hygiene, comfort, privacy and function were associated with higher satisfaction. We used exploratory robust regression analysis to examine the relationship between built spaces and features and maternity waiting home satisfaction. Two maternity waiting homes in Malawi, one that incorporated non-standardized design features to improve hygiene, comfort, privacy, and function (Kasungu maternity waiting home) and the other that had a standard maternity waiting home design (Dowa maternity waiting home). 322 expectant mothers at risk for underutilizing maternity waiting homes (i.e. first-time mothers and those with no pregnancy risk factors) who had stayed at the Kasungu or Dowa maternity waiting homes. There were significant differences in ratings of built spaces and features between the two differently designed maternity waiting homes, with the non-standard design having higher ratings for: adequacy of toilets, and ratings of heating/cooling, air and water quality, sanitation, toilets/showers and kitchen facilities, building maintenance, sleep area, private storage space, comfort level, outdoor spaces and overall satisfaction (p = <.0001 for all). The final regression model showed that built spaces and features that are most important for maternity waiting home user satisfaction are toilets/showers, guardian spaces, safety, building maintenance, sleep area and private storage space (R 2  = 0.28). The design of maternity waiting home built spaces and features is associated with user satisfaction in women at risk for underutilizing maternity waiting homes, especially related to toilets/showers, guardian spaces, safety, building maintenance, sleep area and private storage space. Improving maternity waiting home built spaces and features may offer a promising area for improving maternity waiting home satisfaction and reducing barriers to maternity waiting home use. Copyright © 2018 Elsevier Ltd. All rights reserved.

Brain Time and Physical Time

NASA Astrophysics Data System (ADS)

Fidelman, Uri

The hemispheric paradigm verifies Kant's suggestion that time and space are our subjective modes of perceiving experience. Time and space are two modes of organizing the sensory input by the l- and right-hemispheric neural mechanisms, respectively. The neural structures of the l- and right-hemispheric mechanisms force our consciousness to perceive time as one-dimensional and propagating from the past towards the future, and space as a simultaneously perceived multidimensional structure. The introduction of temporal propagation from the future towards the past by Feynman and other physicists caused the transfer of the concept time from the l hemisphere (which cannot perceive this change of the temporal direction) to the right one. This transfer requires and allows for the introduction of additional temporal axes in order to solve paradoxes in physics.

Spatially organized «vertical city» as a synthesis of tall buildings and airships

NASA Astrophysics Data System (ADS)

Gagulina, Olga; Matovnikov, Sergei

2018-03-01

The paper explores the compact city concept based on the «spatial» urban development principles and describes the prerequisites and possible methods to move from «horizontal» planning to «vertical» urban environments. It highlights the close connection between urban space, high-rise city landscape and conveyance options and sets out the ideas for upgrading the existing architectural and urban planning principles. It also conceptualizes the use of airships to create additional spatial connections between urban structure elements and high-rise buildings. Functional changes are considered in creating both urban environment and internal space of tall buildings, and the environmental aspects of the new spatial model are brought to light. The paper delineates the prospects for making a truly «spatial» multidimensional city space.

Machine Detection of Enhanced Electromechanical Energy Conversion in PbZr 0.2Ti 0.8O 3 Thin Films

DOE PAGES

Agar, Joshua C.; Cao, Ye; Naul, Brett; ...

2018-05-28

Many energy conversion, sensing, and microelectronic applications based on ferroic materials are determined by the domain structure evolution under applied stimuli. New hyperspectral, multidimensional spectroscopic techniques now probe dynamic responses at relevant length and time scales to provide an understanding of how these nanoscale domain structures impact macroscopic properties. Such approaches, however, remain limited in use because of the difficulties that exist in extracting and visualizing scientific insights from these complex datasets. Using multidimensional band-excitation scanning probe spectroscopy and adapting tools from both computer vision and machine learning, an automated workflow is developed to featurize, detect, and classify signatures ofmore » ferroelectric/ferroelastic switching processes in complex ferroelectric domain structures. This approach enables the identification and nanoscale visualization of varied modes of response and a pathway to statistically meaningful quantification of the differences between those modes. Lastly, among other things, the importance of domain geometry is spatially visualized for enhancing nanoscale electromechanical energy conversion.« less

Entropy-conservative spatial discretization of the multidimensional quasi-gasdynamic system of equations

NASA Astrophysics Data System (ADS)

Zlotnik, A. A.

2017-04-01

The multidimensional quasi-gasdynamic system written in the form of mass, momentum, and total energy balance equations for a perfect polytropic gas with allowance for a body force and a heat source is considered. A new conservative symmetric spatial discretization of these equations on a nonuniform rectangular grid is constructed (with the basic unknown functions—density, velocity, and temperature—defined on a common grid and with fluxes and viscous stresses defined on staggered grids). Primary attention is given to the analysis of entropy behavior: the discretization is specially constructed so that the total entropy does not decrease. This is achieved via a substantial revision of the standard discretization and applying numerous original features. A simplification of the constructed discretization serves as a conservative discretization with nondecreasing total entropy for the simpler quasi-hydrodynamic system of equations. In the absence of regularizing terms, the results also hold for the Navier-Stokes equations of a viscous compressible heat-conducting gas.

Binary Bees Algorithm - bioinspiration from the foraging mechanism of honeybees to optimize a multiobjective multidimensional assignment problem

NASA Astrophysics Data System (ADS)

Xu, Shuo; Ji, Ze; Truong Pham, Duc; Yu, Fan

2011-11-01

The simultaneous mission assignment and home allocation for hospital service robots studied is a Multidimensional Assignment Problem (MAP) with multiobjectives and multiconstraints. A population-based metaheuristic, the Binary Bees Algorithm (BBA), is proposed to optimize this NP-hard problem. Inspired by the foraging mechanism of honeybees, the BBA's most important feature is an explicit functional partitioning between global search and local search for exploration and exploitation, respectively. Its key parts consist of adaptive global search, three-step elitism selection (constraint handling, non-dominated solutions selection, and diversity preservation), and elites-centred local search within a Hamming neighbourhood. Two comparative experiments were conducted to investigate its single objective optimization, optimization effectiveness (indexed by the S-metric and C-metric) and optimization efficiency (indexed by computational burden and CPU time) in detail. The BBA outperformed its competitors in almost all the quantitative indices. Hence, the above overall scheme, and particularly the searching history-adapted global search strategy was validated.

Bridging the gap between supernovae and their remnants through multi-dimensional hydrodynamic modeling

NASA Astrophysics Data System (ADS)

Orlando, S.; Miceli, M.; Petruk, O.

2017-02-01

Supernova remnants (SNRs) are diffuse extended sources characterized by a complex morphology and a non-uniform distribution of ejecta. Such a morphology reflects pristine structures and features of the progenitor supernova (SN) and the early interaction of the SN blast wave with the inhomogeneous circumstellar medium (CSM). Deciphering the observations of SNRs might open the possibility to investigate the physical properties of both the interacting ejecta and the shocked CSM. This requires accurate numerical models which describe the evolution from the SN explosion to the remnant development and which connect the emission properties of the remnants to the progenitor SNe. Here we show how multi-dimensional SN-SNR hydrodynamic models have been very effective in deciphering observations of SNR Cassiopeia A and SN 1987A, thus unveiling the structure of ejecta in the immediate aftermath of the SN explosion and constraining the 3D pre-supernova structure and geometry of the environment surrounding the progenitor SN.

Entropy of Leukemia on Multidimensional Morphological and Molecular Landscapes

NASA Astrophysics Data System (ADS)

Vilar, Jose M. G.

2014-04-01

Leukemia epitomizes the class of highly complex diseases that new technologies aim to tackle by using large sets of single-cell-level information. Achieving such a goal depends critically not only on experimental techniques but also on approaches to interpret the data. A most pressing issue is to identify the salient quantitative features of the disease from the resulting massive amounts of information. Here, I show that the entropies of cell-population distributions on specific multidimensional molecular and morphological landscapes provide a set of measures for the precise characterization of normal and pathological states, such as those corresponding to healthy individuals and acute myeloid leukemia (AML) patients. I provide a systematic procedure to identify the specific landscapes and illustrate how, applied to cell samples from peripheral blood and bone marrow aspirates, this characterization accurately diagnoses AML from just flow cytometry data. The methodology can generally be applied to other types of cell populations and establishes a straightforward link between the traditional statistical thermodynamics methodology and biomedical applications.

Measuring Mindreading: A Review of Behavioral Approaches to Testing Cognitive and Affective Mental State Attribution in Neurologically Typical Adults.

PubMed

Turner, Rose; Felisberti, Fatima M

2017-01-01

Mindreading refers to the ability to attribute mental states, including thoughts, intentions and emotions, to oneself and others, and is essential for navigating the social world. Empirical mindreading research has predominantly featured children, groups with autism spectrum disorder and clinical samples, and many standard tasks suffer ceiling effects with neurologically typical (NT) adults. We first outline a case for studying mindreading in NT adults and proceed to review tests of emotion perception, cognitive and affective mentalizing, and multidimensional tasks combining these facets. We focus on selected examples of core experimental paradigms including emotion recognition tests, social vignettes, narrative fiction (prose and film) and participative interaction (in real and virtual worlds), highlighting challenges for studies with NT adult cohorts. We conclude that naturalistic, multidimensional approaches may be productively applied alongside traditional tasks to facilitate a more nuanced picture of mindreading in adulthood, and to ensure construct validity whilst remaining sensitive to variation at the upper echelons of the ability.

A MUSIC-based method for SSVEP signal processing.

PubMed

Chen, Kun; Liu, Quan; Ai, Qingsong; Zhou, Zude; Xie, Sheng Quan; Meng, Wei

2016-03-01

The research on brain computer interfaces (BCIs) has become a hotspot in recent years because it offers benefit to disabled people to communicate with the outside world. Steady state visual evoked potential (SSVEP)-based BCIs are more widely used because of higher signal to noise ratio and greater information transfer rate compared with other BCI techniques. In this paper, a multiple signal classification based method was proposed for multi-dimensional SSVEP feature extraction. 2-second data epochs from four electrodes achieved excellent accuracy rates including idle state detection. In some asynchronous mode experiments, the recognition accuracy reached up to 100%. The experimental results showed that the proposed method attained good frequency resolution. In most situations, the recognition accuracy was higher than canonical correlation analysis, which is a typical method for multi-channel SSVEP signal processing. Also, a virtual keyboard was successfully controlled by different subjects in an unshielded environment, which proved the feasibility of the proposed method for multi-dimensional SSVEP signal processing in practical applications.

Measuring Mindreading: A Review of Behavioral Approaches to Testing Cognitive and Affective Mental State Attribution in Neurologically Typical Adults

PubMed Central

Turner, Rose; Felisberti, Fatima M.

2017-01-01

Mindreading refers to the ability to attribute mental states, including thoughts, intentions and emotions, to oneself and others, and is essential for navigating the social world. Empirical mindreading research has predominantly featured children, groups with autism spectrum disorder and clinical samples, and many standard tasks suffer ceiling effects with neurologically typical (NT) adults. We first outline a case for studying mindreading in NT adults and proceed to review tests of emotion perception, cognitive and affective mentalizing, and multidimensional tasks combining these facets. We focus on selected examples of core experimental paradigms including emotion recognition tests, social vignettes, narrative fiction (prose and film) and participative interaction (in real and virtual worlds), highlighting challenges for studies with NT adult cohorts. We conclude that naturalistic, multidimensional approaches may be productively applied alongside traditional tasks to facilitate a more nuanced picture of mindreading in adulthood, and to ensure construct validity whilst remaining sensitive to variation at the upper echelons of the ability. PMID:28174552

Machine Detection of Enhanced Electromechanical Energy Conversion in PbZr 0.2Ti 0.8O 3 Thin Films

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

Agar, Joshua C.; Cao, Ye; Naul, Brett

Many energy conversion, sensing, and microelectronic applications based on ferroic materials are determined by the domain structure evolution under applied stimuli. New hyperspectral, multidimensional spectroscopic techniques now probe dynamic responses at relevant length and time scales to provide an understanding of how these nanoscale domain structures impact macroscopic properties. Such approaches, however, remain limited in use because of the difficulties that exist in extracting and visualizing scientific insights from these complex datasets. Using multidimensional band-excitation scanning probe spectroscopy and adapting tools from both computer vision and machine learning, an automated workflow is developed to featurize, detect, and classify signatures ofmore » ferroelectric/ferroelastic switching processes in complex ferroelectric domain structures. This approach enables the identification and nanoscale visualization of varied modes of response and a pathway to statistically meaningful quantification of the differences between those modes. Lastly, among other things, the importance of domain geometry is spatially visualized for enhancing nanoscale electromechanical energy conversion.« less

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

Fang, X.; Xia, C.; Keppens, R.

We present the first multidimensional, magnetohydrodynamic simulations that capture the initial formation and long-term sustainment of the enigmatic coronal rain phenomenon. We demonstrate how thermal instability can induce a spectacular display of in situ forming blob-like condensations which then start their intimate ballet on top of initially linear force-free arcades. Our magnetic arcades host a chromospheric, transition region, and coronal plasma. Following coronal rain dynamics for over 80 minutes of physical time, we collect enough statistics to quantify blob widths, lengths, velocity distributions, and other characteristics which directly match modern observational knowledge. Our virtual coronal rain displays the deformation ofmore » blobs into V-shaped features, interactions of blobs due to mostly pressure-mediated levitations, and gives the first views of blobs that evaporate in situ or are siphoned over the apex of the background arcade. Our simulations pave the way for systematic surveys of coronal rain showers in true multidimensional settings to connect parameterized heating prescriptions with rain statistics, ultimately allowing us to quantify the coronal heating input.« less

Visualization of the operational space of edge-localized modes through low-dimensional embedding of probability distributions

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

Shabbir, A., E-mail: aqsa.shabbir@ugent.be; Noterdaeme, J. M.; Max-Planck-Institut für Plasmaphysik, Garching D-85748

2014-11-15

Information visualization aimed at facilitating human perception is an important tool for the interpretation of experiments on the basis of complex multidimensional data characterizing the operational space of fusion devices. This work describes a method for visualizing the operational space on a two-dimensional map and applies it to the discrimination of type I and type III edge-localized modes (ELMs) from a series of carbon-wall ELMy discharges at JET. The approach accounts for stochastic uncertainties that play an important role in fusion data sets, by modeling measurements with probability distributions in a metric space. The method is aimed at contributing tomore » physical understanding of ELMs as well as their control. Furthermore, it is a general method that can be applied to the modeling of various other plasma phenomena as well.« less

Gaze-informed, task-situated representation of space in primate hippocampus during virtual navigation

PubMed Central

Wirth, Sylvia; Baraduc, Pierre; Planté, Aurélie; Pinède, Serge; Duhamel, Jean-René

2017-01-01

To elucidate how gaze informs the construction of mental space during wayfinding in visual species like primates, we jointly examined navigation behavior, visual exploration, and hippocampal activity as macaque monkeys searched a virtual reality maze for a reward. Cells sensitive to place also responded to one or more variables like head direction, point of gaze, or task context. Many cells fired at the sight (and in anticipation) of a single landmark in a viewpoint- or task-dependent manner, simultaneously encoding the animal’s logical situation within a set of actions leading to the goal. Overall, hippocampal activity was best fit by a fine-grained state space comprising current position, view, and action contexts. Our findings indicate that counterparts of rodent place cells in primates embody multidimensional, task-situated knowledge pertaining to the target of gaze, therein supporting self-awareness in the construction of space. PMID:28241007

Categorical dimensions of human odor descriptor space revealed by non-negative matrix factorization

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

Chennubhotla, Chakra; Castro, Jason

2013-01-01

In contrast to most other sensory modalities, the basic perceptual dimensions of olfaction remain un- clear. Here, we use non-negative matrix factorization (NMF) - a dimensionality reduction technique - to uncover structure in a panel of odor profiles, with each odor defined as a point in multi-dimensional descriptor space. The properties of NMF are favorable for the analysis of such lexical and perceptual data, and lead to a high-dimensional account of odor space. We further provide evidence that odor di- mensions apply categorically. That is, odor space is not occupied homogenously, but rather in a discrete and intrinsically clustered manner.more » We discuss the potential implications of these results for the neural coding of odors, as well as for developing classifiers on larger datasets that may be useful for predicting perceptual qualities from chemical structures.« less

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

The use of minimal spanning trees in particle physics

DOE PAGES

Rainbolt, J. Lovelace; Schmitt, M.

2017-02-14

Minimal spanning trees (MSTs) have been used in cosmology and astronomy to distinguish distributions of points in a multi-dimensional space. They are essentially unknown in particle physics, however. We briefly define MSTs and illustrate their properties through a series of examples. We show how they might be applied to study a typical event sample from a collider experiment and conclude that MSTs may prove useful in distinguishing different classes of events.

The use of minimal spanning trees in particle physics

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

Rainbolt, J. Lovelace; Schmitt, M.

Minimal spanning trees (MSTs) have been used in cosmology and astronomy to distinguish distributions of points in a multi-dimensional space. They are essentially unknown in particle physics, however. We briefly define MSTs and illustrate their properties through a series of examples. We show how they might be applied to study a typical event sample from a collider experiment and conclude that MSTs may prove useful in distinguishing different classes of events.

The Hyperwall

NASA Technical Reports Server (NTRS)

Biegel, Bryan A. (Technical Monitor); Sandstrom, Timothy A.; Henze, Chris; Levit, Creon

2003-01-01

This paper presents the hyperwall, a visualization cluster that uses coordinated visualizations for interactive exploration of multidimensional data and simulations. The system strongly leverages the human eye-brain system with a generous 7x7 array offlat panel LCD screens powered by a beowulf clustel: With each screen backed by a workstation class PC, graphic and compute intensive applications can be applied to a broad range of data. Navigational tools are presented that allow for investigation of high dimensional spaces.

Experimental demonstration of a flexible time-domain quantum channel.

PubMed

Xing, Xingxing; Feizpour, Amir; Hayat, Alex; Steinberg, Aephraim M

2014-10-20

We present an experimental realization of a flexible quantum channel where the Hilbert space dimensionality can be controlled electronically. Using electro-optical modulators (EOM) and narrow-band optical filters, quantum information is encoded and decoded in the temporal degrees of freedom of photons from a long-coherence-time single-photon source. Our results demonstrate the feasibility of a generic scheme for encoding and transmitting multidimensional quantum information over the existing fiber-optical telecommunications infrastructure.

Planning Robot-Control Parameters With Qualitative Reasoning

NASA Technical Reports Server (NTRS)

Peters, Stephen F.

1993-01-01

Qualitative-reasoning planning algorithm helps to determine quantitative parameters controlling motion of robot. Algorithm regarded as performing search in multidimensional space of control parameters from starting point to goal region in which desired result of robotic manipulation achieved. Makes use of directed graph representing qualitative physical equations describing task, and interacts, at each sampling period, with history of quantitative control parameters and sensory data, to narrow search for reliable values of quantitative control parameters.

Towards Big Earth Data Analytics: The EarthServer Approach

NASA Astrophysics Data System (ADS)

Baumann, Peter

2013-04-01

Big Data in the Earth sciences, the Tera- to Exabyte archives, mostly are made up from coverage data whereby the term "coverage", according to ISO and OGC, is defined as the digital representation of some space-time varying phenomenon. Common examples include 1-D sensor timeseries, 2-D remote sensing imagery, 3D x/y/t image timeseries and x/y/z geology data, and 4-D x/y/z/t atmosphere and ocean data. Analytics on such data requires on-demand processing of sometimes significant complexity, such as getting the Fourier transform of satellite images. As network bandwidth limits prohibit transfer of such Big Data it is indispensable to devise protocols allowing clients to task flexible and fast processing on the server. The EarthServer initiative, funded by EU FP7 eInfrastructures, unites 11 partners from computer and earth sciences to establish Big Earth Data Analytics. One key ingredient is flexibility for users to ask what they want, not impeded and complicated by system internals. The EarthServer answer to this is to use high-level query languages; these have proven tremendously successful on tabular and XML data, and we extend them with a central geo data structure, multi-dimensional arrays. A second key ingredient is scalability. Without any doubt, scalability ultimately can only be achieved through parallelization. In the past, parallelizing code has been done at compile time and usually with manual intervention. The EarthServer approach is to perform a samentic-based dynamic distribution of queries fragments based on networks optimization and further criteria. The EarthServer platform is comprised by rasdaman, an Array DBMS enabling efficient storage and retrieval of any-size, any-type multi-dimensional raster data. In the project, rasdaman is being extended with several functionality and scalability features, including: support for irregular grids and general meshes; in-situ retrieval (evaluation of database queries on existing archive structures, avoiding data import and, hence, duplication); the aforementioned distributed query processing. Additionally, Web clients for multi-dimensional data visualization are being established. Client/server interfaces are strictly based on OGC and W3C standards, in particular the Web Coverage Processing Service (WCPS) which defines a high-level raster query language. We present the EarthServer project with its vision and approaches, relate it to the current state of standardization, and demonstrate it by way of large-scale data centers and their services using rasdaman.

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.

Meta-modelling, visualization and emulation of multi-dimensional data for virtual production intelligence

NASA Astrophysics Data System (ADS)

Schulz, Wolfgang; Hermanns, Torsten; Al Khawli, Toufik

2017-07-01

Decision making for competitive production in high-wage countries is a daily challenge where rational and irrational methods are used. The design of decision making processes is an intriguing, discipline spanning science. However, there are gaps in understanding the impact of the known mathematical and procedural methods on the usage of rational choice theory. Following Benjamin Franklin's rule for decision making formulated in London 1772, he called "Prudential Algebra" with the meaning of prudential reasons, one of the major ingredients of Meta-Modelling can be identified finally leading to one algebraic value labelling the results (criteria settings) of alternative decisions (parameter settings). This work describes the advances in Meta-Modelling techniques applied to multi-dimensional and multi-criterial optimization by identifying the persistence level of the corresponding Morse-Smale Complex. Implementations for laser cutting and laser drilling are presented, including the generation of fast and frugal Meta-Models with controlled error based on mathematical model reduction Reduced Models are derived to avoid any unnecessary complexity. Both, model reduction and analysis of multi-dimensional parameter space are used to enable interactive communication between Discovery Finders and Invention Makers. Emulators and visualizations of a metamodel are introduced as components of Virtual Production Intelligence making applicable the methods of Scientific Design Thinking and getting the developer as well as the operator more skilled.

Method and system for data clustering for very large databases

NASA Technical Reports Server (NTRS)

Livny, Miron (Inventor); Zhang, Tian (Inventor); Ramakrishnan, Raghu (Inventor)

1998-01-01

Multi-dimensional data contained in very large databases is efficiently and accurately clustered to determine patterns therein and extract useful information from such patterns. Conventional computer processors may be used which have limited memory capacity and conventional operating speed, allowing massive data sets to be processed in a reasonable time and with reasonable computer resources. The clustering process is organized using a clustering feature tree structure wherein each clustering feature comprises the number of data points in the cluster, the linear sum of the data points in the cluster, and the square sum of the data points in the cluster. A dense region of data points is treated collectively as a single cluster, and points in sparsely occupied regions can be treated as outliers and removed from the clustering feature tree. The clustering can be carried out continuously with new data points being received and processed, and with the clustering feature tree being restructured as necessary to accommodate the information from the newly received data points.

Morphological features of IFN-γ–stimulated mesenchymal stromal cells predict overall immunosuppressive capacity

PubMed Central

Klinker, Matthew W.; Marklein, Ross A.; Lo Surdo, Jessica L.; Wei, Cheng-Hong

2017-01-01

Human mesenchymal stromal cell (MSC) lines can vary significantly in their functional characteristics, and the effectiveness of MSC-based therapeutics may be realized by finding predictive features associated with MSC function. To identify features associated with immunosuppressive capacity in MSCs, we developed a robust in vitro assay that uses principal-component analysis to integrate multidimensional flow cytometry data into a single measurement of MSC-mediated inhibition of T-cell activation. We used this assay to correlate single-cell morphological data with overall immunosuppressive capacity in a cohort of MSC lines derived from different donors and manufacturing conditions. MSC morphology after IFN-γ stimulation significantly correlated with immunosuppressive capacity and accurately predicted the immunosuppressive capacity of MSC lines in a validation cohort. IFN-γ enhanced the immunosuppressive capacity of all MSC lines, and morphology predicted the magnitude of IFN-γ–enhanced immunosuppressive activity. Together, these data identify MSC morphology as a predictive feature of MSC immunosuppressive function. PMID:28283659

Hörmander multipliers on two-dimensional dyadic Hardy spaces

NASA Astrophysics Data System (ADS)

Daly, J.; Fridli, S.

2008-12-01

In this paper we are interested in conditions on the coefficients of a two-dimensional Walsh multiplier operator that imply the operator is bounded on certain of the Hardy type spaces Hp, 0

Defining process design space for a hydrophobic interaction chromatography (HIC) purification step: application of quality by design (QbD) principles.

PubMed

Jiang, Canping; Flansburg, Lisa; Ghose, Sanchayita; Jorjorian, Paul; Shukla, Abhinav A

2010-12-15

The concept of design space has been taking root under the quality by design paradigm as a foundation of in-process control strategies for biopharmaceutical manufacturing processes. This paper outlines the development of a design space for a hydrophobic interaction chromatography (HIC) process step. The design space included the impact of raw material lot-to-lot variability and variations in the feed stream from cell culture. A failure modes and effects analysis was employed as the basis for the process characterization exercise. During mapping of the process design space, the multi-dimensional combination of operational variables were studied to quantify the impact on process performance in terms of yield and product quality. Variability in resin hydrophobicity was found to have a significant influence on step yield and high-molecular weight aggregate clearance through the HIC step. A robust operating window was identified for this process step that enabled a higher step yield while ensuring acceptable product quality. © 2010 Wiley Periodicals, Inc.

Chemical Space: Big Data Challenge for Molecular Diversity.

PubMed

Awale, Mahendra; Visini, Ricardo; Probst, Daniel; Arús-Pous, Josep; Reymond, Jean-Louis

2017-10-25

Chemical space describes all possible molecules as well as multi-dimensional conceptual spaces representing the structural diversity of these molecules. Part of this chemical space is available in public databases ranging from thousands to billions of compounds. Exploiting these databases for drug discovery represents a typical big data problem limited by computational power, data storage and data access capacity. Here we review recent developments of our laboratory, including progress in the chemical universe databases (GDB) and the fragment subset FDB-17, tools for ligand-based virtual screening by nearest neighbor searches, such as our multi-fingerprint browser for the ZINC database to select purchasable screening compounds, and their application to discover potent and selective inhibitors for calcium channel TRPV6 and Aurora A kinase, the polypharmacology browser (PPB) for predicting off-target effects, and finally interactive 3D-chemical space visualization using our online tools WebDrugCS and WebMolCS. All resources described in this paper are available for public use at www.gdb.unibe.ch.

The ACTTION–APS–AAPM Pain Taxonomy (AAAPT) Multidimensional Approach to Classifying Acute Pain Conditions

PubMed Central

Kent, Michael L.; Tighe, Patrick J.; Belfer, Inna; Brennan, Timothy J.; Bruehl, Stephen; Brummett, Chad M.; Buckenmaier, Chester C.; Buvanendran, Asokumar; Cohen, Robert I.; Desjardins, Paul; Edwards, David; Fillingim, Roger; Gewandter, Jennifer; Gordon, Debra B.; Hurley, Robert W.; Kehlet, Henrik; Loeser, John D.; Mackey, Sean; McLean, Samuel A.; Polomano, Rosemary; Rahman, Siamak; Raja, Srinivasa; Rowbotham, Michael; Suresh, Santhanam; Schachtel, Bernard; Schreiber, Kristin; Schumacher, Mark; Stacey, Brett; Stanos, Steven; Todd, Knox; Turk, Dennis C.; Weisman, Steven J.; Wu, Christopher; Carr, Daniel B.; Dworkin, Robert H.; Terman, Gregory

2017-01-01

Objective. With the increasing societal awareness of the prevalence and impact of acute pain, there is a need to develop an acute pain classification system that both reflects contemporary mechanistic insights and helps guide future research and treatment. Existing classifications of acute pain conditions are limiting, with a predominant focus on the sensory experience (e.g., pain intensity) and pharmacologic consumption. Consequently, there is a need to more broadly characterize and classify the multidimensional experience of acute pain. Setting. Consensus report following expert panel involving the Analgesic, Anesthetic, and Addiction Clinical Trial Translations, Innovations, Opportunities, and Networks (ACTTION), American Pain Society (APS), and American Academy of Pain Medicine (AAPM). Methods. As a complement to a taxonomy recently developed for chronic pain, the ACTTION public-private partnership with the US Food and Drug Administration, the APS, and the AAPM convened a consensus meeting of experts to develop an acute pain taxonomy using prevailing evidence. Key issues pertaining to the distinct nature of acute pain are presented followed by the agreed-upon taxonomy. The ACTTION-APS-AAPM Acute Pain Taxonomy will include the following dimensions: 1) core criteria, 2) common features, 3) modulating factors, 4) impact/functional consequences, and 5) putative pathophysiologic pain mechanisms. Future efforts will consist of working groups utilizing this taxonomy to develop diagnostic criteria for a comprehensive set of acute pain conditions. Perspective. The ACTTION-APS-AAPM Acute Pain Taxonomy (AAAPT) is a multidimensional acute pain classification system designed to classify acute pain along the following dimensions: 1) core criteria, 2) common features, 3) modulating factors, 4) impact/functional consequences, and 5) putative pathophysiologic pain mechanisms. Conclusions. Significant numbers of patients still suffer from significant acute pain, despite the advent of modern multimodal analgesic strategies. Mismanaged acute pain has a broad societal impact as significant numbers of patients may progress to suffer from chronic pain. An acute pain taxonomy provides a much-needed standardization of clinical diagnostic criteria, which benefits clinical care, research, education, and public policy. For the purposes of the present taxonomy, acute pain is considered to last up to seven days, with prolongation to 30 days being common. The current understanding of acute pain mechanisms poorly differentiates between acute and chronic pain and is often insufficient to distinguish among many types of acute pain conditions. Given the usefulness of the AAPT multidimensional framework, the AAAPT undertook a similar approach to organizing various acute pain conditions. PMID:28482098

The ACTTION-APS-AAPM Pain Taxonomy (AAAPT) Multidimensional Approach to Classifying Acute Pain Conditions.

PubMed

Kent, Michael L; Tighe, Patrick J; Belfer, Inna; Brennan, Timothy J; Bruehl, Stephen; Brummett, Chad M; Buckenmaier, Chester C; Buvanendran, Asokumar; Cohen, Robert I; Desjardins, Paul; Edwards, David; Fillingim, Roger; Gewandter, Jennifer; Gordon, Debra B; Hurley, Robert W; Kehlet, Henrik; Loeser, John D; Mackey, Sean; McLean, Samuel A; Polomano, Rosemary; Rahman, Siamak; Raja, Srinivasa; Rowbotham, Michael; Suresh, Santhanam; Schachtel, Bernard; Schreiber, Kristin; Schumacher, Mark; Stacey, Brett; Stanos, Steven; Todd, Knox; Turk, Dennis C; Weisman, Steven J; Wu, Christopher; Carr, Daniel B; Dworkin, Robert H; Terman, Gregory

2017-05-01

With the increasing societal awareness of the prevalence and impact of acute pain, there is a need to develop an acute pain classification system that both reflects contemporary mechanistic insights and helps guide future research and treatment. Existing classifications of acute pain conditions are limiting, with a predominant focus on the sensory experience (eg, pain intensity) and pharmacologic consumption. Consequently, there is a need to more broadly characterize and classify the multidimensional experience of acute pain. Consensus report following expert panel involving the Analgesic, Anesthetic, and Addiction Clinical Trial Translations, Innovations, Opportunities, and Networks (ACTTION), American Pain Society (APS), and American Academy of Pain Medicine (AAPM). As a complement to a taxonomy recently developed for chronic pain, the ACTTION public-private partnership with the US Food and Drug Administration, the APS, and the AAPM convened a consensus meeting of experts to develop an acute pain taxonomy using prevailing evidence. Key issues pertaining to the distinct nature of acute pain are presented followed by the agreed-upon taxonomy. The ACTTION-APS-AAPM Acute Pain Taxonomy will include the following dimensions: 1) core criteria, 2) common features, 3) modulating factors, 4) impact/functional consequences, and 5) putative pathophysiologic pain mechanisms. Future efforts will consist of working groups utilizing this taxonomy to develop diagnostic criteria for a comprehensive set of acute pain conditions. The ACTTION-APS-AAPM Acute Pain Taxonomy (AAAPT) is a multidimensional acute pain classification system designed to classify acute pain along the following dimensions: 1) core criteria, 2) common features, 3) modulating factors, 4) impact/functional consequences, and 5) putative pathophysiologic pain mechanisms. Significant numbers of patients still suffer from significant acute pain, despite the advent of modern multimodal analgesic strategies. Mismanaged acute pain has a broad societal impact as significant numbers of patients may progress to suffer from chronic pain. An acute pain taxonomy provides a much-needed standardization of clinical diagnostic criteria, which benefits clinical care, research, education, and public policy. For the purposes of the present taxonomy, acute pain is considered to last up to seven days, with prolongation to 30 days being common. The current understanding of acute pain mechanisms poorly differentiates between acute and chronic pain and is often insufficient to distinguish among many types of acute pain conditions. Given the usefulness of the AAPT multidimensional framework, the AAAPT undertook a similar approach to organizing various acute pain conditions. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset | Office of Cancer Genomics

Cancer.gov

Identifying genetic alterations that prime a cancer cell to respond to a particular therapeutic agent can facilitate the development of precision cancer medicines. Cancer cell-line (CCL) profiling of small-molecule sensitivity has emerged as an unbiased method to assess the relationships between genetic or cellular features of CCLs and small-molecule response. Here, we developed annotated cluster multidimensional enrichment analysis to explore the associations between groups of small molecules and groups of CCLs in a new, quantitative sensitivity dataset.

Compressing random microstructures via stochastic Wang tilings.

PubMed

Novák, Jan; Kučerová, Anna; Zeman, Jan

2012-10-01

This Rapid Communication presents a stochastic Wang tiling-based technique to compress or reconstruct disordered microstructures on the basis of given spatial statistics. Unlike the existing approaches based on a single unit cell, it utilizes a finite set of tiles assembled by a stochastic tiling algorithm, thereby allowing to accurately reproduce long-range orientation orders in a computationally efficient manner. Although the basic features of the method are demonstrated for a two-dimensional particulate suspension, the present framework is fully extensible to generic multidimensional media.

A Data Base Management System for Clinical and Epidemiologic Studies In Systemic Lupus Erythematosus: Design and Maintenance

PubMed Central

Kosmides, Victoria S.; Hochberg, Marc C.

1984-01-01

This report describes the development, design specifications, features and implementation of a data base management system (DBMS) for clinical and epidemiologic studies in SLE. The DBMS is multidimensional with arrays formulated across patients, studies and variables. The major impact of this DBMS has been to increase the efficiency of managing and analyzing vast amounts of clinical and laboratory data and, as a result, to allow for continued growth in research productivity in areas related to SLE.

A Hartree-Fock Application Using UPC++ and the New DArray Library

DOE PAGES

Ozog, David; Kamil, Amir; Zheng, Yili; ...

2016-07-21

The Hartree-Fock (HF) method is the fundamental first step for incorporating quantum mechanics into many-electron simulations of atoms and molecules, and it is an important component of computational chemistry toolkits like NWChem. The GTFock code is an HF implementation that, while it does not have all the features in NWChem, represents crucial algorithmic advances that reduce communication and improve load balance by doing an up-front static partitioning of tasks, followed by work stealing whenever necessary. To enable innovations in algorithms and exploit next generation exascale systems, it is crucial to support quantum chemistry codes using expressive and convenient programming modelsmore » and runtime systems that are also efficient and scalable. Here, this paper presents an HF implementation similar to GTFock using UPC++, a partitioned global address space model that includes flexible communication, asynchronous remote computation, and a powerful multidimensional array library. UPC++ offers runtime features that are useful for HF such as active messages, a rich calculus for array operations, hardware-supported fetch-and-add, and functions for ensuring asynchronous runtime progress. We present a new distributed array abstraction, DArray, that is convenient for the kinds of random-access array updates and linear algebra operations on block-distributed arrays with irregular data ownership. Finally, we analyze the performance of atomic fetch-and-add operations (relevant for load balancing) and runtime attentiveness, then compare various techniques and optimizations for each. Our optimized implementation of HF using UPC++ and the DArrays library shows up to 20% improvement over GTFock with Global Arrays at scales up to 24,000 cores.« less

The application of artificial intelligence for the identification of the maceral groups and mineral components of coal

NASA Astrophysics Data System (ADS)

Mlynarczuk, Mariusz; Skiba, Marta

2017-06-01

The correct and consistent identification of the petrographic properties of coal is an important issue for researchers in the fields of mining and geology. As part of the study described in this paper, investigations concerning the application of artificial intelligence methods for the identification of the aforementioned characteristics were carried out. The methods in question were used to identify the maceral groups of coal, i.e. vitrinite, inertinite, and liptinite. Additionally, an attempt was made to identify some non-organic minerals. The analyses were performed using pattern recognition techniques (NN, kNN), as well as artificial neural network techniques (a multilayer perceptron - MLP). The classification process was carried out using microscopy images of polished sections of coals. A multidimensional feature space was defined, which made it possible to classify the discussed structures automatically, based on the methods of pattern recognition and algorithms of the artificial neural networks. Also, from the study we assessed the impact of the parameters for which the applied methods proved effective upon the final outcome of the classification procedure. The result of the analyses was a high percentage (over 97%) of correct classifications of maceral groups and mineral components. The paper discusses also an attempt to analyze particular macerals of the inertinite group. It was demonstrated that using artificial neural networks to this end makes it possible to classify the macerals properly in over 91% of cases. Thus, it was proved that artificial intelligence methods can be successfully applied for the identification of selected petrographic features of coal.

A Hartree-Fock Application Using UPC++ and the New DArray Library

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

Ozog, David; Kamil, Amir; Zheng, Yili

The Hartree-Fock (HF) method is the fundamental first step for incorporating quantum mechanics into many-electron simulations of atoms and molecules, and it is an important component of computational chemistry toolkits like NWChem. The GTFock code is an HF implementation that, while it does not have all the features in NWChem, represents crucial algorithmic advances that reduce communication and improve load balance by doing an up-front static partitioning of tasks, followed by work stealing whenever necessary. To enable innovations in algorithms and exploit next generation exascale systems, it is crucial to support quantum chemistry codes using expressive and convenient programming modelsmore » and runtime systems that are also efficient and scalable. Here, this paper presents an HF implementation similar to GTFock using UPC++, a partitioned global address space model that includes flexible communication, asynchronous remote computation, and a powerful multidimensional array library. UPC++ offers runtime features that are useful for HF such as active messages, a rich calculus for array operations, hardware-supported fetch-and-add, and functions for ensuring asynchronous runtime progress. We present a new distributed array abstraction, DArray, that is convenient for the kinds of random-access array updates and linear algebra operations on block-distributed arrays with irregular data ownership. Finally, we analyze the performance of atomic fetch-and-add operations (relevant for load balancing) and runtime attentiveness, then compare various techniques and optimizations for each. Our optimized implementation of HF using UPC++ and the DArrays library shows up to 20% improvement over GTFock with Global Arrays at scales up to 24,000 cores.« less

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.

Structure Size Enhanced Histogram

NASA Astrophysics Data System (ADS)

Wesarg, Stefan; Kirschner, Matthias

Direct volume visualization requires the definition of transfer functions (TFs) for the assignment of opacity and color. Multi-dimensional TFs are based on at least two image properties, and are specified by means of 2D histograms. In this work we propose a new type of a 2D histogram which combines gray value with information about the size of the structures. This structure size enhanced (SSE) histogram is an intuitive approach for representing anatomical features. Clinicians — the users we are focusing on — are much more familiar with selecting features by their size than by their gradient magnitude value. As a proof of concept, we employ the SSE histogram for the definition of two-dimensional TFs for the visualization of 3D MRI and CT image data.

An Efficient G-XML Data Management Method using XML Spatial Index for Mobile Devices

NASA Astrophysics Data System (ADS)

Tamada, Takashi; Momma, Kei; Seo, Kazuo; Hijikata, Yoshinori; Nishida, Shogo

This paper presents an efficient G-XML data management method for mobile devices. G-XML is XML based encoding for the transport of geographic information. Mobile devices, such as PDA and mobile-phone, performance trail desktop machines, so some techniques are needed for processing G-XML data on mobile devices. In this method, XML-format spatial index file is used to improve an initial display time of G-XML data. This index file contains XML pointer of each feature in G-XML data and classifies these features by multi-dimensional data structures. From the experimental result, we can prove this method speed up about 3-7 times an initial display time of G-XML data on mobile devices.

Multiobjective optimization in a pseudometric objective space as applied to a general model of business activities

NASA Astrophysics Data System (ADS)

Khachaturov, R. V.

2016-09-01

It is shown that finding the equivalence set for solving multiobjective discrete optimization problems is advantageous over finding the set of Pareto optimal decisions. An example of a set of key parameters characterizing the economic efficiency of a commercial firm is proposed, and a mathematical model of its activities is constructed. In contrast to the classical problem of finding the maximum profit for any business, this study deals with a multiobjective optimization problem. A method for solving inverse multiobjective problems in a multidimensional pseudometric space is proposed for finding the best project of firm's activities. The solution of a particular problem of this type is presented.

Improving the Accessibility and Use of NASA Earth Science Data

NASA Technical Reports Server (NTRS)

Tisdale, Matthew; Tisdale, Brian

2015-01-01

Many of the NASA Langley Atmospheric Science Data Center (ASDC) Distributed Active Archive Center (DAAC) multidimensional tropospheric and atmospheric chemistry data products are stored in HDF4, HDF5 or NetCDF format, which traditionally have been difficult to analyze and visualize with geospatial tools. With the rising demand from the diverse end-user communities for geospatial tools to handle multidimensional products, several applications, such as ArcGIS, have refined their software. Many geospatial applications now have new functionalities that enable the end user to: Store, serve, and perform analysis on each individual variable, its time dimension, and vertical dimension. Use NetCDF, GRIB, and HDF raster data formats across applications directly. Publish output within REST image services or WMS for time and space enabled web application development. During this webinar, participants will learn how to leverage geospatial applications such as ArcGIS, OPeNDAP and ncWMS in the production of Earth science information, and in increasing data accessibility and usability.

Multidimensional scaling of D15 caps: color-vision defects among tobacco smokers?

PubMed

Bimler, David; Kirkland, John

2004-01-01

Tobacco smoke contains a range of toxins including carbon monoxide and cyanide. With specialized cells and high metabolic demands, the optic nerve and retina are vulnerable to toxic exposure. We examined the possible effects of smoking on color vision: specifically, whether smokers perceive a different pattern of suprathreshold color dissimilarities from nonsmokers. It is already known that smokers differ in threshold color discrimination, with elevated scores on the Roth 28-Hue Desaturated panel test. Groups of smokers and nonsmokers, matched for sex and age, followed a triadic procedure to compare dissimilarities among 32 pigmented stimuli (the caps of the saturated and desaturated versions of the D15 panel test). Multidimensional scaling was applied to quantify individual variations in the salience of the axes of color space. Despite the briefness, simplicity, and "low-tech" nature of the procedure, subtle but statistically significant differences did emerge: on average the smoking group were significantly less sensitive to red-green differences. This is consistent with some form of injury to the optic nerve.

The Earth is flat when personally significant experiences with the sphericity of the Earth are absent.

PubMed

Carbon, Claus-Christian

2010-07-01

Participants with personal and without personal experiences with the Earth as a sphere estimated large-scale distances between six cities located on different continents. Cognitive distances were submitted to a specific multidimensional scaling algorithm in the 3D Euclidean space with the constraint that all cities had to lie on the same sphere. A simulation was run that calculated respective 3D configurations of the city positions for a wide range of radii of the proposed sphere. People who had personally experienced the Earth as a sphere, at least once in their lifetime, showed a clear optimal solution of the multidimensional scaling (MDS) routine with a mean radius deviating only 8% from the actual radius of the Earth. In contrast, the calculated configurations for people without any personal experience with the Earth as a sphere were compatible with a cognitive concept of a flat Earth. 2010 Elsevier B.V. All rights reserved.

The shape of facial features and the spacing among them generate similar inversion effects: a reply to Rossion (2008).

PubMed

Yovel, Galit

2009-11-01

It is often argued that picture-plane face inversion impairs discrimination of the spacing among face features to a greater extent than the identity of the facial features. However, several recent studies have reported similar inversion effects for both types of face manipulations. In a recent review, Rossion (2008) claimed that similar inversion effects for spacing and features are due to methodological and conceptual shortcomings and that data still support the idea that inversion impairs the discrimination of features less than that of the spacing among them. Here I will claim that when facial features differ primarily in shape, the effect of inversion on features is not smaller than the one on spacing. It is when color/contrast information is added to facial features that the inversion effect on features decreases. This obvious observation accounts for the discrepancy in the literature and suggests that the large inversion effect that was found for features that differ in shape is not a methodological artifact. These findings together with other data that are discussed are consistent with the idea that the shape of facial features and the spacing among them are integrated rather than dissociated in the holistic representation of faces.

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.

Shift-Variant Multidimensional Systems.

DTIC Science & Technology

1985-05-29

i=0,1,** *N-1 in (3.1), one will get 0() i_0,1,* ,N-1 which is nonnegative due to the Perron - Frobenius Theorem [24]. That is, the A nonnegativity ...and the current input. The state-space model was extended in order to model 2-D discrete LSV systems with support on a causality cone . Subsequently...formulated as a special system of linear equations with nonnegative coefficients whose solution is required to satisfy con- straints like nonnegativity in

Accurate Finite Difference Algorithms

NASA Technical Reports Server (NTRS)

Goodrich, John W.

1996-01-01

Two families of finite difference algorithms for computational aeroacoustics are presented and compared. All of the algorithms are single step explicit methods, they have the same order of accuracy in both space and time, with examples up to eleventh order, and they have multidimensional extensions. One of the algorithm families has spectral like high resolution. Propagation with high order and high resolution algorithms can produce accurate results after O(10(exp 6)) periods of propagation with eight grid points per wavelength.

High Definition Information Systems. Hearings before the Subcommittee on Technology and Competitiveness of the Committee on Science, Space, and Technology. U.S. House of Representatives, One Hundred Second Congress, First Session (May 14, 21, 1991).

ERIC Educational Resources Information Center

Congress of the U.S., Washington, DC. House Committee on Science, Space and Technology.

The report of these two hearings on high definition information systems begins by noting that they are digital, and that they are likely to handle computing, telecommunications, home security, computer imaging, storage, fiber optics networks, multi-dimensional libraries, and many other local, national, and international systems. (It is noted that…

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.

Reproducible, high-throughput synthesis of colloidal nanocrystals for optimization in multidimensional parameter space.

PubMed

Chan, Emory M; Xu, Chenxu; Mao, Alvin W; Han, Gang; Owen, Jonathan S; Cohen, Bruce E; Milliron, Delia J

2010-05-12

While colloidal nanocrystals hold tremendous potential for both enhancing fundamental understanding of materials scaling and enabling advanced technologies, progress in both realms can be inhibited by the limited reproducibility of traditional synthetic methods and by the difficulty of optimizing syntheses over a large number of synthetic parameters. Here, we describe an automated platform for the reproducible synthesis of colloidal nanocrystals and for the high-throughput optimization of physical properties relevant to emerging applications of nanomaterials. This robotic platform enables precise control over reaction conditions while performing workflows analogous to those of traditional flask syntheses. We demonstrate control over the size, size distribution, kinetics, and concentration of reactions by synthesizing CdSe nanocrystals with 0.2% coefficient of variation in the mean diameters across an array of batch reactors and over multiple runs. Leveraging this precise control along with high-throughput optical and diffraction characterization, we effectively map multidimensional parameter space to tune the size and polydispersity of CdSe nanocrystals, to maximize the photoluminescence efficiency of CdTe nanocrystals, and to control the crystal phase and maximize the upconverted luminescence of lanthanide-doped NaYF(4) nanocrystals. On the basis of these demonstrative examples, we conclude that this automated synthesis approach will be of great utility for the development of diverse colloidal nanomaterials for electronic assemblies, luminescent biological labels, electroluminescent devices, and other emerging applications.

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

Animated computer graphics models of space and earth sciences data generated via the massively parallel processor

NASA Technical Reports Server (NTRS)

Treinish, Lloyd A.; Gough, Michael L.; Wildenhain, W. David

1987-01-01

The capability was developed of rapidly producing visual representations of large, complex, multi-dimensional space and earth sciences data sets via the implementation of computer graphics modeling techniques on the Massively Parallel Processor (MPP) by employing techniques recently developed for typically non-scientific applications. Such capabilities can provide a new and valuable tool for the understanding of complex scientific data, and a new application of parallel computing via the MPP. A prototype system with such capabilities was developed and integrated into the National Space Science Data Center's (NSSDC) Pilot Climate Data System (PCDS) data-independent environment for computer graphics data display to provide easy access to users. While developing these capabilities, several problems had to be solved independently of the actual use of the MPP, all of which are outlined.

Application Of Empirical Phase Diagrams For Multidimensional Data Visualization Of High Throughput Microbatch Crystallization Experiments.

PubMed

Klijn, Marieke E; Hubbuch, Jürgen

2018-04-27

Protein phase diagrams are a tool to investigate cause and consequence of solution conditions on protein phase behavior. The effects are scored according to aggregation morphologies such as crystals or amorphous precipitates. Solution conditions affect morphological features, such as crystal size, as well as kinetic features, such as crystal growth time. Common used data visualization techniques include individual line graphs or symbols-based phase diagrams. These techniques have limitations in terms of handling large datasets, comprehensiveness or completeness. To eliminate these limitations, morphological and kinetic features obtained from crystallization images generated with high throughput microbatch experiments have been visualized with radar charts in combination with the empirical phase diagram (EPD) method. Morphological features (crystal size, shape, and number, as well as precipitate size) and kinetic features (crystal and precipitate onset and growth time) are extracted for 768 solutions with varying chicken egg white lysozyme concentration, salt type, ionic strength and pH. Image-based aggregation morphology and kinetic features were compiled into a single and easily interpretable figure, thereby showing that the EPD method can support high throughput crystallization experiments in its data amount as well as its data complexity. Copyright © 2018. Published by Elsevier Inc.

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.

THE PLUTO CODE FOR ADAPTIVE MESH COMPUTATIONS IN ASTROPHYSICAL FLUID DYNAMICS

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

Mignone, A.; Tzeferacos, P.; Zanni, C.

We present a description of the adaptive mesh refinement (AMR) implementation of the PLUTO code for solving the equations of classical and special relativistic magnetohydrodynamics (MHD and RMHD). The current release exploits, in addition to the static grid version of the code, the distributed infrastructure of the CHOMBO library for multidimensional parallel computations over block-structured, adaptively refined grids. We employ a conservative finite-volume approach where primary flow quantities are discretized at the cell center in a dimensionally unsplit fashion using the Corner Transport Upwind method. Time stepping relies on a characteristic tracing step where piecewise parabolic method, weighted essentially non-oscillatory,more » or slope-limited linear interpolation schemes can be handily adopted. A characteristic decomposition-free version of the scheme is also illustrated. The solenoidal condition of the magnetic field is enforced by augmenting the equations with a generalized Lagrange multiplier providing propagation and damping of divergence errors through a mixed hyperbolic/parabolic explicit cleaning step. Among the novel features, we describe an extension of the scheme to include non-ideal dissipative processes, such as viscosity, resistivity, and anisotropic thermal conduction without operator splitting. Finally, we illustrate an efficient treatment of point-local, potentially stiff source terms over hierarchical nested grids by taking advantage of the adaptivity in time. Several multidimensional benchmarks and applications to problems of astrophysical relevance assess the potentiality of the AMR version of PLUTO in resolving flow features separated by large spatial and temporal disparities.« less

Particle on a torus knot: Constrained dynamics and semi-classical quantization in a magnetic field

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

Das, Praloy, E-mail: praloydasdurgapur@gmail.com; Pramanik, Souvik, E-mail: souvick.in@gmail.com; Ghosh, Subir, E-mail: subirghosh20@gmail.com

2016-11-15

Kinematics and dynamics of a particle moving on a torus knot poses an interesting problem as a constrained system. In the first part of the paper we have derived the modified symplectic structure or Dirac brackets of the above model in Dirac’s Hamiltonian framework, both in toroidal and Cartesian coordinate systems. This algebra has been used to study the dynamics, in particular small fluctuations in motion around a specific torus. The spatial symmetries of the system have also been studied. In the second part of the paper we have considered the quantum theory of a charge moving in a torusmore » knot in the presence of a uniform magnetic field along the axis of the torus in a semiclassical quantization framework. We exploit the Einstein–Brillouin–Keller (EBK) scheme of quantization that is appropriate for multidimensional systems. Embedding of the knot on a specific torus is inherently two dimensional that gives rise to two quantization conditions. This shows that although the system, after imposing the knot condition reduces to a one dimensional system, even then it has manifested non-planar features which shows up again in the study of fractional angular momentum. Finally we compare the results obtained from EBK (multi-dimensional) and Bohr–Sommerfeld (single dimensional) schemes. The energy levels and fractional spin depend on the torus knot parameters that specifies its non-planar features. Interestingly, we show that there can be non-planar corrections to the planar anyon-like fractional spin.« less

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.

CARBON STARS FROM LAMOST DR2 DATA

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

Ji, Wei; Cui, Wenyuan; Zhang, Bo

2016-09-01

In this work, we present the new catalog of carbon stars from the LAMOST DR2 catalog. In total, 894 carbon stars are identified from multiple line indices measured from the stellar spectra. We are able to identify the carbon stars by combining the CN bands in the red end with C{sub 2} and other lines. Moreover, we also classify the carbon stars into spectral sub-types of C–H, C–R, and C–N. These sub-types show distinct features in the multi-dimensional line indices, implying that in the future they can be used to identify carbon stars from larger spectroscopic data sets. While themore » C–N stars are clearly separated from the others in the line index space, we find no clear separation between the C–R and C–H sub-types. The C–R and C–H stars seem to smoothly transition from one to another. This may hint that the C–R and C–H stars may not be different in their origins, instead their spectra look different because of different metallicities. Due to the relatively low spectral resolution and lower signal-to-noise ratio, the ratio of {sup 12}C/{sup 13}C is not measured and thus the C–J stars are not identified.« less

PROBING THE EXPANSION HISTORY OF THE UNIVERSE BY MODEL-INDEPENDENT RECONSTRUCTION FROM SUPERNOVAE AND GAMMA-RAY BURST MEASUREMENTS

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

Feng, Chao-Jun; Li, Xin-Zhou, E-mail: fengcj@shnu.edu.cn, E-mail: kychz@shnu.edu.cn

To probe the late evolution history of the universe, we adopt two kinds of optimal basis systems. One of them is constructed by performing the principle component analysis, and the other is built by taking the multidimensional scaling approach. Cosmological observables such as the luminosity distance can be decomposed into these basis systems. These basis systems are optimized for different kinds of cosmological models that are based on different physical assumptions, even for a mixture model of them. Therefore, the so-called feature space that is projected from the basis systems is cosmological model independent, and it provides a parameterization for studying and reconstructing themore » Hubble expansion rate from the supernova luminosity distance and even gamma-ray burst (GRB) data with self-calibration. The circular problem when using GRBs as cosmological candles is naturally eliminated in this procedure. By using the Levenberg–Marquardt technique and the Markov Chain Monte Carlo method, we perform an observational constraint on this kind of parameterization. The data we used include the “joint light-curve analysis” data set that consists of 740 Type Ia supernovae and 109 long GRBs with the well-known Amati relation.« less

The theory of n-scales

NASA Astrophysics Data System (ADS)

Dündar, Furkan Semih

2018-01-01

We provide a theory of n-scales previously called as n dimensional time scales. In previous approaches to the theory of time scales, multi-dimensional scales were taken as product space of two time scales [1, 2]. n-scales make the mathematical structure more flexible and appropriate to real world applications in physics and related fields. Here we define an n-scale as an arbitrary closed subset of ℝn. Modified forward and backward jump operators, Δ-derivatives and Δ-integrals on n-scales are defined.

The nonconvex multi-dimensional Riemann problem for Hamilton-Jacobi equations

NASA Technical Reports Server (NTRS)

Osher, Stanley

1989-01-01

Simple inequalities for the Riemann problem for a Hamilton-Jacobi equation in N space dimension when neither the initial data nor the Hamiltonian need be convex (or concave) are presented. The initial data is globally continuous, affine in each orthant, with a possible jump in normal derivative across each coordinate plane, x sub i = 0. The inequalities become equalities wherever a maxmin equals a minmax and thus an exact closed form solution to this problem is then obtained.

Spherical Panoramas for Astrophysical Data Visualization

NASA Astrophysics Data System (ADS)

Kent, Brian R.

2017-05-01

Data immersion has advantages in astrophysical visualization. Complex multi-dimensional data and phase spaces can be explored in a seamless and interactive viewing environment. Putting the user in the data is a first step toward immersive data analysis. We present a technique for creating 360° spherical panoramas with astrophysical data. The three-dimensional software package Blender and the Google Spatial Media module are used together to immerse users in data exploration. Several examples employing these methods exhibit how the technique works using different types of astronomical data.

Displays, instruments, and the multi-dimensional world of cartography

NASA Technical Reports Server (NTRS)

Mccleary, George F., Jr.

1989-01-01

Cartographers are creators and purveyors of maps. Maps are representations of space, geographical images of the environment. Maps organize spatial information for convenience, particularly for use in performing tasks which involve the environment. There are many different kinds of maps, and there are as many different uses of maps as there are spatial problems to be solved. Maps and the display instrument dichotomy are examined. Also examined are the categories of map use along with the characteristics of maps.

Functional magnetic resonance imaging activation detection: fuzzy cluster analysis in wavelet and multiwavelet domains.

PubMed

Jahanian, Hesamoddin; Soltanian-Zadeh, Hamid; Hossein-Zadeh, Gholam-Ali

2005-09-01

To present novel feature spaces, based on multiscale decompositions obtained by scalar wavelet and multiwavelet transforms, to remedy problems associated with high dimension of functional magnetic resonance imaging (fMRI) time series (when they are used directly in clustering algorithms) and their poor signal-to-noise ratio (SNR) that limits accurate classification of fMRI time series according to their activation contents. Using randomization, the proposed method finds wavelet/multiwavelet coefficients that represent the activation content of fMRI time series and combines them to define new feature spaces. Using simulated and experimental fMRI data sets, the proposed feature spaces are compared to the cross-correlation (CC) feature space and their performances are evaluated. In these studies, the false positive detection rate is controlled using randomization. To compare different methods, several points of the receiver operating characteristics (ROC) curves, using simulated data, are estimated and compared. The proposed features suppress the effects of confounding signals and improve activation detection sensitivity. Experimental results show improved sensitivity and robustness of the proposed method compared to the conventional CC analysis. More accurate and sensitive activation detection can be achieved using the proposed feature spaces compared to CC feature space. Multiwavelet features show superior detection sensitivity compared to the scalar wavelet features. (c) 2005 Wiley-Liss, Inc.

Oversampling the Minority Class in the Feature Space.

PubMed

Perez-Ortiz, Maria; Gutierrez, Pedro Antonio; Tino, Peter; Hervas-Martinez, Cesar

2016-09-01

The imbalanced nature of some real-world data is one of the current challenges for machine learning researchers. One common approach oversamples the minority class through convex combination of its patterns. We explore the general idea of synthetic oversampling in the feature space induced by a kernel function (as opposed to input space). If the kernel function matches the underlying problem, the classes will be linearly separable and synthetically generated patterns will lie on the minority class region. Since the feature space is not directly accessible, we use the empirical feature space (EFS) (a Euclidean space isomorphic to the feature space) for oversampling purposes. The proposed method is framed in the context of support vector machines, where the imbalanced data sets can pose a serious hindrance. The idea is investigated in three scenarios: 1) oversampling in the full and reduced-rank EFSs; 2) a kernel learning technique maximizing the data class separation to study the influence of the feature space structure (implicitly defined by the kernel function); and 3) a unified framework for preferential oversampling that spans some of the previous approaches in the literature. We support our investigation with extensive experiments over 50 imbalanced data sets.

Many Specialists for Suppressing Cortical Excitation

PubMed Central

Burkhalter, Andreas

2008-01-01

Cortical computations are critically dependent on GABA-releasing neurons for dynamically balancing excitation with inhibition that is proportional to the overall level of activity. Although it is widely accepted that there are multiple types of interneurons, defining their identities based on qualitative descriptions of morphological, molecular and physiological features has failed to produce a universally accepted ‘parts list’, which is needed to understand the roles that interneurons play in cortical processing. A list of features has been published by the Petilla Interneurons Nomenclature Group, which represents an important step toward an unbiased classification of interneurons. To this end some essential features have recently been studied quantitatively and their association was examined using multidimensional cluster analyses. These studies revealed at least 3 distinct electrophysiological, 6 morphological and 15 molecular phenotypes. This is a conservative estimate of the number of interneuron types, which almost certainly will be revised as more quantitative studies will be performed and similarities will be defined objectively. It is clear that interneurons are organized with physiological attributes representing the most general, molecular characteristics the most detailed and morphological features occupying the middle ground. By themselves, none of these features are sufficient to define classes of interneurons. The challenge will be to determine which features belong together and how cell type-specific feature combinations are genetically specified. PMID:19225588

Tensor-driven extraction of developmental features from varying paediatric EEG datasets.

PubMed

Kinney-Lang, Eli; Spyrou, Loukianos; Ebied, Ahmed; Chin, Richard Fm; Escudero, Javier

2018-05-21

Constant changes in developing children's brains can pose a challenge in EEG dependant technologies. Advancing signal processing methods to identify developmental differences in paediatric populations could help improve function and usability of such technologies. Taking advantage of the multi-dimensional structure of EEG data through tensor analysis may offer a framework for extracting relevant developmental features of paediatric datasets. A proof of concept is demonstrated through identifying latent developmental features in resting-state EEG. Approach. Three paediatric datasets (n = 50, 17, 44) were analyzed using a two-step constrained parallel factor (PARAFAC) tensor decomposition. Subject age was used as a proxy measure of development. Classification used support vector machines (SVM) to test if PARAFAC identified features could predict subject age. The results were cross-validated within each dataset. Classification analysis was complemented by visualization of the high-dimensional feature structures using t-distributed Stochastic Neighbour Embedding (t-SNE) maps. Main Results. Development-related features were successfully identified for the developmental conditions of each dataset. SVM classification showed the identified features could accurately predict subject at a significant level above chance for both healthy and impaired populations. t-SNE maps revealed suitable tensor factorization was key in extracting the developmental features. Significance. The described methods are a promising tool for identifying latent developmental features occurring throughout childhood EEG. © 2018 IOP Publishing Ltd.

A Source Area Approach Demonstrates Moderate Predictive Ability but Pronounced Variability of Invasive Species Traits

PubMed Central

Essl, Franz; Dullinger, Stefan

2016-01-01

The search for traits that make alien species invasive has mostly concentrated on comparing successful invaders and different comparison groups with respect to average trait values. By contrast, little attention has been paid to trait variability among invaders. Here, we combine an analysis of trait differences between invasive and non-invasive species with a comparison of multidimensional trait variability within these two species groups. We collected data on biological and distributional traits for 1402 species of the native, non-woody vascular plant flora of Austria. We then compared the subsets of species recorded and not recorded as invasive aliens anywhere in the world, respectively, first, with respect to the sampled traits using univariate and multiple regression models; and, second, with respect to their multidimensional trait diversity by calculating functional richness and dispersion metrics. Attributes related to competitiveness (strategy type, nitrogen indicator value), habitat use (agricultural and ruderal habitats, occurrence under the montane belt), and propagule pressure (frequency) were most closely associated with invasiveness. However, even the best multiple model, including interactions, only explained a moderate fraction of the differences in invasive success. In addition, multidimensional variability in trait space was even larger among invasive than among non-invasive species. This pronounced variability suggests that invasive success has a considerable idiosyncratic component and is probably highly context specific. We conclude that basing risk assessment protocols on species trait profiles will probably face hardly reducible uncertainties. PMID:27187616

A Source Area Approach Demonstrates Moderate Predictive Ability but Pronounced Variability of Invasive Species Traits.

PubMed

Klonner, Günther; Fischer, Stefan; Essl, Franz; Dullinger, Stefan

2016-01-01

The search for traits that make alien species invasive has mostly concentrated on comparing successful invaders and different comparison groups with respect to average trait values. By contrast, little attention has been paid to trait variability among invaders. Here, we combine an analysis of trait differences between invasive and non-invasive species with a comparison of multidimensional trait variability within these two species groups. We collected data on biological and distributional traits for 1402 species of the native, non-woody vascular plant flora of Austria. We then compared the subsets of species recorded and not recorded as invasive aliens anywhere in the world, respectively, first, with respect to the sampled traits using univariate and multiple regression models; and, second, with respect to their multidimensional trait diversity by calculating functional richness and dispersion metrics. Attributes related to competitiveness (strategy type, nitrogen indicator value), habitat use (agricultural and ruderal habitats, occurrence under the montane belt), and propagule pressure (frequency) were most closely associated with invasiveness. However, even the best multiple model, including interactions, only explained a moderate fraction of the differences in invasive success. In addition, multidimensional variability in trait space was even larger among invasive than among non-invasive species. This pronounced variability suggests that invasive success has a considerable idiosyncratic component and is probably highly context specific. We conclude that basing risk assessment protocols on species trait profiles will probably face hardly reducible uncertainties.

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

A multidimensional anisotropic strength criterion based on Kelvin modes

NASA Astrophysics Data System (ADS)

Arramon, Yves Pierre

A new theory for the prediction of multiaxial strength of anisotropic elastic materials was proposed by Biegler and Mehrabadi (1993). This theory is based on the premise that the total elastic strain energy of an anisotropic material subjected to multiaxial stress can be decomposed into dilatational and deviatoric modes. A multidimensional strength criterion may thus be formulated by postulating that the failure would occur when the energy stored in one of these modes has reached a critical value. However, the logic employed by these authors to formulate a failure criterion based on this theory could not be extended to multiaxial stress. In this thesis, an alternate criterion is presented which redresses the biaxial restriction by reformulating the surfaces of constant modal energy as surfaces of constant eigenstress magnitude. The resulting failure envelope, in a multidimensional stress space, is piecewise smooth. Each facet of the envelope is expected to represent the locus of failure data by a particular Kelvin mode. It is further shown that the Kelvin mode theory alone provides an incomplete description of the failure of some materials, but that this weakness can be addressed by the introduction of a set of complementary modes. A revised theory which combines both Kelvin and complementary modes is thus proposed and applied seven example materials: an isotropic concrete, tetragonal paperboard, two orthotropic softwoods, two orthotropic hardwoods and an orthotropic cortical bone. The resulting failure envelopes for these examples were plotted and, with the exception of concrete, shown to produce intuitively correct failure predictions.

Exploring short-GRB afterglow parameter space for observations in coincidence with gravitational waves

NASA Astrophysics Data System (ADS)

Saleem, M.; Resmi, L.; Misra, Kuntal; Pai, Archana; Arun, K. G.

2018-03-01

Short duration Gamma Ray Bursts (SGRB) and their afterglows are among the most promising electromagnetic (EM) counterparts of Neutron Star (NS) mergers. The afterglow emission is broad-band, visible across the entire electromagnetic window from γ-ray to radio frequencies. The flux evolution in these frequencies is sensitive to the multidimensional afterglow physical parameter space. Observations of gravitational wave (GW) from BNS mergers in spatial and temporal coincidence with SGRB and associated afterglows can provide valuable constraints on afterglow physics. We run simulations of GW-detected BNS events and assuming that all of them are associated with a GRB jet which also produces an afterglow, investigate how detections or non-detections in X-ray, optical and radio frequencies can be influenced by the parameter space. We narrow down the regions of afterglow parameter space for a uniform top-hat jet model, which would result in different detection scenarios. We list inferences which can be drawn on the physics of GRB afterglows from multimessenger astronomy with coincident GW-EM observations.

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.

A self-organized learning strategy for object recognition by an embedded line of attraction

NASA Astrophysics Data System (ADS)

Seow, Ming-Jung; Alex, Ann T.; Asari, Vijayan K.

2012-04-01

For humans, a picture is worth a thousand words, but to a machine, it is just a seemingly random array of numbers. Although machines are very fast and efficient, they are vastly inferior to humans for everyday information processing. Algorithms that mimic the way the human brain computes and learns may be the solution. In this paper we present a theoretical model based on the observation that images of similar visual perceptions reside in a complex manifold in an image space. The perceived features are often highly structured and hidden in a complex set of relationships or high-dimensional abstractions. To model the pattern manifold, we present a novel learning algorithm using a recurrent neural network. The brain memorizes information using a dynamical system made of interconnected neurons. Retrieval of information is accomplished in an associative sense. It starts from an arbitrary state that might be an encoded representation of a visual image and converges to another state that is stable. The stable state is what the brain remembers. In designing a recurrent neural network, it is usually of prime importance to guarantee the convergence in the dynamics of the network. We propose to modify this picture: if the brain remembers by converging to the state representing familiar patterns, it should also diverge from such states when presented with an unknown encoded representation of a visual image belonging to a different category. That is, the identification of an instability mode is an indication that a presented pattern is far away from any stored pattern and therefore cannot be associated with current memories. These properties can be used to circumvent the plasticity-stability dilemma by using the fluctuating mode as an indicator to create new states. We capture this behavior using a novel neural architecture and learning algorithm, in which the system performs self-organization utilizing a stability mode and an instability mode for the dynamical system. Based on this observation we developed a self- organizing line attractor, which is capable of generating new lines in the feature space to learn unrecognized patterns. Experiments performed on UMIST pose database and CMU face expression variant database for face recognition have shown that the proposed nonlinear line attractor is able to successfully identify the individuals and it provided better recognition rate when compared to the state of the art face recognition techniques. Experiments on FRGC version 2 database has also provided excellent recognition rate in images captured in complex lighting environments. Experiments performed on the Japanese female face expression database and Essex Grimace database using the self organizing line attractor have also shown successful expression invariant face recognition. These results show that the proposed model is able to create nonlinear manifolds in a multidimensional feature space to distinguish complex patterns.

Estuarial fingerprinting through multidimensional fluorescence and multivariate analysis.

PubMed

Hall, Gregory J; Clow, Kerin E; Kenny, Jonathan E

2005-10-01

As part of a strategy for preventing the introduction of aquatic nuisance species (ANS) to U.S. estuaries, ballast water exchange (BWE) regulations have been imposed. Enforcing these regulations requires a reliable method for determining the port of origin of water in the ballast tanks of ships entering U.S. waters. This study shows that a three-dimensional fluorescence fingerprinting technique, excitation emission matrix (EEM) spectroscopy, holds great promise as a ballast water analysis tool. In our technique, EEMs are analyzed by multivariate classification and curve resolution methods, such as N-way partial least squares Regression-discriminant analysis (NPLS-DA) and parallel factor analysis (PARAFAC). We demonstrate that classification techniques can be used to discriminate among sampling sites less than 10 miles apart, encompassing Boston Harbor and two tributaries in the Mystic River Watershed. To our knowledge, this work is the first to use multivariate analysis to classify water as to location of origin. Furthermore, it is shown that curve resolution can show seasonal features within the multidimensional fluorescence data sets, which correlate with difficulty in classification.

Behavior analysis of video object in complicated background

NASA Astrophysics Data System (ADS)

Zhao, Wenting; Wang, Shigang; Liang, Chao; Wu, Wei; Lu, Yang

2016-10-01

This paper aims to achieve robust behavior recognition of video object in complicated background. Features of the video object are described and modeled according to the depth information of three-dimensional video. Multi-dimensional eigen vector are constructed and used to process high-dimensional data. Stable object tracing in complex scenes can be achieved with multi-feature based behavior analysis, so as to obtain the motion trail. Subsequently, effective behavior recognition of video object is obtained according to the decision criteria. What's more, the real-time of algorithms and accuracy of analysis are both improved greatly. The theory and method on the behavior analysis of video object in reality scenes put forward by this project have broad application prospect and important practical significance in the security, terrorism, military and many other fields.

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…

Sample-space-based feature extraction and class preserving projection for gene expression data.

PubMed

Wang, Wenjun

2013-01-01

In order to overcome the problems of high computational complexity and serious matrix singularity for feature extraction using Principal Component Analysis (PCA) and Fisher's Linear Discrinimant Analysis (LDA) in high-dimensional data, sample-space-based feature extraction is presented, which transforms the computation procedure of feature extraction from gene space to sample space by representing the optimal transformation vector with the weighted sum of samples. The technique is used in the implementation of PCA, LDA, Class Preserving Projection (CPP) which is a new method for discriminant feature extraction proposed, and the experimental results on gene expression data demonstrate the effectiveness of the method.

Particle-in-cell/accelerator code for space-charge dominated beam simulation

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

2012-05-08

Warp is a multidimensional discrete-particle beam simulation program designed to be applicable where the beam space-charge is non-negligible or dominant. It is being developed in a collaboration among LLNL, LBNL and the University of Maryland. It was originally designed and optimized for heave ion fusion accelerator physics studies, but has received use in a broader range of applications, including for example laser wakefield accelerators, e-cloud studies in high enery accelerators, particle traps and other areas. At present it incorporates 3-D, axisymmetric (r,z) planar (x-z) and transverse slice (x,y) descriptions, with both electrostatic and electro-magnetic fields, and a beam envelope model.more » The code is guilt atop the Python interpreter language.« less

Multidimensional phase space methods for mass measurements and decay topology determination

NASA Astrophysics Data System (ADS)

Altunkaynak, Baris; Kilic, Can; Klimek, Matthew D.

2017-02-01

Collider events with multi-stage cascade decays fill out the kinematically allowed region in phase space with a density that is enhanced at the boundary. The boundary encodes all available information as regards the spectrum and is well populated even with moderate signal statistics due to this enhancement. In previous work, the improvement in the precision of mass measurements for cascade decays with three visible and one invisible particles was demonstrated when the full boundary information is used instead of endpoints of one-dimensional projections. We extend these results to cascade decays with four visible and one invisible particles. We also comment on how the topology of the cascade decay can be determined from the differential distribution of events in these scenarios.

Precision Attitude Determination for an Infrared Space Telescope

NASA Technical Reports Server (NTRS)

Benford, Dominic J.

2008-01-01

We have developed performance simulations for a precision attitude determination system using a focal plane star tracker on an infrared space telescope. The telescope is being designed for the Destiny mission to measure cosmologically distant supernovae as one of the candidate implementations for the Joint Dark Energy Mission. Repeat observations of the supernovae require attitude control at the level of 0.010 arcseconds (0.05 microradians) during integrations and at repeat intervals up to and over a year. While absolute accuracy is not required, the repoint precision is challenging. We have simulated the performance of a focal plane star tracker in a multidimensional parameter space, including pixel size, read noise, and readout rate. Systematic errors such as proper motion, velocity aberration, and parallax can be measured and compensated out. Our prediction is that a relative attitude determination accuracy of 0.001 to 0.002 arcseconds (0.005 to 0.010 microradians) will be achievable.

Local Feature Selection for Data Classification.

PubMed

Armanfard, Narges; Reilly, James P; Komeili, Majid

2016-06-01

Typical feature selection methods choose an optimal global feature subset that is applied over all regions of the sample space. In contrast, in this paper we propose a novel localized feature selection (LFS) approach whereby each region of the sample space is associated with its own distinct optimized feature set, which may vary both in membership and size across the sample space. This allows the feature set to optimally adapt to local variations in the sample space. An associated method for measuring the similarities of a query datum to each of the respective classes is also proposed. The proposed method makes no assumptions about the underlying structure of the samples; hence the method is insensitive to the distribution of the data over the sample space. The method is efficiently formulated as a linear programming optimization problem. Furthermore, we demonstrate the method is robust against the over-fitting problem. Experimental results on eleven synthetic and real-world data sets demonstrate the viability of the formulation and the effectiveness of the proposed algorithm. In addition we show several examples where localized feature selection produces better results than a global feature selection method.

Recent developments in the theory of protein folding: searching for the global energy minimum.

PubMed

Scheraga, H A

1996-04-16

Statistical mechanical theories and computer simulation are being used to gain an understanding of the fundamental features of protein folding. A major obstacle in the computation of protein structures is the multiple-minima problem arising from the existence of many local minima in the multidimensional energy landscape of the protein. This problem has been surmounted for small open-chain and cyclic peptides, and for regular-repeating sequences of models of fibrous proteins. Progress is being made in resolving this problem for globular proteins.

PROC IRT: A SAS Procedure for Item Response Theory

PubMed Central

Matlock Cole, Ki; Paek, Insu

2017-01-01

This article reviews the procedure for item response theory (PROC IRT) procedure in SAS/STAT 14.1 to conduct item response theory (IRT) analyses of dichotomous and polytomous datasets that are unidimensional or multidimensional. The review provides an overview of available features, including models, estimation procedures, interfacing, input, and output files. A small-scale simulation study evaluates the IRT model parameter recovery of the PROC IRT procedure. The use of the IRT procedure in Statistical Analysis Software (SAS) may be useful for researchers who frequently utilize SAS for analyses, research, and teaching.

[Complex hygiene evaluation of the influence of exogenous and endogenous factors on the occurance of urolithiasis in the children of the Primorye Territory].

PubMed

Koval'chuk, V K; Luchaninova, V N; Koldaev, V M

2005-01-01

The paper presents the results of a study of the influence of 43 exogenous and 14 endogenous factors on the occurrence of urolithiasis in children with regional features of the Primorye Territory being kept in mind. Multidimensional mathematical analysis was used to reveal continuous and intermittent risk factors of this disease. The authors show it necessary to take into account the findings in substantiating measures for primary prevention of urolithiasis among the children of the Primorye Territory.

Structural changes in cross-border liabilities: A multidimensional approach

NASA Astrophysics Data System (ADS)

Araújo, Tanya; Spelta, Alessandro

2014-01-01

We study the international interbank market through a geometric analysis of empirical data. The geometric analysis of the time series of cross-country liabilities shows that the systematic information of the interbank international market is contained in a space of small dimension. Geometric spaces of financial relations across countries are developed, for which the space volume, multivariate skewness and multivariate kurtosis are computed. The behavior of these coefficients reveals an important modification acting in the financial linkages since 1997 and allows us to relate the shape of the geometric space that emerges in recent years to the globally turbulent period that has characterized financial systems since the late 1990s. Here we show that, besides a persistent decrease in the volume of the geometric space since 1997, the observation of a generalized increase in the values of the multivariate skewness and kurtosis sheds some light on the behavior of cross-border interdependencies during periods of financial crises. This was found to occur in such a systematic fashion, that these coefficients may be used as a proxy for systemic risk.

Similarity-dissimilarity plot for visualization of high dimensional data in biomedical pattern classification.

PubMed

Arif, Muhammad

2012-06-01

In pattern classification problems, feature extraction is an important step. Quality of features in discriminating different classes plays an important role in pattern classification problems. In real life, pattern classification may require high dimensional feature space and it is impossible to visualize the feature space if the dimension of feature space is greater than four. In this paper, we have proposed a Similarity-Dissimilarity plot which can project high dimensional space to a two dimensional space while retaining important characteristics required to assess the discrimination quality of the features. Similarity-dissimilarity plot can reveal information about the amount of overlap of features of different classes. Separable data points of different classes will also be visible on the plot which can be classified correctly using appropriate classifier. Hence, approximate classification accuracy can be predicted. Moreover, it is possible to know about whom class the misclassified data points will be confused by the classifier. Outlier data points can also be located on the similarity-dissimilarity plot. Various examples of synthetic data are used to highlight important characteristics of the proposed plot. Some real life examples from biomedical data are also used for the analysis. The proposed plot is independent of number of dimensions of the feature space.

Classification of molecular structure images by using ANN, RF, LBP, HOG, and size reduction methods for early stomach cancer detection

NASA Astrophysics Data System (ADS)

Aytaç Korkmaz, Sevcan; Binol, Hamidullah

2018-03-01

Patients who die from stomach cancer are still present. Early diagnosis is crucial in reducing the mortality rate of cancer patients. Therefore, computer aided methods have been developed for early detection in this article. Stomach cancer images were obtained from Fırat University Medical Faculty Pathology Department. The Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG) features of these images are calculated. At the same time, Sammon mapping, Stochastic Neighbor Embedding (SNE), Isomap, Classical multidimensional scaling (MDS), Local Linear Embedding (LLE), Linear Discriminant Analysis (LDA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Laplacian Eigenmaps methods are used for dimensional the reduction of the features. The high dimension of these features has been reduced to lower dimensions using dimensional reduction methods. Artificial neural networks (ANN) and Random Forest (RF) classifiers were used to classify stomach cancer images with these new lower feature sizes. New medical systems have developed to measure the effects of these dimensions by obtaining features in different dimensional with dimensional reduction methods. When all the methods developed are compared, it has been found that the best accuracy results are obtained with LBP_MDS_ANN and LBP_LLE_ANN methods.

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.

Neural correlates of processing facial identity based on features versus their spacing.

PubMed

Maurer, D; O'Craven, K M; Le Grand, R; Mondloch, C J; Springer, M V; Lewis, T L; Grady, C L

2007-04-08

Adults' expertise in recognizing facial identity involves encoding subtle differences among faces in the shape of individual facial features (featural processing) and in the spacing among features (a type of configural processing called sensitivity to second-order relations). We used fMRI to investigate the neural mechanisms that differentiate these two types of processing. Participants made same/different judgments about pairs of faces that differed only in the shape of the eyes and mouth, with minimal differences in spacing (featural blocks), or pairs of faces that had identical features but differed in the positions of those features (spacing blocks). From a localizer scan with faces, objects, and houses, we identified regions with comparatively more activity for faces, including the fusiform face area (FFA) in the right fusiform gyrus, other extrastriate regions, and prefrontal cortices. Contrasts between the featural and spacing conditions revealed distributed patterns of activity differentiating the two conditions. A region of the right fusiform gyrus (near but not overlapping the localized FFA) showed greater activity during the spacing task, along with multiple areas of right frontal cortex, whereas left prefrontal activity increased for featural processing. These patterns of activity were not related to differences in performance between the two tasks. The results indicate that the processing of facial features is distinct from the processing of second-order relations in faces, and that these functions are mediated by separate and lateralized networks involving the right fusiform gyrus, although the FFA as defined from a localizer scan is not differentially involved.

Behavioral Hypervolumes of Predator Groups and Predator-Predator Interactions Shape Prey Survival Rates and Selection on Prey Behavior

PubMed Central

Pruitt, Jonathan N.; Howell, Kimberly A.; Gladney, Shaniqua J.; Yang, Yusan; Lichtenstein, James L. L.; Spicer, Michelle Elise; Echeverri, Sebastian A.; Pinter-Wollman, Noa

2017-01-01

Predator-prey interactions often vary on the basis of the traits of the individual predators and prey involved. Here we examine whether the multidimensional behavioral diversity of predator groups shapes prey mortality rates and selection on prey behavior. We ran individual sea stars (Pisaster ochraceus) through three behavioral assays to characterize individuals’ behavioral phenotype along three axes. We then created groups that varied in the volume of behavioral space that they occupied. We further manipulated the ability of predators to interact with one another physically via the addition of barriers. Prey snails (Chlorostome funebralis) were also run through an assay to evaluate their predator avoidance behavior before their use in mesocosm experiments. We then subjected pools of prey to predator groups and recorded the number of prey consumed and their behavioral phenotypes. We found that predator-predator interactions changed survival selection on prey traits: when predators were prevented from interacting, more fearful snails had higher survival rates, whereas prey fearfulness had no effect on survival when predators were free to interact. We also found that groups of predators that occupied a larger volume in behavioral trait space consumed 35% more prey snails than homogeneous predator groups. Finally, we found that behavioral hypervolumes were better predictors of prey survival rates than single behavioral traits or other multivariate statistics (i.e., principal component analysis). Taken together, predator-predator interactions and multidimensional behavioral diversity determine prey survival rates and selection on prey traits in this system. PMID:28221831

Behavioral Hypervolumes of Predator Groups and Predator-Predator Interactions Shape Prey Survival Rates and Selection on Prey Behavior.

PubMed

Pruitt, Jonathan N; Howell, Kimberly A; Gladney, Shaniqua J; Yang, Yusan; Lichtenstein, James L L; Spicer, Michelle Elise; Echeverri, Sebastian A; Pinter-Wollman, Noa

2017-03-01

Predator-prey interactions often vary on the basis of the traits of the individual predators and prey involved. Here we examine whether the multidimensional behavioral diversity of predator groups shapes prey mortality rates and selection on prey behavior. We ran individual sea stars (Pisaster ochraceus) through three behavioral assays to characterize individuals' behavioral phenotype along three axes. We then created groups that varied in the volume of behavioral space that they occupied. We further manipulated the ability of predators to interact with one another physically via the addition of barriers. Prey snails (Chlorostome funebralis) were also run through an assay to evaluate their predator avoidance behavior before their use in mesocosm experiments. We then subjected pools of prey to predator groups and recorded the number of prey consumed and their behavioral phenotypes. We found that predator-predator interactions changed survival selection on prey traits: when predators were prevented from interacting, more fearful snails had higher survival rates, whereas prey fearfulness had no effect on survival when predators were free to interact. We also found that groups of predators that occupied a larger volume in behavioral trait space consumed 35% more prey snails than homogeneous predator groups. Finally, we found that behavioral hypervolumes were better predictors of prey survival rates than single behavioral traits or other multivariate statistics (i.e., principal component analysis). Taken together, predator-predator interactions and multidimensional behavioral diversity determine prey survival rates and selection on prey traits in this system.

Topological data analysis of financial time series: Landscapes of crashes

NASA Astrophysics Data System (ADS)

Gidea, Marian; Katz, Yuri

2018-02-01

We explore the evolution of daily returns of four major US stock market indices during the technology crash of 2000, and the financial crisis of 2007-2009. Our methodology is based on topological data analysis (TDA). We use persistence homology to detect and quantify topological patterns that appear in multidimensional time series. Using a sliding window, we extract time-dependent point cloud data sets, to which we associate a topological space. We detect transient loops that appear in this space, and we measure their persistence. This is encoded in real-valued functions referred to as a 'persistence landscapes'. We quantify the temporal changes in persistence landscapes via their Lp-norms. We test this procedure on multidimensional time series generated by various non-linear and non-equilibrium models. We find that, in the vicinity of financial meltdowns, the Lp-norms exhibit strong growth prior to the primary peak, which ascends during a crash. Remarkably, the average spectral density at low frequencies of the time series of Lp-norms of the persistence landscapes demonstrates a strong rising trend for 250 trading days prior to either dotcom crash on 03/10/2000, or to the Lehman bankruptcy on 09/15/2008. Our study suggests that TDA provides a new type of econometric analysis, which complements the standard statistical measures. The method can be used to detect early warning signals of imminent market crashes. We believe that this approach can be used beyond the analysis of financial time series presented here.

Machine Learning Classification of Heterogeneous Fields to Estimate Physical Responses

NASA Astrophysics Data System (ADS)

McKenna, S. A.; Akhriev, A.; Alzate, C.; Zhuk, S.

2017-12-01

The promise of machine learning to enhance physics-based simulation is examined here using the transient pressure response to a pumping well in a heterogeneous aquifer. 10,000 random fields of log10 hydraulic conductivity (K) are created and conditioned on a single K measurement at the pumping well. Each K-field is used as input to a forward simulation of drawdown (pressure decline). The differential equations governing groundwater flow to the well serve as a non-linear transform of the input K-field to an output drawdown field. The results are stored and the data set is split into training and testing sets for classification. A Euclidean distance measure between any two fields is calculated and the resulting distances between all pairs of fields define a similarity matrix. Similarity matrices are calculated for both input K-fields and the resulting drawdown fields at the end of the simulation. The similarity matrices are then used as input to spectral clustering to determine groupings of similar input and output fields. Additionally, the similarity matrix is used as input to multi-dimensional scaling to visualize the clustering of fields in lower dimensional spaces. We examine the ability to cluster both input K-fields and output drawdown fields separately with the goal of identifying K-fields that create similar drawdowns and, conversely, given a set of simulated drawdown fields, identify meaningful clusters of input K-fields. Feature extraction based on statistical parametric mapping provides insight into what features of the fields drive the classification results. The final goal is to successfully classify input K-fields into the correct output class, and also, given an output drawdown field, be able to infer the correct class of input field that created it.

Using Multidimensional Grief Theory to Explore Effects of Deployment, Reintegration, and Death on Military Youth and Families

PubMed Central

Kaplow, Julie B.; Layne, Christopher M.; Saltzman, William R.; Cozza, Stephen J.; Pynoos, Robert S.

2015-01-01

To date, the U.S. military has made major strides in acknowledging and therapeutically addressing trauma and Posttraumatic Stress Disorder (PTSD) in service members and their families. However, given the nature of warfare and high rates of losses sustained by both military members (e.g., deaths of fellow unit members) and military families (e.g., loss of a young parent who served in the military), as well as the ongoing threat of loss that military families face during deployment, we propose that a similar focus on grief is also needed to properly understand and address many of the challenges encountered by bereaved service members, spouses, and children. In this article, we describe a newly developed theory of grief (Multidimensional Grief Theory) and apply it to the task of exploring major features of military-related experiences during the phases of deployment, reintegration, and the aftermath of combat death—especially as they impact children. We also describe implications for designing preventive interventions during each phase and conclude with recommended avenues for future research. Primary aims are to illustrate: (1) the indispensable role of theory in guiding efforts to describe, explain, predict, prevent, and treat maladaptive grief in military service members, children, and families; (2) the relevance of multidimensional grief theory for addressing both losses due to physical death as well as losses brought about by extended physical separations to which military children and families are exposed during and after deployment; and (3) a focus on military-related grief as a much-needed complement to an already-established focus on military-related PTSD. PMID:23760905

Using multidimensional grief theory to explore the effects of deployment, reintegration, and death on military youth and families.

PubMed

Kaplow, Julie B; Layne, Christopher M; Saltzman, William R; Cozza, Stephen J; Pynoos, Robert S

2013-09-01

To date, the US military has made major strides in acknowledging and therapeutically addressing trauma and post-traumatic stress disorder (PTSD) in service members and their families. However, given the nature of warfare and high rates of losses sustained by both military members (e.g., deaths of fellow unit members) and military families (e.g., loss of a young parent who served in the military), as well as the ongoing threat of loss that military families face during deployment, we propose that a similar focus on grief is also needed to properly understand and address many of the challenges encountered by bereaved service members, spouses, and children. In this article, we describe a newly developed theory of grief (multidimensional grief theory) and apply it to the task of exploring major features of military-related experiences during the phases of deployment, reintegration, and the aftermath of combat death--especially as they impact children. We also describe implications for designing preventive interventions during each phase and conclude with recommended avenues for future research. Primary aims are to illustrate: (1) the indispensable role of theory in guiding efforts to describe, explain, predict, prevent, and treat maladaptive grief in military service members, children, and families; (2) the relevance of multidimensional grief theory for addressing both losses due to physical death as well as losses brought about by extended physical separations to which military children and families are exposed during and after deployment; and (3) a focus on military-related grief as a much-needed complement to an already-established focus on military-related PTSD.

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…

Quantifying Stream-Aquifer Exchanges Over Scales: the Concept of Nested Interfaces

NASA Astrophysics Data System (ADS)

Flipo, N.; Mouhri, A.; Labarthe, B.; Saleh, F. S.

2013-12-01

Recent developments in hydrological modelling are based on a view of the interface being a single continuum through which water flows. These coupled hydrological-hydrogeological models, emphasizing the importance of the stream-aquifer interface (SAI), are more and more used in hydrological sciences for pluri-disciplinary studies aiming at questioning environmental issues. This notion of a single continuum comes from the historical modelling of hydrosystems based on the hypothesis of a homogeneous media that led to the Darcy law. Nowadays, there is a need to first bridge the gap between hydrological and eco-hydrological views of the SAIs, and, second, to rationalize the modelling of SAI within a consistent framework that fully takes into account the multi-dimensionality of the SAIs. We first define the concept of nested SAIs as a key transitional component of continental hydrosystem. We then demonstrate the usefulness of the concept for the multi-dimensional study of the SAI, with a special emphasis on the stream network which is identified as the key component for scaling hydrological processes occurring at the interface. Finally we focus on SAI modelling at various scales with up-to-date methodologies and give some guidance for the multi-dimensional modelling of the interface using the innovative methodology MIM (Measurements-Interpolation-Modelling), which is graphically developed. MIM scales in space three pools of methods needed to fully understand SAIs. The outcome of MIM is the localization in space of the type of SAI that can be studied by a given approach. The efficiency of the method is illustrated from the local (approx. 1m) to the regional scale (> 10 000 km2) with two examples from the Paris basin (France). The first one consists in the implementation of a sampling system of stream-aquifer exchanges, which is coupled with local 2D thermo-hydro models and a pseudo 3D hydro(geo)logical model at the watershed scale (40 km2). The quantification of monthly stream-aquifer exchanges over 14 000 km of river network in the Paris basin (74 000 km2) corresponds to a unique regional scale example.

Automated Spatiotemporal Analysis of Fibrils and Coronal Rain Using the Rolling Hough Transform

NASA Astrophysics Data System (ADS)

Schad, Thomas

2017-09-01

A technique is presented that automates the direction characterization of curvilinear features in multidimensional solar imaging datasets. It is an extension of the Rolling Hough Transform (RHT) technique presented by Clark, Peek, and Putman ( Astrophys. J. 789, 82, 2014), and it excels at rapid quantification of spatial and spatiotemporal feature orientation even for applications with a low signal-to-noise ratio. It operates on a pixel-by-pixel basis within a dataset and reliably quantifies orientation even for locations not centered on a feature ridge, which is used here to derive a quasi-continuous map of the chromospheric fine-structure projection angle. For time-series analysis, a procedure is developed that uses a hierarchical application of the RHT to automatically derive the apparent motion of coronal rain observed off-limb. Essential to the success of this technique is the formulation presented in this article for the RHT error analysis as it provides a means to properly filter results.

Color Image Segmentation Based on Statistics of Location and Feature Similarity

NASA Astrophysics Data System (ADS)

Mori, Fumihiko; Yamada, Hiromitsu; Mizuno, Makoto; Sugano, Naotoshi

The process of “image segmentation and extracting remarkable regions” is an important research subject for the image understanding. However, an algorithm based on the global features is hardly found. The requisite of such an image segmentation algorism is to reduce as much as possible the over segmentation and over unification. We developed an algorithm using the multidimensional convex hull based on the density as the global feature. In the concrete, we propose a new algorithm in which regions are expanded according to the statistics of the region such as the mean value, standard deviation, maximum value and minimum value of pixel location, brightness and color elements and the statistics are updated. We also introduced a new concept of conspicuity degree and applied it to the various 21 images to examine the effectiveness. The remarkable object regions, which were extracted by the presented system, highly coincided with those which were pointed by the sixty four subjects who attended the psychological experiment.

Microtopographic and depth controls on active layer chemistry in Arctic polygonal ground

DOE PAGES

Newman, Brent D.; Throckmorton, Heather M.; Graham, David E.; ...

2015-03-24

Polygonal ground is a signature characteristic of Arctic lowlands, and carbon release from permafrost thaw can alter feedbacks to Arctic ecosystems and climate. This study describes the first comprehensive spatial examination of active layer biogeochemistry that extends across high- and low-centered, ice wedge polygons, their features, and with depth. Water chemistry measurements of 54 analytes were made on surface and active layer pore waters collected near Barrow, Alaska, USA. Significant differences were observed between high- and low-centered polygons suggesting that polygon types may be useful for landscape-scale geochemical classification. However, differences were found for polygon features (centers and troughs) formore » analytes that were not significant for polygon type, suggesting that finer-scale features affect biogeochemistry differently from polygon types. Depth variations were also significant, demonstrating important multidimensional aspects of polygonal ground biogeochemistry. These results have major implications for understanding how polygonal ground ecosystems function, and how they may respond to future change.« less

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.

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

Li, Minghai; Duan, Mojie; Fan, Jue

The thermodynamics and kinetics of protein folding and protein conformational changes are governed by the underlying free energy landscape. However, the multidimensional nature of the free energy landscape makes it difficult to describe. We propose to use a weighted-graph approach to depict the free energy landscape with the nodes on the graph representing the conformational states and the edge weights reflecting the free energy barriers between the states. Our graph is constructed from a molecular dynamics trajectory and does not involve projecting the multi-dimensional free energy landscape onto a low-dimensional space defined by a few order parameters. The calculation ofmore » free energy barriers was based on transition-path theory using the MSMBuilder2 package. We compare our graph with the widely used transition disconnectivity graph (TRDG) which is constructed from the same trajectory and show that our approach gives more accurate description of the free energy landscape than the TRDG approach even though the latter can be organized into a simple tree representation. The weighted-graph is a general approach and can be used on any complex system.« less

On the chaotic diffusion in multidimensional Hamiltonian systems

NASA Astrophysics Data System (ADS)

Cincotta, P. M.; Giordano, C. M.; Martí, J. G.; Beaugé, C.

2018-01-01

We present numerical evidence that diffusion in the herein studied multidimensional near-integrable Hamiltonian systems departs from a normal process, at least for realistic timescales. Therefore, the derivation of a diffusion coefficient from a linear fit on the variance evolution of the unperturbed integrals fails. We review some topics on diffusion in the Arnold Hamiltonian and yield numerical and theoretical arguments to show that in the examples we considered, a standard coefficient would not provide a good estimation of the speed of diffusion. However, numerical experiments concerning diffusion would provide reliable information about the stability of the motion within chaotic regions of the phase space. In this direction, we present an extension of previous results concerning the dynamical structure of the Laplace resonance in Gliese-876 planetary system considering variations of the orbital parameters accordingly to the error introduced by the radial velocity determination. We found that a slight variation of the eccentricity of planet c would destabilize the inner region of the resonance that, though chaotic, shows stable when adopting the best fit values for the parameters.

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.

Stochastic Evolutionary Algorithms for Planning Robot Paths

NASA Technical Reports Server (NTRS)

Fink, Wolfgang; Aghazarian, Hrand; Huntsberger, Terrance; Terrile, Richard

2006-01-01

A computer program implements stochastic evolutionary algorithms for planning and optimizing collision-free paths for robots and their jointed limbs. Stochastic evolutionary algorithms can be made to produce acceptably close approximations to exact, optimal solutions for path-planning problems while often demanding much less computation than do exhaustive-search and deterministic inverse-kinematics algorithms that have been used previously for this purpose. Hence, the present software is better suited for application aboard robots having limited computing capabilities (see figure). The stochastic aspect lies in the use of simulated annealing to (1) prevent trapping of an optimization algorithm in local minima of an energy-like error measure by which the fitness of a trial solution is evaluated while (2) ensuring that the entire multidimensional configuration and parameter space of the path-planning problem is sampled efficiently with respect to both robot joint angles and computation time. Simulated annealing is an established technique for avoiding local minima in multidimensional optimization problems, but has not, until now, been applied to planning collision-free robot paths by use of low-power computers.

Machine Learning of Human Pluripotent Stem Cell-Derived Engineered Cardiac Tissue Contractility for Automated Drug Classification.

PubMed

Lee, Eugene K; Tran, David D; Keung, Wendy; Chan, Patrick; Wong, Gabriel; Chan, Camie W; Costa, Kevin D; Li, Ronald A; Khine, Michelle

2017-11-14

Accurately predicting cardioactive effects of new molecular entities for therapeutics remains a daunting challenge. Immense research effort has been focused toward creating new screening platforms that utilize human pluripotent stem cell (hPSC)-derived cardiomyocytes and three-dimensional engineered cardiac tissue constructs to better recapitulate human heart function and drug responses. As these new platforms become increasingly sophisticated and high throughput, the drug screens result in larger multidimensional datasets. Improved automated analysis methods must therefore be developed in parallel to fully comprehend the cellular response across a multidimensional parameter space. Here, we describe the use of machine learning to comprehensively analyze 17 functional parameters derived from force readouts of hPSC-derived ventricular cardiac tissue strips (hvCTS) electrically paced at a range of frequencies and exposed to a library of compounds. A generated metric is effective for then determining the cardioactivity of a given drug. Furthermore, we demonstrate a classification model that can automatically predict the mechanistic action of an unknown cardioactive drug. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Software Defined Networking (SDN) controlled all optical switching networks with multi-dimensional switching architecture

NASA Astrophysics Data System (ADS)

Zhao, Yongli; Ji, Yuefeng; Zhang, Jie; Li, Hui; Xiong, Qianjin; Qiu, Shaofeng

2014-08-01

Ultrahigh throughout capacity requirement is challenging the current optical switching nodes with the fast development of data center networks. Pbit/s level all optical switching networks need to be deployed soon, which will cause the high complexity of node architecture. How to control the future network and node equipment together will become a new problem. An enhanced Software Defined Networking (eSDN) control architecture is proposed in the paper, which consists of Provider NOX (P-NOX) and Node NOX (N-NOX). With the cooperation of P-NOX and N-NOX, the flexible control of the entire network can be achieved. All optical switching network testbed has been experimentally demonstrated with efficient control of enhanced Software Defined Networking (eSDN). Pbit/s level all optical switching nodes in the testbed are implemented based on multi-dimensional switching architecture, i.e. multi-level and multi-planar. Due to the space and cost limitation, each optical switching node is only equipped with four input line boxes and four output line boxes respectively. Experimental results are given to verify the performance of our proposed control and switching architecture.

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.

Correlative visualization techniques for multidimensional data

NASA Technical Reports Server (NTRS)

Treinish, Lloyd A.; Goettsche, Craig

1989-01-01

Critical to the understanding of data is the ability to provide pictorial or visual representation of those data, particularly in support of correlative data analysis. Despite the advancement of visualization techniques for scientific data over the last several years, there are still significant problems in bringing today's hardware and software technology into the hands of the typical scientist. For example, there are other computer science domains outside of computer graphics that are required to make visualization effective such as data management. Well-defined, flexible mechanisms for data access and management must be combined with rendering algorithms, data transformation, etc. to form a generic visualization pipeline. A generalized approach to data visualization is critical for the correlative analysis of distinct, complex, multidimensional data sets in the space and Earth sciences. Different classes of data representation techniques must be used within such a framework, which can range from simple, static two- and three-dimensional line plots to animation, surface rendering, and volumetric imaging. Static examples of actual data analyses will illustrate the importance of an effective pipeline in data visualization system.