H-Metric: Characterizing Image Datasets via Homogenization Based on KNN-Queries

Directory of Open Access Journals (Sweden)

Welington M da Silva

2012-01-01

Full Text Available Precision-Recall is one of the main metrics for evaluating content-based image retrieval techniques. However, it does not provide an ample perception of the properties of an image dataset immersed in a metric space. In this work, we describe an alternative metric named H-Metric, which is determined along a sequence of controlled modifications in the image dataset. The process is named homogenization and works by altering the homogeneity characteristics of the classes of the images. The result is a process that measures how hard it is to deal with a set of images in respect to content-based retrieval, offering support in the task of analyzing configurations of distance functions and of features extractors.

Series distance – an intuitive metric to quantify hydrograph similarity in terms of occurrence, amplitude and timing of hydrological events

Directory of Open Access Journals (Sweden)

U. Ehret

2011-03-01

Full Text Available Applying metrics to quantify the similarity or dissimilarity of hydrographs is a central task in hydrological modelling, used both in model calibration and the evaluation of simulations or forecasts. Motivated by the shortcomings of standard objective metrics such as the Root Mean Square Error (RMSE or the Mean Absolute Peak Time Error (MAPTE and the advantages of visual inspection as a powerful tool for simultaneous, case-specific and multi-criteria (yet subjective evaluation, we propose a new objective metric termed Series Distance, which is in close accordance with visual evaluation. The Series Distance quantifies the similarity of two hydrographs neither in a time-aggregated nor in a point-by-point manner, but on the scale of hydrological events. It consists of three parts, namely a Threat Score which evaluates overall agreement of event occurrence, and the overall distance of matching observed and simulated events with respect to amplitude and timing. The novelty of the latter two is the way in which matching point pairs on the observed and simulated hydrographs are identified: not by equality in time (as is the case with the RMSE, but by the same relative position in matching segments (rise or recession of the event, indicating the same underlying hydrological process. Thus, amplitude and timing errors are calculated simultaneously but separately, from point pairs that also match visually, considering complete events rather than only individual points (as is the case with MAPTE. Relative weights can freely be assigned to each component of the Series Distance, which allows (subjective customization of the metric to various fields of application, but in a traceable way. Each of the three components of the Series Distance can be used in an aggregated or non-aggregated way, which makes the Series Distance a suitable tool for differentiated, process-based model diagnostics.

After discussing the applicability of established time series

WE-E-213CD-11: A New Automatically Generated Metric for Evaluating the Spatial Precision of Deformable Image Registrations: The Distance Discordance Metric.

Science.gov (United States)

Saleh, Z; Apte, A; Sharp, G; Deasy, J

2012-06-01

We propose a new metric called Distance Discordance (DD), which is defined as the distance between two anatomic points from two moving images, which are co-located on some reference image, when deformed onto another reference image. To demonstrate the concept of DD, we created a reference software phantom which contains two objects. The first object (1) consists of a hollow box with a fixed size core and variable wall thickness. The second object (2) consists of a solid box of fixed size and arbitrary location. 7 different variations of the fixed phantom were created. Each phantom was deformed onto every other phantom using two B-Spline DIR algorithms available in Elastix and Plastimatch. Voxels were sampled from the reference phantom [1], which were also deformed from moving phantoms [2…6], and we find the differences in their corresponding location on phantom [7]. Each voxel results in a distribution of DD values, which we call distance discordance histogram (DDH). We also demonstrate this concept in 8 Head & Neck patients. The two image registration algorithms produced two different DD results for the same phantom image set. The mean values of the DDH were slightly lower for Elastix (0-1.28 cm) as compared to the values produced by Plastimatch (0-1.43 cm). The combined DDH for the H&N patients followed a lognormal distribution with a mean of 0.45 cm and std. deviation of 0.42 cm. The proposed distance discordance (DD) metric is an easily interpretable, quantitative tool that can be used to evaluate the effect of inter-patient variability on the goodness of the registration in different parts of the patient anatomy. Therefore, it can be utilized to exclude certain images based on their DDH characteristics. In addition, this metric does not rely on 'ground truth' or the presence of contoured structures. Partially supported by NIH grant R01 CA85181. © 2012 American Association of Physicists in Medicine.

A PEG Construction of LDPC Codes Based on the Betweenness Centrality Metric

Directory of Open Access Journals (Sweden)

BHURTAH-SEEWOOSUNGKUR, I.

2016-05-01

Full Text Available Progressive Edge Growth (PEG constructions are usually based on optimizing the distance metric by using various methods. In this work however, the distance metric is replaced by a different one, namely the betweenness centrality metric, which was shown to enhance routing performance in wireless mesh networks. A new type of PEG construction for Low-Density Parity-Check (LDPC codes is introduced based on the betweenness centrality metric borrowed from social networks terminology given that the bipartite graph describing the LDPC is analogous to a network of nodes. The algorithm is very efficient in filling edges on the bipartite graph by adding its connections in an edge-by-edge manner. The smallest graph size the new code could construct surpasses those obtained from a modified PEG algorithm - the RandPEG algorithm. To the best of the authors' knowledge, this paper produces the best regular LDPC column-weight two graphs. In addition, the technique proves to be competitive in terms of error-correcting performance. When compared to MacKay, PEG and other recent modified-PEG codes, the algorithm gives better performance over high SNR due to its particular edge and local graph properties.

An uncertainty importance measure using a distance metric for the change in a cumulative distribution function

International Nuclear Information System (INIS)

Chun, Moon-Hyun; Han, Seok-Jung; Tak, Nam-IL

2000-01-01

A simple measure of uncertainty importance using the entire change of cumulative distribution functions (CDFs) has been developed for use in probability safety assessments (PSAs). The entire change of CDFs is quantified in terms of the metric distance between two CDFs. The metric distance measure developed in this study reflects the relative impact of distributional changes of inputs on the change of an output distribution, while most of the existing uncertainty importance measures reflect the magnitude of relative contribution of input uncertainties to the output uncertainty. The present measure has been evaluated analytically for various analytical distributions to examine its characteristics. To illustrate the applicability and strength of the present measure, two examples are provided. The first example is an application of the present measure to a typical problem of a system fault tree analysis and the second one is for a hypothetical non-linear model. Comparisons of the present result with those obtained by existing uncertainty importance measures show that the metric distance measure is a useful tool to express the measure of uncertainty importance in terms of the relative impact of distributional changes of inputs on the change of an output distribution

Discriminatory Data Mapping by Matrix-Based Supervised Learning Metrics

NARCIS (Netherlands)

Strickert, M.; Schneider, P.; Keilwagen, J.; Villmann, T.; Biehl, M.; Hammer, B.

2008-01-01

Supervised attribute relevance detection using cross-comparisons (SARDUX), a recently proposed method for data-driven metric learning, is extended from dimension-weighted Minkowski distances to metrics induced by a data transformation matrix Ω for modeling mutual attribute dependence. Given class

Multi-site Study of Diffusion Metric Variability: Characterizing the Effects of Site, Vendor, Field Strength, and Echo Time using the Histogram Distance

Science.gov (United States)

Helmer, K. G.; Chou, M-C.; Preciado, R. I.; Gimi, B.; Rollins, N. K.; Song, A.; Turner, J.; Mori, S.

2016-01-01

MRI-based multi-site trials now routinely include some form of diffusion-weighted imaging (DWI) in their protocol. These studies can include data originating from scanners built by different vendors, each with their own set of unique protocol restrictions, including restrictions on the number of available gradient directions, whether an externally-generated list of gradient directions can be used, and restrictions on the echo time (TE). One challenge of multi-site studies is to create a common imaging protocol that will result in a reliable and accurate set of diffusion metrics. The present study describes the effect of site, scanner vendor, field strength, and TE on two common metrics: the first moment of the diffusion tensor field (mean diffusivity, MD), and the fractional anisotropy (FA). We have shown in earlier work that ROI metrics and the mean of MD and FA histograms are not sufficiently sensitive for use in site characterization. Here we use the distance between whole brain histograms of FA and MD to investigate within- and between-site effects. We concluded that the variability of DTI metrics due to site, vendor, field strength, and echo time could influence the results in multi-center trials and that histogram distance is sensitive metrics for each of these variables. PMID:27350723

Multi-site Study of Diffusion Metric Variability: Characterizing the Effects of Site, Vendor, Field Strength, and Echo Time using the Histogram Distance.

Science.gov (United States)

Helmer, K G; Chou, M-C; Preciado, R I; Gimi, B; Rollins, N K; Song, A; Turner, J; Mori, S

2016-02-27

MRI-based multi-site trials now routinely include some form of diffusion-weighted imaging (DWI) in their protocol. These studies can include data originating from scanners built by different vendors, each with their own set of unique protocol restrictions, including restrictions on the number of available gradient directions, whether an externally-generated list of gradient directions can be used, and restrictions on the echo time (TE). One challenge of multi-site studies is to create a common imaging protocol that will result in a reliable and accurate set of diffusion metrics. The present study describes the effect of site, scanner vendor, field strength, and TE on two common metrics: the first moment of the diffusion tensor field (mean diffusivity, MD), and the fractional anisotropy (FA). We have shown in earlier work that ROI metrics and the mean of MD and FA histograms are not sufficiently sensitive for use in site characterization. Here we use the distance between whole brain histograms of FA and MD to investigate within- and between-site effects. We concluded that the variability of DTI metrics due to site, vendor, field strength, and echo time could influence the results in multi-center trials and that histogram distance is sensitive metrics for each of these variables.

Drift correction for single-molecule imaging by molecular constraint field, a distance minimum metric

International Nuclear Information System (INIS)

Han, Renmin; Wang, Liansan; Xu, Fan; Zhang, Yongdeng; Zhang, Mingshu; Liu, Zhiyong; Ren, Fei; Zhang, Fa

2015-01-01

The recent developments of far-field optical microscopy (single molecule imaging techniques) have overcome the diffraction barrier of light and improve image resolution by a factor of ten compared with conventional light microscopy. These techniques utilize the stochastic switching of probe molecules to overcome the diffraction limit and determine the precise localizations of molecules, which often requires a long image acquisition time. However, long acquisition times increase the risk of sample drift. In the case of high resolution microscopy, sample drift would decrease the image resolution. In this paper, we propose a novel metric based on the distance between molecules to solve the drift correction. The proposed metric directly uses the position information of molecules to estimate the frame drift. We also designed an algorithm to implement the metric for the general application of drift correction. There are two advantages of our method: First, because our method does not require space binning of positions of molecules but directly operates on the positions, it is more natural for single molecule imaging techniques. Second, our method can estimate drift with a small number of positions in each temporal bin, which may extend its potential application. The effectiveness of our method has been demonstrated by both simulated data and experiments on single molecular images

Encyclopedia of distances

CERN Document Server

Deza, Michel Marie

2016-01-01

This 4th edition of the leading reference volume on distance metrics is characterized by updated and rewritten sections on some items suggested by experts and readers, as well a general streamlining of content and the addition of essential new topics. Though the structure remains unchanged, the new edition also explores recent advances in the use of distances and metrics for e.g. generalized distances, probability theory, graph theory, coding theory, data analysis. New topics in the purely mathematical sections include e.g. the Vitanyi multiset-metric, algebraic point-conic distance, triangular ratio metric, Rossi-Hamming metric, Taneja distance, spectral semimetric between graphs, channel metrization, and Maryland bridge distance. The multidisciplinary sections have also been supplemented with new topics, including: dynamic time wrapping distance, memory distance, allometry, atmospheric depth, elliptic orbit distance, VLBI distance measurements, the astronomical system of units, and walkability distance. Lea...

Randomized Approaches for Nearest Neighbor Search in Metric Space When Computing the Pairwise Distance Is Extremely Expensive

Science.gov (United States)

Wang, Lusheng; Yang, Yong; Lin, Guohui

Finding the closest object for a query in a database is a classical problem in computer science. For some modern biological applications, computing the similarity between two objects might be very time consuming. For example, it takes a long time to compute the edit distance between two whole chromosomes and the alignment cost of two 3D protein structures. In this paper, we study the nearest neighbor search problem in metric space, where the pair-wise distance between two objects in the database is known and we want to minimize the number of distances computed on-line between the query and objects in the database in order to find the closest object. We have designed two randomized approaches for indexing metric space databases, where objects are purely described by their distances with each other. Analysis and experiments show that our approaches only need to compute O(logn) objects in order to find the closest object, where n is the total number of objects in the database.

Encyclopedia of distances

CERN Document Server

Deza, Michel Marie

2014-01-01

This updated and revised third edition of the leading reference volume on distance metrics includes new items from very active research areas in the use of distances and metrics such as geometry, graph theory, probability theory and analysis. Among the new topics included are, for example, polyhedral metric space, nearness matrix problems, distances between belief assignments, distance-related animal settings, diamond-cutting distances, natural units of length, Heidegger’s de-severance distance, and brain distances. The publication of this volume coincides with intensifying research efforts into metric spaces and especially distance design for applications. Accurate metrics have become a crucial goal in computational biology, image analysis, speech recognition and information retrieval. Leaving aside the practical questions that arise during the selection of a ‘good’ distance function, this work focuses on providing the research community with an invaluable comprehensive listing of the main available di...

Fixed point results for contractions involving generalized altering distances in ordered metric spaces

Directory of Open Access Journals (Sweden)

Samet Bessem

2011-01-01

Full Text Available Abstract In this article, we establish coincidence point and common fixed point theorems for mappings satisfying a contractive inequality which involves two generalized altering distance functions in ordered complete metric spaces. As application, we study the existence of a common solution to a system of integral equations. 2000 Mathematics subject classification. Primary 47H10, Secondary 54H25

Analysis and Comparison of Information Theory-based Distances for Genomic Strings

Science.gov (United States)

Balzano, Walter; Cicalese, Ferdinando; Del Sorbo, Maria Rosaria; Vaccaro, Ugo

2008-07-01

Genomic string comparison via alignment are widely applied for mining and retrieval of information in biological databases. In some situation, the effectiveness of such alignment based comparison is still unclear, e.g., for sequences with non-uniform length and with significant shuffling of identical substrings. An alternative approach is the one based on information theory distances. Biological data information content is stored in very long strings of only four characters. In last ten years, several entropic measures have been proposed for genomic string analysis. Notwithstanding their individual merit and experimental validation, to the nest of our knowledge, there is no direct comparison of these different metrics. We shall present four of the most representative alignment-free distance measures, based on mutual information. Each one has a different origin and expression. Our comparison involves a sort of arrangement, to reduce different concepts to a unique formalism, so as it has been possible to construct a phylogenetic tree for each of them. The trees produced via these metrics are compared to the ones widely accepted as biologically validated. In general the results provided more evidence of the reliability of the alignment-free distance models. Also, we observe that one of the metrics appeared to be more robust than the other three. We believe that this result can be object of further researches and observations. Many of the results of experimentation, the graphics and the table are available at the following URL: http://people.na.infn.it/˜wbalzano/BIO

Classification in medical images using adaptive metric k-NN

Science.gov (United States)

Chen, C.; Chernoff, K.; Karemore, G.; Lo, P.; Nielsen, M.; Lauze, F.

2010-03-01

The performance of the k-nearest neighborhoods (k-NN) classifier is highly dependent on the distance metric used to identify the k nearest neighbors of the query points. The standard Euclidean distance is commonly used in practice. This paper investigates the performance of k-NN classifier with respect to different adaptive metrics in the context of medical imaging. We propose using adaptive metrics such that the structure of the data is better described, introducing some unsupervised learning knowledge in k-NN. We investigated four different metrics are estimated: a theoretical metric based on the assumption that images are drawn from Brownian Image Model (BIM), the normalized metric based on variance of the data, the empirical metric is based on the empirical covariance matrix of the unlabeled data, and an optimized metric obtained by minimizing the classification error. The spectral structure of the empirical covariance also leads to Principal Component Analysis (PCA) performed on it which results the subspace metrics. The metrics are evaluated on two data sets: lateral X-rays of the lumbar aortic/spine region, where we use k-NN for performing abdominal aorta calcification detection; and mammograms, where we use k-NN for breast cancer risk assessment. The results show that appropriate choice of metric can improve classification.

Supplier selection using different metric functions

Directory of Open Access Journals (Sweden)

Omosigho S.E.

2015-01-01

Full Text Available Supplier selection is an important component of supply chain management in today’s global competitive environment. Hence, the evaluation and selection of suppliers have received considerable attention in the literature. Many attributes of suppliers, other than cost, are considered in the evaluation and selection process. Therefore, the process of evaluation and selection of suppliers is a multi-criteria decision making process. The methodology adopted to solve the supplier selection problem is intuitionistic fuzzy TOPSIS (Technique for Order Preference by Similarity to the Ideal Solution. Generally, TOPSIS is based on the concept of minimum distance from the positive ideal solution and maximum distance from the negative ideal solution. We examine the deficiencies of using only one metric function in TOPSIS and propose the use of spherical metric function in addition to the commonly used metric functions. For empirical supplier selection problems, more than one metric function should be used.

SU-E-J-159: Intra-Patient Deformable Image Registration Uncertainties Quantified Using the Distance Discordance Metric

International Nuclear Information System (INIS)

Saleh, Z; Thor, M; Apte, A; Deasy, J; Sharp, G; Muren, L

2014-01-01

Purpose: The quantitative evaluation of deformable image registration (DIR) is currently challenging due to lack of a ground truth. In this study we test a new method proposed for quantifying multiple-image based DIRrelated uncertainties, for DIR of pelvic images. Methods: 19 patients were analyzed, each with 6 CT scans, who previously had radiotherapy for prostate cancer. Manually delineated structures for rectum and bladder, which served as ground truth structures, were delineated on the planning CT and each subsequent scan. For each patient, voxel-by-voxel DIR-related uncertainties were evaluated, following B-spline based DIR, by applying a previously developed metric, the distance discordance metric (DDM; Saleh et al., PMB (2014) 59:733). The DDM map was superimposed on the first acquired CT scan and DDM statistics were assessed, also relative to two metrics estimating the agreement between the propagated and the manually delineated structures. Results: The highest DDM values which correspond to greatest spatial uncertainties were observed near the body surface and in the bowel due to the presence of gas. The mean rectal and bladder DDM values ranged from 1.1–11.1 mm and 1.5–12.7 mm, respectively. There was a strong correlation in the DDMs between the rectum and bladder (Pearson R = 0.68 for the max DDM). For both structures, DDM was correlated with the ratio between the DIR-propagated and manually delineated volumes (R = 0.74 for the max rectal DDM). The maximum rectal DDM was negatively correlated with the Dice Similarity Coefficient between the propagated and the manually delineated volumes (R= −0.52). Conclusion: The multipleimage based DDM map quantified considerable DIR variability across different structures and among patients. Besides using the DDM for quantifying DIR-related uncertainties it could potentially be used to adjust for uncertainties in DIR-based accumulated dose distributions

Value-based metrics and Internet-based enterprises

Science.gov (United States)

Gupta, Krishan M.

2001-10-01

Within the last few years, a host of value-based metrics like EVA, MVA, TBR, CFORI, and TSR have evolved. This paper attempts to analyze the validity and applicability of EVA and Balanced Scorecard for Internet based organizations. Despite the collapse of the dot-com model, the firms engaged in e- commerce continue to struggle to find new ways to account for customer-base, technology, employees, knowledge, etc, as part of the value of the firm. While some metrics, like the Balance Scorecard are geared towards internal use, others like EVA are for external use. Value-based metrics are used for performing internal audits as well as comparing firms against one another; and can also be effectively utilized by individuals outside the firm looking to determine if the firm is creating value for its stakeholders.

Stochastic Learning of Multi-Instance Dictionary for Earth Mover's Distance based Histogram Comparison

OpenAIRE

Fan, Jihong; Liang, Ru-Ze

2016-01-01

Dictionary plays an important role in multi-instance data representation. It maps bags of instances to histograms. Earth mover's distance (EMD) is the most effective histogram distance metric for the application of multi-instance retrieval. However, up to now, there is no existing multi-instance dictionary learning methods designed for EMD based histogram comparison. To fill this gap, we develop the first EMD-optimal dictionary learning method using stochastic optimization method. In the stoc...

Clustering by Partitioning around Medoids using Distance-Based ...

African Journals Online (AJOL)

OLUWASOGO

outperforms both the Euclidean and Manhattan distance metrics in certain situations. KEYWORDS: PAM ... version of a dataset, compare the quality of clusters obtained from the Euclidean .... B. Theoretical Framework and Methodology.

Continuity Properties of Distances for Markov Processes

DEFF Research Database (Denmark)

Jaeger, Manfred; Mao, Hua; Larsen, Kim Guldstrand

2014-01-01

In this paper we investigate distance functions on finite state Markov processes that measure the behavioural similarity of non-bisimilar processes. We consider both probabilistic bisimilarity metrics, and trace-based distances derived from standard Lp and Kullback-Leibler distances. Two desirable...

Metric diffusion along foliations

CERN Document Server

Walczak, Szymon M

2017-01-01

Up-to-date research in metric diffusion along compact foliations is presented in this book. Beginning with fundamentals from the optimal transportation theory and the theory of foliations; this book moves on to cover Wasserstein distance, Kantorovich Duality Theorem, and the metrization of the weak topology by the Wasserstein distance. Metric diffusion is defined, the topology of the metric space is studied and the limits of diffused metrics along compact foliations are discussed. Essentials on foliations, holonomy, heat diffusion, and compact foliations are detailed and vital technical lemmas are proved to aide understanding. Graduate students and researchers in geometry, topology and dynamics of foliations and laminations will find this supplement useful as it presents facts about the metric diffusion along non-compact foliation and provides a full description of the limit for metrics diffused along foliation with at least one compact leaf on the two dimensions.

Semantic metrics

OpenAIRE

Hu, Bo; Kalfoglou, Yannis; Dupplaw, David; Alani, Harith; Lewis, Paul; Shadbolt, Nigel

2006-01-01

In the context of the Semantic Web, many ontology-related operations, e.g. ontology ranking, segmentation, alignment, articulation, reuse, evaluation, can be boiled down to one fundamental operation: computing the similarity and/or dissimilarity among ontological entities, and in some cases among ontologies themselves. In this paper, we review standard metrics for computing distance measures and we propose a series of semantic metrics. We give a formal account of semantic metrics drawn from a...

A metric for the Radial Basis Function Network - Application on Real Radar Data

NARCIS (Netherlands)

Heiden, R. van der; Groen, F.C.A.

1996-01-01

A Radial Basis Functions (RBF) network for pattern recognition is considered. Classification with such a network is based on distances between patterns, so a metric is always present. Using real radar data, the Euclidean metric is shown to perform poorly - a metric based on the so called Box-Cox

Active Metric Learning for Supervised Classification

OpenAIRE

Kumaran, Krishnan; Papageorgiou, Dimitri; Chang, Yutong; Li, Minhan; Takáč, Martin

2018-01-01

Clustering and classification critically rely on distance metrics that provide meaningful comparisons between data points. We present mixed-integer optimization approaches to find optimal distance metrics that generalize the Mahalanobis metric extensively studied in the literature. Additionally, we generalize and improve upon leading methods by removing reliance on pre-designated "target neighbors," "triplets," and "similarity pairs." Another salient feature of our method is its ability to en...

Distinguishability notion based on Wootters statistical distance: Application to discrete maps

Science.gov (United States)

Gomez, Ignacio S.; Portesi, M.; Lamberti, P. W.

2017-08-01

We study the distinguishability notion given by Wootters for states represented by probability density functions. This presents the particularity that it can also be used for defining a statistical distance in chaotic unidimensional maps. Based on that definition, we provide a metric d ¯ for an arbitrary discrete map. Moreover, from d ¯ , we associate a metric space with each invariant density of a given map, which results to be the set of all distinguished points when the number of iterations of the map tends to infinity. Also, we give a characterization of the wandering set of a map in terms of the metric d ¯ , which allows us to identify the dissipative regions in the phase space. We illustrate the results in the case of the logistic and the circle maps numerically and analytically, and we obtain d ¯ and the wandering set for some characteristic values of their parameters. Finally, an extension of the metric space associated for arbitrary probability distributions (not necessarily invariant densities) is given along with some consequences. The statistical properties of distributions given by histograms are characterized in terms of the cardinal of the associated metric space. For two conjugate variables, the uncertainty principle is expressed in terms of the diameters of the associated metric space with those variables.

Normalized compression distance of multisets with applications

NARCIS (Netherlands)

Cohen, A.R.; Vitányi, P.M.B.

Pairwise normalized compression distance (NCD) is a parameter-free, feature-free, alignment-free, similarity metric based on compression. We propose an NCD of multisets that is also metric. Previously, attempts to obtain such an NCD failed. For classification purposes it is superior to the pairwise

Distance Metric Tracking

Science.gov (United States)

2016-03-02

whereBψ is any Bregman divergence and ηt is the learning rate parameter. From (Hall & Willett, 2015) we have: Theorem 1. G` = max θ∈Θ,`∈L ‖∇f(θ)‖ φmax = 1...Kullback-Liebler divergence between an initial guess of the matrix that parameterizes the Mahalanobis distance and a solution that satisfies a set of...Bregman divergence and ηt is the learning rate parameter. M̂0, µ̂0 are initialized to some initial value. In [18] a closed-form algorithm for solving

Equivalence Testing of Complex Particle Size Distribution Profiles Based on Earth Mover's Distance.

Science.gov (United States)

Hu, Meng; Jiang, Xiaohui; Absar, Mohammad; Choi, Stephanie; Kozak, Darby; Shen, Meiyu; Weng, Yu-Ting; Zhao, Liang; Lionberger, Robert

2018-04-12

Particle size distribution (PSD) is an important property of particulates in drug products. In the evaluation of generic drug products formulated as suspensions, emulsions, and liposomes, the PSD comparisons between a test product and the branded product can provide useful information regarding in vitro and in vivo performance. Historically, the FDA has recommended the population bioequivalence (PBE) statistical approach to compare the PSD descriptors D50 and SPAN from test and reference products to support product equivalence. In this study, the earth mover's distance (EMD) is proposed as a new metric for comparing PSD particularly when the PSD profile exhibits complex distribution (e.g., multiple peaks) that is not accurately described by the D50 and SPAN descriptor. EMD is a statistical metric that measures the discrepancy (distance) between size distribution profiles without a prior assumption of the distribution. PBE is then adopted to perform statistical test to establish equivalence based on the calculated EMD distances. Simulations show that proposed EMD-based approach is effective in comparing test and reference profiles for equivalence testing and is superior compared to commonly used distance measures, e.g., Euclidean and Kolmogorov-Smirnov distances. The proposed approach was demonstrated by evaluating equivalence of cyclosporine ophthalmic emulsion PSDs that were manufactured under different conditions. Our results show that proposed approach can effectively pass an equivalent product (e.g., reference product against itself) and reject an inequivalent product (e.g., reference product against negative control), thus suggesting its usefulness in supporting bioequivalence determination of a test product to the reference product which both possess multimodal PSDs.

Managing distance and covariate information with point-based clustering

Directory of Open Access Journals (Sweden)

Peter A. Whigham

2016-09-01

Full Text Available Abstract Background Geographic perspectives of disease and the human condition often involve point-based observations and questions of clustering or dispersion within a spatial context. These problems involve a finite set of point observations and are constrained by a larger, but finite, set of locations where the observations could occur. Developing a rigorous method for pattern analysis in this context requires handling spatial covariates, a method for constrained finite spatial clustering, and addressing bias in geographic distance measures. An approach, based on Ripley’s K and applied to the problem of clustering with deliberate self-harm (DSH, is presented. Methods Point-based Monte-Carlo simulation of Ripley’s K, accounting for socio-economic deprivation and sources of distance measurement bias, was developed to estimate clustering of DSH at a range of spatial scales. A rotated Minkowski L1 distance metric allowed variation in physical distance and clustering to be assessed. Self-harm data was derived from an audit of 2 years’ emergency hospital presentations (n = 136 in a New Zealand town (population ~50,000. Study area was defined by residential (housing land parcels representing a finite set of possible point addresses. Results Area-based deprivation was spatially correlated. Accounting for deprivation and distance bias showed evidence for clustering of DSH for spatial scales up to 500 m with a one-sided 95 % CI, suggesting that social contagion may be present for this urban cohort. Conclusions Many problems involve finite locations in geographic space that require estimates of distance-based clustering at many scales. A Monte-Carlo approach to Ripley’s K, incorporating covariates and models for distance bias, are crucial when assessing health-related clustering. The case study showed that social network structure defined at the neighbourhood level may account for aspects of neighbourhood clustering of DSH. Accounting for

Energy-Based Metrics for Arthroscopic Skills Assessment.

Science.gov (United States)

Poursartip, Behnaz; LeBel, Marie-Eve; McCracken, Laura C; Escoto, Abelardo; Patel, Rajni V; Naish, Michael D; Trejos, Ana Luisa

2017-08-05

Minimally invasive skills assessment methods are essential in developing efficient surgical simulators and implementing consistent skills evaluation. Although numerous methods have been investigated in the literature, there is still a need to further improve the accuracy of surgical skills assessment. Energy expenditure can be an indication of motor skills proficiency. The goals of this study are to develop objective metrics based on energy expenditure, normalize these metrics, and investigate classifying trainees using these metrics. To this end, different forms of energy consisting of mechanical energy and work were considered and their values were divided by the related value of an ideal performance to develop normalized metrics. These metrics were used as inputs for various machine learning algorithms including support vector machines (SVM) and neural networks (NNs) for classification. The accuracy of the combination of the normalized energy-based metrics with these classifiers was evaluated through a leave-one-subject-out cross-validation. The proposed method was validated using 26 subjects at two experience levels (novices and experts) in three arthroscopic tasks. The results showed that there are statistically significant differences between novices and experts for almost all of the normalized energy-based metrics. The accuracy of classification using SVM and NN methods was between 70% and 95% for the various tasks. The results show that the normalized energy-based metrics and their combination with SVM and NN classifiers are capable of providing accurate classification of trainees. The assessment method proposed in this study can enhance surgical training by providing appropriate feedback to trainees about their level of expertise and can be used in the evaluation of proficiency.

Inferring feature relevances from metric learning

DEFF Research Database (Denmark)

Schulz, Alexander; Mokbel, Bassam; Biehl, Michael

2015-01-01

Powerful metric learning algorithms have been proposed in the last years which do not only greatly enhance the accuracy of distance-based classifiers and nearest neighbor database retrieval, but which also enable the interpretability of these operations by assigning explicit relevance weights...

On the importance of the distance measures used to train and test knowledge-based potentials for proteins

DEFF Research Database (Denmark)

Carlsen, Martin; Koehl, Patrice; Røgen, Peter

2014-01-01

(PPD), while the other had the set of all distances filtered to reflect consistency in an ensemble of decoys (PPE). We tested four types of metric to characterize the distance between the decoy and the native structure, two based on extrinsic geometry (RMSD and GTD-TS*), and two based on intrinsic...... geometry (Q* and MT). The corresponding eight potentials were tested on a large collection of decoy sets. We found that it is usually better to train a potential using an intrinsic distance measure. We also found that PPE outperforms PPD, emphasizing the benefits of capturing consistent information...

Metric Learning for Hyperspectral Image Segmentation

Science.gov (United States)

Bue, Brian D.; Thompson, David R.; Gilmore, Martha S.; Castano, Rebecca

2011-01-01

We present a metric learning approach to improve the performance of unsupervised hyperspectral image segmentation. Unsupervised spatial segmentation can assist both user visualization and automatic recognition of surface features. Analysts can use spatially-continuous segments to decrease noise levels and/or localize feature boundaries. However, existing segmentation methods use tasks-agnostic measures of similarity. Here we learn task-specific similarity measures from training data, improving segment fidelity to classes of interest. Multiclass Linear Discriminate Analysis produces a linear transform that optimally separates a labeled set of training classes. The defines a distance metric that generalized to a new scenes, enabling graph-based segmentation that emphasizes key spectral features. We describe tests based on data from the Compact Reconnaissance Imaging Spectrometer (CRISM) in which learned metrics improve segment homogeneity with respect to mineralogical classes.

Image retrieval by information fusion based on scalable vocabulary tree and robust Hausdorff distance

Science.gov (United States)

Che, Chang; Yu, Xiaoyang; Sun, Xiaoming; Yu, Boyang

2017-12-01

In recent years, Scalable Vocabulary Tree (SVT) has been shown to be effective in image retrieval. However, for general images where the foreground is the object to be recognized while the background is cluttered, the performance of the current SVT framework is restricted. In this paper, a new image retrieval framework that incorporates a robust distance metric and information fusion is proposed, which improves the retrieval performance relative to the baseline SVT approach. First, the visual words that represent the background are diminished by using a robust Hausdorff distance between different images. Second, image matching results based on three image signature representations are fused, which enhances the retrieval precision. We conducted intensive experiments on small-scale to large-scale image datasets: Corel-9, Corel-48, and PKU-198, where the proposed Hausdorff metric and information fusion outperforms the state-of-the-art methods by about 13, 15, and 15%, respectively.

Moving from gamma passing rates to patient DVH-based QA metrics in pretreatment dose QA

Energy Technology Data Exchange (ETDEWEB)

Zhen, Heming; Nelms, Benjamin E.; Tome, Wolfgang A. [Department of Medical Physics, University of Wisconsin, Madison, Wisconsin 53705 (United States); Department of Human Oncology, University of Wisconsin, Madison, Wisconsin 53792 and Canis Lupus LLC, Merrimac, Wisconsin 53561 (United States); Department of Medical Physics, University of Wisconsin, Madison, Wisconsin 53705 and Department of Human Oncology, University of Wisconsin, Madison, Wisconsin 53792 (United States)

2011-10-15

Purpose: The purpose of this work is to explore the usefulness of the gamma passing rate metric for per-patient, pretreatment dose QA and to validate a novel patient-dose/DVH-based method and its accuracy and correlation. Specifically, correlations between: (1) gamma passing rates for three 3D dosimeter detector geometries vs clinically relevant patient DVH-based metrics; (2) Gamma passing rates of whole patient dose grids vs DVH-based metrics, (3) gamma passing rates filtered by region of interest (ROI) vs DVH-based metrics, and (4) the capability of a novel software algorithm that estimates corrected patient Dose-DVH based on conventional phan-tom QA data are analyzed. Methods: Ninety six unique ''imperfect'' step-and-shoot IMRT plans were generated by applying four different types of errors on 24 clinical Head/Neck patients. The 3D patient doses as well as the dose to a cylindrical QA phantom were then recalculated using an error-free beam model to serve as a simulated measurement for comparison. Resulting deviations to the planned vs simulated measured DVH-based metrics were generated, as were gamma passing rates for a variety of difference/distance criteria covering: dose-in-phantom comparisons and dose-in-patient comparisons, with the in-patient results calculated both over the whole grid and per-ROI volume. Finally, patient dose and DVH were predicted using the conventional per-beam planar data as input into a commercial ''planned dose perturbation'' (PDP) algorithm, and the results of these predicted DVH-based metrics were compared to the known values. Results: A range of weak to moderate correlations were found between clinically relevant patient DVH metrics (CTV-D95, parotid D{sub mean}, spinal cord D1cc, and larynx D{sub mean}) and both 3D detector and 3D patient gamma passing rate (3%/3 mm, 2%/2 mm) for dose-in-phantom along with dose-in-patient for both whole patient volume and filtered per-ROI. There was

Kerr metric in cosmological background

Energy Technology Data Exchange (ETDEWEB)

Vaidya, P C [Gujarat Univ., Ahmedabad (India). Dept. of Mathematics

1977-06-01

A metric satisfying Einstein's equation is given which in the vicinity of the source reduces to the well-known Kerr metric and which at large distances reduces to the Robertson-Walker metric of a nomogeneous cosmological model. The radius of the event horizon of the Kerr black hole in the cosmological background is found out.

Generalized Distance Transforms and Skeletons in Graphics Hardware

NARCIS (Netherlands)

Strzodka, R.; Telea, A.

2004-01-01

We present a framework for computing generalized distance transforms and skeletons of two-dimensional objects using graphics hardware. Our method is based on the concept of footprint splatting. Combining different splats produces weighted distance transforms for different metrics, as well as the

Metrics for Performance Evaluation of Patient Exercises during Physical Therapy.

Science.gov (United States)

Vakanski, Aleksandar; Ferguson, Jake M; Lee, Stephen

2017-06-01

The article proposes a set of metrics for evaluation of patient performance in physical therapy exercises. Taxonomy is employed that classifies the metrics into quantitative and qualitative categories, based on the level of abstraction of the captured motion sequences. Further, the quantitative metrics are classified into model-less and model-based metrics, in reference to whether the evaluation employs the raw measurements of patient performed motions, or whether the evaluation is based on a mathematical model of the motions. The reviewed metrics include root-mean square distance, Kullback Leibler divergence, log-likelihood, heuristic consistency, Fugl-Meyer Assessment, and similar. The metrics are evaluated for a set of five human motions captured with a Kinect sensor. The metrics can potentially be integrated into a system that employs machine learning for modelling and assessment of the consistency of patient performance in home-based therapy setting. Automated performance evaluation can overcome the inherent subjectivity in human performed therapy assessment, and it can increase the adherence to prescribed therapy plans, and reduce healthcare costs.

Product Differentiation and Brand Competition in the Italian Breakfast Cereal Market: a Distance Metric Approach

Directory of Open Access Journals (Sweden)

Paolo Sckokai

2013-03-01

Full Text Available This article employs a nation-wide sample of supermarket scanner data to study product and brand competition in the Italian breakfast cereal market. A modified Almost Ideal Demand System (AIDS, that includes Distance Metrics (DMs as proposed by Pinkse, Slade and Brett (2002, is estimated to study demand responses, substitution patterns, own-price and cross-price elasticities. Estimation results provide evidence of some degree of brand loyalty, while consumers do not seem loyal to the product type. Elasticity estimates point out the presence of patterns of substitution within products sharing the same brand and similar nutritional characteristics.

MESUR: USAGE-BASED METRICS OF SCHOLARLY IMPACT

Energy Technology Data Exchange (ETDEWEB)

BOLLEN, JOHAN [Los Alamos National Laboratory; RODRIGUEZ, MARKO A. [Los Alamos National Laboratory; VAN DE SOMPEL, HERBERT [Los Alamos National Laboratory

2007-01-30

The evaluation of scholarly communication items is now largely a matter of expert opinion or metrics derived from citation data. Both approaches can fail to take into account the myriad of factors that shape scholarly impact. Usage data has emerged as a promising complement to existing methods o fassessment but the formal groundwork to reliably and validly apply usage-based metrics of schlolarly impact is lacking. The Andrew W. Mellon Foundation funded MESUR project constitutes a systematic effort to define, validate and cross-validate a range of usage-based metrics of schlolarly impact by creating a semantic model of the scholarly communication process. The constructed model will serve as the basis of a creating a large-scale semantic network that seamlessly relates citation, bibliographic and usage data from a variety of sources. A subsequent program that uses the established semantic network as a reference data set will determine the characteristics and semantics of a variety of usage-based metrics of schlolarly impact. This paper outlines the architecture and methodology adopted by the MESUR project and its future direction.

Stochastic learning of multi-instance dictionary for earth mover’s distance-based histogram comparison

KAUST Repository

Fan, Jihong; Liang, Ru-Ze

2016-01-01

Dictionary plays an important role in multi-instance data representation. It maps bags of instances to histograms. Earth mover’s distance (EMD) is the most effective histogram distance metric for the application of multi-instance retrieval. However

Metrics for Probabilistic Geometries

DEFF Research Database (Denmark)

Tosi, Alessandra; Hauberg, Søren; Vellido, Alfredo

2014-01-01

the distribution over mappings is given by a Gaussian process. We treat the corresponding latent variable model as a Riemannian manifold and we use the expectation of the metric under the Gaussian process prior to define interpolating paths and measure distance between latent points. We show how distances...

A Metric on Phylogenetic Tree Shapes.

Science.gov (United States)

Colijn, C; Plazzotta, G

2018-01-01

The shapes of evolutionary trees are influenced by the nature of the evolutionary process but comparisons of trees from different processes are hindered by the challenge of completely describing tree shape. We present a full characterization of the shapes of rooted branching trees in a form that lends itself to natural tree comparisons. We use this characterization to define a metric, in the sense of a true distance function, on tree shapes. The metric distinguishes trees from random models known to produce different tree shapes. It separates trees derived from tropical versus USA influenza A sequences, which reflect the differing epidemiology of tropical and seasonal flu. We describe several metrics based on the same core characterization, and illustrate how to extend the metric to incorporate trees' branch lengths or other features such as overall imbalance. Our approach allows us to construct addition and multiplication on trees, and to create a convex metric on tree shapes which formally allows computation of average tree shapes. © The Author(s) 2017. Published by Oxford University Press, on behalf of the Society of Systematic Biologists.

Distance metric learning for complex networks: Towards size-independent comparison of network structures

Science.gov (United States)

Aliakbary, Sadegh; Motallebi, Sadegh; Rashidian, Sina; Habibi, Jafar; Movaghar, Ali

2015-02-01

Real networks show nontrivial topological properties such as community structure and long-tail degree distribution. Moreover, many network analysis applications are based on topological comparison of complex networks. Classification and clustering of networks, model selection, and anomaly detection are just some applications of network comparison. In these applications, an effective similarity metric is needed which, given two complex networks of possibly different sizes, evaluates the amount of similarity between the structural features of the two networks. Traditional graph comparison approaches, such as isomorphism-based methods, are not only too time consuming but also inappropriate to compare networks with different sizes. In this paper, we propose an intelligent method based on the genetic algorithms for integrating, selecting, and weighting the network features in order to develop an effective similarity measure for complex networks. The proposed similarity metric outperforms state of the art methods with respect to different evaluation criteria.

Metric learning

CERN Document Server

Bellet, Aurelien; Sebban, Marc

2015-01-01

Similarity between objects plays an important role in both human cognitive processes and artificial systems for recognition and categorization. How to appropriately measure such similarities for a given task is crucial to the performance of many machine learning, pattern recognition and data mining methods. This book is devoted to metric learning, a set of techniques to automatically learn similarity and distance functions from data that has attracted a lot of interest in machine learning and related fields in the past ten years. In this book, we provide a thorough review of the metric learnin

Assessment of six dissimilarity metrics for climate analogues

Science.gov (United States)

Grenier, Patrick; Parent, Annie-Claude; Huard, David; Anctil, François; Chaumont, Diane

2013-04-01

Spatial analogue techniques consist in identifying locations whose recent-past climate is similar in some aspects to the future climate anticipated at a reference location. When identifying analogues, one key step is the quantification of the dissimilarity between two climates separated in time and space, which involves the choice of a metric. In this communication, spatial analogues and their usefulness are briefly discussed. Next, six metrics are presented (the standardized Euclidean distance, the Kolmogorov-Smirnov statistic, the nearest-neighbor distance, the Zech-Aslan energy statistic, the Friedman-Rafsky runs statistic and the Kullback-Leibler divergence), along with a set of criteria used for their assessment. The related case study involves the use of numerical simulations performed with the Canadian Regional Climate Model (CRCM-v4.2.3), from which three annual indicators (total precipitation, heating degree-days and cooling degree-days) are calculated over 30-year periods (1971-2000 and 2041-2070). Results indicate that the six metrics identify comparable analogue regions at a relatively large scale, but best analogues may differ substantially. For best analogues, it is also shown that the uncertainty stemming from the metric choice does generally not exceed that stemming from the simulation or model choice. A synthesis of the advantages and drawbacks of each metric is finally presented, in which the Zech-Aslan energy statistic stands out as the most recommended metric for analogue studies, whereas the Friedman-Rafsky runs statistic is the least recommended, based on this case study.

Deep Correlated Holistic Metric Learning for Sketch-Based 3D Shape Retrieval.

Science.gov (United States)

Dai, Guoxian; Xie, Jin; Fang, Yi

2018-07-01

How to effectively retrieve desired 3D models with simple queries is a long-standing problem in computer vision community. The model-based approach is quite straightforward but nontrivial, since people could not always have the desired 3D query model available by side. Recently, large amounts of wide-screen electronic devices are prevail in our daily lives, which makes the sketch-based 3D shape retrieval a promising candidate due to its simpleness and efficiency. The main challenge of sketch-based approach is the huge modality gap between sketch and 3D shape. In this paper, we proposed a novel deep correlated holistic metric learning (DCHML) method to mitigate the discrepancy between sketch and 3D shape domains. The proposed DCHML trains two distinct deep neural networks (one for each domain) jointly, which learns two deep nonlinear transformations to map features from both domains into a new feature space. The proposed loss, including discriminative loss and correlation loss, aims to increase the discrimination of features within each domain as well as the correlation between different domains. In the new feature space, the discriminative loss minimizes the intra-class distance of the deep transformed features and maximizes the inter-class distance of the deep transformed features to a large margin within each domain, while the correlation loss focused on mitigating the distribution discrepancy across different domains. Different from existing deep metric learning methods only with loss at the output layer, our proposed DCHML is trained with loss at both hidden layer and output layer to further improve the performance by encouraging features in the hidden layer also with desired properties. Our proposed method is evaluated on three benchmarks, including 3D Shape Retrieval Contest 2013, 2014, and 2016 benchmarks, and the experimental results demonstrate the superiority of our proposed method over the state-of-the-art methods.

Active Metric Learning from Relative Comparisons

OpenAIRE

Xiong, Sicheng; Rosales, Rómer; Pei, Yuanli; Fern, Xiaoli Z.

2014-01-01

This work focuses on active learning of distance metrics from relative comparison information. A relative comparison specifies, for a data point triplet $(x_i,x_j,x_k)$, that instance $x_i$ is more similar to $x_j$ than to $x_k$. Such constraints, when available, have been shown to be useful toward defining appropriate distance metrics. In real-world applications, acquiring constraints often require considerable human effort. This motivates us to study how to select and query the most useful ...

Predicting speech release from masking through spatial separation in distance

DEFF Research Database (Denmark)

Chabot-Leclerc, Alexandre; Dau, Torsten

2014-01-01

of spatial release from masking (SRM) where the masker is moved, on-axis, away from the target. Two binaural models, which use the conventional audio signal-to-noise ratio (SNR) in the decision metric, and two monaural models, using a decision metric based on the SNR in the envelope domain (SNRenv), were...... considered. The predictions were compared to data from Westermann et al. [2013, POMA, 19, 050156] in condi- tions where the target was located 0.5 m in front of the listener and the masker was presented at a distance of 0.5, 2, 5 or 10 m in front of the listener. The data showed an SRM of 10 dB when moving...... the masker from a distance of 0.5 m to a distance of 10 m. The long-term monaural model based on the SNRenv metric was able to account for most of the SRM data, whereas the models that used the audio SNR did not predict any SRM, even when they included an equalizationcancellation-like process. The short...

Information-theoretic semi-supervised metric learning via entropy regularization.

Science.gov (United States)

Niu, Gang; Dai, Bo; Yamada, Makoto; Sugiyama, Masashi

2014-08-01

We propose a general information-theoretic approach to semi-supervised metric learning called SERAPH (SEmi-supervised metRic leArning Paradigm with Hypersparsity) that does not rely on the manifold assumption. Given the probability parameterized by a Mahalanobis distance, we maximize its entropy on labeled data and minimize its entropy on unlabeled data following entropy regularization. For metric learning, entropy regularization improves manifold regularization by considering the dissimilarity information of unlabeled data in the unsupervised part, and hence it allows the supervised and unsupervised parts to be integrated in a natural and meaningful way. Moreover, we regularize SERAPH by trace-norm regularization to encourage low-dimensional projections associated with the distance metric. The nonconvex optimization problem of SERAPH could be solved efficiently and stably by either a gradient projection algorithm or an EM-like iterative algorithm whose M-step is convex. Experiments demonstrate that SERAPH compares favorably with many well-known metric learning methods, and the learned Mahalanobis distance possesses high discriminability even under noisy environments.

Evaluation of Vehicle-Based Crash Severity Metrics.

Science.gov (United States)

Tsoi, Ada H; Gabler, Hampton C

2015-01-01

Vehicle change in velocity (delta-v) is a widely used crash severity metric used to estimate occupant injury risk. Despite its widespread use, delta-v has several limitations. Of most concern, delta-v is a vehicle-based metric which does not consider the crash pulse or the performance of occupant restraints, e.g. seatbelts and airbags. Such criticisms have prompted the search for alternative impact severity metrics based upon vehicle kinematics. The purpose of this study was to assess the ability of the occupant impact velocity (OIV), acceleration severity index (ASI), vehicle pulse index (VPI), and maximum delta-v (delta-v) to predict serious injury in real world crashes. The study was based on the analysis of event data recorders (EDRs) downloaded from the National Automotive Sampling System / Crashworthiness Data System (NASS-CDS) 2000-2013 cases. All vehicles in the sample were GM passenger cars and light trucks involved in a frontal collision. Rollover crashes were excluded. Vehicles were restricted to single-event crashes that caused an airbag deployment. All EDR data were checked for a successful, completed recording of the event and that the crash pulse was complete. The maximum abbreviated injury scale (MAIS) was used to describe occupant injury outcome. Drivers were categorized into either non-seriously injured group (MAIS2-) or seriously injured group (MAIS3+), based on the severity of any injuries to the thorax, abdomen, and spine. ASI and OIV were calculated according to the Manual for Assessing Safety Hardware. VPI was calculated according to ISO/TR 12353-3, with vehicle-specific parameters determined from U.S. New Car Assessment Program crash tests. Using binary logistic regression, the cumulative probability of injury risk was determined for each metric and assessed for statistical significance, goodness-of-fit, and prediction accuracy. The dataset included 102,744 vehicles. A Wald chi-square test showed each vehicle-based crash severity metric

Some Metric Properties of Planar Gaussian Free Field

Science.gov (United States)

Goswami, Subhajit

In this thesis we study the properties of some metrics arising from two-dimensional Gaussian free field (GFF), namely the Liouville first-passage percolation (Liouville FPP), the Liouville graph distance and an effective resistance metric. In Chapter 1, we define these metrics as well as discuss the motivations for studying them. Roughly speaking, Liouville FPP is the shortest path metric in a planar domain D where the length of a path P is given by ∫Pe gammah(z)|dz| where h is the GFF on D and gamma > 0. In Chapter 2, we present an upper bound on the expected Liouville FPP distance between two typical points for small values of gamma (the near-Euclidean regime). A similar upper bound is derived in Chapter 3 for the Liouville graph distance which is, roughly, the minimal number of Euclidean balls with comparable Liouville quantum gravity (LQG) measure whose union contains a continuous path between two endpoints. Our bounds seem to be in disagreement with Watabiki's prediction (1993) on the random metric of Liouville quantum gravity in this regime. The contents of these two chapters are based on a joint work with Jian Ding. In Chapter 4, we derive some asymptotic estimates for effective resistances on a random network which is defined as follows. Given any gamma > 0 and for eta = {etav}v∈Z2 denoting a sample of the two-dimensional discrete Gaussian free field on Z2 pinned at the origin, we equip the edge ( u, v) with conductance egamma(etau + eta v). The metric structure of effective resistance plays a crucial role in our proof of the main result in Chapter 4. The primary motivation behind this metric is to understand the random walk on Z 2 where the edge (u, v) has weight egamma(etau + etav). Using the estimates from Chapter 4 we show in Chapter 5 that for almost every eta, this random walk is recurrent and that, with probability tending to 1 as T → infinity, the return probability at time 2T decays as T-1+o(1). In addition, we prove a version of subdiffusive

Evaluating hydrological model performance using information theory-based metrics

Science.gov (United States)

The accuracy-based model performance metrics not necessarily reflect the qualitative correspondence between simulated and measured streamflow time series. The objective of this work was to use the information theory-based metrics to see whether they can be used as complementary tool for hydrologic m...

Local adjacency metric dimension of sun graph and stacked book graph

Science.gov (United States)

Yulisda Badri, Alifiah; Darmaji

2018-03-01

A graph is a mathematical system consisting of a non-empty set of nodes and a set of empty sides. One of the topics to be studied in graph theory is the metric dimension. Application in the metric dimension is the navigation robot system on a path. Robot moves from one vertex to another vertex in the field by minimizing the errors that occur in translating the instructions (code) obtained from the vertices of that location. To move the robot must give different instructions (code). In order for the robot to move efficiently, the robot must be fast to translate the code of the nodes of the location it passes. so that the location vertex has a minimum distance. However, if the robot must move with the vertex location on a very large field, so the robot can not detect because the distance is too far.[6] In this case, the robot can determine its position by utilizing location vertices based on adjacency. The problem is to find the minimum cardinality of the required location vertex, and where to put, so that the robot can determine its location. The solution to this problem is the dimension of adjacency metric and adjacency metric bases. Rodrguez-Velzquez and Fernau combine the adjacency metric dimensions with local metric dimensions, thus becoming the local adjacency metric dimension. In the local adjacency metric dimension each vertex in the graph may have the same adjacency representation as the terms of the vertices. To obtain the local metric dimension of values in the graph of the Sun and the stacked book graph is used the construction method by considering the representation of each adjacent vertex of the graph.

Encyclopedia of distances

CERN Document Server

Deza, Michel Marie

2009-01-01

Distance metrics and distances have become an essential tool in many areas of pure and applied Mathematics. This title offers both independent introductions and definitions, while at the same time making cross-referencing easy through hyperlink-like boldfaced references to original definitions.

A guide to phylogenetic metrics for conservation, community ecology and macroecology

Science.gov (United States)

Cadotte, Marc W.; Carvalho, Silvia B.; Davies, T. Jonathan; Ferrier, Simon; Fritz, Susanne A.; Grenyer, Rich; Helmus, Matthew R.; Jin, Lanna S.; Mooers, Arne O.; Pavoine, Sandrine; Purschke, Oliver; Redding, David W.; Rosauer, Dan F.; Winter, Marten; Mazel, Florent

2016-01-01

ABSTRACT The use of phylogenies in ecology is increasingly common and has broadened our understanding of biological diversity. Ecological sub‐disciplines, particularly conservation, community ecology and macroecology, all recognize the value of evolutionary relationships but the resulting development of phylogenetic approaches has led to a proliferation of phylogenetic diversity metrics. The use of many metrics across the sub‐disciplines hampers potential meta‐analyses, syntheses, and generalizations of existing results. Further, there is no guide for selecting the appropriate metric for a given question, and different metrics are frequently used to address similar questions. To improve the choice, application, and interpretation of phylo‐diversity metrics, we organize existing metrics by expanding on a unifying framework for phylogenetic information. Generally, questions about phylogenetic relationships within or between assemblages tend to ask three types of question: how much; how different; or how regular? We show that these questions reflect three dimensions of a phylogenetic tree: richness, divergence, and regularity. We classify 70 existing phylo‐diversity metrics based on their mathematical form within these three dimensions and identify ‘anchor’ representatives: for α‐diversity metrics these are PD (Faith's phylogenetic diversity), MPD (mean pairwise distance), and VPD (variation of pairwise distances). By analysing mathematical formulae and using simulations, we use this framework to identify metrics that mix dimensions, and we provide a guide to choosing and using the most appropriate metrics. We show that metric choice requires connecting the research question with the correct dimension of the framework and that there are logical approaches to selecting and interpreting metrics. The guide outlined herein will help researchers navigate the current jungle of indices. PMID:26785932

Assessment of the Log-Euclidean Metric Performance in Diffusion Tensor Image Segmentation

Directory of Open Access Journals (Sweden)

Mostafa Charmi

2010-06-01

Full Text Available Introduction: Appropriate definition of the distance measure between diffusion tensors has a deep impact on Diffusion Tensor Image (DTI segmentation results. The geodesic metric is the best distance measure since it yields high-quality segmentation results. However, the important problem with the geodesic metric is a high computational cost of the algorithms based on it. The main goal of this paper is to assess the possible substitution of the geodesic metric with the Log-Euclidean one to reduce the computational cost of a statistical surface evolution algorithm. Materials and Methods: We incorporated the Log-Euclidean metric in the statistical surface evolution algorithm framework. To achieve this goal, the statistics and gradients of diffusion tensor images were defined using the Log-Euclidean metric. Numerical implementation of the segmentation algorithm was performed in the MATLAB software using the finite difference techniques. Results: In the statistical surface evolution framework, the Log-Euclidean metric was able to discriminate the torus and helix patterns in synthesis datasets and rat spinal cords in biological phantom datasets from the background better than the Euclidean and J-divergence metrics. In addition, similar results were obtained with the geodesic metric. However, the main advantage of the Log-Euclidean metric over the geodesic metric was the dramatic reduction of computational cost of the segmentation algorithm, at least by 70 times. Discussion and Conclusion: The qualitative and quantitative results have shown that the Log-Euclidean metric is a good substitute for the geodesic metric when using a statistical surface evolution algorithm in DTIs segmentation.

Ideal Based Cyber Security Technical Metrics for Control Systems

Energy Technology Data Exchange (ETDEWEB)

W. F. Boyer; M. A. McQueen

2007-10-01

Much of the world's critical infrastructure is at risk from attack through electronic networks connected to control systems. Security metrics are important because they provide the basis for management decisions that affect the protection of the infrastructure. A cyber security technical metric is the security relevant output from an explicit mathematical model that makes use of objective measurements of a technical object. A specific set of technical security metrics are proposed for use by the operators of control systems. Our proposed metrics are based on seven security ideals associated with seven corresponding abstract dimensions of security. We have defined at least one metric for each of the seven ideals. Each metric is a measure of how nearly the associated ideal has been achieved. These seven ideals provide a useful structure for further metrics development. A case study shows how the proposed metrics can be applied to an operational control system.

Operator-based metric for nuclear operations automation assessment

Energy Technology Data Exchange (ETDEWEB)

Zacharias, G.L.; Miao, A.X.; Kalkan, A. [Charles River Analytics Inc., Cambridge, MA (United States)] [and others

1995-04-01

Continuing advances in real-time computational capabilities will support enhanced levels of smart automation and AI-based decision-aiding systems in the nuclear power plant (NPP) control room of the future. To support development of these aids, we describe in this paper a research tool, and more specifically, a quantitative metric, to assess the impact of proposed automation/aiding concepts in a manner that can account for a number of interlinked factors in the control room environment. In particular, we describe a cognitive operator/plant model that serves as a framework for integrating the operator`s information-processing capabilities with his procedural knowledge, to provide insight as to how situations are assessed by the operator, decisions made, procedures executed, and communications conducted. Our focus is on the situation assessment (SA) behavior of the operator, the development of a quantitative metric reflecting overall operator awareness, and the use of this metric in evaluating automation/aiding options. We describe the results of a model-based simulation of a selected emergency scenario, and metric-based evaluation of a range of contemplated NPP control room automation/aiding options. The results demonstrate the feasibility of model-based analysis of contemplated control room enhancements, and highlight the need for empirical validation.

Metric space construction for the boundary of space-time

International Nuclear Information System (INIS)

Meyer, D.A.

1986-01-01

A distance function between points in space-time is defined and used to consider the manifold as a topological metric space. The properties of the distance function are investigated: conditions under which the metric and manifold topologies agree, the relationship with the causal structure of the space-time and with the maximum lifetime function of Wald and Yip, and in terms of the space of causal curves. The space-time is then completed as a topological metric space; the resultant boundary is compared with the causal boundary and is also calculated for some pertinent examples

Performance evaluation of a distance learning program.

OpenAIRE

Dailey, D. J.; Eno, K. R.; Brinkley, J. F.

1994-01-01

This paper presents a performance metric which uses a single number to characterize the response time for a non-deterministic client-server application operating over the Internet. When applied to a Macintosh-based distance learning application called the Digital Anatomist Browser, the metric allowed us to observe that "A typical student doing a typical mix of Browser commands on a typical data set will experience the same delay if they use a slow Macintosh on a local network or a fast Macint...

Emergence of the scale-invariant proportion in a flock from the metric-topological interaction.

Science.gov (United States)

Niizato, Takayuki; Murakami, Hisashi; Gunji, Yukio-Pegio

2014-05-01

Recently, it has become possible to more precisely analyze flocking behavior. Such research has prompted a reconsideration of the notion of neighborhoods in the theoretical model. Flocking based on topological distance is one such result. In a topological flocking model, a bird does not interact with its neighbors on the basis of a fixed-size neighborhood (i.e., on the basis of metric distance), but instead interacts with its nearest seven neighbors. Cavagna et al., moreover, found a new phenomenon in flocks that can be explained by neither metric distance nor topological distance: they found that correlated domains in a flock were larger than the metric and topological distance and that these domains were proportional to the total flock size. However, the role of scale-free correlation is still unclear. In a previous study, we constructed a metric-topological interaction model on three-dimensional spaces and showed that this model exhibited scale-free correlation. In this study, we found that scale-free correlation in a two-dimensional flock was more robust than in a three-dimensional flock for the threshold parameter. Furthermore, we also found a qualitative difference in behavior from using the fluctuation coherence, which we observed on three-dimensional flocking behavior. Our study suggests that two-dimensional flocks try to maintain a balance between the flock size and flock mobility by breaking into several smaller flocks. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Comparison of luminance based metrics in different lighting conditions

DEFF Research Database (Denmark)

Wienold, J.; Kuhn, T.E.; Christoffersen, J.

In this study, we evaluate established and newly developed metrics for predicting glare using data from three different research studies. The evaluation covers two different targets: 1. How well the user’s perception of glare magnitude correlates to the prediction of the glare metrics? 2. How well...... do the glare metrics describe the subjects’ disturbance by glare? We applied Spearman correlations, logistic regressions and an accuracy evaluation, based on an ROC-analysis. The results show that five of the twelve investigated metrics are failing at least one of the statistical tests. The other...... seven metrics CGI, modified DGI, DGP, Ev, average Luminance of the image Lavg, UGP and UGR are passing all statistical tests. DGP, CGI, DGI_mod and UGP have largest AUC and might be slightly more robust. The accuracy of the predictions of afore mentioned seven metrics for the disturbance by glare lies...

Metrics for assessing retailers based on consumer perception

Directory of Open Access Journals (Sweden)

Klimin Anastasii

2017-01-01

Full Text Available The article suggests a new look at trading platforms, which is called “metrics.” Metrics are a way to look at the point of sale in a large part from the buyer’s side. The buyer enters the store and make buying decision based on those factors that the seller often does not consider, or considers in part, because “does not see” them, since he is not a buyer. The article proposes the classification of retailers, metrics and a methodology for their determination, presents the results of an audit of retailers in St. Petersburg on the proposed methodology.

Finite Metric Spaces of Strictly negative Type

DEFF Research Database (Denmark)

Hjorth, Poul G.

If a finite metric space is of strictly negative type then its transfinite diameter is uniquely realized by an infinite extent (“load vector''). Finite metric spaces that have this property include all trees, and all finite subspaces of Euclidean and Hyperbolic spaces. We prove that if the distance...

On the importance of the distance measures used to train and test knowledge-based potentials for proteins.

Directory of Open Access Journals (Sweden)

Martin Carlsen

Full Text Available Knowledge-based potentials are energy functions derived from the analysis of databases of protein structures and sequences. They can be divided into two classes. Potentials from the first class are based on a direct conversion of the distributions of some geometric properties observed in native protein structures into energy values, while potentials from the second class are trained to mimic quantitatively the geometric differences between incorrectly folded models and native structures. In this paper, we focus on the relationship between energy and geometry when training the second class of knowledge-based potentials. We assume that the difference in energy between a decoy structure and the corresponding native structure is linearly related to the distance between the two structures. We trained two distance-based knowledge-based potentials accordingly, one based on all inter-residue distances (PPD, while the other had the set of all distances filtered to reflect consistency in an ensemble of decoys (PPE. We tested four types of metric to characterize the distance between the decoy and the native structure, two based on extrinsic geometry (RMSD and GTD-TS*, and two based on intrinsic geometry (Q* and MT. The corresponding eight potentials were tested on a large collection of decoy sets. We found that it is usually better to train a potential using an intrinsic distance measure. We also found that PPE outperforms PPD, emphasizing the benefits of capturing consistent information in an ensemble. The relevance of these results for the design of knowledge-based potentials is discussed.

Gamut Volume Index: a color preference metric based on meta-analysis and optimized colour samples.

Science.gov (United States)

Liu, Qiang; Huang, Zheng; Xiao, Kaida; Pointer, Michael R; Westland, Stephen; Luo, M Ronnier

2017-07-10

A novel metric named Gamut Volume Index (GVI) is proposed for evaluating the colour preference of lighting. This metric is based on the absolute gamut volume of optimized colour samples. The optimal colour set of the proposed metric was obtained by optimizing the weighted average correlation between the metric predictions and the subjective ratings for 8 psychophysical studies. The performance of 20 typical colour metrics was also investigated, which included colour difference based metrics, gamut based metrics, memory based metrics as well as combined metrics. It was found that the proposed GVI outperformed the existing counterparts, especially for the conditions where correlated colour temperatures differed.

Defining functional distances over Gene Ontology

Directory of Open Access Journals (Sweden)

del Pozo Angela

2008-01-01

Full Text Available Abstract Background A fundamental problem when trying to define the functional relationships between proteins is the difficulty in quantifying functional similarities, even when well-structured ontologies exist regarding the activity of proteins (i.e. 'gene ontology' -GO-. However, functional metrics can overcome the problems in the comparing and evaluating functional assignments and predictions. As a reference of proximity, previous approaches to compare GO terms considered linkage in terms of ontology weighted by a probability distribution that balances the non-uniform 'richness' of different parts of the Direct Acyclic Graph. Here, we have followed a different approach to quantify functional similarities between GO terms. Results We propose a new method to derive 'functional distances' between GO terms that is based on the simultaneous occurrence of terms in the same set of Interpro entries, instead of relying on the structure of the GO. The coincidence of GO terms reveals natural biological links between the GO functions and defines a distance model Df which fulfils the properties of a Metric Space. The distances obtained in this way can be represented as a hierarchical 'Functional Tree'. Conclusion The method proposed provides a new definition of distance that enables the similarity between GO terms to be quantified. Additionally, the 'Functional Tree' defines groups with biological meaning enhancing its utility for protein function comparison and prediction. Finally, this approach could be for function-based protein searches in databases, and for analysing the gene clusters produced by DNA array experiments.

Prioritizing Urban Habitats for Connectivity Conservation: Integrating Centrality and Ecological Metrics.

Science.gov (United States)

Poodat, Fatemeh; Arrowsmith, Colin; Fraser, David; Gordon, Ascelin

2015-09-01

Connectivity among fragmented areas of habitat has long been acknowledged as important for the viability of biological conservation, especially within highly modified landscapes. Identifying important habitat patches in ecological connectivity is a priority for many conservation strategies, and the application of 'graph theory' has been shown to provide useful information on connectivity. Despite the large number of metrics for connectivity derived from graph theory, only a small number have been compared in terms of the importance they assign to nodes in a network. This paper presents a study that aims to define a new set of metrics and compares these with traditional graph-based metrics, used in the prioritization of habitat patches for ecological connectivity. The metrics measured consist of "topological" metrics, "ecological metrics," and "integrated metrics," Integrated metrics are a combination of topological and ecological metrics. Eight metrics were applied to the habitat network for the fat-tailed dunnart within Greater Melbourne, Australia. A non-directional network was developed in which nodes were linked to adjacent nodes. These links were then weighted by the effective distance between patches. By applying each of the eight metrics for the study network, nodes were ranked according to their contribution to the overall network connectivity. The structured comparison revealed the similarity and differences in the way the habitat for the fat-tailed dunnart was ranked based on different classes of metrics. Due to the differences in the way the metrics operate, a suitable metric should be chosen that best meets the objectives established by the decision maker.

Computing Best and Worst Shortcuts of Graphs Embedded in Metric Spaces

DEFF Research Database (Denmark)

Wulff-Nilsen, Christian; Luo, Jun

2008-01-01

Given a graph embedded in a metric space, its dilation is the maximum over all distinct pairs of vertices of the ratio between their distance in the graph and the metric distance between them. Given such a graph G with n vertices and m edges and consisting of at most two connected components, we ...

Characterizing the round sphere by mean distance

DEFF Research Database (Denmark)

Kokkendorff, Simon Lyngby

2008-01-01

We discuss the measure theoretic metric invariants extent, rendezvous number and mean distance of a general compact metric space X and relate these to classical metric invariants such as diameter and radius. In the final section we focus attention to the category of Riemannian manifolds. The main...

Nonlinear Semi-Supervised Metric Learning Via Multiple Kernels and Local Topology.

Science.gov (United States)

Li, Xin; Bai, Yanqin; Peng, Yaxin; Du, Shaoyi; Ying, Shihui

2018-03-01

Changing the metric on the data may change the data distribution, hence a good distance metric can promote the performance of learning algorithm. In this paper, we address the semi-supervised distance metric learning (ML) problem to obtain the best nonlinear metric for the data. First, we describe the nonlinear metric by the multiple kernel representation. By this approach, we project the data into a high dimensional space, where the data can be well represented by linear ML. Then, we reformulate the linear ML by a minimization problem on the positive definite matrix group. Finally, we develop a two-step algorithm for solving this model and design an intrinsic steepest descent algorithm to learn the positive definite metric matrix. Experimental results validate that our proposed method is effective and outperforms several state-of-the-art ML methods.

A Kerr-NUT metric

International Nuclear Information System (INIS)

Vaidya, P.C.; Patel, L.K.; Bhatt, P.V.

1976-01-01

Using Galilean time and retarded distance as coordinates the usual Kerr metric is expressed in form similar to the Newman-Unti-Tamburino (NUT) metric. The combined Kerr-NUT metric is then investigated. In addition to the Kerr and NUT solutions of Einstein's equations, three other types of solutions are derived. These are (i) the radiating Kerr solution, (ii) the radiating NUT solution satisfying Rsub(ik) = sigmaxisub(i)xisub(k), xisub(i)xisup(i) = 0, and (iii) the associated Kerr solution satisfying Rsub(ik) = 0. Solution (i) is distinct from and simpler than the one reported earlier by Vaidya and Patel (Phys. Rev.; D7:3590 (1973)). Solutions (ii) and (iii) gave line elements which have the axis of symmetry as a singular line. (author)

Standardized reporting of functioning information on ICF-based common metrics.

Science.gov (United States)

Prodinger, Birgit; Tennant, Alan; Stucki, Gerold

2018-02-01

In clinical practice and research a variety of clinical data collection tools are used to collect information on people's functioning for clinical practice and research and national health information systems. Reporting on ICF-based common metrics enables standardized documentation of functioning information in national health information systems. The objective of this methodological note on applying the ICF in rehabilitation is to demonstrate how to report functioning information collected with a data collection tool on ICF-based common metrics. We first specify the requirements for the standardized reporting of functioning information. Secondly, we introduce the methods needed for transforming functioning data to ICF-based common metrics. Finally, we provide an example. The requirements for standardized reporting are as follows: 1) having a common conceptual framework to enable content comparability between any health information; and 2) a measurement framework so that scores between two or more clinical data collection tools can be directly compared. The methods needed to achieve these requirements are the ICF Linking Rules and the Rasch measurement model. Using data collected incorporating the 36-item Short Form Health Survey (SF-36), the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0), and the Stroke Impact Scale 3.0 (SIS 3.0), the application of the standardized reporting based on common metrics is demonstrated. A subset of items from the three tools linked to common chapters of the ICF (d4 Mobility, d5 Self-care and d6 Domestic life), were entered as "super items" into the Rasch model. Good fit was achieved with no residual local dependency and a unidimensional metric. A transformation table allows for comparison between scales, and between a scale and the reporting common metric. Being able to report functioning information collected with commonly used clinical data collection tools with ICF-based common metrics enables clinicians

Quasi-metrics, midpoints and applications

Energy Technology Data Exchange (ETDEWEB)

Valero, O.

2017-07-01

In applied sciences, the scientific community uses simultaneously different kinds of information coming from several sources in order to infer a conclusion or working decision. In the literature there are many techniques for merging the information and providing, hence, a meaningful fused data. In mostpractical cases such fusion methods are based on aggregation operators on somenumerical values, i.e. the aim of the fusion process is to obtain arepresentative number from a finite sequence of numerical data. In the aforementioned cases, the input data presents some kind of imprecision and for thisreason it is represented as fuzzy sets. Moreover, in such problems the comparisons between the numerical values that represent the information described by the fuzzy sets become necessary. The aforementioned comparisons are made by means of a distance defined on fuzzy sets. Thus, the numerical operators aggregating distances between fuzzy sets as incoming data play a central role in applied problems. Recently, J.J. Nieto and A. Torres gave some applications of the aggregation of distances on fuzzy sets to the study of real medical data in /cite{Nieto}. These applications are based on the notion of segment joining two given fuzzy sets and on the notion of set of midpoints between fuzzy sets. A few results obtained by Nieto and Torres have been generalized in turn by Casasnovas and Rossell/'{o} in /cite{Casas,Casas2}. Nowadays, quasi-metrics provide efficient tools in some fields of computer science and in bioinformatics. Motivated by the exposed facts, a study of segments joining two fuzzy sets and of midpoints between fuzzy sets when the measure, used for comparisons, is a quasi-metric has been made in /cite{Casas3, SebVal2013,TiradoValero}. (Author)

A Single Conjunction Risk Assessment Metric: the F-Value

Science.gov (United States)

Frigm, Ryan Clayton; Newman, Lauri K.

2009-01-01

The Conjunction Assessment Team at NASA Goddard Space Flight Center provides conjunction risk assessment for many NASA robotic missions. These risk assessments are based on several figures of merit, such as miss distance, probability of collision, and orbit determination solution quality. However, these individual metrics do not singly capture the overall risk associated with a conjunction, making it difficult for someone without this complete understanding to take action, such as an avoidance maneuver. The goal of this analysis is to introduce a single risk index metric that can easily convey the level of risk without all of the technical details. The proposed index is called the conjunction "F-value." This paper presents the concept of the F-value and the tuning of the metric for use in routine Conjunction Assessment operations.

Algorithms for Planar Graphs and Graphs in Metric Spaces

DEFF Research Database (Denmark)

Wulff-Nilsen, Christian

structural properties that can be exploited. For instance, a road network or a wire layout on a microchip is typically (near-)planar and distances in the network are often defined w.r.t. the Euclidean or the rectilinear metric. Specialized algorithms that take advantage of such properties are often orders...... of magnitude faster than the corresponding algorithms for general graphs. The first and main part of this thesis focuses on the development of efficient planar graph algorithms. The most important contributions include a faster single-source shortest path algorithm, a distance oracle with subquadratic...... for geometric graphs and graphs embedded in metric spaces. Roughly speaking, the stretch factor is a real value expressing how well a (geo-)metric graph approximates the underlying complete graph w.r.t. distances. We give improved algorithms for computing the stretch factor of a given graph and for augmenting...

PSQM-based RR and NR video quality metrics

Science.gov (United States)

Lu, Zhongkang; Lin, Weisi; Ong, Eeping; Yang, Xiaokang; Yao, Susu

2003-06-01

This paper presents a new and general concept, PQSM (Perceptual Quality Significance Map), to be used in measuring the visual distortion. It makes use of the selectivity characteristic of HVS (Human Visual System) that it pays more attention to certain area/regions of visual signal due to one or more of the following factors: salient features in image/video, cues from domain knowledge, and association of other media (e.g., speech or audio). PQSM is an array whose elements represent the relative perceptual-quality significance levels for the corresponding area/regions for images or video. Due to its generality, PQSM can be incorporated into any visual distortion metrics: to improve effectiveness or/and efficiency of perceptual metrics; or even to enhance a PSNR-based metric. A three-stage PQSM estimation method is also proposed in this paper, with an implementation of motion, texture, luminance, skin-color and face mapping. Experimental results show the scheme can improve the performance of current image/video distortion metrics.

Quality Evaluation in Wireless Imaging Using Feature-Based Objective Metrics

OpenAIRE

Engelke, Ulrich; Zepernick, Hans-Jürgen

2007-01-01

This paper addresses the evaluation of image quality in the context of wireless systems using feature-based objective metrics. The considered metrics comprise of a weighted combination of feature values that are used to quantify the extend by which the related artifacts are present in a processed image. In view of imaging applications in mobile radio and wireless communication systems, reduced-reference objective quality metrics are investigated for quantifying user-perceived quality. The exa...

A Feeling for Numbers: Shared Metric for Symbolic and Tactile Numerosities

Directory of Open Access Journals (Sweden)

Florian eKrause

2013-01-01

Full Text Available Evidence for an approximate analogue system of numbers has been provided by the finding that the comparison of two numerals takes longer and is more error prone if the semantic distance between the numbers becomes smaller (so-called numerical distance effect. Recent embodied theories suggest that analogue number representations are based on previous sensory experiences and constitute therefore a common magnitude metric shared by multiple domains. Here we demonstrate the existence of a cross-modal semantic distance effect between symbolic and tactile numerosities. Participants received tactile stimulations of different amounts of fingers while reading Arabic digits and indicated verbally whether the amount of stimulated fingers was different from the simultaneously presented digit or not. The larger the semantic distance was between the two numerosities, the faster and more accurate participants made their judgements. This cross-modal numerosity distance effect suggests a direct connection between tactile sensations and the concept of numerical magnitude. A second experiment replicated the interaction between symbolic and tactile numerosities and showed that this effect is not modulated by the participants' finger counting habits. Taken together, our data provide novel evidence for a shared metric for symbolic and tactile numerosites as an instance of an embodied representation of numbers.

Distance between Behaviors and Rational Representations

NARCIS (Netherlands)

Trentelman, H.L.; Gottimukkala, S.V.

2013-01-01

In this paper we study notions of distance between behaviors of linear differential systems. We introduce four metrics on the space of all controllable behaviors which generalize existing metrics on the space of input-output systems represented by transfer matrices. Three of these are defined in

Contextual Distance Refining for Image Retrieval

KAUST Repository

Islam, Almasri

2014-01-01

Recently, a number of methods have been proposed to improve image retrieval accuracy by capturing context information. These methods try to compensate for the fact that a visually less similar image might be more relevant because it depicts the same object. We propose a new quick method for refining any pairwise distance metric, it works by iteratively discovering the object in the image from the most similar images, and then refine the distance metric accordingly. Test show that our technique improves over the state of art in terms of accuracy over the MPEG7 dataset.

Contextual Distance Refining for Image Retrieval

KAUST Repository

Islam, Almasri

2014-09-16

Recently, a number of methods have been proposed to improve image retrieval accuracy by capturing context information. These methods try to compensate for the fact that a visually less similar image might be more relevant because it depicts the same object. We propose a new quick method for refining any pairwise distance metric, it works by iteratively discovering the object in the image from the most similar images, and then refine the distance metric accordingly. Test show that our technique improves over the state of art in terms of accuracy over the MPEG7 dataset.

Evaluative Usage-Based Metrics for the Selection of E-Journals.

Science.gov (United States)

Hahn, Karla L.; Faulkner, Lila A.

2002-01-01

Explores electronic journal usage statistics and develops three metrics and three benchmarks based on those metrics. Topics include earlier work that assessed the value of print journals and was modified for the electronic format; the evaluation of potential purchases; and implications for standards development, including the need for content…

Metrics in Keplerian orbits quotient spaces

Science.gov (United States)

Milanov, Danila V.

2018-03-01

Quotient spaces of Keplerian orbits are important instruments for the modelling of orbit samples of celestial bodies on a large time span. We suppose that variations of the orbital eccentricities, inclinations and semi-major axes remain sufficiently small, while arbitrary perturbations are allowed for the arguments of pericentres or longitudes of the nodes, or both. The distance between orbits or their images in quotient spaces serves as a numerical criterion for such problems of Celestial Mechanics as search for common origin of meteoroid streams, comets, and asteroids, asteroid families identification, and others. In this paper, we consider quotient sets of the non-rectilinear Keplerian orbits space H. Their elements are identified irrespective of the values of pericentre arguments or node longitudes. We prove that distance functions on the quotient sets, introduced in Kholshevnikov et al. (Mon Not R Astron Soc 462:2275-2283, 2016), satisfy metric space axioms and discuss theoretical and practical importance of this result. Isometric embeddings of the quotient spaces into R^n, and a space of compact subsets of H with Hausdorff metric are constructed. The Euclidean representations of the orbits spaces find its applications in a problem of orbit averaging and computational algorithms specific to Euclidean space. We also explore completions of H and its quotient spaces with respect to corresponding metrics and establish a relation between elements of the extended spaces and rectilinear trajectories. Distance between an orbit and subsets of elliptic and hyperbolic orbits is calculated. This quantity provides an upper bound for the metric value in a problem of close orbits identification. Finally the invariance of the equivalence relations in H under coordinates change is discussed.

Converging from Branching to Linear Metrics on Markov Chains

DEFF Research Database (Denmark)

Bacci, Giorgio; Bacci, Giovanni; Larsen, Kim Guldstrand

2015-01-01

time in the size of the MC. The upper-approximants are Kantorovich-like pseudometrics, i.e. branching-time distances, that converge point-wise to the linear-time metrics. This convergence is interesting in itself, since it reveals a nontrivial relation between branching and linear-time metric...

Validation of network communicability metrics for the analysis of brain structural networks.

Directory of Open Access Journals (Sweden)

Jennifer Andreotti

Full Text Available Computational network analysis provides new methods to analyze the brain's structural organization based on diffusion imaging tractography data. Networks are characterized by global and local metrics that have recently given promising insights into diagnosis and the further understanding of psychiatric and neurologic disorders. Most of these metrics are based on the idea that information in a network flows along the shortest paths. In contrast to this notion, communicability is a broader measure of connectivity which assumes that information could flow along all possible paths between two nodes. In our work, the features of network metrics related to communicability were explored for the first time in the healthy structural brain network. In addition, the sensitivity of such metrics was analysed using simulated lesions to specific nodes and network connections. Results showed advantages of communicability over conventional metrics in detecting densely connected nodes as well as subsets of nodes vulnerable to lesions. In addition, communicability centrality was shown to be widely affected by the lesions and the changes were negatively correlated with the distance from lesion site. In summary, our analysis suggests that communicability metrics that may provide an insight into the integrative properties of the structural brain network and that these metrics may be useful for the analysis of brain networks in the presence of lesions. Nevertheless, the interpretation of communicability is not straightforward; hence these metrics should be used as a supplement to the more standard connectivity network metrics.

Connection Setup Signaling Scheme with Flooding-Based Path Searching for Diverse-Metric Network

Science.gov (United States)

Kikuta, Ko; Ishii, Daisuke; Okamoto, Satoru; Oki, Eiji; Yamanaka, Naoaki

Connection setup on various computer networks is now achieved by GMPLS. This technology is based on the source-routing approach, which requires the source node to store metric information of the entire network prior to computing a route. Thus all metric information must be distributed to all network nodes and kept up-to-date. However, as metric information become more diverse and generalized, it is hard to update all information due to the huge update overhead. Emerging network services and applications require the network to support diverse metrics for achieving various communication qualities. Increasing the number of metrics supported by the network causes excessive processing of metric update messages. To reduce the number of metric update messages, another scheme is required. This paper proposes a connection setup scheme that uses flooding-based signaling rather than the distribution of metric information. The proposed scheme requires only flooding of signaling messages with requested metric information, no routing protocol is required. Evaluations confirm that the proposed scheme achieves connection establishment without excessive overhead. Our analysis shows that the proposed scheme greatly reduces the number of control messages compared to the conventional scheme, while their blocking probabilities are comparable.

Use of different exposure metrics for understanding multi-modal travel injury risk

Directory of Open Access Journals (Sweden)

S. Ilgin Guler

2016-08-01

Full Text Available The objective of this work is to identify characteristics of different metrics of exposure for quantifying multi-modal travel injury risk. First, a discussion on the use of time-based and trip-based metrics for road user exposure to injury risk, considering multiple travel modes, is presented. The main difference between a time-based and trip-based metric is argued to be that a time-based metric reflects the actual duration of time spent on the road exposed to the travel risks. This can be proven to be important when considering multiple modes since different modes typically different speeds and average travel distances. Next, the use of total number of trips, total time traveled, and mode share (time-based or trip-based is considered to compare the injury risk of a given mode at different locations. It is argued that using mode share the safety concept which focuses on absolute numbers can be generalized. Quantitative results are also obtained from combining travel survey data with police collision reports for ten counties in California. The data are aggregated for five modes: (i cars, (ii SUVs, (iii transit riders, (iv bicyclists, and (v pedestrians. These aggregated data are used to compare travel risk of different modes with time-based or trip-based exposure metrics. These quantitative results confirm the initial qualitative discussions. As the penetration of mobile probes for transportation data collection increases, the insights of this study can provide guidance on how to best utilize the added value of such data to better quantify travel injury risk, and improve safety.

An image processing approach to computing distances between RNA secondary structures dot plots

Directory of Open Access Journals (Sweden)

Sapiro Guillermo

2009-02-01

Full Text Available Abstract Background Computing the distance between two RNA secondary structures can contribute in understanding the functional relationship between them. When used repeatedly, such a procedure may lead to finding a query RNA structure of interest in a database of structures. Several methods are available for computing distances between RNAs represented as strings or graphs, but none utilize the RNA representation with dot plots. Since dot plots are essentially digital images, there is a clear motivation to devise an algorithm for computing the distance between dot plots based on image processing methods. Results We have developed a new metric dubbed 'DoPloCompare', which compares two RNA structures. The method is based on comparing dot plot diagrams that represent the secondary structures. When analyzing two diagrams and motivated by image processing, the distance is based on a combination of histogram correlations and a geometrical distance measure. We introduce, describe, and illustrate the procedure by two applications that utilize this metric on RNA sequences. The first application is the RNA design problem, where the goal is to find the nucleotide sequence for a given secondary structure. Examples where our proposed distance measure outperforms others are given. The second application locates peculiar point mutations that induce significant structural alternations relative to the wild type predicted secondary structure. The approach reported in the past to solve this problem was tested on several RNA sequences with known secondary structures to affirm their prediction, as well as on a data set of ribosomal pieces. These pieces were computationally cut from a ribosome for which an experimentally derived secondary structure is available, and on each piece the prediction conveys similarity to the experimental result. Our newly proposed distance measure shows benefit in this problem as well when compared to standard methods used for assessing

GPS Device Testing Based on User Performance Metrics

Science.gov (United States)

2015-10-02

1. Rationale for a Test Program Based on User Performance Metrics ; 2. Roberson and Associates Test Program ; 3. Status of, and Revisions to, the Roberson and Associates Test Program ; 4. Comparison of Roberson and DOT/Volpe Programs

Monitor-Based Statistical Model Checking for Weighted Metric Temporal Logic

DEFF Research Database (Denmark)

Bulychev, Petr; David, Alexandre; Larsen, Kim Guldstrand

2012-01-01

We present a novel approach and implementation for ana- lysing weighted timed automata (WTA) with respect to the weighted metric temporal logic (WMTL≤ ). Based on a stochastic semantics of WTAs, we apply statistical model checking (SMC) to estimate and test probabilities of satisfaction with desi......We present a novel approach and implementation for ana- lysing weighted timed automata (WTA) with respect to the weighted metric temporal logic (WMTL≤ ). Based on a stochastic semantics of WTAs, we apply statistical model checking (SMC) to estimate and test probabilities of satisfaction...

INFORMATIVE ENERGY METRIC FOR SIMILARITY MEASURE IN REPRODUCING KERNEL HILBERT SPACES

Directory of Open Access Journals (Sweden)

Songhua Liu

2012-02-01

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

Performance evaluation of a distance learning program.

Science.gov (United States)

Dailey, D J; Eno, K R; Brinkley, J F

1994-01-01

This paper presents a performance metric which uses a single number to characterize the response time for a non-deterministic client-server application operating over the Internet. When applied to a Macintosh-based distance learning application called the Digital Anatomist Browser, the metric allowed us to observe that "A typical student doing a typical mix of Browser commands on a typical data set will experience the same delay if they use a slow Macintosh on a local network or a fast Macintosh on the other side of the country accessing the data over the Internet." The methodology presented is applicable to other client-server applications that are rapidly appearing on the Internet.

Phylo_dCor: distance correlation as a novel metric for phylogenetic profiling.

Science.gov (United States)

Sferra, Gabriella; Fratini, Federica; Ponzi, Marta; Pizzi, Elisabetta

2017-09-05

Elaboration of powerful methods to predict functional and/or physical protein-protein interactions from genome sequence is one of the main tasks in the post-genomic era. Phylogenetic profiling allows the prediction of protein-protein interactions at a whole genome level in both Prokaryotes and Eukaryotes. For this reason it is considered one of the most promising methods. Here, we propose an improvement of phylogenetic profiling that enables handling of large genomic datasets and infer global protein-protein interactions. This method uses the distance correlation as a new measure of phylogenetic profile similarity. We constructed robust reference sets and developed Phylo-dCor, a parallelized version of the algorithm for calculating the distance correlation that makes it applicable to large genomic data. Using Saccharomyces cerevisiae and Escherichia coli genome datasets, we showed that Phylo-dCor outperforms phylogenetic profiling methods previously described based on the mutual information and Pearson's correlation as measures of profile similarity. In this work, we constructed and assessed robust reference sets and propose the distance correlation as a measure for comparing phylogenetic profiles. To make it applicable to large genomic data, we developed Phylo-dCor, a parallelized version of the algorithm for calculating the distance correlation. Two R scripts that can be run on a wide range of machines are available upon request.

Wireless sensor network performance metrics for building applications

Energy Technology Data Exchange (ETDEWEB)

Jang, W.S. (Department of Civil Engineering Yeungnam University 214-1 Dae-Dong, Gyeongsan-Si Gyeongsangbuk-Do 712-749 South Korea); Healy, W.M. [Building and Fire Research Laboratory, 100 Bureau Drive, Gaithersburg, MD 20899-8632 (United States)

2010-06-15

Metrics are investigated to help assess the performance of wireless sensors in buildings. Wireless sensor networks present tremendous opportunities for energy savings and improvement in occupant comfort in buildings by making data about conditions and equipment more readily available. A key barrier to their adoption, however, is the uncertainty among users regarding the reliability of the wireless links through building construction. Tests were carried out that examined three performance metrics as a function of transmitter-receiver separation distance, transmitter power level, and obstruction type. These tests demonstrated, via the packet delivery rate, a clear transition from reliable to unreliable communications at different separation distances. While the packet delivery rate is difficult to measure in actual applications, the received signal strength indication correlated well with the drop in packet delivery rate in the relatively noise-free environment used in these tests. The concept of an equivalent distance was introduced to translate the range of reliability in open field operation to that seen in a typical building, thereby providing wireless system designers a rough estimate of the necessary spacing between sensor nodes in building applications. It is anticipated that the availability of straightforward metrics on the range of wireless sensors in buildings will enable more widespread sensing in buildings for improved control and fault detection. (author)

Objectively Quantifying Radiation Esophagitis With Novel Computed Tomography–Based Metrics

Energy Technology Data Exchange (ETDEWEB)

Niedzielski, Joshua S., E-mail: jsniedzielski@mdanderson.org [Department of Radiation Physics, The University of Texas M. D. Anderson Cancer Center, Houston, Texas (United States); University of Texas Houston Graduate School of Biomedical Science, Houston, Texas (United States); Yang, Jinzhong [Department of Radiation Physics, The University of Texas M. D. Anderson Cancer Center, Houston, Texas (United States); University of Texas Houston Graduate School of Biomedical Science, Houston, Texas (United States); Stingo, Francesco [Department of Biostatistics, The University of Texas M. D. Anderson Cancer Center, Houston, Texas (United States); Martel, Mary K.; Mohan, Radhe [Department of Radiation Physics, The University of Texas M. D. Anderson Cancer Center, Houston, Texas (United States); University of Texas Houston Graduate School of Biomedical Science, Houston, Texas (United States); Gomez, Daniel R. [Department of Radiation Oncology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas (United States); Briere, Tina M. [Department of Radiation Physics, The University of Texas M. D. Anderson Cancer Center, Houston, Texas (United States); University of Texas Houston Graduate School of Biomedical Science, Houston, Texas (United States); Liao, Zhongxing [Department of Radiation Oncology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas (United States); Court, Laurence E. [Department of Radiation Physics, The University of Texas M. D. Anderson Cancer Center, Houston, Texas (United States); University of Texas Houston Graduate School of Biomedical Science, Houston, Texas (United States)

2016-02-01

Purpose: To study radiation-induced esophageal expansion as an objective measure of radiation esophagitis in patients with non-small cell lung cancer (NSCLC) treated with intensity modulated radiation therapy. Methods and Materials: Eighty-five patients had weekly intra-treatment CT imaging and esophagitis scoring according to Common Terminlogy Criteria for Adverse Events 4.0, (24 Grade 0, 45 Grade 2, and 16 Grade 3). Nineteen esophageal expansion metrics based on mean, maximum, spatial length, and volume of expansion were calculated as voxel-based relative volume change, using the Jacobian determinant from deformable image registration between the planning and weekly CTs. An anatomic variability correction method was validated and applied to these metrics to reduce uncertainty. An analysis of expansion metrics and radiation esophagitis grade was conducted using normal tissue complication probability from univariate logistic regression and Spearman rank for grade 2 and grade 3 esophagitis endpoints, as well as the timing of expansion and esophagitis grade. Metrics' performance in classifying esophagitis was tested with receiver operating characteristic analysis. Results: Expansion increased with esophagitis grade. Thirteen of 19 expansion metrics had receiver operating characteristic area under the curve values >0.80 for both grade 2 and grade 3 esophagitis endpoints, with the highest performance from maximum axial expansion (MaxExp1) and esophageal length with axial expansion ≥30% (LenExp30%) with area under the curve values of 0.93 and 0.91 for grade 2, 0.90 and 0.90 for grade 3 esophagitis, respectively. Conclusions: Esophageal expansion may be a suitable objective measure of esophagitis, particularly maximum axial esophageal expansion and esophageal length with axial expansion ≥30%, with 2.1 Jacobian value and 98.6 mm as the metric value for 50% probability of grade 3 esophagitis. The uncertainty in esophageal Jacobian calculations can be reduced

Objectively Quantifying Radiation Esophagitis With Novel Computed Tomography–Based Metrics

International Nuclear Information System (INIS)

Niedzielski, Joshua S.; Yang, Jinzhong; Stingo, Francesco; Martel, Mary K.; Mohan, Radhe; Gomez, Daniel R.; Briere, Tina M.; Liao, Zhongxing; Court, Laurence E.

2016-01-01

Purpose: To study radiation-induced esophageal expansion as an objective measure of radiation esophagitis in patients with non-small cell lung cancer (NSCLC) treated with intensity modulated radiation therapy. Methods and Materials: Eighty-five patients had weekly intra-treatment CT imaging and esophagitis scoring according to Common Terminlogy Criteria for Adverse Events 4.0, (24 Grade 0, 45 Grade 2, and 16 Grade 3). Nineteen esophageal expansion metrics based on mean, maximum, spatial length, and volume of expansion were calculated as voxel-based relative volume change, using the Jacobian determinant from deformable image registration between the planning and weekly CTs. An anatomic variability correction method was validated and applied to these metrics to reduce uncertainty. An analysis of expansion metrics and radiation esophagitis grade was conducted using normal tissue complication probability from univariate logistic regression and Spearman rank for grade 2 and grade 3 esophagitis endpoints, as well as the timing of expansion and esophagitis grade. Metrics' performance in classifying esophagitis was tested with receiver operating characteristic analysis. Results: Expansion increased with esophagitis grade. Thirteen of 19 expansion metrics had receiver operating characteristic area under the curve values >0.80 for both grade 2 and grade 3 esophagitis endpoints, with the highest performance from maximum axial expansion (MaxExp1) and esophageal length with axial expansion ≥30% (LenExp30%) with area under the curve values of 0.93 and 0.91 for grade 2, 0.90 and 0.90 for grade 3 esophagitis, respectively. Conclusions: Esophageal expansion may be a suitable objective measure of esophagitis, particularly maximum axial esophageal expansion and esophageal length with axial expansion ≥30%, with 2.1 Jacobian value and 98.6 mm as the metric value for 50% probability of grade 3 esophagitis. The uncertainty in esophageal Jacobian calculations can be reduced

Stochastic learning of multi-instance dictionary for earth mover’s distance-based histogram comparison

KAUST Repository

Fan, Jihong

2016-09-17

Dictionary plays an important role in multi-instance data representation. It maps bags of instances to histograms. Earth mover’s distance (EMD) is the most effective histogram distance metric for the application of multi-instance retrieval. However, up to now, there is no existing multi-instance dictionary learning methods designed for EMD-based histogram comparison. To fill this gap, we develop the first EMD-optimal dictionary learning method using stochastic optimization method. In the stochastic learning framework, we have one triplet of bags, including one basic bag, one positive bag, and one negative bag. These bags are mapped to histograms using a multi-instance dictionary. We argue that the EMD between the basic histogram and the positive histogram should be smaller than that between the basic histogram and the negative histogram. Base on this condition, we design a hinge loss. By minimizing this hinge loss and some regularization terms of the dictionary, we update the dictionary instances. The experiments over multi-instance retrieval applications shows its effectiveness when compared to other dictionary learning methods over the problems of medical image retrieval and natural language relation classification. © 2016 The Natural Computing Applications Forum

Concordance-based Kendall's Correlation for Computationally-Light vs. Computationally-Heavy Centrality Metrics: Lower Bound for Correlation

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Natarajan Meghanathan

2017-01-01

Full Text Available We identify three different levels of correlation (pair-wise relative ordering, network-wide ranking and linear regression that could be assessed between a computationally-light centrality metric and a computationally-heavy centrality metric for real-world networks. The Kendall's concordance-based correlation measure could be used to quantitatively assess how well we could consider the relative ordering of two vertices vi and vj with respect to a computationally-light centrality metric as the relative ordering of the same two vertices with respect to a computationally-heavy centrality metric. We hypothesize that the pair-wise relative ordering (concordance-based assessment of the correlation between centrality metrics is the most strictest of all the three levels of correlation and claim that the Kendall's concordance-based correlation coefficient will be lower than the correlation coefficient observed with the more relaxed levels of correlation measures (linear regression-based Pearson's product-moment correlation coefficient and the network wide ranking-based Spearman's correlation coefficient. We validate our hypothesis by evaluating the three correlation coefficients between two sets of centrality metrics: the computationally-light degree and local clustering coefficient complement-based degree centrality metrics and the computationally-heavy eigenvector centrality, betweenness centrality and closeness centrality metrics for a diverse collection of 50 real-world networks.

Two projects in theoretical neuroscience: A convolution-based metric for neural membrane potentials and a combinatorial connectionist semantic network method

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Evans, Garrett Nolan

In this work, I present two projects that both contribute to the aim of discovering how intelligence manifests in the brain. The first project is a method for analyzing recorded neural signals, which takes the form of a convolution-based metric on neural membrane potential recordings. Relying only on integral and algebraic operations, the metric compares the timing and number of spikes within recordings as well as the recordings' subthreshold features: summarizing differences in these with a single "distance" between the recordings. Like van Rossum's (2001) metric for spike trains, the metric is based on a convolution operation that it performs on the input data. The kernel used for the convolution is carefully chosen such that it produces a desirable frequency space response and, unlike van Rossum's kernel, causes the metric to be first order both in differences between nearby spike times and in differences between same-time membrane potential values: an important trait. The second project is a combinatorial syntax method for connectionist semantic network encoding. Combinatorial syntax has been a point on which those who support a symbol-processing view of intelligent processing and those who favor a connectionist view have had difficulty seeing eye-to-eye. Symbol-processing theorists have persuasively argued that combinatorial syntax is necessary for certain intelligent mental operations, such as reasoning by analogy. Connectionists have focused on the versatility and adaptability offered by self-organizing networks of simple processing units. With this project, I show that there is a way to reconcile the two perspectives and to ascribe a combinatorial syntax to a connectionist network. The critical principle is to interpret nodes, or units, in the connectionist network as bound integrations of the interpretations for nodes that they share links with. Nodes need not correspond exactly to neurons and may correspond instead to distributed sets, or assemblies, of

Fusion set selection with surrogate metric in multi-atlas based image segmentation

International Nuclear Information System (INIS)

Zhao, Tingting; Ruan, Dan

2016-01-01

Multi-atlas based image segmentation sees unprecedented opportunities but also demanding challenges in the big data era. Relevant atlas selection before label fusion plays a crucial role in reducing potential performance loss from heterogeneous data quality and high computation cost from extensive data. This paper starts with investigating the image similarity metric (termed ‘surrogate’), an alternative to the inaccessible geometric agreement metric (termed ‘oracle’) in atlas relevance assessment, and probes into the problem of how to select the ‘most-relevant’ atlases and how many such atlases to incorporate. We propose an inference model to relate the surrogates and the oracle geometric agreement metrics. Based on this model, we quantify the behavior of the surrogates in mimicking oracle metrics for atlas relevance ordering. Finally, analytical insights on the choice of fusion set size are presented from a probabilistic perspective, with the integrated goal of including the most relevant atlases and excluding the irrelevant ones. Empirical evidence and performance assessment are provided based on prostate and corpus callosum segmentation. (paper)

Classification in medical image analysis using adaptive metric k-NN

DEFF Research Database (Denmark)

Chen, Chen; Chernoff, Konstantin; Karemore, Gopal

2010-01-01

The performance of the k-nearest neighborhoods (k-NN) classifier is highly dependent on the distance metric used to identify the k nearest neighbors of the query points. The standard Euclidean distance is commonly used in practice. This paper investigates the performance of k-NN classifier...

Networks and centroid metrics for understanding football

African Journals Online (AJOL)

Gonçalo Dias

games. However, it seems that the centroid metric, supported only by the position of players in the field ...... the strategy adopted by the coach (Gama et al., 2014). ... centroid distance as measures of team's tactical performance in youth football.

An Improved EMD-Based Dissimilarity Metric for Unsupervised Linear Subspace Learning

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Xiangchun Yu

2018-01-01

Full Text Available We investigate a novel way of robust face image feature extraction by adopting the methods based on Unsupervised Linear Subspace Learning to extract a small number of good features. Firstly, the face image is divided into blocks with the specified size, and then we propose and extract pooled Histogram of Oriented Gradient (pHOG over each block. Secondly, an improved Earth Mover’s Distance (EMD metric is adopted to measure the dissimilarity between blocks of one face image and the corresponding blocks from the rest of face images. Thirdly, considering the limitations of the original Locality Preserving Projections (LPP, we proposed the Block Structure LPP (BSLPP, which effectively preserves the structural information of face images. Finally, an adjacency graph is constructed and a small number of good features of a face image are obtained by methods based on Unsupervised Linear Subspace Learning. A series of experiments have been conducted on several well-known face databases to evaluate the effectiveness of the proposed algorithm. In addition, we construct the noise, geometric distortion, slight translation, slight rotation AR, and Extended Yale B face databases, and we verify the robustness of the proposed algorithm when faced with a certain degree of these disturbances.

Computations of Wall Distances Based on Differential Equations

Science.gov (United States)

Tucker, Paul G.; Rumsey, Chris L.; Spalart, Philippe R.; Bartels, Robert E.; Biedron, Robert T.

2004-01-01

The use of differential equations such as Eikonal, Hamilton-Jacobi and Poisson for the economical calculation of the nearest wall distance d, which is needed by some turbulence models, is explored. Modifications that could palliate some turbulence-modeling anomalies are also discussed. Economy is of especial value for deforming/adaptive grid problems. For these, ideally, d is repeatedly computed. It is shown that the Eikonal and Hamilton-Jacobi equations can be easy to implement when written in implicit (or iterated) advection and advection-diffusion equation analogous forms, respectively. These, like the Poisson Laplacian term, are commonly occurring in CFD solvers, allowing the re-use of efficient algorithms and code components. The use of the NASA CFL3D CFD program to solve the implicit Eikonal and Hamilton-Jacobi equations is explored. The re-formulated d equations are easy to implement, and are found to have robust convergence. For accurate Eikonal solutions, upwind metric differences are required. The Poisson approach is also found effective, and easiest to implement. Modified distances are not found to affect global outputs such as lift and drag significantly, at least in common situations such as airfoil flows.

The Metric of Colour Space

DEFF Research Database (Denmark)

Gravesen, Jens

2015-01-01

and found the MacAdam ellipses which are often interpreted as defining the metric tensor at their centres. An important question is whether it is possible to define colour coordinates such that the Euclidean distance in these coordinates correspond to human perception. Using cubic splines to represent......The space of colours is a fascinating space. It is a real vector space, but no matter what inner product you put on the space the resulting Euclidean distance does not correspond to human perception of difference between colours. In 1942 MacAdam performed the first experiments on colour matching...

Is Time the Best Metric to Measure Carbon-Related Climate Change Potential and Tune the Economy Toward Reduced Fossil Carbon Extraction?

Science.gov (United States)

DeGroff, F. A.

2016-12-01

Anthropogenic changes to non-anthropogenic carbon fluxes are a primary driver of climate change. There currently exists no comprehensive metric to measure and value anthropogenic changes in carbon flux between all states of carbon. Focusing on atmospheric carbon emissions as a measure of anthropogenic activity on the environment ignores the fungible characteristics of carbon that are crucial in both the biosphere and the worldwide economy. Focusing on a single form of inorganic carbon as a proxy metric for the plethora of anthropogenic activity and carbon compounds will prove inadequate, convoluted, and unmanageable. A broader, more basic metric is needed to capture the entirety of carbon activity, particularly in an economic, profit-driven environment. We propose a new metric to measure changes in the temporal distance of any form or state of carbon from one state to another. Such a metric would be especially useful to measure the temporal distance of carbon from sinks such as the atmosphere or oceans. The effect of changes in carbon flux as a result of any human activity can be measured by the difference between the anthropogenic and non-anthropogenic temporal distance. The change in the temporal distance is a measure of the climate change potential much like voltage is a measure of electrical potential. The integral of the climate change potential is proportional to the anthropogenic climate change. We also propose a logarithmic vector scale for carbon quality, cq, as a measure of anthropogenic changes in carbon flux. The distance between the cq vector starting and ending temporal distances represents the change in cq. A base-10 logarithmic scale would allow the addition and subtraction of exponents to calculate changes in cq. As anthropogenic activity changes the temporal distance of carbon, the change in cq is measured as: cq = ß ( log10 [mean carbon temporal distance] ) where ß represents the carbon price coefficient for a particular country. For any

Distance Based Root Cause Analysis and Change Impact Analysis of Performance Regressions

Directory of Open Access Journals (Sweden)

Junzan Zhou

2015-01-01

Full Text Available Performance regression testing is applied to uncover both performance and functional problems of software releases. A performance problem revealed by performance testing can be high response time, low throughput, or even being out of service. Mature performance testing process helps systematically detect software performance problems. However, it is difficult to identify the root cause and evaluate the potential change impact. In this paper, we present an approach leveraging server side logs for identifying root causes of performance problems. Firstly, server side logs are used to recover call tree of each business transaction. We define a novel distance based metric computed from call trees for root cause analysis and apply inverted index from methods to business transactions for change impact analysis. Empirical studies show that our approach can effectively and efficiently help developers diagnose root cause of performance problems.

Selective Distance-Based K+ Quantification on Paper-Based Microfluidics.

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Gerold, Chase T; Bakker, Eric; Henry, Charles S

2018-04-03

In this study, paper-based microfluidic devices (μPADs) capable of K + quantification in aqueous samples, as well as in human serum, using both colorimetric and distance-based methods are described. A lipophilic phase containing potassium ionophore I (valinomycin) was utilized to achieve highly selective quantification of K + in the presence of Na + , Li + , and Mg 2+ ions. Successful addition of a suspended lipophilic phase to a wax printed paper-based device is described and offers a solution to current approaches that rely on organic solvents, which damage wax barriers. The approach provides an avenue for future alkali/alkaline quantification utilizing μPADs. Colorimetric spot tests allowed for K + quantification from 0.1-5.0 mM using only 3.00 μL of sample solution. Selective distance-based quantification required small sample volumes (6.00 μL) and gave responses sensitive enough to distinguish between 1.0 and 2.5 mM of sample K + . μPADs using distance-based methods were also capable of differentiating between 4.3 and 6.9 mM K + in human serum samples. Distance-based methods required no digital analysis, electronic hardware, or pumps; any steps required for quantification could be carried out using the naked eye.

Towards Video Quality Metrics Based on Colour Fractal Geometry

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Richard Noël

2010-01-01

Full Text Available Vision is a complex process that integrates multiple aspects of an image: spatial frequencies, topology and colour. Unfortunately, so far, all these elements were independently took into consideration for the development of image and video quality metrics, therefore we propose an approach that blends together all of them. Our approach allows for the analysis of the complexity of colour images in the RGB colour space, based on the probabilistic algorithm for calculating the fractal dimension and lacunarity. Given that all the existing fractal approaches are defined only for gray-scale images, we extend them to the colour domain. We show how these two colour fractal features capture the multiple aspects that characterize the degradation of the video signal, based on the hypothesis that the quality degradation perceived by the user is directly proportional to the modification of the fractal complexity. We claim that the two colour fractal measures can objectively assess the quality of the video signal and they can be used as metrics for the user-perceived video quality degradation and we validated them through experimental results obtained for an MPEG-4 video streaming application; finally, the results are compared against the ones given by unanimously-accepted metrics and subjective tests.

Turbulence Hazard Metric Based on Peak Accelerations for Jetliner Passengers

Science.gov (United States)

Stewart, Eric C.

2005-01-01

Calculations are made of the approximate hazard due to peak normal accelerations of an airplane flying through a simulated vertical wind field associated with a convective frontal system. The calculations are based on a hazard metric developed from a systematic application of a generic math model to 1-cosine discrete gusts of various amplitudes and gust lengths. The math model simulates the three degree-of- freedom longitudinal rigid body motion to vertical gusts and includes (1) fuselage flexibility, (2) the lag in the downwash from the wing to the tail, (3) gradual lift effects, (4) a simplified autopilot, and (5) motion of an unrestrained passenger in the rear cabin. Airplane and passenger response contours are calculated for a matrix of gust amplitudes and gust lengths. The airplane response contours are used to develop an approximate hazard metric of peak normal accelerations as a function of gust amplitude and gust length. The hazard metric is then applied to a two-dimensional simulated vertical wind field of a convective frontal system. The variations of the hazard metric with gust length and airplane heading are demonstrated.

Censoring distances based on labeled cortical distance maps in cortical morphometry.

Science.gov (United States)

Ceyhan, Elvan; Nishino, Tomoyuki; Alexopolous, Dimitrios; Todd, Richard D; Botteron, Kelly N; Miller, Michael I; Ratnanather, J Tilak

2013-01-01

It has been demonstrated that shape differences in cortical structures may be manifested in neuropsychiatric disorders. Such morphometric differences can be measured by labeled cortical distance mapping (LCDM) which characterizes the morphometry of the laminar cortical mantle of cortical structures. LCDM data consist of signed/labeled distances of gray matter (GM) voxels with respect to GM/white matter (WM) surface. Volumes and other summary measures for each subject and the pooled distances can help determine the morphometric differences between diagnostic groups, however they do not reveal all the morphometric information contained in LCDM distances. To extract more information from LCDM data, censoring of the pooled distances is introduced for each diagnostic group where the range of LCDM distances is partitioned at a fixed increment size; and at each censoring step, the distances not exceeding the censoring distance are kept. Censored LCDM distances inherit the advantages of the pooled distances but also provide information about the location of morphometric differences which cannot be obtained from the pooled distances. However, at each step, the censored distances aggregate, which might confound the results. The influence of data aggregation is investigated with an extensive Monte Carlo simulation analysis and it is demonstrated that this influence is negligible. As an illustrative example, GM of ventral medial prefrontal cortices (VMPFCs) of subjects with major depressive disorder (MDD), subjects at high risk (HR) of MDD, and healthy control (Ctrl) subjects are used. A significant reduction in laminar thickness of the VMPFC in MDD and HR subjects is observed compared to Ctrl subjects. Moreover, the GM LCDM distances (i.e., locations with respect to the GM/WM surface) for which these differences start to occur are determined. The methodology is also applicable to LCDM-based morphometric measures of other cortical structures affected by disease.

Censoring Distances Based on Labeled Cortical Distance Maps in Cortical Morphometry

Directory of Open Access Journals (Sweden)

Elvan eCeyhan

2013-10-01

Full Text Available It has been demonstrated that shape differences are manifested in cortical structures due to neuropsychiatric disorders. Such morphometric differences can be measured by labeled cortical distance mapping (LCDM which characterizes the morphometry of the laminar cortical mantle of cortical structures. LCDM data consist of signed/labeled distances of gray matter (GM voxels with respect to GM/white matter (WM surface. Volumes and other summary measures for each subject and the pooled distances can help determine the morphometric differences between diagnostic groups, however they do not reveal all the morphometric information con-tained in LCDM distances. To extract more information from LCDM data, censoring of the pooled distances is introduced for each diagnostic group where the range of LCDM distances is partitioned at a fixed increment size; and at each censoring step, the distances not exceeding the censoring distance are kept. Censored LCDM distances inherit the advantages of the pooled distances but also provide information about the location of morphometric differences which cannot be obtained from the pooled distances. However, at each step, the censored distances aggregate, which might confound the results. The influence of data aggregation is investigated with an extensive Monte Carlo simulation analysis and it is demonstrated that this influence is negligible. As an illustrative example, GM of ventral medial prefrontal cortices (VMPFCs of subjects with major depressive disorder (MDD, subjects at high risk (HR of MDD, and healthy control (Ctrl subjects are used. A significant reduction in laminar thickness of the VMPFC in MDD and HR subjects is observed compared to Ctrl subjects. Moreover, the GM LCDM distances (i.e., locations with respect to the GM/WM surface for which these differences start to occur are determined. The methodology is also applicable to LCDM-based morphometric measures of other cortical structures affected by disease.

Clustering by Partitioning around Medoids using Distance-Based Similarity Measures on Interval-Scaled Variables

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D. L. Nkweteyim

2018-03-01

Full Text Available It is reported in this paper, the results of a study of the partitioning around medoids (PAM clustering algorithm applied to four datasets, both standardized and not, and of varying sizes and numbers of clusters. The angular distance proximity measure in addition to the two more traditional proximity measures, namely the Euclidean distance and Manhattan distance, was used to compute object-object similarity. The data used in the study comprise three widely available datasets, and one that was constructed from publicly available climate data. Results replicate some of the well known facts about the PAM algorithm, namely that the quality of the clusters generated tend to be much better for small datasets, that the silhouette value is a good, even if not perfect, guide for the optimal number of clusters to generate, and that human intervention is required to interpret generated clusters. Additionally, results also indicate that the angular distance measure, which traditionally has not been widely used in clustering, outperforms both the Euclidean and Manhattan distance metrics in certain situations.

Grading the Metrics: Performance-Based Funding in the Florida State University System

Science.gov (United States)

Cornelius, Luke M.; Cavanaugh, Terence W.

2016-01-01

A policy analysis of Florida's 10-factor Performance-Based Funding system for state universities. The focus of the article is on the system of performance metrics developed by the state Board of Governors and their impact on institutions and their missions. The paper also discusses problems and issues with the metrics, their ongoing evolution, and…

Distance-Based Opportunistic Mobile Data Offloading.

Science.gov (United States)

Lu, Xiaofeng; Lio, Pietro; Hui, Pan

2016-06-15

Cellular network data traffic can be offload onto opportunistic networks. This paper proposes a Distance-based Opportunistic Publish/Subscribe (DOPS) content dissemination model, which is composed of three layers: application layer, decision-making layer and network layer. When a user wants new content, he/she subscribes on a subscribing server. Users having the contents decide whether to deliver the contents to the subscriber based on the distance information. If in the meantime a content owner has traveled further in the immediate past time than the distance between the owner and the subscriber, the content owner will send the content to the subscriber through opportunistic routing. Simulations provide an evaluation of the data traffic offloading efficiency of DOPS.

Individuality evaluation for paper based artifact-metrics using transmitted light image

Science.gov (United States)

Yamakoshi, Manabu; Tanaka, Junichi; Furuie, Makoto; Hirabayashi, Masashi; Matsumoto, Tsutomu

2008-02-01

Artifact-metrics is an automated method of authenticating artifacts based on a measurable intrinsic characteristic. Intrinsic characters, such as microscopic random-patterns made during the manufacturing process, are very difficult to copy. A transmitted light image of the distribution can be used for artifact-metrics, since the fiber distribution of paper is random. Little is known about the individuality of the transmitted light image although it is an important requirement for intrinsic characteristic artifact-metrics. Measuring individuality requires that the intrinsic characteristic of each artifact significantly differs, so having sufficient individuality can make an artifact-metric system highly resistant to brute force attack. Here we investigate the influence of paper category, matching size of sample, and image-resolution on the individuality of a transmitted light image of paper through a matching test using those images. More concretely, we evaluate FMR/FNMR curves by calculating similarity scores with matches using correlation coefficients between pairs of scanner input images, and the individuality of paper by way of estimated EER with probabilistic measure through a matching method based on line segments, which can localize the influence of rotation gaps of a sample in the case of large matching size. As a result, we found that the transmitted light image of paper has a sufficient individuality.

Distance-Based Opportunistic Mobile Data Offloading

Directory of Open Access Journals (Sweden)

Xiaofeng Lu

2016-06-01

Full Text Available Cellular network data traffic can be offload onto opportunistic networks. This paper proposes a Distance-based Opportunistic Publish/Subscribe (DOPS content dissemination model, which is composed of three layers: application layer, decision-making layer and network layer. When a user wants new content, he/she subscribes on a subscribing server. Users having the contents decide whether to deliver the contents to the subscriber based on the distance information. If in the meantime a content owner has traveled further in the immediate past time than the distance between the owner and the subscriber, the content owner will send the content to the subscriber through opportunistic routing. Simulations provide an evaluation of the data traffic offloading efficiency of DOPS.

Metric interpretation of gauge fields in noncommutative geometry

International Nuclear Information System (INIS)

Martinetti, P.

2007-01-01

We shall give an overview of the metric interpretation of gauge fields in noncommutative geometry, via Connes distance formula. Especially we shall focus on the Higgs fields in the standard model, and gauge fields in various models of fiber bundle. (author)

Comparing Phylogenetic Trees by Matching Nodes Using the Transfer Distance Between Partitions.

Science.gov (United States)

Bogdanowicz, Damian; Giaro, Krzysztof

2017-05-01

Ability to quantify dissimilarity of different phylogenetic trees describing the relationship between the same group of taxa is required in various types of phylogenetic studies. For example, such metrics are used to assess the quality of phylogeny construction methods, to define optimization criteria in supertree building algorithms, or to find horizontal gene transfer (HGT) events. Among the set of metrics described so far in the literature, the most commonly used seems to be the Robinson-Foulds distance. In this article, we define a new metric for rooted trees-the Matching Pair (MP) distance. The MP metric uses the concept of the minimum-weight perfect matching in a complete bipartite graph constructed from partitions of all pairs of leaves of the compared phylogenetic trees. We analyze the properties of the MP metric and present computational experiments showing its potential applicability in tasks related to finding the HGT events.

Are contemporary tourists consuming distance?

DEFF Research Database (Denmark)

Larsen, Gunvor Riber

2012. Background The background for this research, which explores how tourists represent distance and whether or not distance can be said to be consumed by contemporary tourists, is the increasing leisure mobility of people. Travelling for the purpose of visiting friends and relatives is increasing...... of understanding mobility at a conceptual level, and distance matters to people's manifest mobility: how they travel and how far they travel are central elements of their movements. Therefore leisure mobility (indeed all mobility) is the activity of relating across distance, either through actual corporeal...... metric representation. These representations are the focus for this research. Research Aim and Questions The aim of this research is thus to explore how distance is being represented within the context of leisure mobility. Further the aim is to explore how or whether distance is being consumed...

Effect of Image Linearization on Normalized Compression Distance

Science.gov (United States)

Mortensen, Jonathan; Wu, Jia Jie; Furst, Jacob; Rogers, John; Raicu, Daniela

Normalized Information Distance, based on Kolmogorov complexity, is an emerging metric for image similarity. It is approximated by the Normalized Compression Distance (NCD) which generates the relative distance between two strings by using standard compression algorithms to compare linear strings of information. This relative distance quantifies the degree of similarity between the two objects. NCD has been shown to measure similarity effectively on information which is already a string: genomic string comparisons have created accurate phylogeny trees and NCD has also been used to classify music. Currently, to find a similarity measure using NCD for images, the images must first be linearized into a string, and then compared. To understand how linearization of a 2D image affects the similarity measure, we perform four types of linearization on a subset of the Corel image database and compare each for a variety of image transformations. Our experiment shows that different linearization techniques produce statistically significant differences in NCD for identical spatial transformations.

Consumer Neuroscience-Based Metrics Predict Recall, Liking and Viewing Rates in Online Advertising.

Science.gov (United States)

Guixeres, Jaime; Bigné, Enrique; Ausín Azofra, Jose M; Alcañiz Raya, Mariano; Colomer Granero, Adrián; Fuentes Hurtado, Félix; Naranjo Ornedo, Valery

2017-01-01

The purpose of the present study is to investigate whether the effectiveness of a new ad on digital channels (YouTube) can be predicted by using neural networks and neuroscience-based metrics (brain response, heart rate variability and eye tracking). Neurophysiological records from 35 participants were exposed to 8 relevant TV Super Bowl commercials. Correlations between neurophysiological-based metrics, ad recall, ad liking, the ACE metrix score and the number of views on YouTube during a year were investigated. Our findings suggest a significant correlation between neuroscience metrics and self-reported of ad effectiveness and the direct number of views on the YouTube channel. In addition, and using an artificial neural network based on neuroscience metrics, the model classifies (82.9% of average accuracy) and estimate the number of online views (mean error of 0.199). The results highlight the validity of neuromarketing-based techniques for predicting the success of advertising responses. Practitioners can consider the proposed methodology at the design stages of advertising content, thus enhancing advertising effectiveness. The study pioneers the use of neurophysiological methods in predicting advertising success in a digital context. This is the first article that has examined whether these measures could actually be used for predicting views for advertising on YouTube.

Consumer Neuroscience-Based Metrics Predict Recall, Liking and Viewing Rates in Online Advertising

Directory of Open Access Journals (Sweden)

Jaime Guixeres

2017-10-01

Full Text Available The purpose of the present study is to investigate whether the effectiveness of a new ad on digital channels (YouTube can be predicted by using neural networks and neuroscience-based metrics (brain response, heart rate variability and eye tracking. Neurophysiological records from 35 participants were exposed to 8 relevant TV Super Bowl commercials. Correlations between neurophysiological-based metrics, ad recall, ad liking, the ACE metrix score and the number of views on YouTube during a year were investigated. Our findings suggest a significant correlation between neuroscience metrics and self-reported of ad effectiveness and the direct number of views on the YouTube channel. In addition, and using an artificial neural network based on neuroscience metrics, the model classifies (82.9% of average accuracy and estimate the number of online views (mean error of 0.199. The results highlight the validity of neuromarketing-based techniques for predicting the success of advertising responses. Practitioners can consider the proposed methodology at the design stages of advertising content, thus enhancing advertising effectiveness. The study pioneers the use of neurophysiological methods in predicting advertising success in a digital context. This is the first article that has examined whether these measures could actually be used for predicting views for advertising on YouTube.

Consumer Neuroscience-Based Metrics Predict Recall, Liking and Viewing Rates in Online Advertising

Science.gov (United States)

Guixeres, Jaime; Bigné, Enrique; Ausín Azofra, Jose M.; Alcañiz Raya, Mariano; Colomer Granero, Adrián; Fuentes Hurtado, Félix; Naranjo Ornedo, Valery

2017-01-01

The purpose of the present study is to investigate whether the effectiveness of a new ad on digital channels (YouTube) can be predicted by using neural networks and neuroscience-based metrics (brain response, heart rate variability and eye tracking). Neurophysiological records from 35 participants were exposed to 8 relevant TV Super Bowl commercials. Correlations between neurophysiological-based metrics, ad recall, ad liking, the ACE metrix score and the number of views on YouTube during a year were investigated. Our findings suggest a significant correlation between neuroscience metrics and self-reported of ad effectiveness and the direct number of views on the YouTube channel. In addition, and using an artificial neural network based on neuroscience metrics, the model classifies (82.9% of average accuracy) and estimate the number of online views (mean error of 0.199). The results highlight the validity of neuromarketing-based techniques for predicting the success of advertising responses. Practitioners can consider the proposed methodology at the design stages of advertising content, thus enhancing advertising effectiveness. The study pioneers the use of neurophysiological methods in predicting advertising success in a digital context. This is the first article that has examined whether these measures could actually be used for predicting views for advertising on YouTube. PMID:29163251

Model assessment using a multi-metric ranking technique

Science.gov (United States)

Fitzpatrick, P. J.; Lau, Y.; Alaka, G.; Marks, F.

2017-12-01

Validation comparisons of multiple models presents challenges when skill levels are similar, especially in regimes dominated by the climatological mean. Assessing skill separation will require advanced validation metrics and identifying adeptness in extreme events, but maintain simplicity for management decisions. Flexibility for operations is also an asset. This work postulates a weighted tally and consolidation technique which ranks results by multiple types of metrics. Variables include absolute error, bias, acceptable absolute error percentages, outlier metrics, model efficiency, Pearson correlation, Kendall's Tau, reliability Index, multiplicative gross error, and root mean squared differences. Other metrics, such as root mean square difference and rank correlation were also explored, but removed when the information was discovered to be generally duplicative to other metrics. While equal weights are applied, weights could be altered depending for preferred metrics. Two examples are shown comparing ocean models' currents and tropical cyclone products, including experimental products. The importance of using magnitude and direction for tropical cyclone track forecasts instead of distance, along-track, and cross-track are discussed. Tropical cyclone intensity and structure prediction are also assessed. Vector correlations are not included in the ranking process, but found useful in an independent context, and will be briefly reported.

The transposition distance for phylogenetic trees

OpenAIRE

Rossello, Francesc; Valiente, Gabriel

2006-01-01

The search for similarity and dissimilarity measures on phylogenetic trees has been motivated by the computation of consensus trees, the search by similarity in phylogenetic databases, and the assessment of clustering results in bioinformatics. The transposition distance for fully resolved phylogenetic trees is a recent addition to the extensive collection of available metrics for comparing phylogenetic trees. In this paper, we generalize the transposition distance from fully resolved to arbi...

MyLibrary@LANL: proximity and semi-metric networks for a collaborative and recommender web service

Energy Technology Data Exchange (ETDEWEB)

Rocha, L. M. [Indiana Univ., Bloomington, IN (United States). School of Informatics and Cognitive Science Program; Simas, T. [Indiana Univ., Bloomington, IN (United States). School of Informatics and Cognitive Science Program; Rechtsteiner, A. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); DiGiacomo, M. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Research Library; Luce, R. E. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Research Library

2005-09-01

We describe a network approach to building recommendation systems for a WWW service. We employ two different types of weighted graphs in our analysis and development: Proximity graphs, a type of Fuzzy Graphs based on a co-occurrence probability, and semi-metric distance graphs, which do not observe the triangle inequality of Euclidean distances. Both types of graphs are used to develop intelligent recommendation and collaboration systems for the MyLibrary@LANL web service, a user-centered front-end to the Los Alamos National Laboratory's (LANL) digital library collections and WWW resources.

Information Entropy-Based Metrics for Measuring Emergences in Artificial Societies

Directory of Open Access Journals (Sweden)

Mingsheng Tang

2014-08-01

Full Text Available Emergence is a common phenomenon, and it is also a general and important concept in complex dynamic systems like artificial societies. Usually, artificial societies are used for assisting in resolving several complex social issues (e.g., emergency management, intelligent transportation system with the aid of computer science. The levels of an emergence may have an effect on decisions making, and the occurrence and degree of an emergence are generally perceived by human observers. However, due to the ambiguity and inaccuracy of human observers, to propose a quantitative method to measure emergences in artificial societies is a meaningful and challenging task. This article mainly concentrates upon three kinds of emergences in artificial societies, including emergence of attribution, emergence of behavior, and emergence of structure. Based on information entropy, three metrics have been proposed to measure emergences in a quantitative way. Meanwhile, the correctness of these metrics has been verified through three case studies (the spread of an infectious influenza, a dynamic microblog network, and a flock of birds with several experimental simulations on the Netlogo platform. These experimental results confirm that these metrics increase with the rising degree of emergences. In addition, this article also has discussed the limitations and extended applications of these metrics.

A Lorentzian Gromov-Hausdorff notion of distance

International Nuclear Information System (INIS)

Noldus, Johan

2004-01-01

This paper is the first of three in which I study the moduli space of isometry classes of (compact) globally hyperbolic spacetimes (with boundary). I introduce a notion of Gromov-Hausdorff distance which makes this moduli space into a metric space. Further properties of this metric space are studied in the next two papers. The importance of the work is in fields such as cosmology, quantum gravity and - for the mathematicians - global Lorentzian geometry

80537 based distance relay

DEFF Research Database (Denmark)

Pedersen, Knud Ole Helgesen

1999-01-01

A method for implementing a digital distance relay in the power system is described.Instructions are given on how to program this relay on a 80537 based microcomputer system.The problem is used as a practical case study in the course 53113: Micocomputer applications in the power system.The relay...

Video Analytics Evaluation: Survey of Datasets, Performance Metrics and Approaches

Science.gov (United States)

2014-09-01

people with different ethnicity and gender . Cur- rently we have four subjects, but more can be added in the future. • Lighting Variations. We consider...is however not a proper distance as the triangular inequality condition is not met. For this reason, the next metric should be preferred. • the...and Alan F. Smeaton and Georges Quenot, An Overview of the Goals, Tasks, Data, Evaluation Mechanisms and Metrics, Proceedings of TRECVID 2011, NIST, USA

Alternatives to accuracy and bias metrics based on percentage errors for radiation belt modeling applications

Energy Technology Data Exchange (ETDEWEB)

Morley, Steven Karl [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2016-07-01

This report reviews existing literature describing forecast accuracy metrics, concentrating on those based on relative errors and percentage errors. We then review how the most common of these metrics, the mean absolute percentage error (MAPE), has been applied in recent radiation belt modeling literature. Finally, we describe metrics based on the ratios of predicted to observed values (the accuracy ratio) that address the drawbacks inherent in using MAPE. Specifically, we define and recommend the median log accuracy ratio as a measure of bias and the median symmetric accuracy as a measure of accuracy.

Assessment and improvement of radiation oncology trainee contouring ability utilizing consensus-based penalty metrics

International Nuclear Information System (INIS)

Hallock, Abhirami; Read, Nancy; D'Souza, David

2012-01-01

The objective of this study was to develop and assess the feasibility of utilizing consensus-based penalty metrics for the purpose of critical structure and organ at risk (OAR) contouring quality assurance and improvement. A Delphi study was conducted to obtain consensus on contouring penalty metrics to assess trainee-generated OAR contours. Voxel-based penalty metric equations were used to score regions of discordance between trainee and expert contour sets. The utility of these penalty metric scores for objective feedback on contouring quality was assessed by using cases prepared for weekly radiation oncology radiation oncology trainee treatment planning rounds. In two Delphi rounds, six radiation oncology specialists reached agreement on clinical importance/impact and organ radiosensitivity as the two primary criteria for the creation of the Critical Structure Inter-comparison of Segmentation (CriSIS) penalty functions. Linear/quadratic penalty scoring functions (for over- and under-contouring) with one of four levels of severity (none, low, moderate and high) were assigned for each of 20 OARs in order to generate a CriSIS score when new OAR contours are compared with reference/expert standards. Six cases (central nervous system, head and neck, gastrointestinal, genitourinary, gynaecological and thoracic) then were used to validate 18 OAR metrics through comparison of trainee and expert contour sets using the consensus derived CriSIS functions. For 14 OARs, there was an improvement in CriSIS score post-educational intervention. The use of consensus-based contouring penalty metrics to provide quantitative information for contouring improvement is feasible.

Genomic signal processing methods for computation of alignment-free distances from DNA sequences.

Science.gov (United States)

Borrayo, Ernesto; Mendizabal-Ruiz, E Gerardo; Vélez-Pérez, Hugo; Romo-Vázquez, Rebeca; Mendizabal, Adriana P; Morales, J Alejandro

2014-01-01

Genomic signal processing (GSP) refers to the use of digital signal processing (DSP) tools for analyzing genomic data such as DNA sequences. A possible application of GSP that has not been fully explored is the computation of the distance between a pair of sequences. In this work we present GAFD, a novel GSP alignment-free distance computation method. We introduce a DNA sequence-to-signal mapping function based on the employment of doublet values, which increases the number of possible amplitude values for the generated signal. Additionally, we explore the use of three DSP distance metrics as descriptors for categorizing DNA signal fragments. Our results indicate the feasibility of employing GAFD for computing sequence distances and the use of descriptors for characterizing DNA fragments.

Nonlinear partial least squares with Hellinger distance for nonlinear process monitoring

KAUST Repository

Harrou, Fouzi; Madakyaru, Muddu; Sun, Ying

2017-01-01

This paper proposes an efficient data-based anomaly detection method that can be used for monitoring nonlinear processes. The proposed method merges advantages of nonlinear projection to latent structures (NLPLS) modeling and those of Hellinger distance (HD) metric to identify abnormal changes in highly correlated multivariate data. Specifically, the HD is used to quantify the dissimilarity between current NLPLS-based residual and reference probability distributions. The performances of the developed anomaly detection using NLPLS-based HD technique is illustrated using simulated plug flow reactor data.

Nonlinear partial least squares with Hellinger distance for nonlinear process monitoring

KAUST Repository

Harrou, Fouzi

2017-02-16

This paper proposes an efficient data-based anomaly detection method that can be used for monitoring nonlinear processes. The proposed method merges advantages of nonlinear projection to latent structures (NLPLS) modeling and those of Hellinger distance (HD) metric to identify abnormal changes in highly correlated multivariate data. Specifically, the HD is used to quantify the dissimilarity between current NLPLS-based residual and reference probability distributions. The performances of the developed anomaly detection using NLPLS-based HD technique is illustrated using simulated plug flow reactor data.

METRIC context unit architecture

Energy Technology Data Exchange (ETDEWEB)

Simpson, R.O.

1988-01-01

METRIC is an architecture for a simple but powerful Reduced Instruction Set Computer (RISC). Its speed comes from the simultaneous processing of several instruction streams, with instructions from the various streams being dispatched into METRIC's execution pipeline as they become available for execution. The pipeline is thus kept full, with a mix of instructions for several contexts in execution at the same time. True parallel programming is supported within a single execution unit, the METRIC Context Unit. METRIC's architecture provides for expansion through the addition of multiple Context Units and of specialized Functional Units. The architecture thus spans a range of size and performance from a single-chip microcomputer up through large and powerful multiprocessors. This research concentrates on the specification of the METRIC Context Unit at the architectural level. Performance tradeoffs made during METRIC's design are discussed, and projections of METRIC's performance are made based on simulation studies.

Comparison of continuous versus categorical tumor measurement-based metrics to predict overall survival in cancer treatment trials

Science.gov (United States)

An, Ming-Wen; Mandrekar, Sumithra J.; Branda, Megan E.; Hillman, Shauna L.; Adjei, Alex A.; Pitot, Henry; Goldberg, Richard M.; Sargent, Daniel J.

2011-01-01

Purpose The categorical definition of response assessed via the Response Evaluation Criteria in Solid Tumors has documented limitations. We sought to identify alternative metrics for tumor response that improve prediction of overall survival. Experimental Design Individual patient data from three North Central Cancer Treatment Group trials (N0026, n=117; N9741, n=1109; N9841, n=332) were used. Continuous metrics of tumor size based on longitudinal tumor measurements were considered in addition to a trichotomized response (TriTR: Response vs. Stable vs. Progression). Cox proportional hazards models, adjusted for treatment arm and baseline tumor burden, were used to assess the impact of the metrics on subsequent overall survival, using a landmark analysis approach at 12-, 16- and 24-weeks post baseline. Model discrimination was evaluated using the concordance (c) index. Results The overall best response rates for the three trials were 26%, 45%, and 25% respectively. While nearly all metrics were statistically significantly associated with overall survival at the different landmark time points, the c-indices for the traditional response metrics ranged from 0.59-0.65; for the continuous metrics from 0.60-0.66 and for the TriTR metrics from 0.64-0.69. The c-indices for TriTR at 12-weeks were comparable to those at 16- and 24-weeks. Conclusions Continuous tumor-measurement-based metrics provided no predictive improvement over traditional response based metrics or TriTR; TriTR had better predictive ability than best TriTR or confirmed response. If confirmed, TriTR represents a promising endpoint for future Phase II trials. PMID:21880789

A Simple Metric for Determining Resolution in Optical, Ion, and Electron Microscope Images.

Science.gov (United States)

Curtin, Alexandra E; Skinner, Ryan; Sanders, Aric W

2015-06-01

A resolution metric intended for resolution analysis of arbitrary spatially calibrated images is presented. By fitting a simple sigmoidal function to pixel intensities across slices of an image taken perpendicular to light-dark edges, the mean distance over which the light-dark transition occurs can be determined. A fixed multiple of this characteristic distance is then reported as the image resolution. The prefactor is determined by analysis of scanning transmission electron microscope high-angle annular dark field images of Si. This metric has been applied to optical, scanning electron microscope, and helium ion microscope images. This method provides quantitative feedback about image resolution, independent of the tool on which the data were collected. In addition, our analysis provides a nonarbitrary and self-consistent framework that any end user can utilize to evaluate the resolution of multiple microscopes from any vendor using the same metric.

Assessing Measurement Distances for OTA Testing of Massive MIMO Base Station at 28 GHz

DEFF Research Database (Denmark)

Kyösti, Pekka; Fan, Wei; Kyrolainen, Jukka

2017-01-01

metrics. The first metric is a new power metric based on assumptions of a code book of fixed beams and planar waves. The second one is the multi-user (MU) MIMO sum rate capacity. The intention is to evaluate physical dimensions in metres with respect to different BS array sizes. Simulation results...

Proposed Performance-Based Metrics for the Future Funding of Graduate Medical Education: Starting the Conversation.

Science.gov (United States)

Caverzagie, Kelly J; Lane, Susan W; Sharma, Niraj; Donnelly, John; Jaeger, Jeffrey R; Laird-Fick, Heather; Moriarty, John P; Moyer, Darilyn V; Wallach, Sara L; Wardrop, Richard M; Steinmann, Alwin F

2017-12-12

Graduate medical education (GME) in the United States is financed by contributions from both federal and state entities that total over $15 billion annually. Within institutions, these funds are distributed with limited transparency to achieve ill-defined outcomes. To address this, the Institute of Medicine convened a committee on the governance and financing of GME to recommend finance reform that would promote a physician training system that meets society's current and future needs. The resulting report provided several recommendations regarding the oversight and mechanisms of GME funding, including implementation of performance-based GME payments, but did not provide specific details about the content and development of metrics for these payments. To initiate a national conversation about performance-based GME funding, the authors asked: What should GME be held accountable for in exchange for public funding? In answer to this question, the authors propose 17 potential performance-based metrics for GME funding that could inform future funding decisions. Eight of the metrics are described as exemplars to add context and to help readers obtain a deeper understanding of the inherent complexities of performance-based GME funding. The authors also describe considerations and precautions for metric implementation.

Cloud-based Computing and Applications of New Snow Metrics for Societal Benefit

Science.gov (United States)

Nolin, A. W.; Sproles, E. A.; Crumley, R. L.; Wilson, A.; Mar, E.; van de Kerk, M.; Prugh, L.

2017-12-01

Seasonal and interannual variability in snow cover affects socio-environmental systems including water resources, forest ecology, freshwater and terrestrial habitat, and winter recreation. We have developed two new seasonal snow metrics: snow cover frequency (SCF) and snow disappearance date (SDD). These metrics are calculated at 500-m resolution using NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover data (MOD10A1). SCF is the number of times snow is observed in a pixel over the user-defined observation period. SDD is the last date of observed snow in a water year. These pixel-level metrics are calculated rapidly and globally in the Google Earth Engine cloud-based environment. SCF and SDD can be interactively visualized in a map-based interface, allowing users to explore spatial and temporal snowcover patterns from 2000-present. These metrics are especially valuable in regions where snow data are sparse or non-existent. We have used these metrics in several ongoing projects. When SCF was linked with a simple hydrologic model in the La Laguna watershed in northern Chile, it successfully predicted summer low flows with a Nash-Sutcliffe value of 0.86. SCF has also been used to help explain changes in Dall sheep populations in Alaska where sheep populations are negatively impacted by late snow cover and low snowline elevation during the spring lambing season. In forest management, SCF and SDD appear to be valuable predictors of post-wildfire vegetation growth. We see a positive relationship between winter SCF and subsequent summer greening for several years post-fire. For western US winter recreation, we are exploring trends in SDD and SCF for regions where snow sports are economically important. In a world with declining snowpacks and increasing uncertainty, these metrics extend across elevations and fill data gaps to provide valuable information for decision-making. SCF and SDD are being produced so that anyone with Internet access and a Google

Application of Technology in Project-Based Distance Learning

Directory of Open Access Journals (Sweden)

Ali Mehrabian

2008-06-01

Full Text Available Present technology and the accessibility of internet have made distance learning easier, more efficient, and more convenient for students. This technology allows instructors and students to communicate asynchronously, at times and locations of their own choosing, by exchanging printed or electronic information. The use of project-based approach is being recognized in the literature as a potential component of courses in the faculties of engineering, science, and technology. Instructors may have to restructure their course differently to accommodate and facilitate the effectiveness of distance learning. A project-based engineering course, traditionally taught in a classroom settings using live mode at the College of Engineering and Computer Sciences at the University of Central Florida (UCF has been transformed to a distance course taught using distance modes. In this case, pedagogical transitions and adjustments are required, in particular for obtaining an optimal balance between the course material and the project work. Project collaboration in groups requires communication, which is possible with extensive utilization of new information and communication technology, such as virtual meetings. This paper discusses the course transition from live to distance modes and touches on some issues as they relate to the effectiveness of this methodology and the lessons learned from its application within different context. More specifically, this discussion includes the benefit of implementing project-based work in the domain of the distance learning courses.

Can Tweets Predict Citations? Metrics of Social Impact Based on Twitter and Correlation with Traditional Metrics of Scientific Impact

Science.gov (United States)

2011-01-01

predictors (P < .001) could explain 27% of the variation of citations. Highly tweeted articles were 11 times more likely to be highly cited than less-tweeted articles (9/12 or 75% of highly tweeted article were highly cited, while only 3/43 or 7% of less-tweeted articles were highly cited; rate ratio 0.75/0.07 = 10.75, 95% confidence interval, 3.4–33.6). Top-cited articles can be predicted from top-tweeted articles with 93% specificity and 75% sensitivity. Conclusions Tweets can predict highly cited articles within the first 3 days of article publication. Social media activity either increases citations or reflects the underlying qualities of the article that also predict citations, but the true use of these metrics is to measure the distinct concept of social impact. Social impact measures based on tweets are proposed to complement traditional citation metrics. The proposed twimpact factor may be a useful and timely metric to measure uptake of research findings and to filter research findings resonating with the public in real time. PMID:22173204

Metrics for Polyphonic Sound Event Detection

Directory of Open Access Journals (Sweden)

Annamaria Mesaros

2016-05-01

Full Text Available This paper presents and discusses various metrics proposed for evaluation of polyphonic sound event detection systems used in realistic situations where there are typically multiple sound sources active simultaneously. The system output in this case contains overlapping events, marked as multiple sounds detected as being active at the same time. The polyphonic system output requires a suitable procedure for evaluation against a reference. Metrics from neighboring fields such as speech recognition and speaker diarization can be used, but they need to be partially redefined to deal with the overlapping events. We present a review of the most common metrics in the field and the way they are adapted and interpreted in the polyphonic case. We discuss segment-based and event-based definitions of each metric and explain the consequences of instance-based and class-based averaging using a case study. In parallel, we provide a toolbox containing implementations of presented metrics.

Natural metrics and least-committed priors for articulated tracking

DEFF Research Database (Denmark)

Hauberg, Søren; Sommer, Stefan Horst; Pedersen, Kim Steenstrup

2012-01-01

of joint positions, which is embedded in a high dimensional Euclidean space. This Riemannian manifold inherits the metric from the embedding space, such that distances are measured as the combined physical length that joints travel during movements. We then develop a least-committed Brownian motion model...

Converging from branching to linear metrics on Markov chains

DEFF Research Database (Denmark)

Bacci, Giorgio; Bacci, Giovanni; Larsen, Kim G.

2017-01-01

-approximant is computable in polynomial time in the size of the MC. The upper-approximants are bisimilarity-like pseudometrics (hence, branching-time distances) that converge point-wise to the linear-time metrics. This convergence is interesting in itself, because it reveals a nontrivial relation between branching...

Utility of ck metrics in predicting size of board-based software games

International Nuclear Information System (INIS)

Sabhat, N.; Azam, F.; Malik, A.A.

2017-01-01

Software size is one of the most important inputs of many software cost and effort estimation models. Early estimation of software plays an important role at the time of project inception. An accurate estimate of software size is, therefore, crucial for planning, managing, and controlling software development projects dealing with the development of software games. However, software size is unavailable during early phase of software development. This research determines the utility of CK (Chidamber and Kemerer) metrics, a well-known suite of object-oriented metrics, in estimating the size of software applications using the information from its UML (Unified Modeling Language) class diagram. This work focuses on a small subset dealing with board-based software games. Almost sixty games written using an object-oriented programming language are downloaded from open source repositories, analyzed and used to calibrate a regression-based size estimation model. Forward stepwise MLR (Multiple Linear Regression) is used for model fitting. The model thus obtained is assessed using a variety of accuracy measures such as MMRE (Mean Magnitude of Relative Error), Prediction of x(PRED(x)), MdMRE (Median of Relative Error) and validated using K-fold cross validation. The accuracy of this model is also compared with an existing model tailored for size estimation of board games. Based on a small subset of desktop games developed in various object-oriented languages, we obtained a model using CK metrics and forward stepwise multiple linear regression with reasonable estimation accuracy as indicated by the value of the coefficient of determination (R2 = 0.756).Comparison results indicate that the existing size estimation model outperforms the model derived using CK metrics in terms of accuracy of prediction. (author)

Improved Iris Recognition through Fusion of Hamming Distance and Fragile Bit Distance.

Science.gov (United States)

Hollingsworth, Karen P; Bowyer, Kevin W; Flynn, Patrick J

2011-12-01

The most common iris biometric algorithm represents the texture of an iris using a binary iris code. Not all bits in an iris code are equally consistent. A bit is deemed fragile if its value changes across iris codes created from different images of the same iris. Previous research has shown that iris recognition performance can be improved by masking these fragile bits. Rather than ignoring fragile bits completely, we consider what beneficial information can be obtained from the fragile bits. We find that the locations of fragile bits tend to be consistent across different iris codes of the same eye. We present a metric, called the fragile bit distance, which quantitatively measures the coincidence of the fragile bit patterns in two iris codes. We find that score fusion of fragile bit distance and Hamming distance works better for recognition than Hamming distance alone. To our knowledge, this is the first and only work to use the coincidence of fragile bit locations to improve the accuracy of matches.

Site-to-Source Finite Fault Distance Probability Distribution in Probabilistic Seismic Hazard and the Relationship Between Minimum Distances

Science.gov (United States)

Ortega, R.; Gutierrez, E.; Carciumaru, D. D.; Huesca-Perez, E.

2017-12-01

We present a method to compute the conditional and no-conditional probability density function (PDF) of the finite fault distance distribution (FFDD). Two cases are described: lines and areas. The case of lines has a simple analytical solution while, in the case of areas, the geometrical probability of a fault based on the strike, dip, and fault segment vertices is obtained using the projection of spheres in a piecewise rectangular surface. The cumulative distribution is computed by measuring the projection of a sphere of radius r in an effective area using an algorithm that estimates the area of a circle within a rectangle. In addition, we introduce the finite fault distance metrics. This distance is the distance where the maximum stress release occurs within the fault plane and generates a peak ground motion. Later, we can apply the appropriate ground motion prediction equations (GMPE) for PSHA. The conditional probability of distance given magnitude is also presented using different scaling laws. A simple model of constant distribution of the centroid at the geometrical mean is discussed, in this model hazard is reduced at the edges because the effective size is reduced. Nowadays there is a trend of using extended source distances in PSHA, however it is not possible to separate the fault geometry from the GMPE. With this new approach, it is possible to add fault rupture models separating geometrical and propagation effects.

Term Based Comparison Metrics for Controlled and Uncontrolled Indexing Languages

Science.gov (United States)

Good, B. M.; Tennis, J. T.

2009-01-01

Introduction: We define a collection of metrics for describing and comparing sets of terms in controlled and uncontrolled indexing languages and then show how these metrics can be used to characterize a set of languages spanning folksonomies, ontologies and thesauri. Method: Metrics for term set characterization and comparison were identified and…

Adapting observationally based metrics of biogeophysical feedbacks from land cover/land use change to climate modeling

International Nuclear Information System (INIS)

Chen, Liang; Dirmeyer, Paul A

2016-01-01

To assess the biogeophysical impacts of land cover/land use change (LCLUC) on surface temperature, two observation-based metrics and their applicability in climate modeling were explored in this study. Both metrics were developed based on the surface energy balance, and provided insight into the contribution of different aspects of land surface change (such as albedo, surface roughness, net radiation and surface heat fluxes) to changing climate. A revision of the first metric, the intrinsic biophysical mechanism, can be used to distinguish the direct and indirect effects of LCLUC on surface temperature. The other, a decomposed temperature metric, gives a straightforward depiction of separate contributions of all components of the surface energy balance. These two metrics well capture observed and model simulated surface temperature changes in response to LCLUC. Results from paired FLUXNET sites and land surface model sensitivity experiments indicate that surface roughness effects usually dominate the direct biogeophysical feedback of LCLUC, while other effects play a secondary role. However, coupled climate model experiments show that these direct effects can be attenuated by large scale atmospheric changes (indirect feedbacks). When applied to real-time transient LCLUC experiments, the metrics also demonstrate usefulness for assessing the performance of climate models and quantifying land–atmosphere interactions in response to LCLUC. (letter)

A Distance Bounding Protocol for Location-Cloaked Applications.

Science.gov (United States)

Molina-Martínez, Cristián; Galdames, Patricio; Duran-Faundez, Cristian

2018-04-26

Location-based services (LBSs) assume that users are willing to release trustworthy and useful details about their whereabouts. However, many location privacy concerns have arisen. For location privacy protection, several algorithms build a cloaking region to hide a user’s location. However, many applications may not operate adequately on cloaked locations. For example, a traditional distance bounding protocol (DBP)—which is run by two nodes called the prover and the verifier—may conclude an untight and useless distance between these two entities. An LBS (verifier) may use this distance as a metric of usefulness and trustworthiness of the location claimed by the user (prover). However, we show that if a tight distance is desired, traditional DBP can refine a user’s cloaked location and compromise its location privacy. To find a proper balance, we propose a location-privacy-aware DBP protocol. Our solution consists of adding some small delays before submitting any user’s response. We show that several issues arise when a certain delay is chosen, and we propose some solutions. The effectiveness of our techniques in balancing location refinement and utility is demonstrated through simulation.

A Distance Bounding Protocol for Location-Cloaked Applications

Directory of Open Access Journals (Sweden)

Cristián Molina-Martínez

2018-04-01

Full Text Available Location-based services (LBSs assume that users are willing to release trustworthy and useful details about their whereabouts. However, many location privacy concerns have arisen. For location privacy protection, several algorithms build a cloaking region to hide a user’s location. However, many applications may not operate adequately on cloaked locations. For example, a traditional distance bounding protocol (DBP—which is run by two nodes called the prover and the verifier—may conclude an untight and useless distance between these two entities. An LBS (verifier may use this distance as a metric of usefulness and trustworthiness of the location claimed by the user (prover. However, we show that if a tight distance is desired, traditional DBP can refine a user’s cloaked location and compromise its location privacy. To find a proper balance, we propose a location-privacy-aware DBP protocol. Our solution consists of adding some small delays before submitting any user’s response. We show that several issues arise when a certain delay is chosen, and we propose some solutions. The effectiveness of our techniques in balancing location refinement and utility is demonstrated through simulation.

A GPS Phase-Locked Loop Performance Metric Based on the Phase Discriminator Output.

Science.gov (United States)

Stevanovic, Stefan; Pervan, Boris

2018-01-19

We propose a novel GPS phase-lock loop (PLL) performance metric based on the standard deviation of tracking error (defined as the discriminator's estimate of the true phase error), and explain its advantages over the popular phase jitter metric using theory, numerical simulation, and experimental results. We derive an augmented GPS phase-lock loop (PLL) linear model, which includes the effect of coherent averaging, to be used in conjunction with this proposed metric. The augmented linear model allows more accurate calculation of tracking error standard deviation in the presence of additive white Gaussian noise (AWGN) as compared to traditional linear models. The standard deviation of tracking error, with a threshold corresponding to half of the arctangent discriminator pull-in region, is shown to be a more reliable/robust measure of PLL performance under interference conditions than the phase jitter metric. In addition, the augmented linear model is shown to be valid up until this threshold, which facilitates efficient performance prediction, so that time-consuming direct simulations and costly experimental testing can be reserved for PLL designs that are much more likely to be successful. The effect of varying receiver reference oscillator quality on the tracking error metric is also considered.

Comparison of SOAP and REST Based Web Services Using Software Evaluation Metrics

Directory of Open Access Journals (Sweden)

Tihomirovs Juris

2016-12-01

Full Text Available The usage of Web services has recently increased. Therefore, it is important to select right type of Web services at the project design stage. The most common implementations are based on SOAP (Simple Object Access Protocol and REST (Representational State Transfer Protocol styles. Maintainability of REST and SOAP Web services has become an important issue as popularity of Web services is increasing. Choice of the right approach is not an easy decision since it is influenced by development requirements and maintenance considerations. In the present research, we present the comparison of SOAP and REST based Web services using software evaluation metrics. To achieve this aim, a systematic literature review will be made to compare REST and SOAP Web services in terms of the software evaluation metrics.

A string matching based algorithm for performance evaluation of ...

Indian Academy of Sciences (India)

Zanibbi et al (2011) have proposed performance metrics based on bipartite graphs at stroke level. ... bipartite graphs on which metrics based on hamming distances are defined. ...... Document Image Analysis for Libraries 320–331 ... Lee H J and Wang J S 1997 Design of a mathematical expression understanding system.

Application of Metric-based Software Reliability Analysis to Example Software

International Nuclear Information System (INIS)

Kim, Man Cheol; Smidts, Carol

2008-07-01

The software reliability of TELLERFAST ATM software is analyzed by using two metric-based software reliability analysis methods, a state transition diagram-based method and a test coverage-based method. The procedures for the software reliability analysis by using the two methods and the analysis results are provided in this report. It is found that the two methods have a relation of complementary cooperation, and therefore further researches on combining the two methods to reflect the benefit of the complementary cooperative effect to the software reliability analysis are recommended

Measuring reliability under epistemic uncertainty: Review on non-probabilistic reliability metrics

Directory of Open Access Journals (Sweden)

Kang Rui

2016-06-01

Full Text Available In this paper, a systematic review of non-probabilistic reliability metrics is conducted to assist the selection of appropriate reliability metrics to model the influence of epistemic uncertainty. Five frequently used non-probabilistic reliability metrics are critically reviewed, i.e., evidence-theory-based reliability metrics, interval-analysis-based reliability metrics, fuzzy-interval-analysis-based reliability metrics, possibility-theory-based reliability metrics (posbist reliability and uncertainty-theory-based reliability metrics (belief reliability. It is pointed out that a qualified reliability metric that is able to consider the effect of epistemic uncertainty needs to (1 compensate the conservatism in the estimations of the component-level reliability metrics caused by epistemic uncertainty, and (2 satisfy the duality axiom, otherwise it might lead to paradoxical and confusing results in engineering applications. The five commonly used non-probabilistic reliability metrics are compared in terms of these two properties, and the comparison can serve as a basis for the selection of the appropriate reliability metrics.

Numerical Calabi-Yau metrics

International Nuclear Information System (INIS)

Douglas, Michael R.; Karp, Robert L.; Lukic, Sergio; Reinbacher, Rene

2008-01-01

We develop numerical methods for approximating Ricci flat metrics on Calabi-Yau hypersurfaces in projective spaces. Our approach is based on finding balanced metrics and builds on recent theoretical work by Donaldson. We illustrate our methods in detail for a one parameter family of quintics. We also suggest several ways to extend our results

Optimizing distance-based methods for large data sets

Science.gov (United States)

Scholl, Tobias; Brenner, Thomas

2015-10-01

Distance-based methods for measuring spatial concentration of industries have received an increasing popularity in the spatial econometrics community. However, a limiting factor for using these methods is their computational complexity since both their memory requirements and running times are in {{O}}(n^2). In this paper, we present an algorithm with constant memory requirements and shorter running time, enabling distance-based methods to deal with large data sets. We discuss three recent distance-based methods in spatial econometrics: the D&O-Index by Duranton and Overman (Rev Econ Stud 72(4):1077-1106, 2005), the M-function by Marcon and Puech (J Econ Geogr 10(5):745-762, 2010) and the Cluster-Index by Scholl and Brenner (Reg Stud (ahead-of-print):1-15, 2014). Finally, we present an alternative calculation for the latter index that allows the use of data sets with millions of firms.

Questionable validity of the catheter-associated urinary tract infection metric used for value-based purchasing.

Science.gov (United States)

Calderon, Lindsay E; Kavanagh, Kevin T; Rice, Mara K

2015-10-01

Catheter-associated urinary tract infections (CAUTIs) occur in 290,000 US hospital patients annually, with an estimated cost of $290 million. Two different measurement systems are being used to track the US health care system's performance in lowering the rate of CAUTIs. Since 2010, the Agency for Healthcare Research and Quality (AHRQ) metric has shown a 28.2% decrease in CAUTI, whereas the Centers for Disease Control and Prevention metric has shown a 3%-6% increase in CAUTI since 2009. Differences in data acquisition and the definition of the denominator may explain this discrepancy. The AHRQ metric analyzes chart-audited data and reflects both catheter use and care. The Centers for Disease Control and Prevention metric analyzes self-reported data and primarily reflects catheter care. Because analysis of the AHRQ metric showed a progressive change in performance over time and the scientific literature supports the importance of catheter use in the prevention of CAUTI, it is suggested that risk-adjusted catheter-use data be incorporated into metrics that are used for determining facility performance and for value-based purchasing initiatives. Copyright © 2015 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

On using multiple routing metrics with destination sequenced distance vector protocol for MultiHop wireless ad hoc networks

Science.gov (United States)

Mehic, M.; Fazio, P.; Voznak, M.; Partila, P.; Komosny, D.; Tovarek, J.; Chmelikova, Z.

2016-05-01

A mobile ad hoc network is a collection of mobile nodes which communicate without a fixed backbone or centralized infrastructure. Due to the frequent mobility of nodes, routes connecting two distant nodes may change. Therefore, it is not possible to establish a priori fixed paths for message delivery through the network. Because of its importance, routing is the most studied problem in mobile ad hoc networks. In addition, if the Quality of Service (QoS) is demanded, one must guarantee the QoS not only over a single hop but over an entire wireless multi-hop path which may not be a trivial task. In turns, this requires the propagation of QoS information within the network. The key to the support of QoS reporting is QoS routing, which provides path QoS information at each source. To support QoS for real-time traffic one needs to know not only minimum delay on the path to the destination but also the bandwidth available on it. Therefore, throughput, end-to-end delay, and routing overhead are traditional performance metrics used to evaluate the performance of routing protocol. To obtain additional information about the link, most of quality-link metrics are based on calculation of the lost probabilities of links by broadcasting probe packets. In this paper, we address the problem of including multiple routing metrics in existing routing packets that are broadcasted through the network. We evaluate the efficiency of such approach with modified version of DSDV routing protocols in ns-3 simulator.

SU-G-BRB-16: Vulnerabilities in the Gamma Metric

International Nuclear Information System (INIS)

Neal, B; Siebers, J

2016-01-01

Purpose: To explore vulnerabilities in the gamma index metric that undermine its wide use as a radiation therapy quality assurance tool. Methods: 2D test field pairs (images) are created specifically to achieve high gamma passing rates, but to also include gross errors by exploiting the distance-to-agreement and percent-passing components of the metric. The first set has no requirement of clinical practicality, but is intended to expose vulnerabilities. The second set exposes clinically realistic vulnerabilities. To circumvent limitations inherent to user-specific tuning of prediction algorithms to match measurements, digital test cases are manually constructed, thereby mimicking high-quality image prediction. Results: With a 3 mm distance-to-agreement metric, changing field size by ±6 mm results in a gamma passing rate over 99%. For a uniform field, a lattice of passing points spaced 5 mm apart results in a passing rate of 100%. Exploiting the percent-passing component, a 10×10 cm"2 field can have a 95% passing rate when an 8 cm"2=2.8×2.8 cm"2 highly out-of-tolerance (e.g. zero dose) square is missing from the comparison image. For clinically realistic vulnerabilities, an arc plan for which a 2D image is created can have a >95% passing rate solely due to agreement in the lateral spillage, with the failing 5% in the critical target region. A field with an integrated boost (e.g whole brain plus small metastases) could neglect the metastases entirely, yet still pass with a 95% threshold. All the failure modes described would be visually apparent on a gamma-map image. Conclusion: The %gamma<1 metric has significant vulnerabilities. High passing rates can obscure critical faults in hypothetical and delivered radiation doses. Great caution should be used with gamma as a QA metric; users should inspect the gamma-map. Visual analysis of gamma-maps may be impractical for cine acquisition.

SU-G-BRB-16: Vulnerabilities in the Gamma Metric

Energy Technology Data Exchange (ETDEWEB)

Neal, B; Siebers, J [University of Virginia Health System, Charlottesville, VA (United States)

2016-06-15

Purpose: To explore vulnerabilities in the gamma index metric that undermine its wide use as a radiation therapy quality assurance tool. Methods: 2D test field pairs (images) are created specifically to achieve high gamma passing rates, but to also include gross errors by exploiting the distance-to-agreement and percent-passing components of the metric. The first set has no requirement of clinical practicality, but is intended to expose vulnerabilities. The second set exposes clinically realistic vulnerabilities. To circumvent limitations inherent to user-specific tuning of prediction algorithms to match measurements, digital test cases are manually constructed, thereby mimicking high-quality image prediction. Results: With a 3 mm distance-to-agreement metric, changing field size by ±6 mm results in a gamma passing rate over 99%. For a uniform field, a lattice of passing points spaced 5 mm apart results in a passing rate of 100%. Exploiting the percent-passing component, a 10×10 cm{sup 2} field can have a 95% passing rate when an 8 cm{sup 2}=2.8×2.8 cm{sup 2} highly out-of-tolerance (e.g. zero dose) square is missing from the comparison image. For clinically realistic vulnerabilities, an arc plan for which a 2D image is created can have a >95% passing rate solely due to agreement in the lateral spillage, with the failing 5% in the critical target region. A field with an integrated boost (e.g whole brain plus small metastases) could neglect the metastases entirely, yet still pass with a 95% threshold. All the failure modes described would be visually apparent on a gamma-map image. Conclusion: The %gamma<1 metric has significant vulnerabilities. High passing rates can obscure critical faults in hypothetical and delivered radiation doses. Great caution should be used with gamma as a QA metric; users should inspect the gamma-map. Visual analysis of gamma-maps may be impractical for cine acquisition.

Permutation-invariant distance between atomic configurations

Science.gov (United States)

Ferré, Grégoire; Maillet, Jean-Bernard; Stoltz, Gabriel

2015-09-01

We present a permutation-invariant distance between atomic configurations, defined through a functional representation of atomic positions. This distance enables us to directly compare different atomic environments with an arbitrary number of particles, without going through a space of reduced dimensionality (i.e., fingerprints) as an intermediate step. Moreover, this distance is naturally invariant through permutations of atoms, avoiding the time consuming associated minimization required by other common criteria (like the root mean square distance). Finally, the invariance through global rotations is accounted for by a minimization procedure in the space of rotations solved by Monte Carlo simulated annealing. A formal framework is also introduced, showing that the distance we propose verifies the property of a metric on the space of atomic configurations. Two examples of applications are proposed. The first one consists in evaluating faithfulness of some fingerprints (or descriptors), i.e., their capacity to represent the structural information of a configuration. The second application concerns structural analysis, where our distance proves to be efficient in discriminating different local structures and even classifying their degree of similarity.

Permutation-invariant distance between atomic configurations

International Nuclear Information System (INIS)

Ferré, Grégoire; Maillet, Jean-Bernard; Stoltz, Gabriel

2015-01-01

We present a permutation-invariant distance between atomic configurations, defined through a functional representation of atomic positions. This distance enables us to directly compare different atomic environments with an arbitrary number of particles, without going through a space of reduced dimensionality (i.e., fingerprints) as an intermediate step. Moreover, this distance is naturally invariant through permutations of atoms, avoiding the time consuming associated minimization required by other common criteria (like the root mean square distance). Finally, the invariance through global rotations is accounted for by a minimization procedure in the space of rotations solved by Monte Carlo simulated annealing. A formal framework is also introduced, showing that the distance we propose verifies the property of a metric on the space of atomic configurations. Two examples of applications are proposed. The first one consists in evaluating faithfulness of some fingerprints (or descriptors), i.e., their capacity to represent the structural information of a configuration. The second application concerns structural analysis, where our distance proves to be efficient in discriminating different local structures and even classifying their degree of similarity

Identifying multiple influential spreaders in term of the distance-based coloring

Energy Technology Data Exchange (ETDEWEB)

Guo, Lei; Lin, Jian-Hong; Guo, Qiang [Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093 (China); Liu, Jian-Guo, E-mail: liujg004@ustc.edu.cn [Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093 (China); Data Science and Cloud Service Research Centre, Shanghai University of Finance and Economics, Shanghai 200433 (China)

2016-02-22

Identifying influential nodes is of significance for understanding the dynamics of information diffusion process in complex networks. In this paper, we present an improved distance-based coloring method to identify the multiple influential spreaders. In our method, each node is colored by a kind of color with the rule that the distance between initial nodes is close to the average distance of a network. When all nodes are colored, nodes with the same color are sorted into an independent set. Then we choose the nodes at the top positions of the ranking list according to their centralities. The experimental results for an artificial network and three empirical networks show that, comparing with the performance of traditional coloring method, the improvement ratio of our distance-based coloring method could reach 12.82%, 8.16%, 4.45%, 2.93% for the ER, Erdős, Polblogs and Routers networks respectively. - Highlights: • We present an improved distance-based coloring method to identify the multiple influential spreaders. • Each node is colored by a kind of color where the distance between initial nodes is close to the average distance. • For three empirical networks show that the improvement ratio of our distance-based coloring method could reach 8.16% for the Erdos network.

Identifying multiple influential spreaders in term of the distance-based coloring

International Nuclear Information System (INIS)

Guo, Lei; Lin, Jian-Hong; Guo, Qiang; Liu, Jian-Guo

2016-01-01

Identifying influential nodes is of significance for understanding the dynamics of information diffusion process in complex networks. In this paper, we present an improved distance-based coloring method to identify the multiple influential spreaders. In our method, each node is colored by a kind of color with the rule that the distance between initial nodes is close to the average distance of a network. When all nodes are colored, nodes with the same color are sorted into an independent set. Then we choose the nodes at the top positions of the ranking list according to their centralities. The experimental results for an artificial network and three empirical networks show that, comparing with the performance of traditional coloring method, the improvement ratio of our distance-based coloring method could reach 12.82%, 8.16%, 4.45%, 2.93% for the ER, Erdős, Polblogs and Routers networks respectively. - Highlights: • We present an improved distance-based coloring method to identify the multiple influential spreaders. • Each node is colored by a kind of color where the distance between initial nodes is close to the average distance. • For three empirical networks show that the improvement ratio of our distance-based coloring method could reach 8.16% for the Erdos network.

Resilience-based performance metrics for water resources management under uncertainty

Science.gov (United States)

Roach, Tom; Kapelan, Zoran; Ledbetter, Ralph

2018-06-01

This paper aims to develop new, resilience type metrics for long-term water resources management under uncertain climate change and population growth. Resilience is defined here as the ability of a water resources management system to 'bounce back', i.e. absorb and then recover from a water deficit event, restoring the normal system operation. Ten alternative metrics are proposed and analysed addressing a range of different resilience aspects including duration, magnitude, frequency and volume of related water deficit events. The metrics were analysed on a real-world case study of the Bristol Water supply system in the UK and compared with current practice. The analyses included an examination of metrics' sensitivity and correlation, as well as a detailed examination into the behaviour of metrics during water deficit periods. The results obtained suggest that multiple metrics which cover different aspects of resilience should be used simultaneously when assessing the resilience of a water resources management system, leading to a more complete understanding of resilience compared with current practice approaches. It was also observed that calculating the total duration of a water deficit period provided a clearer and more consistent indication of system performance compared to splitting the deficit periods into the time to reach and time to recover from the worst deficit events.

New Sensors for Cultural Heritage Metric Survey: The ToF Cameras

Directory of Open Access Journals (Sweden)

Filiberto Chiabrando

2011-12-01

Full Text Available ToF cameras are new instruments based on CCD/CMOS sensors which measure distances instead of radiometry. The resulting point clouds show the same properties (both in terms of accuracy and resolution of the point clouds acquired by means of traditional LiDAR devices. ToF cameras are cheap instruments (less than 10.000 € based on video real time distance measurements and can represent an interesting alternative to the more expensive LiDAR instruments. In addition, the limited weight and dimensions of ToF cameras allow a reduction of some practical problems such as transportation and on-site management. Most of the commercial ToF cameras use the phase-shift method to measure distances. Due to the use of only one wavelength, most of them have limited range of application (usually about 5 or 10 m. After a brief description of the main characteristics of these instruments, this paper explains and comments the results of the first experimental applications of ToF cameras in Cultural Heritage 3D metric survey.  The possibility to acquire more than 30 frames/s and future developments of these devices in terms of use of more than one wavelength to overcome the ambiguity problem allow to foresee new interesting applications.

The Knowledge Base as an Extension of Distance Learning Reference Service

Science.gov (United States)

Casey, Anne Marie

2012-01-01

This study explores knowledge bases as extension of reference services for distance learners. Through a survey and follow-up interviews with distance learning librarians, this paper discusses their interest in creating and maintaining a knowledge base as a resource for reference services to distance learners. It also investigates their perceptions…

Project-Based Collaborative Learning in Distance Education

DEFF Research Database (Denmark)

Knudsen, Morten; Bajard, C.; Helbo, Jan

2003-01-01

This article describes the experiences drawn from an experiment in transferring positive experience with a project-organised on-campus engineering programme to a technology supported distance education programme. Three years of experience with the Master of Industrial Information Technology (MII)......, didactic adjustments have been made based on feedback, in particular from evaluation questionnaires. This process has been very constructive in approaching the goal: a successful model for project organized learning in distance education.......) programme indicates, however, that adjustments are required in transforming the on-campus model to distance education. The main problem is that while project work is an excellent regulator of the learning process for on-campus students, this does not seem to be the case for off-campus students. Consequently......This article describes the experiences drawn from an experiment in transferring positive experience with a project-organised on-campus engineering programme to a technology supported distance education programme. Three years of experience with the Master of Industrial Information Technology (MII...

Evidence-based Metrics Toolkit for Measuring Safety and Efficiency in Human-Automation Systems

Data.gov (United States)

National Aeronautics and Space Administration — APRIL 2016 NOTE: Principal Investigator moved to Rice University in mid-2015. Project continues at Rice with the same title (Evidence-based Metrics Toolkit for...

Resilience Metrics for the Electric Power System: A Performance-Based Approach.

Energy Technology Data Exchange (ETDEWEB)

Vugrin, Eric D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Castillo, Andrea R [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Silva-Monroy, Cesar Augusto [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2017-02-01

Grid resilience is a concept related to a power system's ability to continue operating and delivering power even in the event that low probability, high-consequence disruptions such as hurricanes, earthquakes, and cyber-attacks occur. Grid resilience objectives focus on managing and, ideally, minimizing potential consequences that occur as a result of these disruptions. Currently, no formal grid resilience definitions, metrics, or analysis methods have been universally accepted. This document describes an effort to develop and describe grid resilience metrics and analysis methods. The metrics and methods described herein extend upon the Resilience Analysis Process (RAP) developed by Watson et al. for the 2015 Quadrennial Energy Review. The extension allows for both outputs from system models and for historical data to serve as the basis for creating grid resilience metrics and informing grid resilience planning and response decision-making. This document describes the grid resilience metrics and analysis methods. Demonstration of the metrics and methods is shown through a set of illustrative use cases.

Taylor expansion of luminosity distance in Szekeres cosmological models: effects of local structures evolution on cosmographic parameters

Energy Technology Data Exchange (ETDEWEB)

Villani, Mattia, E-mail: villani@fi.infn.it [Sezione INFN di Firenze, Polo Scientifico Via Sansone 1, 50019, Sesto Fiorentino (Italy)

2014-06-01

We consider the Goode-Wainwright representation of the Szekeres cosmological models and calculate the Taylor expansion of the luminosity distance in order to study the effects of the inhomogeneities on cosmographic parameters. Without making a particular choice for the arbitrary functions defining the metric, we Taylor expand up to the second order in redshift for Family I and up to the third order for Family II Szekeres metrics under the hypotesis, based on observation, that local structure formation is over. In a conservative fashion, we also allow for the existence of a non null cosmological constant.

Project-based Collaborative learning in distance education

DEFF Research Database (Denmark)

Knudsen, Morten; Bajard, Christine; Helbo, Jan

2004-01-01

) programme indicates, however, that adjustments are required in transforming the on-campus model to distance education. The main problem is that while project work is an excellent regulator of the learning process for on-campus students, this does not seem to be the case for off-campus students. Consequently......This article describes the experiences drawn from an experiment in transferring positive experience with a project-organised on-campus engineering programme to a technology supported distance education programme. Three years of experience with the Master of Industrial Information Technology (MII......, didactic adjustments have been made based on feedback, in particular from evaluation questionnaires. This process has been very constructive in approaching the goal: a successful model for project organized learning in distance education....

SU-E-I-71: Quality Assessment of Surrogate Metrics in Multi-Atlas-Based Image Segmentation

International Nuclear Information System (INIS)

Zhao, T; Ruan, D

2015-01-01

Purpose: With the ever-growing data of heterogeneous quality, relevance assessment of atlases becomes increasingly critical for multi-atlas-based image segmentation. However, there is no universally recognized best relevance metric and even a standard to compare amongst candidates remains elusive. This study, for the first time, designs a quantification to assess relevance metrics’ quality, based on a novel perspective of the metric as surrogate for inferring the inaccessible oracle geometric agreement. Methods: We first develop an inference model to relate surrogate metrics in image space to the underlying oracle relevance metric in segmentation label space, with a monotonically non-decreasing function subject to random perturbations. Subsequently, we investigate model parameters to reveal key contributing factors to surrogates’ ability in prognosticating the oracle relevance value, for the specific task of atlas selection. Finally, we design an effective contract-to-noise ratio (eCNR) to quantify surrogates’ quality based on insights from these analyses and empirical observations. Results: The inference model was specialized to a linear function with normally distributed perturbations, with surrogate metric exemplified by several widely-used image similarity metrics, i.e., MSD/NCC/(N)MI. Surrogates’ behaviors in selecting the most relevant atlases were assessed under varying eCNR, showing that surrogates with high eCNR dominated those with low eCNR in retaining the most relevant atlases. In an end-to-end validation, NCC/(N)MI with eCNR of 0.12 compared to MSD with eCNR of 0.10 resulted in statistically better segmentation with mean DSC of about 0.85 and the first and third quartiles of (0.83, 0.89), compared to MSD with mean DSC of 0.84 and the first and third quartiles of (0.81, 0.89). Conclusion: The designed eCNR is capable of characterizing surrogate metrics’ quality in prognosticating the oracle relevance value. It has been demonstrated to be

Comparison of Highly Resolved Model-Based Exposure Metrics for Traffic-Related Air Pollutants to Support Environmental Health Studies

Directory of Open Access Journals (Sweden)

Shih Ying Chang

2015-12-01

Full Text Available Human exposure to air pollution in many studies is represented by ambient concentrations from space-time kriging of observed values. Space-time kriging techniques based on a limited number of ambient monitors may fail to capture the concentration from local sources. Further, because people spend more time indoors, using ambient concentration to represent exposure may cause error. To quantify the associated exposure error, we computed a series of six different hourly-based exposure metrics at 16,095 Census blocks of three Counties in North Carolina for CO, NOx, PM2.5, and elemental carbon (EC during 2012. These metrics include ambient background concentration from space-time ordinary kriging (STOK, ambient on-road concentration from the Research LINE source dispersion model (R-LINE, a hybrid concentration combining STOK and R-LINE, and their associated indoor concentrations from an indoor infiltration mass balance model. Using a hybrid-based indoor concentration as the standard, the comparison showed that outdoor STOK metrics yielded large error at both population (67% to 93% and individual level (average bias between −10% to 95%. For pollutants with significant contribution from on-road emission (EC and NOx, the on-road based indoor metric performs the best at the population level (error less than 52%. At the individual level, however, the STOK-based indoor concentration performs the best (average bias below 30%. For PM2.5, due to the relatively low contribution from on-road emission (7%, STOK-based indoor metric performs the best at both population (error below 40% and individual level (error below 25%. The results of the study will help future epidemiology studies to select appropriate exposure metric and reduce potential bias in exposure characterization.

EDUCATEE'S THESAURUS AS AN OBJECT OF MEASURING LEARNED MATERIAL OF THE DISTANCE LEARNING COURSE

Directory of Open Access Journals (Sweden)

Alexander Aleksandrovich RYBANOV

2013-10-01

Full Text Available Monitoring and control over the process of studying the distance learning course are based on solving the problem of making out an adequate integral mark to the educatee for mastering entire study course, by testing results. It is suggested to use the degree of correspondence between educatee's thesaurus and the study course thesaurus as an integral mark for the degree of mastering the distance learning course. Study course thesaurus is a set of the course objects with relations between them specified. The article considers metrics of the study course thesaurus complexity, made on the basis of the graph theory and the information theory. It is suggested to use the amount of information contained in the study course thesaurus graph as the metrics of the study course thesaurus complexity. Educatee's thesaurus is considered as an object of measuring educational material learned at the semantic level and is assessed on the basis of amount of information contained in its graph, taking into account the factors of learning the thesaurus objects.

Monoparametric family of metrics derived from classical Jensen-Shannon divergence

Science.gov (United States)

Osán, Tristán M.; Bussandri, Diego G.; Lamberti, Pedro W.

2018-04-01

Jensen-Shannon divergence is a well known multi-purpose measure of dissimilarity between probability distributions. It has been proven that the square root of this quantity is a true metric in the sense that, in addition to the basic properties of a distance, it also satisfies the triangle inequality. In this work we extend this last result to prove that in fact it is possible to derive a monoparametric family of metrics from the classical Jensen-Shannon divergence. Motivated by our results, an application into the field of symbolic sequences segmentation is explored. Additionally, we analyze the possibility to extend this result into the quantum realm.

Multi-image acquisition-based distance sensor using agile laser spot beam.

Science.gov (United States)

Riza, Nabeel A; Amin, M Junaid

2014-09-01

We present a novel laser-based distance measurement technique that uses multiple-image-based spatial processing to enable distance measurements. Compared with the first-generation distance sensor using spatial processing, the modified sensor is no longer hindered by the classic Rayleigh axial resolution limit for the propagating laser beam at its minimum beam waist location. The proposed high-resolution distance sensor design uses an electronically controlled variable focus lens (ECVFL) in combination with an optical imaging device, such as a charged-coupled device (CCD), to produce and capture different laser spot size images on a target with these beam spot sizes different from the minimal spot size possible at this target distance. By exploiting the unique relationship of the target located spot sizes with the varying ECVFL focal length for each target distance, the proposed distance sensor can compute the target distance with a distance measurement resolution better than the axial resolution via the Rayleigh resolution criterion. Using a 30 mW 633 nm He-Ne laser coupled with an electromagnetically actuated liquid ECVFL, along with a 20 cm focal length bias lens, and using five spot images captured per target position by a CCD-based Nikon camera, a proof-of-concept proposed distance sensor is successfully implemented in the laboratory over target ranges from 10 to 100 cm with a demonstrated sub-cm axial resolution, which is better than the axial Rayleigh resolution limit at these target distances. Applications for the proposed potentially cost-effective distance sensor are diverse and include industrial inspection and measurement and 3D object shape mapping and imaging.

On Information Metrics for Spatial Coding.

Science.gov (United States)

Souza, Bryan C; Pavão, Rodrigo; Belchior, Hindiael; Tort, Adriano B L

2018-04-01

The hippocampal formation is involved in navigation, and its neuronal activity exhibits a variety of spatial correlates (e.g., place cells, grid cells). The quantification of the information encoded by spikes has been standard procedure to identify which cells have spatial correlates. For place cells, most of the established metrics derive from Shannon's mutual information (Shannon, 1948), and convey information rate in bits/s or bits/spike (Skaggs et al., 1993, 1996). Despite their widespread use, the performance of these metrics in relation to the original mutual information metric has never been investigated. In this work, using simulated and real data, we find that the current information metrics correlate less with the accuracy of spatial decoding than the original mutual information metric. We also find that the top informative cells may differ among metrics, and show a surrogate-based normalization that yields comparable spatial information estimates. Since different information metrics may identify different neuronal populations, we discuss current and alternative definitions of spatially informative cells, which affect the metric choice. Copyright © 2018 IBRO. Published by Elsevier Ltd. All rights reserved.

Riemannian metric optimization on surfaces (RMOS) for intrinsic brain mapping in the Laplace-Beltrami embedding space.

Science.gov (United States)

Gahm, Jin Kyu; Shi, Yonggang

2018-05-01

Surface mapping methods play an important role in various brain imaging studies from tracking the maturation of adolescent brains to mapping gray matter atrophy patterns in Alzheimer's disease. Popular surface mapping approaches based on spherical registration, however, have inherent numerical limitations when severe metric distortions are present during the spherical parameterization step. In this paper, we propose a novel computational framework for intrinsic surface mapping in the Laplace-Beltrami (LB) embedding space based on Riemannian metric optimization on surfaces (RMOS). Given a diffeomorphism between two surfaces, an isometry can be defined using the pullback metric, which in turn results in identical LB embeddings from the two surfaces. The proposed RMOS approach builds upon this mathematical foundation and achieves general feature-driven surface mapping in the LB embedding space by iteratively optimizing the Riemannian metric defined on the edges of triangular meshes. At the core of our framework is an optimization engine that converts an energy function for surface mapping into a distance measure in the LB embedding space, which can be effectively optimized using gradients of the LB eigen-system with respect to the Riemannian metrics. In the experimental results, we compare the RMOS algorithm with spherical registration using large-scale brain imaging data, and show that RMOS achieves superior performance in the prediction of hippocampal subfields and cortical gyral labels, and the holistic mapping of striatal surfaces for the construction of a striatal connectivity atlas from substantia nigra. Copyright © 2018 Elsevier B.V. All rights reserved.

Model-Based Referenceless Quality Metric of 3D Synthesized Images Using Local Image Description.

Science.gov (United States)

Gu, Ke; Jakhetiya, Vinit; Qiao, Jun-Fei; Li, Xiaoli; Lin, Weisi; Thalmann, Daniel

2017-07-28

New challenges have been brought out along with the emerging of 3D-related technologies such as virtual reality (VR), augmented reality (AR), and mixed reality (MR). Free viewpoint video (FVV), due to its applications in remote surveillance, remote education, etc, based on the flexible selection of direction and viewpoint, has been perceived as the development direction of next-generation video technologies and has drawn a wide range of researchers' attention. Since FVV images are synthesized via a depth image-based rendering (DIBR) procedure in the "blind" environment (without reference images), a reliable real-time blind quality evaluation and monitoring system is urgently required. But existing assessment metrics do not render human judgments faithfully mainly because geometric distortions are generated by DIBR. To this end, this paper proposes a novel referenceless quality metric of DIBR-synthesized images using the autoregression (AR)-based local image description. It was found that, after the AR prediction, the reconstructed error between a DIBR-synthesized image and its AR-predicted image can accurately capture the geometry distortion. The visual saliency is then leveraged to modify the proposed blind quality metric to a sizable margin. Experiments validate the superiority of our no-reference quality method as compared with prevailing full-, reduced- and no-reference models.

Developing a Security Metrics Scorecard for Healthcare Organizations.

Science.gov (United States)

Elrefaey, Heba; Borycki, Elizabeth; Kushniruk, Andrea

2015-01-01

In healthcare, information security is a key aspect of protecting a patient's privacy and ensuring systems availability to support patient care. Security managers need to measure the performance of security systems and this can be achieved by using evidence-based metrics. In this paper, we describe the development of an evidence-based security metrics scorecard specific to healthcare organizations. Study participants were asked to comment on the usability and usefulness of a prototype of a security metrics scorecard that was developed based on current research in the area of general security metrics. Study findings revealed that scorecards need to be customized for the healthcare setting in order for the security information to be useful and usable in healthcare organizations. The study findings resulted in the development of a security metrics scorecard that matches the healthcare security experts' information requirements.

Bin Ratio-Based Histogram Distances and Their Application to Image Classification.

Science.gov (United States)

Hu, Weiming; Xie, Nianhua; Hu, Ruiguang; Ling, Haibin; Chen, Qiang; Yan, Shuicheng; Maybank, Stephen

2014-12-01

Large variations in image background may cause partial matching and normalization problems for histogram-based representations, i.e., the histograms of the same category may have bins which are significantly different, and normalization may produce large changes in the differences between corresponding bins. In this paper, we deal with this problem by using the ratios between bin values of histograms, rather than bin values' differences which are used in the traditional histogram distances. We propose a bin ratio-based histogram distance (BRD), which is an intra-cross-bin distance, in contrast with previous bin-to-bin distances and cross-bin distances. The BRD is robust to partial matching and histogram normalization, and captures correlations between bins with only a linear computational complexity. We combine the BRD with the ℓ1 histogram distance and the χ(2) histogram distance to generate the ℓ1 BRD and the χ(2) BRD, respectively. These combinations exploit and benefit from the robustness of the BRD under partial matching and the robustness of the ℓ1 and χ(2) distances to small noise. We propose a method for assessing the robustness of histogram distances to partial matching. The BRDs and logistic regression-based histogram fusion are applied to image classification. The experimental results on synthetic data sets show the robustness of the BRDs to partial matching, and the experiments on seven benchmark data sets demonstrate promising results of the BRDs for image classification.

Spatial generalised linear mixed models based on distances.

Science.gov (United States)

Melo, Oscar O; Mateu, Jorge; Melo, Carlos E

2016-10-01

Risk models derived from environmental data have been widely shown to be effective in delineating geographical areas of risk because they are intuitively easy to understand. We present a new method based on distances, which allows the modelling of continuous and non-continuous random variables through distance-based spatial generalised linear mixed models. The parameters are estimated using Markov chain Monte Carlo maximum likelihood, which is a feasible and a useful technique. The proposed method depends on a detrending step built from continuous or categorical explanatory variables, or a mixture among them, by using an appropriate Euclidean distance. The method is illustrated through the analysis of the variation in the prevalence of Loa loa among a sample of village residents in Cameroon, where the explanatory variables included elevation, together with maximum normalised-difference vegetation index and the standard deviation of normalised-difference vegetation index calculated from repeated satellite scans over time. © The Author(s) 2013.

Distance matrix-based approach to protein structure prediction.

Science.gov (United States)

Kloczkowski, Andrzej; Jernigan, Robert L; Wu, Zhijun; Song, Guang; Yang, Lei; Kolinski, Andrzej; Pokarowski, Piotr

2009-03-01

Much structural information is encoded in the internal distances; a distance matrix-based approach can be used to predict protein structure and dynamics, and for structural refinement. Our approach is based on the square distance matrix D = [r(ij)(2)] containing all square distances between residues in proteins. This distance matrix contains more information than the contact matrix C, that has elements of either 0 or 1 depending on whether the distance r (ij) is greater or less than a cutoff value r (cutoff). We have performed spectral decomposition of the distance matrices D = sigma lambda(k)V(k)V(kT), in terms of eigenvalues lambda kappa and the corresponding eigenvectors v kappa and found that it contains at most five nonzero terms. A dominant eigenvector is proportional to r (2)--the square distance of points from the center of mass, with the next three being the principal components of the system of points. By predicting r (2) from the sequence we can approximate a distance matrix of a protein with an expected RMSD value of about 7.3 A, and by combining it with the prediction of the first principal component we can improve this approximation to 4.0 A. We can also explain the role of hydrophobic interactions for the protein structure, because r is highly correlated with the hydrophobic profile of the sequence. Moreover, r is highly correlated with several sequence profiles which are useful in protein structure prediction, such as contact number, the residue-wise contact order (RWCO) or mean square fluctuations (i.e. crystallographic temperature factors). We have also shown that the next three components are related to spatial directionality of the secondary structure elements, and they may be also predicted from the sequence, improving overall structure prediction. We have also shown that the large number of available HIV-1 protease structures provides a remarkable sampling of conformations, which can be viewed as direct structural information about the

Open Problem: Kernel methods on manifolds and metric spaces

DEFF Research Database (Denmark)

Feragen, Aasa; Hauberg, Søren

2016-01-01

Radial kernels are well-suited for machine learning over general geodesic metric spaces, where pairwise distances are often the only computable quantity available. We have recently shown that geodesic exponential kernels are only positive definite for all bandwidths when the input space has strong...... linear properties. This negative result hints that radial kernel are perhaps not suitable over geodesic metric spaces after all. Here, however, we present evidence that large intervals of bandwidths exist where geodesic exponential kernels have high probability of being positive definite over finite...... datasets, while still having significant predictive power. From this we formulate conjectures on the probability of a positive definite kernel matrix for a finite random sample, depending on the geometry of the data space and the spread of the sample....

Quantum metric spaces as a model for pregeometry

International Nuclear Information System (INIS)

Alvarez, E.; Cespedes, J.; Verdaguer, E.

1992-01-01

A new arena for the dynamics of spacetime is proposed, in which the basic quantum variable is the two-point distance on a metric space. The scaling dimension (that is, the Kolmogorov capacity) in the neighborhood of each point then defines in a natural way a local concept of dimension. We study our model in the region of parameter space in which the resulting spacetime is not too different from a smooth manifold

Metric-based approach and tool for modeling the I and C system using Markov chains

International Nuclear Information System (INIS)

Butenko, Valentyna; Kharchenko, Vyacheslav; Odarushchenko, Elena; Butenko, Dmitriy

2015-01-01

Markov's chains (MC) are well-know and widely applied in dependability and performability analysis of safety-critical systems, because of the flexible representation of system components dependencies and synchronization. There are few radblocks for greater application of the MC: accounting the additional system components increases the model state-space and complicates analysis; the non-numerically sophisticated user may find it difficult to decide between the variety of numerical methods to determine the most suitable and accurate for their application. Thus obtaining the high accurate and trusted modeling results becomes a nontrivial task. In this paper, we present the metric-based approach for selection of the applicable solution approach, based on the analysis of MCs stiffness, decomposability, sparsity and fragmentedness. Using this selection procedure the modeler can provide the verification of earlier obtained results. The presented approach was implemented in utility MSMC, which supports the MC construction, metric-based analysis, recommendations shaping and model solution. The model can be exported to the wall-known off-the-shelf mathematical packages for verification. The paper presents the case study of the industrial NPP I and C system, manufactured by RPC Radiy. The paper shows an application of metric-based approach and MSMC fool for dependability and safety analysis of RTS, and procedure of results verification. (author)

Long-distance quantum communication over noisy networks without long-time quantum memory

Science.gov (United States)

Mazurek, Paweł; Grudka, Andrzej; Horodecki, Michał; Horodecki, Paweł; Łodyga, Justyna; Pankowski, Łukasz; PrzysieŻna, Anna

2014-12-01

The problem of sharing entanglement over large distances is crucial for implementations of quantum cryptography. A possible scheme for long-distance entanglement sharing and quantum communication exploits networks whose nodes share Einstein-Podolsky-Rosen (EPR) pairs. In Perseguers et al. [Phys. Rev. A 78, 062324 (2008), 10.1103/PhysRevA.78.062324] the authors put forward an important isomorphism between storing quantum information in a dimension D and transmission of quantum information in a D +1 -dimensional network. We show that it is possible to obtain long-distance entanglement in a noisy two-dimensional (2D) network, even when taking into account that encoding and decoding of a state is exposed to an error. For 3D networks we propose a simple encoding and decoding scheme based solely on syndrome measurements on 2D Kitaev topological quantum memory. Our procedure constitutes an alternative scheme of state injection that can be used for universal quantum computation on 2D Kitaev code. It is shown that the encoding scheme is equivalent to teleporting the state, from a specific node into a whole two-dimensional network, through some virtual EPR pair existing within the rest of network qubits. We present an analytic lower bound on fidelity of the encoding and decoding procedure, using as our main tool a modified metric on space-time lattice, deviating from a taxicab metric at the first and the last time slices.

Contact- and distance-based principal component analysis of protein dynamics

Energy Technology Data Exchange (ETDEWEB)

Ernst, Matthias; Sittel, Florian; Stock, Gerhard, E-mail: stock@physik.uni-freiburg.de [Biomolecular Dynamics, Institute of Physics, Albert Ludwigs University, 79104 Freiburg (Germany)

2015-12-28

To interpret molecular dynamics simulations of complex systems, systematic dimensionality reduction methods such as principal component analysis (PCA) represent a well-established and popular approach. Apart from Cartesian coordinates, internal coordinates, e.g., backbone dihedral angles or various kinds of distances, may be used as input data in a PCA. Adopting two well-known model problems, folding of villin headpiece and the functional dynamics of BPTI, a systematic study of PCA using distance-based measures is presented which employs distances between C{sub α}-atoms as well as distances between inter-residue contacts including side chains. While this approach seems prohibitive for larger systems due to the quadratic scaling of the number of distances with the size of the molecule, it is shown that it is sufficient (and sometimes even better) to include only relatively few selected distances in the analysis. The quality of the PCA is assessed by considering the resolution of the resulting free energy landscape (to identify metastable conformational states and barriers) and the decay behavior of the corresponding autocorrelation functions (to test the time scale separation of the PCA). By comparing results obtained with distance-based, dihedral angle, and Cartesian coordinates, the study shows that the choice of input variables may drastically influence the outcome of a PCA.

Statistical distance and the approach to KNO scaling

International Nuclear Information System (INIS)

Diosi, L.; Hegyi, S.; Krasznovszky, S.

1990-05-01

A new method is proposed for characterizing the approach to KNO scaling. The essence of our method lies in the concept of statistical distance between nearby KNO distributions which reflects their distinguishability in spite of multiplicity fluctuations. It is shown that the geometry induced by the distance function defines a natural metric on the parameter space of a certain family of KNO distributions. Some examples are given in which the energy dependences of distinguishability of neighbouring KNO distributions are compared in nondiffractive hadron-hadron collisions and electron-positron annihilation. (author) 19 refs.; 4 figs

High resolution metric imaging payload

Science.gov (United States)

Delclaud, Y.

2017-11-01

Alcatel Space Industries has become Europe's leader in the field of high and very high resolution optical payloads, in the frame work of earth observation system able to provide military government with metric images from space. This leadership allowed ALCATEL to propose for the export market, within a French collaboration frame, a complete space based system for metric observation.

[Applicability of traditional landscape metrics in evaluating urban heat island effect].

Science.gov (United States)

Chen, Ai-Lian; Sun, Ran-Hao; Chen, Li-Ding

2012-08-01

By using 24 landscape metrics, this paper evaluated the urban heat island effect in parts of Beijing downtown area. QuickBird (QB) images were used to extract the landscape type information, and the thermal bands from Landsat Enhanced Thematic Mapper Plus (ETM+) images were used to extract the land surface temperature (LST) in four seasons of the same year. The 24 landscape pattern metrics were calculated at landscape and class levels in a fixed window with 120 mx 120 m in size, with the applicability of these traditional landscape metrics in evaluating the urban heat island effect examined. Among the 24 landscape metrics, only the percentage composition of landscape (PLAND), patch density (PD), largest patch index (LPI), coefficient of Euclidean nearest-neighbor distance variance (ENN_CV), and landscape division index (DIVISION) at landscape level were significantly correlated with the LST in March, May, and November, and the PLAND, LPI, DIVISION, percentage of like adjacencies, and interspersion and juxtaposition index at class level showed significant correlations with the LST in March, May, July, and December, especially in July. Some metrics such as PD, edge density, clumpiness index, patch cohesion index, effective mesh size, splitting index, aggregation index, and normalized landscape shape index showed varying correlations with the LST at different class levels. The traditional landscape metrics could not be appropriate in evaluating the effects of river on LST, while some of the metrics could be useful in characterizing urban LST and analyzing the urban heat island effect, but screening and examining should be made on the metrics.

Editorial ~ Does "Lean Thinking" Relate to Network-based Distance Education

Directory of Open Access Journals (Sweden)

Peter S. Cookson

2003-10-01

Full Text Available Pointing to the “objectivised, rationalized, technologically-based interaction,” Peters (1973 referred to the then prevailing correspondence forms of distance education as “the most industrialized form of education” (p. 313. With such features as assembly line methods; division of labor; centralized processes of teaching materials development, production and dispatching; student admissions enrollment systems; automated registration, course allocation, and student support, and personnel management systems, distance education institutions demonstrated management structures and practices utilized in industrial and business organizations. Large numbers of courses and students were thus “processed” in correspondence, radio, and television-based distance education systems.

A robust metric for screening outliers from analogue product manufacturing tests responses

NARCIS (Netherlands)

Krishnan, S.; Kerkhoff, H.G.

2011-01-01

Mahalanobis distance is one of the commonly used multivariate metrics for finely segregating defective devices from non-defective ones. An associated problem with this approach is the estimation of a robust mean and a covariance matrix. In the absence of such robust estimates, especially in the

A Robust Metric for Screening Outliers from Analogue Product Manufacturing Tests Responses

NARCIS (Netherlands)

Krishnan, Shaji; Krishnan, Shaji; Kerkhoff, Hans G.

2011-01-01

Mahalanobis distance is one of the commonly used multivariate metrics for finely segregating defective devices from non-defective ones. An associated problem with this approach is the estimation of a robust mean and a covariance matrix. In the absence of such robust estimates, especially in the

Factor structure of the Tomimatsu-Sato metrics

International Nuclear Information System (INIS)

Perjes, Z.

1989-02-01

Based on an earlier result stating that δ = 3 Tomimatsu-Sato (TS) metrics can be factored over the field of integers, an analogous representation for higher TS metrics was sought. It is shown that the factoring property of TS metrics follows from the structure of special Hankel determinants. A set of linear algebraic equations determining the factors was defined, and the factors of the first five TS metrics were tabulated, together with their primitive factors. (R.P.) 4 refs.; 2 tabs

Sharp metric obstructions for quasi-Einstein metrics

Science.gov (United States)

Case, Jeffrey S.

2013-02-01

Using the tractor calculus to study smooth metric measure spaces, we adapt results of Gover and Nurowski to give sharp metric obstructions to the existence of quasi-Einstein metrics on suitably generic manifolds. We do this by introducing an analogue of the Weyl tractor W to the setting of smooth metric measure spaces. The obstructions we obtain can be realized as tensorial invariants which are polynomial in the Riemann curvature tensor and its divergence. By taking suitable limits of their tensorial forms, we then find obstructions to the existence of static potentials, generalizing to higher dimensions a result of Bartnik and Tod, and to the existence of potentials for gradient Ricci solitons.

Symmetric Kullback-Leibler Metric Based Tracking Behaviors for Bioinspired Robotic Eyes.

Science.gov (United States)

Liu, Hengli; Luo, Jun; Wu, Peng; Xie, Shaorong; Li, Hengyu

2015-01-01

A symmetric Kullback-Leibler metric based tracking system, capable of tracking moving targets, is presented for a bionic spherical parallel mechanism to minimize a tracking error function to simulate smooth pursuit of human eyes. More specifically, we propose a real-time moving target tracking algorithm which utilizes spatial histograms taking into account symmetric Kullback-Leibler metric. In the proposed algorithm, the key spatial histograms are extracted and taken into particle filtering framework. Once the target is identified, an image-based control scheme is implemented to drive bionic spherical parallel mechanism such that the identified target is to be tracked at the center of the captured images. Meanwhile, the robot motion information is fed forward to develop an adaptive smooth tracking controller inspired by the Vestibuloocular Reflex mechanism. The proposed tracking system is designed to make the robot track dynamic objects when the robot travels through transmittable terrains, especially bumpy environment. To perform bumpy-resist capability under the condition of violent attitude variation when the robot works in the bumpy environment mentioned, experimental results demonstrate the effectiveness and robustness of our bioinspired tracking system using bionic spherical parallel mechanism inspired by head-eye coordination.

Symmetric Kullback-Leibler Metric Based Tracking Behaviors for Bioinspired Robotic Eyes

Directory of Open Access Journals (Sweden)

Hengli Liu

2015-01-01

Full Text Available A symmetric Kullback-Leibler metric based tracking system, capable of tracking moving targets, is presented for a bionic spherical parallel mechanism to minimize a tracking error function to simulate smooth pursuit of human eyes. More specifically, we propose a real-time moving target tracking algorithm which utilizes spatial histograms taking into account symmetric Kullback-Leibler metric. In the proposed algorithm, the key spatial histograms are extracted and taken into particle filtering framework. Once the target is identified, an image-based control scheme is implemented to drive bionic spherical parallel mechanism such that the identified target is to be tracked at the center of the captured images. Meanwhile, the robot motion information is fed forward to develop an adaptive smooth tracking controller inspired by the Vestibuloocular Reflex mechanism. The proposed tracking system is designed to make the robot track dynamic objects when the robot travels through transmittable terrains, especially bumpy environment. To perform bumpy-resist capability under the condition of violent attitude variation when the robot works in the bumpy environment mentioned, experimental results demonstrate the effectiveness and robustness of our bioinspired tracking system using bionic spherical parallel mechanism inspired by head-eye coordination.

The International Safeguards Technology Base: How is the Patient Doing? An Exploration of Effective Metrics

International Nuclear Information System (INIS)

Schanfein, Mark J.; Gouveia, Fernando S.

2010-01-01

The term 'Technology Base' is commonly used but what does it mean? Is there a common understanding of the components that comprise a technology base? Does a formal process exist to assess the health of a given technology base? These are important questions the relevance of which is even more pressing given the USDOE/NNSA initiatives to strengthen the safeguards technology base through investments in research and development and human capital development. Accordingly, the authors will establish a high-level framework to define and understand what comprises a technology base. Potential goal-driven metrics to assess the health of a technology base will also be explored, such as linear demographics and resource availability, in the hope that they can be used to better understand and improve the health of the U.S. safeguards technology base. Finally, through the identification of such metrics, the authors will offer suggestions and highlight choices for addressing potential shortfalls.

PERMANOVA-S: association test for microbial community composition that accommodates confounders and multiple distances.

Science.gov (United States)

Tang, Zheng-Zheng; Chen, Guanhua; Alekseyenko, Alexander V

2016-09-01

Recent advances in sequencing technology have made it possible to obtain high-throughput data on the composition of microbial communities and to study the effects of dysbiosis on the human host. Analysis of pairwise intersample distances quantifies the association between the microbiome diversity and covariates of interest (e.g. environmental factors, clinical outcomes, treatment groups). In the design of these analyses, multiple choices for distance metrics are available. Most distance-based methods, however, use a single distance and are underpowered if the distance is poorly chosen. In addition, distance-based tests cannot flexibly handle confounding variables, which can result in excessive false-positive findings. We derive presence-weighted UniFrac to complement the existing UniFrac distances for more powerful detection of the variation in species richness. We develop PERMANOVA-S, a new distance-based method that tests the association of microbiome composition with any covariates of interest. PERMANOVA-S improves the commonly-used Permutation Multivariate Analysis of Variance (PERMANOVA) test by allowing flexible confounder adjustments and ensembling multiple distances. We conducted extensive simulation studies to evaluate the performance of different distances under various patterns of association. Our simulation studies demonstrate that the power of the test relies on how well the selected distance captures the nature of the association. The PERMANOVA-S unified test combines multiple distances and achieves good power regardless of the patterns of the underlying association. We demonstrate the usefulness of our approach by reanalyzing several real microbiome datasets. miProfile software is freely available at https://medschool.vanderbilt.edu/tang-lab/software/miProfile z.tang@vanderbilt.edu or g.chen@vanderbilt.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Software architecture analysis tool : software architecture metrics collection

NARCIS (Netherlands)

Muskens, J.; Chaudron, M.R.V.; Westgeest, R.

2002-01-01

The Software Engineering discipline lacks the ability to evaluate software architectures. Here we describe a tool for software architecture analysis that is based on metrics. Metrics can be used to detect possible problems and bottlenecks in software architectures. Even though metrics do not give a

A Novel Riemannian Metric Based on Riemannian Structure and Scaling Information for Fixed Low-Rank Matrix Completion.

Science.gov (United States)

Mao, Shasha; Xiong, Lin; Jiao, Licheng; Feng, Tian; Yeung, Sai-Kit

2017-05-01

Riemannian optimization has been widely used to deal with the fixed low-rank matrix completion problem, and Riemannian metric is a crucial factor of obtaining the search direction in Riemannian optimization. This paper proposes a new Riemannian metric via simultaneously considering the Riemannian geometry structure and the scaling information, which is smoothly varying and invariant along the equivalence class. The proposed metric can make a tradeoff between the Riemannian geometry structure and the scaling information effectively. Essentially, it can be viewed as a generalization of some existing metrics. Based on the proposed Riemanian metric, we also design a Riemannian nonlinear conjugate gradient algorithm, which can efficiently solve the fixed low-rank matrix completion problem. By experimenting on the fixed low-rank matrix completion, collaborative filtering, and image and video recovery, it illustrates that the proposed method is superior to the state-of-the-art methods on the convergence efficiency and the numerical performance.

New method for distance-based close following safety indicator.

Science.gov (United States)

Sharizli, A A; Rahizar, R; Karim, M R; Saifizul, A A

2015-01-01

The increase in the number of fatalities caused by road accidents involving heavy vehicles every year has raised the level of concern and awareness on road safety in developing countries like Malaysia. Changes in the vehicle dynamic characteristics such as gross vehicle weight, travel speed, and vehicle classification will affect a heavy vehicle's braking performance and its ability to stop safely in emergency situations. As such, the aim of this study is to establish a more realistic new distance-based safety indicator called the minimum safe distance gap (MSDG), which incorporates vehicle classification (VC), speed, and gross vehicle weight (GVW). Commercial multibody dynamics simulation software was used to generate braking distance data for various heavy vehicle classes under various loads and speeds. By applying nonlinear regression analysis to the simulation results, a mathematical expression of MSDG has been established. The results show that MSDG is dynamically changed according to GVW, VC, and speed. It is envisaged that this new distance-based safety indicator would provide a more realistic depiction of the real traffic situation for safety analysis.

Long-distance configuration of FPGA based on serial communication

International Nuclear Information System (INIS)

Liu Xiang; Song Kezhu; Zhang Sifeng

2010-01-01

To solve FPGA configuration in some nuclear electronics, which works in radioactivity environment, the article introduces a way of long-distance configuration with PC and CPLD, based on serial communication. Taking CYCLONE series FPGA and EPCS configuration chip from ALTERA for example, and using the AS configuration mode, we described our design from the aspects of basic theory, hardware connection, software function and communication protocol. With this design, we could configure several FPGAs in the distance of 100 meters, or we could configure on FPGA in the distance of 150 meters. (authors)

VCSEL-based sensors for distance and velocity

Science.gov (United States)

Moench, Holger; Carpaij, Mark; Gerlach, Philipp; Gronenborn, Stephan; Gudde, Ralph; Hellmig, Jochen; Kolb, Johanna; van der Lee, Alexander

2016-03-01

VCSEL based sensors can measure distance and velocity in three dimensional space and are already produced in high quantities for professional and consumer applications. Several physical principles are used: VCSELs are applied as infrared illumination for surveillance cameras. High power arrays combined with imaging optics provide a uniform illumination of scenes up to a distance of several hundred meters. Time-of-flight methods use a pulsed VCSEL as light source, either with strong single pulses at low duty cycle or with pulse trains. Because of the sensitivity to background light and the strong decrease of the signal with distance several Watts of laser power are needed at a distance of up to 100m. VCSEL arrays enable power scaling and can provide very short pulses at higher power density. Applications range from extended functions in a smartphone over industrial sensors up to automotive LIDAR for driver assistance and autonomous driving. Self-mixing interference works with coherent laser photons scattered back into the cavity. It is therefore insensitive to environmental light. The method is used to measure target velocity and distance with very high accuracy at distances up to one meter. Single-mode VCSELs with integrated photodiode and grating stabilized polarization enable very compact and cost effective products. Besides the well know application as computer input device new applications with even higher accuracy or for speed over ground measurement in automobiles and up to 250km/h are investigated. All measurement methods exploit the known VCSEL properties like robustness, stability over temperature and the potential for packages with integrated optics and electronics. This makes VCSEL sensors ideally suited for new mass applications in consumer and automotive markets.

The International Safeguards Technology Base: How is the Patient Doing? An Exploration of Effective Metrics

Energy Technology Data Exchange (ETDEWEB)

Schanfein, Mark J; Gouveia, Fernando S

2010-07-01

The term “Technology Base” is commonly used but what does it mean? Is there a common understanding of the components that comprise a technology base? Does a formal process exist to assess the health of a given technology base? These are important questions the relevance of which is even more pressing given the USDOE/NNSA initiatives to strengthen the safeguards technology base through investments in research & development and human capital development. Accordingly, the authors will establish a high-level framework to define and understand what comprises a technology base. Potential goal-driven metrics to assess the health of a technology base will also be explored, such as linear demographics and resource availability, in the hope that they can be used to better understand and improve the health of the U.S. safeguards technology base. Finally, through the identification of such metrics, the authors will offer suggestions and highlight choices for addressing potential shortfalls.

Measurable Control System Security through Ideal Driven Technical Metrics

Energy Technology Data Exchange (ETDEWEB)

Miles McQueen; Wayne Boyer; Sean McBride; Marie Farrar; Zachary Tudor

2008-01-01

The Department of Homeland Security National Cyber Security Division supported development of a small set of security ideals as a framework to establish measurable control systems security. Based on these ideals, a draft set of proposed technical metrics was developed to allow control systems owner-operators to track improvements or degradations in their individual control systems security posture. The technical metrics development effort included review and evaluation of over thirty metrics-related documents. On the bases of complexity, ambiguity, or misleading and distorting effects the metrics identified during the reviews were determined to be weaker than necessary to aid defense against the myriad threats posed by cyber-terrorism to human safety, as well as to economic prosperity. Using the results of our metrics review and the set of security ideals as a starting point for metrics development, we identified thirteen potential technical metrics - with at least one metric supporting each ideal. Two case study applications of the ideals and thirteen metrics to control systems were then performed to establish potential difficulties in applying both the ideals and the metrics. The case studies resulted in no changes to the ideals, and only a few deletions and refinements to the thirteen potential metrics. This led to a final proposed set of ten core technical metrics. To further validate the security ideals, the modifications made to the original thirteen potential metrics, and the final proposed set of ten core metrics, seven separate control systems security assessments performed over the past three years were reviewed for findings and recommended mitigations. These findings and mitigations were then mapped to the security ideals and metrics to assess gaps in their coverage. The mappings indicated that there are no gaps in the security ideals and that the ten core technical metrics provide significant coverage of standard security issues with 87% coverage. Based

Distance-based microfluidic quantitative detection methods for point-of-care testing.

Science.gov (United States)

Tian, Tian; Li, Jiuxing; Song, Yanling; Zhou, Leiji; Zhu, Zhi; Yang, Chaoyong James

2016-04-07

Equipment-free devices with quantitative readout are of great significance to point-of-care testing (POCT), which provides real-time readout to users and is especially important in low-resource settings. Among various equipment-free approaches, distance-based visual quantitative detection methods rely on reading the visual signal length for corresponding target concentrations, thus eliminating the need for sophisticated instruments. The distance-based methods are low-cost, user-friendly and can be integrated into portable analytical devices. Moreover, such methods enable quantitative detection of various targets by the naked eye. In this review, we first introduce the concept and history of distance-based visual quantitative detection methods. Then, we summarize the main methods for translation of molecular signals to distance-based readout and discuss different microfluidic platforms (glass, PDMS, paper and thread) in terms of applications in biomedical diagnostics, food safety monitoring, and environmental analysis. Finally, the potential and future perspectives are discussed.

Analysis of transitions at two-fold redundant sites in mammalian genomes. Transition redundant approach-to-equilibrium (TREx distance metrics

Directory of Open Access Journals (Sweden)

Liberles David A

2006-03-01

sites within two-fold redundant coding systems were examined in the mouse, rat, and human genomes. The key metric (f2, the fraction of those sites that holds the same nucleotide, was measured for putative ortholog pairs. A transition redundant exchange (TREx distance was calculated from f2 for these pairs. Pyrimidine-pyrimidine transitions at these sites occur approximately 14% faster than purine-purine transitions in various lineages. Transition rate constants were similar in different genes within the same lineages; within a set of orthologs, the f2 distribution is only modest overdispersed. No correlation between disparity and overdispersion is observed. In rodents, evidence was found for greater conservation of TREx sites in genes on the X chromosome, accounting for a small part of the overdispersion, however. Conclusion The TREx metric is useful to analyze the history of transition rate constants within these mammals over the past 100 million years. The TREx metric estimates the extent to which silent nucleotide substitutions accumulate in different genes, on different chromosomes, with different compositions, in different lineages, and at different times.

$\\eta$-metric structures

OpenAIRE

Gaba, Yaé Ulrich

2017-01-01

In this paper, we discuss recent results about generalized metric spaces and fixed point theory. We introduce the notion of $\\eta$-cone metric spaces, give some topological properties and prove some fixed point theorems for contractive type maps on these spaces. In particular we show that theses $\\eta$-cone metric spaces are natural generalizations of both cone metric spaces and metric type spaces.

Directional output distance functions: endogenous directions based on exogenous normalization constraints

Science.gov (United States)

In this paper we develop a model for computing directional output distance functions with endogenously determined direction vectors. We show how this model is related to the slacks-based directional distance function introduced by Fare and Grosskopf and show how to use the slacks-based function to e...

Enhancing Authentication Models Characteristic Metrics via ...

African Journals Online (AJOL)

In this work, we derive the universal characteristic metrics set for authentication models based on security, usability and design issues. We then compute the probability of the occurrence of each characteristic metrics in some single factor and multifactor authentication models in order to determine the effectiveness of these ...

Detection of image structures using the Fisher information and the Rao metric.

Science.gov (United States)

Maybank, Stephen J

2004-12-01

In many detection problems, the structures to be detected are parameterized by the points of a parameter space. If the conditional probability density function for the measurements is known, then detection can be achieved by sampling the parameter space at a finite number of points and checking each point to see if the corresponding structure is supported by the data. The number of samples and the distances between neighboring samples are calculated using the Rao metric on the parameter space. The Rao metric is obtained from the Fisher information which is, in turn, obtained from the conditional probability density function. An upper bound is obtained for the probability of a false detection. The calculations are simplified in the low noise case by making an asymptotic approximation to the Fisher information. An application to line detection is described. Expressions are obtained for the asymptotic approximation to the Fisher information, the volume of the parameter space, and the number of samples. The time complexity for line detection is estimated. An experimental comparison is made with a Hough transform-based method for detecting lines.

Simulation of devices mobility to estimate wireless channel quality metrics in 5G networks

Science.gov (United States)

Orlov, Yu.; Fedorov, S.; Samuylov, A.; Gaidamaka, Yu.; Molchanov, D.

2017-07-01

The problem of channel quality estimation for devices in a wireless 5G network is formulated. As a performance metrics of interest we choose the signal-to-interference-plus-noise ratio, which depends essentially on the distance between the communicating devices. A model with a plurality of moving devices in a bounded three-dimensional space and a simulation algorithm to determine the distances between the devices for a given motion model are devised.

New exposure-based metric approach for evaluating O3 risk to North American aspen forests

International Nuclear Information System (INIS)

Percy, K.E.; Nosal, M.; Heilman, W.; Dann, T.; Sober, J.; Legge, A.H.; Karnosky, D.F.

2007-01-01

The United States and Canada currently use exposure-based metrics to protect vegetation from O 3 . Using 5 years (1999-2003) of co-measured O 3 , meteorology and growth response, we have developed exposure-based regression models that predict Populus tremuloides growth change within the North American ambient air quality context. The models comprised growing season fourth-highest daily maximum 8-h average O 3 concentration, growing degree days, and wind speed. They had high statistical significance, high goodness of fit, include 95% confidence intervals for tree growth change, and are simple to use. Averaged across a wide range of clonal sensitivity, historical 2001-2003 growth change over most of the 26 M ha P. tremuloides distribution was estimated to have ranged from no impact (0%) to strong negative impacts (-31%). With four aspen clones responding negatively (one responded positively) to O 3 , the growing season fourth-highest daily maximum 8-h average O 3 concentration performed much better than growing season SUM06, AOT40 or maximum 1 h average O 3 concentration metrics as a single indicator of aspen stem cross-sectional area growth. - A new exposure-based metric approach to predict O 3 risk to North American aspen forests has been developed

What can article-level metrics do for you?

Science.gov (United States)

Fenner, Martin

2013-10-01

Article-level metrics (ALMs) provide a wide range of metrics about the uptake of an individual journal article by the scientific community after publication. They include citations, usage statistics, discussions in online comments and social media, social bookmarking, and recommendations. In this essay, we describe why article-level metrics are an important extension of traditional citation-based journal metrics and provide a number of example from ALM data collected for PLOS Biology.

Vehicular Networking Enhancement And Multi-Channel Routing Optimization, Based on Multi-Objective Metric and Minimum Spanning Tree

Directory of Open Access Journals (Sweden)

Peppino Fazio

2013-01-01

Full Text Available Vehicular Ad hoc NETworks (VANETs represent a particular mobile technology that permits the communication among vehicles, offering security and comfort. Nowadays, distributed mobile wireless computing is becoming a very important communications paradigm, due to its flexibility to adapt to different mobile applications. VANETs are a practical example of data exchanging among real mobile nodes. To enable communications within an ad-hoc network, characterized by continuous node movements, routing protocols are needed to react to frequent changes in network topology. In this paper, the attention is focused mainly on the network layer of VANETs, proposing a novel approach to reduce the interference level during mobile transmission, based on the multi-channel nature of IEEE 802.11p (1609.4 standard. In this work a new routing protocol based on Distance Vector algorithm is presented to reduce the delay end to end and to increase packet delivery ratio (PDR and throughput in VANETs. A new metric is also proposed, based on the maximization of the average Signal-to-Interference Ratio (SIR level and the link duration probability between two VANET nodes. In order to relieve the effects of the co-channel interference perceived by mobile nodes, transmission channels are switched on a basis of a periodical SIR evaluation. A Network Simulator has been used for implementing and testing the proposed idea.

A Process-Based Transport-Distance Model of Aeolian Transport

Science.gov (United States)

Naylor, A. K.; Okin, G.; Wainwright, J.; Parsons, A. J.

2017-12-01

We present a new approach to modeling aeolian transport based on transport distance. Particle fluxes are based on statistical probabilities of particle detachment and distributions of transport lengths, which are functions of particle size classes. A computational saltation model is used to simulate transport distances over a variety of sizes. These are fit to an exponential distribution, which has the advantages of computational economy, concordance with current field measurements, and a meaningful relationship to theoretical assumptions about mean and median particle transport distance. This novel approach includes particle-particle interactions, which are important for sustaining aeolian transport and dust emission. Results from this model are compared with results from both bulk- and particle-sized-specific transport equations as well as empirical wind tunnel studies. The transport-distance approach has been successfully used for hydraulic processes, and extending this methodology from hydraulic to aeolian transport opens up the possibility of modeling joint transport by wind and water using consistent physics. Particularly in nutrient-limited environments, modeling the joint action of aeolian and hydraulic transport is essential for understanding the spatial distribution of biomass across landscapes and how it responds to climatic variability and change.

Attenuation-based size metric for estimating organ dose to patients undergoing tube current modulated CT exams

Energy Technology Data Exchange (ETDEWEB)

Bostani, Maryam, E-mail: mbostani@mednet.ucla.edu; McMillan, Kyle; Lu, Peiyun; Kim, Hyun J.; Cagnon, Chris H.; McNitt-Gray, Michael F. [Departments of Biomedical Physics and Radiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California 90024 (United States); DeMarco, John J. [Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, California 90095 (United States)

2015-02-15

Purpose: Task Group 204 introduced effective diameter (ED) as the patient size metric used to correlate size-specific-dose-estimates. However, this size metric fails to account for patient attenuation properties and has been suggested to be replaced by an attenuation-based size metric, water equivalent diameter (D{sub W}). The purpose of this study is to investigate different size metrics, effective diameter, and water equivalent diameter, in combination with regional descriptions of scanner output to establish the most appropriate size metric to be used as a predictor for organ dose in tube current modulated CT exams. Methods: 101 thoracic and 82 abdomen/pelvis scans from clinically indicated CT exams were collected retrospectively from a multidetector row CT (Sensation 64, Siemens Healthcare) with Institutional Review Board approval to generate voxelized patient models. Fully irradiated organs (lung and breasts in thoracic scans and liver, kidneys, and spleen in abdominal scans) were segmented and used as tally regions in Monte Carlo simulations for reporting organ dose. Along with image data, raw projection data were collected to obtain tube current information for simulating tube current modulation scans using Monte Carlo methods. Additionally, previously described patient size metrics [ED, D{sub W}, and approximated water equivalent diameter (D{sub Wa})] were calculated for each patient and reported in three different ways: a single value averaged over the entire scan, a single value averaged over the region of interest, and a single value from a location in the middle of the scan volume. Organ doses were normalized by an appropriate mAs weighted CTDI{sub vol} to reflect regional variation of tube current. Linear regression analysis was used to evaluate the correlations between normalized organ doses and each size metric. Results: For the abdominal organs, the correlations between normalized organ dose and size metric were overall slightly higher for all three

Algorithms for Speeding up Distance-Based Outlier Detection

Data.gov (United States)

National Aeronautics and Space Administration — The problem of distance-based outlier detection is difficult to solve efficiently in very large datasets because of potential quadratic time complexity. We address...

Challenges and Opportunities for Learning Biology in Distance-Based Settings

Science.gov (United States)

Hallyburton, Chad L.; Lunsford, Eddie

2013-01-01

The history of learning biology through distance education is documented. A review of terminology and unique problems associated with biology instruction is presented. Using published research and their own teaching experience, the authors present recommendations and best practices for managing biology in distance-based formats. They offer ideas…

THE ROLE OF ARTICLE LEVEL METRICS IN SCIENTIFIC PUBLISHING

Directory of Open Access Journals (Sweden)

Vladimir TRAJKOVSKI

2016-04-01

Full Text Available Emerging metrics based on article-level does not exclude traditional metrics based on citations to the journal, but complements them. Article-level metrics (ALMs provide a wide range of metrics about the uptake of an individual journal article by the scientific community after publication. They include citations, statistics of usage, discussions in online comments and social media, social bookmarking, and recommendations. In this editorial, the role of article level metrics in publishing scientific papers has been described. Article-Level Metrics (ALMs are rapidly emerging as important tools to quantify how individual articles are being discussed, shared, and used. Data sources depend on the tool, but they include classic metrics indicators depending on citations, academic social networks (Mendeley, CiteULike, Delicious and social media (Facebook, Twitter, blogs, and Youtube. The most popular tools used to apply this new metrics are: Public Library of Science - Article-Level Metrics, Altmetric, Impactstory and Plum Analytics. Journal Impact Factor (JIF does not consider impact or influence beyond citations count as this count reflected only through Thomson Reuters’ Web of Science® database. JIF provides indicator related to the journal, but not related to a published paper. Thus, altmetrics now becomes an alternative metrics for performance assessment of individual scientists and their contributed scholarly publications. Macedonian scholarly publishers have to work on implementing of article level metrics in their e-journals. It is the way to increase their visibility and impact in the world of science.

Multiscale Distance Coherence Vector Algorithm for Content-Based Image Retrieval

Science.gov (United States)

Jiexian, Zeng; Xiupeng, Liu

2014-01-01

Multiscale distance coherence vector algorithm for content-based image retrieval (CBIR) is proposed due to the same descriptor with different shapes and the shortcomings of antinoise performance of the distance coherence vector algorithm. By this algorithm, the image contour curve is evolved by Gaussian function first, and then the distance coherence vector is, respectively, extracted from the contour of the original image and evolved images. Multiscale distance coherence vector was obtained by reasonable weight distribution of the distance coherence vectors of evolved images contour. This algorithm not only is invariable to translation, rotation, and scaling transformation but also has good performance of antinoise. The experiment results show us that the algorithm has a higher recall rate and precision rate for the retrieval of images polluted by noise. PMID:24883416

Distance Based Method for Outlier Detection of Body Sensor Networks

Directory of Open Access Journals (Sweden)

Haibin Zhang

2016-01-01

Full Text Available We propose a distance based method for the outlier detection of body sensor networks. Firstly, we use a Kernel Density Estimation (KDE to calculate the probability of the distance to k nearest neighbors for diagnosed data. If the probability is less than a threshold, and the distance of this data to its left and right neighbors is greater than a pre-defined value, the diagnosed data is decided as an outlier. Further, we formalize a sliding window based method to improve the outlier detection performance. Finally, to estimate the KDE by training sensor readings with errors, we introduce a Hidden Markov Model (HMM based method to estimate the most probable ground truth values which have the maximum probability to produce the training data. Simulation results show that the proposed method possesses a good detection accuracy with a low false alarm rate.

Performance metrics for the evaluation of hyperspectral chemical identification systems

Science.gov (United States)

Truslow, Eric; Golowich, Steven; Manolakis, Dimitris; Ingle, Vinay

2016-02-01

Remote sensing of chemical vapor plumes is a difficult but important task for many military and civilian applications. Hyperspectral sensors operating in the long-wave infrared regime have well-demonstrated detection capabilities. However, the identification of a plume's chemical constituents, based on a chemical library, is a multiple hypothesis testing problem which standard detection metrics do not fully describe. We propose using an additional performance metric for identification based on the so-called Dice index. Our approach partitions and weights a confusion matrix to develop both the standard detection metrics and identification metric. Using the proposed metrics, we demonstrate that the intuitive system design of a detector bank followed by an identifier is indeed justified when incorporating performance information beyond the standard detection metrics.

Distances on Cosmological Scales with VLTI

OpenAIRE

Karovska, Margarita; Elvis, Martin; Marengo, Massimo

2003-01-01

We present here a new method using interferometric measurements of quasars, that allows the determination of direct geometrical distances on cosmic scales. Quasar Broad Emission Line Regions sizes provide a "meter rule" with which to measure the metric of the Universe. This method is less dependent of model assumptions, and even of variations in the fundamental constants (other than c). We discuss the spectral and spatial requirements on the VLTI observations needed to carry out these measure...

Sigma Routing Metric for RPL Protocol

Directory of Open Access Journals (Sweden)

Paul Sanmartin

2018-04-01

Full Text Available This paper presents the adaptation of a specific metric for the RPL protocol in the objective function MRHOF. Among the functions standardized by IETF, we find OF0, which is based on the minimum hop count, as well as MRHOF, which is based on the Expected Transmission Count (ETX. However, when the network becomes denser or the number of nodes increases, both OF0 and MRHOF introduce long hops, which can generate a bottleneck that restricts the network. The adaptation is proposed to optimize both OFs through a new routing metric. To solve the above problem, the metrics of the minimum number of hops and the ETX are combined by designing a new routing metric called SIGMA-ETX, in which the best route is calculated using the standard deviation of ETX values between each node, as opposed to working with the ETX average along the route. This method ensures a better routing performance in dense sensor networks. The simulations are done through the Cooja simulator, based on the Contiki operating system. The simulations showed that the proposed optimization outperforms at a high margin in both OF0 and MRHOF, in terms of network latency, packet delivery ratio, lifetime, and power consumption.

Distance Ranging Based on Quantum Entanglement

International Nuclear Information System (INIS)

Xiao Jun-Jun; Han Xiao-Chun; Zeng Gui-Hua; Fang Chen; Zhao Jian-Kang

2013-01-01

In the quantum metrology, applications of quantum techniques based on entanglement bring in some better performances than conventional approaches. We experimentally investigate an application of entanglement in accurate ranging based on the second-order coherence in the time domain. By a fitting algorithm in the data processing, the optimization results show a precision of ±200 μm at a distance of 1043.3m. In addition, the influence of jamming noise on the ranging scheme is studied. With some different fitting parameters, the result shows that the proposed scheme has a powerful anti-jamming capability for white noise

Reference-free ground truth metric for metal artifact evaluation in CT images

International Nuclear Information System (INIS)

Kratz, Baerbel; Ens, Svitlana; Mueller, Jan; Buzug, Thorsten M.

2011-01-01

Purpose: In computed tomography (CT), metal objects in the region of interest introduce data inconsistencies during acquisition. Reconstructing these data results in an image with star shaped artifacts induced by the metal inconsistencies. To enhance image quality, the influence of the metal objects can be reduced by different metal artifact reduction (MAR) strategies. For an adequate evaluation of new MAR approaches a ground truth reference data set is needed. In technical evaluations, where phantoms can be measured with and without metal inserts, ground truth data can easily be obtained by a second reference data acquisition. Obviously, this is not possible for clinical data. Here, an alternative evaluation method is presented without the need of an additionally acquired reference data set. Methods: The proposed metric is based on an inherent ground truth for metal artifacts as well as MAR methods comparison, where no reference information in terms of a second acquisition is needed. The method is based on the forward projection of a reconstructed image, which is compared to the actually measured projection data. Results: The new evaluation technique is performed on phantom and on clinical CT data with and without MAR. The metric results are then compared with methods using a reference data set as well as an expert-based classification. It is shown that the new approach is an adequate quantification technique for artifact strength in reconstructed metal or MAR CT images. Conclusions: The presented method works solely on the original projection data itself, which yields some advantages compared to distance measures in image domain using two data sets. Beside this, no parameters have to be manually chosen. The new metric is a useful evaluation alternative when no reference data are available.

A convergence theory for probabilistic metric spaces | Jäger ...

African Journals Online (AJOL)

We develop a theory of probabilistic convergence spaces based on Tardiff's neighbourhood systems for probabilistic metric spaces. We show that the resulting category is a topological universe and we characterize a subcategory that is isomorphic to the category of probabilistic metric spaces. Keywords: Probabilistic metric ...

Species-Level Differences in Hyperspectral Metrics among Tropical Rainforest Trees as Determined by a Tree-Based Classifier

Directory of Open Access Journals (Sweden)

Dar A. Roberts

2012-06-01

Full Text Available This study explores a method to classify seven tropical rainforest tree species from full-range (400–2,500 nm hyperspectral data acquired at tissue (leaf and bark, pixel and crown scales using laboratory and airborne sensors. Metrics that respond to vegetation chemistry and structure were derived using narrowband indices, derivative- and absorption-based techniques, and spectral mixture analysis. We then used the Random Forests tree-based classifier to discriminate species with minimally-correlated, importance-ranked metrics. At all scales, best overall accuracies were achieved with metrics derived from all four techniques and that targeted chemical and structural properties across the visible to shortwave infrared spectrum (400–2500 nm. For tissue spectra, overall accuracies were 86.8% for leaves, 74.2% for bark, and 84.9% for leaves plus bark. Variation in tissue metrics was best explained by an axis of red absorption related to photosynthetic leaves and an axis distinguishing bark water and other chemical absorption features. Overall accuracies for individual tree crowns were 71.5% for pixel spectra, 70.6% crown-mean spectra, and 87.4% for a pixel-majority technique. At pixel and crown scales, tree structure and phenology at the time of image acquisition were important factors that determined species spectral separability.

Construction of Einstein-Sasaki metrics in D≥7

International Nuclear Information System (INIS)

Lue, H.; Pope, C. N.; Vazquez-Poritz, J. F.

2007-01-01

We construct explicit Einstein-Kaehler metrics in all even dimensions D=2n+4≥6, in terms of a 2n-dimensional Einstein-Kaehler base metric. These are cohomogeneity 2 metrics which have the new feature of including a NUT-type parameter, or gravomagnetic charge, in addition to..' in addition to mass and rotation parameters. Using a canonical construction, these metrics all yield Einstein-Sasaki metrics in dimensions D=2n+5≥7. As is commonly the case in this type of construction, for suitable choices of the free parameters the Einstein-Sasaki metrics can extend smoothly onto complete and nonsingular manifolds, even though the underlying Einstein-Kaehler metric has conical singularities. We discuss some explicit examples in the case of seven-dimensional Einstein-Sasaki spaces. These new spaces can provide supersymmetric backgrounds in M theory, which play a role in the AdS 4 /CFT 3 correspondence

National Metrical Types in Nineteenth Century Art Song

Directory of Open Access Journals (Sweden)

Leigh VanHandel

2010-01-01

Full Text Available William Rothstein’s article “National metrical types in music of the eighteenth and early nineteenth centuries” (2008 proposes a distinction between the metrical habits of 18th and early 19th century German music and those of Italian and French music of that period. Based on theoretical treatises and compositional practice, he outlines these national metrical types and discusses the characteristics of each type. This paper presents the results of a study designed to determine whether, and to what degree, Rothstein’s characterizations of national metrical types are present in 19th century French and German art song. Studying metrical habits in this genre may provide a lens into changing metrical conceptions of 19th century theorists and composers, as well as to the metrical habits and compositional style of individual 19th century French and German art song composers.

Image Inpainting Based on Coherence Transport with Adapted Distance Functions

KAUST Repository

März, Thomas

2011-01-01

We discuss an extension of our method image inpainting based on coherence transport. For the latter method the pixels of the inpainting domain have to be serialized into an ordered list. Until now, to induce the serialization we have used the distance to boundary map. But there are inpainting problems where the distance to boundary serialization causes unsatisfactory inpainting results. In the present work we demonstrate cases where we can resolve the difficulties by employing other distance functions which better suit the problem at hand. © 2011 Society for Industrial and Applied Mathematics.

Using Publication Metrics to Highlight Academic Productivity and Research Impact

Science.gov (United States)

Carpenter, Christopher R.; Cone, David C.; Sarli, Cathy C.

2016-01-01

This article provides a broad overview of widely available measures of academic productivity and impact using publication data and highlights uses of these metrics for various purposes. Metrics based on publication data include measures such as number of publications, number of citations, the journal impact factor score, and the h-index, as well as emerging metrics based on document-level metrics. Publication metrics can be used for a variety of purposes for tenure and promotion, grant applications and renewal reports, benchmarking, recruiting efforts, and administrative purposes for departmental or university performance reports. The authors also highlight practical applications of measuring and reporting academic productivity and impact to emphasize and promote individual investigators, grant applications, or department output. PMID:25308141

Metrics-based assessments of research: incentives for 'institutional plagiarism'?

Science.gov (United States)

Berry, Colin

2013-06-01

The issue of plagiarism--claiming credit for work that is not one's own, rightly, continues to cause concern in the academic community. An analysis is presented that shows the effects that may arise from metrics-based assessments of research, when credit for an author's outputs (chiefly publications) is given to an institution that did not support the research but which subsequently employs the author. The incentives for what is termed here "institutional plagiarism" are demonstrated with reference to the UK Research Assessment Exercise in which submitting units of assessment are shown in some instances to derive around twice the credit for papers produced elsewhere by new recruits, compared to papers produced 'in-house'.

KM-FCM: A fuzzy clustering optimization algorithm based on Mahalanobis distance

Directory of Open Access Journals (Sweden)

Zhiwen ZU

2018-04-01

Full Text Available The traditional fuzzy clustering algorithm uses Euclidean distance as the similarity criterion, which is disadvantageous to the multidimensional data processing. In order to solve this situation, Mahalanobis distance is used instead of the traditional Euclidean distance, and the optimization of fuzzy clustering algorithm based on Mahalanobis distance is studied to enhance the clustering effect and ability. With making the initialization means by Heuristic search algorithm combined with k-means algorithm, and in terms of the validity function which could automatically adjust the optimal clustering number, an optimization algorithm KM-FCM is proposed. The new algorithm is compared with FCM algorithm, FCM-M algorithm and M-FCM algorithm in three standard data sets. The experimental results show that the KM-FCM algorithm is effective. It has higher clustering accuracy than FCM, FCM-M and M-FCM, recognizing high-dimensional data clustering well. It has global optimization effect, and the clustering number has no need for setting in advance. The new algorithm provides a reference for the optimization of fuzzy clustering algorithm based on Mahalanobis distance.

The difference between presence-based education and distance learning

OpenAIRE

Fernández Rodríguez, Mònica

2002-01-01

Attempts to define distance learning always involve comparisons with presence-based education, as the latter is the most direct reference that the former has. It is on this basis that the convergent points, similarities and differences of the two types of approach are established. This article opens with such a comparison, before going on to focus mainly on distance learning and to examine methodological strategies that should be borne in mind when implementing an e-learning system.

Measuring the distance from saddle points and driving to locate them over quantum control landscapes

International Nuclear Information System (INIS)

Sun, Qiuyang; Riviello, Gregory; Rabitz, Herschel; Wu, Re-Bing

2015-01-01

Optimal control of quantum phenomena involves the introduction of a cost functional J to characterize the degree of achieving a physical objective by a chosen shaped electromagnetic field. The cost functional dependence upon the control forms a control landscape. Two theoretically important canonical cases are the landscapes associated with seeking to achieve either a physical observable or a unitary transformation. Upon satisfaction of particular assumptions, both landscapes are analytically known to be trap-free, yet possess saddle points at precise suboptimal J values. The presence of saddles on the landscapes can influence the effort needed to find an optimal field. As a foundation to future algorithm development and analyzes, we define metrics that identify the ‘distance’ from a given saddle based on the sufficient and necessary conditions for the existence of the saddles. Algorithms are introduced utilizing the metrics to find a control such that the dynamics arrive at a targeted saddle. The saddle distance metric and saddle-seeking methodology is tested numerically in several model systems. (paper)

Overlapping community detection based on link graph using distance dynamics

Science.gov (United States)

Chen, Lei; Zhang, Jing; Cai, Li-Jun

2018-01-01

The distance dynamics model was recently proposed to detect the disjoint community of a complex network. To identify the overlapping structure of a network using the distance dynamics model, an overlapping community detection algorithm, called L-Attractor, is proposed in this paper. The process of L-Attractor mainly consists of three phases. In the first phase, L-Attractor transforms the original graph to a link graph (a new edge graph) to assure that one node has multiple distances. In the second phase, using the improved distance dynamics model, a dynamic interaction process is introduced to simulate the distance dynamics (shrink or stretch). Through the dynamic interaction process, all distances converge, and the disjoint community structure of the link graph naturally manifests itself. In the third phase, a recovery method is designed to convert the disjoint community structure of the link graph to the overlapping community structure of the original graph. Extensive experiments are conducted on the LFR benchmark networks as well as real-world networks. Based on the results, our algorithm demonstrates higher accuracy and quality than other state-of-the-art algorithms.

Metric modular spaces

CERN Document Server

Chistyakov, Vyacheslav

2015-01-01

Aimed toward researchers and graduate students familiar with elements of functional analysis, linear algebra, and general topology; this book contains a general study of modulars, modular spaces, and metric modular spaces. Modulars may be thought of as generalized velocity fields and serve two important purposes: generate metric spaces in a unified manner and provide a weaker convergence, the modular convergence, whose topology is non-metrizable in general. Metric modular spaces are extensions of metric spaces, metric linear spaces, and classical modular linear spaces. The topics covered include the classification of modulars, metrizability of modular spaces, modular transforms and duality between modular spaces, metric  and modular topologies. Applications illustrated in this book include: the description of superposition operators acting in modular spaces, the existence of regular selections of set-valued mappings, new interpretations of spaces of Lipschitzian and absolutely continuous mappings, the existe...

INVESTIGATION AND EVALUATION OF SPATIAL PATTERNS IN TABRIZ PARKS USING LANDSCAPE METRICS

Directory of Open Access Journals (Sweden)

Ali Majnouni Toutakhane

2016-01-01

Full Text Available Nowadays, the green spaces in cities and especially metropolises have adopted a variety of functions. In addition to improving the environmental conditions, they are suitable places for spending free times and mitigating nervous pressures of the machinery life based on their distribution and dispersion in the cities. In this research, in order to study the spatial distribution and composition of the parks and green spaces in Tabriz metropolis, the map of Parks prepared using the digital atlas of Tabriz parks and Arc Map and IDRISI softwares. Then, quantitative information of spatial patterns of Tabriz parks provided using Fragstats software and a selection of landscape metrics including: the area of class, patch density, percentage of landscape, average patch size, average patch area, largest patch index, landscape shape index, average Euclidean distance of the nearest neighborhood and average index of patch shape. Then the spatial distribution, composition, extent and continuity of the parks was evaluated. Overall, only 8.5 percent of the landscape is assigned to the parks, and they are studied in three classes of neighborhood, district and regional parks. Neighborhood parks and green spaces have a better spatial distribution pattern compared to the other classes and the studied metrics showed better results for this class. In contrast, the quantitative results of the metrics calculated for regional parks, showed the most unfavorable spatial status for this class of parks among the three classes studied in Tabriz city.

Test of the FLRW Metric and Curvature with Strong Lens Time Delays

International Nuclear Information System (INIS)

Liao, Kai; Li, Zhengxiang; Wang, Guo-Jian; Fan, Xi-Long

2017-01-01

We present a new model-independent strategy for testing the Friedmann–Lemaître–Robertson–Walker (FLRW) metric and constraining cosmic curvature, based on future time-delay measurements of strongly lensed quasar-elliptical galaxy systems from the Large Synoptic Survey Telescope and supernova observations from the Dark Energy Survey. The test only relies on geometric optics. It is independent of the energy contents of the universe and the validity of the Einstein equation on cosmological scales. The study comprises two levels: testing the FLRW metric through the distance sum rule (DSR) and determining/constraining cosmic curvature. We propose an effective and efficient (redshift) evolution model for performing the former test, which allows us to concretely specify the violation criterion for the FLRW DSR. If the FLRW metric is consistent with the observations, then on the second level the cosmic curvature parameter will be constrained to ∼0.057 or ∼0.041 (1 σ ), depending on the availability of high-redshift supernovae, which is much more stringent than current model-independent techniques. We also show that the bias in the time-delay method might be well controlled, leading to robust results. The proposed method is a new independent tool for both testing the fundamental assumptions of homogeneity and isotropy in cosmology and for determining cosmic curvature. It is complementary to cosmic microwave background plus baryon acoustic oscillation analyses, which normally assume a cosmological model with dark energy domination in the late-time universe.

Test of the FLRW Metric and Curvature with Strong Lens Time Delays

Energy Technology Data Exchange (ETDEWEB)

Liao, Kai [School of Science, Wuhan University of Technology, Wuhan 430070 (China); Li, Zhengxiang; Wang, Guo-Jian [Department of Astronomy, Beijing Normal University, Beijing 100875 (China); Fan, Xi-Long, E-mail: liaokai@whut.edu.cn, E-mail: xilong.fan@glasgow.ac.uk [Department of Physics and Mechanical and Electrical Engineering, Hubei University of Education, Wuhan 430205 (China)

2017-04-20

We present a new model-independent strategy for testing the Friedmann–Lemaître–Robertson–Walker (FLRW) metric and constraining cosmic curvature, based on future time-delay measurements of strongly lensed quasar-elliptical galaxy systems from the Large Synoptic Survey Telescope and supernova observations from the Dark Energy Survey. The test only relies on geometric optics. It is independent of the energy contents of the universe and the validity of the Einstein equation on cosmological scales. The study comprises two levels: testing the FLRW metric through the distance sum rule (DSR) and determining/constraining cosmic curvature. We propose an effective and efficient (redshift) evolution model for performing the former test, which allows us to concretely specify the violation criterion for the FLRW DSR. If the FLRW metric is consistent with the observations, then on the second level the cosmic curvature parameter will be constrained to ∼0.057 or ∼0.041 (1 σ ), depending on the availability of high-redshift supernovae, which is much more stringent than current model-independent techniques. We also show that the bias in the time-delay method might be well controlled, leading to robust results. The proposed method is a new independent tool for both testing the fundamental assumptions of homogeneity and isotropy in cosmology and for determining cosmic curvature. It is complementary to cosmic microwave background plus baryon acoustic oscillation analyses, which normally assume a cosmological model with dark energy domination in the late-time universe.

Next-Generation Metrics: Responsible Metrics & Evaluation for Open Science

Energy Technology Data Exchange (ETDEWEB)

Wilsdon, J.; Bar-Ilan, J.; Peters, I.; Wouters, P.

2016-07-01

Metrics evoke a mixed reaction from the research community. A commitment to using data to inform decisions makes some enthusiastic about the prospect of granular, real-time analysis o of research and its wider impacts. Yet we only have to look at the blunt use of metrics such as journal impact factors, h-indices and grant income targets, to be reminded of the pitfalls. Some of the most precious qualities of academic culture resist simple quantification, and individual indicators often struggle to do justice to the richness and plurality of research. Too often, poorly designed evaluation criteria are “dominating minds, distorting behaviour and determining careers (Lawrence, 2007).” Metrics hold real power: they are constitutive of values, identities and livelihoods. How to exercise that power to more positive ends has been the focus of several recent and complementary initiatives, including the San Francisco Declaration on Research Assessment (DORA1), the Leiden Manifesto2 and The Metric Tide3 (a UK government review of the role of metrics in research management and assessment). Building on these initiatives, the European Commission, under its new Open Science Policy Platform4, is now looking to develop a framework for responsible metrics for research management and evaluation, which can be incorporated into the successor framework to Horizon 2020. (Author)

Low-complexity atlas-based prostate segmentation by combining global, regional, and local metrics

Energy Technology Data Exchange (ETDEWEB)

Xie, Qiuliang; Ruan, Dan, E-mail: druan@mednet.ucla.edu [The Department of Radiation Oncology, University of California Los Angeles, California 90095 (United States)

2014-04-15

Purpose: To improve the efficiency of atlas-based segmentation without compromising accuracy, and to demonstrate the validity of the proposed method on MRI-based prostate segmentation application. Methods: Accurate and efficient automatic structure segmentation is an important task in medical image processing. Atlas-based methods, as the state-of-the-art, provide good segmentation at the cost of a large number of computationally intensive nonrigid registrations, for anatomical sites/structures that are subject to deformation. In this study, the authors propose to utilize a combination of global, regional, and local metrics to improve the accuracy yet significantly reduce the number of required nonrigid registrations. The authors first perform an affine registration to minimize the global mean squared error (gMSE) to coarsely align each atlas image to the target. Subsequently, atarget-specific regional MSE (rMSE), demonstrated to be a good surrogate for dice similarity coefficient (DSC), is used to select a relevant subset from the training atlas. Only within this subset are nonrigid registrations performed between the training images and the target image, to minimize a weighted combination of gMSE and rMSE. Finally, structure labels are propagated from the selected training samples to the target via the estimated deformation fields, and label fusion is performed based on a weighted combination of rMSE and local MSE (lMSE) discrepancy, with proper total-variation-based spatial regularization. Results: The proposed method was applied to a public database of 30 prostate MR images with expert-segmented structures. The authors’ method, utilizing only eight nonrigid registrations, achieved a performance with a median/mean DSC of over 0.87/0.86, outperforming the state-of-the-art full-fledged atlas-based segmentation approach of which the median/mean DSC was 0.84/0.82 when applying to their data set. Conclusions: The proposed method requires a fixed number of nonrigid

Development of a perceptually calibrated objective metric of noise

Science.gov (United States)

Keelan, Brian W.; Jin, Elaine W.; Prokushkin, Sergey

2011-01-01

A system simulation model was used to create scene-dependent noise masks that reflect current performance of mobile phone cameras. Stimuli with different overall magnitudes of noise and with varying mixtures of red, green, blue, and luminance noises were included in the study. Eleven treatments in each of ten pictorial scenes were evaluated by twenty observers using the softcopy ruler method. In addition to determining the quality loss function in just noticeable differences (JNDs) for the average observer and scene, transformations for different combinations of observer sensitivity and scene susceptibility were derived. The psychophysical results were used to optimize an objective metric of isotropic noise based on system noise power spectra (NPS), which were integrated over a visual frequency weighting function to yield perceptually relevant variances and covariances in CIE L*a*b* space. Because the frequency weighting function is expressed in terms of cycles per degree at the retina, it accounts for display pixel size and viewing distance effects, so application-specific predictions can be made. Excellent results were obtained using only L* and a* variances and L*a* covariance, with relative weights of 100, 5, and 12, respectively. The positive a* weight suggests that the luminance (photopic) weighting is slightly narrow on the long wavelength side for predicting perceived noisiness. The L*a* covariance term, which is normally negative, reflects masking between L* and a* noise, as confirmed in informal evaluations. Test targets in linear sRGB and rendered L*a*b* spaces for each treatment are available at http://www.aptina.com/ImArch/ to enable other researchers to test metrics of their own design and calibrate them to JNDs of quality loss without performing additional observer experiments. Such JND-calibrated noise metrics are particularly valuable for comparing the impact of noise and other attributes, and for computing overall image quality.

In the pursuit of a semantic similarity metric based on UMLS annotations for articles in PubMed Central Open Access.

Science.gov (United States)

Garcia Castro, Leyla Jael; Berlanga, Rafael; Garcia, Alexander

2015-10-01

Although full-text articles are provided by the publishers in electronic formats, it remains a challenge to find related work beyond the title and abstract context. Identifying related articles based on their abstract is indeed a good starting point; this process is straightforward and does not consume as many resources as full-text based similarity would require. However, further analyses may require in-depth understanding of the full content. Two articles with highly related abstracts can be substantially different regarding the full content. How similarity differs when considering title-and-abstract versus full-text and which semantic similarity metric provides better results when dealing with full-text articles are the main issues addressed in this manuscript. We have benchmarked three similarity metrics - BM25, PMRA, and Cosine, in order to determine which one performs best when using concept-based annotations on full-text documents. We also evaluated variations in similarity values based on title-and-abstract against those relying on full-text. Our test dataset comprises the Genomics track article collection from the 2005 Text Retrieval Conference. Initially, we used an entity recognition software to semantically annotate titles and abstracts as well as full-text with concepts defined in the Unified Medical Language System (UMLS®). For each article, we created a document profile, i.e., a set of identified concepts, term frequency, and inverse document frequency; we then applied various similarity metrics to those document profiles. We considered correlation, precision, recall, and F1 in order to determine which similarity metric performs best with concept-based annotations. For those full-text articles available in PubMed Central Open Access (PMC-OA), we also performed dispersion analyses in order to understand how similarity varies when considering full-text articles. We have found that the PubMed Related Articles similarity metric is the most suitable for

A remodelling metric for angular fibre distributions and its application to diseased carotid bifurcations.

LENUS (Irish Health Repository)

Creane, Arthur

2012-07-01

Many soft biological tissues contain collagen fibres, which act as major load bearing constituents. The orientation and the dispersion of these fibres influence the macroscopic mechanical properties of the tissue and are therefore of importance in several areas of research including constitutive model development, tissue engineering and mechanobiology. Qualitative comparisons between these fibre architectures can be made using vector plots of mean orientations and contour plots of fibre dispersion but quantitative comparison cannot be achieved using these methods. We propose a \\'remodelling metric\\' between two angular fibre distributions, which represents the mean rotational effort required to transform one into the other. It is an adaptation of the earth mover\\'s distance, a similarity measure between two histograms\\/signatures used in image analysis, which represents the minimal cost of transforming one distribution into the other by moving distribution mass around. In this paper, its utility is demonstrated by considering the change in fibre architecture during a period of plaque growth in finite element models of the carotid bifurcation. The fibre architecture is predicted using a strain-based remodelling algorithm. We investigate the remodelling metric\\'s potential as a clinical indicator of plaque vulnerability by comparing results between symptomatic and asymptomatic carotid bifurcations. Fibre remodelling was found to occur at regions of plaque burden. As plaque thickness increased, so did the remodelling metric. A measure of the total predicted fibre remodelling during plaque growth, TRM, was found to be higher in the symptomatic group than in the asymptomatic group. Furthermore, a measure of the total fibre remodelling per plaque size, TRM\\/TPB, was found to be significantly higher in the symptomatic vessels. The remodelling metric may prove to be a useful tool in other soft tissues and engineered scaffolds where fibre adaptation is also present.

Project organized Problem-based learning in Distance Education

DEFF Research Database (Denmark)

Jensen, Lars Peter; Helbo, Jan; Knudsen, Morten

2002-01-01

Project organized problem based learning is a successful concept for on-campus engineering education at Aalborg University. Recently this "Aalborg concept" has been used in networked distance education as well. This paper describes the experiences from two years of Internet-mediated project work...... in a new Master of Information Technology education. The main conclusions are, that the project work is a strong learning motivator, enhancing peer collaboration, for off-campus students as well. However, the concept cannot be directly transferred to off-campus learning. In this paper, the main problems...... experienced with group organized project work in distance education are described, and some possible solutions are listed....

Project-Organized Problem-Based Learning in Distance Education

DEFF Research Database (Denmark)

Jensen, Lars Peter; Helbo, Jan; Knudsen, Morten

2003-01-01

Project organized problem based learning is a successful concept for on-campus engineering education at Aalborg University. Recently this "Aalborg concept" has been used in networked distance education as well. This paper describes the experiences from two years of Internet-mediated project work...... in a new Master of Information Technology education. The main conclusions are, that the project work is a strong learning motivator, enhancing peer collaboration, for off-campus students as well. However, the concept cannot be directly transferred to off-campus learning. In this paper, the main problems...... experienced with group organized project work in distance education are described, and some possible solutions are listed....

Metrics for energy resilience

International Nuclear Information System (INIS)

Roege, Paul E.; Collier, Zachary A.; Mancillas, James; McDonagh, John A.; Linkov, Igor

2014-01-01

Energy lies at the backbone of any advanced society and constitutes an essential prerequisite for economic growth, social order and national defense. However there is an Achilles heel to today's energy and technology relationship; namely a precarious intimacy between energy and the fiscal, social, and technical systems it supports. Recently, widespread and persistent disruptions in energy systems have highlighted the extent of this dependence and the vulnerability of increasingly optimized systems to changing conditions. Resilience is an emerging concept that offers to reconcile considerations of performance under dynamic environments and across multiple time frames by supplementing traditionally static system performance measures to consider behaviors under changing conditions and complex interactions among physical, information and human domains. This paper identifies metrics useful to implement guidance for energy-related planning, design, investment, and operation. Recommendations are presented using a matrix format to provide a structured and comprehensive framework of metrics relevant to a system's energy resilience. The study synthesizes previously proposed metrics and emergent resilience literature to provide a multi-dimensional model intended for use by leaders and practitioners as they transform our energy posture from one of stasis and reaction to one that is proactive and which fosters sustainable growth. - Highlights: • Resilience is the ability of a system to recover from adversity. • There is a need for methods to quantify and measure system resilience. • We developed a matrix-based approach to generate energy resilience metrics. • These metrics can be used in energy planning, system design, and operations

Distance learning, problem based learning and dynamic knowledge networks.

Science.gov (United States)

Giani, U; Martone, P

1998-06-01

This paper is an attempt to develop a distance learning model grounded upon a strict integration of problem based learning (PBL), dynamic knowledge networks (DKN) and web tools, such as hypermedia documents, synchronous and asynchronous communication facilities, etc. The main objective is to develop a theory of distance learning based upon the idea that learning is a highly dynamic cognitive process aimed at connecting different concepts in a network of mutually supporting concepts. Moreover, this process is supposed to be the result of a social interaction that has to be facilitated by the web. The model was tested by creating a virtual classroom of medical and nursing students and activating a learning session on the concept of knowledge representation in health sciences.

Retrieval evaluation and distance learning from perceived similarity between endomicroscopy videos.

Science.gov (United States)

André, Barbara; Vercauteren, Tom; Buchner, Anna M; Wallace, Michael B; Ayache, Nicholas

2011-01-01

Evaluating content-based retrieval (CBR) is challenging because it requires an adequate ground-truth. When the available groundtruth is limited to textual metadata such as pathological classes, retrieval results can only be evaluated indirectly, for example in terms of classification performance. In this study we first present a tool to generate perceived similarity ground-truth that enables direct evaluation of endomicroscopic video retrieval. This tool uses a four-points Likert scale and collects subjective pairwise similarities perceived by multiple expert observers. We then evaluate against the generated ground-truth a previously developed dense bag-of-visual-words method for endomicroscopic video retrieval. Confirming the results of previous indirect evaluation based on classification, our direct evaluation shows that this method significantly outperforms several other state-of-the-art CBR methods. In a second step, we propose to improve the CBR method by learning an adjusted similarity metric from the perceived similarity ground-truth. By minimizing a margin-based cost function that differentiates similar and dissimilar video pairs, we learn a weight vector applied to the visual word signatures of videos. Using cross-validation, we demonstrate that the learned similarity distance is significantly better correlated with the perceived similarity than the original visual-word-based distance.

Combination of evidence in recommendation systems characterized by distance functions

Energy Technology Data Exchange (ETDEWEB)

Rocha, L. M. (Luis Mateus)

2002-01-01

Recommendation systems for different Document Networks (DN) such as the World Wide Web (WWW), Digitnl Libarries, or Scientific Databases, often make use of distance functions extracted from relationships among documents and between documents and semantic tags. For instance, documents In the WWW are related via a hyperlink network, while documents in bibliographic databases are related by citation and collaboration networks.Furthermore, documents can be related to semantic tags such as keywords used to describe their content, The distance functions computed from these relations establish associative networks among items of the DN, and allow recommendation systems to identify relevant associations for iudividoal users. The process of recommendation can be improved by integrating associative data from different sources. Thus we are presented with a problem of combining evidence (about assochaons between items) from different sonrces characterized by distance functions. In this paper we summarize our work on (1) inferring associations from semi-metric distance functions and (2) combining evidence from different (distance) associative DN.

Landscape pattern metrics and regional assessment

Science.gov (United States)

O'Neill, R. V.; Riitters, K.H.; Wickham, J.D.; Jones, K.B.

1999-01-01

The combination of remote imagery data, geographic information systems software, and landscape ecology theory provides a unique basis for monitoring and assessing large-scale ecological systems. The unique feature of the work has been the need to develop and interpret quantitative measures of spatial pattern-the landscape indices. This article reviews what is known about the statistical properties of these pattern metrics and suggests some additional metrics based on island biogeography, percolation theory, hierarchy theory, and economic geography. Assessment applications of this approach have required interpreting the pattern metrics in terms of specific environmental endpoints, such as wildlife and water quality, and research into how to represent synergystic effects of many overlapping sources of stress.

Baby universe metric equivalent to an interior black-hole metric

International Nuclear Information System (INIS)

Gonzalez-Diaz, P.F.

1991-01-01

It is shown that the maximally extended metric corresponding to a large wormhole is the unique possible wormhole metric whose baby universe sector is conformally equivalent ot the maximal inextendible Kruskal metric corresponding to the interior region of a Schwarzschild black hole whose gravitational radius is half the wormhole neck radius. The physical implications of this result in the black hole evaporation process are discussed. (orig.)

Benchmarking Distance Control and Virtual Drilling for Lateral Skull Base Surgery.

Science.gov (United States)

Voormolen, Eduard H J; Diederen, Sander; van Stralen, Marijn; Woerdeman, Peter A; Noordmans, Herke Jan; Viergever, Max A; Regli, Luca; Robe, Pierre A; Berkelbach van der Sprenkel, Jan Willem

2018-01-01

Novel audiovisual feedback methods were developed to improve image guidance during skull base surgery by providing audiovisual warnings when the drill tip enters a protective perimeter set at a distance around anatomic structures ("distance control") and visualizing bone drilling ("virtual drilling"). To benchmark the drill damage risk reduction provided by distance control, to quantify the accuracy of virtual drilling, and to investigate whether the proposed feedback methods are clinically feasible. In a simulated surgical scenario using human cadavers, 12 unexperienced users (medical students) drilled 12 mastoidectomies. Users were divided into a control group using standard image guidance and 3 groups using distance control with protective perimeters of 1, 2, or 3 mm. Damage to critical structures (sigmoid sinus, semicircular canals, facial nerve) was assessed. Neurosurgeons performed another 6 mastoidectomy/trans-labyrinthine and retro-labyrinthine approaches. Virtual errors as compared with real postoperative drill cavities were calculated. In a clinical setting, 3 patients received lateral skull base surgery with the proposed feedback methods. Users drilling with distance control protective perimeters of 3 mm did not damage structures, whereas the groups using smaller protective perimeters and the control group injured structures. Virtual drilling maximum cavity underestimations and overestimations were 2.8 ± 0.1 and 3.3 ± 0.4 mm, respectively. Feedback methods functioned properly in the clinical setting. Distance control reduced the risks of drill damage proportional to the protective perimeter distance. Errors in virtual drilling reflect spatial errors of the image guidance system. These feedback methods are clinically feasible. Copyright © 2017 Elsevier Inc. All rights reserved.

On the Averaging of Cardiac Diffusion Tensor MRI Data: The Effect of Distance Function Selection

Science.gov (United States)

Giannakidis, Archontis; Melkus, Gerd; Yang, Guang; Gullberg, Grant T.

2016-01-01

Diffusion tensor magnetic resonance imaging (DT-MRI) allows a unique insight into the microstructure of highly-directional tissues. The selection of the most proper distance function for the space of diffusion tensors is crucial in enhancing the clinical application of this imaging modality. Both linear and nonlinear metrics have been proposed in the literature over the years. The debate on the most appropriate DT-MRI distance function is still ongoing. In this paper, we presented a framework to compare the Euclidean, affine-invariant Riemannian and log-Euclidean metrics using actual high-resolution DT-MRI rat heart data. We employed temporal averaging at the diffusion tensor level of three consecutive and identically-acquired DT-MRI datasets from each of five rat hearts as a means to rectify the background noise-induced loss of myocyte directional regularity. This procedure is applied here for the first time in the context of tensor distance function selection. When compared with previous studies that used a different concrete application to juxtapose the various DT-MRI distance functions, this work is unique in that it combined the following: (i) Metrics were judged by quantitative –rather than qualitative– criteria, (ii) the comparison tools were non-biased, (iii) a longitudinal comparison operation was used on a same-voxel basis. The statistical analyses of the comparison showed that the three DT-MRI distance functions tend to provide equivalent results. Hence, we came to the conclusion that the tensor manifold for cardiac DT-MRI studies is a curved space of almost zero curvature. The signal to noise ratio dependence of the operations was investigated through simulations. Finally, the “swelling effect” occurrence following Euclidean averaging was found to be too unimportant to be worth consideration. PMID:27754986

On the averaging of cardiac diffusion tensor MRI data: the effect of distance function selection

Science.gov (United States)

Giannakidis, Archontis; Melkus, Gerd; Yang, Guang; Gullberg, Grant T.

2016-11-01

Diffusion tensor magnetic resonance imaging (DT-MRI) allows a unique insight into the microstructure of highly-directional tissues. The selection of the most proper distance function for the space of diffusion tensors is crucial in enhancing the clinical application of this imaging modality. Both linear and nonlinear metrics have been proposed in the literature over the years. The debate on the most appropriate DT-MRI distance function is still ongoing. In this paper, we presented a framework to compare the Euclidean, affine-invariant Riemannian and log-Euclidean metrics using actual high-resolution DT-MRI rat heart data. We employed temporal averaging at the diffusion tensor level of three consecutive and identically-acquired DT-MRI datasets from each of five rat hearts as a means to rectify the background noise-induced loss of myocyte directional regularity. This procedure is applied here for the first time in the context of tensor distance function selection. When compared with previous studies that used a different concrete application to juxtapose the various DT-MRI distance functions, this work is unique in that it combined the following: (i) metrics were judged by quantitative—rather than qualitative—criteria, (ii) the comparison tools were non-biased, (iii) a longitudinal comparison operation was used on a same-voxel basis. The statistical analyses of the comparison showed that the three DT-MRI distance functions tend to provide equivalent results. Hence, we came to the conclusion that the tensor manifold for cardiac DT-MRI studies is a curved space of almost zero curvature. The signal to noise ratio dependence of the operations was investigated through simulations. Finally, the ‘swelling effect’ occurrence following Euclidean averaging was found to be too unimportant to be worth consideration.

Pythagoras's theorem on a two-dimensional lattice from a `natural' Dirac operator and Connes's distance formula

Science.gov (United States)

Dai, Jian; Song, Xing-Chang

2001-07-01

One of the key ingredients of Connes's noncommutative geometry is a generalized Dirac operator which induces a metric (Connes's distance) on the pure state space. We generalize such a Dirac operator devised by Dimakis et al, whose Connes distance recovers the linear distance on an one-dimensional lattice, to the two-dimensional case. This Dirac operator has the local eigenvalue property and induces a Euclidean distance on this two-dimensional lattice, which is referred to as `natural'. This kind of Dirac operator can be easily generalized into any higher-dimensional lattices.

PERMANOVA-S: association test for microbial community composition that accommodates confounders and multiple distances

OpenAIRE

Tang, Zheng-Zheng; Chen, Guanhua; Alekseyenko, Alexander V.

2016-01-01

Motivation: Recent advances in sequencing technology have made it possible to obtain high-throughput data on the composition of microbial communities and to study the effects of dysbiosis on the human host. Analysis of pairwise intersample distances quantifies the association between the microbiome diversity and covariates of interest (e.g. environmental factors, clinical outcomes, treatment groups). In the design of these analyses, multiple choices for distance metrics are available. Most di...

Calibration Base Lines for Electronic Distance Measuring Instruments (EDMI)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — A calibration base line (CBL) is a precisely measured, straight-line course of approximately 1,400 m used to calibrate Electronic Distance Measuring Instruments...

Comparison of frequency-distance relationship and Gaussian-diffusion-based methods of compensation for distance-dependent spatial resolution in SPECT imaging

International Nuclear Information System (INIS)

Kohli, Vandana; King, Micgael A.; Glick, Stephen J.; Pan, Tin-Su

1998-01-01

The goal of this investigation was to compare resolution recovery versus noise level of two methods for compensation of distance-dependent resolution (DDR) in SPECT imaging. The two methods of compensation were restoration filtering based on the frequency-distance relationship (FDR) prior to iterative reconstruction, and modelling DDR in the projector/backprojector pair employed in iterative reconstruction. FDR restoration filtering was computationally faster than modelling the detector response in iterative reconstruction. Using Gaussian diffusion to model the detector response in iterative reconstruction sped up the process by a factor of 2.5 over frequency domain filtering in the projector/backprojector pair. Gaussian diffusion modelling resulted in a better resolution versus noise tradeoff than either FDR restoration filtering or solely modelling attenuation in the projector/backprojector pair of iterative reconstruction. For the pixel size investigated herein (0.317 cm), accounting for DDR in the projector/backprojector pair by Gaussian diffusion, or by applying a blurring function based on the distance from the face of the collimator at each distance, resulted in very similar resolution recovery and slice noise level. (author)

Properties of C-metric spaces

Science.gov (United States)

Croitoru, Anca; Apreutesei, Gabriela; Mastorakis, Nikos E.

2017-09-01

The subject of this paper belongs to the theory of approximate metrics [23]. An approximate metric on X is a real application defined on X × X that satisfies only a part of the metric axioms. In a recent paper [23], we introduced a new type of approximate metric, named C-metric, that is an application which satisfies only two metric axioms: symmetry and triangular inequality. The remarkable fact in a C-metric space is that a topological structure induced by the C-metric can be defined. The innovative idea of this paper is that we obtain some convergence properties of a C-metric space in the absence of a metric. In this paper we investigate C-metric spaces. The paper is divided into four sections. Section 1 is for Introduction. In Section 2 we recall some concepts and preliminary results. In Section 3 we present some properties of C-metric spaces, such as convergence properties, a canonical decomposition and a C-fixed point theorem. Finally, in Section 4 some conclusions are highlighted.

Learning Low-Dimensional Metrics

OpenAIRE

Jain, Lalit; Mason, Blake; Nowak, Robert

2017-01-01

This paper investigates the theoretical foundations of metric learning, focused on three key questions that are not fully addressed in prior work: 1) we consider learning general low-dimensional (low-rank) metrics as well as sparse metrics; 2) we develop upper and lower (minimax)bounds on the generalization error; 3) we quantify the sample complexity of metric learning in terms of the dimension of the feature space and the dimension/rank of the underlying metric;4) we also bound the accuracy ...

Gravitomagnetic bending angle of light with finite-distance corrections in stationary axisymmetric spacetimes

Science.gov (United States)

Ono, Toshiaki; Ishihara, Asahi; Asada, Hideki

2017-11-01

By using the Gauss-Bonnet theorem, the bending angle of light in a static, spherically symmetric and asymptotically flat spacetime has been recently discussed, especially by taking account of the finite distance from a lens object to a light source and a receiver [Ishihara, Suzuki, Ono, Asada, Phys. Rev. D 95, 044017 (2017), 10.1103/PhysRevD.95.044017]. We discuss a possible extension of the method of calculating the bending angle of light to stationary, axisymmetric and asymptotically flat spacetimes. For this purpose, we consider the light rays on the equatorial plane in the axisymmetric spacetime. We introduce a spatial metric to define the bending angle of light in the finite-distance situation. We show that the proposed bending angle of light is coordinate-invariant by using the Gauss-Bonnet theorem. The nonvanishing geodesic curvature of the photon orbit with the spatial metric is caused in gravitomagnetism, even though the light ray in the four-dimensional spacetime follows the null geodesic. Finally, we consider Kerr spacetime as an example in order to examine how the bending angle of light is computed by the present method. The finite-distance correction to the gravitomagnetic deflection angle due to the Sun's spin is around a pico-arcsecond level. The finite-distance corrections for Sgr A* also are estimated to be very small. Therefore, the gravitomagnetic finite-distance corrections for these objects are unlikely to be observed with present technology.

Lipschitz Metrics for a Class of Nonlinear Wave Equations

Science.gov (United States)

Bressan, Alberto; Chen, Geng

2017-12-01

The nonlinear wave equation {u_{tt}-c(u)(c(u)u_x)_x=0} determines a flow of conservative solutions taking values in the space {H^1(R)}. However, this flow is not continuous with respect to the natural H 1 distance. The aim of this paper is to construct a new metric which renders the flow uniformly Lipschitz continuous on bounded subsets of {H^1(R)}. For this purpose, H 1 is given the structure of a Finsler manifold, where the norm of tangent vectors is defined in terms of an optimal transportation problem. For paths of piecewise smooth solutions, one can carefully estimate how the weighted length grows in time. By the generic regularity result proved in [7], these piecewise regular paths are dense and can be used to construct a geodesic distance with the desired Lipschitz property.

The effects of alignment quality, distance calculation method, sequence filtering, and region on the analysis of 16S rRNA gene-based studies.

Directory of Open Access Journals (Sweden)

Patrick D Schloss

Full Text Available Pyrosequencing of PCR-amplified fragments that target variable regions within the 16S rRNA gene has quickly become a powerful method for analyzing the membership and structure of microbial communities. This approach has revealed and introduced questions that were not fully appreciated by those carrying out traditional Sanger sequencing-based methods. These include the effects of alignment quality, the best method of calculating pairwise genetic distances for 16S rRNA genes, whether it is appropriate to filter variable regions, and how the choice of variable region relates to the genetic diversity observed in full-length sequences. I used a diverse collection of 13,501 high-quality full-length sequences to assess each of these questions. First, alignment quality had a significant impact on distance values and downstream analyses. Specifically, the greengenes alignment, which does a poor job of aligning variable regions, predicted higher genetic diversity, richness, and phylogenetic diversity than the SILVA and RDP-based alignments. Second, the effect of different gap treatments in determining pairwise genetic distances was strongly affected by the variation in sequence length for a region; however, the effect of different calculation methods was subtle when determining the sample's richness or phylogenetic diversity for a region. Third, applying a sequence mask to remove variable positions had a profound impact on genetic distances by muting the observed richness and phylogenetic diversity. Finally, the genetic distances calculated for each of the variable regions did a poor job of correlating with the full-length gene. Thus, while it is tempting to apply traditional cutoff levels derived for full-length sequences to these shorter sequences, it is not advisable. Analysis of beta-diversity metrics showed that each of these factors can have a significant impact on the comparison of community membership and structure. Taken together, these results

Tide or Tsunami? The Impact of Metrics on Scholarly Research

Science.gov (United States)

Bonnell, Andrew G.

2016-01-01

Australian universities are increasingly resorting to the use of journal metrics such as impact factors and ranking lists in appraisal and promotion processes, and are starting to set quantitative "performance expectations" which make use of such journal-based metrics. The widespread use and misuse of research metrics is leading to…

a tensor theory of gravitation in a curved metric on a flat background

International Nuclear Information System (INIS)

Drummond, J.E.

1979-01-01

A theory of gravity is proposed using a tensor potential for the field on a flat metric. This potential cannot be isolated by local observations, but some details can be deduced from measurements at a distance. The requirement that the field equations for the tensor potential shall be deducible from an action integral, that the action and field equations are gauge invariant, and, conversely, that the Lagrangian in the action integral can be integrated from the field equations leads to Einstein's field equations. The requirement that the field energy-momentum tensor exists leads to a constraint on the tensor potential. If the constraint is a differential gauge condition, then it can only be the Hilbert condition giving a unique background tensor, metric tensor and tensor potential. For a continuous field inside a solid sphere the metric must be homogeneous in the spatial coordinates, and the associated field energy-momentum tensor has properties consistent with Newtonian dynamics. (author)

A guide to calculating habitat-quality metrics to inform conservation of highly mobile species

Science.gov (United States)

Bieri, Joanna A.; Sample, Christine; Thogmartin, Wayne E.; Diffendorfer, James E.; Earl, Julia E.; Erickson, Richard A.; Federico, Paula; Flockhart, D. T. Tyler; Nicol, Sam; Semmens, Darius J.; Skraber, T.; Wiederholt, Ruscena; Mattsson, Brady J.

2018-01-01

Many metrics exist for quantifying the relative value of habitats and pathways used by highly mobile species. Properly selecting and applying such metrics requires substantial background in mathematics and understanding the relevant management arena. To address this multidimensional challenge, we demonstrate and compare three measurements of habitat quality: graph-, occupancy-, and demographic-based metrics. Each metric provides insights into system dynamics, at the expense of increasing amounts and complexity of data and models. Our descriptions and comparisons of diverse habitat-quality metrics provide means for practitioners to overcome the modeling challenges associated with management or conservation of such highly mobile species. Whereas previous guidance for applying habitat-quality metrics has been scattered in diversified tracks of literature, we have brought this information together into an approachable format including accessible descriptions and a modeling case study for a typical example that conservation professionals can adapt for their own decision contexts and focal populations.Considerations for Resource ManagersManagement objectives, proposed actions, data availability and quality, and model assumptions are all relevant considerations when applying and interpreting habitat-quality metrics.Graph-based metrics answer questions related to habitat centrality and connectivity, are suitable for populations with any movement pattern, quantify basic spatial and temporal patterns of occupancy and movement, and require the least data.Occupancy-based metrics answer questions about likelihood of persistence or colonization, are suitable for populations that undergo localized extinctions, quantify spatial and temporal patterns of occupancy and movement, and require a moderate amount of data.Demographic-based metrics answer questions about relative or absolute population size, are suitable for populations with any movement pattern, quantify demographic

The Hidden Flow Structure and Metric Space of Network Embedding Algorithms Based on Random Walks.

Science.gov (United States)

Gu, Weiwei; Gong, Li; Lou, Xiaodan; Zhang, Jiang

2017-10-13

Network embedding which encodes all vertices in a network as a set of numerical vectors in accordance with it's local and global structures, has drawn widespread attention. Network embedding not only learns significant features of a network, such as the clustering and linking prediction but also learns the latent vector representation of the nodes which provides theoretical support for a variety of applications, such as visualization, link prediction, node classification, and recommendation. As the latest progress of the research, several algorithms based on random walks have been devised. Although those algorithms have drawn much attention for their high scores in learning efficiency and accuracy, there is still a lack of theoretical explanation, and the transparency of those algorithms has been doubted. Here, we propose an approach based on the open-flow network model to reveal the underlying flow structure and its hidden metric space of different random walk strategies on networks. We show that the essence of embedding based on random walks is the latent metric structure defined on the open-flow network. This not only deepens our understanding of random- walk-based embedding algorithms but also helps in finding new potential applications in network embedding.

Scalar-metric and scalar-metric-torsion gravitational theories

International Nuclear Information System (INIS)

Aldersley, S.J.

1977-01-01

The techniques of dimensional analysis and of the theory of tensorial concomitants are employed to study field equations in gravitational theories which incorporate scalar fields of the Brans-Dicke type. Within the context of scalar-metric gravitational theories, a uniqueness theorem for the geometric (or gravitational) part of the field equations is proven and a Lagrangian is determined which is uniquely specified by dimensional analysis. Within the context of scalar-metric-torsion gravitational theories a uniqueness theorem for field Lagrangians is presented and the corresponding Euler-Lagrange equations are given. Finally, an example of a scalar-metric-torsion theory is presented which is similar in many respects to the Brans-Dicke theory and the Einstein-Cartan theory

Metrics of quantum states

International Nuclear Information System (INIS)

Ma Zhihao; Chen Jingling

2011-01-01

In this work we study metrics of quantum states, which are natural generalizations of the usual trace metric and Bures metric. Some useful properties of the metrics are proved, such as the joint convexity and contractivity under quantum operations. Our result has a potential application in studying the geometry of quantum states as well as the entanglement detection.

High Precision Infrared Temperature Measurement System Based on Distance Compensation

Directory of Open Access Journals (Sweden)

Chen Jing

2017-01-01

Full Text Available To meet the need of real-time remote monitoring of human body surface temperature for optical rehabilitation therapy, a non-contact high-precision real-time temperature measurement method based on distance compensation was proposed, and the system design was carried out. The microcontroller controls the infrared temperature measurement module and the laser range module to collect temperature and distance data. The compensation formula of temperature with distance wass fitted according to the least square method. Testing had been performed on different individuals to verify the accuracy of the system. The results indicate that the designed non-contact infrared temperature measurement system has a residual error of less than 0.2°C and the response time isless than 0.1s in the range of 0 to 60cm. This provides a reference for developing long-distance temperature measurement equipment in optical rehabilitation therapy.

A heuristic way of obtaining the Kerr metric

International Nuclear Information System (INIS)

Enderlein, J.

1997-01-01

An intuitive, straightforward way of finding the metric of a rotating black hole is presented, based on the algebra of differential forms. The representation obtained for the metric displays a simplicity which is not obvious in the usual Boyer Lindquist coordinates. copyright 1997 American Association of Physics Teachers

Healthcare4VideoStorm: Making Smart Decisions Based on Storm Metrics.

Science.gov (United States)

Zhang, Weishan; Duan, Pengcheng; Chen, Xiufeng; Lu, Qinghua

2016-04-23

Storm-based stream processing is widely used for real-time large-scale distributed processing. Knowing the run-time status and ensuring performance is critical to providing expected dependability for some applications, e.g., continuous video processing for security surveillance. The existing scheduling strategies' granularity is too coarse to have good performance, and mainly considers network resources without computing resources while scheduling. In this paper, we propose Healthcare4Storm, a framework that finds Storm insights based on Storm metrics to gain knowledge from the health status of an application, finally ending up with smart scheduling decisions. It takes into account both network and computing resources and conducts scheduling at a fine-grained level using tuples instead of topologies. The comprehensive evaluation shows that the proposed framework has good performance and can improve the dependability of the Storm-based applications.

Parameter Search Algorithms for Microwave Radar-Based Breast Imaging: Focal Quality Metrics as Fitness Functions.

Science.gov (United States)

O'Loughlin, Declan; Oliveira, Bárbara L; Elahi, Muhammad Adnan; Glavin, Martin; Jones, Edward; Popović, Milica; O'Halloran, Martin

2017-12-06

Inaccurate estimation of average dielectric properties can have a tangible impact on microwave radar-based breast images. Despite this, recent patient imaging studies have used a fixed estimate although this is known to vary from patient to patient. Parameter search algorithms are a promising technique for estimating the average dielectric properties from the reconstructed microwave images themselves without additional hardware. In this work, qualities of accurately reconstructed images are identified from point spread functions. As the qualities of accurately reconstructed microwave images are similar to the qualities of focused microscopic and photographic images, this work proposes the use of focal quality metrics for average dielectric property estimation. The robustness of the parameter search is evaluated using experimental dielectrically heterogeneous phantoms on the three-dimensional volumetric image. Based on a very broad initial estimate of the average dielectric properties, this paper shows how these metrics can be used as suitable fitness functions in parameter search algorithms to reconstruct clear and focused microwave radar images.

Content-based image retrieval using spatial layout information in brain tumor T1-weighted contrast-enhanced MR images.

Science.gov (United States)

Huang, Meiyan; Yang, Wei; Wu, Yao; Jiang, Jun; Gao, Yang; Chen, Yang; Feng, Qianjin; Chen, Wufan; Lu, Zhentai

2014-01-01

This study aims to develop content-based image retrieval (CBIR) system for the retrieval of T1-weighted contrast-enhanced MR (CE-MR) images of brain tumors. When a tumor region is fed to the CBIR system as a query, the system attempts to retrieve tumors of the same pathological category. The bag-of-visual-words (BoVW) model with partition learning is incorporated into the system to extract informative features for representing the image contents. Furthermore, a distance metric learning algorithm called the Rank Error-based Metric Learning (REML) is proposed to reduce the semantic gap between low-level visual features and high-level semantic concepts. The effectiveness of the proposed method is evaluated on a brain T1-weighted CE-MR dataset with three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor). Using the BoVW model with partition learning, the mean average precision (mAP) of retrieval increases beyond 4.6% with the learned distance metrics compared with the spatial pyramid BoVW method. The distance metric learned by REML significantly outperforms three other existing distance metric learning methods in terms of mAP. The mAP of the CBIR system is as high as 91.8% using the proposed method, and the precision can reach 93.1% when the top 10 images are returned by the system. These preliminary results demonstrate that the proposed method is effective and feasible for the retrieval of brain tumors in T1-weighted CE-MR Images.

Content-based image retrieval using spatial layout information in brain tumor T1-weighted contrast-enhanced MR images.

Directory of Open Access Journals (Sweden)

Meiyan Huang

Full Text Available This study aims to develop content-based image retrieval (CBIR system for the retrieval of T1-weighted contrast-enhanced MR (CE-MR images of brain tumors. When a tumor region is fed to the CBIR system as a query, the system attempts to retrieve tumors of the same pathological category. The bag-of-visual-words (BoVW model with partition learning is incorporated into the system to extract informative features for representing the image contents. Furthermore, a distance metric learning algorithm called the Rank Error-based Metric Learning (REML is proposed to reduce the semantic gap between low-level visual features and high-level semantic concepts. The effectiveness of the proposed method is evaluated on a brain T1-weighted CE-MR dataset with three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor. Using the BoVW model with partition learning, the mean average precision (mAP of retrieval increases beyond 4.6% with the learned distance metrics compared with the spatial pyramid BoVW method. The distance metric learned by REML significantly outperforms three other existing distance metric learning methods in terms of mAP. The mAP of the CBIR system is as high as 91.8% using the proposed method, and the precision can reach 93.1% when the top 10 images are returned by the system. These preliminary results demonstrate that the proposed method is effective and feasible for the retrieval of brain tumors in T1-weighted CE-MR Images.

An Artificial Intelligence-Based Distance Education System: Artimat

Science.gov (United States)

Nabiyev, Vasif; Karal, Hasan; Arslan, Selahattin; Erumit, Ali Kursat; Cebi, Ayca

2013-01-01

The purpose of this study is to evaluate the artificial intelligence-based distance education system called ARTIMAT, which has been prepared in order to improve mathematical problem solving skills of the students, in terms of conceptual proficiency and ease of use with the opinions of teachers and students. The implementation has been performed…

Prototypic Development and Evaluation of a Medium Format Metric Camera

Science.gov (United States)

Hastedt, H.; Rofallski, R.; Luhmann, T.; Rosenbauer, R.; Ochsner, D.; Rieke-Zapp, D.

2018-05-01

Engineering applications require high-precision 3D measurement techniques for object sizes that vary between small volumes (2-3 m in each direction) and large volumes (around 20 x 20 x 1-10 m). The requested precision in object space (1σ RMS) is defined to be within 0.1-0.2 mm for large volumes and less than 0.01 mm for small volumes. In particular, focussing large volume applications the availability of a metric camera would have different advantages for several reasons: 1) high-quality optical components and stabilisations allow for a stable interior geometry of the camera itself, 2) a stable geometry leads to a stable interior orientation that enables for an a priori camera calibration, 3) a higher resulting precision can be expected. With this article the development and accuracy evaluation of a new metric camera, the ALPA 12 FPS add|metric will be presented. Its general accuracy potential is tested against calibrated lengths in a small volume test environment based on the German Guideline VDI/VDE 2634.1 (2002). Maximum length measurement errors of less than 0.025 mm are achieved with different scenarios having been tested. The accuracy potential for large volumes is estimated within a feasibility study on the application of photogrammetric measurements for the deformation estimation on a large wooden shipwreck in the German Maritime Museum. An accuracy of 0.2 mm-0.4 mm is reached for a length of 28 m (given by a distance from a lasertracker network measurement). All analyses have proven high stabilities of the interior orientation of the camera and indicate the applicability for a priori camera calibration for subsequent 3D measurements.

PROTOTYPIC DEVELOPMENT AND EVALUATION OF A MEDIUM FORMAT METRIC CAMERA

Directory of Open Access Journals (Sweden)

H. Hastedt

2018-05-01

Full Text Available Engineering applications require high-precision 3D measurement techniques for object sizes that vary between small volumes (2–3 m in each direction and large volumes (around 20 x 20 x 1–10 m. The requested precision in object space (1σ RMS is defined to be within 0.1–0.2 mm for large volumes and less than 0.01 mm for small volumes. In particular, focussing large volume applications the availability of a metric camera would have different advantages for several reasons: 1 high-quality optical components and stabilisations allow for a stable interior geometry of the camera itself, 2 a stable geometry leads to a stable interior orientation that enables for an a priori camera calibration, 3 a higher resulting precision can be expected. With this article the development and accuracy evaluation of a new metric camera, the ALPA 12 FPS add|metric will be presented. Its general accuracy potential is tested against calibrated lengths in a small volume test environment based on the German Guideline VDI/VDE 2634.1 (2002. Maximum length measurement errors of less than 0.025 mm are achieved with different scenarios having been tested. The accuracy potential for large volumes is estimated within a feasibility study on the application of photogrammetric measurements for the deformation estimation on a large wooden shipwreck in the German Maritime Museum. An accuracy of 0.2 mm–0.4 mm is reached for a length of 28 m (given by a distance from a lasertracker network measurement. All analyses have proven high stabilities of the interior orientation of the camera and indicate the applicability for a priori camera calibration for subsequent 3D measurements.

Rapporteur Report: Sources and Exposure Metrics for RF Epidemiology (Part 1) (invited paper)

Energy Technology Data Exchange (ETDEWEB)

Allen, S

1999-07-01

A variety of sources was considered illustrating the predominantly uniform exposure at a distance and highly non-uniform exposures close to sources. The measurement of both electric and magnetic fields was considered for near field situations but where possible the use of induced body currents was judged to be the preferred metric. The salient features affecting exposure to fields from mobile telephones and their base stations were discussed for both existing and third generation systems. As an aid to future cancer studies, high resolution numerical modelling was used to illustrate left/right exposure discrimination of bilateral organs in the head. Factors influencing both numerical and experimental dosimetry were discussed and studies to investigate the ability to appropriately rank exposure were considered important areas for research. (author)

Rapporteur Report: Sources and Exposure Metrics for RF Epidemiology (Part 1) (invited paper)

International Nuclear Information System (INIS)

Allen, S.

1999-01-01

A variety of sources was considered illustrating the predominantly uniform exposure at a distance and highly non-uniform exposures close to sources. The measurement of both electric and magnetic fields was considered for near field situations but where possible the use of induced body currents was judged to be the preferred metric. The salient features affecting exposure to fields from mobile telephones and their base stations were discussed for both existing and third generation systems. As an aid to future cancer studies, high resolution numerical modelling was used to illustrate left/right exposure discrimination of bilateral organs in the head. Factors influencing both numerical and experimental dosimetry were discussed and studies to investigate the ability to appropriately rank exposure were considered important areas for research. (author)

Preparedness of NGO Health Service Providers in Bangladesh about Distance Based Learning

Directory of Open Access Journals (Sweden)

AKM ALAMGIR

2006-07-01

Full Text Available This cross-sectional survey was conducted countrywide from 15 January to 01 March 2004 to explore the potentials of health care service providers (physicians, nurses, paramedics etc. for using distance-based learning materials. Face-to-face in-depth interview was taken from 99 randomly selected direct service providers, 45 midlevel clinic mangers/physicians and 06 administrators or policy planners. Quasi-open questionnaire was developed for three different levels. Pre-trained interviewer team assisted data collection at field level. Total procedure was stringently monitored for completeness and consistency to ensure quality data. SPSS software was used to process and analyze both univariate and multivariate multiple responses. Identified need for training areas were- STD/HIV, tuberculosis updates, family planning, treatment of locally endemic diseases, behavioral change communication & marketing and quality management system for managers. About 76.7% clinic managers and 89.1% service providers had primary information about distance-based learning in spite showed interest. About 51.5% desired monthly, 20.6% biweekly and 26.8% wanted bimonthly circulation of the distance-based study materials. About 35.1% expected print materials with regular facilitators while 58.8% demanded stand-by facilitators. The study suggested wide acceptance of distance-based learning methods as supplementary to the continuing medical education among the countrywide health service providers.

An improved initialization center k-means clustering algorithm based on distance and density

Science.gov (United States)

Duan, Yanling; Liu, Qun; Xia, Shuyin

2018-04-01

Aiming at the problem of the random initial clustering center of k means algorithm that the clustering results are influenced by outlier data sample and are unstable in multiple clustering, a method of central point initialization method based on larger distance and higher density is proposed. The reciprocal of the weighted average of distance is used to represent the sample density, and the data sample with the larger distance and the higher density are selected as the initial clustering centers to optimize the clustering results. Then, a clustering evaluation method based on distance and density is designed to verify the feasibility of the algorithm and the practicality, the experimental results on UCI data sets show that the algorithm has a certain stability and practicality.

Observable traces of non-metricity: New constraints on metric-affine gravity

Science.gov (United States)

Delhom-Latorre, Adrià; Olmo, Gonzalo J.; Ronco, Michele

2018-05-01

Relaxing the Riemannian condition to incorporate geometric quantities such as torsion and non-metricity may allow to explore new physics associated with defects in a hypothetical space-time microstructure. Here we show that non-metricity produces observable effects in quantum fields in the form of 4-fermion contact interactions, thereby allowing us to constrain the scale of non-metricity to be greater than 1 TeV by using results on Bahbah scattering. Our analysis is carried out in the framework of a wide class of theories of gravity in the metric-affine approach. The bound obtained represents an improvement of several orders of magnitude to previous experimental constraints.

Distance-based relative orbital elements determination for formation flying system

Science.gov (United States)

He, Yanchao; Xu, Ming; Chen, Xi

2016-01-01

The present paper deals with determination of relative orbital elements based only on distance between satellites in the formation flying system, which has potential application in engineering, especially suited for rapid orbit determination required missions. A geometric simplification is performed to reduce the formation configuration in three-dimensional space to a plane. Then the equivalent actual configuration deviating from its nominal design is introduced to derive a group of autonomous linear equations on the mapping between the relative orbital elements differences and distance errors. The primary linear equations-based algorithm is initially proposed to conduct the rapid and precise determination of the relative orbital elements without the complex computation, which is further improved by least-squares method with more distance measurements taken into consideration. Numerical simulations and comparisons with traditional approaches are presented to validate the effectiveness of the proposed methods. To assess the performance of the two proposed algorithms, accuracy validation and Monte Carlo simulations are implemented in the presence of noises of distance measurements and the leader's absolute orbital elements. It is demonstrated that the relative orbital elements determination accuracy of two approaches reaches more than 90% and even close to the actual values for the least-squares improved one. The proposed approaches can be alternates for relative orbit determination without assistance of additional facilities in engineering for their fairly high efficiency with accuracy and autonomy.

Application of Entropy-Based Metrics to Identify Emotional Distress from Electroencephalographic Recordings

Directory of Open Access Journals (Sweden)

Beatriz García-Martínez

2016-06-01

Full Text Available Recognition of emotions is still an unresolved challenge, which could be helpful to improve current human-machine interfaces. Recently, nonlinear analysis of some physiological signals has shown to play a more relevant role in this context than their traditional linear exploration. Thus, the present work introduces for the first time the application of three recent entropy-based metrics: sample entropy (SE, quadratic SE (QSE and distribution entropy (DE to discern between emotional states of calm and negative stress (also called distress. In the last few years, distress has received growing attention because it is a common negative factor in the modern lifestyle of people from developed countries and, moreover, it may lead to serious mental and physical health problems. Precisely, 279 segments of 32-channel electroencephalographic (EEG recordings from 32 subjects elicited to be calm or negatively stressed have been analyzed. Results provide that QSE is the first single metric presented to date with the ability to identify negative stress. Indeed, this metric has reported a discriminant ability of around 70%, which is only slightly lower than the one obtained by some previous works. Nonetheless, discriminant models from dozens or even hundreds of features have been previously obtained by using advanced classifiers to yield diagnostic accuracies about 80%. Moreover, in agreement with previous neuroanatomy findings, QSE has also revealed notable differences for all the brain regions in the neural activation triggered by the two considered emotions. Consequently, given these results, as well as easy interpretation of QSE, this work opens a new standpoint in the detection of emotional distress, which may gain new insights about the brain’s behavior under this negative emotion.

Evaluating a stochastic parametrization for a fast–slow system using the Wasserstein distance

Directory of Open Access Journals (Sweden)

G. Vissio

2018-06-01

Full Text Available Constructing accurate, flexible, and efficient parametrizations is one of the great challenges in the numerical modeling of geophysical fluids. We consider here the simple yet paradigmatic case of a Lorenz 84 model forced by a Lorenz 63 model and derive a parametrization using a recently developed statistical mechanical methodology based on the Ruelle response theory. We derive an expression for the deterministic and the stochastic component of the parametrization and we show that the approach allows for dealing seamlessly with the case of the Lorenz 63 being a fast as well as a slow forcing compared to the characteristic timescales of the Lorenz 84 model. We test our results using both standard metrics based on the moments of the variables of interest as well as Wasserstein distance between the projected measure of the original system on the Lorenz 84 model variables and the measure of the parametrized one. By testing our methods on reduced-phase spaces obtained by projection, we find support for the idea that comparisons based on the Wasserstein distance might be of relevance in many applications despite the curse of dimensionality.

Validation of Metrics as Error Predictors

Science.gov (United States)

Mendling, Jan

In this chapter, we test the validity of metrics that were defined in the previous chapter for predicting errors in EPC business process models. In Section 5.1, we provide an overview of how the analysis data is generated. Section 5.2 describes the sample of EPCs from practice that we use for the analysis. Here we discuss a disaggregation by the EPC model group and by error as well as a correlation analysis between metrics and error. Based on this sample, we calculate a logistic regression model for predicting error probability with the metrics as input variables in Section 5.3. In Section 5.4, we then test the regression function for an independent sample of EPC models from textbooks as a cross-validation. Section 5.5 summarizes the findings.

A Complete Quantitative Deduction System for the Bisimilarity Distance on Markov Chains

DEFF Research Database (Denmark)

Bacci, Giovanni; Bacci, Giorgio; Larsen, Kim Guldstrand

2017-01-01

In this paper we propose a complete axiomatization of the bisimilarity distance of Desharnais et al. for the class of finite labelled Markov chains. Our axiomatization is given in the style of a quantitative extension of equational logic recently proposed by Mardare, Panangaden, and Plotkin (LICS...... an axiom for dealing with the Kantorovich distance between probability distributions. The axiomatization is then used to propose a metric extension of a Kleene's style representation theorem for finite labelled Markov chains, that was proposed (in a more general coalgebraic fashion) by Silva et al. (Inf...

Evaluating and Estimating the WCET Criticality Metric

DEFF Research Database (Denmark)

Jordan, Alexander

2014-01-01

a programmer (or compiler) from targeting optimizations the right way. A possible resort is to use a metric that targets WCET and which can be efficiently computed for all code parts of a program. Similar to dynamic profiling techniques, which execute code with input that is typically expected...... for the application, based on WCET analysis we can indicate how critical a code fragment is, in relation to the worst-case bound. Computing such a metric on top of static analysis, incurs a certain overhead though, which increases with the complexity of the underlying WCET analysis. We present our approach...... to estimate the Criticality metric, by relaxing the precision of WCET analysis. Through this, we can reduce analysis time by orders of magnitude, while only introducing minor error. To evaluate our estimation approach and share our garnered experience using the metric, we evaluate real-time programs, which...

Designing Industrial Networks Using Ecological Food Web Metrics.

Science.gov (United States)

Layton, Astrid; Bras, Bert; Weissburg, Marc

2016-10-18

Biologically Inspired Design (biomimicry) and Industrial Ecology both look to natural systems to enhance the sustainability and performance of engineered products, systems and industries. Bioinspired design (BID) traditionally has focused on a unit operation and single product level. In contrast, this paper describes how principles of network organization derived from analysis of ecosystem properties can be applied to industrial system networks. Specifically, this paper examines the applicability of particular food web matrix properties as design rules for economically and biologically sustainable industrial networks, using an optimization model developed for a carpet recycling network. Carpet recycling network designs based on traditional cost and emissions based optimization are compared to designs obtained using optimizations based solely on ecological food web metrics. The analysis suggests that networks optimized using food web metrics also were superior from a traditional cost and emissions perspective; correlations between optimization using ecological metrics and traditional optimization ranged generally from 0.70 to 0.96, with flow-based metrics being superior to structural parameters. Four structural food parameters provided correlations nearly the same as that obtained using all structural parameters, but individual structural parameters provided much less satisfactory correlations. The analysis indicates that bioinspired design principles from ecosystems can lead to both environmentally and economically sustainable industrial resource networks, and represent guidelines for designing sustainable industry networks.

How accurately can the peak skin dose in fluoroscopy be determined using indirect dose metrics?

International Nuclear Information System (INIS)

Jones, A. Kyle; Ensor, Joe E.; Pasciak, Alexander S.

2014-01-01

Purpose: Skin dosimetry is important for fluoroscopically-guided interventions, as peak skin doses (PSD) that result in skin reactions can be reached during these procedures. There is no consensus as to whether or not indirect skin dosimetry is sufficiently accurate for fluoroscopically-guided interventions. However, measuring PSD with film is difficult and the decision to do so must be madea priori. The purpose of this study was to assess the accuracy of different types of indirect dose estimates and to determine if PSD can be calculated within ±50% using indirect dose metrics for embolization procedures. Methods: PSD were measured directly using radiochromic film for 41 consecutive embolization procedures at two sites. Indirect dose metrics from the procedures were collected, including reference air kerma. Four different estimates of PSD were calculated from the indirect dose metrics and compared along with reference air kerma to the measured PSD for each case. The four indirect estimates included a standard calculation method, the use of detailed information from the radiation dose structured report, and two simplified calculation methods based on the standard method. Indirect dosimetry results were compared with direct measurements, including an analysis of uncertainty associated with film dosimetry. Factors affecting the accuracy of the different indirect estimates were examined. Results: When using the standard calculation method, calculated PSD were within ±35% for all 41 procedures studied. Calculated PSD were within ±50% for a simplified method using a single source-to-patient distance for all calculations. Reference air kerma was within ±50% for all but one procedure. Cases for which reference air kerma or calculated PSD exhibited large (±35%) differences from the measured PSD were analyzed, and two main causative factors were identified: unusually small or large source-to-patient distances and large contributions to reference air kerma from cone

Joining of Ukraine to the European scientific and metric systems

Directory of Open Access Journals (Sweden)

O.M. Sazonets

2015-09-01

Full Text Available At the present stage of development it is necessary to form the knowledge which structures knowledge as the object of management. In conditions of technological globalism there are structural changes in the information environment of countries. Scientific metrics is sufficiently developed in other countries, especially in the EU. The article contains the description of the first index calculation system of scientific references called Science Citation Index (SCI. The main advantage of this project was searching for information not only by the author and thematic categories, but also by the list of cited literature. The authors define the scientific and metric base in the following way: scientific and metric database (SMBD is the bibliographic and abstract database with the tools for tracking citations of articles published in scientific journals. The most prominent European scientific and metric bases are examined. The authors show that the bases have the performance assessment tools which track down the impact of scientific papers and publications of individual scientists and research institutions. The state of crisis in scientific and technological activities in Ukraine as well as the economy as a whole, needs immediate organization of national scientific and metric system.

A novel three-stage distance-based consensus ranking method

Science.gov (United States)

Aghayi, Nazila; Tavana, Madjid

2018-05-01

In this study, we propose a three-stage weighted sum method for identifying the group ranks of alternatives. In the first stage, a rank matrix, similar to the cross-efficiency matrix, is obtained by computing the individual rank position of each alternative based on importance weights. In the second stage, a secondary goal is defined to limit the vector of weights since the vector of weights obtained in the first stage is not unique. Finally, in the third stage, the group rank position of alternatives is obtained based on a distance of individual rank positions. The third stage determines a consensus solution for the group so that the ranks obtained have a minimum distance from the ranks acquired by each alternative in the previous stage. A numerical example is presented to demonstrate the applicability and exhibit the efficacy of the proposed method and algorithms.

Metric reconstruction from Weyl scalars

Energy Technology Data Exchange (ETDEWEB)

Whiting, Bernard F; Price, Larry R [Department of Physics, PO Box 118440, University of Florida, Gainesville, FL 32611 (United States)

2005-08-07

The Kerr geometry has remained an elusive world in which to explore physics and delve into the more esoteric implications of general relativity. Following the discovery, by Kerr in 1963, of the metric for a rotating black hole, the most major advance has been an understanding of its Weyl curvature perturbations based on Teukolsky's discovery of separable wave equations some ten years later. In the current research climate, where experiments across the globe are preparing for the first detection of gravitational waves, a more complete understanding than concerns just the Weyl curvature is now called for. To understand precisely how comparatively small masses move in response to the gravitational waves they emit, a formalism has been developed based on a description of the whole spacetime metric perturbation in the neighbourhood of the emission region. Presently, such a description is not available for the Kerr geometry. While there does exist a prescription for obtaining metric perturbations once curvature perturbations are known, it has become apparent that there are gaps in that formalism which are still waiting to be filled. The most serious gaps include gauge inflexibility, the inability to include sources-which are essential when the emitting masses are considered-and the failure to describe the l = 0 and 1 perturbation properties. Among these latter properties of the perturbed spacetime, arising from a point mass in orbit, are the perturbed mass and axial component of angular momentum, as well as the very elusive Carter constant for non-axial angular momentum. A status report is given on recent work which begins to repair these deficiencies in our current incomplete description of Kerr metric perturbations.

Metric reconstruction from Weyl scalars

International Nuclear Information System (INIS)

Whiting, Bernard F; Price, Larry R

2005-01-01

The Kerr geometry has remained an elusive world in which to explore physics and delve into the more esoteric implications of general relativity. Following the discovery, by Kerr in 1963, of the metric for a rotating black hole, the most major advance has been an understanding of its Weyl curvature perturbations based on Teukolsky's discovery of separable wave equations some ten years later. In the current research climate, where experiments across the globe are preparing for the first detection of gravitational waves, a more complete understanding than concerns just the Weyl curvature is now called for. To understand precisely how comparatively small masses move in response to the gravitational waves they emit, a formalism has been developed based on a description of the whole spacetime metric perturbation in the neighbourhood of the emission region. Presently, such a description is not available for the Kerr geometry. While there does exist a prescription for obtaining metric perturbations once curvature perturbations are known, it has become apparent that there are gaps in that formalism which are still waiting to be filled. The most serious gaps include gauge inflexibility, the inability to include sources-which are essential when the emitting masses are considered-and the failure to describe the l = 0 and 1 perturbation properties. Among these latter properties of the perturbed spacetime, arising from a point mass in orbit, are the perturbed mass and axial component of angular momentum, as well as the very elusive Carter constant for non-axial angular momentum. A status report is given on recent work which begins to repair these deficiencies in our current incomplete description of Kerr metric perturbations

Analytic processing of distance.

Science.gov (United States)

Dopkins, Stephen; Galyer, Darin

2018-01-01

How does a human observer extract from the distance between two frontal points the component corresponding to an axis of a rectangular reference frame? To find out we had participants classify pairs of small circles, varying on the horizontal and vertical axes of a computer screen, in terms of the horizontal distance between them. A response signal controlled response time. The error rate depended on the irrelevant vertical as well as the relevant horizontal distance between the test circles with the relevant distance effect being larger than the irrelevant distance effect. The results implied that the horizontal distance between the test circles was imperfectly extracted from the overall distance between them. The results supported an account, derived from the Exemplar Based Random Walk model (Nosofsky & Palmieri, 1997), under which distance classification is based on the overall distance between the test circles, with relevant distance being extracted from overall distance to the extent that the relevant and irrelevant axes are differentially weighted so as to reduce the contribution of irrelevant distance to overall distance. The results did not support an account, derived from the General Recognition Theory (Ashby & Maddox, 1994), under which distance classification is based on the relevant distance between the test circles, with the irrelevant distance effect arising because a test circle's perceived location on the relevant axis depends on its location on the irrelevant axis, and with relevant distance being extracted from overall distance to the extent that this dependency is absent. Copyright © 2017 Elsevier B.V. All rights reserved.

A Validation of Object-Oriented Design Metrics as Quality Indicators

Science.gov (United States)

Basili, Victor R.; Briand, Lionel C.; Melo, Walcelio

1997-01-01

This paper presents the results of a study in which we empirically investigated the suits of object-oriented (00) design metrics introduced in another work. More specifically, our goal is to assess these metrics as predictors of fault-prone classes and, therefore, determine whether they can be used as early quality indicators. This study is complementary to the work described where the same suite of metrics had been used to assess frequencies of maintenance changes to classes. To perform our validation accurately, we collected data on the development of eight medium-sized information management systems based on identical requirements. All eight projects were developed using a sequential life cycle model, a well-known 00 analysis/design method and the C++ programming language. Based on empirical and quantitative analysis, the advantages and drawbacks of these 00 metrics are discussed. Several of Chidamber and Kamerer's 00 metrics appear to be useful to predict class fault-proneness during the early phases of the life-cycle. Also, on our data set, they are better predictors than 'traditional' code metrics, which can only be collected at a later phase of the software development processes.

Fault Management Metrics

Science.gov (United States)

Johnson, Stephen B.; Ghoshal, Sudipto; Haste, Deepak; Moore, Craig

2017-01-01

This paper describes the theory and considerations in the application of metrics to measure the effectiveness of fault management. Fault management refers here to the operational aspect of system health management, and as such is considered as a meta-control loop that operates to preserve or maximize the system's ability to achieve its goals in the face of current or prospective failure. As a suite of control loops, the metrics to estimate and measure the effectiveness of fault management are similar to those of classical control loops in being divided into two major classes: state estimation, and state control. State estimation metrics can be classified into lower-level subdivisions for detection coverage, detection effectiveness, fault isolation and fault identification (diagnostics), and failure prognosis. State control metrics can be classified into response determination effectiveness and response effectiveness. These metrics are applied to each and every fault management control loop in the system, for each failure to which they apply, and probabilistically summed to determine the effectiveness of these fault management control loops to preserve the relevant system goals that they are intended to protect.

Completion of a Dislocated Metric Space

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P. Sumati Kumari

2015-01-01

Full Text Available We provide a construction for the completion of a dislocated metric space (abbreviated d-metric space; we also prove that the completion of the metric associated with a d-metric coincides with the metric associated with the completion of the d-metric.

Development of a clinician reputation metric to identify appropriate problem-medication pairs in a crowdsourced knowledge base.

Science.gov (United States)

McCoy, Allison B; Wright, Adam; Rogith, Deevakar; Fathiamini, Safa; Ottenbacher, Allison J; Sittig, Dean F

2014-04-01

Correlation of data within electronic health records is necessary for implementation of various clinical decision support functions, including patient summarization. A key type of correlation is linking medications to clinical problems; while some databases of problem-medication links are available, they are not robust and depend on problems and medications being encoded in particular terminologies. Crowdsourcing represents one approach to generating robust knowledge bases across a variety of terminologies, but more sophisticated approaches are necessary to improve accuracy and reduce manual data review requirements. We sought to develop and evaluate a clinician reputation metric to facilitate the identification of appropriate problem-medication pairs through crowdsourcing without requiring extensive manual review. We retrieved medications from our clinical data warehouse that had been prescribed and manually linked to one or more problems by clinicians during e-prescribing between June 1, 2010 and May 31, 2011. We identified measures likely to be associated with the percentage of accurate problem-medication links made by clinicians. Using logistic regression, we created a metric for identifying clinicians who had made greater than or equal to 95% appropriate links. We evaluated the accuracy of the approach by comparing links made by those physicians identified as having appropriate links to a previously manually validated subset of problem-medication pairs. Of 867 clinicians who asserted a total of 237,748 problem-medication links during the study period, 125 had a reputation metric that predicted the percentage of appropriate links greater than or equal to 95%. These clinicians asserted a total of 2464 linked problem-medication pairs (983 distinct pairs). Compared to a previously validated set of problem-medication pairs, the reputation metric achieved a specificity of 99.5% and marginally improved the sensitivity of previously described knowledge bases. A

Swarm-based wayfinding support in open and distance learning

NARCIS (Netherlands)

Tattersall, Colin; Manderveld, Jocelyn; Van den Berg, Bert; Van Es, René; Janssen, José; Koper, Rob

2005-01-01

Please refer to the original source: Tattersall, C. Manderveld, J., Van den Berg, B., Van Es, R., Janssen, J., & Koper, R. (2005). Swarm-based wayfinding support in open and distance learning. In Alkhalifa, E.M. (Ed). Cognitively Informed Systems: Utilizing Practical Approaches to Enrich Information

Development of soil quality metrics using mycorrhizal fungi

Energy Technology Data Exchange (ETDEWEB)

Baar, J.

2010-07-01

Based on the Treaty on Biological Diversity of Rio de Janeiro in 1992 for maintaining and increasing biodiversity, several countries have started programmes monitoring soil quality and the above- and below ground biodiversity. Within the European Union, policy makers are working on legislation for soil protection and management. Therefore, indicators are needed to monitor the status of the soils and these indicators reflecting the soil quality, can be integrated in working standards or soil quality metrics. Soil micro-organisms, particularly arbuscular mycorrhizal fungi (AMF), are indicative of soil changes. These soil fungi live in symbiosis with the great majority of plants and are sensitive to changes in the physico-chemical conditions of the soil. The aim of this study was to investigate whether AMF are reliable and sensitive indicators for disturbances in the soils and can be used for the development of soil quality metrics. Also, it was studied whether soil quality metrics based on AMF meet requirements to applicability by users and policy makers. Ecological criterions were set for the development of soil quality metrics for different soils. Multiple root samples containing AMF from various locations in The Netherlands were analyzed. The results of the analyses were related to the defined criterions. This resulted in two soil quality metrics, one for sandy soils and a second one for clay soils, with six different categories ranging from very bad to very good. These soil quality metrics meet the majority of requirements for applicability and are potentially useful for the development of legislations for the protection of soil quality. (Author) 23 refs.

Novel Clustering Method Based on K-Medoids and Mobility Metric

Directory of Open Access Journals (Sweden)

Y. Hamzaoui

2018-06-01

Full Text Available The structure and constraint of MANETS influence negatively the performance of QoS, moreover the main routing protocols proposed generally operate in flat routing. Hence, this structure gives the bad results of QoS when the network becomes larger and denser. To solve this problem we use one of the most popular methods named clustering. The present paper comes within the frameworks of research to improve the QoS in MANETs. In this paper we propose a new algorithm of clustering based on the new mobility metric and K-Medoid to distribute the nodes into several clusters. Intuitively our algorithm can give good results in terms of stability of the cluster, and can also extend life time of cluster head.

A newly developed dispersal metric indicates the succession of benthic invertebrates in restored rivers.

Science.gov (United States)

Li, Fengqing; Sundermann, Andrea; Stoll, Stefan; Haase, Peter

2016-11-01

Dispersal capacity plays a fundamental role in the riverine benthic invertebrate colonization of new habitats that emerges following flash floods or restoration. However, an appropriate measure of dispersal capacity for benthic invertebrates is still lacking. The dispersal of benthic invertebrates occurs mainly during the aquatic (larval) and aerial (adult) life stages, and the dispersal of each stage can be further subdivided into active and passive modes. Based on these four possible dispersal modes, we first developed a metric (which is very similar to the well-known and widely used saprobic index) to estimate the dispersal capacity for 802 benthic invertebrate taxa by incorporating a weight for each mode. Second, we tested this metric using benthic invertebrate community data from a) 23 large restored river sites with substantial improvements of river bottom habitats dating back 1 to 10years, b) 23 unrestored sites very close to the restored sites, and c) 298 adjacent surrounding sites (mean±standard deviation: 13.0±9.5 per site) within a distance of up to 5km for each restored site in the low mountain and lowland areas of Germany. We hypothesize that our metric will reflect the temporal succession process of benthic invertebrate communities colonizing the restored sites, whereas no temporal changes are expected in the unrestored and surrounding sites. By applying our metric to these three river treatment categories, we found that the average dispersal capacity of benthic invertebrate communities in the restored sites significantly decreased in the early years following restoration, whereas there were no changes in either the unrestored or the surrounding sites. After all taxa had been divided into quartiles representing weak to strong dispersers, this pattern became even more obvious; strong dispersers colonized the restored sites during the first year after restoration and then significantly decreased over time, whereas weak dispersers continued to increase

Metrics with vanishing quantum corrections

International Nuclear Information System (INIS)

Coley, A A; Hervik, S; Gibbons, G W; Pope, C N

2008-01-01

We investigate solutions of the classical Einstein or supergravity equations that solve any set of quantum corrected Einstein equations in which the Einstein tensor plus a multiple of the metric is equated to a symmetric conserved tensor T μν (g αβ , ∂ τ g αβ , ∂ τ ∂ σ g αβ , ...,) constructed from sums of terms, the involving contractions of the metric and powers of arbitrary covariant derivatives of the curvature tensor. A classical solution, such as an Einstein metric, is called universal if, when evaluated on that Einstein metric, T μν is a multiple of the metric. A Ricci flat classical solution is called strongly universal if, when evaluated on that Ricci flat metric, T μν vanishes. It is well known that pp-waves in four spacetime dimensions are strongly universal. We focus attention on a natural generalization; Einstein metrics with holonomy Sim(n - 2) in which all scalar invariants are zero or constant. In four dimensions we demonstrate that the generalized Ghanam-Thompson metric is weakly universal and that the Goldberg-Kerr metric is strongly universal; indeed, we show that universality extends to all four-dimensional Sim(2) Einstein metrics. We also discuss generalizations to higher dimensions

Implementation of a microcomputer based distance relay for parallel transmission lines

International Nuclear Information System (INIS)

Phadke, A.G.; Jihuang, L.

1986-01-01

Distance relaying for parallel transmission lines is a difficult application problem with conventional phase and ground distance relays. It is known that for cross-country faults involving dissimilar phases and ground, three phase tripping may result. This paper summarizes a newly developed microcomputer based relay which is capable of classifying the cross-country fault correctly. The paper describes the principle of operation and results of laboratory tests of this relay

SOCIAL METRICS APPLIED TO SMART TOURISM

Directory of Open Access Journals (Sweden)

O. Cervantes

2016-09-01

Full Text Available We present a strategy to make productive use of semantically-related social data, from a user-centered semantic network, in order to help users (tourists and citizens in general to discover cultural heritage, points of interest and available services in a smart city. This data can be used to personalize recommendations in a smart tourism application. Our approach is based on flow centrality metrics typically used in social network analysis: flow betweenness, flow closeness and eccentricity. These metrics are useful to discover relevant nodes within the network yielding nodes that can be interpreted as suggestions (venues or services to users. We describe the semantic network built on graph model, as well as social metrics algorithms used to produce recommendations. We also present challenges and results from a prototypical implementation applied to the case study of the City of Puebla, Mexico.

Social Metrics Applied to Smart Tourism

Science.gov (United States)

Cervantes, O.; Gutiérrez, E.; Gutiérrez, F.; Sánchez, J. A.

2016-09-01

We present a strategy to make productive use of semantically-related social data, from a user-centered semantic network, in order to help users (tourists and citizens in general) to discover cultural heritage, points of interest and available services in a smart city. This data can be used to personalize recommendations in a smart tourism application. Our approach is based on flow centrality metrics typically used in social network analysis: flow betweenness, flow closeness and eccentricity. These metrics are useful to discover relevant nodes within the network yielding nodes that can be interpreted as suggestions (venues or services) to users. We describe the semantic network built on graph model, as well as social metrics algorithms used to produce recommendations. We also present challenges and results from a prototypical implementation applied to the case study of the City of Puebla, Mexico.

Remarks on G-Metric Spaces

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Bessem Samet

2013-01-01

Full Text Available In 2005, Mustafa and Sims (2006 introduced and studied a new class of generalized metric spaces, which are called G-metric spaces, as a generalization of metric spaces. We establish some useful propositions to show that many fixed point theorems on (nonsymmetric G-metric spaces given recently by many authors follow directly from well-known theorems on metric spaces. Our technique can be easily extended to other results as shown in application.

A software quality model and metrics for risk assessment

Science.gov (United States)

Hyatt, L.; Rosenberg, L.

1996-01-01

A software quality model and its associated attributes are defined and used as the model for the basis for a discussion on risk. Specific quality goals and attributes are selected based on their importance to a software development project and their ability to be quantified. Risks that can be determined by the model's metrics are identified. A core set of metrics relating to the software development process and its products is defined. Measurements for each metric and their usability and applicability are discussed.

An Initialization Method Based on Hybrid Distance for k-Means Algorithm.

Science.gov (United States)

Yang, Jie; Ma, Yan; Zhang, Xiangfen; Li, Shunbao; Zhang, Yuping

2017-11-01

The traditional [Formula: see text]-means algorithm has been widely used as a simple and efficient clustering method. However, the performance of this algorithm is highly dependent on the selection of initial cluster centers. Therefore, the method adopted for choosing initial cluster centers is extremely important. In this letter, we redefine the density of points according to the number of its neighbors, as well as the distance between points and their neighbors. In addition, we define a new distance measure that considers both Euclidean distance and density. Based on that, we propose an algorithm for selecting initial cluster centers that can dynamically adjust the weighting parameter. Furthermore, we propose a new internal clustering validation measure, the clustering validation index based on the neighbors (CVN), which can be exploited to select the optimal result among multiple clustering results. Experimental results show that the proposed algorithm outperforms existing initialization methods on real-world data sets and demonstrates the adaptability of the proposed algorithm to data sets with various characteristics.

Crowdsourcing metrics of digital collections

Directory of Open Access Journals (Sweden)

Tuula Pääkkönen

2015-12-01

Full Text Available In the National Library of Finland (NLF there are millions of digitized newspaper and journal pages, which are openly available via the public website  http://digi.kansalliskirjasto.fi. To serve users better, last year the front end was completely overhauled with its main aim in crowdsourcing features, e.g., by giving end-users the opportunity to create digital clippings and a personal scrapbook from the digital collections. But how can you know whether crowdsourcing has had an impact? How much crowdsourcing functionalities have been used so far? Did crowdsourcing work? In this paper the statistics and metrics of a recent crowdsourcing effort are analysed across the different digitized material types (newspapers, journals, ephemera. The subjects, categories and keywords given by the users are analysed to see which topics are the most appealing. Some notable public uses of the crowdsourced article clippings are highlighted. These metrics give us indications on how the end-users, based on their own interests, are investigating and using the digital collections. Therefore, the suggested metrics illustrate the versatility of the information needs of the users, varying from citizen science to research purposes. By analysing the user patterns, we can respond to the new needs of the users by making minor changes to accommodate the most active participants, while still making the service more approachable for those who are trying out the functionalities for the first time. Participation in the clippings and annotations can enrich the materials in unexpected ways and can possibly pave the way for opportunities of using crowdsourcing more also in research contexts. This creates more opportunities for the goals of open science since source data becomes ­available, making it possible for researchers to reach out to the general public for help. In the long term, utilizing, for example, text mining methods can allow these different end-user segments to

Adaptive metric learning with deep neural networks for video-based facial expression recognition

Science.gov (United States)

Liu, Xiaofeng; Ge, Yubin; Yang, Chao; Jia, Ping

2018-01-01

Video-based facial expression recognition has become increasingly important for plenty of applications in the real world. Despite that numerous efforts have been made for the single sequence, how to balance the complex distribution of intra- and interclass variations well between sequences has remained a great difficulty in this area. We propose the adaptive (N+M)-tuplet clusters loss function and optimize it with the softmax loss simultaneously in the training phrase. The variations introduced by personal attributes are alleviated using the similarity measurements of multiple samples in the feature space with many fewer comparison times as conventional deep metric learning approaches, which enables the metric calculations for large data applications (e.g., videos). Both the spatial and temporal relations are well explored by a unified framework that consists of an Inception-ResNet network with long short term memory and the two fully connected layer branches structure. Our proposed method has been evaluated with three well-known databases, and the experimental results show that our method outperforms many state-of-the-art approaches.

Resource-level QoS metric for CPU-based guarantees in cloud providers

OpenAIRE

Goiri Presa, Íñigo; Julià Massó, Ferran; Fitó, Josep Oriol; Macías Lloret, Mario; Guitart Fernández, Jordi

2010-01-01

Success of Cloud computing requires that both customers and providers can be confident that signed Service Level Agreements (SLA) are supporting their respective business activities to their best extent. Currently used SLAs fail in providing such confidence, especially when providers outsource resources to other providers. These resource providers typically support very simple metrics, or metrics that hinder an efficient exploitation of their resources. In this paper, we propose a re...

Anchoring quartet-based phylogenetic distances and applications to species tree reconstruction.

Science.gov (United States)

Sayyari, Erfan; Mirarab, Siavash

2016-11-11

Inferring species trees from gene trees using the coalescent-based summary methods has been the subject of much attention, yet new scalable and accurate methods are needed. We introduce DISTIQUE, a new statistically consistent summary method for inferring species trees from gene trees under the coalescent model. We generalize our results to arbitrary phylogenetic inference problems; we show that two arbitrarily chosen leaves, called anchors, can be used to estimate relative distances between all other pairs of leaves by inferring relevant quartet trees. This results in a family of distance-based tree inference methods, with running times ranging between quadratic to quartic in the number of leaves. We show in simulated studies that DISTIQUE has comparable accuracy to leading coalescent-based summary methods and reduced running times.

Accuracy and precision in the calculation of phenology metrics

DEFF Research Database (Denmark)

Ferreira, Ana Sofia; Visser, Andre; MacKenzie, Brian

2014-01-01

a phenology metric is first determined from a noise- and gap-free time series, and again once it has been modified. We show that precision is a greater concern than accuracy for many of these metrics, an important point that has been hereto overlooked in the literature. The variability in precision between...... phenology metrics is substantial, but it can be improved by the use of preprocessing techniques (e.g., gap-filling or smoothing). Furthermore, there are important differences in the inherent variability of the metrics that may be crucial in the interpretation of studies based upon them. Of the considered......Phytoplankton phenology (the timing of seasonal events) is a commonly used indicator for evaluating responses of marine ecosystems to climate change. However, phenological metrics are vulnerable to observation-(bloom amplitude, missing data, and observational noise) and analysis-related (temporal...

APF-Based Car Following Behavior Considering Lateral Distance

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Zhao-Sheng Yang

2013-01-01

Full Text Available Considering the influence of lateral distance on consecutive vehicles, this paper proposes a new car following model based on the artificial potential field theory (APF. Traditional car following behaviors all assume that the vehicles are driving along the middle of a lane. Different from the traditional car following principles, this incorporation of APF offers a potential breakthrough in the fields of car following theory. The individual vehicle can be represented as a unit point charge in electric field, and the interaction of the attractive potential energy and the repellent potential energy between vehicles simplifies the various influence factors on the target vehicle in actual following behavior. Consequently, it can make a better analysis of the following behavior under the lateral separation. Then, the proposed model has been demonstrated in simulation environment, through which the space-time trajectories and the potential energy change regulation are obtained. Simulations verify that the following vehicle's behavior is vulnerable to be affected by lateral distance, where the attractive potential energy tends to become repellent potential energy as the longitudinal distance decreases. The search results prove that the proposed model quantifies the relations between headway and potential energy and better reflects the following process in real-world situation.

Handwritten Digit Recognition using Edit Distance-Based KNN

OpenAIRE

Bernard , Marc; Fromont , Elisa; Habrard , Amaury; Sebban , Marc

2012-01-01

We discuss the student project given for the last 5 years to the 1st year Master Students which follow the Machine Learning lecture at the University Jean Monnet in Saint Etienne, France. The goal of this project is to develop a GUI that can recognize digits and/or letters drawn manually. The system is based on a string representation of the dig- its using Freeman codes and on the use of an edit-distance-based K-Nearest Neighbors classifier. In addition to the machine learning knowledge about...

Metric-adjusted skew information

DEFF Research Database (Denmark)

Liang, Cai; Hansen, Frank

2010-01-01

on a bipartite system and proved superadditivity of the Wigner-Yanase-Dyson skew informations for such states. We extend this result to the general metric-adjusted skew information. We finally show that a recently introduced extension to parameter values 1 ...We give a truly elementary proof of the convexity of metric-adjusted skew information following an idea of Effros. We extend earlier results of weak forms of superadditivity to general metric-adjusted skew information. Recently, Luo and Zhang introduced the notion of semi-quantum states...... of (unbounded) metric-adjusted skew information....

The metrics and correlates of physician migration from Africa

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Arah Onyebuchi A

2007-05-01

Full Text Available Abstract Background Physician migration from poor to rich countries is considered an important contributor to the growing health workforce crisis in the developing world. This is particularly true for Africa. The perceived magnitude of such migration for each source country might, however, depend on the choice of metrics used in the analysis. This study examined the influence of choice of migration metrics on the rankings of African countries that suffered the most physician migration, and investigated the correlates of physician migration. Methods Ranking and correlational analyses were conducted on African physician migration data adjusted for bilateral net flows, and supplemented with developmental, economic and health system data. The setting was the 53 African birth countries of African-born physicians working in nine wealthier destination countries. Three metrics of physician migration were used: total number of physician émigrés; emigration fraction defined as the proportion of the potential physician pool working in destination countries; and physician migration density defined as the number of physician émigrés per 1000 population of the African source country. Results Rankings based on any of the migration metrics differed substantially from those based on the other two metrics. Although the emigration fraction and physician migration density metrics gave proportionality to the migration crisis, only the latter was consistently associated with source countries' workforce capacity, health, health spending, economic and development characteristics. As such, higher physician migration density was seen among African countries with relatively higher health workforce capacity (0.401 ≤ r ≤ 0.694, p ≤ 0.011, health status, health spending, and development. Conclusion The perceived magnitude of physician migration is sensitive to the choice of metrics. Complementing the emigration fraction, the physician migration density is a metric

A Deep Similarity Metric Learning Model for Matching Text Chunks to Spatial Entities

Science.gov (United States)

Ma, K.; Wu, L.; Tao, L.; Li, W.; Xie, Z.

2017-12-01

The matching of spatial entities with related text is a long-standing research topic that has received considerable attention over the years. This task aims at enrich the contents of spatial entity, and attach the spatial location information to the text chunk. In the data fusion field, matching spatial entities with the corresponding describing text chunks has a big range of significance. However, the most traditional matching methods often rely fully on manually designed, task-specific linguistic features. This work proposes a Deep Similarity Metric Learning Model (DSMLM) based on Siamese Neural Network to learn similarity metric directly from the textural attributes of spatial entity and text chunk. The low-dimensional feature representation of the space entity and the text chunk can be learned separately. By employing the Cosine distance to measure the matching degree between the vectors, the model can make the matching pair vectors as close as possible. Mearnwhile, it makes the mismatching as far apart as possible through supervised learning. In addition, extensive experiments and analysis on geological survey data sets show that our DSMLM model can effectively capture the matching characteristics between the text chunk and the spatial entity, and achieve state-of-the-art performance.

Software metrics: Software quality metrics for distributed systems. [reliability engineering

Science.gov (United States)

Post, J. V.

1981-01-01

Software quality metrics was extended to cover distributed computer systems. Emphasis is placed on studying embedded computer systems and on viewing them within a system life cycle. The hierarchy of quality factors, criteria, and metrics was maintained. New software quality factors were added, including survivability, expandability, and evolvability.

Localized Multi-Model Extremes Metrics for the Fourth National Climate Assessment

Science.gov (United States)

Thompson, T. R.; Kunkel, K.; Stevens, L. E.; Easterling, D. R.; Biard, J.; Sun, L.

2017-12-01

We have performed localized analysis of scenario-based datasets for the Fourth National Climate Assessment (NCA4). These datasets include CMIP5-based Localized Constructed Analogs (LOCA) downscaled simulations at daily temporal resolution and 1/16th-degree spatial resolution. Over 45 temperature and precipitation extremes metrics have been processed using LOCA data, including threshold, percentile, and degree-days calculations. The localized analysis calculates trends in the temperature and precipitation extremes metrics for relatively small regions such as counties, metropolitan areas, climate zones, administrative areas, or economic zones. For NCA4, we are currently addressing metropolitan areas as defined by U.S. Census Bureau Metropolitan Statistical Areas. Such localized analysis provides essential information for adaptation planning at scales relevant to local planning agencies and businesses. Nearly 30 such regions have been analyzed to date. Each locale is defined by a closed polygon that is used to extract LOCA-based extremes metrics specific to the area. For each metric, single-model data at each LOCA grid location are first averaged over several 30-year historical and future periods. Then, for each metric, the spatial average across the region is calculated using model weights based on both model independence and reproducibility of current climate conditions. The range of single-model results is also captured on the same localized basis, and then combined with the weighted ensemble average for each region and each metric. For example, Boston-area cooling degree days and maximum daily temperature is shown below for RCP8.5 (red) and RCP4.5 (blue) scenarios. We also discuss inter-regional comparison of these metrics, as well as their relevance to risk analysis for adaptation planning.

Evaluation of Subjective and Objective Performance Metrics for Haptically Controlled Robotic Systems

Directory of Open Access Journals (Sweden)

Cong Dung Pham

2014-07-01

Full Text Available This paper studies in detail how different evaluation methods perform when it comes to describing the performance of haptically controlled mobile manipulators. Particularly, we investigate how well subjective metrics perform compared to objective metrics. To find the best metrics to describe the performance of a control scheme is challenging when human operators are involved; how the user perceives the performance of the controller does not necessarily correspond to the directly measurable metrics normally used in controller evaluation. It is therefore important to study whether there is any correspondence between how the user perceives the performance of a controller, and how it performs in terms of directly measurable metrics such as the time used to perform a task, number of errors, accuracy, and so on. To perform these tests we choose a system that consists of a mobile manipulator that is controlled by an operator through a haptic device. This is a good system for studying different performance metrics as the performance can be determined by subjective metrics based on feedback from the users, and also as objective and directly measurable metrics. The system consists of a robotic arm which provides for interaction and manipulation, which is mounted on a mobile base which extends the workspace of the arm. The operator thus needs to perform both interaction and locomotion using a single haptic device. While the position of the on-board camera is determined by the base motion, the principal control objective is the motion of the manipulator arm. This calls for intelligent control allocation between the base and the manipulator arm in order to obtain intuitive control of both the camera and the arm. We implement three different approaches to the control allocation problem, i.e., whether the vehicle or manipulator arm actuation is applied to generate the desired motion. The performance of the different control schemes is evaluated, and our

Contaminant classification using cosine distances based on multiple conventional sensors.

Science.gov (United States)

Liu, Shuming; Che, Han; Smith, Kate; Chang, Tian

2015-02-01

Emergent contamination events have a significant impact on water systems. After contamination detection, it is important to classify the type of contaminant quickly to provide support for remediation attempts. Conventional methods generally either rely on laboratory-based analysis, which requires a long analysis time, or on multivariable-based geometry analysis and sequence analysis, which is prone to being affected by the contaminant concentration. This paper proposes a new contaminant classification method, which discriminates contaminants in a real time manner independent of the contaminant concentration. The proposed method quantifies the similarities or dissimilarities between sensors' responses to different types of contaminants. The performance of the proposed method was evaluated using data from contaminant injection experiments in a laboratory and compared with a Euclidean distance-based method. The robustness of the proposed method was evaluated using an uncertainty analysis. The results show that the proposed method performed better in identifying the type of contaminant than the Euclidean distance based method and that it could classify the type of contaminant in minutes without significantly compromising the correct classification rate (CCR).

A p-Adic Metric for Particle Mass Scale Organization with Genetic Divisors

International Nuclear Information System (INIS)

Dai, Yang; Borisov, Alexey B.; Boyer, Keith; Rhodes, Charles K.

2001-01-01

The concept of genetic divisors can be given a quantitative measure with a non-Archimedean p-adic metric that is both computationally convenient and physically motivated. For two particles possessing distinct mass parameters x and y, the metric distance D(x, y) is expressed on the field of rational numbers Q as the inverse of the greatest common divisor [gcd (x , y)]. As a measure of genetic similarity, this metric can be applied to (1) the mass numbers of particle states and (2) the corresponding subgroup orders of these systems. The use of the Bezout identity in the form of a congruence for the expression of the gcd (x , y) corresponding to the v e and μ neutrinos (a) connects the genetic divisor concept to the cosmic seesaw congruence, (b) provides support for the δ-conjecture concerning the subgroup structure of particle states, and (c) quantitatively strengthens the interlocking relationships joining the values of the prospectively derived (i) electron neutrino (v e ) mass (0.808 meV), (ii) muon neutrino (v μ ) mass (27.68 meV), and (iii) unified strong-electroweak coupling constant (α* -1 = 34.26)

The metric system: An introduction

Science.gov (United States)

Lumley, Susan M.

On 13 Jul. 1992, Deputy Director Duane Sewell restated the Laboratory's policy on conversion to the metric system which was established in 1974. Sewell's memo announced the Laboratory's intention to continue metric conversion on a reasonable and cost effective basis. Copies of the 1974 and 1992 Administrative Memos are contained in the Appendix. There are three primary reasons behind the Laboratory's conversion to the metric system. First, Public Law 100-418, passed in 1988, states that by the end of fiscal year 1992 the Federal Government must begin using metric units in grants, procurements, and other business transactions. Second, on 25 Jul. 1991, President George Bush signed Executive Order 12770 which urged Federal agencies to expedite conversion to metric units. Third, the contract between the University of California and the Department of Energy calls for the Laboratory to convert to the metric system. Thus, conversion to the metric system is a legal requirement and a contractual mandate with the University of California. Public Law 100-418 and Executive Order 12770 are discussed in more detail later in this section, but first they examine the reasons behind the nation's conversion to the metric system. The second part of this report is on applying the metric system.

The metric system: An introduction

Energy Technology Data Exchange (ETDEWEB)

Lumley, S.M.

1995-05-01

On July 13, 1992, Deputy Director Duane Sewell restated the Laboratory`s policy on conversion to the metric system which was established in 1974. Sewell`s memo announced the Laboratory`s intention to continue metric conversion on a reasonable and cost effective basis. Copies of the 1974 and 1992 Administrative Memos are contained in the Appendix. There are three primary reasons behind the Laboratory`s conversion to the metric system. First, Public Law 100-418, passed in 1988, states that by the end of fiscal year 1992 the Federal Government must begin using metric units in grants, procurements, and other business transactions. Second, on July 25, 1991, President George Bush signed Executive Order 12770 which urged Federal agencies to expedite conversion to metric units. Third, the contract between the University of California and the Department of Energy calls for the Laboratory to convert to the metric system. Thus, conversion to the metric system is a legal requirement and a contractual mandate with the University of California. Public Law 100-418 and Executive Order 12770 are discussed in more detail later in this section, but first they examine the reasons behind the nation`s conversion to the metric system. The second part of this report is on applying the metric system.

Classification of Pulse Waveforms Using Edit Distance with Real Penalty

Directory of Open Access Journals (Sweden)

Zhang Dongyu

2010-01-01

Full Text Available Abstract Advances in sensor and signal processing techniques have provided effective tools for quantitative research in traditional Chinese pulse diagnosis (TCPD. Because of the inevitable intraclass variation of pulse patterns, the automatic classification of pulse waveforms has remained a difficult problem. In this paper, by referring to the edit distance with real penalty (ERP and the recent progress in -nearest neighbors (KNN classifiers, we propose two novel ERP-based KNN classifiers. Taking advantage of the metric property of ERP, we first develop an ERP-induced inner product and a Gaussian ERP kernel, then embed them into difference-weighted KNN classifiers, and finally develop two novel classifiers for pulse waveform classification. The experimental results show that the proposed classifiers are effective for accurate classification of pulse waveform.

Attack-Resistant Trust Metrics

Science.gov (United States)

Levien, Raph

The Internet is an amazingly powerful tool for connecting people together, unmatched in human history. Yet, with that power comes great potential for spam and abuse. Trust metrics are an attempt to compute the set of which people are trustworthy and which are likely attackers. This chapter presents two specific trust metrics developed and deployed on the Advogato Website, which is a community blog for free software developers. This real-world experience demonstrates that the trust metrics fulfilled their goals, but that for good results, it is important to match the assumptions of the abstract trust metric computation to the real-world implementation.

A family of metric gravities

Science.gov (United States)

Shuler, Robert

2018-04-01

The goal of this paper is to take a completely fresh approach to metric gravity, in which the metric principle is strictly adhered to but its properties in local space-time are derived from conservation principles, not inferred from a global field equation. The global field strength variation then gains some flexibility, but only in the regime of very strong fields (2nd-order terms) whose measurement is now being contemplated. So doing provides a family of similar gravities, differing only in strong fields, which could be developed into meaningful verification targets for strong fields after the manner in which far-field variations were used in the 20th century. General Relativity (GR) is shown to be a member of the family and this is demonstrated by deriving the Schwarzschild metric exactly from a suitable field strength assumption. The method of doing so is interesting in itself because it involves only one differential equation rather than the usual four. Exact static symmetric field solutions are also given for one pedagogical alternative based on potential, and one theoretical alternative based on inertia, and the prospects of experimentally differentiating these are analyzed. Whether the method overturns the conventional wisdom that GR is the only metric theory of gravity and that alternatives must introduce additional interactions and fields is somewhat semantical, depending on whether one views the field strength assumption as a field and whether the assumption that produces GR is considered unique in some way. It is of course possible to have other fields, and the local space-time principle can be applied to field gravities which usually are weak-field approximations having only time dilation, giving them the spatial factor and promoting them to full metric theories. Though usually pedagogical, some of them are interesting from a quantum gravity perspective. Cases are noted where mass measurement errors, or distributions of dark matter, can cause one

Adaptive density trajectory cluster based on time and space distance

Science.gov (United States)

Liu, Fagui; Zhang, Zhijie

2017-10-01

There are some hotspot problems remaining in trajectory cluster for discovering mobile behavior regularity, such as the computation of distance between sub trajectories, the setting of parameter values in cluster algorithm and the uncertainty/boundary problem of data set. As a result, based on the time and space, this paper tries to define the calculation method of distance between sub trajectories. The significance of distance calculation for sub trajectories is to clearly reveal the differences in moving trajectories and to promote the accuracy of cluster algorithm. Besides, a novel adaptive density trajectory cluster algorithm is proposed, in which cluster radius is computed through using the density of data distribution. In addition, cluster centers and number are selected by a certain strategy automatically, and uncertainty/boundary problem of data set is solved by designed weighted rough c-means. Experimental results demonstrate that the proposed algorithm can perform the fuzzy trajectory cluster effectively on the basis of the time and space distance, and obtain the optimal cluster centers and rich cluster results information adaptably for excavating the features of mobile behavior in mobile and sociology network.

Symmetries of the dual metrics

International Nuclear Information System (INIS)

Baleanu, D.

1998-01-01

The geometric duality between the metric g μν and a Killing tensor K μν is studied. The conditions were found when the symmetries of the metric g μν and the dual metric K μν are the same. Dual spinning space was constructed without introduction of torsion. The general results are applied to the case of Kerr-Newmann metric

Complexity Management Using Metrics for Trajectory Flexibility Preservation and Constraint Minimization

Science.gov (United States)

Idris, Husni; Shen, Ni; Wing, David J.

2011-01-01

The growing demand for air travel is increasing the need for mitigating air traffic congestion and complexity problems, which are already at high levels. At the same time new surveillance, navigation, and communication technologies are enabling major transformations in the air traffic management system, including net-based information sharing and collaboration, performance-based access to airspace resources, and trajectory-based rather than clearance-based operations. The new system will feature different schemes for allocating tasks and responsibilities between the ground and airborne agents and between the human and automation, with potential capacity and cost benefits. Therefore, complexity management requires new metrics and methods that can support these new schemes. This paper presents metrics and methods for preserving trajectory flexibility that have been proposed to support a trajectory-based approach for complexity management by airborne or ground-based systems. It presents extensions to these metrics as well as to the initial research conducted to investigate the hypothesis that using these metrics to guide user and service provider actions will naturally mitigate traffic complexity. The analysis showed promising results in that: (1) Trajectory flexibility preservation mitigated traffic complexity as indicated by inducing self-organization in the traffic patterns and lowering traffic complexity indicators such as dynamic density and traffic entropy. (2)Trajectory flexibility preservation reduced the potential for secondary conflicts in separation assurance. (3) Trajectory flexibility metrics showed potential application to support user and service provider negotiations for minimizing the constraints imposed on trajectories without jeopardizing their objectives.

A condition metric for Eucalyptus woodland derived from expert evaluations.

Science.gov (United States)

Sinclair, Steve J; Bruce, Matthew J; Griffioen, Peter; Dodd, Amanda; White, Matthew D

2018-02-01

The evaluation of ecosystem quality is important for land-management and land-use planning. Evaluation is unavoidably subjective, and robust metrics must be based on consensus and the structured use of observations. We devised a transparent and repeatable process for building and testing ecosystem metrics based on expert data. We gathered quantitative evaluation data on the quality of hypothetical grassy woodland sites from experts. We used these data to train a model (an ensemble of 30 bagged regression trees) capable of predicting the perceived quality of similar hypothetical woodlands based on a set of 13 site variables as inputs (e.g., cover of shrubs, richness of native forbs). These variables can be measured at any site and the model implemented in a spreadsheet as a metric of woodland quality. We also investigated the number of experts required to produce an opinion data set sufficient for the construction of a metric. The model produced evaluations similar to those provided by experts, as shown by assessing the model's quality scores of expert-evaluated test sites not used to train the model. We applied the metric to 13 woodland conservation reserves and asked managers of these sites to independently evaluate their quality. To assess metric performance, we compared the model's evaluation of site quality with the managers' evaluations through multidimensional scaling. The metric performed relatively well, plotting close to the center of the space defined by the evaluators. Given the method provides data-driven consensus and repeatability, which no single human evaluator can provide, we suggest it is a valuable tool for evaluating ecosystem quality in real-world contexts. We believe our approach is applicable to any ecosystem. © 2017 State of Victoria.

The Graph, Geometry and Symmetries of the Genetic Code with Hamming Metric

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Reijer Lenstra

2015-07-01

Full Text Available The similarity patterns of the genetic code result from similar codons encoding similar messages. We develop a new mathematical model to analyze these patterns. The physicochemical characteristics of amino acids objectively quantify their differences and similarities; the Hamming metric does the same for the 64 codons of the codon set. (Hamming distances equal the number of different codon positions: AAA and AAC are at 1-distance; codons are maximally at 3-distance. The CodonPolytope, a 9-dimensional geometric object, is spanned by 64 vertices that represent the codons and the Euclidian distances between these vertices correspond one-to-one with intercodon Hamming distances. The CodonGraph represents the vertices and edges of the polytope; each edge equals a Hamming 1-distance. The mirror reflection symmetry group of the polytope is isomorphic to the largest permutation symmetry group of the codon set that preserves Hamming distances. These groups contain 82,944 symmetries. Many polytope symmetries coincide with the degeneracy and similarity patterns of the genetic code. These code symmetries are strongly related with the face structure of the polytope with smaller faces displaying stronger code symmetries. Splitting the polytope stepwise into smaller faces models an early evolution of the code that generates this hierarchy of code symmetries. The canonical code represents a class of 41,472 codes with equivalent symmetries; a single class among an astronomical number of symmetry classes comprising all possible codes.

Thickness and clearance visualization based on distance field of 3D objects

Directory of Open Access Journals (Sweden)

Masatomo Inui

2015-07-01

Full Text Available This paper proposes a novel method for visualizing the thickness and clearance of 3D objects in a polyhedral representation. The proposed method uses the distance field of the objects in the visualization. A parallel algorithm is developed for constructing the distance field of polyhedral objects using the GPU. The distance between a voxel and the surface polygons of the model is computed many times in the distance field construction. Similar sets of polygons are usually selected as close polygons for close voxels. By using this spatial coherence, a parallel algorithm is designed to compute the distances between a cluster of close voxels and the polygons selected by the culling operation so that the fast shared memory mechanism of the GPU can be fully utilized. The thickness/clearance of the objects is visualized by distributing points on the visible surfaces of the objects and painting them with a unique color corresponding to the thickness/clearance values at those points. A modified ray casting method is developed for computing the thickness/clearance using the distance field of the objects. A system based on these algorithms can compute the distance field of complex objects within a few minutes for most cases. After the distance field construction, thickness/clearance visualization at a near interactive rate is achieved.

Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics

Directory of Open Access Journals (Sweden)

Bernardin Keni

2008-01-01

Full Text Available Abstract Simultaneous tracking of multiple persons in real-world environments is an active research field and several approaches have been proposed, based on a variety of features and algorithms. Recently, there has been a growing interest in organizing systematic evaluations to compare the various techniques. Unfortunately, the lack of common metrics for measuring the performance of multiple object trackers still makes it hard to compare their results. In this work, we introduce two intuitive and general metrics to allow for objective comparison of tracker characteristics, focusing on their precision in estimating object locations, their accuracy in recognizing object configurations and their ability to consistently label objects over time. These metrics have been extensively used in two large-scale international evaluations, the 2006 and 2007 CLEAR evaluations, to measure and compare the performance of multiple object trackers for a wide variety of tracking tasks. Selected performance results are presented and the advantages and drawbacks of the presented metrics are discussed based on the experience gained during the evaluations.

Assessing Software Quality Through Visualised Cohesion Metrics

Directory of Open Access Journals (Sweden)

Timothy Shih

2001-05-01

Full Text Available Cohesion is one of the most important factors for software quality as well as maintainability, reliability and reusability. Module cohesion is defined as a quality attribute that seeks for measuring the singleness of the purpose of a module. The module of poor quality can be a serious obstacle to the system quality. In order to design a good software quality, software managers and engineers need to introduce cohesion metrics to measure and produce desirable software. A highly cohesion software is thought to be a desirable constructing. In this paper, we propose a function-oriented cohesion metrics based on the analysis of live variables, live span and the visualization of processing element dependency graph. We give six typical cohesion examples to be measured as our experiments and justification. Therefore, a well-defined, well-normalized, well-visualized and well-experimented cohesion metrics is proposed to indicate and thus enhance software cohesion strength. Furthermore, this cohesion metrics can be easily incorporated with software CASE tool to help software engineers to improve software quality.

AN ARTIFICIAL INTELLIGENCE-BASED DISTANCE EDUCATION SYSTEM: Artimat

Directory of Open Access Journals (Sweden)

Vasif NABIYEV

2013-04-01

Full Text Available The purpose of this study is to evaluate the artificial intelligence-based distance education system called as ARTIMAT, which has been prepared in order to improve mathematical problem solving skills of the students, in terms of conceptual proficiency and ease of use with the opinions of teachers and students. The implementation has been performed with 4 teachers and 59 students in 10th grade in an Anatolian High School in Trabzon. Many institutions and organizations in the world approach seriously to distance education besides traditional education. It is inevitable to use the distance education in teaching the problem solving skills in this different dimension of the education. In the studies in Turkey and abroad in the field of mathematics teaching, problem solving skills are generally stated not to be at the desired level and often expressed to have difficulty in teaching. For this reason, difficulties of the students in problem solving have initially been evaluated and the system has been prepared utilizing artificial intelligence algorithms according to the obtained results. In the evaluation of the findings obtained from the application, it has been concluded that the system is responsive to the needs of the students and is successful in general, but that conceptual changes should be made in order that students adapt to the system quickly.

Overview of journal metrics

Directory of Open Access Journals (Sweden)

Kihong Kim

2018-02-01

Full Text Available Various kinds of metrics used for the quantitative evaluation of scholarly journals are reviewed. The impact factor and related metrics including the immediacy index and the aggregate impact factor, which are provided by the Journal Citation Reports, are explained in detail. The Eigenfactor score and the article influence score are also reviewed. In addition, journal metrics such as CiteScore, Source Normalized Impact per Paper, SCImago Journal Rank, h-index, and g-index are discussed. Limitations and problems that these metrics have are pointed out. We should be cautious to rely on those quantitative measures too much when we evaluate journals or researchers.

Mahalanobis Distance Based Iterative Closest Point

DEFF Research Database (Denmark)

Hansen, Mads Fogtmann; Blas, Morten Rufus; Larsen, Rasmus

2007-01-01

the notion of a mahalanobis distance map upon a point set with associated covariance matrices which in addition to providing correlation weighted distance implicitly provides a method for assigning correspondence during alignment. This distance map provides an easy formulation of the ICP problem that permits...... a fast optimization. Initially, the covariance matrices are set to the identity matrix, and all shapes are aligned to a randomly selected shape (equivalent to standard ICP). From this point the algorithm iterates between the steps: (a) obtain mean shape and new estimates of the covariance matrices from...... the aligned shapes, (b) align shapes to the mean shape. Three different methods for estimating the mean shape with associated covariance matrices are explored in the paper. The proposed methods are validated experimentally on two separate datasets (IMM face dataset and femur-bones). The superiority of ICP...

Fisher information metrics for binary classifier evaluation and training

CERN Multimedia

CERN. Geneva

2018-01-01

Different evaluation metrics for binary classifiers are appropriate to different scientific domains and even to different problems within the same domain. This presentation focuses on the optimisation of event selection to minimise statistical errors in HEP parameter estimation, a problem that is best analysed in terms of the maximisation of Fisher information about the measured parameters. After describing a general formalism to derive evaluation metrics based on Fisher information, three more specific metrics are introduced for the measurements of signal cross sections in counting experiments (FIP1) or distribution fits (FIP2) and for the measurements of other parameters from distribution fits (FIP3). The FIP2 metric is particularly interesting because it can be derived from any ROC curve, provided that prevalence is also known. In addition to its relation to measurement errors when used as an evaluation criterion (which makes it more interesting that the ROC AUC), a further advantage of the FIP2 metric is ...

The challenge of global water access monitoring: evaluating straight-line distance versus self-reported travel time among rural households in Mozambique.

Science.gov (United States)

Ho, Jeff C; Russel, Kory C; Davis, Jennifer

2014-03-01

Support is growing for the incorporation of fetching time and/or distance considerations in the definition of access to improved water supply used for global monitoring. Current efforts typically rely on self-reported distance and/or travel time data that have been shown to be unreliable. To date, however, there has been no head-to-head comparison of such indicators with other possible distance/time metrics. This study provides such a comparison. We examine the association between both straight-line distance and self-reported one-way travel time with measured route distances to water sources for 1,103 households in Nampula province, Mozambique. We find straight-line, or Euclidean, distance to be a good proxy for route distance (R(2) = 0.98), while self-reported travel time is a poor proxy (R(2) = 0.12). We also apply a variety of time- and distance-based indicators proposed in the literature to our sample data, finding that the share of households classified as having versus lacking access would differ by more than 70 percentage points depending on the particular indicator employed. This work highlights the importance of the ongoing debate regarding valid, reliable, and feasible strategies for monitoring progress in the provision of improved water supply services.

A distance weighted-based approach for self-organized aggregation in robot swarms

KAUST Repository

Khaldi, Belkacem

2017-12-14

In this paper, a Distance-Weighted K Nearest Neighboring (DW-KNN) topology is proposed to study self-organized aggregation as an emergent swarming behavior within robot swarms. A virtual physics approach is applied among the proposed neighborhood topology to keep the robots together. A distance-weighted function based on a Smoothed Particle Hydrodynamic (SPH) interpolation approach is used as a key factor to identify the K-Nearest neighbors taken into account when aggregating the robots. The intra virtual physical connectivity among these neighbors is achieved using a virtual viscoelastic-based proximity model. With the ARGoS based-simulator, we model and evaluate the proposed approach showing various self-organized aggregations performed by a swarm of N foot-bot robots.

Holographic Spherically Symmetric Metrics

Science.gov (United States)

Petri, Michael

The holographic principle (HP) conjectures, that the maximum number of degrees of freedom of any realistic physical system is proportional to the system's boundary area. The HP has its roots in the study of black holes. It has recently been applied to cosmological solutions. In this article we apply the HP to spherically symmetric static space-times. We find that any regular spherically symmetric object saturating the HP is subject to tight constraints on the (interior) metric, energy-density, temperature and entropy-density. Whenever gravity can be described by a metric theory, gravity is macroscopically scale invariant and the laws of thermodynamics hold locally and globally, the (interior) metric of a regular holographic object is uniquely determined up to a constant factor and the interior matter-state must follow well defined scaling relations. When the metric theory of gravity is general relativity, the interior matter has an overall string equation of state (EOS) and a unique total energy-density. Thus the holographic metric derived in this article can serve as simple interior 4D realization of Mathur's string fuzzball proposal. Some properties of the holographic metric and its possible experimental verification are discussed. The geodesics of the holographic metric describe an isotropically expanding (or contracting) universe with a nearly homogeneous matter-distribution within the local Hubble volume. Due to the overall string EOS the active gravitational mass-density is zero, resulting in a coasting expansion with Ht = 1, which is compatible with the recent GRB-data.

Critical thinking skills in nursing students: comparison of simulation-based performance with metrics

Science.gov (United States)

Fero, Laura J.; O’Donnell, John M.; Zullo, Thomas G.; Dabbs, Annette DeVito; Kitutu, Julius; Samosky, Joseph T.; Hoffman, Leslie A.

2018-01-01

Aim This paper is a report of an examination of the relationship between metrics of critical thinking skills and performance in simulated clinical scenarios. Background Paper and pencil assessments are commonly used to assess critical thinking but may not reflect simulated performance. Methods In 2007, a convenience sample of 36 nursing students participated in measurement of critical thinking skills and simulation-based performance using videotaped vignettes, high-fidelity human simulation, the California Critical Thinking Disposition Inventory and California Critical Thinking Skills Test. Simulation- based performance was rated as ‘meeting’ or ‘not meeting’ overall expectations. Test scores were categorized as strong, average, or weak. Results Most (75·0%) students did not meet overall performance expectations using videotaped vignettes or high-fidelity human simulation; most difficulty related to problem recognition and reporting findings to the physician. There was no difference between overall performance based on method of assessment (P = 0·277). More students met subcategory expectations for initiating nursing interventions (P ≤ 0·001) using high-fidelity human simulation. The relationship between video-taped vignette performance and critical thinking disposition or skills scores was not statistically significant, except for problem recognition and overall critical thinking skills scores (Cramer’s V = 0·444, P = 0·029). There was a statistically significant relationship between overall high-fidelity human simulation performance and overall critical thinking disposition scores (Cramer’s V = 0·413, P = 0·047). Conclusion Students’ performance reflected difficulty meeting expectations in simulated clinical scenarios. High-fidelity human simulation performance appeared to approximate scores on metrics of critical thinking best. Further research is needed to determine if simulation-based performance correlates with critical thinking skills

Critical thinking skills in nursing students: comparison of simulation-based performance with metrics.

Science.gov (United States)

Fero, Laura J; O'Donnell, John M; Zullo, Thomas G; Dabbs, Annette DeVito; Kitutu, Julius; Samosky, Joseph T; Hoffman, Leslie A

2010-10-01

This paper is a report of an examination of the relationship between metrics of critical thinking skills and performance in simulated clinical scenarios. Paper and pencil assessments are commonly used to assess critical thinking but may not reflect simulated performance. In 2007, a convenience sample of 36 nursing students participated in measurement of critical thinking skills and simulation-based performance using videotaped vignettes, high-fidelity human simulation, the California Critical Thinking Disposition Inventory and California Critical Thinking Skills Test. Simulation-based performance was rated as 'meeting' or 'not meeting' overall expectations. Test scores were categorized as strong, average, or weak. Most (75.0%) students did not meet overall performance expectations using videotaped vignettes or high-fidelity human simulation; most difficulty related to problem recognition and reporting findings to the physician. There was no difference between overall performance based on method of assessment (P = 0.277). More students met subcategory expectations for initiating nursing interventions (P ≤ 0.001) using high-fidelity human simulation. The relationship between videotaped vignette performance and critical thinking disposition or skills scores was not statistically significant, except for problem recognition and overall critical thinking skills scores (Cramer's V = 0.444, P = 0.029). There was a statistically significant relationship between overall high-fidelity human simulation performance and overall critical thinking disposition scores (Cramer's V = 0.413, P = 0.047). Students' performance reflected difficulty meeting expectations in simulated clinical scenarios. High-fidelity human simulation performance appeared to approximate scores on metrics of critical thinking best. Further research is needed to determine if simulation-based performance correlates with critical thinking skills in the clinical setting. © 2010 The Authors. Journal of Advanced

Advanced spatial metrics analysis in cellular automata land use and cover change modeling

International Nuclear Information System (INIS)

Zamyatin, Alexander; Cabral, Pedro

2011-01-01

This paper proposes an approach for a more effective definition of cellular automata transition rules for landscape change modeling using an advanced spatial metrics analysis. This approach considers a four-stage methodology based on: (i) the search for the appropriate spatial metrics with minimal correlations; (ii) the selection of the appropriate neighborhood size; (iii) the selection of the appropriate technique for spatial metrics application; and (iv) the analysis of the contribution level of each spatial metric for joint use. The case study uses an initial set of 7 spatial metrics of which 4 are selected for modeling. Results show a better model performance when compared to modeling without any spatial metrics or with the initial set of 7 metrics.

Landscape metrics for three-dimension urban pattern recognition

Science.gov (United States)

Liu, M.; Hu, Y.; Zhang, W.; Li, C.

2017-12-01

Understanding how landscape pattern determines population or ecosystem dynamics is crucial for managing our landscapes. Urban areas are becoming increasingly dominant social-ecological systems, so it is important to understand patterns of urbanization. Most studies of urban landscape pattern examine land-use maps in two dimensions because the acquisition of 3-dimensional information is difficult. We used Brista software based on Quickbird images and aerial photos to interpret the height of buildings, thus incorporating a 3-dimensional approach. We estimated the feasibility and accuracy of this approach. A total of 164,345 buildings in the Liaoning central urban agglomeration of China, which included seven cities, were measured. Twelve landscape metrics were proposed or chosen to describe the urban landscape patterns in 2- and 3-dimensional scales. The ecological and social meaning of landscape metrics were analyzed with multiple correlation analysis. The results showed that classification accuracy compared with field surveys was 87.6%, which means this method for interpreting building height was acceptable. The metrics effectively reflected the urban architecture in relation to number of buildings, area, height, 3-D shape and diversity aspects. We were able to describe the urban characteristics of each city with these metrics. The metrics also captured ecological and social meanings. The proposed landscape metrics provided a new method for urban landscape analysis in three dimensions.

Metrics to describe the effects of landscape pattern on hydrology in a lotic peatland

Science.gov (United States)

Yuan, J.; Cohen, M. J.; Kaplan, D. A.; Acharya, S.; Larsen, L.; Nungesser, M.

2013-12-01

Strong reciprocal interactions exist between landscape patterns and ecological processes. Hydrology is the dominant abiotic driver of ecological processes in wetlands, particularly flowing wetlands, but is both the control on and controlled by the geometry of vegetation patterning. Landscape metrics are widely used to quantitatively link pattern and process. Our goal here was to use several candidate spatial pattern metrics to predict the effects of wetland vegetation pattern on hydrologic regime, specifically hydroperiod, in the ridge-slough patterned landscape of the Everglades. The metrics focus on the capacity for longitudinally connected flow, and thus the ability of this low-gradient patterned landscape to route water from upstream. We first explored flow friction cost (FFC), a weighted spatial distance procedure wherein ridges have a high flow cost than sloughs by virtue of their elevation and vegetation structure, to evaluate water movement through different landscape configurations. We also investigated existing published flow metrics, specifically the Directional Connectivity Index (DCI) and Landscape Discharge Competence (LDC), that seek to quantify connectivity, one of the sentinel targets of ecological restoration. Hydroperiod was estimated using a numerical hydrologic model (SWIFT 2D) in real and synthetic landscapes with varying vegetation properties ( patch anisotropy, ridge density). Synthetic landscapes were constrained by the geostatistical properties of the best conserved patterned, and contained five anisotropy levels and seven ridge density levels. These were used to construct the relationship between landscape metrics and hydroperiod. Then, using historical images from 1940 to 2004, we applied the metrics toback-cast hydroperiod. Current vegetation maps were used to test scale dependency for each metric. Our results suggest that both FFC and DCI are good predictors of hydroperiod under free flowing conditions, and that they can be used

Evaluation metrics for biostatistical and epidemiological collaborations.

Science.gov (United States)

Rubio, Doris McGartland; Del Junco, Deborah J; Bhore, Rafia; Lindsell, Christopher J; Oster, Robert A; Wittkowski, Knut M; Welty, Leah J; Li, Yi-Ju; Demets, Dave

2011-10-15

Increasing demands for evidence-based medicine and for the translation of biomedical research into individual and public health benefit have been accompanied by the proliferation of special units that offer expertise in biostatistics, epidemiology, and research design (BERD) within academic health centers. Objective metrics that can be used to evaluate, track, and improve the performance of these BERD units are critical to their successful establishment and sustainable future. To develop a set of reliable but versatile metrics that can be adapted easily to different environments and evolving needs, we consulted with members of BERD units from the consortium of academic health centers funded by the Clinical and Translational Science Award Program of the National Institutes of Health. Through a systematic process of consensus building and document drafting, we formulated metrics that covered the three identified domains of BERD practices: the development and maintenance of collaborations with clinical and translational science investigators, the application of BERD-related methods to clinical and translational research, and the discovery of novel BERD-related methodologies. In this article, we describe the set of metrics and advocate their use for evaluating BERD practices. The routine application, comparison of findings across diverse BERD units, and ongoing refinement of the metrics will identify trends, facilitate meaningful changes, and ultimately enhance the contribution of BERD activities to biomedical research. Copyright © 2011 John Wiley & Sons, Ltd.

Left-invariant Einstein metrics on S3 ×S3

Science.gov (United States)

Belgun, Florin; Cortés, Vicente; Haupt, Alexander S.; Lindemann, David

2018-06-01

The classification of homogeneous compact Einstein manifolds in dimension six is an open problem. We consider the remaining open case, namely left-invariant Einstein metrics g on G = SU(2) × SU(2) =S3 ×S3. Einstein metrics are critical points of the total scalar curvature functional for fixed volume. The scalar curvature S of a left-invariant metric g is constant and can be expressed as a rational function in the parameters determining the metric. The critical points of S, subject to the volume constraint, are given by the zero locus of a system of polynomials in the parameters. In general, however, the determination of the zero locus is apparently out of reach. Instead, we consider the case where the isotropy group K of g in the group of motions is non-trivial. When K ≇Z2 we prove that the Einstein metrics on G are given by (up to homothety) either the standard metric or the nearly Kähler metric, based on representation-theoretic arguments and computer algebra. For the remaining case K ≅Z2 we present partial results.

Metric regularity and subdifferential calculus

International Nuclear Information System (INIS)

Ioffe, A D

2000-01-01

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

Ant-Based Phylogenetic Reconstruction (ABPR: A new distance algorithm for phylogenetic estimation based on ant colony optimization

Directory of Open Access Journals (Sweden)

Karla Vittori

2008-12-01

Full Text Available We propose a new distance algorithm for phylogenetic estimation based on Ant Colony Optimization (ACO, named Ant-Based Phylogenetic Reconstruction (ABPR. ABPR joins two taxa iteratively based on evolutionary distance among sequences, while also accounting for the quality of the phylogenetic tree built according to the total length of the tree. Similar to optimization algorithms for phylogenetic estimation, the algorithm allows exploration of a larger set of nearly optimal solutions. We applied the algorithm to four empirical data sets of mitochondrial DNA ranging from 12 to 186 sequences, and from 898 to 16,608 base pairs, and covering taxonomic levels from populations to orders. We show that ABPR performs better than the commonly used Neighbor-Joining algorithm, except when sequences are too closely related (e.g., population-level sequences. The phylogenetic relationships recovered at and above species level by ABPR agree with conventional views. However, like other algorithms of phylogenetic estimation, the proposed algorithm failed to recover expected relationships when distances are too similar or when rates of evolution are very variable, leading to the problem of long-branch attraction. ABPR, as well as other ACO-based algorithms, is emerging as a fast and accurate alternative method of phylogenetic estimation for large data sets.

Context-dependent ATC complexity metric

NARCIS (Netherlands)

Mercado Velasco, G.A.; Borst, C.

2015-01-01

Several studies have investigated Air Traffic Control (ATC) complexity metrics in a search for a metric that could best capture workload. These studies have shown how daunting the search for a universal workload metric (one that could be applied in different contexts: sectors, traffic patterns,

Nodal distances for rooted phylogenetic trees.

Science.gov (United States)

Cardona, Gabriel; Llabrés, Mercè; Rosselló, Francesc; Valiente, Gabriel

2010-08-01

Dissimilarity measures for (possibly weighted) phylogenetic trees based on the comparison of their vectors of path lengths between pairs of taxa, have been present in the systematics literature since the early seventies. For rooted phylogenetic trees, however, these vectors can only separate non-weighted binary trees, and therefore these dissimilarity measures are metrics only on this class of rooted phylogenetic trees. In this paper we overcome this problem, by splitting in a suitable way each path length between two taxa into two lengths. We prove that the resulting splitted path lengths matrices single out arbitrary rooted phylogenetic trees with nested taxa and arcs weighted in the set of positive real numbers. This allows the definition of metrics on this general class of rooted phylogenetic trees by comparing these matrices through metrics in spaces M(n)(R) of real-valued n x n matrices. We conclude this paper by establishing some basic facts about the metrics for non-weighted phylogenetic trees defined in this way using L(p) metrics on M(n)(R), with p [epsilon] R(>0).

Statistical rice yield modeling using blended MODIS-Landsat based crop phenology metrics in Taiwan

Science.gov (United States)

Chen, C. R.; Chen, C. F.; Nguyen, S. T.; Lau, K. V.

2015-12-01

Taiwan is a populated island with a majority of residents settled in the western plains where soils are suitable for rice cultivation. Rice is not only the most important commodity, but also plays a critical role for agricultural and food marketing. Information of rice production is thus important for policymakers to devise timely plans for ensuring sustainably socioeconomic development. Because rice fields in Taiwan are generally small and yet crop monitoring requires information of crop phenology associating with the spatiotemporal resolution of satellite data, this study used Landsat-MODIS fusion data for rice yield modeling in Taiwan. We processed the data for the first crop (Feb-Mar to Jun-Jul) and the second (Aug-Sep to Nov-Dec) in 2014 through five main steps: (1) data pre-processing to account for geometric and radiometric errors of Landsat data, (2) Landsat-MODIS data fusion using using the spatial-temporal adaptive reflectance fusion model, (3) construction of the smooth time-series enhanced vegetation index 2 (EVI2), (4) rice yield modeling using EVI2-based crop phenology metrics, and (5) error verification. The fusion results by a comparison bewteen EVI2 derived from the fusion image and that from the reference Landsat image indicated close agreement between the two datasets (R2 > 0.8). We analysed smooth EVI2 curves to extract phenology metrics or phenological variables for establishment of rice yield models. The results indicated that the established yield models significantly explained more than 70% variability in the data (p-value 0.8), in both cases. The root mean square error (RMSE) and mean absolute error (MAE) used to measure the model accuracy revealed the consistency between the estimated yields and the government's yield statistics. This study demonstrates advantages of using EVI2-based phenology metrics (derived from Landsat-MODIS fusion data) for rice yield estimation in Taiwan prior to the harvest period.

DLA Energy Biofuel Feedstock Metrics Study

Science.gov (United States)

2012-12-11

moderately/highly in- vasive  Metric 2: Genetically modified organism ( GMO ) hazard, Yes/No and Hazard Category  Metric 3: Species hybridization...4– biofuel distribution Stage # 5– biofuel use Metric 1: State inva- siveness ranking Yes Minimal Minimal No No Metric 2: GMO hazard Yes...may utilize GMO microbial or microalgae species across the applicable biofuel life cycles (stages 1–3). The following consequence Metrics 4–6 then

Handwritten document age classification based on handwriting styles

Science.gov (United States)

Ramaiah, Chetan; Kumar, Gaurav; Govindaraju, Venu

2012-01-01

Handwriting styles are constantly changing over time. We approach the novel problem of estimating the approximate age of Historical Handwritten Documents using Handwriting styles. This system will have many applications in handwritten document processing engines where specialized processing techniques can be applied based on the estimated age of the document. We propose to learn a distribution over styles across centuries using Topic Models and to apply a classifier over weights learned in order to estimate the approximate age of the documents. We present a comparison of different distance metrics such as Euclidean Distance and Hellinger Distance within this application.

Modified fuzzy c-means applied to a Bragg grating-based spectral imager for material clustering

Science.gov (United States)

Rodríguez, Aida; Nieves, Juan Luis; Valero, Eva; Garrote, Estíbaliz; Hernández-Andrés, Javier; Romero, Javier

2012-01-01

We have modified the Fuzzy C-Means algorithm for an application related to segmentation of hyperspectral images. Classical fuzzy c-means algorithm uses Euclidean distance for computing sample membership to each cluster. We have introduced a different distance metric, Spectral Similarity Value (SSV), in order to have a more convenient similarity measure for reflectance information. SSV distance metric considers both magnitude difference (by the use of Euclidean distance) and spectral shape (by the use of Pearson correlation). Experiments confirmed that the introduction of this metric improves the quality of hyperspectral image segmentation, creating spectrally more dense clusters and increasing the number of correctly classified pixels.

Water quality assessment with hierarchical cluster analysis based on Mahalanobis distance.

Science.gov (United States)

Du, Xiangjun; Shao, Fengjing; Wu, Shunyao; Zhang, Hanlin; Xu, Si

2017-07-01

Water quality assessment is crucial for assessment of marine eutrophication, prediction of harmful algal blooms, and environment protection. Previous studies have developed many numeric modeling methods and data driven approaches for water quality assessment. The cluster analysis, an approach widely used for grouping data, has also been employed. However, there are complex correlations between water quality variables, which play important roles in water quality assessment but have always been overlooked. In this paper, we analyze correlations between water quality variables and propose an alternative method for water quality assessment with hierarchical cluster analysis based on Mahalanobis distance. Further, we cluster water quality data collected form coastal water of Bohai Sea and North Yellow Sea of China, and apply clustering results to evaluate its water quality. To evaluate the validity, we also cluster the water quality data with cluster analysis based on Euclidean distance, which are widely adopted by previous studies. The results show that our method is more suitable for water quality assessment with many correlated water quality variables. To our knowledge, it is the first attempt to apply Mahalanobis distance for coastal water quality assessment.

Temporal variability of daily personal magnetic field exposure metrics in pregnant women.

Science.gov (United States)

Lewis, Ryan C; Evenson, Kelly R; Savitz, David A; Meeker, John D

2015-01-01

Recent epidemiology studies of power-frequency magnetic fields and reproductive health have characterized exposures using data collected from personal exposure monitors over a single day, possibly resulting in exposure misclassification due to temporal variability in daily personal magnetic field exposure metrics, but relevant data in adults are limited. We assessed the temporal variability of daily central tendency (time-weighted average, median) and peak (upper percentiles, maximum) personal magnetic field exposure metrics over 7 consecutive days in 100 pregnant women. When exposure was modeled as a continuous variable, central tendency metrics had substantial reliability, whereas peak metrics had fair (maximum) to moderate (upper percentiles) reliability. The predictive ability of a single-day metric to accurately classify participants into exposure categories based on a weeklong metric depended on the selected exposure threshold, with sensitivity decreasing with increasing exposure threshold. Consistent with the continuous measures analysis, sensitivity was higher for central tendency metrics than for peak metrics. If there is interest in peak metrics, more than 1 day of measurement is needed over the window of disease susceptibility to minimize measurement error, but 1 day may be sufficient for central tendency metrics.

A Model-Based Approach to Constructing Music Similarity Functions

Directory of Open Access Journals (Sweden)

Lamere Paul

2007-01-01

Full Text Available Several authors have presented systems that estimate the audio similarity of two pieces of music through the calculation of a distance metric, such as the Euclidean distance, between spectral features calculated from the audio, related to the timbre or pitch of the signal. These features can be augmented with other, temporally or rhythmically based features such as zero-crossing rates, beat histograms, or fluctuation patterns to form a more well-rounded music similarity function. It is our contention that perceptual or cultural labels, such as the genre, style, or emotion of the music, are also very important features in the perception of music. These labels help to define complex regions of similarity within the available feature spaces. We demonstrate a machine-learning-based approach to the construction of a similarity metric, which uses this contextual information to project the calculated features into an intermediate space where a music similarity function that incorporates some of the cultural information may be calculated.

A Model-Based Approach to Constructing Music Similarity Functions

Science.gov (United States)

West, Kris; Lamere, Paul

2006-12-01

Several authors have presented systems that estimate the audio similarity of two pieces of music through the calculation of a distance metric, such as the Euclidean distance, between spectral features calculated from the audio, related to the timbre or pitch of the signal. These features can be augmented with other, temporally or rhythmically based features such as zero-crossing rates, beat histograms, or fluctuation patterns to form a more well-rounded music similarity function. It is our contention that perceptual or cultural labels, such as the genre, style, or emotion of the music, are also very important features in the perception of music. These labels help to define complex regions of similarity within the available feature spaces. We demonstrate a machine-learning-based approach to the construction of a similarity metric, which uses this contextual information to project the calculated features into an intermediate space where a music similarity function that incorporates some of the cultural information may be calculated.

MOND rotation curves for spiral galaxies with Cepheid-based distances

NARCIS (Netherlands)

Bottema, R; Pestana, JLG; Rothberg, B; Sanders, RH

2002-01-01

Rotation curves for four spiral galaxies with recently determined Cepheid-based distances are reconsidered in terms of modified Newtonian dynamics (MOND). For two of the objects, NGC 2403 and NGC 7331, the rotation curves predicted by MOND are compatible with the observed curves when these galaxies

METRICS FOR DYNAMIC SCALING OF DATABASE IN CLOUDS

Directory of Open Access Journals (Sweden)

Alexander V. Boichenko

2013-01-01

Full Text Available This article analyzes the main methods of scaling databases (replication, sharding and their support at the popular relational databases and NoSQL solutions with different data models: a document-oriented, key-value, column-oriented, graph. The article provides an assessment of the capabilities of modern cloud-based solution and gives a model for the organization of dynamic scaling in the cloud infrastructure. In the article are analyzed different types of metrics and are included the basic metrics that characterize the functioning parameters and database technology, as well as sets the goals of the integral metrics, necessary for the implementation of adaptive algorithms for dynamic scaling databases in the cloud infrastructure. This article was prepared with the support of RFBR grant № 13-07-00749.

Biomechanical CT Metrics Are Associated With Patient Outcomes in COPD

Science.gov (United States)

Bodduluri, Sandeep; Bhatt, Surya P; Hoffman, Eric A.; Newell, John D.; Martinez, Carlos H.; Dransfield, Mark T.; Han, Meilan K.; Reinhardt, Joseph M.

2017-01-01

Background Traditional metrics of lung disease such as those derived from spirometry and static single-volume CT images are used to explain respiratory morbidity in patients with chronic obstructive pulmonary disease (COPD), but are insufficient. We hypothesized that the mean Jacobian determinant, a measure of local lung expansion and contraction with respiration, would contribute independently to clinically relevant functional outcomes. Methods We applied image registration techniques to paired inspiratory-expiratory CT scans and derived the Jacobian determinant of the deformation field between the two lung volumes to map local volume change with respiration. We analyzed 490 participants with COPD with multivariable regression models to assess strengths of association between traditional CT metrics of disease and the Jacobian determinant with respiratory morbidity including dyspnea (mMRC), St Georges Respiratory Questionnaire (SGRQ) score, six-minute walk distance (6MWD), and the BODE index, as well as all-cause mortality. Results The Jacobian determinant was significantly associated with SGRQ (adjusted regression co-efficient β = −11.75,95%CI −21.6 to −1.7;p=0.020), and with 6MWD (β=321.15, 95%CI 134.1 to 508.1;p<0.001), independent of age, sex, race, body-mass-index, FEV1, smoking pack-years, CT emphysema, CT gas trapping, airway wall thickness, and CT scanner protocol. The mean Jacobian determinant was also independently associated with the BODE index (β= −0.41, 95%CI −0.80 to −0.02; p = 0.039), and mortality on follow-up (adjusted hazards ratio = 4.26, 95%CI = 0.93 to 19.23; p = 0.064). Conclusion Biomechanical metrics representing local lung expansion and contraction improve prediction of respiratory morbidity and mortality and offer additional prognostic information beyond traditional measures of lung function and static single-volume CT metrics. PMID:28044005

A no-reference image and video visual quality metric based on machine learning

Science.gov (United States)

Frantc, Vladimir; Voronin, Viacheslav; Semenishchev, Evgenii; Minkin, Maxim; Delov, Aliy

2018-04-01

The paper presents a novel visual quality metric for lossy compressed video quality assessment. High degree of correlation with subjective estimations of quality is due to using of a convolutional neural network trained on a large amount of pairs video sequence-subjective quality score. We demonstrate how our predicted no-reference quality metric correlates with qualitative opinion in a human observer study. Results are shown on the EVVQ dataset with comparison existing approaches.

An Evaluation of the IntelliMetric[SM] Essay Scoring System

Science.gov (United States)

Rudner, Lawrence M.; Garcia, Veronica; Welch, Catherine

2006-01-01

This report provides a two-part evaluation of the IntelliMetric[SM] automated essay scoring system based on its performance scoring essays from the Analytic Writing Assessment of the Graduate Management Admission Test[TM] (GMAT[TM]). The IntelliMetric system performance is first compared to that of individual human raters, a Bayesian system…

Symmetries of Taub-NUT dual metrics

International Nuclear Information System (INIS)

Baleanu, D.; Codoban, S.

1998-01-01

Recently geometric duality was analyzed for a metric which admits Killing tensors. An interesting example arises when the manifold has Killing-Yano tensors. The symmetries of the dual metrics in the case of Taub-NUT metric are investigated. Generic and non-generic symmetries of dual Taub-NUT metric are analyzed

Computational Analysis of Distance Operators for the Iterative Closest Point Algorithm.

Directory of Open Access Journals (Sweden)

Higinio Mora

Full Text Available The Iterative Closest Point (ICP algorithm is currently one of the most popular methods for rigid registration so that it has become the standard in the Robotics and Computer Vision communities. Many applications take advantage of it to align 2D/3D surfaces due to its popularity and simplicity. Nevertheless, some of its phases present a high computational cost thus rendering impossible some of its applications. In this work, it is proposed an efficient approach for the matching phase of the Iterative Closest Point algorithm. This stage is the main bottleneck of that method so that any efficiency improvement has a great positive impact on the performance of the algorithm. The proposal consists in using low computational cost point-to-point distance metrics instead of classic Euclidean one. The candidates analysed are the Chebyshev and Manhattan distance metrics due to their simpler formulation. The experiments carried out have validated the performance, robustness and quality of the proposal. Different experimental cases and configurations have been set up including a heterogeneous set of 3D figures, several scenarios with partial data and random noise. The results prove that an average speed up of 14% can be obtained while preserving the convergence properties of the algorithm and the quality of the final results.

REPRESENTATIONS OF DISTANCE: DIFFERENCES IN UNDERSTANDING DISTANCE ACCORDING TO TRAVEL METHOD

Directory of Open Access Journals (Sweden)

Gunvor Riber Larsen

2017-12-01

Full Text Available This paper explores how Danish tourists represent distance in relation to their holiday mobility and how these representations of distance are a result of being aero-mobile as opposed to being land-mobile. Based on interviews with Danish tourists, whose holiday mobility ranges from the European continent to global destinations, the first part of this qualitative study identifies three categories of representations of distance that show how distance is being ‘translated’ by the tourists into non-geometric forms: distance as resources, distance as accessibility, and distance as knowledge. The representations of distance articulated by the Danish tourists show that distance is often not viewed in ‘just’ kilometres. Rather, it is understood in forms that express how transcending the physical distance through holiday mobility is dependent on individual social and economic contexts, and on whether the journey was undertaken by air or land. The analysis also shows that being aeromobile is the holiday transportation mode that removes the tourists the furthest away from physical distance, resulting in the distance travelled by air being represented in ways that have the least correlation, in the tourists’ minds, with physical distance measured in kilometres.

Retrospective group fusion similarity search based on eROCE evaluation metric.

Science.gov (United States)

Avram, Sorin I; Crisan, Luminita; Bora, Alina; Pacureanu, Liliana M; Avram, Stefana; Kurunczi, Ludovic

2013-03-01

In this study, a simple evaluation metric, denoted as eROCE was proposed to measure the early enrichment of predictive methods. We demonstrated the superior robustness of eROCE compared to other known metrics throughout several active to inactive ratios ranging from 1:10 to 1:1000. Group fusion similarity search was investigated by varying 16 similarity coefficients, five molecular representations (binary and non-binary) and two group fusion rules using two reference structure set sizes. We used a dataset of 3478 actives and 43,938 inactive molecules and the enrichment was analyzed by means of eROCE. This retrospective study provides optimal similarity search parameters in the case of ALDH1A1 inhibitors. Copyright © 2013 Elsevier Ltd. All rights reserved.

Femtosecond frequency comb based distance measurement in air.

Science.gov (United States)

Balling, Petr; Kren, Petr; Masika, Pavel; van den Berg, S A

2009-05-25

Interferometric measurement of distance using a femtosecond frequency comb is demonstrated and compared with a counting interferometer displacement measurement. A numerical model of pulse propagation in air is developed and the results are compared with experimental data for short distances. The relative agreement for distance measurement in known laboratory conditions is better than 10(-7). According to the model, similar precision seems feasible even for long-distance measurement in air if conditions are sufficiently known. It is demonstrated that the relative width of the interferogram envelope even decreases with the measured length, and a fringe contrast higher than 90% could be obtained for kilometer distances in air, if optimal spectral width for that length and wavelength is used. The possibility of comb radiation delivery to the interferometer by an optical fiber is shown by model and experiment, which is important from a practical point of view.

Technical Privacy Metrics: a Systematic Survey

OpenAIRE

Wagner, Isabel; Eckhoff, David

2018-01-01

The file attached to this record is the author's final peer reviewed version The goal of privacy metrics is to measure the degree of privacy enjoyed by users in a system and the amount of protection offered by privacy-enhancing technologies. In this way, privacy metrics contribute to improving user privacy in the digital world. The diversity and complexity of privacy metrics in the literature makes an informed choice of metrics challenging. As a result, instead of using existing metrics, n...

Measures of agreement between computation and experiment:validation metrics.

Energy Technology Data Exchange (ETDEWEB)

Barone, Matthew Franklin; Oberkampf, William Louis

2005-08-01

With the increasing role of computational modeling in engineering design, performance estimation, and safety assessment, improved methods are needed for comparing computational results and experimental measurements. Traditional methods of graphically comparing computational and experimental results, though valuable, are essentially qualitative. Computable measures are needed that can quantitatively compare computational and experimental results over a range of input, or control, variables and sharpen assessment of computational accuracy. This type of measure has been recently referred to as a validation metric. We discuss various features that we believe should be incorporated in a validation metric and also features that should be excluded. We develop a new validation metric that is based on the statistical concept of confidence intervals. Using this fundamental concept, we construct two specific metrics: one that requires interpolation of experimental data and one that requires regression (curve fitting) of experimental data. We apply the metrics to three example problems: thermal decomposition of a polyurethane foam, a turbulent buoyant plume of helium, and compressibility effects on the growth rate of a turbulent free-shear layer. We discuss how the present metrics are easily interpretable for assessing computational model accuracy, as well as the impact of experimental measurement uncertainty on the accuracy assessment.

A method for assigning species into groups based on generalized Mahalanobis distance between habitat model coefficients

Science.gov (United States)

Williams, C.J.; Heglund, P.J.

2009-01-01

Habitat association models are commonly developed for individual animal species using generalized linear modeling methods such as logistic regression. We considered the issue of grouping species based on their habitat use so that management decisions can be based on sets of species rather than individual species. This research was motivated by a study of western landbirds in northern Idaho forests. The method we examined was to separately fit models to each species and to use a generalized Mahalanobis distance between coefficient vectors to create a distance matrix among species. Clustering methods were used to group species from the distance matrix, and multidimensional scaling methods were used to visualize the relations among species groups. Methods were also discussed for evaluating the sensitivity of the conclusions because of outliers or influential data points. We illustrate these methods with data from the landbird study conducted in northern Idaho. Simulation results are presented to compare the success of this method to alternative methods using Euclidean distance between coefficient vectors and to methods that do not use habitat association models. These simulations demonstrate that our Mahalanobis-distance- based method was nearly always better than Euclidean-distance-based methods or methods not based on habitat association models. The methods used to develop candidate species groups are easily explained to other scientists and resource managers since they mainly rely on classical multivariate statistical methods. ?? 2008 Springer Science+Business Media, LLC.

Generalized Painleve-Gullstrand metrics

Energy Technology Data Exchange (ETDEWEB)

Lin Chunyu [Department of Physics, National Cheng Kung University, Tainan 70101, Taiwan (China)], E-mail: l2891112@mail.ncku.edu.tw; Soo Chopin [Department of Physics, National Cheng Kung University, Tainan 70101, Taiwan (China)], E-mail: cpsoo@mail.ncku.edu.tw

2009-02-02

An obstruction to the implementation of spatially flat Painleve-Gullstrand (PG) slicings is demonstrated, and explicitly discussed for Reissner-Nordstroem and Schwarzschild-anti-deSitter spacetimes. Generalizations of PG slicings which are not spatially flat but which remain regular at the horizons are introduced. These metrics can be obtained from standard spherically symmetric metrics by physical Lorentz boosts. With these generalized PG metrics, problematic contributions to the imaginary part of the action in the Parikh-Wilczek derivation of Hawking radiation due to the obstruction can be avoided.

Flight Crew State Monitoring Metrics, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — eSky will develop specific crew state metrics based on the timeliness, tempo and accuracy of pilot inputs required by the H-mode Flight Control System (HFCS)....

Kerr metric in the deSitter background

International Nuclear Information System (INIS)

Vaidya, P.C.

1984-01-01

In addition to the Kerr metric with cosmological constant Λ several other metrics are presented giving a Kerr-like solution of Einstein's equations in the background of deSitter universe. A new metric of what may be termed as rotating deSitter space-time devoid of matter but containing null fluid with twisting null rays, has been presented. This metric reduces to the standard deSitter metric when the twist in the rays vanishes. Kerr metric in this background is the immediate generalization of Schwarzschild's exterior metric with cosmological constant. (author)

FACTORS AND METRICS THAT INFLUENCE FRANCHISEE PERFORMANCE: AN APPROACH BASED ON BRAZILIAN FRANCHISES

OpenAIRE

Aguiar, Helder de Souza; Consoni, Flavia

2017-01-01

The article searches to map the manager’s decisions in order to understand what has been the franchisor system for choose regarding to characteristics, and what the metrics has been adopted to measure the performance Though 15 interviews with Brazilian franchise there was confirmation that revenue is the main metric used by national franchises to measure performance, although other indicators are also used in a complementary way. In addition, two other factors were cited by the interviewees a...

Relevance of motion-related assessment metrics in laparoscopic surgery.

Science.gov (United States)

Oropesa, Ignacio; Chmarra, Magdalena K; Sánchez-González, Patricia; Lamata, Pablo; Rodrigues, Sharon P; Enciso, Silvia; Sánchez-Margallo, Francisco M; Jansen, Frank-Willem; Dankelman, Jenny; Gómez, Enrique J

2013-06-01

Motion metrics have become an important source of information when addressing the assessment of surgical expertise. However, their direct relationship with the different surgical skills has not been fully explored. The purpose of this study is to investigate the relevance of motion-related metrics in the evaluation processes of basic psychomotor laparoscopic skills and their correlation with the different abilities sought to measure. A framework for task definition and metric analysis is proposed. An explorative survey was first conducted with a board of experts to identify metrics to assess basic psychomotor skills. Based on the output of that survey, 3 novel tasks for surgical assessment were designed. Face and construct validation was performed, with focus on motion-related metrics. Tasks were performed by 42 participants (16 novices, 22 residents, and 4 experts). Movements of the laparoscopic instruments were registered with the TrEndo tracking system and analyzed. Time, path length, and depth showed construct validity for all 3 tasks. Motion smoothness and idle time also showed validity for tasks involving bimanual coordination and tasks requiring a more tactical approach, respectively. Additionally, motion smoothness and average speed showed a high internal consistency, proving them to be the most task-independent of all the metrics analyzed. Motion metrics are complementary and valid for assessing basic psychomotor skills, and their relevance depends on the skill being evaluated. A larger clinical implementation, combined with quality performance information, will give more insight on the relevance of the results shown in this study.

Shape anisotropy: tensor distance to anisotropy measure

Science.gov (United States)

Weldeselassie, Yonas T.; El-Hilo, Saba; Atkins, M. S.

2011-03-01

Fractional anisotropy, defined as the distance of a diffusion tensor from its closest isotropic tensor, has been extensively studied as quantitative anisotropy measure for diffusion tensor magnetic resonance images (DT-MRI). It has been used to reveal the white matter profile of brain images, as guiding feature for seeding and stopping in fiber tractography and for the diagnosis and assessment of degenerative brain diseases. Despite its extensive use in DT-MRI community, however, not much attention has been given to the mathematical correctness of its derivation from diffusion tensors which is achieved using Euclidean dot product in 9D space. But, recent progress in DT-MRI has shown that the space of diffusion tensors does not form a Euclidean vector space and thus Euclidean dot product is not appropriate for tensors. In this paper, we propose a novel and robust rotationally invariant diffusion anisotropy measure derived using the recently proposed Log-Euclidean and J-divergence tensor distance measures. An interesting finding of our work is that given a diffusion tensor, its closest isotropic tensor is different for different tensor distance metrics used. We demonstrate qualitatively that our new anisotropy measure reveals superior white matter profile of DT-MR brain images and analytically show that it has a higher signal to noise ratio than fractional anisotropy.

Dialect distances based on orthographic and phonetic transcriptions

CSIR Research Space (South Africa)

Zulu, N

2006-11-01

Full Text Available , where transcription segments were compared using the algorithm. In 2003 Gooskens and Heeringa [5] calculated Levenshtein distances between 15 Norwegian dialects and compared them to the distances as perceived by Norwegian listeners... by a clustering algorithm. Figure 2 illustrates the dendrogram derived from the clustering of perceptual distances as perceived by Norwegian listeners for the 15 Norwegian dialects investigated in this research [6]. Figure 2: Dendrogram...

Tourists consuming distance

DEFF Research Database (Denmark)

Larsen, Gunvor Riber

The environmental impact of tourism mobility is linked to the distances travelled in order to reach a holiday destination, and with tourists travelling more and further than previously, an understanding of how the tourists view the distance they travel across becomes relevant. Based on interviews...... contribute to an understanding of how it is possible to change tourism travel behaviour towards becoming more sustainable. How tourists 'consume distance' is discussed, from the practical level of actually driving the car or sitting in the air plane, to the symbolic consumption of distance that occurs when...... travelling on holiday becomes part of a lifestyle and a social positioning game. Further, different types of tourist distance consumers are identified, ranging from the reluctant to the deliberate and nonchalant distance consumers, who display very differing attitudes towards the distance they all travel...

Physics in space-time with scale-dependent metrics

Science.gov (United States)

Balankin, Alexander S.

2013-10-01

We construct three-dimensional space Rγ3 with the scale-dependent metric and the corresponding Minkowski space-time Mγ,β4 with the scale-dependent fractal (DH) and spectral (DS) dimensions. The local derivatives based on scale-dependent metrics are defined and differential vector calculus in Rγ3 is developed. We state that Mγ,β4 provides a unified phenomenological framework for dimensional flow observed in quite different models of quantum gravity. Nevertheless, the main attention is focused on the special case of flat space-time M1/3,14 with the scale-dependent Cantor-dust-like distribution of admissible states, such that DH increases from DH=2 on the scale ≪ℓ0 to DH=4 in the infrared limit ≫ℓ0, where ℓ0 is the characteristic length (e.g. the Planck length, or characteristic size of multi-fractal features in heterogeneous medium), whereas DS≡4 in all scales. Possible applications of approach based on the scale-dependent metric to systems of different nature are briefly discussed.

Analysis on the Metrics used in Optimizing Electronic Business based on Learning Techniques

Directory of Open Access Journals (Sweden)

Irina-Steliana STAN

2014-09-01

Full Text Available The present paper proposes a methodology of analyzing the metrics related to electronic business. The drafts of the optimizing models include KPIs that can highlight the business specific, if only they are integrated by using learning-based techniques. Having set the most important and high-impact elements of the business, the models should get in the end the link between them, by automating business flows. The human resource will be found in the situation of collaborating more and more with the optimizing models which will translate into high quality decisions followed by profitability increase.

Metrics for Electronic-Nursing-Record-Based Narratives: Cross-sectional Analysis

Science.gov (United States)

Kim, Kidong; Jeong, Suyeon; Lee, Kyogu; Park, Hyeoun-Ae; Min, Yul Ha; Lee, Joo Yun; Kim, Yekyung; Yoo, Sooyoung; Doh, Gippeum

2016-01-01

Summary Objectives We aimed to determine the characteristics of quantitative metrics for nursing narratives documented in electronic nursing records and their association with hospital admission traits and diagnoses in a large data set not limited to specific patient events or hypotheses. Methods We collected 135,406,873 electronic, structured coded nursing narratives from 231,494 hospital admissions of patients discharged between 2008 and 2012 at a tertiary teaching institution that routinely uses an electronic health records system. The standardized number of nursing narratives (i.e., the total number of nursing narratives divided by the length of the hospital stay) was suggested to integrate the frequency and quantity of nursing documentation. Results The standardized number of nursing narratives was higher for patients aged 70 years (median = 30.2 narratives/day, interquartile range [IQR] = 24.0–39.4 narratives/day), long (8 days) hospital stays (median = 34.6 narratives/day, IQR = 27.2–43.5 narratives/day), and hospital deaths (median = 59.1 narratives/day, IQR = 47.0–74.8 narratives/day). The standardized number of narratives was higher in “pregnancy, childbirth, and puerperium” (median = 46.5, IQR = 39.0–54.7) and “diseases of the circulatory system” admissions (median = 35.7, IQR = 29.0–43.4). Conclusions Diverse hospital admissions can be consistently described with nursing-document-derived metrics for similar hospital admissions and diagnoses. Some areas of hospital admissions may have consistently increasing volumes of nursing documentation across years. Usability of electronic nursing document metrics for evaluating healthcare requires multiple aspects of hospital admissions to be considered. PMID:27901174

Cophenetic metrics for phylogenetic trees, after Sokal and Rohlf.

Science.gov (United States)

Cardona, Gabriel; Mir, Arnau; Rosselló, Francesc; Rotger, Lucía; Sánchez, David

2013-01-16

Phylogenetic tree comparison metrics are an important tool in the study of evolution, and hence the definition of such metrics is an interesting problem in phylogenetics. In a paper in Taxon fifty years ago, Sokal and Rohlf proposed to measure quantitatively the difference between a pair of phylogenetic trees by first encoding them by means of their half-matrices of cophenetic values, and then comparing these matrices. This idea has been used several times since then to define dissimilarity measures between phylogenetic trees but, to our knowledge, no proper metric on weighted phylogenetic trees with nested taxa based on this idea has been formally defined and studied yet. Actually, the cophenetic values of pairs of different taxa alone are not enough to single out phylogenetic trees with weighted arcs or nested taxa. For every (rooted) phylogenetic tree T, let its cophenetic vectorφ(T) consist of all pairs of cophenetic values between pairs of taxa in T and all depths of taxa in T. It turns out that these cophenetic vectors single out weighted phylogenetic trees with nested taxa. We then define a family of cophenetic metrics dφ,p by comparing these cophenetic vectors by means of Lp norms, and we study, either analytically or numerically, some of their basic properties: neighbors, diameter, distribution, and their rank correlation with each other and with other metrics. The cophenetic metrics can be safely used on weighted phylogenetic trees with nested taxa and no restriction on degrees, and they can be computed in O(n2) time, where n stands for the number of taxa. The metrics dφ,1 and dφ,2 have positive skewed distributions, and they show a low rank correlation with the Robinson-Foulds metric and the nodal metrics, and a very high correlation with each other and with the splitted nodal metrics. The diameter of dφ,p, for p⩾1 , is in O(n(p+2)/p), and thus for low p they are more discriminative, having a wider range of values.

IMPACT OF COMPUTER BASED ONLINE ENTREPRENEURSHIP DISTANCE EDUCATION IN INDIA

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Bhagwan SHREE RAM

2012-07-01

Full Text Available The success of Indian enterprises and professionals in the computer and information technology (CIT domain during the twenty year has been spectacular. Entrepreneurs, bureaucrats and technocrats are now advancing views about how India can ride CIT bandwagon and leapfrog into a knowledge-based economy in the area of entrepreneurship distance education on-line. Isolated instances of remotely located villagers sending and receiving email messages, effective application of mobile communications and surfing the Internet are being promoted as examples of how the nation can achieve this transformation, while vanquishing socio-economic challenges such as illiteracy, high growth of population, poverty, and the digital divide along the way. Likewise, even while a small fraction of the urban population in India has access to computers and the Internet, e-governance is being projected as the way of the future. There is no dearth of fascinating stories about CIT enabled changes, yet there is little discussion about whether such changes are effective and sustainable in the absence of the basic infrastructure that is accessible to the citizens of more advanced economies. When used appropriately, different CITs are said to help expand access to entrepreneurship distance education, strengthen the relevance of education to the increasingly digital workplace, and raise technical and managerial educational quality by, among others, helping make teaching and learning into an engaging, active process connected to real life. This research paper investigates on the impact of computer based online entrepreneurship distance education in India.

Metrical presentation boosts implicit learning of artificial grammar.

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Selchenkova, Tatiana; François, Clément; Schön, Daniele; Corneyllie, Alexandra; Perrin, Fabien; Tillmann, Barbara

2014-01-01

The present study investigated whether a temporal hierarchical structure favors implicit learning. An artificial pitch grammar implemented with a set of tones was presented in two different temporal contexts, notably with either a strongly metrical structure or an isochronous structure. According to the Dynamic Attending Theory, external temporal regularities can entrain internal oscillators that guide attention over time, allowing for temporal expectations that influence perception of future events. Based on this framework, it was hypothesized that the metrical structure provides a benefit for artificial grammar learning in comparison to an isochronous presentation. Our study combined behavioral and event-related potential measurements. Behavioral results demonstrated similar learning in both participant groups. By contrast, analyses of event-related potentials showed a larger P300 component and an earlier N2 component for the strongly metrical group during the exposure phase and the test phase, respectively. These findings suggests that the temporal expectations in the strongly metrical condition helped listeners to better process the pitch dimension, leading to improved learning of the artificial grammar.

Teaching Reading Comprehension in English in a Distance Web-Based Course: New Roles for Teachers

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Jorge Hugo Muñoz Marín

2010-10-01

Full Text Available Distance web-based learning is a popular strategy in ELT teaching in Colombia. Despite of the growth of experiences, there are very few studies regarding teachers' participation in these courses. This paper reports preliminary findings of an on-going study aiming at exploring the roles that a teacher plays in an efl reading comprehension distance web-based course. Data analysis suggests that teachers play new roles solving technical problems, providing immediate feedback, interacting with students in a non traditional way, providing time management advice, and acting as a constant motivator. The authors conclude that EFL teachers require training for this new teaching roles and the analysis of web-based distance learning environments as an option under permanent construction that requires their active participation.

Two classes of metric spaces

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Isabel Garrido

2016-04-01

Full Text Available The class of metric spaces (X,d known as small-determined spaces, introduced by Garrido and Jaramillo, are properly defined by means of some type of real-valued Lipschitz functions on X. On the other hand, B-simple metric spaces introduced by Hejcman are defined in terms of some kind of bornologies of bounded subsets of X. In this note we present a common framework where both classes of metric spaces can be studied which allows us to see not only the relationships between them but also to obtain new internal characterizations of these metric properties.

An Emperical Analysis of Co-Movements in High- and Low-Frequency Metrics for Financial Market Efficiency

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D.M. Rösch (Dominik); A. Subrahmanyam (Avanidhar); M.A. van Dijk (Mathijs)

2014-01-01

textabstractSeveral high- and low-frequency metrics for financial market efficiency have been proposed in distinct lines of research. We explore the joint dynamics of these metrics. High-frequency metrics co-move across individual stocks, and also co-move with lower-frequency metrics based on

Datasets related to in-land water for limnology and remote sensing applications: distance-to-land, distance-to-water, water-body identifier and lake-centre co-ordinates.

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Carrea, Laura; Embury, Owen; Merchant, Christopher J

2015-11-01

Datasets containing information to locate and identify water bodies have been generated from data locating static-water-bodies with resolution of about 300 m (1/360 ∘ ) recently released by the Land Cover Climate Change Initiative (LC CCI) of the European Space Agency. The LC CCI water-bodies dataset has been obtained from multi-temporal metrics based on time series of the backscattered intensity recorded by ASAR on Envisat between 2005 and 2010. The new derived datasets provide coherently: distance to land, distance to water, water-body identifiers and lake-centre locations. The water-body identifier dataset locates the water bodies assigning the identifiers of the Global Lakes and Wetlands Database (GLWD), and lake centres are defined for in-land waters for which GLWD IDs were determined. The new datasets therefore link recent lake/reservoir/wetlands extent to the GLWD, together with a set of coordinates which locates unambiguously the water bodies in the database. Information on distance-to-land for each water cell and the distance-to-water for each land cell has many potential applications in remote sensing, where the applicability of geophysical retrieval algorithms may be affected by the presence of water or land within a satellite field of view (image pixel). During the generation and validation of the datasets some limitations of the GLWD database and of the LC CCI water-bodies mask have been found. Some examples of the inaccuracies/limitations are presented and discussed. Temporal change in water-body extent is common. Future versions of the LC CCI dataset are planned to represent temporal variation, and this will permit these derived datasets to be updated.