Timed Testing under Partial Observability
DEFF Research Database (Denmark)
David, Alexandre; Larsen, Kim Guldstrand; Li, Shuhao
2009-01-01
observability of SUT using a set of predicates over the TGA state space, and specify the test purposes in Computation Tree Logic (CTL) formulas. A recently developed partially observable timed game solver is used to generate winning strategies, which are used as test cases. We propose a conformance testing...
Emulating facial biomechanics using multivariate partial least squares surrogate models.
Wu, Tim; Martens, Harald; Hunter, Peter; Mithraratne, Kumar
2014-11-01
A detailed biomechanical model of the human face driven by a network of muscles is a useful tool in relating the muscle activities to facial deformations. However, lengthy computational times often hinder its applications in practical settings. The objective of this study is to replace precise but computationally demanding biomechanical model by a much faster multivariate meta-model (surrogate model), such that a significant speedup (to real-time interactive speed) can be achieved. Using a multilevel fractional factorial design, the parameter space of the biomechanical system was probed from a set of sample points chosen to satisfy maximal rank optimality and volume filling. The input-output relationship at these sampled points was then statistically emulated using linear and nonlinear, cross-validated, partial least squares regression models. It was demonstrated that these surrogate models can mimic facial biomechanics efficiently and reliably in real-time. Copyright © 2014 John Wiley & Sons, Ltd.
Emulating facial biomechanics using multivariate partial least squares surrogate models
Martens, Harald; Wu, Tim; Hunter, Peter; Mithraratne, Kumar
2014-01-01
This is the author’s final, accepted and refereed manuscript to the article. Locked until 2015-05-06 A detailed biomechanical model of the human face driven by a network of muscles is a useful tool in relating the muscle activities to facial deformations. However, lengthy computational times often hinder its applications in practical settings. The objective of this study is to replace precise but computationally demanding biomechanical model by a much faster multivariate meta-mode...
Method for statistical data analysis of multivariate observations
Gnanadesikan, R
1997-01-01
A practical guide for multivariate statistical techniques-- now updated and revised In recent years, innovations in computer technology and statistical methodologies have dramatically altered the landscape of multivariate data analysis. This new edition of Methods for Statistical Data Analysis of Multivariate Observations explores current multivariate concepts and techniques while retaining the same practical focus of its predecessor. It integrates methods and data-based interpretations relevant to multivariate analysis in a way that addresses real-world problems arising in many areas of inte
Synchronizing Strategies under Partial Observability
DEFF Research Database (Denmark)
Larsen, Kim Guldstrand; Laursen, Simon; Srba, Jiri
2014-01-01
Embedded devices usually share only partial information about their current configurations as the communication bandwidth can be restricted. Despite this, we may wish to bring a failed device into a given predetermined configuration. This problem, also known as resetting or synchronizing words, has...... been intensively studied for systems that do not provide any information about their configurations. In order to capture more general scenarios, we extend the existing theory of synchronizing words to synchronizing strategies, and study the synchronization, short-synchronization and subset...
Spreading paths in partially observed social networks
Onnela, Jukka-Pekka; Christakis, Nicholas A.
2012-03-01
Understanding how and how far information, behaviors, or pathogens spread in social networks is an important problem, having implications for both predicting the size of epidemics, as well as for planning effective interventions. There are, however, two main challenges for inferring spreading paths in real-world networks. One is the practical difficulty of observing a dynamic process on a network, and the other is the typical constraint of only partially observing a network. Using static, structurally realistic social networks as platforms for simulations, we juxtapose three distinct paths: (1) the stochastic path taken by a simulated spreading process from source to target; (2) the topologically shortest path in the fully observed network, and hence the single most likely stochastic path, between the two nodes; and (3) the topologically shortest path in a partially observed network. In a sampled network, how closely does the partially observed shortest path (3) emulate the unobserved spreading path (1)? Although partial observation inflates the length of the shortest path, the stochastic nature of the spreading process also frequently derails the dynamic path from the shortest path. We find that the partially observed shortest path does not necessarily give an inflated estimate of the length of the process path; in fact, partial observation may, counterintuitively, make the path seem shorter than it actually is.
Spreading paths in partially observed social networks.
Onnela, Jukka-Pekka; Christakis, Nicholas A
2012-03-01
Understanding how and how far information, behaviors, or pathogens spread in social networks is an important problem, having implications for both predicting the size of epidemics, as well as for planning effective interventions. There are, however, two main challenges for inferring spreading paths in real-world networks. One is the practical difficulty of observing a dynamic process on a network, and the other is the typical constraint of only partially observing a network. Using static, structurally realistic social networks as platforms for simulations, we juxtapose three distinct paths: (1) the stochastic path taken by a simulated spreading process from source to target; (2) the topologically shortest path in the fully observed network, and hence the single most likely stochastic path, between the two nodes; and (3) the topologically shortest path in a partially observed network. In a sampled network, how closely does the partially observed shortest path (3) emulate the unobserved spreading path (1)? Although partial observation inflates the length of the shortest path, the stochastic nature of the spreading process also frequently derails the dynamic path from the shortest path. We find that the partially observed shortest path does not necessarily give an inflated estimate of the length of the process path; in fact, partial observation may, counterintuitively, make the path seem shorter than it actually is.
Directory of Open Access Journals (Sweden)
Veyis Turut
2013-01-01
Full Text Available Two tecHniques were implemented, the Adomian decomposition method (ADM and multivariate Padé approximation (MPA, for solving nonlinear partial differential equations of fractional order. The fractional derivatives are described in Caputo sense. First, the fractional differential equation has been solved and converted to power series by Adomian decomposition method (ADM, then power series solution of fractional differential equation was put into multivariate Padé series. Finally, numerical results were compared and presented in tables and figures.
Observability of discretized partial differential equations
Cohn, Stephen E.; Dee, Dick P.
1988-01-01
It is shown that complete observability of the discrete model used to assimilate data from a linear partial differential equation (PDE) system is necessary and sufficient for asymptotic stability of the data assimilation process. The observability theory for discrete systems is reviewed and applied to obtain simple observability tests for discretized constant-coefficient PDEs. Examples are used to show how numerical dispersion can result in discrete dynamics with multiple eigenvalues, thereby detracting from observability.
Monitoring as a partially observable decision problem
Paul L. Fackler; Robert G. Haight
2014-01-01
Monitoring is an important and costly activity in resource man-agement problems such as containing invasive species, protectingendangered species, preventing soil erosion, and regulating con-tracts for environmental services. Recent studies have viewedoptimal monitoring as a Partially Observable Markov Decision Pro-cess (POMDP), which provides a framework for...
Spreading paths in partially observed social networks
Onnela, Jukka-Pekka; Christakis, Nicholas A.
2012-01-01
Understanding how and how far information, behaviors, or pathogens spread in social networks is an important problem, having implications for both predicting the size of epidemics, as well as for planning effective interventions. There are, however, two main challenges for inferring spreading paths in real-world networks. One is the practical difficulty of observing a dynamic process on a network, and the other is the typical constraint of only partially observing a network. Using a static, s...
Multivariate Statistical Inference of Lightning Occurrence, and Using Lightning Observations
Boccippio, Dennis
2004-01-01
Two classes of multivariate statistical inference using TRMM Lightning Imaging Sensor, Precipitation Radar, and Microwave Imager observation are studied, using nonlinear classification neural networks as inferential tools. The very large and globally representative data sample provided by TRMM allows both training and validation (without overfitting) of neural networks with many degrees of freedom. In the first study, the flashing / or flashing condition of storm complexes is diagnosed using radar, passive microwave and/or environmental observations as neural network inputs. The diagnostic skill of these simple lightning/no-lightning classifiers can be quite high, over land (above 80% Probability of Detection; below 20% False Alarm Rate). In the second, passive microwave and lightning observations are used to diagnose radar reflectivity vertical structure. A priori diagnosis of hydrometeor vertical structure is highly important for improved rainfall retrieval from either orbital radars (e.g., the future Global Precipitation Mission "mothership") or radiometers (e.g., operational SSM/I and future Global Precipitation Mission passive microwave constellation platforms), we explore the incremental benefit to such diagnosis provided by lightning observations.
Yan, Zhengbing; Kuang, Te-Hui; Yao, Yuan
2017-09-01
In recent years, multivariate statistical monitoring of batch processes has become a popular research topic, wherein multivariate fault isolation is an important step aiming at the identification of the faulty variables contributing most to the detected process abnormality. Although contribution plots have been commonly used in statistical fault isolation, such methods suffer from the smearing effect between correlated variables. In particular, in batch process monitoring, the high autocorrelations and cross-correlations that exist in variable trajectories make the smearing effect unavoidable. To address such a problem, a variable selection-based fault isolation method is proposed in this research, which transforms the fault isolation problem into a variable selection problem in partial least squares discriminant analysis and solves it by calculating a sparse partial least squares model. As different from the traditional methods, the proposed method emphasizes the relative importance of each process variable. Such information may help process engineers in conducting root-cause diagnosis. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Partial observation control in an anticipating environment
International Nuclear Information System (INIS)
Oeksendal, B; Sulem, A
2004-01-01
A study is made of a controlled stochastic system whose state X(t) at time t is described by a stochastic differential equation driven by Levy processes with filtration {F t } telementof[0,T] . The system is assumed to be anticipating, in the sense that the coefficients are assumed to be adapted to a filtration {G t } t≥0 with F t subset of equal G t for all t element of [0,T]. The corresponding anticipating stochastic differential equation is interpreted in the sense of forward integrals, which naturally generalize semimartingale integrals. The admissible controls are assumed to be adapted to a filtration {E t } telementof[0,T] such that E t subset of equal F t for all t element of [0,T]. The general problem is to maximize a given performance functional of this system over all admissible controls. This is a partial observation stochastic control problem in an anticipating environment. Examples of applications include stochastic volatity models in finance, insider influenced financial markets, and stochastic control of systems with delayed noise effects. Some particular cases in finance, involving optimal portfolios with logarithmic utility, are solved explicitly
Energy Technology Data Exchange (ETDEWEB)
Clegg, Samuel M [Los Alamos National Laboratory; Barefield, James E [Los Alamos National Laboratory; Wiens, Roger C [Los Alamos National Laboratory; Sklute, Elizabeth [MT HOLYOKE COLLEGE; Dyare, Melinda D [MT HOLYOKE COLLEGE
2008-01-01
Quantitative analysis with LIBS traditionally employs calibration curves that are complicated by the chemical matrix effects. These chemical matrix effects influence the LIBS plasma and the ratio of elemental composition to elemental emission line intensity. Consequently, LIBS calibration typically requires a priori knowledge of the unknown, in order for a series of calibration standards similar to the unknown to be employed. In this paper, three new Multivariate Analysis (MV A) techniques are employed to analyze the LIBS spectra of 18 disparate igneous and highly-metamorphosed rock samples. Partial Least Squares (PLS) analysis is used to generate a calibration model from which unknown samples can be analyzed. Principal Components Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA) are employed to generate a model and predict the rock type of the samples. These MV A techniques appear to exploit the matrix effects associated with the chemistries of these 18 samples.
Statistical Inference for Partially Observed Diffusion Processes
DEFF Research Database (Denmark)
Jensen, Anders Christian
This thesis is concerned with parameter estimation for multivariate diffusion models. It gives a short introduction to diffusion models, and related mathematical concepts. we then introduce the method of prediction-based estimating functions and describe in detail the application for a two......-Uhlenbeck process, while chapter eight describes the detials of an R-package that was developed in relations to the application of the estimationprocedure of chapters five and six....
Gravity Effects Observed In Partially Premixed Flames
Puri, Ishwar K.; Aggarwal, Suresh K.; Lock, Andrew J.; Gauguly, Ranjan; Hegde, Uday
2003-01-01
Partially premixed flames (PPFs) contain a rich premixed fuel air mixture in a pocket or stream, and, for complete combustion to occur, they require the transport of oxidizer from an appropriately oxidizer-rich (or fuel-lean) mixture that is present in another pocket or stream. Partial oxidation reactions occur in fuel-rich portions of the mixture and any remaining unburned fuel and/or intermediate species are consumed in the oxidizer-rich portions. Partial premixing, therefore, represents that condition when the equivalence ratio (phi) in one portion of the flowfield is greater than unity, and in another section its value is less than unity. In general, for combustion to occur efficiently, the global equivalence ratio is in the range fuel-lean to stoichiometric. These flames can be established by design by placing a fuel-rich mixture in contact with a fuel-lean mixture, but they also occur otherwise in many practical systems, which include nonpremixed lifted flames, turbulent nonpremixed combustion, spray flames, and unwanted fires. Other practical applications of PPFs are reported elsewhere. Although extensive experimental studies have been conducted on premixed and nonpremixed flames under microgravity, there is a absence of previous experimental work on burner stabilized PPFs in this regard. Previous numerical studies by our group employing a detailed numerical model showed gravity effects to be significant on the PPF structure. We report on the results of microgravity experiments conducted on two-dimensional (established on a Wolfhard-Parker slot burner) and axisymmetric flames (on a coannular burner) that were investigated in a self-contained multipurpose rig. Thermocouple and radiometer data were also used to characterize the thermal transport in the flame.
Reasoning about Strategies under Partial Observability and Fairness Constraints
Directory of Open Access Journals (Sweden)
Simon Busard
2013-03-01
Full Text Available A number of extensions exist for Alternating-time Temporal Logic; some of these mix strategies and partial observability but, to the best of our knowledge, no work provides a unified framework for strategies, partial observability and fairness constraints. In this paper we propose ATLK^F_po, a logic mixing strategies under partial observability and epistemic properties of agents in a system with fairness constraints on states, and we provide a model checking algorithm for it.
Stenlund, Hans; Johansson, Erik; Gottfries, Johan; Trygg, Johan
2009-01-01
Near infrared spectroscopy (NIR) was developed primarily for applications such as the quantitative determination of nutrients in the agricultural and food industries. Examples include the determination of water, protein, and fat within complex samples such as grain and milk. Because of its useful properties, NIR analysis has spread to other areas such as chemistry and pharmaceutical production. NIR spectra consist of infrared overtones and combinations thereof, making interpretation of the results complicated. It can be very difficult to assign peaks to known constituents in the sample. Thus, multivariate analysis (MVA) has been crucial in translating spectral data into information, mainly for predictive purposes. Orthogonal partial least squares (OPLS), a new MVA method, has prediction and modeling properties similar to those of other MVA techniques, e.g., partial least squares (PLS), a method with a long history of use for the analysis of NIR data. OPLS provides an intrinsic algorithmic improvement for the interpretation of NIR data. In this report, four sets of NIR data were analyzed to demonstrate the improved interpretation provided by OPLS. The first two sets included simulated data to demonstrate the overall principles; the third set comprised a statistically replicated design of experiments (DoE), to demonstrate how instrumental difference could be accurately visualized and correctly attributed to Wood's anomaly phenomena; the fourth set was chosen to challenge the MVA by using data relating to powder mixing, a crucial step in the pharmaceutical industry prior to tabletting. Improved interpretation by OPLS was demonstrated for all four examples, as compared to alternative MVA approaches. It is expected that OPLS will be used mostly in applications where improved interpretation is crucial; one such area is process analytical technology (PAT). PAT involves fewer independent samples, i.e., batches, than would be associated with agricultural applications; in
Observable consequences of partially degenerate leptogenesis
Ellis, Jonathan Richard; Yanagida, T; Ellis, John; Raidal, Martti
2002-01-01
In the context of the seesaw mechanism, it is natural that the large solar and atmospheric neutrino mixing angles originate separately from large 2 by 2 mixings in the neutrino and charged-lepton sectors, respectively, and large mixing in the neutrino couplings is in turn more plausible if two of the heavy singlet neutrinos are nearly degenerate. We study the phenomenology of this scenario, calculating leptogenesis by solving numerically the set of coupled Boltzmann equations for out-of-equilibrium heavy singlet neutrino decays in the minimal supersymmetric seesaw model. The near-degenerate neutrinos may weigh < 10^8 GeV, avoiding the cosmological gravitino problem. This scenario predicts that Br(mu to e gamma) should be strongly suppressed, because of the small singlet neutrino masses, whilst Br(tau to mu gamma) may be large enough to be observable in B-factory or LHC experiments. If the light neutrino masses are hierarchical, we predict that the neutrinoless double-beta decay parameter m_{ee} is approxim...
Nash Equilibria in Symmetric Graph Games with Partial Observation
DEFF Research Database (Denmark)
Bouyer, Patricia; Markey, Nicolas; Vester, Steen
2017-01-01
We investigate a model for representing large multiplayer games, which satisfy strong symmetry properties. This model is made of multiple copies of an arena; each player plays in his own arena, and can partially observe what the other players do. Therefore, this game has partial information...... and symmetry constraints, which make the computation of Nash equilibria difficult. We show several undecidability results, and for bounded-memory strategies, we precisely characterize the complexity of computing pure Nash equilibria for qualitative objectives in this game model....
Nash Equilibria in Symmetric Games with Partial Observation
DEFF Research Database (Denmark)
Bouyer, Patricia; Markey, Nicolas; Vester, Steen
2014-01-01
We investigate a model for representing large multiplayer games, which satisfy strong symmetry properties. This model is made of multiple copies of an arena; each player plays in his own arena, and can partially observe what the other players do. Therefore, this game has partial information...... and symmetry constraints, which make the computation of Nash equilibria difficult. We show several undecidability results, and for bounded-memory strategies, we precisely characterize the complexity of computing pure Nash equilibria (for qualitative objectives) in this game model....
MacDonald, Shannon E; Schopflocher, Donald P; Vaudry, Wendy
2014-01-01
Children who begin but do not fully complete the recommended series of childhood vaccines by 2 y of age are a much larger group than those who receive no vaccines. While parents who refuse all vaccines typically express concern about vaccine safety, it is critical to determine what influences parents of 'partially' immunized children. This case-control study examined whether parental concern about vaccine safety was responsible for partial immunization, and whether other personal or system-level factors played an important role. A random sample of parents of partially and completely immunized 2 y old children were selected from a Canadian regional immunization registry and completed a postal survey assessing various personal and system-level factors. Unadjusted odds ratios (OR) and adjusted ORs (aOR) were calculated with logistic regression. While vaccine safety concern was associated with partial immunization (OR 7.338, 95% CI 4.138-13.012), other variables were more strongly associated and reduced the strength of the relationship between concern and partial immunization in multivariable analysis (aOR 2.829, 95% CI 1.151-6.957). Other important factors included perceived disease susceptibility and severity (aOR 4.629, 95% CI 2.017-10.625), residential mobility (aOR 3.908, 95% CI 2.075-7.358), daycare use (aOR 0.310, 95% CI 0.144-0.671), number of needles administered at each visit (aOR 7.734, 95% CI 2.598-23.025) and access to a regular physician (aOR 0.219, 95% CI 0.057-0.846). While concern about vaccine safety may be addressed through educational strategies, this study suggests that additional program and policy-level strategies may positively impact immunization uptake.
A Partially Observed Markov Decision Process for Dynamic Pricing
Yossi Aviv; Amit Pazgal
2005-01-01
In this paper, we develop a stylized partially observed Markov decision process (POMDP) framework to study a dynamic pricing problem faced by sellers of fashion-like goods. We consider a retailer that plans to sell a given stock of items during a finite sales season. The objective of the retailer is to dynamically price the product in a way that maximizes expected revenues. Our model brings together various types of uncertainties about the demand, some of which are resolvable through sales ob...
Burgués, Javier; Marco, Santiago
2018-08-17
Metal oxide semiconductor (MOX) sensors are usually temperature-modulated and calibrated with multivariate models such as partial least squares (PLS) to increase the inherent low selectivity of this technology. The multivariate sensor response patterns exhibit heteroscedastic and correlated noise, which suggests that maximum likelihood methods should outperform PLS. One contribution of this paper is the comparison between PLS and maximum likelihood principal components regression (MLPCR) in MOX sensors. PLS is often criticized by the lack of interpretability when the model complexity increases beyond the chemical rank of the problem. This happens in MOX sensors due to cross-sensitivities to interferences, such as temperature or humidity and non-linearity. Additionally, the estimation of fundamental figures of merit, such as the limit of detection (LOD), is still not standardized in multivariate models. Orthogonalization methods, such as orthogonal projection to latent structures (O-PLS), have been successfully applied in other fields to reduce the complexity of PLS models. In this work, we propose a LOD estimation method based on applying the well-accepted univariate LOD formulas to the scores of the first component of an orthogonal PLS model. The resulting LOD is compared to the multivariate LOD range derived from error-propagation. The methodology is applied to data extracted from temperature-modulated MOX sensors (FIS SB-500-12 and Figaro TGS 3870-A04), aiming at the detection of low concentrations of carbon monoxide in the presence of uncontrolled humidity (chemical noise). We found that PLS models were simpler and more accurate than MLPCR models. Average LOD values of 0.79 ppm (FIS) and 1.06 ppm (Figaro) were found using the approach described in this paper. These values were contained within the LOD ranges obtained with the error-propagation approach. The mean LOD increased to 1.13 ppm (FIS) and 1.59 ppm (Figaro) when considering validation samples
Directory of Open Access Journals (Sweden)
M. Flach
2017-08-01
Full Text Available Today, many processes at the Earth's surface are constantly monitored by multiple data streams. These observations have become central to advancing our understanding of vegetation dynamics in response to climate or land use change. Another set of important applications is monitoring effects of extreme climatic events, other disturbances such as fires, or abrupt land transitions. One important methodological question is how to reliably detect anomalies in an automated and generic way within multivariate data streams, which typically vary seasonally and are interconnected across variables. Although many algorithms have been proposed for detecting anomalies in multivariate data, only a few have been investigated in the context of Earth system science applications. In this study, we systematically combine and compare feature extraction and anomaly detection algorithms for detecting anomalous events. Our aim is to identify suitable workflows for automatically detecting anomalous patterns in multivariate Earth system data streams. We rely on artificial data that mimic typical properties and anomalies in multivariate spatiotemporal Earth observations like sudden changes in basic characteristics of time series such as the sample mean, the variance, changes in the cycle amplitude, and trends. This artificial experiment is needed as there is no gold standard for the identification of anomalies in real Earth observations. Our results show that a well-chosen feature extraction step (e.g., subtracting seasonal cycles, or dimensionality reduction is more important than the choice of a particular anomaly detection algorithm. Nevertheless, we identify three detection algorithms (k-nearest neighbors mean distance, kernel density estimation, a recurrence approach and their combinations (ensembles that outperform other multivariate approaches as well as univariate extreme-event detection methods. Our results therefore provide an effective workflow to
The energy efficiency paradox revisited through a partial observability approach
International Nuclear Information System (INIS)
Kounetas, Kostas; Tsekouras, Kostas
2008-01-01
The present paper examines the energy efficiency paradox demonstrated in Greek manufacturing firms through a partial observability approach. The data set used has resulted from a survey carried out among 161 energy-saving technology firm adopters. Maximum likelihood estimates that arise from an incidental truncation model reveal that the adoption of the energy-saving technologies is indeed strongly correlated to the returns of assets that are required in order to undertake the corresponding investments. The source of the energy efficiency paradox lies within a wide range of factors. Policy schemes that aim to increase the adoption rate of energy-saving technologies within the field of manufacturing are significantly affected by differences in the size of firms. Finally, mixed policies seem to be more effective than policies that are only capital subsidy or regulation oriented
Partially Observed Mixtures of IRT Models: An Extension of the Generalized Partial-Credit Model
Von Davier, Matthias; Yamamoto, Kentaro
2004-01-01
The generalized partial-credit model (GPCM) is used frequently in educational testing and in large-scale assessments for analyzing polytomous data. Special cases of the generalized partial-credit model are the partial-credit model--or Rasch model for ordinal data--and the two parameter logistic (2PL) model. This article extends the GPCM to the…
Bayesian inference for multivariate point processes observed at sparsely distributed times
DEFF Research Database (Denmark)
Rasmussen, Jakob Gulddahl; Møller, Jesper; Aukema, B.H.
We consider statistical and computational aspects of simulation-based Bayesian inference for a multivariate point process which is only observed at sparsely distributed times. For specicity we consider a particular data set which has earlier been analyzed by a discrete time model involving unknown...... normalizing constants. We discuss the advantages and disadvantages of using continuous time processes compared to discrete time processes in the setting of the present paper as well as other spatial-temporal situations. Keywords: Bark beetle, conditional intensity, forest entomology, Markov chain Monte Carlo...
Robust Dynamics and Control of a Partially Observed Markov Chain
International Nuclear Information System (INIS)
Elliott, R. J.; Malcolm, W. P.; Moore, J. P.
2007-01-01
In a seminal paper, Martin Clark (Communications Systems and Random Process Theory, Darlington, 1977, pp. 721-734, 1978) showed how the filtered dynamics giving the optimal estimate of a Markov chain observed in Gaussian noise can be expressed using an ordinary differential equation. These results offer substantial benefits in filtering and in control, often simplifying the analysis and an in some settings providing numerical benefits, see, for example Malcolm et al. (J. Appl. Math. Stoch. Anal., 2007, to appear).Clark's method uses a gauge transformation and, in effect, solves the Wonham-Zakai equation using variation of constants. In this article, we consider the optimal control of a partially observed Markov chain. This problem is discussed in Elliott et al. (Hidden Markov Models Estimation and Control, Applications of Mathematics Series, vol. 29, 1995). The innovation in our results is that the robust dynamics of Clark are used to compute forward in time dynamics for a simplified adjoint process. A stochastic minimum principle is established
International Nuclear Information System (INIS)
Chernov, N.I.; Kurbatov, V.S.; Ososkov, G.A.
1988-01-01
Parameter estimation for multivariate probability distributions is studied in experiments where data are presented as one-dimensional hystograms. For this model a statistics defined as a quadratic form of the observed frequencies which has a limitig x 2 -distribution is proposed. The efficiency of the estimator minimizing the value of that statistics is proved whithin the class of all unibased estimates obtained via minimization of quadratic forms of observed frequencies. The elaborated method was applied to the physical problem of analysis of the secondary pion energy distribution in the isobar model of pion-nucleon interactions with the production of an additional pion. The numerical experiments showed that the accuracy of estimation is twice as much if comparing the conventional methods
Partial Linearization of Mechanical Systems with Application to Observer Design
Sarras, Ioannis; Venkatraman, Aneesh; Ortega, Romeo; Schaft, Arjan van der
2008-01-01
We consider general mechanical systems and establish a necessary and sufficient condition for the existence of a suitable change in the generalized momentum coordinates such that the new dynamics become linear in the transformed momenta. The class of systems which can be (partially) linearized by
An observation of a partially albinistic zenaida macroura (Mourning Dove)
Berdeen, James; Otis, D.L.
2011-01-01
Abstract Three of the 4 forms of albinism that occur in avifauna have been detected in Zenaida macroura (Mourning Dove). Albinism is rare in this species, and the incidence rate of each age and sex cohort is not well known. Consequently, we examined the pigmentation of Mourning Doves encountered in the Coastal Plain of South Carolina, and classified the age and sex of all individuals. One adult male Mourning Dove had unusually light coloration of some feathers and the upper mandible. This pigmentation is consistent with partial albinism. This was the only individual out of 10,749 examined that appeared to be albinistic. This low incidence rate of albinism supports the conclusion that this condition is relatively rare in Mourning Doves (Mirarchi 1993).
Polynomial Time Decidability of Weighted Synchronization under Partial Observability
DEFF Research Database (Denmark)
Kretínsky, Jan; Larsen, Kim Guldstrand; Laursen, Simon
2015-01-01
We consider weighted automata with both positive and negative integer weights on edges and study the problem of synchronization using adaptive strategies that may only observe whether the current weight-level is negative or nonnegative. We show that the synchronization problem is decidable...
Likelihood based inference for partially observed renewal processes
van Lieshout, Maria Nicolette Margaretha
2016-01-01
This paper is concerned with inference for renewal processes on the real line that are observed in a broken interval. For such processes, the classic history-based approach cannot be used. Instead, we adapt tools from sequential spatial point process theory to propose a Monte Carlo maximum
Testing for Change in Mean of Independent Multivariate Observations with Time Varying Covariance
Directory of Open Access Journals (Sweden)
Mohamed Boutahar
2012-01-01
Full Text Available We consider a nonparametric CUSUM test for change in the mean of multivariate time series with time varying covariance. We prove that under the null, the test statistic has a Kolmogorov limiting distribution. The asymptotic consistency of the test against a large class of alternatives which contains abrupt, smooth and continuous changes is established. We also perform a simulation study to analyze the size distortion and the power of the proposed test.
Vujačić, Ivan; Dattner, Itai
In this paper we use the sieve framework to prove consistency of the ‘direct integral estimator’ of parameters for partially observed systems of ordinary differential equations, which are commonly used for modeling dynamic processes.
Uncovering Multivariate Structure in Classroom Observations in the Presence of Rater Errors
McCaffrey, Daniel F.; Yuan, Kun; Savitsky, Terrance D.; Lockwood, J. R.; Edelen, Maria O.
2015-01-01
We examine the factor structure of scores from the CLASS-S protocol obtained from observations of middle school classroom teaching. Factor analysis has been used to support both interpretations of scores from classroom observation protocols, like CLASS-S, and the theories about teaching that underlie them. However, classroom observations contain…
Delwiche, Stephen R; Reeves, James B
2010-01-01
In multivariate regression analysis of spectroscopy data, spectral preprocessing is often performed to reduce unwanted background information (offsets, sloped baselines) or accentuate absorption features in intrinsically overlapping bands. These procedures, also known as pretreatments, are commonly smoothing operations or derivatives. While such operations are often useful in reducing the number of latent variables of the actual decomposition and lowering residual error, they also run the risk of misleading the practitioner into accepting calibration equations that are poorly adapted to samples outside of the calibration. The current study developed a graphical method to examine this effect on partial least squares (PLS) regression calibrations of near-infrared (NIR) reflection spectra of ground wheat meal with two analytes, protein content and sodium dodecyl sulfate sedimentation (SDS) volume (an indicator of the quantity of the gluten proteins that contribute to strong doughs). These two properties were chosen because of their differing abilities to be modeled by NIR spectroscopy: excellent for protein content, fair for SDS sedimentation volume. To further demonstrate the potential pitfalls of preprocessing, an artificial component, a randomly generated value, was included in PLS regression trials. Savitzky-Golay (digital filter) smoothing, first-derivative, and second-derivative preprocess functions (5 to 25 centrally symmetric convolution points, derived from quadratic polynomials) were applied to PLS calibrations of 1 to 15 factors. The results demonstrated the danger of an over reliance on preprocessing when (1) the number of samples used in a multivariate calibration is low (<50), (2) the spectral response of the analyte is weak, and (3) the goodness of the calibration is based on the coefficient of determination (R(2)) rather than a term based on residual error. The graphical method has application to the evaluation of other preprocess functions and various
Guglielminotti, Jean; Dechartres, Agnès; Mentré, France; Montravers, Philippe; Longrois, Dan; Laouénan, Cedric
2015-10-01
Prognostic research studies in anesthesiology aim to identify risk factors for an outcome (explanatory studies) or calculate the risk of this outcome on the basis of patients' risk factors (predictive studies). Multivariable models express the relationship between predictors and an outcome and are used in both explanatory and predictive studies. Model development demands a strict methodology and a clear reporting to assess its reliability. In this methodological descriptive review, we critically assessed the reporting and methodology of multivariable analysis used in observational prognostic studies published in anesthesiology journals. A systematic search was conducted on Medline through Web of Knowledge, PubMed, and journal websites to identify observational prognostic studies with multivariable analysis published in Anesthesiology, Anesthesia & Analgesia, British Journal of Anaesthesia, and Anaesthesia in 2010 and 2011. Data were extracted by 2 independent readers. First, studies were analyzed with respect to reporting of outcomes, design, size, methods of analysis, model performance (discrimination and calibration), model validation, clinical usefulness, and STROBE (i.e., Strengthening the Reporting of Observational Studies in Epidemiology) checklist. A reporting rate was calculated on the basis of 21 items of the aforementioned points. Second, they were analyzed with respect to some predefined methodological points. Eighty-six studies were included: 87.2% were explanatory and 80.2% investigated a postoperative event. The reporting was fairly good, with a median reporting rate of 79% (75% in explanatory studies and 100% in predictive studies). Six items had a reporting rate website. Limiting the number of candidate variables, including cases with missing data, and not arbitrarily categorizing continuous variables should be encouraged.
Peng, Ying; Li, Su-Ning; Pei, Xuexue; Hao, Kun
2018-03-01
Amultivariate regression statisticstrategy was developed to clarify multi-components content-effect correlation ofpanaxginseng saponins extract and predict the pharmacological effect by components content. In example 1, firstly, we compared pharmacological effects between panax ginseng saponins extract and individual saponin combinations. Secondly, we examined the anti-platelet aggregation effect in seven different saponin combinations of ginsenoside Rb1, Rg1, Rh, Rd, Ra3 and notoginsenoside R1. Finally, the correlation between anti-platelet aggregation and the content of multiple components was analyzed by a partial least squares algorithm. In example 2, firstly, 18 common peaks were identified in ten different batches of panax ginseng saponins extracts from different origins. Then, we investigated the anti-myocardial ischemia reperfusion injury effects of the ten different panax ginseng saponins extracts. Finally, the correlation between the fingerprints and the cardioprotective effects was analyzed by a partial least squares algorithm. Both in example 1 and 2, the relationship between the components content and pharmacological effect was modeled well by the partial least squares regression equations. Importantly, the predicted effect curve was close to the observed data of dot marked on the partial least squares regression model. This study has given evidences that themulti-component content is a promising information for predicting the pharmacological effects of traditional Chinese medicine.
Directory of Open Access Journals (Sweden)
Ying Peng
2018-03-01
Full Text Available Amultivariate regression statisticstrategy was developed to clarify multi-components content-effect correlation ofpanaxginseng saponins extract and predict the pharmacological effect by components content. In example 1, firstly, we compared pharmacological effects between panax ginseng saponins extract and individual saponin combinations. Secondly, we examined the anti-platelet aggregation effect in seven different saponin combinations of ginsenoside Rb1, Rg1, Rh, Rd, Ra3 and notoginsenoside R1. Finally, the correlation between anti-platelet aggregation and the content of multiple components was analyzed by a partial least squares algorithm. In example 2, firstly, 18 common peaks were identified in ten different batches of panax ginseng saponins extracts from different origins. Then, we investigated the anti-myocardial ischemia reperfusion injury effects of the ten different panax ginseng saponins extracts. Finally, the correlation between the fingerprints and the cardioprotective effects was analyzed by a partial least squares algorithm. Both in example 1 and 2, the relationship between the components content and pharmacological effect was modeled well by the partial least squares regression equations. Importantly, the predicted effect curve was close to the observed data of dot marked on the partial least squares regression model. This study has given evidences that themulti-component content is a promising information for predicting the pharmacological effects of traditional Chinese medicine.
de Brito, Aila Riany; Santos Reis, Nadabe Dos; Silva, Tatielle Pereira; Ferreira Bonomo, Renata Cristina; Trovatti Uetanabaro, Ana Paula; de Assis, Sandra Aparecida; da Silva, Erik Galvão Paranhos; Aguiar-Oliveira, Elizama; Oliveira, Julieta Rangel; Franco, Marcelo
2017-11-26
Endoglucanase production by Aspergillus oryzae ATCC 10124 cultivated in rice husks or peanut shells was optimized by experimental design as a function of humidity, time, and temperature. The optimum temperature for the endoglucanase activity was estimated by a univariate analysis (one factor at the time) as 50°C (rice husks) and 60°C (peanut shells), however, by a multivariate analysis (synergism of factors), it was determined a different temperature (56°C) for endoglucanase from peanut shells. For the optimum pH, values determined by univariate and multivariate analysis were 5 and 5.2 (rice husk) and 5 and 7.6 (peanut shells). In addition, the best half-lives were observed at 50°C as 22.8 hr (rice husks) and 7.3 hr (peanut shells), also, 80% of residual activities was obtained between 30 and 50°C for both substrates, and the pH stability was improved at 5-7 (rice hulls) and 6-9 (peanut shells). Both endoglucanases obtained presented different characteristics as a result of the versatility of fungi in different substrates.
Yu, Xiang
2011-01-01
We consider a model of optimal investment and consumption with both habit formation and partial observations in incomplete It\\^{o} processes market. The investor chooses his consumption under the addictive habits constraint while only observing the market stock prices but not the instantaneous rate of return. Applying the Kalman-Bucy filtering theorem and the Dynamic Programming arguments, we solve the associated Hamilton-Jacobi-Bellman (HJB) equation explicitly for the path dependent stochas...
A Method for Speeding Up Value Iteration in Partially Observable Markov Decision Processes
Zhang, Nevin Lianwen; Lee, Stephen S.; Zhang, Weihong
2013-01-01
We present a technique for speeding up the convergence of value iteration for partially observable Markov decisions processes (POMDPs). The underlying idea is similar to that behind modified policy iteration for fully observable Markov decision processes (MDPs). The technique can be easily incorporated into any existing POMDP value iteration algorithms. Experiments have been conducted on several test problems with one POMDP value iteration algorithm called incremental pruning. We find that th...
DEFF Research Database (Denmark)
Raja, Muhammad Imran; Hussain, Dil muhammed Akbar; Soltani, Mohsen
2017-01-01
Multivariable disturbance accommodated observer based control (DOBC) scheme is presented to mitigate loads generated due to wind shear and tower shadow using individual blade pitch for above-rated wind speed condition of wind turbine. Wind shear and tower shadow add flickers as 1p, 3p, 6p and so on......, (p is the rotor rotational frequency) for three-bladed wind turbine. Novel DOBC with individual pitch control (IPC) to mitigate the flickers is presented and linear state-space model of wind turbine with tower dynamics is developed. The proposed controller is tuned using optimal control theory...... density of generator speed, drive-train torsion and tower fore-aft moment shows better mitigation to the flickers by proposed controller as compared with proportional–integral (PI) and disturbance accommodation control (DAC) with collective pitch control. Furthermore, it shows less degradation...
Directory of Open Access Journals (Sweden)
S. Sparnocchia
Full Text Available Multivariate vertical Empirical Orthogonal Functions (EOF are calculated for the entire Mediterranean Sea both from observations and model simulations, in order to find the optimal number of vertical modes to represent the upper thermocline vertical structure. For the first time, we show that the large-scale Mediterranean thermohaline vertical structure can be represented by a limited number of vertical multivariate EOFs, and that the "optimal set" can be selected on the basis of general principles. In particular, the EOFs are calculated for the combined temperature and salinity statistics, dividing the Mediterranean Sea into 9 regions and grouping the data seasonally. The criterion used to establish whether a reduced set of EOFs is optimal is based on the analysis of the root mean square residual error between the original data and the profiles reconstructed by the reduced set of EOFs. It was found that the number of EOFs needed to capture the variability contained in the original data changes with geographical region and seasons. In particular, winter data require a smaller number of modes (4–8, depending on the region than the other seasons (8–9 in summer. Moreover, western Mediterranean regions require more modes than the eastern Mediterranean ones, but this result may depend on the data scarcity in the latter regions. The EOFs computed from the in situ data set are compared to those calculated using data obtained from a model simulation. The main results of this exercise are that the two groups of modes are not strictly comparable but their ability to reproduce observations is the same. Thus, they may be thought of as equivalent sets of basis functions, upon which to project the thermohaline variability of the basin.
Key words. Oceanography: general (water masses – Oceanography: physical (hydrography; instruments and techniques
Directory of Open Access Journals (Sweden)
S. Sparnocchia
2003-01-01
Full Text Available Multivariate vertical Empirical Orthogonal Functions (EOF are calculated for the entire Mediterranean Sea both from observations and model simulations, in order to find the optimal number of vertical modes to represent the upper thermocline vertical structure. For the first time, we show that the large-scale Mediterranean thermohaline vertical structure can be represented by a limited number of vertical multivariate EOFs, and that the "optimal set" can be selected on the basis of general principles. In particular, the EOFs are calculated for the combined temperature and salinity statistics, dividing the Mediterranean Sea into 9 regions and grouping the data seasonally. The criterion used to establish whether a reduced set of EOFs is optimal is based on the analysis of the root mean square residual error between the original data and the profiles reconstructed by the reduced set of EOFs. It was found that the number of EOFs needed to capture the variability contained in the original data changes with geographical region and seasons. In particular, winter data require a smaller number of modes (4–8, depending on the region than the other seasons (8–9 in summer. Moreover, western Mediterranean regions require more modes than the eastern Mediterranean ones, but this result may depend on the data scarcity in the latter regions. The EOFs computed from the in situ data set are compared to those calculated using data obtained from a model simulation. The main results of this exercise are that the two groups of modes are not strictly comparable but their ability to reproduce observations is the same. Thus, they may be thought of as equivalent sets of basis functions, upon which to project the thermohaline variability of the basin. Key words. Oceanography: general (water masses – Oceanography: physical (hydrography; instruments and techniques
Ahmad, Hamzah; Namerikawa, Toru
2010-01-01
This paper presents H∞ Filter SLAM, which is also known as the minimax filter to estimate the robot and landmarks location with the analysis on partial observability. Some convergence conditions are also presented to aid the analysis. Due to SLAM is a controllable but unobservable problem, it's difficult to estimate the position of robot and landmarks even though the control inputs are given to the system. As a result, Covariance Inflation which is a method of adding a pseudo positive semidef...
Energy Technology Data Exchange (ETDEWEB)
Harris, Candace [Florida Agriculture & Mechanic Univ.; Profeta, Luisa [Alakai Defense Systems, Inc.; Akpovo, Codjo [Florida Agriculture & Mechanic Univ.; Stowe, Ashley [Y-12 National Security Complex, Oak Ridge, TN (United States); Johnson, Lewis [Florida Agriculture & Mechanic Univ.
2017-10-09
The psuedo univariate limit of detection was calculated to compare to the multivariate interval. ompared with results from the psuedounivariate LOD, the multivariate LOD includes other factors (i.e. signal uncertainties) and the reveals the significance in creating models that not only use the analyte’s emission line but also its entire molecular spectra.
International Nuclear Information System (INIS)
Gershgorin, B.; Majda, A.J.
2011-01-01
A statistically exactly solvable model for passive tracers is introduced as a test model for the authors' Nonlinear Extended Kalman Filter (NEKF) as well as other filtering algorithms. The model involves a Gaussian velocity field and a passive tracer governed by the advection-diffusion equation with an imposed mean gradient. The model has direct relevance to engineering problems such as the spread of pollutants in the air or contaminants in the water as well as climate change problems concerning the transport of greenhouse gases such as carbon dioxide with strongly intermittent probability distributions consistent with the actual observations of the atmosphere. One of the attractive properties of the model is the existence of the exact statistical solution. In particular, this unique feature of the model provides an opportunity to design and test fast and efficient algorithms for real-time data assimilation based on rigorous mathematical theory for a turbulence model problem with many active spatiotemporal scales. Here, we extensively study the performance of the NEKF which uses the exact first and second order nonlinear statistics without any approximations due to linearization. The role of partial and sparse observations, the frequency of observations and the observation noise strength in recovering the true signal, its spectrum, and fat tail probability distribution are the central issues discussed here. The results of our study provide useful guidelines for filtering realistic turbulent systems with passive tracers through partial observations.
Sarasola, Xabier; Brenner, Paul; Hahn, Michael; Pedersen, Thomas
2009-11-01
The Columbia Non-neutral Torus (CNT) is the first stellarator devoted to the study of pure electron, partially neutralized and positron-electron plasmas. To date, CNT usually operates with electron rich plasmas (with negligible ion density) [1], but a stellarator can also confine plasmas of arbitrary degree of neutralization. In CNT the accumulation of ions alters the equilibrium of electron plasmas and a global instability has been observed when the ion fraction exceeds 10 %. A characterization of this instability is presented in [2], analyzing its parameter dependence and spatial structure (non- resonant with rational surfaces). A new set of experiments is currently underway studying plasmas of arbitrary degree of neutralization, ranging from pure electron to quasineutral plasmas. Basic observations show that the plasma potential decouples from emitter bias when we increase the degree of the neutralization of our plasmas. Partially neutralized plasmas are also characterized by multiple mode behavior with dominant modes between 20 and 200 kHz. When the plasma becomes quasineutral, it reverts to single mode behavior. The first results on partially neutralized plasmas confined on magnetic surfaces will be presented. [1] J. Kremer, PRL 97, (2006) 095003 [2] Q. Marksteiner, PRL 100 (2008) 065002
An Optimal Medium Access Control with Partial Observations for Sensor Networks
Directory of Open Access Journals (Sweden)
Servetto Sergio D
2005-01-01
Full Text Available We consider medium access control (MAC in multihop sensor networks, where only partial information about the shared medium is available to the transmitter. We model our setting as a queuing problem in which the service rate of a queue is a function of a partially observed Markov chain representing the available bandwidth, and in which the arrivals are controlled based on the partial observations so as to keep the system in a desirable mildly unstable regime. The optimal controller for this problem satisfies a separation property: we first compute a probability measure on the state space of the chain, namely the information state, then use this measure as the new state on which the control decisions are based. We give a formal description of the system considered and of its dynamics, we formalize and solve an optimal control problem, and we show numerical simulations to illustrate with concrete examples properties of the optimal control law. We show how the ergodic behavior of our queuing model is characterized by an invariant measure over all possible information states, and we construct that measure. Our results can be specifically applied for designing efficient and stable algorithms for medium access control in multiple-accessed systems, in particular for sensor networks.
Seah, Regina K H; Garland, Marc; Loo, Joachim S C; Widjaja, Effendi
2009-02-15
In the present contribution, the biomimetic growth of carbonated hydroxyapatite (HA) on bioactive glass were investigated by Raman microscopy. Bioactive glass samples were immersed in simulated body fluid (SBF) buffered solution at pH 7.40 up to 17 days at 37 degrees C. Raman microscopy mapping was performed on the bioglass samples immersed in SBF solution for different periods of time. The collected data was then analyzed using the band-target entropy minimization technique to extract the observable pure component Raman spectral information. In this study, the pure component Raman spectra of the precursor amorphous calcium phosphate, transient octacalcium phosphate, and matured HA were all recovered. In addition, pure component Raman spectra of calcite, silica glass, and some organic impurities were also recovered. The resolved pure component spectra were fit to the normalized measured Raman data to provide the spatial distribution of these species on the sample surfaces. The current results show that Raman microscopy and multivariate data analysis provide a sensitive and accurate tool to characterize the surface morphology, as well as to give more specific information on the chemical species present and the phase transformation of phosphate species during the formation of HA on bioactive glass.
Nonlinear Inference in Partially Observed Physical Systems and Deep Neural Networks
Rozdeba, Paul J.
The problem of model state and parameter estimation is a significant challenge in nonlinear systems. Due to practical considerations of experimental design, it is often the case that physical systems are partially observed, meaning that data is only available for a subset of the degrees of freedom required to fully model the observed system's behaviors and, ultimately, predict future observations. Estimation in this context is highly complicated by the presence of chaos, stochasticity, and measurement noise in dynamical systems. One of the aims of this dissertation is to simultaneously analyze state and parameter estimation in as a regularized inverse problem, where the introduction of a model makes it possible to reverse the forward problem of partial, noisy observation; and as a statistical inference problem using data assimilation to transfer information from measurements to the model states and parameters. Ultimately these two formulations achieve the same goal. Similar aspects that appear in both are highlighted as a means for better understanding the structure of the nonlinear inference problem. An alternative approach to data assimilation that uses model reduction is then examined as a way to eliminate unresolved nonlinear gating variables from neuron models. In this formulation, only measured variables enter into the model, and the resulting errors are themselves modeled by nonlinear stochastic processes with memory. Finally, variational annealing, a data assimilation method previously applied to dynamical systems, is introduced as a potentially useful tool for understanding deep neural network training in machine learning by exploiting similarities between the two problems.
Screening for a Chronic Disease: A Multiple Stage Duration Model with Partial Observability.
Mroz, Thomas A; Picone, Gabriel; Sloan, Frank; Yashkin, Arseniy P
2016-08-01
We estimate a dynamic multi-stage duration model to investigate how early detection of diabetes can delay the onset of lower extremity complications and death. We allow for partial observability of the disease stage, unmeasured heterogeneity, and endogenous timing of diabetes screening. Timely diagnosis appears important. We evaluate the effectiveness of two potential policies to reduce the monetary costs of frequent screening in terms of lost longevity. Compared to the status quo, the more restrictive policy yields an implicit value for an additional year of life of about $50,000, while the less restrictive policy implies a value of about $120,000.
Partial Variance of Increments Method in Solar Wind Observations and Plasma Simulations
Greco, A.; Matthaeus, W. H.; Perri, S.; Osman, K. T.; Servidio, S.; Wan, M.; Dmitruk, P.
2018-02-01
The method called "PVI" (Partial Variance of Increments) has been increasingly used in analysis of spacecraft and numerical simulation data since its inception in 2008. The purpose of the method is to study the kinematics and formation of coherent structures in space plasmas, a topic that has gained considerable attention, leading the development of identification methods, observations, and associated theoretical research based on numerical simulations. This review paper will summarize key features of the method and provide a synopsis of the main results obtained by various groups using the method. This will enable new users or those considering methods of this type to find details and background collected in one place.
Determination of sulfamethoxazole and trimethoprim mixtures by multivariate electronic spectroscopy
Cordeiro, Gilcélia A.; Peralta-Zamora, Patricio; Nagata, Noemi; Pontarollo, Roberto
2008-01-01
In this work a multivariate spectroscopic methodology is proposed for quantitative determination of sulfamethoxazole and trimethoprim in pharmaceutical associations. The multivariate model was developed by partial least-squares regression, using twenty synthetic mixtures and the spectral region between 190 and 350 nm. In the validation stage, which involved the analysis of five synthetic mixtures, prediction errors lower that 3% were observed. The predictive capacity of the multivariate model...
MULTIWAVELENGTH OBSERVATIONS OF A PARTIALLY ERUPTIVE FILAMENT ON 2011 SEPTEMBER 8
Energy Technology Data Exchange (ETDEWEB)
Zhang, Q. M.; Ning, Z. J.; Zhou, T. H.; Ji, H. S.; Feng, L. [Key Laboratory for Dark Matter and Space Science, Purple Mountain Observatory, CAS, Nanjing 210008 (China); Guo, Y.; Cheng, X. [School of Astronomy and Space Science, Nanjing University, Nanjing 210093 (China); Wiegelmann, T., E-mail: zhangqm@pmo.ac.cn [Max-Planck-Institut für Sonnensystemforschung, Justus-von-Liebig-Weg-3, D-37077 Göttingen (Germany)
2015-05-20
In this paper, we report our multiwavelength observations of a partial filament eruption event in NOAA active region (AR) 11283 on 8 September 2011. A magnetic null point and the corresponding spine and separatrix surface are found in the AR. Beneath the null point, a sheared arcade supports the filament along the highly complex and fragmented polarity inversion line. After being activated, the sigmoidal filament erupted and split into two parts. The major part rose at speeds of 90–150 km s{sup −1} before reaching the maximum apparent height of ∼115 Mm. Afterward, it returned to the solar surface in a bumpy way at speeds of 20–80 km s{sup −1}. The rising and falling motions were clearly observed in the extreme-ultraviolet, UV, and Hα wavelengths. The failed eruption of the main part was associated with an M6.7 flare with a single hard X-ray source. The runaway part of the filament, however, separated from and rotated around the major part for ∼1 turn at the eastern leg before escaping from the corona, probably along large-scale open magnetic field lines. The ejection of the runaway part resulted in a very faint coronal mass ejection that propagated at an apparent speed of 214 km s{sup −1} in the outer corona. The filament eruption also triggered a transverse kink-mode oscillation of the adjacent coronal loops in the same AR. The amplitude and period of the oscillation were 1.6 Mm and 225 s. Our results are important for understanding the mechanisms of partial filament eruptions, and provide new constraints to theoretical models. The multiwavelength observations also shed light on space weather prediction.
Is partially automated driving a bad idea? Observations from an on-road study.
Banks, Victoria A; Eriksson, Alexander; O'Donoghue, Jim; Stanton, Neville A
2018-04-01
The automation of longitudinal and lateral control has enabled drivers to become "hands and feet free" but they are required to remain in an active monitoring state with a requirement to resume manual control if required. This represents the single largest allocation of system function problem with vehicle automation as the literature suggests that humans are notoriously inefficient at completing prolonged monitoring tasks. To further explore whether partially automated driving solutions can appropriately support the driver in completing their new monitoring role, video observations were collected as part of an on-road study using a Tesla Model S being operated in Autopilot mode. A thematic analysis of video data suggests that drivers are not being properly supported in adhering to their new monitoring responsibilities and instead demonstrate behaviour indicative of complacency and over-trust. These attributes may encourage drivers to take more risks whilst out on the road. Copyright © 2017 Elsevier Ltd. All rights reserved.
Modeling treatment of ischemic heart disease with partially observable Markov decision processes.
Hauskrecht, M; Fraser, H
1998-01-01
Diagnosis of a disease and its treatment are not separate, one-shot activities. Instead they are very often dependent and interleaved over time, mostly due to uncertainty about the underlying disease, uncertainty associated with the response of a patient to the treatment and varying cost of different diagnostic (investigative) and treatment procedures. The framework of Partially observable Markov decision processes (POMDPs) developed and used in operations research, control theory and artificial intelligence communities is particularly suitable for modeling such a complex decision process. In the paper, we show how the POMDP framework could be used to model and solve the problem of the management of patients with ischemic heart disease, and point out modeling advantages of the framework over standard decision formalisms.
Planning treatment of ischemic heart disease with partially observable Markov decision processes.
Hauskrecht, M; Fraser, H
2000-03-01
Diagnosis of a disease and its treatment are not separate, one-shot activities. Instead, they are very often dependent and interleaved over time. This is mostly due to uncertainty about the underlying disease, uncertainty associated with the response of a patient to the treatment and varying cost of different diagnostic (investigative) and treatment procedures. The framework of partially observable Markov decision processes (POMDPs) developed and used in the operations research, control theory and artificial intelligence communities is particularly suitable for modeling such a complex decision process. In this paper, we show how the POMDP framework can be used to model and solve the problem of the management of patients with ischemic heart disease (IHD), and demonstrate the modeling advantages of the framework over standard decision formalisms.
A simple method for identifying parameter correlations in partially observed linear dynamic models.
Li, Pu; Vu, Quoc Dong
2015-12-14
Parameter estimation represents one of the most significant challenges in systems biology. This is because biological models commonly contain a large number of parameters among which there may be functional interrelationships, thus leading to the problem of non-identifiability. Although identifiability analysis has been extensively studied by analytical as well as numerical approaches, systematic methods for remedying practically non-identifiable models have rarely been investigated. We propose a simple method for identifying pairwise correlations and higher order interrelationships of parameters in partially observed linear dynamic models. This is made by derivation of the output sensitivity matrix and analysis of the linear dependencies of its columns. Consequently, analytical relations between the identifiability of the model parameters and the initial conditions as well as the input functions can be achieved. In the case of structural non-identifiability, identifiable combinations can be obtained by solving the resulting homogenous linear equations. In the case of practical non-identifiability, experiment conditions (i.e. initial condition and constant control signals) can be provided which are necessary for remedying the non-identifiability and unique parameter estimation. It is noted that the approach does not consider noisy data. In this way, the practical non-identifiability issue, which is popular for linear biological models, can be remedied. Several linear compartment models including an insulin receptor dynamics model are taken to illustrate the application of the proposed approach. Both structural and practical identifiability of partially observed linear dynamic models can be clarified by the proposed method. The result of this method provides important information for experimental design to remedy the practical non-identifiability if applicable. The derivation of the method is straightforward and thus the algorithm can be easily implemented into a
Majeed, Muhammad Usman
2017-07-19
Steady-state elliptic partial differential equations (PDEs) are frequently used to model a diverse range of physical phenomena. The source and boundary data estimation problems for such PDE systems are of prime interest in various engineering disciplines including biomedical engineering, mechanics of materials and earth sciences. Almost all existing solution strategies for such problems can be broadly classified as optimization-based techniques, which are computationally heavy especially when the problems are formulated on higher dimensional space domains. However, in this dissertation, feedback based state estimation algorithms, known as state observers, are developed to solve such steady-state problems using one of the space variables as time-like. In this regard, first, an iterative observer algorithm is developed that sweeps over regular-shaped domains and solves boundary estimation problems for steady-state Laplace equation. It is well-known that source and boundary estimation problems for the elliptic PDEs are highly sensitive to noise in the data. For this, an optimal iterative observer algorithm, which is a robust counterpart of the iterative observer, is presented to tackle the ill-posedness due to noise. The iterative observer algorithm and the optimal iterative algorithm are then used to solve source localization and estimation problems for Poisson equation for noise-free and noisy data cases respectively. Next, a divide and conquer approach is developed for three-dimensional domains with two congruent parallel surfaces to solve the boundary and the source data estimation problems for the steady-state Laplace and Poisson kind of systems respectively. Theoretical results are shown using a functional analysis framework, and consistent numerical simulation results are presented for several test cases using finite difference discretization schemes.
Lewis, F L; Vamvoudakis, Kyriakos G
2011-02-01
Approximate dynamic programming (ADP) is a class of reinforcement learning methods that have shown their importance in a variety of applications, including feedback control of dynamical systems. ADP generally requires full information about the system internal states, which is usually not available in practical situations. In this paper, we show how to implement ADP methods using only measured input/output data from the system. Linear dynamical systems with deterministic behavior are considered herein, which are systems of great interest in the control system community. In control system theory, these types of methods are referred to as output feedback (OPFB). The stochastic equivalent of the systems dealt with in this paper is a class of partially observable Markov decision processes. We develop both policy iteration and value iteration algorithms that converge to an optimal controller that requires only OPFB. It is shown that, similar to Q -learning, the new methods have the important advantage that knowledge of the system dynamics is not needed for the implementation of these learning algorithms or for the OPFB control. Only the order of the system, as well as an upper bound on its "observability index," must be known. The learned OPFB controller is in the form of a polynomial autoregressive moving-average controller that has equivalent performance with the optimal state variable feedback gain.
Hard X-Ray Emission from Partially Occulted Solar Flares: RHESSI Observations in Two Solar Cycles
Energy Technology Data Exchange (ETDEWEB)
Effenberger, Frederic; Costa, Fatima Rubio da; Petrosian, Vahé [Department of Physics and KIPAC, Stanford University, Stanford, CA 94305 (United States); Oka, Mitsuo; Saint-Hilaire, Pascal; Krucker, Säm [Space Sciences Laboratory, University of California, Berkeley, CA 94720-7450 (United States); Liu, Wei [Bay Area Environmental Research Institute, 625 2nd Street, Suite 209, Petaluma, CA 94952 (United States); Glesener, Lindsay, E-mail: feffen@stanford.edu, E-mail: frubio@stanford.edu [School of Physics and Astronomy, University of Minnesota, Minneapolis, MN 55455 (United States)
2017-02-01
Flares close to the solar limb, where the footpoints are occulted, can reveal the spectrum and structure of the coronal looptop source in X-rays. We aim at studying the properties of the corresponding energetic electrons near their acceleration site, without footpoint contamination. To this end, a statistical study of partially occulted flares observed with Reuven Ramaty High-Energy Solar Spectroscopic Imager is presented here, covering a large part of solar cycles 23 and 24. We perform detailed spectra, imaging, and light curve analyses for 116 flares and include contextual observations from SDO and STEREO when available, providing further insights into flare emission that were previously not accessible. We find that most spectra are fitted well with a thermal component plus a broken power-law, non-thermal component. A thin-target kappa distribution model gives satisfactory fits after the addition of a thermal component. X-ray imaging reveals small spatial separation between the thermal and non-thermal components, except for a few flares with a richer coronal source structure. A comprehensive light curve analysis shows a very good correlation between the derivative of the soft X-ray flux (from GOES ) and the hard X-rays for a substantial number of flares, indicative of the Neupert effect. The results confirm that non-thermal particles are accelerated in the corona and estimated timescales support the validity of a thin-target scenario with similar magnitudes of thermal and non-thermal energy fluxes.
Real-time characterization of partially observed epidemics using surrogate models.
Energy Technology Data Exchange (ETDEWEB)
Safta, Cosmin; Ray, Jaideep; Lefantzi, Sophia; Crary, David (Applied Research Associates, Arlington, VA); Sargsyan, Khachik; Cheng, Karen (Applied Research Associates, Arlington, VA)
2011-09-01
We present a statistical method, predicated on the use of surrogate models, for the 'real-time' characterization of partially observed epidemics. Observations consist of counts of symptomatic patients, diagnosed with the disease, that may be available in the early epoch of an ongoing outbreak. Characterization, in this context, refers to estimation of epidemiological parameters that can be used to provide short-term forecasts of the ongoing epidemic, as well as to provide gross information on the dynamics of the etiologic agent in the affected population e.g., the time-dependent infection rate. The characterization problem is formulated as a Bayesian inverse problem, and epidemiological parameters are estimated as distributions using a Markov chain Monte Carlo (MCMC) method, thus quantifying the uncertainty in the estimates. In some cases, the inverse problem can be computationally expensive, primarily due to the epidemic simulator used inside the inversion algorithm. We present a method, based on replacing the epidemiological model with computationally inexpensive surrogates, that can reduce the computational time to minutes, without a significant loss of accuracy. The surrogates are created by projecting the output of an epidemiological model on a set of polynomial chaos bases; thereafter, computations involving the surrogate model reduce to evaluations of a polynomial. We find that the epidemic characterizations obtained with the surrogate models is very close to that obtained with the original model. We also find that the number of projections required to construct a surrogate model is O(10)-O(10{sup 2}) less than the number of samples required by the MCMC to construct a stationary posterior distribution; thus, depending upon the epidemiological models in question, it may be possible to omit the offline creation and caching of surrogate models, prior to their use in an inverse problem. The technique is demonstrated on synthetic data as well as
Burke, Christopher J; Baddeley, Michelle; Tobler, Philippe N; Schultz, Wolfram
2016-09-28
Given that the range of rewarding and punishing outcomes of actions is large but neural coding capacity is limited, efficient processing of outcomes by the brain is necessary. One mechanism to increase efficiency is to rescale neural output to the range of outcomes expected in the current context, and process only experienced deviations from this expectation. However, this mechanism comes at the cost of not being able to discriminate between unexpectedly low losses when times are bad versus unexpectedly high gains when times are good. Thus, too much adaptation would result in disregarding information about the nature and absolute magnitude of outcomes, preventing learning about the longer-term value structure of the environment. Here we investigate the degree of adaptation in outcome coding brain regions in humans, for directly experienced outcomes and observed outcomes. We scanned participants while they performed a social learning task in gain and loss blocks. Multivariate pattern analysis showed two distinct networks of brain regions adapt to the most likely outcomes within a block. Frontostriatal areas adapted to directly experienced outcomes, whereas lateral frontal and temporoparietal regions adapted to observed social outcomes. Critically, in both cases, adaptation was incomplete and information about whether the outcomes arose in a gain block or a loss block was retained. Univariate analysis confirmed incomplete adaptive coding in these regions but also detected nonadapting outcome signals. Thus, although neural areas rescale their responses to outcomes for efficient coding, they adapt incompletely and keep track of the longer-term incentives available in the environment. Optimal value-based choice requires that the brain precisely and efficiently represents positive and negative outcomes. One way to increase efficiency is to adapt responding to the most likely outcomes in a given context. However, too strong adaptation would result in loss of precise
Stochastic simulations of conditional states of partially observed systems, quantum and classical
International Nuclear Information System (INIS)
Gambetta, Jay; Wiseman, H M
2005-01-01
In a partially observed quantum or classical system the information that we cannot access results in our description of the system becoming mixed, even if we have perfect initial knowledge. That is, if the system is quantum the conditional state will be given by a state matrix ρ r (t), and if classical, the conditional state will be given by a probability distribution P r (x,t), where r is the result of the measurement. Thus to determine the evolution of this conditional state, under continuous-in-time monitoring, requires a numerically expensive calculation. In this paper we demonstrate a numerical technique based on linear measurement theory that allows us to determine the conditional state using only pure states. That is, our technique reduces the problem size by a factor of N, the number of basis states for the system. Furthermore we show that our method can be applied to joint classical and quantum systems such as arise in modelling realistic (finite bandwidth, noisy) measurement
Rufo Campos, M; Carreño, M
2009-01-01
It is important to conduct studies on the utilization of new antiepileptic drugs in order to improve their use. Our objective is to describe the use patterns of carbamazepine and oxcarbazepine. Observational, cross-sectional, national study with 58 investigators that included 185 pediatric patients with partial epilepsy. We recorded prescription patterns, quality of life (QoL) using the QoL scale in childhood epilepsy (CAVE) and use of resources. 134 patients were under treatment with oxcarbazepine (72.4 %), with a mean dose of 22.3 mg/kg/day; standard deviation (SD): 8.04; 95 % confidence interval (CI): 20.9 to 23.7, and 51 (27.6%) with carbamazepine, mean dose of 14 mg/kg/day; SD: 6.2; 95 % CI: 12.3 to 15.8. A total of 19.4% and 21.6 %, respectively, followed multiple drug treatment. The mean scores on functional dimensions of CAVE were (out of 5): school attendance: 4.5; SD: 0.7; social relationships: 4.1; SD: 0.9, and autonomy: 3.9; SD: 1.9. Patients receiving multiple drug therapy had worse results in quality of life (p used in lower doses than recommended and the dosing is not adjusted for weight. Underdosing may lead to regimes of multiple drug therapy that should be reviewed individually.
Neustifter, Benjamin; Rathbun, Stephen L; Shiffman, Saul
2012-01-01
Ecological Momentary Assessment is an emerging method of data collection in behavioral research that may be used to capture the times of repeated behavioral events on electronic devices, and information on subjects' psychological states through the electronic administration of questionnaires at times selected from a probability-based design as well as the event times. A method for fitting a mixed Poisson point process model is proposed for the impact of partially-observed, time-varying covariates on the timing of repeated behavioral events. A random frailty is included in the point-process intensity to describe variation among subjects in baseline rates of event occurrence. Covariate coefficients are estimated using estimating equations constructed by replacing the integrated intensity in the Poisson score equations with a design-unbiased estimator. An estimator is also proposed for the variance of the random frailties. Our estimators are robust in the sense that no model assumptions are made regarding the distribution of the time-varying covariates or the distribution of the random effects. However, subject effects are estimated under gamma frailties using an approximate hierarchical likelihood. The proposed approach is illustrated using smoking data.
Directory of Open Access Journals (Sweden)
Rajesh P N Rao
2010-11-01
Full Text Available A fundamental problem faced by animals is learning to select actions based on noisy sensory information and incomplete knowledge of the world. It has been suggested that the brain engages in Bayesian inference during perception but how such probabilistic representations are used to select actions has remained unclear. Here we propose a neural model of action selection and decision making based on the theory of partially observable Markov decision processes (POMDPs. Actions are selected based not on a single optimal estimate of state but on the posterior distribution over states (the belief state. We show how such a model provides a unified framework for explaining experimental results in decision making that involve both information gathering and overt actions. The model utilizes temporal difference (TD learning for maximizing expected reward. The resulting neural architecture posits an active role for the neocortex in belief computation while ascribing a role to the basal ganglia in belief representation, value computation, and action selection. When applied to the random dots motion discrimination task, model neurons representing belief exhibit responses similar to those of LIP neurons in primate neocortex. The appropriate threshold for switching from information gathering to overt actions emerges naturally during reward maximization. Additionally, the time course of reward prediction error in the model shares similarities with dopaminergic responses in the basal ganglia during the random dots task. For tasks with a deadline, the model learns a decision making strategy that changes with elapsed time, predicting a collapsing decision threshold consistent with some experimental studies. The model provides a new framework for understanding neural decision making and suggests an important role for interactions between the neocortex and the basal ganglia in learning the mapping between probabilistic sensory representations and actions that maximize
Duarte, Janaína; Pacheco, Marcos T. T.; Villaverde, Antonio Balbin; Machado, Rosangela Z.; Zângaro, Renato A.; Silveira, Landulfo
2010-07-01
Toxoplasmosis is an important zoonosis in public health because domestic cats are the main agents responsible for the transmission of this disease in Brazil. We investigate a method for diagnosing toxoplasmosis based on Raman spectroscopy. Dispersive near-infrared Raman spectra are used to quantify anti-Toxoplasma gondii (IgG) antibodies in blood sera from domestic cats. An 830-nm laser is used for sample excitation, and a dispersive spectrometer is used to detect the Raman scattering. A serological test is performed in all serum samples by the enzyme-linked immunosorbent assay (ELISA) for validation. Raman spectra are taken from 59 blood serum samples and a quantification model is implemented based on partial least squares (PLS) to quantify the sample's serology by Raman spectra compared to the results provided by the ELISA test. Based on the serological values provided by the Raman/PLS model, diagnostic parameters such as sensitivity, specificity, accuracy, positive prediction values, and negative prediction values are calculated to discriminate negative from positive samples, obtaining 100, 80, 90, 83.3, and 100%, respectively. Raman spectroscopy, associated with the PLS, is promising as a serological assay for toxoplasmosis, enabling fast and sensitive diagnosis.
Cobben, M.M.P.; Van Noordwijk, A.J.
2017-01-01
Migration is a widespread phenomenon across the animal kingdom as a response to seasonality in environmental conditions. Partially migratory populations are populations that consist of both migratory and residential individuals. Such populations are very common, yet their stability has long been
Routing policies for a partially observable two-server queueing system
Ellens, W.; Kovács, P.; Núñez-Queija, R.; Berg, H. van den
2015-01-01
We consider a queueing system controlled by decisions based on partial state information. The motivation for this work stems from road traffic, in which drivers may, or may not, be subscribed to a smartphone application for dynamic route planning. Our model consists of two queues with independent
Routing policies for a partially observable two-server queueing system
W. Ellens; P. Kovacs; J.L. van den Berg (Hans); R. Núñez Queija (Rudesindo); A. Busic; M. Gribaudo; P. Reinecke
2015-01-01
htmlabstractWe consider a queueing system controlled by decisions based on partial state information. The motivation for this work stems from road traffic, in which drivers may, or may not, be subscribed to a smartphone application for dynamic route planning. Our model consists of two queues
International Nuclear Information System (INIS)
Leij, Femke van der; Elkhuizen, Paula H.M.; Janssen, Tomas M.; Poortmans, Philip; Sangen, Maurice van der; Scholten, Astrid N.; Vliet-Vroegindeweij, Corine van; Boersma, Liesbeth J.
2014-01-01
The challenge of adequate target volume definition in external beam partial breast irradiation (PBI) could be overcome with preoperative irradiation, due to less inter-observer variation. We compared the target volume delineation for external beam PBI on preoperative versus postoperative CT scans of twenty-four breast cancer patients
Leij, F. van der; Elkhuizen, P.H.M.; Janssen, T.M.; Poortmans, P.M.P.; Sangen, M. van der; Scholten, A.N.; Vliet-Vroegindeweij, C. van; Boersma, L.J.
2014-01-01
The challenge of adequate target volume definition in external beam partial breast irradiation (PBI) could be overcome with preoperative irradiation, due to less inter-observer variation. We compared the target volume delineation for external beam PBI on preoperative versus postoperative CT scans of
Cobben, Marleen M P; van Noordwijk, Arie J
2017-10-01
Migration is a widespread phenomenon across the animal kingdom as a response to seasonality in environmental conditions. Partially migratory populations are populations that consist of both migratory and residential individuals. Such populations are very common, yet their stability has long been debated. The inheritance of migratory activity is currently best described by the threshold model of quantitative genetics. The inclusion of such a genetic threshold model for migratory behavior leads to a stable zone in time and space of partially migratory populations under a wide range of demographic parameter values, when assuming stable environmental conditions and unlimited genetic diversity. Migratory species are expected to be particularly sensitive to global warming, as arrival at the breeding grounds might be increasingly mistimed as a result of the uncoupling of long-used cues and actual environmental conditions, with decreasing reproduction as a consequence. Here, we investigate the consequences for migratory behavior and the stability of partially migratory populations under five climate change scenarios and the assumption of a genetic threshold value for migratory behavior in an individual-based model. The results show a spatially and temporally stable zone of partially migratory populations after different lengths of time in all scenarios. In the scenarios in which the species expands its range from a particular set of starting populations, the genetic diversity and location at initialization determine the species' colonization speed across the zone of partial migration and therefore across the entire landscape. Abruptly changing environmental conditions after model initialization never caused a qualitative change in phenotype distributions, or complete extinction. This suggests that climate change-induced shifts in species' ranges as well as changes in survival probabilities and reproductive success can be met with flexibility in migratory behavior at the
Deconinck, E; Zhang, M H; Petitet, F; Dubus, E; Ijjaali, I; Coomans, D; Vander Heyden, Y
2008-02-18
The use of some unconventional non-linear modeling techniques, i.e. classification and regression trees and multivariate adaptive regression splines-based methods, was explored to model the blood-brain barrier (BBB) passage of drugs and drug-like molecules. The data set contains BBB passage values for 299 structural and pharmacological diverse drugs, originating from a structured knowledge-based database. Models were built using boosted regression trees (BRT) and multivariate adaptive regression splines (MARS), as well as their respective combinations with stepwise multiple linear regression (MLR) and partial least squares (PLS) regression in two-step approaches. The best models were obtained using combinations of MARS with either stepwise MLR or PLS. It could be concluded that the use of combinations of a linear with a non-linear modeling technique results in some improved properties compared to the individual linear and non-linear models and that, when the use of such a combination is appropriate, combinations using MARS as non-linear technique should be preferred over those with BRT, due to some serious drawbacks of the BRT approaches.
Directory of Open Access Journals (Sweden)
Rami Alazrai
2017-03-01
Full Text Available This paper presents a new approach for fall detection from partially-observed depth-map video sequences. The proposed approach utilizes the 3D skeletal joint positions obtained from the Microsoft Kinect sensor to build a view-invariant descriptor for human activity representation, called the motion-pose geometric descriptor (MPGD. Furthermore, we have developed a histogram-based representation (HBR based on the MPGD to construct a length-independent representation of the observed video subsequences. Using the constructed HBR, we formulate the fall detection problem as a posterior-maximization problem in which the posteriori probability for each observed video subsequence is estimated using a multi-class SVM (support vector machine classifier. Then, we combine the computed posteriori probabilities from all of the observed subsequences to obtain an overall class posteriori probability of the entire partially-observed depth-map video sequence. To evaluate the performance of the proposed approach, we have utilized the Kinect sensor to record a dataset of depth-map video sequences that simulates four fall-related activities of elderly people, including: walking, sitting, falling form standing and falling from sitting. Then, using the collected dataset, we have developed three evaluation scenarios based on the number of unobserved video subsequences in the testing videos, including: fully-observed video sequence scenario, single unobserved video subsequence of random lengths scenarios and two unobserved video subsequences of random lengths scenarios. Experimental results show that the proposed approach achieved an average recognition accuracy of 93 . 6 % , 77 . 6 % and 65 . 1 % , in recognizing the activities during the first, second and third evaluation scenario, respectively. These results demonstrate the feasibility of the proposed approach to detect falls from partially-observed videos.
Analysis of partially pulsating fatigue process on carbon steel with microstructural observation
International Nuclear Information System (INIS)
Shimano, Hiroyuki; Faiz, M. Khairi; Hara, Asato; Yoshizumi, Kyoko; Yoshida, Makoto; Horibe, Susumu
2016-01-01
Pulsating low-cycle fatigue processes, up to the present, have been divided into three states: the transient state, steady state, and accelerating state of ratcheting. In our previous work, we suggested that fatigue behavior of pulsating fatigue process should be classified into five stages in which the plastic strain amplitude and the ratcheting strain rate are plotted on the X and Y axis, respectively. In this study, at the condition of R=−0.3 (partially pulsating fatigue), the change in the plastic strain amplitude and ratcheting strain rate for each cycle to failure was examined on AISI 1025 carbon steel. The dislocation substructure was examined using transmission electron microscopy (TEM) for each stage, except for stage I. It was also demonstrated that the fatigue process can be divided into five stages: stage I corresponds to the un-pinning of dislocations from the Cottrell atmosphere and propagation of the Luders band. Stage II corresponds to the restriction of dislocation movement by dislocation tangles. Stage III corresponds to the formation of dislocation cells. Stage IV corresponds to the promotion of the to-and-fro (back-and-forth) motion of dislocations by a re-arrangement of the dislocations in the cells. Stage V corresponds to the release of dislocation movement by the collapse of dislocation cells.
Analysis of partially pulsating fatigue process on carbon steel with microstructural observation
Energy Technology Data Exchange (ETDEWEB)
Shimano, Hiroyuki, E-mail: tales-of-destiny@akane.waseda.jp [Department of Modern Mechanical Engineering, Graduate School of Creative Science and Engineering, Waseda University, 3-4-1 Shinjyuku-ku Okubo, Tokyo 169-8555 (Japan); Faiz, M. Khairi; Hara, Asato; Yoshizumi, Kyoko [Department of Modern Mechanical Engineering, Graduate School of Creative Science and Engineering, Waseda University, 3-4-1 Shinjyuku-ku Okubo, Tokyo 169-8555 (Japan); Yoshida, Makoto [Department of Modern Mechanical Engineering, Graduate School of Creative Science and Engineering, Waseda University, 3-4-1 Shinjyuku-ku Okubo, Tokyo 169-8555 (Japan); Kagami Memorial Research Institute for Materials Science and Technology, Waseda University, 2-8-26, Nishi-Waseda, Shinjyuku-ku, Tokyo 169-0051 (Japan); Horibe, Susumu [Department of Modern Mechanical Engineering, Graduate School of Creative Science and Engineering, Waseda University, 3-4-1 Shinjyuku-ku Okubo, Tokyo 169-8555 (Japan)
2016-01-10
Pulsating low-cycle fatigue processes, up to the present, have been divided into three states: the transient state, steady state, and accelerating state of ratcheting. In our previous work, we suggested that fatigue behavior of pulsating fatigue process should be classified into five stages in which the plastic strain amplitude and the ratcheting strain rate are plotted on the X and Y axis, respectively. In this study, at the condition of R=−0.3 (partially pulsating fatigue), the change in the plastic strain amplitude and ratcheting strain rate for each cycle to failure was examined on AISI 1025 carbon steel. The dislocation substructure was examined using transmission electron microscopy (TEM) for each stage, except for stage I. It was also demonstrated that the fatigue process can be divided into five stages: stage I corresponds to the un-pinning of dislocations from the Cottrell atmosphere and propagation of the Luders band. Stage II corresponds to the restriction of dislocation movement by dislocation tangles. Stage III corresponds to the formation of dislocation cells. Stage IV corresponds to the promotion of the to-and-fro (back-and-forth) motion of dislocations by a re-arrangement of the dislocations in the cells. Stage V corresponds to the release of dislocation movement by the collapse of dislocation cells.
Suzaku Observation of the Dwarf Nova V893 Scorpii: The Discovery of a Partial X-Ray Eclipse
Mukai, Koji; Zietsman, E.; Still, M.
2008-01-01
V893 Sco is an eclipsing dwarf nova that had attracted little attention from X-ray astronomers until it was proposed as the identification of an RXTE all-sky slew survey (XSS) source. Here we report on the po inted X-ray observations of this object using Suzaku. We confirm V893 Sco to be X-ray bright, whose spectrum is highly absorbed for a dwar f nova. We have also discovered a partial X-ray eclipse in V893 Sco. This is the first time that a partial eclipse is seen in Xray light c urves of a dwarf nova. We have successfully modeled the gross features of the optical and X-ray eclipse light curves using a boundary layer geometry of the X-ray emission region. Future observations may lead to confirmation of this basic picture, and allow us to place tight co nstraints on the size of the X-ray emission region. The partial X-ray eclipse therefore should make V893 Sco a key object in understanding the physics of accretion in quiescent dwarf nova.
Majeed, Muhammad Usman
2017-01-01
the problems are formulated on higher dimensional space domains. However, in this dissertation, feedback based state estimation algorithms, known as state observers, are developed to solve such steady-state problems using one of the space variables as time
Multivariate calculus and geometry
Dineen, Seán
2014-01-01
Multivariate calculus can be understood best by combining geometric insight, intuitive arguments, detailed explanations and mathematical reasoning. This textbook has successfully followed this programme. It additionally provides a solid description of the basic concepts, via familiar examples, which are then tested in technically demanding situations. In this new edition the introductory chapter and two of the chapters on the geometry of surfaces have been revised. Some exercises have been replaced and others provided with expanded solutions. Familiarity with partial derivatives and a course in linear algebra are essential prerequisites for readers of this book. Multivariate Calculus and Geometry is aimed primarily at higher level undergraduates in the mathematical sciences. The inclusion of many practical examples involving problems of several variables will appeal to mathematics, science and engineering students.
Autonomous Navigation in Partially Observable Environments Using Hierarchical Q-Learning
Zhou, Y.; van Kampen, E.; Chu, Q.
2016-01-01
Flapping-wing MAVs represent an attractive alternative to conventional designs with rotary wings, since they promise a much higher efficiency in forward flight. However, further insight into the flapping-wing aerodynamics is still needed to get closer to the flight performance observed in natural
A partial ensemble Kalman filtering approach to enable use of range limited observations
DEFF Research Database (Denmark)
Borup, Morten; Grum, Morten; Madsen, Henrik
2015-01-01
The ensemble Kalman filter (EnKF) relies on the assumption that an observed quantity can be regarded as a stochastic variable that is Gaussian distributed with mean and variance that equals the measurement and the measurement noise, respectively. When a gauge has a minimum and/or maximum detection...
Applied multivariate statistical analysis
Härdle, Wolfgang Karl
2015-01-01
Focusing on high-dimensional applications, this 4th edition presents the tools and concepts used in multivariate data analysis in a style that is also accessible for non-mathematicians and practitioners. It surveys the basic principles and emphasizes both exploratory and inferential statistics; a new chapter on Variable Selection (Lasso, SCAD and Elastic Net) has also been added. All chapters include practical exercises that highlight applications in different multivariate data analysis fields: in quantitative financial studies, where the joint dynamics of assets are observed; in medicine, where recorded observations of subjects in different locations form the basis for reliable diagnoses and medication; and in quantitative marketing, where consumers’ preferences are collected in order to construct models of consumer behavior. All of these examples involve high to ultra-high dimensions and represent a number of major fields in big data analysis. The fourth edition of this book on Applied Multivariate ...
Rate estimation in partially observed Markov jump processes with measurement errors
Amrein, Michael; Kuensch, Hans R.
2010-01-01
We present a simulation methodology for Bayesian estimation of rate parameters in Markov jump processes arising for example in stochastic kinetic models. To handle the problem of missing components and measurement errors in observed data, we embed the Markov jump process into the framework of a general state space model. We do not use diffusion approximations. Markov chain Monte Carlo and particle filter type algorithms are introduced, which allow sampling from the posterior distribution of t...
A note on identification in discrete choice models with partial observability
DEFF Research Database (Denmark)
Fosgerau, Mogens; Ranjan, Abhishek
2017-01-01
This note establishes a new identification result for additive random utility discrete choice models. A decision-maker associates a random utility Uj+ mj to each alternative in a finite set j∈ {1 , … , J} , where U= {U1, … , UJ} is unobserved by the researcher and random with an unknown joint dis...... for applications where choices are observed aggregated into groups while prices and attributes vary at the level of individual alternatives....
Shen, Chuan-an; Chai, Jia-ke; Tuo, Xiao-ye; Cai, Jian-hua; Li, Dong-jie; Zhang, Lin; Zhu, Hua; Cai, Jin-dong
2013-02-01
To observe the effect of negative pressure therapy in the treatment of superficial partial-thickness scald in children. Three hundred and seven children with superficial partial-thickness scald hospitalized from August 2009 to May 2012 were divided into negative pressure therapy group (NPT, n = 145) and control group (C, n = 162) according to the random number table. Patients in group NPT were treated with negative pressure from within post injury day (PID) 3 to PID 9 (with -16 kPa pressure), while traditional occlusive dressing method was used in group C. Changes in body temperature, wound healing condition, frequency of dressing change were compared between group NPT and group C. Bacterial culture results of wounds were compared before and after treatment in group NPT. Volume of drained transudate per one percent of wound area was recorded in group NPT on PID 1 to PID 3. Data were processed with t test or chi-square test. The incidence of high fever was significantly lower in group NPT (26.9%, 39/145) than in group C (63.6%, 103/162, χ(2) = 41.419, P partial-thickness scald.
Multivariable calculus with applications
Lax, Peter D
2017-01-01
This text in multivariable calculus fosters comprehension through meaningful explanations. Written with students in mathematics, the physical sciences, and engineering in mind, it extends concepts from single variable calculus such as derivative, integral, and important theorems to partial derivatives, multiple integrals, Stokes’ and divergence theorems. Students with a background in single variable calculus are guided through a variety of problem solving techniques and practice problems. Examples from the physical sciences are utilized to highlight the essential relationship between calculus and modern science. The symbiotic relationship between science and mathematics is shown by deriving and discussing several conservation laws, and vector calculus is utilized to describe a number of physical theories via partial differential equations. Students will learn that mathematics is the language that enables scientific ideas to be precisely formulated and that science is a source for the development of mathemat...
Hand gesture recognition in confined spaces with partial observability and occultation constraints
Shirkhodaie, Amir; Chan, Alex; Hu, Shuowen
2016-05-01
Human activity detection and recognition capabilities have broad applications for military and homeland security. These tasks are very complicated, however, especially when multiple persons are performing concurrent activities in confined spaces that impose significant obstruction, occultation, and observability uncertainty. In this paper, our primary contribution is to present a dedicated taxonomy and kinematic ontology that are developed for in-vehicle group human activities (IVGA). Secondly, we describe a set of hand-observable patterns that represents certain IVGA examples. Thirdly, we propose two classifiers for hand gesture recognition and compare their performance individually and jointly. Finally, we present a variant of Hidden Markov Model for Bayesian tracking, recognition, and annotation of hand motions, which enables spatiotemporal inference to human group activity perception and understanding. To validate our approach, synthetic (graphical data from virtual environment) and real physical environment video imagery are employed to verify the performance of these hand gesture classifiers, while measuring their efficiency and effectiveness based on the proposed Hidden Markov Model for tracking and interpreting dynamic spatiotemporal IVGA scenarios.
The missing link: Predicting connectomes from noisy and partially observed tract tracing data
DEFF Research Database (Denmark)
Hinne, Max; Meijers, Annet; Bakker, Rembrandt
2017-01-01
a high chance of being connected, while regions far apart are most likely disconnected in the connectome. After learning the latent embedding from the connections that we did observe, the latent space allows us to predict connections that have not been probed previously. We apply the methodology to two....... In this paper, we suggest that instead of probing all possible connections, hitherto unknown connections may be predicted from the data that is already available. Our approach uses a 'latent space model' that embeds the connectivity in an abstract physical space. Regions that are close in the latent space have...... connectivity data sets of the macaque, where we demonstrate that the latent space model is successful in predicting unobserved connectivity, outperforming two baselines and an alternative model in nearly all cases. Furthermore, we show how the latent spatial embedding may be used to integrate multimodal...
SLOW RISE AND PARTIAL ERUPTION OF A DOUBLE-DECKER FILAMENT. I. OBSERVATIONS AND INTERPRETATION
International Nuclear Information System (INIS)
Liu Rui; Kliem, Bernhard; Török, Tibor; Titov, Viacheslav S.; Lionello, Roberto; Linker, Jon A.; Liu Chang; Wang Haimin
2012-01-01
We study an active-region dextral filament that was composed of two branches separated in height by about 13 Mm, as inferred from three-dimensional reconstruction by combining SDO and STEREO-B observations. This 'double-decker' configuration sustained for days before the upper branch erupted with a GOES-class M1.0 flare on 2010 August 7. Analyzing this evolution, we obtain the following main results. (1) During the hours before the eruption, filament threads within the lower branch were observed to intermittently brighten up, lift upward, and then merge with the upper branch. The merging process contributed magnetic flux and current to the upper branch, resulting in its quasi-static ascent. (2) This transfer might serve as the key mechanism for the upper branch to lose equilibrium by reaching the limiting flux that can be stably held down by the overlying field or by reaching the threshold of the torus instability. (3) The erupting branch first straightened from a reverse S shape that followed the polarity inversion line and then writhed into a forward S shape. This shows a transfer of left-handed helicity in a sequence of writhe-twist-writhe. The fact that the initial writhe is converted into the twist of the flux rope excludes the helical kink instability as the trigger process of the eruption, but supports the occurrence of the instability in the main phase, which is indeed indicated by the very strong writhing motion. (4) A hard X-ray sigmoid, likely of coronal origin, formed in the gap between the two original filament branches in the impulsive phase of the associated flare. This supports a model of transient sigmoids forming in the vertical flare current sheet. (5) Left-handed magnetic helicity is inferred for both branches of the dextral filament. (6) Two types of force-free magnetic configurations are compatible with the data, a double flux rope equilibrium and a single flux rope situated above a loop arcade.
Litvak, Leonid M; Spahr, Anthony J; Emadi, Gulam
2007-08-01
Most cochlear implant strategies utilize monopolar stimulation, likely inducing relatively broad activation of the auditory neurons. The spread of activity may be narrowed with a tripolar stimulation scheme, wherein compensating current of opposite polarity is simultaneously delivered to two adjacent electrodes. In this study, a model and cochlear implant subjects were used to examine loudness growth for varying amounts of tripolar compensation, parameterized by a coefficient sigma, ranging from 0 (monopolar) to 1 (full tripolar). In both the model and the subjects, current required for threshold activation could be approximated by I(sigma)=Ithr(0)(1-sigmaK), with fitted constants Ithr(0) and K. Three of the subjects had a "positioner," intended to place their electrode arrays closer to their neural tissue. The values of K were smaller for the positioner users and for a "close" electrode-to-tissue distance in the model. Above threshold, equal-loudness contours for some subjects deviated significantly from a linear scale-up of the threshold approximations. The patterns of deviation were similar to those observed in the model for conditions in which most of the neurons near the center electrode were excited.
Energy Technology Data Exchange (ETDEWEB)
Wunderlich, Y.; Afzal, F.; Thiel, A.; Beck, R. [Universitaet Bonn, Helmholtz-Institut fuer Strahlen- und Kernphysik, Bonn (Germany)
2017-05-15
This work presents a simple method to determine the significant partial wave contributions to experimentally determined observables in pseudoscalar meson photoproduction. First, fits to angular distributions are presented and the maximum orbital angular momentum L{sub max} needed to achieve a good fit is determined. Then, recent polarization measurements for γp → π{sup 0}p from ELSA, GRAAL, JLab and MAMI are investigated according to the proposed method. This method allows us to project high-spin partial wave contributions to any observable as long as the measurement has the necessary statistical accuracy. We show, that high precision and large angular coverage in the polarization data are needed in order to be sensitive to high-spin resonance states and thereby also for the finding of small resonance contributions. This task can be achieved via interference of these resonances with the well-known states. For the channel γp → π{sup 0}p, those are the N(1680)(5)/(2){sup +} and Δ(1950)(7)/(2){sup +}, contributing to the F-waves. (orig.)
Valchev, Nikola; Zijdewind, Inge; Keysers, Christian; Gazzola, Valeria; Avenanti, Alessio; Maurits, Natasha M
2015-01-01
Seeing others performing an action induces the observers' motor cortex to "resonate" with the observed action. Transcranial magnetic stimulation (TMS) studies suggest that such motor resonance reflects the encoding of various motor features of the observed action, including the apparent motor effort. However, it is unclear whether such encoding requires direct observation or whether force requirements can be inferred when the moving body part is partially occluded. To address this issue, we presented participants with videos of a right hand lifting a box of three different weights and asked them to estimate its weight. During each trial we delivered one transcranial magnetic stimulation (TMS) pulse over the left primary motor cortex of the observer and recorded the motor evoked potentials (MEPs) from three muscles of the right hand (first dorsal interosseous, FDI, abductor digiti minimi, ADM, and brachioradialis, BR). Importantly, because the hand shown in the videos was hidden behind a screen, only the contractions in the actor's BR muscle under the bare skin were observable during the entire videos, while the contractions in the actor's FDI and ADM muscles were hidden during the grasp and actual lift. The amplitudes of the MEPs recorded from the BR (observable) and FDI (hidden) muscle increased with the weight of the box. These findings indicate that the modulation of motor excitability induced by action observation extends to the cortical representation of muscles with contractions that could not be observed. Thus, motor resonance appears to reflect force requirements of observed lifting actions even when the moving body part is occluded from view. Copyright © 2014 Elsevier Ltd. All rights reserved.
Mcclenny, Levi D; Imani, Mahdi; Braga-Neto, Ulisses M
2017-11-25
Gene regulatory networks govern the function of key cellular processes, such as control of the cell cycle, response to stress, DNA repair mechanisms, and more. Boolean networks have been used successfully in modeling gene regulatory networks. In the Boolean network model, the transcriptional state of each gene is represented by 0 (inactive) or 1 (active), and the relationship among genes is represented by logical gates updated at discrete time points. However, the Boolean gene states are never observed directly, but only indirectly and incompletely through noisy measurements based on expression technologies such as cDNA microarrays, RNA-Seq, and cell imaging-based assays. The Partially-Observed Boolean Dynamical System (POBDS) signal model is distinct from other deterministic and stochastic Boolean network models in removing the requirement of a directly observable Boolean state vector and allowing uncertainty in the measurement process, addressing the scenario encountered in practice in transcriptomic analysis. BoolFilter is an R package that implements the POBDS model and associated algorithms for state and parameter estimation. It allows the user to estimate the Boolean states, network topology, and measurement parameters from time series of transcriptomic data using exact and approximated (particle) filters, as well as simulate the transcriptomic data for a given Boolean network model. Some of its infrastructure, such as the network interface, is the same as in the previously published R package for Boolean Networks BoolNet, which enhances compatibility and user accessibility to the new package. We introduce the R package BoolFilter for Partially-Observed Boolean Dynamical Systems (POBDS). The BoolFilter package provides a useful toolbox for the bioinformatics community, with state-of-the-art algorithms for simulation of time series transcriptomic data as well as the inverse process of system identification from data obtained with various expression
International Nuclear Information System (INIS)
Haupage, Samantha; Branski, Ryan C.; Kraus, Dennis; Peck, Kyung K.; Hsu, Meier; Holodny, Andrei
2010-01-01
The current study seeks to provide preliminary data regarding this central, adaptive response during tongue motor tasks utilizing functional magnetic resonance imaging (fMRI) before and after glossectomy. Six patients, with confirmed histological diagnoses of oral tongue cancer, underwent fMRI before and 6 months after partial glossectomy. These data were compared to nine healthy controls. All subjects performed three tongue motor tasks during fMRI: tongue tapping (TT), dry swallow (Dry), and wet swallow (Wet). Following surgery, increased activation was subjectively observed in the superior parietal lobule, supplementary motor area, and anterior cingulate. Region of interest (ROI) analysis of the precentral gyrus confirmed increased cortical activity following surgery. In addition, comparisons between pre-surgical scans and controls suggested the dry swallow task was sensitive to elicit tongue-related activation in the precentral gyrus (p ≤ 0.05). The adaptive changes in the cortex following partial glossectomy reflect recruitment of the parietal, frontal, and cingulate cortex during tongue motor tasks. In addition, post-operative activation patterns more closely approximated control levels than the pre-operative scans. Furthermore, the dry swallow task appears most specific to elicit tongue-related cortical activity. (orig.)
Energy Technology Data Exchange (ETDEWEB)
Haupage, Samantha; Branski, Ryan C.; Kraus, Dennis [Memorial Sloan-Kettering Cancer Center, Head and Neck Surgery, New York, NY (United States); Peck, Kyung K. [Memorial Sloan-Kettering Cancer Center, Department of Radiology, New York, NY (United States); Memorial Sloan-Kettering Cancer Center, Medical Physics, New York, NY (United States); Memorial Sloan-Kettering Cancer Center, Department of Medical Physics and Radiology, New York, NY (United States); Hsu, Meier [Memorial Sloan-Kettering Cancer Center, Department of Epidemiology and Biostatistics, New York, NY (United States); Holodny, Andrei [Memorial Sloan-Kettering Cancer Center, Department of Radiology, New York, NY (United States)
2010-12-15
The current study seeks to provide preliminary data regarding this central, adaptive response during tongue motor tasks utilizing functional magnetic resonance imaging (fMRI) before and after glossectomy. Six patients, with confirmed histological diagnoses of oral tongue cancer, underwent fMRI before and 6 months after partial glossectomy. These data were compared to nine healthy controls. All subjects performed three tongue motor tasks during fMRI: tongue tapping (TT), dry swallow (Dry), and wet swallow (Wet). Following surgery, increased activation was subjectively observed in the superior parietal lobule, supplementary motor area, and anterior cingulate. Region of interest (ROI) analysis of the precentral gyrus confirmed increased cortical activity following surgery. In addition, comparisons between pre-surgical scans and controls suggested the dry swallow task was sensitive to elicit tongue-related activation in the precentral gyrus (p {<=} 0.05). The adaptive changes in the cortex following partial glossectomy reflect recruitment of the parietal, frontal, and cingulate cortex during tongue motor tasks. In addition, post-operative activation patterns more closely approximated control levels than the pre-operative scans. Furthermore, the dry swallow task appears most specific to elicit tongue-related cortical activity. (orig.)
Qin, Ning; Wen, John Z.; Ren, Carolyn L.
2017-04-01
This is the first part of a two-part study on a partially miscible liquid-liquid flow (liquid carbon dioxide and deionized water) which is highly pressurized and confined in a microfluidic T-junction. Our main focuses are to understand the flow regimes as a result of varying flow conditions and investigate the characteristics of drop flow distinct from coflow, with a capillary number, C ac , that is calculated based on the continuous liquid, ranging from 10-3 to 10-2 (10-4 for coflow). Here in part I, we present our experimental observation of drop formation cycle by tracking drop length, spacing, frequency, and after-generation speed using high-speed video and image analysis. The drop flow is chronologically composed of a stagnating and filling stage, an elongating and squeezing stage, and a truncating stage. The common "necking" time during the elongating and squeezing stage (with C ac˜10-3 ) for the truncation of the dispersed liquid stream is extended, and the truncation point is subsequently shifted downstream from the T-junction corner. This temporal postponement effect modifies the scaling function reported in the literature for droplet formation with two immiscible fluids. Our experimental measurements also demonstrate the drop speed immediately following their generations can be approximated by the mean velocity from averaging the total flow rate over the channel cross section. Further justifications of the quantitative analysis by considering the mass transfer at the interface of the two partially miscible fluids are provided in part II.
Buckley, Lisa; Bingham, C Raymond; Flannagan, Carol A; Carter, Patrick M; Almani, Farideh; Cicchino, Jessica B
2016-10-01
Motorcycle crashes result in a significant health burden, including many fatal injuries and serious non-fatal head injuries. Helmets are highly effective in preventing such trauma, and jurisdictions that require helmet use of all motorcyclists have higher rates of helmet use and lower rates of head injuries among motorcyclists. The current study examines helmet use and characteristics of helmeted operators and their riding conditions in Michigan, following a weakening of the state's universal motorcycle helmet use law in April 2012. Data on police-reported crashes occurring during 2012-14 and from a stratified roadside observational survey undertaken in Southeast Michigan during May-September 2014 were used to estimate statewide helmet use rates. Observed helmet use was more common among operators of sports motorcycles, on freeways, and in the morning, and least common among operators of cruisers, on minor arterials, and in the afternoon. The rate of helmet use across the state was estimated at 75%, adjusted for roadway type, motorcycle class, and time of day. Similarly, the helmet use rate found from examination of crash records was 73%. In the observation survey, 47% of operators wore jackets, 94% wore long pants, 54% wore boots, and 80% wore gloves. Protective clothing of jackets and gloves was most often worn by sport motorcycle operators and long pants and boots most often by riders of touring motorcycles. Findings highlight the much lower rate of helmet use in Michigan compared with states that have a universal helmet use law, although the rate is higher than observed in many states with partial helmet laws. Targeted interventions aimed at specific groups of motorcyclists and situations where helmet use rates are particularly low should be considered to increase helmet use. Copyright © 2016 Elsevier Ltd. All rights reserved.
Yamashita, Yoshifumi; Nakata, Ryu; Nishikawa, Takeshi; Hada, Masaki; Hayashi, Yasuhiko
2018-04-01
We studied the dynamics of the expansion of a Shockley-type stacking fault (SSF) with 30° Si(g) partial dislocations (PDs) using a scanning electron microscope. We observed SSFs as dark lines (DLs), which formed the contrast at the intersection between the surface and the SSF on the (0001) face inclined by 8° from the surface. We performed experiments at different electron-beam scanning speeds, observing magnifications, and irradiation areas. The results indicated that the elongation of a DL during one-frame scanning depended on the time for which the electron beam irradiated the PD segment in the frame of view. From these results, we derived a formula to express the velocity of the PD using the elongation rate of the corresponding DL during one-frame scanning. We also obtained the result that the elongation velocity of the DL was not influenced by changing the direction in which the electron beam irradiates the PD. From this result, we deduced that the geometrical kink motion of the PD was enhanced by diffusing carriers that were generated by the electron-beam irradiation.
Skopina, Maria; Protasov, Vladimir
2016-01-01
This book presents a systematic study of multivariate wavelet frames with matrix dilation, in particular, orthogonal and bi-orthogonal bases, which are a special case of frames. Further, it provides algorithmic methods for the construction of dual and tight wavelet frames with a desirable approximation order, namely compactly supported wavelet frames, which are commonly required by engineers. It particularly focuses on methods of constructing them. Wavelet bases and frames are actively used in numerous applications such as audio and graphic signal processing, compression and transmission of information. They are especially useful in image recovery from incomplete observed data due to the redundancy of frame systems. The construction of multivariate wavelet frames, especially bases, with desirable properties remains a challenging problem as although a general scheme of construction is well known, its practical implementation in the multidimensional setting is difficult. Another important feature of wavelet is ...
Directory of Open Access Journals (Sweden)
Rozana Alik
2016-03-01
Full Text Available This paper presents a simple checking algorithm for maximum power point tracking (MPPT technique for Photovoltaic (PV system using Perturb and Observe (P&O algorithm. The main benefit of this checking algorithm is the simplicity and efficiency of the system whose duty cycle produced by the MPPT is smoother and changes faster according to maximum power point (MPP. This checking algorithm can determine the maximum power first before the P&O algorithm takes place to identify the voltage at MPP (VMPP, which is needed to calculate the duty cycle for the boost converter. To test the effectiveness of the algorithm, a simulation model of PV system has been carried out using MATLAB/Simulink under different level of irradiation; or in other words partially shaded condition of PV array. The results from the system using the proposed approach prove to have faster response and low ripple. Besides, the results are close to the desired outputs and exhibit an approximately 98.25% of the system efficiency. On the other hand, the system with conventional P&O MPPT seems to be unstable and has higher percentage of error. In summary, the proposed method is useful under varying level of irradiation with higher efficiency of the system.
Directory of Open Access Journals (Sweden)
Jian Jiao
2017-09-01
Full Text Available The Ka-band and higher Q/V band channels can provide an appealing capacity for the future deep-space communications and Space Information Networks (SIN, which are viewed as a primary solution to satisfy the increasing demands for high data rate services. However, Ka-band channel is much more sensitive to the weather conditions than the conventional communication channels. Moreover, due to the huge distance and long propagation delay in SINs, the transmitter can only obtain delayed Channel State Information (CSI from feedback. In this paper, the noise temperature of time-varying rain attenuation at Ka-band channels is modeled to a two-state Gilbert–Elliot channel, to capture the channel capacity that randomly ranging from good to bad state. An optimal transmission scheme based on Partially Observable Markov Decision Processes (POMDP is proposed, and the key thresholds for selecting the optimal transmission method in the SIN communications are derived. Simulation results show that our proposed scheme can effectively improve the throughput.
Li, Jian; Jiang, Ting; Li, Sai; Chen, Wei
2013-02-18
To investigate design methods of dual insertion paths and observe a short-term clinic overview of rotational path removable partial dentures (RPDs). In the study, 40 patients with partial edentulous arches were included and divided into two groups. The patients in group one were restored with rotational path RPDs (10 Kennedy class III and 10 Kennedy class IV respectively). The patients in group two (20 patients), whose edentulous area was matched with the patients' in group one, were restored with the linear path RPDs. After surveying and simulative preparation on diagnostic casts, the basic laws of designing rotational path RPDs were summarized. The oral preparation was accurately performed under the guidance of indices made on diagnostic casts after simulative preparation. The 40 dentures were recalled two weeks and one year after the insertion. The evaluations of the clinic outcome, including retention, stability, mastication function, esthetics and wearing convenience, were marked out as good, acceptable, and poor. The comparison of the evaluation results was performed between the two groups. In the rotational path design for Kennedy class III or IV RPDs, the angles (α) of dual insertion paths should be designed within a scope, approximate 10°-15°.When the angle (α) became larger, the denture retention turned to be better, but accordingly the posterior abutments needed more preparation. In the clinical application, the first insertions of the 40 dentures were all favorably accomplished. When the rotational path RPDs were compared to linear path RPDs, the time consuming on first insertion had no statistical difference[(32±8) min and (33±8) min respectively, P>0.05]. Recalled two weeks and one year after the insertion, in the esthetics evaluation, 20 rotational path RPDs were all evaluated as "A", but only 7(two weeks after) and 6 (one year after) linear path RPDs were evaluated as "A"(P<0.05). There was no significant difference in other evaluation results
A kernel version of multivariate alteration detection
DEFF Research Database (Denmark)
Nielsen, Allan Aasbjerg; Vestergaard, Jacob Schack
2013-01-01
Based on the established methods kernel canonical correlation analysis and multivariate alteration detection we introduce a kernel version of multivariate alteration detection. A case study with SPOT HRV data shows that the kMAD variates focus on extreme change observations.......Based on the established methods kernel canonical correlation analysis and multivariate alteration detection we introduce a kernel version of multivariate alteration detection. A case study with SPOT HRV data shows that the kMAD variates focus on extreme change observations....
International Nuclear Information System (INIS)
Jung, Joon-Yong; Jee, Won-Hee; Chun, Ho Jong; Ahn, Myeong Im; Kim, Yang-Soo
2010-01-01
Background: Partial-thickness tear of the rotator cuff is a common cause of shoulder pain. Magnetic resonance (MR) arthrography has been described as a useful measure to diagnose rotator cuff abnormalities. Purpose: To determine the reliability and accuracy of MR arthrography with abduction and external rotation (ABER) view for the diagnosis of partial-thickness tears of the rotator cuff. Material and Methods: Among patients who underwent MR arthrographies, 22 patients (12 men, 10 women; mean age 45 years) who had either partial-thickness tear or normal tendon on arthroscopy were included. MR images were independently scored by two observers for partial-thickness tears of the rotator cuff. Interobserver and intraobserver agreements for detection of partial-thickness tears of the rotator cuff were calculated by using κ coefficients. The differences in areas under the receiver operating characteristic (ROC) curves were assessed with a univariate Z-score test. Differences in sensitivity and specificity for interpretations based on different imaging series were tested for significance using the McNemar statistic. Results: Sensitivity, specificity, and accuracy of each reader on MR imaging without ABER view were 83%, 90%, and 86%, and 83%, 80%, and 82%, respectively, whereas on overall interpretation including ABER view, the sensitivity, specificity, and accuracy of each reader were 92%, 70%, and 82%, and 92%, 80%, and 86%, respectively. Including ABER view, interobserver agreement for partial-thickness tear increased from κ=0.55 to κ=0.68. Likewise, intraobserver agreements increased from κ=0.79 and 0.53 to κ=0.81 and 0.70 for each reader, respectively. The areas under the ROC curves for each reader were 0.96 and 0.90, which were not significantly different. Conclusion: Including ABER view in routine sequences of MR arthrography increases the sensitivity, and inter- and intraobserver agreements for detecting partial-thickness tear of rotator cuff tendon
Energy Technology Data Exchange (ETDEWEB)
Jung, Joon-Yong; Jee, Won-Hee; Chun, Ho Jong; Ahn, Myeong Im (Dept. of Radiology, Seoul St. Mary' s Hospital, School of Medicine, Catholic Univ. of Korea, Seoul (Korea)), e-mail: whjee@catholic.ac.kr; Kim, Yang-Soo (Dept. of Orthopedic Surgery, Seoul St. Mary' s Hospital, School of Medicine, Catholic Univ. of Korea, Seoul (Korea))
2010-03-15
Background: Partial-thickness tear of the rotator cuff is a common cause of shoulder pain. Magnetic resonance (MR) arthrography has been described as a useful measure to diagnose rotator cuff abnormalities. Purpose: To determine the reliability and accuracy of MR arthrography with abduction and external rotation (ABER) view for the diagnosis of partial-thickness tears of the rotator cuff. Material and Methods: Among patients who underwent MR arthrographies, 22 patients (12 men, 10 women; mean age 45 years) who had either partial-thickness tear or normal tendon on arthroscopy were included. MR images were independently scored by two observers for partial-thickness tears of the rotator cuff. Interobserver and intraobserver agreements for detection of partial-thickness tears of the rotator cuff were calculated by using kappa coefficients. The differences in areas under the receiver operating characteristic (ROC) curves were assessed with a univariate Z-score test. Differences in sensitivity and specificity for interpretations based on different imaging series were tested for significance using the McNemar statistic. Results: Sensitivity, specificity, and accuracy of each reader on MR imaging without ABER view were 83%, 90%, and 86%, and 83%, 80%, and 82%, respectively, whereas on overall interpretation including ABER view, the sensitivity, specificity, and accuracy of each reader were 92%, 70%, and 82%, and 92%, 80%, and 86%, respectively. Including ABER view, interobserver agreement for partial-thickness tear increased from kappa=0.55 to kappa=0.68. Likewise, intraobserver agreements increased from kappa=0.79 and 0.53 to kappa=0.81 and 0.70 for each reader, respectively. The areas under the ROC curves for each reader were 0.96 and 0.90, which were not significantly different. Conclusion: Including ABER view in routine sequences of MR arthrography increases the sensitivity, and inter- and intraobserver agreements for detecting partial-thickness tear of rotator cuff
International Nuclear Information System (INIS)
Chen Shibing
2011-01-01
Objective: To observe the long-term efficacy of partial spleen embolization combined with vincristine infusion in treating refractory idiopathic thrombocytopenic purpura (ITP) and Evans syndrome. Methods: During the period of 2000-2007, partial spleen embolization together with vincristine infusion was carried out in 30 patients with refractory idiopathic thrombocytopenic purpura (n=24) or Evans syndrome (n=6). Vincristine infusion (2 mg) via splenic artery was performed before partial spleen embolization procedure. The long-term effectiveness was observed and analyzed. Results: One week after the treatment, the platelet count was increased from preoperative (10.23±8.28) × 10 9 /L to (140.28±85.45) × 10 9 /L in patients with ITP, while the platelet count was increased from preoperative (12±8) × 10 9 /L to (210±60) × 10 9 /L in patients with Evans syndrome. Meanwhile, the hemoglobin level showed an increase in different degrees, from preoperative (63.00±13.62) g/L to postoperative (123.00±13.14) g/L. The therapeutic effectiveness was 100%. During the follow-up time lasting for 3-5 years, recurrence was seen in 11 patients (36.7%) and the overall efficacy rate was 63.3%. Conclusion: For the treatment of refractory idiopathic thrombocytopenic purpura and Evans syndrome, partial spleen embolization combined with vincristine infusion carries reliable long-term efficacy. (author)
Multivariate analysis with LISREL
Jöreskog, Karl G; Y Wallentin, Fan
2016-01-01
This book traces the theory and methodology of multivariate statistical analysis and shows how it can be conducted in practice using the LISREL computer program. It presents not only the typical uses of LISREL, such as confirmatory factor analysis and structural equation models, but also several other multivariate analysis topics, including regression (univariate, multivariate, censored, logistic, and probit), generalized linear models, multilevel analysis, and principal component analysis. It provides numerous examples from several disciplines and discusses and interprets the results, illustrated with sections of output from the LISREL program, in the context of the example. The book is intended for masters and PhD students and researchers in the social, behavioral, economic and many other sciences who require a basic understanding of multivariate statistical theory and methods for their analysis of multivariate data. It can also be used as a textbook on various topics of multivariate statistical analysis.
J Olive, David
2017-01-01
This text presents methods that are robust to the assumption of a multivariate normal distribution or methods that are robust to certain types of outliers. Instead of using exact theory based on the multivariate normal distribution, the simpler and more applicable large sample theory is given. The text develops among the first practical robust regression and robust multivariate location and dispersion estimators backed by theory. The robust techniques are illustrated for methods such as principal component analysis, canonical correlation analysis, and factor analysis. A simple way to bootstrap confidence regions is also provided. Much of the research on robust multivariate analysis in this book is being published for the first time. The text is suitable for a first course in Multivariate Statistical Analysis or a first course in Robust Statistics. This graduate text is also useful for people who are familiar with the traditional multivariate topics, but want to know more about handling data sets with...
Valchev, Nikola; Zijdewind, Inge; Keysers, Christian; Gazzola, Valeria; Avenanti, Alessio; Maurits, Natasha M.
Seeing others performing an action induces the observers' motor cortex to "resonate" with the observed action. Transcranial magnetic stimulation (TMS) studies suggest that such motor resonance reflects the encoding of various motor features of the observed action, including the apparent motor
Valchev, N.; Zijdewind, I.; Keysers, C.; Gazzola, V.; Avenanti, A.; Maurits, N.M.
2015-01-01
Seeing others performing an action induces the observers' motor cortex to "resonate" with the observed action. Transcranial magnetic stimulation (TMS) studies suggest that such motor resonance reflects the encoding of various motor features of the observed action, including the apparent motor
Potegal, Michael; Drewel, Elena H; MacDonald, John T
2018-01-01
We explored associations between EEG pathophysiology and emotional/behavioral (E/B) problems of children with two types of epilepsy using standard parent questionnaires and two new indicators: tantrums recorded by parents at home and brief, emotion-eliciting situations in the laboratory. Children with Benign Rolandic epilepsy (BRE, N = 6) reportedly had shorter, more angry tantrums from which they recovered quickly. Children with Complex Partial Seizures (CPS, N = 13) had longer, sadder tantrums often followed by bad moods. More generally, BRE correlated with anger and aggression; CPS with sadness and withdrawal. Scores of a composite group of siblings ( N = 11) were generally intermediate between the BRE and CPS groups. Across all children, high voltage theta and/or interictal epileptiform discharges (IEDs) correlated with negative emotional reactions. Such EEG abnormalities in left hemisphere correlated with greater social fear, right hemisphere EEG abnormalities with greater anger. Right hemisphere localization in CPS was also associated with parent-reported problems at home. If epilepsy alters neural circuitry thereby increasing negative emotions, additional assessment of anti-epileptic drug treatment of epilepsy-related E/B problems would be warranted.
Directory of Open Access Journals (Sweden)
Michael Potegal
2018-03-01
Full Text Available We explored associations between EEG pathophysiology and emotional/behavioral (E/B problems of children with two types of epilepsy using standard parent questionnaires and two new indicators: tantrums recorded by parents at home and brief, emotion-eliciting situations in the laboratory. Children with Benign Rolandic epilepsy (BRE, N = 6 reportedly had shorter, more angry tantrums from which they recovered quickly. Children with Complex Partial Seizures (CPS, N = 13 had longer, sadder tantrums often followed by bad moods. More generally, BRE correlated with anger and aggression; CPS with sadness and withdrawal. Scores of a composite group of siblings (N = 11 were generally intermediate between the BRE and CPS groups. Across all children, high voltage theta and/or interictal epileptiform discharges (IEDs correlated with negative emotional reactions. Such EEG abnormalities in left hemisphere correlated with greater social fear, right hemisphere EEG abnormalities with greater anger. Right hemisphere localization in CPS was also associated with parent-reported problems at home. If epilepsy alters neural circuitry thereby increasing negative emotions, additional assessment of anti-epileptic drug treatment of epilepsy-related E/B problems would be warranted.
DEFF Research Database (Denmark)
Ditlevsen, Susanne; Samson, Adeline
2014-01-01
Parameter estimation in multidimensional diffusion models with only one coordinate observed is highly relevant in many biological applications, but a statistically difficult problem. In neuroscience, the membrane potential evolution in single neurons can be measured at high frequency, but biophys...
Montopoli, Mario; Vulpiani, Gianfranco; Riccci, Matteo; Corradini, Stefano; Merucci, Luca; Marzano, Frank S.
2015-04-01
Ground based weather radar observations of volcanic ash clouds are gaining momentum after recent works which demonstrated their potential use either as stand alone tool or in combination with satellite retrievals. From an operational standpoint, radar data have been mainly exploited to derive the height of ash plume and its temporal-spatial development, taking into account the radar limitation of detecting coarse ash particles (from approximately 20 microns to 10 millimeters and above in terms of particle's radius). More sophisticated radar retrievals can include airborne ash concentration, ash fall rate and out-flux rate. Marzano et al. developed several volcanic ash radar retrieval (VARR) schemes, even though their practical use is still subject to a robust validation activity. The latter is made particularly difficult due to the lack of field campaigns with multiple observations and the scarce repetition of volcanic events. The radar variable, often used to infer the physical features of actual ash clouds, is the radar reflectivity named ZHH. It is related to ash particle size distribution and it shows a nice power law relationship with ash concentration. This makes ZHH largely used in radar-volcanology studies. However, weather radars are often able to detect Doppler frequency shifts and, more and more, they have a polarization-diversity capability. The former means that wind speed spectrum of the ash cloud is potentially inferable, whereas the latter implies that variables other than ZHH are available. Theoretically, these additional radar variables are linked to the degree of eccentricity of ash particles, their orientation and density as well as the presence of strong turbulence effects. Thus, the opportunity to refine the ash radar estimates so far developed can benefit from the thorough analysis of radar Doppler and polarization diversity. In this work we show a detailed analysis of Doppler shifts and polarization variables measured by the X band radar
Toledano, Rafael; Jovel, Camilo Espinosa; Jiménez-Huete, Adolfo; Bayarri, Pau Giner; Campos, Dulce; Gomariz, Elena López; Giráldez, Beatriz González; García-Morales, Irene; Falip, Mercé; Agredano, Paula Martínez; Palao, Susana; Prior, María José Aguilar Amat; Pascual, María Rosa Querol; Navacerrada, Francisco José; González, Francisco Javier López; Ojeda, Joaquín; Sáez, Aránzazu Alfaro; Bermejo, Pedro Emilio; Gil-Nagel, Antonio
2017-08-01
Eslicarbazepine acetate (ESL, Aptiom™) is a once-daily anticonvulsant, approved as adjunctive treatment of partial-onset seizures (POS). Historical-controlled trials investigating the use of ESL as monotherapy have demonstrated a favorable efficacy and tolerability profile in patients with POS. This prospective, non-interventional study recruited POS patients in 17 hospitals in Spain. After a 3-month baseline period, ESL therapy was initiated as 400mg QD and up-titrated to an optimal maintenance dose based on clinical response and tolerance. The incidence of seizures was assessed via seizure calendars and the nature and severity of adverse events (AEs) were also recorded. A total of 117 patients (aged 9-87years) enrolled in the study and were treated with ESL at either 400mg/day (3.4% patients), 800mg/day (61% patients), 1200mg/day (27.1% patients) or 1600mg/day (8.5% patients). At 3months, 82.0% (n=72) of patients achieved a ≥50% reduction in seizure frequency, compared to 79.7% (n=67) of patients at 6months and 83.0% (n=49) at 12months. Patients who suffered secondary generalized tonic-clonic (SGTC) seizures had seizure-free rates of 71% (n=27), 69.6% (n=29), and 72.7% (n=16) at 3, 6, and 12months, respectively. Overall, 18 patients (15.3%) reported AEs of instability and dizziness (n=9), somnolence (n=3), mild hyponatremia (n=3), headache (n=1), hypertriglyceridemia (n=1), and allergic reaction (n=1), which caused ESL discontinuation of ESL treatment. ESL is effective and well tolerated as monotherapy for patients with POS, which supports previous findings. Early use is supported by its frequent use as monotherapy in this study and lack of severe side effects. Copyright © 2017 Elsevier Inc. All rights reserved.
Continuous multivariate exponential extension
International Nuclear Information System (INIS)
Block, H.W.
1975-01-01
The Freund-Weinman multivariate exponential extension is generalized to the case of nonidentically distributed marginal distributions. A fatal shock model is given for the resulting distribution. Results in the bivariate case and the concept of constant multivariate hazard rate lead to a continuous distribution related to the multivariate exponential distribution (MVE) of Marshall and Olkin. This distribution is shown to be a special case of the extended Freund-Weinman distribution. A generalization of the bivariate model of Proschan and Sullo leads to a distribution which contains both the extended Freund-Weinman distribution and the MVE
Methods of Multivariate Analysis
Rencher, Alvin C
2012-01-01
Praise for the Second Edition "This book is a systematic, well-written, well-organized text on multivariate analysis packed with intuition and insight . . . There is much practical wisdom in this book that is hard to find elsewhere."-IIE Transactions Filled with new and timely content, Methods of Multivariate Analysis, Third Edition provides examples and exercises based on more than sixty real data sets from a wide variety of scientific fields. It takes a "methods" approach to the subject, placing an emphasis on how students and practitioners can employ multivariate analysis in real-life sit
DEFF Research Database (Denmark)
Silvennoinen, Annastiina; Teräsvirta, Timo
This article contains a review of multivariate GARCH models. Most common GARCH models are presented and their properties considered. This also includes nonparametric and semiparametric models. Existing specification and misspecification tests are discussed. Finally, there is an empirical example...
Multivariate Time Series Search
National Aeronautics and Space Administration — Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical...
Estimating the decomposition of predictive information in multivariate systems
Faes, Luca; Kugiumtzis, Dimitris; Nollo, Giandomenico; Jurysta, Fabrice; Marinazzo, Daniele
2015-03-01
In the study of complex systems from observed multivariate time series, insight into the evolution of one system may be under investigation, which can be explained by the information storage of the system and the information transfer from other interacting systems. We present a framework for the model-free estimation of information storage and information transfer computed as the terms composing the predictive information about the target of a multivariate dynamical process. The approach tackles the curse of dimensionality employing a nonuniform embedding scheme that selects progressively, among the past components of the multivariate process, only those that contribute most, in terms of conditional mutual information, to the present target process. Moreover, it computes all information-theoretic quantities using a nearest-neighbor technique designed to compensate the bias due to the different dimensionality of individual entropy terms. The resulting estimators of prediction entropy, storage entropy, transfer entropy, and partial transfer entropy are tested on simulations of coupled linear stochastic and nonlinear deterministic dynamic processes, demonstrating the superiority of the proposed approach over the traditional estimators based on uniform embedding. The framework is then applied to multivariate physiologic time series, resulting in physiologically well-interpretable information decompositions of cardiovascular and cardiorespiratory interactions during head-up tilt and of joint brain-heart dynamics during sleep.
Experts' understanding of partial derivatives using the Partial Derivative Machine
Roundy, David; Dorko, Allison; Dray, Tevian; Manogue, Corinne A.; Weber, Eric
2014-01-01
Partial derivatives are used in a variety of different ways within physics. Most notably, thermodynamics uses partial derivatives in ways that students often find confusing. As part of a collaboration with mathematics faculty, we are at the beginning of a study of the teaching of partial derivatives, a goal of better aligning the teaching of multivariable calculus with the needs of students in STEM disciplines. As a part of this project, we have performed a pilot study of expert understanding...
An Improved Wavelet‐Based Multivariable Fault Detection Scheme
Harrou, Fouzi
2017-07-06
Data observed from environmental and engineering processes are usually noisy and correlated in time, which makes the fault detection more difficult as the presence of noise degrades fault detection quality. Multiscale representation of data using wavelets is a powerful feature extraction tool that is well suited to denoising and decorrelating time series data. In this chapter, we combine the advantages of multiscale partial least squares (MSPLSs) modeling with those of the univariate EWMA (exponentially weighted moving average) monitoring chart, which results in an improved fault detection system, especially for detecting small faults in highly correlated, multivariate data. Toward this end, we applied EWMA chart to the output residuals obtained from MSPLS model. It is shown through simulated distillation column data the significant improvement in fault detection can be obtained by using the proposed methods as compared to the use of the conventional partial least square (PLS)‐based Q and EWMA methods and MSPLS‐based Q method.
Multivariate Birkhoff interpolation
Lorentz, Rudolph A
1992-01-01
The subject of this book is Lagrange, Hermite and Birkhoff (lacunary Hermite) interpolation by multivariate algebraic polynomials. It unifies and extends a new algorithmic approach to this subject which was introduced and developed by G.G. Lorentz and the author. One particularly interesting feature of this algorithmic approach is that it obviates the necessity of finding a formula for the Vandermonde determinant of a multivariate interpolation in order to determine its regularity (which formulas are practically unknown anyways) by determining the regularity through simple geometric manipulations in the Euclidean space. Although interpolation is a classical problem, it is surprising how little is known about its basic properties in the multivariate case. The book therefore starts by exploring its fundamental properties and its limitations. The main part of the book is devoted to a complete and detailed elaboration of the new technique. A chapter with an extensive selection of finite elements follows as well a...
Stoll, Richard; Cappel, I; Jablonski-Momeni, Anahita; Pieper, K; Stachniss, V
2007-01-01
This study evaluated the long-term survival of inlays and partial crowns made of IPS Empress. For this purpose, the patient data of a prospective study were examined in retrospect and statistically evaluated. All of the inlays and partial crowns fabricated of IPS-Empress within the Department of Operative Dentistry at the School of Dental Medicine of Philipps University, Marburg, Germany were systematically recorded in a database between 1991 and 2001. The corresponding patient files were revised at the end of 2001. The information gathered in this way was used to evaluate the survival of the restorations using the method described by Kaplan and Meyer. A total of n = 1624 restorations were fabricated of IPS-Empress within the observation period. During this time, n = 53 failures were recorded. The remaining restorations were observed for a mean period of 18.77 months. The failures were mainly attributed to fractures, endodontic problems and cementation errors. The last failure was established after 82 months. At this stage, a cumulative survival probability of p = 0.81 was registered with a standard error of 0.04. At this time, n = 30 restorations were still being observed. Restorations on vital teeth (n = 1588) showed 46 failures, with a cumulative survival probability of p = 0.82. Restorations performed on non-vital teeth (n = 36) showed seven failures, with a cumulative survival probability of p = 0.53. Highly significant differences were found between the two groups (p < 0.0001) in a log-rank test. No significant difference (p = 0.41) was found between the patients treated by students (n = 909) and those treated by qualified dentists (n = 715). Likewise, no difference (p = 0.13) was established between the restorations seated with a high viscosity cement (n = 295) and those placed with a low viscosity cement (n = 1329).
A MULTIVARIATE WEIBULL DISTRIBUTION
Directory of Open Access Journals (Sweden)
Cheng Lee
2010-07-01
Full Text Available A multivariate survival function of Weibull Distribution is developed by expanding the theorem by Lu and Bhattacharyya. From the survival function, the probability density function, the cumulative probability function, the determinant of the Jacobian Matrix, and the general moment are derived.
DEFF Research Database (Denmark)
Barndorff-Nielsen, Ole; Hansen, Peter Reinhard; Lunde, Asger
We propose a multivariate realised kernel to estimate the ex-post covariation of log-prices. We show this new consistent estimator is guaranteed to be positive semi-definite and is robust to measurement noise of certain types and can also handle non-synchronous trading. It is the first estimator...
DEFF Research Database (Denmark)
Hansen, Michael Adsetts Edberg
Interest in statistical methodology is increasing so rapidly in the astronomical community that accessible introductory material in this area is long overdue. This book fills the gap by providing a presentation of the most useful techniques in multivariate statistics. A wide-ranging annotated set...
Multivariate pattern dependence.
Directory of Open Access Journals (Sweden)
Stefano Anzellotti
2017-11-01
Full Text Available When we perform a cognitive task, multiple brain regions are engaged. Understanding how these regions interact is a fundamental step to uncover the neural bases of behavior. Most research on the interactions between brain regions has focused on the univariate responses in the regions. However, fine grained patterns of response encode important information, as shown by multivariate pattern analysis. In the present article, we introduce and apply multivariate pattern dependence (MVPD: a technique to study the statistical dependence between brain regions in humans in terms of the multivariate relations between their patterns of responses. MVPD characterizes the responses in each brain region as trajectories in region-specific multidimensional spaces, and models the multivariate relationship between these trajectories. We applied MVPD to the posterior superior temporal sulcus (pSTS and to the fusiform face area (FFA, using a searchlight approach to reveal interactions between these seed regions and the rest of the brain. Across two different experiments, MVPD identified significant statistical dependence not detected by standard functional connectivity. Additionally, MVPD outperformed univariate connectivity in its ability to explain independent variance in the responses of individual voxels. In the end, MVPD uncovered different connectivity profiles associated with different representational subspaces of FFA: the first principal component of FFA shows differential connectivity with occipital and parietal regions implicated in the processing of low-level properties of faces, while the second and third components show differential connectivity with anterior temporal regions implicated in the processing of invariant representations of face identity.
Chen, Ming; Hu, Xiang-long; Wu, Zu-xing
2010-06-01
To observe changes of the partial oxygen pressure in the deep tissues along the Large Intestine Meridian (LIM) during acupuncture stimulation, so as to reveal the characteristics of energy metabolism in the tissues along the LIM. Thirty-one healthy volunteer subjects were enlisted in the present study. Partial oxygen pressure (POP) in the tissues (at a depth of about 1.5 cm) of acupoints Binao (LI 14), Shouwuli (LI 13), Shousanli (LI 10), 2 non-acupoints [the midpoints between Quchi (LI 11) and LI 14, and between Yangxi (LI 5) and LI 11) of the LIM, and 10 non-meridian points, 1.5-2.0 cm lateral and medial to each of the tested points of the LIM was detected before, during and after electroacupuncture (EA) stimulation of Hegu (LI 4) by using a tissue oxygen tension needle-like sensor. In normal condition, the POP values in the deep tissues along the LIM were significantly higher than those of the non-meridian control points on its bilateral sides. During and after EA of Hegu (LI 4), the POP levels decreased significantly in the deep tissues along the LIM in comparison with pre-EA (P 0.05). POP is significantly higher in the deep tissues along the LIM of healthy subjects under normal conditions, which can be downregulated by EA of Hegu (LI 4), suggesting an increase of both the utilization rate of oxygen and energy metabolism after EA.
Intelligent multivariate process supervision
International Nuclear Information System (INIS)
Visuri, Pertti.
1986-01-01
This thesis addresses the difficulties encountered in managing large amounts of data in supervisory control of complex systems. Some previous alarm and disturbance analysis concepts are reviewed and a method for improving the supervision of complex systems is presented. The method, called multivariate supervision, is based on adding low level intelligence to the process control system. By using several measured variables linked together by means of deductive logic, the system can take into account the overall state of the supervised system. Thus, it can present to the operators fewer messages with higher information content than the conventional control systems which are based on independent processing of each variable. In addition, the multivariate method contains a special information presentation concept for improving the man-machine interface. (author)
Multivariate rational data fitting
Cuyt, Annie; Verdonk, Brigitte
1992-12-01
Sections 1 and 2 discuss the advantages of an object-oriented implementation combined with higher floating-point arithmetic, of the algorithms available for multivariate data fitting using rational functions. Section 1 will in particular explain what we mean by "higher arithmetic". Section 2 will concentrate on the concepts of "object orientation". In sections 3 and 4 we shall describe the generality of the data structure that can be dealt with: due to some new results virtually every data set is acceptable right now, with possible coalescence of coordinates or points. In order to solve the multivariate rational interpolation problem the data sets are fed to different algorithms depending on the structure of the interpolation points in then-variate space.
Transient multivariable sensor evaluation
Energy Technology Data Exchange (ETDEWEB)
Vilim, Richard B.; Heifetz, Alexander
2017-02-21
A method and system for performing transient multivariable sensor evaluation. The method and system includes a computer system for identifying a model form, providing training measurement data, generating a basis vector, monitoring system data from sensor, loading the system data in a non-transient memory, performing an estimation to provide desired data and comparing the system data to the desired data and outputting an alarm for a defective sensor.
Likelihood estimators for multivariate extremes
Huser, Raphaë l; Davison, Anthony C.; Genton, Marc G.
2015-01-01
The main approach to inference for multivariate extremes consists in approximating the joint upper tail of the observations by a parametric family arising in the limit for extreme events. The latter may be expressed in terms of componentwise maxima, high threshold exceedances or point processes, yielding different but related asymptotic characterizations and estimators. The present paper clarifies the connections between the main likelihood estimators, and assesses their practical performance. We investigate their ability to estimate the extremal dependence structure and to predict future extremes, using exact calculations and simulation, in the case of the logistic model.
Likelihood estimators for multivariate extremes
Huser, Raphaël
2015-11-17
The main approach to inference for multivariate extremes consists in approximating the joint upper tail of the observations by a parametric family arising in the limit for extreme events. The latter may be expressed in terms of componentwise maxima, high threshold exceedances or point processes, yielding different but related asymptotic characterizations and estimators. The present paper clarifies the connections between the main likelihood estimators, and assesses their practical performance. We investigate their ability to estimate the extremal dependence structure and to predict future extremes, using exact calculations and simulation, in the case of the logistic model.
Control Multivariable por Desacoplo
Directory of Open Access Journals (Sweden)
Fernando Morilla
2013-01-01
Full Text Available Resumen: La interacción entre variables es una característica inherente de los procesos multivariables, que dificulta su operación y el diseño de sus sistemas de control. Bajo el paradigma de Control por desacoplo se agrupan un conjunto de metodologías, que tradicionalmente han estado orientadas a eliminar o reducir la interacción, y que recientemente algunos investigadores han reorientado con objetivos de solucionar un problema tan complejo como es el control multivariable. Parte del material descrito en este artículo es bien conocido en el campo del control de procesos, pero la mayor parte de él son resultados de varios años de investigación de los autores en los que han primado la generalización del problema, la búsqueda de soluciones de fácil implementación y la combinación de bloques elementales de control PID. Esta conjunción de intereses provoca que no siempre se pueda conseguir un desacoplo perfecto, pero que sí se pueda conseguir una considerable reducción de la interacción en el nivel básico de la pirámide de control, en beneficio de otros sistemas de control que ocupan niveles jerárquicos superiores. El artículo resume todos los aspectos básicos del Control por desacoplo y su aplicación a dos procesos representativos: una planta experimental de cuatro tanques acoplados y un modelo 4×4 de un sistema experimental de calefacción, ventilación y aire acondicionado. Abstract: The interaction between variables is inherent in multivariable processes and this fact may complicate their operation and control system design. Under the paradigm of decoupling control, several methodologies that traditionally have been addressed to cancel or reduce the interactions are gathered. Recently, this approach has been reoriented by several researchers with the aim to solve such a complex problem as the multivariable control. Parts of the material in this work are well known in the process control field; however, most of them are
Introduction to multivariate discrimination
Kégl, Balázs
2013-07-01
Multivariate discrimination or classification is one of the best-studied problem in machine learning, with a plethora of well-tested and well-performing algorithms. There are also several good general textbooks [1-9] on the subject written to an average engineering, computer science, or statistics graduate student; most of them are also accessible for an average physics student with some background on computer science and statistics. Hence, instead of writing a generic introduction, we concentrate here on relating the subject to a practitioner experimental physicist. After a short introduction on the basic setup (Section 1) we delve into the practical issues of complexity regularization, model selection, and hyperparameter optimization (Section 2), since it is this step that makes high-complexity non-parametric fitting so different from low-dimensional parametric fitting. To emphasize that this issue is not restricted to classification, we illustrate the concept on a low-dimensional but non-parametric regression example (Section 2.1). Section 3 describes the common algorithmic-statistical formal framework that unifies the main families of multivariate classification algorithms. We explain here the large-margin principle that partly explains why these algorithms work. Section 4 is devoted to the description of the three main (families of) classification algorithms, neural networks, the support vector machine, and AdaBoost. We do not go into the algorithmic details; the goal is to give an overview on the form of the functions these methods learn and on the objective functions they optimize. Besides their technical description, we also make an attempt to put these algorithm into a socio-historical context. We then briefly describe some rather heterogeneous applications to illustrate the pattern recognition pipeline and to show how widespread the use of these methods is (Section 5). We conclude the chapter with three essentially open research problems that are either
Introduction to multivariate discrimination
International Nuclear Information System (INIS)
Kegl, B.
2013-01-01
Multivariate discrimination or classification is one of the best-studied problem in machine learning, with a plethora of well-tested and well-performing algorithms. There are also several good general textbooks [1-9] on the subject written to an average engineering, computer science, or statistics graduate student; most of them are also accessible for an average physics student with some background on computer science and statistics. Hence, instead of writing a generic introduction, we concentrate here on relating the subject to a practitioner experimental physicist. After a short introduction on the basic setup (Section 1) we delve into the practical issues of complexity regularization, model selection, and hyper-parameter optimization (Section 2), since it is this step that makes high-complexity non-parametric fitting so different from low-dimensional parametric fitting. To emphasize that this issue is not restricted to classification, we illustrate the concept on a low-dimensional but non-parametric regression example (Section 2.1). Section 3 describes the common algorithmic-statistical formal framework that unifies the main families of multivariate classification algorithms. We explain here the large-margin principle that partly explains why these algorithms work. Section 4 is devoted to the description of the three main (families of) classification algorithms, neural networks, the support vector machine, and AdaBoost. We do not go into the algorithmic details; the goal is to give an overview on the form of the functions these methods learn and on the objective functions they optimize. Besides their technical description, we also make an attempt to put these algorithm into a socio-historical context. We then briefly describe some rather heterogeneous applications to illustrate the pattern recognition pipeline and to show how widespread the use of these methods is (Section 5). We conclude the chapter with three essentially open research problems that are either
DEFF Research Database (Denmark)
Barndorff-Nielsen, Ole Eiler; Hansen, Peter Reinhard; Lunde, Asger
2011-01-01
We propose a multivariate realised kernel to estimate the ex-post covariation of log-prices. We show this new consistent estimator is guaranteed to be positive semi-definite and is robust to measurement error of certain types and can also handle non-synchronous trading. It is the first estimator...... which has these three properties which are all essential for empirical work in this area. We derive the large sample asymptotics of this estimator and assess its accuracy using a Monte Carlo study. We implement the estimator on some US equity data, comparing our results to previous work which has used...
Directory of Open Access Journals (Sweden)
Gilcélia A. Cordeiro
2008-01-01
Full Text Available In this work a multivariate spectroscopic methodology is proposed for quantitative determination of sulfamethoxazole and trimethoprim in pharmaceutical associations. The multivariate model was developed by partial least-squares regression, using twenty synthetic mixtures and the spectral region between 190 and 350 nm. In the validation stage, which involved the analysis of five synthetic mixtures, prediction errors lower that 3% were observed. The predictive capacity of the multivariate models is seriously affected by spectral changes induced by pH variations, a fact that acquires a great significance in the analysis of real samples (pharmaceuticals that contain chemical additives.
Solt, Andras S; Bostock, Mark J; Shrestha, Binesh; Kumar, Prashant; Warne, Tony; Tate, Christopher G; Nietlispach, Daniel
2017-11-27
A complex conformational energy landscape determines G-protein-coupled receptor (GPCR) signalling via intracellular binding partners (IBPs), e.g., G s and β-arrestin. Using 13 C methyl methionine NMR for the β 1 -adrenergic receptor, we identify ligand efficacy-dependent equilibria between an inactive and pre-active state and, in complex with G s -mimetic nanobody, between more and less active ternary complexes. Formation of a basal activity complex through ligand-free nanobody-receptor interaction reveals structural differences on the cytoplasmic receptor side compared to the full agonist-bound nanobody-coupled form, suggesting that ligand-induced variations in G-protein interaction underpin partial agonism. Significant differences in receptor dynamics are observed ranging from rigid nanobody-coupled states to extensive μs-to-ms timescale dynamics when bound to a full agonist. We suggest that the mobility of the full agonist-bound form primes the GPCR to couple to IBPs. On formation of the ternary complex, ligand efficacy determines the quality of the interaction between the rigidified receptor and an IBP and consequently the signalling level.
Multivariate Statistical Process Control
DEFF Research Database (Denmark)
Kulahci, Murat
2013-01-01
As sensor and computer technology continues to improve, it becomes a normal occurrence that we confront with high dimensional data sets. As in many areas of industrial statistics, this brings forth various challenges in statistical process control (SPC) and monitoring for which the aim...... is to identify “out-of-control” state of a process using control charts in order to reduce the excessive variation caused by so-called assignable causes. In practice, the most common method of monitoring multivariate data is through a statistic akin to the Hotelling’s T2. For high dimensional data with excessive...... amount of cross correlation, practitioners are often recommended to use latent structures methods such as Principal Component Analysis to summarize the data in only a few linear combinations of the original variables that capture most of the variation in the data. Applications of these control charts...
Directory of Open Access Journals (Sweden)
Zhi-Hui Deng
2015-12-01
Full Text Available AIM:To observe the availability and tolerance of 0.1% bromfenac sodium hydrate ophthalmic solution in the partial substitution of glucocorticoid after laser subepithelial keratomileusis(LASEK. METHODS:Totally 180 patients(180 eyesreceived LASEK were selected and divided into study group and control group according to different medications. The study group adopted 0.1% bromfenac sodium hydrate ophthalmic solution combined with glucocorticoid; the control group adopted glucocorticoid. The changes of visual acuity and intraocular pressure(IOPof two groups were recorded before and after surgery and the occurrence of diffuse larnellar kerafitis(DLKafter surgery were observed. RESULTS:After 1mo of surgery, visual acuity of study group was 1.25±0.22 while that of control group was 0.97±0.23(PP>0.05. After 1 and 3mo of surgery, IOP of study group was 12.29±2.71 and 12.67±2.33mmHg while that of control group was 14.26±2.65 and 14.56±2.61mmHg, the difference was statistically significant(PP>0.05. In terms of tolerance, the control group had 4 cases(4 eyestaking the IOP-lowering medication. The study group had no uncomfortable cases. The DLK level of the study group at 0, 1, 2 was 93.33%, 6.67%, 0%, respectively and those in control group was 75.56%, 17.78% and 6.67%, respectively, and the differences were significant(PCONCLUSION:0.1% bromfenac sodium hydrate ophthalmic solution can efficiently stabilize the patient's IOP after LASEK. The patient has a better visual acuity, visual function and fewer complications. The tolerance is also favorable. It is worthy of promotion.
Practical multivariate analysis
Afifi, Abdelmonem; Clark, Virginia A
2011-01-01
""First of all, it is very easy to read. … The authors manage to introduce and (at least partially) explain even quite complex concepts, e.g. eigenvalues, in an easy and pedagogical way that I suppose is attractive to readers without deeper statistical knowledge. The text is also sprinkled with references for those who want to probe deeper into a certain topic. Secondly, I personally find the book's emphasis on practical data handling very appealing. … Thirdly, the book gives very nice coverage of regression analysis. … this is a nicely written book that gives a good overview of a large number
Ilić, L.; Kuzmanoski, M.; Kolarž, P.; Nina, A.; Srećković, V.; Mijić, Z.; Bajčetić, J.; Andrić, M.
2018-06-01
Measurements of atmospheric parameters were carried out during the partial solar eclipse (51% coverage of solar disc) observed in Belgrade on 20 March 2015. The measured parameters included height of the planetary boundary layer (PBL), meteorological parameters, solar radiation, surface ozone and air ions, as well as Very Low Frequency (VLF, 3-30 kHz) and Low Frequency (LF, 30-300 kHz) signals to detect low-ionospheric plasma perturbations. The observed decrease of global solar and UV-B radiation was 48%, similar to the solar disc coverage. Meteorological parameters showed similar behavior at two measurement sites, with different elevations and different measurement heights. Air temperature change due to solar eclipse was more pronounced at the lower measurement height, showing a decrease of 2.6 °C, with 15-min time delay relative to the eclipse maximum. However, at the other site temperature did not decrease; its morning increase ceased with the start of the eclipse, and continued after the eclipse maximum. Relative humidity at both sites remained almost constant until the eclipse maximum and then decreased as the temperature increased. The wind speed decreased and reached minimum 35 min after the last contact. The eclipse-induced decrease of PBL height was about 200 m, with minimum reached 20 min after the eclipse maximum. Although dependent on UV radiation, surface ozone concentration did not show the expected decrease, possibly due to less significant influence of photochemical reactions at the measurement site and decline of PBL height. Air-ion concentration decreased during the solar eclipse, with minimum almost coinciding with the eclipse maximum. Additionally, the referential Line-of-Sight (LOS) radio link was set in the area of Belgrade, using the carrier frequency of 3 GHz. Perturbation of the receiving signal level (RSL) was observed on March 20, probably induced by the solar eclipse. Eclipse-related perturbations in ionospheric D-region were detected
Multivariate statistical methods a primer
Manly, Bryan FJ
2004-01-01
THE MATERIAL OF MULTIVARIATE ANALYSISExamples of Multivariate DataPreview of Multivariate MethodsThe Multivariate Normal DistributionComputer ProgramsGraphical MethodsChapter SummaryReferencesMATRIX ALGEBRAThe Need for Matrix AlgebraMatrices and VectorsOperations on MatricesMatrix InversionQuadratic FormsEigenvalues and EigenvectorsVectors of Means and Covariance MatricesFurther Reading Chapter SummaryReferencesDISPLAYING MULTIVARIATE DATAThe Problem of Displaying Many Variables in Two DimensionsPlotting index VariablesThe Draftsman's PlotThe Representation of Individual Data P:ointsProfiles o
Processing data collected from radiometric experiments by multivariate technique
International Nuclear Information System (INIS)
Urbanski, P.; Kowalska, E.; Machaj, B.; Jakowiuk, A.
2005-01-01
Multivariate techniques applied for processing data collected from radiometric experiments can provide more efficient extraction of the information contained in the spectra. Several techniques are considered: (i) multivariate calibration using Partial Least Square Regression and Artificial Neural Network, (ii) standardization of the spectra, (iii) smoothing of collected spectra were autocorrelation function and bootstrap were used for the assessment of the processed data, (iv) image processing using Principal Component Analysis. Application of these techniques is illustrated on examples of some industrial applications. (author)
Multivariable dynamic calculus on time scales
Bohner, Martin
2016-01-01
This book offers the reader an overview of recent developments of multivariable dynamic calculus on time scales, taking readers beyond the traditional calculus texts. Covering topics from parameter-dependent integrals to partial differentiation on time scales, the book’s nine pedagogically oriented chapters provide a pathway to this active area of research that will appeal to students and researchers in mathematics and the physical sciences. The authors present a clear and well-organized treatment of the concept behind the mathematics and solution techniques, including many practical examples and exercises.
Multivariate statistics exercises and solutions
Härdle, Wolfgang Karl
2015-01-01
The authors present tools and concepts of multivariate data analysis by means of exercises and their solutions. The first part is devoted to graphical techniques. The second part deals with multivariate random variables and presents the derivation of estimators and tests for various practical situations. The last part introduces a wide variety of exercises in applied multivariate data analysis. The book demonstrates the application of simple calculus and basic multivariate methods in real life situations. It contains altogether more than 250 solved exercises which can assist a university teacher in setting up a modern multivariate analysis course. All computer-based exercises are available in the R language. All R codes and data sets may be downloaded via the quantlet download center www.quantlet.org or via the Springer webpage. For interactive display of low-dimensional projections of a multivariate data set, we recommend GGobi.
Indian Academy of Sciences (India)
First page Back Continue Last page Overview Graphics. Partial Cancellation. Full Cancellation is desirable. But complexity requirements are enormous. 4000 tones, 100 Users billions of flops !!! Main Idea: Challenge: To determine which cross-talker to cancel on what “tone” for a given victim. Constraint: Total complexity is ...
Directory of Open Access Journals (Sweden)
Liberti Gian Luigi
2016-01-01
Full Text Available This study reports some preliminary analyses of multichannel lidar measurements taken in Rome Tor Vergata (Italy during the 20th March 2015 partial solar eclipse. The objective is assessing the capability of the instrument to document the effect of the eclipse in the lower troposphere, with a particular emphasis on the information content at relatively small temporal and spatial scales.
Liberti, Gian Luigi; Dionisi, Davide; Federico, Stefano; Congeduti, Fernando
2016-06-01
This study reports some preliminary analyses of multichannel lidar measurements taken in Rome Tor Vergata (Italy) during the 20th March 2015 partial solar eclipse. The objective is assessing the capability of the instrument to document the effect of the eclipse in the lower troposphere, with a particular emphasis on the information content at relatively small temporal and spatial scales.
Multivariate analysis: models and method
International Nuclear Information System (INIS)
Sanz Perucha, J.
1990-01-01
Data treatment techniques are increasingly used since computer methods result of wider access. Multivariate analysis consists of a group of statistic methods that are applied to study objects or samples characterized by multiple values. A final goal is decision making. The paper describes the models and methods of multivariate analysis
Model Checking Multivariate State Rewards
DEFF Research Database (Denmark)
Nielsen, Bo Friis; Nielson, Flemming; Nielson, Hanne Riis
2010-01-01
We consider continuous stochastic logics with state rewards that are interpreted over continuous time Markov chains. We show how results from multivariate phase type distributions can be used to obtain higher-order moments for multivariate state rewards (including covariance). We also generalise...
Multivariate analysis methods in physics
International Nuclear Information System (INIS)
Wolter, M.
2007-01-01
A review of multivariate methods based on statistical training is given. Several multivariate methods useful in high-energy physics analysis are discussed. Selected examples from current research in particle physics are discussed, both from the on-line trigger selection and from the off-line analysis. Also statistical training methods are presented and some new application are suggested [ru
International Nuclear Information System (INIS)
1978-11-01
This discussion paper considers the possibility of applying to the recycle of plutonium in thermal reactors a particular method of partial processing based on the PUREX process but named CIVEX to emphasise the differences. The CIVEX process is based primarily on the retention of short-lived fission products. The paper suggests: (1) the recycle of fission products with uranium and plutonium in thermal reactor fuel would be technically feasible; (2) it would, however, take ten years or more to develop the CIVEX process to the point where it could be launched on a commercial scale; (3) since the majority of spent fuel to be reprocessed this century will have been in storage for ten years or more, the recycling of short-lived fission products with the U-Pu would not provide an effective means of making refabrication fuel ''inaccessible'' because the radioactivity associated with the fission products would have decayed. There would therefore be no advantage in partial processing
Directory of Open Access Journals (Sweden)
М.М. Karimova
2017-05-01
Full Text Available A girl with partial gigantism (the increased I and II fingers of the left foot is being examined. This condition is a rare and unresolved problem, as the definite reason of its development is not determined. Wait-and-see strategy is recommended, as well as correcting operations after closing of growth zones, and forming of data pool for generalization and development of schemes of drug and radial therapeutic methods.
Multivariate Max-Stable Spatial Processes
Genton, Marc G.
2014-01-06
Analysis of spatial extremes is currently based on univariate processes. Max-stable processes allow the spatial dependence of extremes to be modelled and explicitly quantified, they are therefore widely adopted in applications. For a better understanding of extreme events of real processes, such as environmental phenomena, it may be useful to study several spatial variables simultaneously. To this end, we extend some theoretical results and applications of max-stable processes to the multivariate setting to analyze extreme events of several variables observed across space. In particular, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. Then, we define a Poisson process construction in the multivariate setting and introduce multivariate versions of the Smith Gaussian extremevalue, the Schlather extremal-Gaussian and extremal-t, and the BrownResnick models. Inferential aspects of those models based on composite likelihoods are developed. We present results of various Monte Carlo simulations and of an application to a dataset of summer daily temperature maxima and minima in Oklahoma, U.S.A., highlighting the utility of working with multivariate models in contrast to the univariate case. Based on joint work with Simone Padoan and Huiyan Sang.
Multivariate Max-Stable Spatial Processes
Genton, Marc G.
2014-01-01
Analysis of spatial extremes is currently based on univariate processes. Max-stable processes allow the spatial dependence of extremes to be modelled and explicitly quantified, they are therefore widely adopted in applications. For a better understanding of extreme events of real processes, such as environmental phenomena, it may be useful to study several spatial variables simultaneously. To this end, we extend some theoretical results and applications of max-stable processes to the multivariate setting to analyze extreme events of several variables observed across space. In particular, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. Then, we define a Poisson process construction in the multivariate setting and introduce multivariate versions of the Smith Gaussian extremevalue, the Schlather extremal-Gaussian and extremal-t, and the BrownResnick models. Inferential aspects of those models based on composite likelihoods are developed. We present results of various Monte Carlo simulations and of an application to a dataset of summer daily temperature maxima and minima in Oklahoma, U.S.A., highlighting the utility of working with multivariate models in contrast to the univariate case. Based on joint work with Simone Padoan and Huiyan Sang.
Multivariate covariance generalized linear models
DEFF Research Database (Denmark)
Bonat, W. H.; Jørgensen, Bent
2016-01-01
are fitted by using an efficient Newton scoring algorithm based on quasi-likelihood and Pearson estimating functions, using only second-moment assumptions. This provides a unified approach to a wide variety of types of response variables and covariance structures, including multivariate extensions......We propose a general framework for non-normal multivariate data analysis called multivariate covariance generalized linear models, designed to handle multivariate response variables, along with a wide range of temporal and spatial correlation structures defined in terms of a covariance link...... function combined with a matrix linear predictor involving known matrices. The method is motivated by three data examples that are not easily handled by existing methods. The first example concerns multivariate count data, the second involves response variables of mixed types, combined with repeated...
Constructing general partial differential equations using polynomial and neural networks.
Zjavka, Ladislav; Pedrycz, Witold
2016-01-01
Sum fraction terms can approximate multi-variable functions on the basis of discrete observations, replacing a partial differential equation definition with polynomial elementary data relation descriptions. Artificial neural networks commonly transform the weighted sum of inputs to describe overall similarity relationships of trained and new testing input patterns. Differential polynomial neural networks form a new class of neural networks, which construct and solve an unknown general partial differential equation of a function of interest with selected substitution relative terms using non-linear multi-variable composite polynomials. The layers of the network generate simple and composite relative substitution terms whose convergent series combinations can describe partial dependent derivative changes of the input variables. This regression is based on trained generalized partial derivative data relations, decomposed into a multi-layer polynomial network structure. The sigmoidal function, commonly used as a nonlinear activation of artificial neurons, may transform some polynomial items together with the parameters with the aim to improve the polynomial derivative term series ability to approximate complicated periodic functions, as simple low order polynomials are not able to fully make up for the complete cycles. The similarity analysis facilitates substitutions for differential equations or can form dimensional units from data samples to describe real-world problems. Copyright © 2015 Elsevier Ltd. All rights reserved.
Oxidative stability of frozen mackerel batches ― A multivariate data analysis approach
DEFF Research Database (Denmark)
Helbo Ekgreen, M.; Frosch, Stina; Baron, Caroline Pascale
2011-01-01
deterioration and texture changes. The aim was to investigate the correlation between the raw material history and the quality loss observed during frozen storage using relevant multivariate data analysis such as Principal Component Analysis (PCA) and Partial Least Square Analysis (PLS). Preliminary results...... showed that it was possible to differentiate between the different batches depending on their history and that some batches were more oxidised than others. Furthermore, based on the results from the data analysis, critical control points in the entire production chain will be identified and strategies...
Goicoechea, H C; Olivieri, A C
1999-08-01
The use of multivariate spectrophotometric calibration is presented for the simultaneous determination of the active components of tablets used in the treatment of pulmonary tuberculosis. The resolution of ternary mixtures of rifampicin, isoniazid and pyrazinamide has been accomplished by using partial least squares (PLS-1) regression analysis. Although the components show an important degree of spectral overlap, they have been simultaneously determined with high accuracy and precision, rapidly and with no need of nonaqueous solvents for dissolving the samples. No interference has been observed from the tablet excipients. A comparison is presented with the related multivariate method of classical least squares (CLS) analysis, which is shown to yield less reliable results due to the severe spectral overlap among the studied compounds. This is highlighted in the case of isoniazid, due to the small absorbances measured for this component.
A primer of multivariate statistics
Harris, Richard J
2014-01-01
Drawing upon more than 30 years of experience in working with statistics, Dr. Richard J. Harris has updated A Primer of Multivariate Statistics to provide a model of balance between how-to and why. This classic text covers multivariate techniques with a taste of latent variable approaches. Throughout the book there is a focus on the importance of describing and testing one's interpretations of the emergent variables that are produced by multivariate analysis. This edition retains its conversational writing style while focusing on classical techniques. The book gives the reader a feel for why
Multivariate and semiparametric kernel regression
Härdle, Wolfgang; Müller, Marlene
1997-01-01
The paper gives an introduction to theory and application of multivariate and semiparametric kernel smoothing. Multivariate nonparametric density estimation is an often used pilot tool for examining the structure of data. Regression smoothing helps in investigating the association between covariates and responses. We concentrate on kernel smoothing using local polynomial fitting which includes the Nadaraya-Watson estimator. Some theory on the asymptotic behavior and bandwidth selection is pro...
Partially Hidden Markov Models
DEFF Research Database (Denmark)
Forchhammer, Søren Otto; Rissanen, Jorma
1996-01-01
Partially Hidden Markov Models (PHMM) are introduced. They differ from the ordinary HMM's in that both the transition probabilities of the hidden states and the output probabilities are conditioned on past observations. As an illustration they are applied to black and white image compression where...
Multivariate Statistical Process Control Charts: An Overview
Bersimis, Sotiris; Psarakis, Stelios; Panaretos, John
2006-01-01
In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as p...
Multivariate Generalized Multiscale Entropy Analysis
Directory of Open Access Journals (Sweden)
Anne Humeau-Heurtier
2016-11-01
Full Text Available Multiscale entropy (MSE was introduced in the 2000s to quantify systems’ complexity. MSE relies on (i a coarse-graining procedure to derive a set of time series representing the system dynamics on different time scales; (ii the computation of the sample entropy for each coarse-grained time series. A refined composite MSE (rcMSE—based on the same steps as MSE—also exists. Compared to MSE, rcMSE increases the accuracy of entropy estimation and reduces the probability of inducing undefined entropy for short time series. The multivariate versions of MSE (MMSE and rcMSE (MrcMSE have also been introduced. In the coarse-graining step used in MSE, rcMSE, MMSE, and MrcMSE, the mean value is used to derive representations of the original data at different resolutions. A generalization of MSE was recently published, using the computation of different moments in the coarse-graining procedure. However, so far, this generalization only exists for univariate signals. We therefore herein propose an extension of this generalized MSE to multivariate data. The multivariate generalized algorithms of MMSE and MrcMSE presented herein (MGMSE and MGrcMSE, respectively are first analyzed through the processing of synthetic signals. We reveal that MGrcMSE shows better performance than MGMSE for short multivariate data. We then study the performance of MGrcMSE on two sets of short multivariate electroencephalograms (EEG available in the public domain. We report that MGrcMSE may show better performance than MrcMSE in distinguishing different types of multivariate EEG data. MGrcMSE could therefore supplement MMSE or MrcMSE in the processing of multivariate datasets.
Multivariate Welch t-test on distances
Alekseyenko, Alexander V.
2016-01-01
Motivation: Permutational non-Euclidean analysis of variance, PERMANOVA, is routinely used in exploratory analysis of multivariate datasets to draw conclusions about the significance of patterns visualized through dimension reduction. This method recognizes that pairwise distance matrix between observations is sufficient to compute within and between group sums of squares necessary to form the (pseudo) F statistic. Moreover, not only Euclidean, but arbitrary distances can be used. This method...
Geometric noise reduction for multivariate time series.
Mera, M Eugenia; Morán, Manuel
2006-03-01
We propose an algorithm for the reduction of observational noise in chaotic multivariate time series. The algorithm is based on a maximum likelihood criterion, and its goal is to reduce the mean distance of the points of the cleaned time series to the attractor. We give evidence of the convergence of the empirical measure associated with the cleaned time series to the underlying invariant measure, implying the possibility to predict the long run behavior of the true dynamics.
Applied multivariate statistics with R
Zelterman, Daniel
2015-01-01
This book brings the power of multivariate statistics to graduate-level practitioners, making these analytical methods accessible without lengthy mathematical derivations. Using the open source, shareware program R, Professor Zelterman demonstrates the process and outcomes for a wide array of multivariate statistical applications. Chapters cover graphical displays, linear algebra, univariate, bivariate and multivariate normal distributions, factor methods, linear regression, discrimination and classification, clustering, time series models, and additional methods. Zelterman uses practical examples from diverse disciplines to welcome readers from a variety of academic specialties. Those with backgrounds in statistics will learn new methods while they review more familiar topics. Chapters include exercises, real data sets, and R implementations. The data are interesting, real-world topics, particularly from health and biology-related contexts. As an example of the approach, the text examines a sample from the B...
Multivariate stochastic simulation with subjective multivariate normal distributions
P. J. Ince; J. Buongiorno
1991-01-01
In many applications of Monte Carlo simulation in forestry or forest products, it may be known that some variables are correlated. However, for simplicity, in most simulations it has been assumed that random variables are independently distributed. This report describes an alternative Monte Carlo simulation technique for subjectively assesed multivariate normal...
Multivariate Matrix-Exponential Distributions
DEFF Research Database (Denmark)
Bladt, Mogens; Nielsen, Bo Friis
2010-01-01
be written as linear combinations of the elements in the exponential of a matrix. For this reason we shall refer to multivariate distributions with rational Laplace transform as multivariate matrix-exponential distributions (MVME). The marginal distributions of an MVME are univariate matrix......-exponential distributions. We prove a characterization that states that a distribution is an MVME distribution if and only if all non-negative, non-null linear combinations of the coordinates have a univariate matrix-exponential distribution. This theorem is analog to a well-known characterization theorem...
Assenza, G; Mecarelli, O; Lanzone, J; Assenza, F; Tombini, M; Di Lazzaro, V; Pulitano, P
2018-05-01
Eslicarbazepine acetate (ESL) is a third-generation member of the dibenzazepine family approved in 2009 by the European Medicines Agency with the indication of adjunctive therapy in adult people with partial-onset seizures (PPOS). We aimed at assessing the ESL impact on seizure frequency and quality of life in PPOS with a particular attention to sleepiness and depression. We evaluated 50 adult PPOS (>18 years; 48 ± 14 years-old; 23 males) treated with adjunctive ESL for ≥2months with a retrospective multi-centric design. Clinical files of the last 2 years were reviewed checking for monthly seizure frequency, treatment retention rate, adverse drug reactions (ADRs), concomitant anti-epileptic drugs and behavioural scales for sleepiness (Stanford Sleepiness Scale, SSS, and Epworth Sleepiness Scale, ESS), depression (Beck Depression Inventory-II, BDI) and overall quality of life (QOLIE-31). At the end of 96 ± 28 days of ESL treatment, the mean seizure reduction was 56%; 60% of patients had seizure reduction above 50%, with a 31% of the whole population becoming seizure free. We reported 16 ADRs with 4 hyponatremia. Retention rate was 76%. Patient reported less sleepiness after ESL (SSS, p = 0.031; ESS, p = 0.0000002). Before ESL, 38% of patients had pathologic BDI scores, which normalized in most of them (73%) after ESL (BDI improvement, p = 0.000012). These scores resulted in an amelioration of quality of life (QOLIE-31, p = 0.000002). ESL is a safe and effective anti-epileptic drug in a real life scenario, with an excellent behavioural profile for the overall quality of life and, in particular, for sleepiness and depression. Copyright © 2018 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.
Prospective surveillance of multivariate spatial disease data
Corberán-Vallet, A
2012-01-01
Surveillance systems are often focused on more than one disease within a predefined area. On those occasions when outbreaks of disease are likely to be correlated, the use of multivariate surveillance techniques integrating information from multiple diseases allows us to improve the sensitivity and timeliness of outbreak detection. In this article, we present an extension of the surveillance conditional predictive ordinate to monitor multivariate spatial disease data. The proposed surveillance technique, which is defined for each small area and time period as the conditional predictive distribution of those counts of disease higher than expected given the data observed up to the previous time period, alerts us to both small areas of increased disease incidence and the diseases causing the alarm within each area. We investigate its performance within the framework of Bayesian hierarchical Poisson models using a simulation study. An application to diseases of the respiratory system in South Carolina is finally presented. PMID:22534429
The Multivariate Gaussian Probability Distribution
DEFF Research Database (Denmark)
Ahrendt, Peter
2005-01-01
This technical report intends to gather information about the multivariate gaussian distribution, that was previously not (at least to my knowledge) to be found in one place and written as a reference manual. Additionally, some useful tips and tricks are collected that may be useful in practical ...
A "Model" Multivariable Calculus Course.
Beckmann, Charlene E.; Schlicker, Steven J.
1999-01-01
Describes a rich, investigative approach to multivariable calculus. Introduces a project in which students construct physical models of surfaces that represent real-life applications of their choice. The models, along with student-selected datasets, serve as vehicles to study most of the concepts of the course from both continuous and discrete…
Directory of Open Access Journals (Sweden)
Rodrigo H. Tardin
2014-02-01
Full Text Available Difficulties in quantifying behavioral events can cause loss of information about cetacean behavior, especially behaviors whose functions are still debated. The lack of knowledge is greater for South American species such as Sotalia guianensis (Van Benédén, 1864. Our objective was to contextualize the behavioral events inside behavioral states using a Permutational Multivariate Analysis of Variance (MANOVA. Three events occurred in the Feeding, Socio-Sexual and Travelling states (Porpoising, Side flop, Tail out dive, and five events occurred in the Feeding and Travelling states (Back flop, Horizontal jump, Lobtail, Spy-hop, Partial flop ahead. Three events (Belly exposure, Club, and Heading occurred exclusively in the Socio-sexual state. Partial Back flop and Head flop occurred exclusively in the Feeding state. For the events that occurred in multiple states, we observed that some events occurred more frequently in one of the states (p < 0.001, such as Lobtail, Tail out dive horizontal Jump, Partial flop ahead and Side flop. Our multivariate analysis, which separated Socio-sexual behavior from Feeding and Travelling, showed that the abundance of behavioral events differs between states. This differentiation indicates that some events are associated with specific behavioral states. Almost 40% of the events observed were exclusively performed in one state, which indicates a high specialization for some events. Proper discrimination and contextualization of behavioral events may be efficient tools to better understand dolphin behaviors. Similar studies in other habitats and with other species, will help build a broader scenario to aid our understanding of the functions of dolphin behavioral events.
Multivariate spectral-analysis of movement-related EEG data
International Nuclear Information System (INIS)
Andrew, C. M.
1997-01-01
The univariate method of event-related desynchronization (ERD) analysis, which quantifies the temporal evolution of power within specific frequency bands from electroencephalographic (EEG) data recorded during a task or event, is extended to an event related multivariate spectral analysis method. With this method, time courses of cross-spectra, phase spectra, coherence spectra, band-averaged coherence values (event-related coherence, ERCoh), partial power spectra and partial coherence spectra are estimated from an ensemble of multivariate event-related EEG trials. This provides a means of investigating relationships between EEG signals recorded over different scalp areas during the performance of a task or the occurrence of an event. The multivariate spectral analysis method is applied to EEG data recorded during three different movement-related studies involving discrete right index finger movements. The first study investigates the impact of the EEG derivation type on the temporal evolution of interhemispheric coherence between activity recorded at electrodes overlying the left and right sensorimotor hand areas during cued finger movement. The question results whether changes in coherence necessarily reflect changes in functional coupling of the cortical structures underlying the recording electrodes. The method is applied to data recorded during voluntary finger movement and a hypothesis, based on an existing global/local model of neocortical dynamics, is formulated to explain the coherence results. The third study applies partial spectral analysis too, and investigates phase relationships of, movement-related data recorded from a full head montage, thereby providing further results strengthening the global/local hypothesis. (author)
Hamer, Hajo; Baulac, Michel; McMurray, Rob; Kockelmann, Edgar
2016-01-01
Zonisamide is licensed for adjunctive therapy for partial-onset seizures with or without secondary generalisation in patients 6 years and older and as monotherapy for the treatment of partial seizures in adult patients with newly diagnosed epilepsy, and shows a favourable pharmacokinetic profile with low interaction potential with other drugs. The aim of the present study was to gather real-life data on retention and modalities of zonisamide use when administered as only add-on treatment to a current AED monotherapy in adult patients with partial-onset seizures. This multicenter observational study was performed in 4 European countries and comprised three visits: baseline, and after 3 and 6 months. Data on patients' retention, reported efficacy, tolerability and safety, and quality of life was collected. Of 100 included patients, 93 could be evaluated. After 6 months, the retention rate of zonisamide add-on therapy was 82.8%. At this time, a reduction of seizure frequency of at least 50% was observed in 79.7% of patients, with 43.6% reporting seizure freedom over the last 3 months of the study period. Adverse events were reported by 19.4% of patients, with fatigue, agitation, dizziness, and headache being most frequent. Approximately 25% of patients were older than 60 years, many of whom suffered from late-onset epilepsy. Compared to younger patients, these patients showed considerable differences with regard to their antiepileptic drug regimen at baseline, and slightly higher responder and retention rates at 6 months. Despite limitations due to the non-interventional open-label design and the low sample size, the results show that zonisamide as only add-on therapy is well retained, indicating effectiveness in the majority of patients under real-life conditions. Copyright © 2015. Published by Elsevier Ltd.
Multivariate calibration applied to the quantitative analysis of infrared spectra
Energy Technology Data Exchange (ETDEWEB)
Haaland, D.M.
1991-01-01
Multivariate calibration methods are very useful for improving the precision, accuracy, and reliability of quantitative spectral analyses. Spectroscopists can more effectively use these sophisticated statistical tools if they have a qualitative understanding of the techniques involved. A qualitative picture of the factor analysis multivariate calibration methods of partial least squares (PLS) and principal component regression (PCR) is presented using infrared calibrations based upon spectra of phosphosilicate glass thin films on silicon wafers. Comparisons of the relative prediction abilities of four different multivariate calibration methods are given based on Monte Carlo simulations of spectral calibration and prediction data. The success of multivariate spectral calibrations is demonstrated for several quantitative infrared studies. The infrared absorption and emission spectra of thin-film dielectrics used in the manufacture of microelectronic devices demonstrate rapid, nondestructive at-line and in-situ analyses using PLS calibrations. Finally, the application of multivariate spectral calibrations to reagentless analysis of blood is presented. We have found that the determination of glucose in whole blood taken from diabetics can be precisely monitored from the PLS calibration of either mind- or near-infrared spectra of the blood. Progress toward the non-invasive determination of glucose levels in diabetics is an ultimate goal of this research. 13 refs., 4 figs.
Sparse Linear Identifiable Multivariate Modeling
DEFF Research Database (Denmark)
Henao, Ricardo; Winther, Ole
2011-01-01
and bench-marked on artificial and real biological data sets. SLIM is closest in spirit to LiNGAM (Shimizu et al., 2006), but differs substantially in inference, Bayesian network structure learning and model comparison. Experimentally, SLIM performs equally well or better than LiNGAM with comparable......In this paper we consider sparse and identifiable linear latent variable (factor) and linear Bayesian network models for parsimonious analysis of multivariate data. We propose a computationally efficient method for joint parameter and model inference, and model comparison. It consists of a fully...
Improved multivariate polynomial factoring algorithm
International Nuclear Information System (INIS)
Wang, P.S.
1978-01-01
A new algorithm for factoring multivariate polynomials over the integers based on an algorithm by Wang and Rothschild is described. The new algorithm has improved strategies for dealing with the known problems of the original algorithm, namely, the leading coefficient problem, the bad-zero problem and the occurrence of extraneous factors. It has an algorithm for correctly predetermining leading coefficients of the factors. A new and efficient p-adic algorithm named EEZ is described. Bascially it is a linearly convergent variable-by-variable parallel construction. The improved algorithm is generally faster and requires less store then the original algorithm. Machine examples with comparative timing are included
Simulation of multivariate diffusion bridges
DEFF Research Database (Denmark)
Bladt, Mogens; Finch, Samuel; Sørensen, Michael
We propose simple methods for multivariate diffusion bridge simulation, which plays a fundamental role in simulation-based likelihood and Bayesian inference for stochastic differential equations. By a novel application of classical coupling methods, the new approach generalizes a previously...... proposed simulation method for one-dimensional bridges to the mulit-variate setting. First a method of simulating approzimate, but often very accurate, diffusion bridges is proposed. These approximate bridges are used as proposal for easily implementable MCMC algorithms that produce exact diffusion bridges...
Essentials of multivariate data analysis
Spencer, Neil H
2013-01-01
""… this text provides an overview at an introductory level of several methods in multivariate data analysis. It contains in-depth examples from one data set woven throughout the text, and a free [Excel] Add-In to perform the analyses in Excel, with step-by-step instructions provided for each technique. … could be used as a text (possibly supplemental) for courses in other fields where researchers wish to apply these methods without delving too deeply into the underlying statistics.""-The American Statistician, February 2015
Multivariate process monitoring of EAFs
Energy Technology Data Exchange (ETDEWEB)
Sandberg, E.; Lennox, B.; Marjanovic, O.; Smith, K.
2005-06-01
Improved knowledge of the effect of scrap grades on the electric steelmaking process and optimised scrap loading practices increase the potential for process automation. As part of an ongoing programme, process data from four Scandinavian EAFs have been analysed, using the multivariate process monitoring approach, to develop predictive models for end point conditions such as chemical composition, yield and energy consumption. The models developed generally predict final Cr, Ni and Mo and tramp element contents well, but electrical energy consumption, yield and content of oxidisable and impurity elements (C, Si, Mn, P, S) are at present more difficult to predict. Potential scrap management applications of the prediction models are also presented. (author)
Aspects of multivariate statistical theory
Muirhead, Robb J
2009-01-01
The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "". . . the wealth of material on statistics concerning the multivariate normal distribution is quite exceptional. As such it is a very useful source of information for the general statistician and a must for anyone wanting to pen
Evidence Estimation for Bayesian Partially Observed MRFs
Chen, Y.; Welling, M.
2013-01-01
Bayesian estimation in Markov random fields is very hard due to the intractability of the partition function. The introduction of hidden units makes the situation even worse due to the presence of potentially very many modes in the posterior distribution. For the first time we propose a
Logic for specifying partially observable stochastic domains
CSIR Research Space (South Africa)
Rens, G
2011-07-01
Full Text Available to place it back on the floor. In situations where the oil-can is full, the robot gets 5 units of reward for grabbing the can, and it gets 10 units for a drink action. Otherwise, the robot gets no rewards. Rewards motivate an agent to behave as desired... with notions of probability. It will be shown how stochastic domains can be specified, including new kinds of axioms dealing with perception and a frame solution for the proposed logic. 1 Introduction and Motivation In the physical real world...
Integrated environmental monitoring and multivariate data analysis-A case study.
Eide, Ingvar; Westad, Frank; Nilssen, Ingunn; de Freitas, Felipe Sales; Dos Santos, Natalia Gomes; Dos Santos, Francisco; Cabral, Marcelo Montenegro; Bicego, Marcia Caruso; Figueira, Rubens; Johnsen, Ståle
2017-03-01
The present article describes integration of environmental monitoring and discharge data and interpretation using multivariate statistics, principal component analysis (PCA), and partial least squares (PLS) regression. The monitoring was carried out at the Peregrino oil field off the coast of Brazil. One sensor platform and 3 sediment traps were placed on the seabed. The sensors measured current speed and direction, turbidity, temperature, and conductivity. The sediment trap samples were used to determine suspended particulate matter that was characterized with respect to a number of chemical parameters (26 alkanes, 16 PAHs, N, C, calcium carbonate, and Ba). Data on discharges of drill cuttings and water-based drilling fluid were provided on a daily basis. The monitoring was carried out during 7 campaigns from June 2010 to October 2012, each lasting 2 to 3 months due to the capacity of the sediment traps. The data from the campaigns were preprocessed, combined, and interpreted using multivariate statistics. No systematic difference could be observed between campaigns or traps despite the fact that the first campaign was carried out before drilling, and 1 of 3 sediment traps was located in an area not expected to be influenced by the discharges. There was a strong covariation between suspended particulate matter and total N and organic C suggesting that the majority of the sediment samples had a natural and biogenic origin. Furthermore, the multivariate regression showed no correlation between discharges of drill cuttings and sediment trap or turbidity data taking current speed and direction into consideration. Because of this lack of correlation with discharges from the drilling location, a more detailed evaluation of chemical indicators providing information about origin was carried out in addition to numerical modeling of dispersion and deposition. The chemical indicators and the modeling of dispersion and deposition support the conclusions from the multivariate
Leachate/domestic wastewater aerobic co-treatment: A pilot-scale study using multivariate analysis.
Ferraz, F M; Bruni, A T; Povinelli, J; Vieira, E M
2016-01-15
Multivariate analysis was used to identify the variables affecting the performance of pilot-scale activated sludge (AS) reactors treating old leachate from a landfill and from domestic wastewater. Raw leachate was pre-treated using air stripping to partially remove the total ammoniacal nitrogen (TAN). The control AS reactor (AS-0%) was loaded only with domestic wastewater, whereas the other reactor was loaded with mixtures containing leachate at volumetric ratios of 2 and 5%. The best removal efficiencies were obtained for a ratio of 2%, as follows: 70 ± 4% for total suspended solids (TSS), 70 ± 3% for soluble chemical oxygen demand (SCOD), 70 ± 4% for dissolved organic carbon (DOC), and 51 ± 9% for the leachate slowly biodegradable organic matter (SBOM). Fourier transform infrared (FTIR) spectroscopic analysis confirmed that most of the SBOM was removed by partial biodegradation rather than dilution or adsorption of organics in the sludge. Nitrification was approximately 80% in the AS-0% and AS-2% reactors. No significant accumulation of heavy metals was observed for any of the tested volumetric ratios. Principal component analysis (PCA) and partial least squares (PLS) indicated that the data dimension could be reduced and that TAN, SCOD, DOC and nitrification efficiency were the main variables that affected the performance of the AS reactors. Copyright © 2015 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Pramita Suwal
2017-03-01
Full Text Available Aim: To compare the effects of cast partial denture with conventional all acrylic denture in respect to retention, stability, masticatory efficiency, comfort and periodontal health of abutments. Methods: 50 adult partially edentulous patient seeking for replacement of missing teeth having Kennedy class I and II arches with or without modification areas were selected for the study. Group-A was treated with cast partial denture and Group-B with acrylic partial denture. Data collected during follow-up visit of 3 months, 6 months, and 1 year by evaluating retention, stability, masticatory efficiency, comfort, periodontal health of abutment. Results: Chi-square test was applied to find out differences between the groups at 95% confidence interval where p = 0.05. One year comparison shows that cast partial denture maintained retention and stability better than acrylic partial denture (p< 0.05. The masticatory efficiency was significantly compromising from 3rd month to 1 year in all acrylic partial denture groups (p< 0.05. The comfort of patient with cast partial denture was maintained better during the observation period (p< 0.05. Periodontal health of abutment was gradually deteriorated in all acrylic denture group (p
Multivariate methods for particle identification
Visan, Cosmin
2013-01-01
The purpose of this project was to evaluate several MultiVariate methods in order to determine which one, if any, offers better results in Particle Identification (PID) than a simple n$\\sigma$ cut on the response of the ALICE PID detectors. The particles considered in the analysis were Pions, Kaons and Protons and the detectors used were TPC and TOF. When used with the same input n$\\sigma$ variables, the results show similar perfoance between the Rectangular Cuts Optimization method and the simple n$\\sigma$ cuts. The method MLP and BDT show poor results for certain ranges of momentum. The KNN method is the best performing, showing similar results for Pions and Protons as the Cuts method, and better results for Kaons. The extension of the methods to include additional input variables leads to poor results, related to instabilities still to be investigated.
Acoustic multivariate condition monitoring - AMCM
Energy Technology Data Exchange (ETDEWEB)
Rosenhave, P E [Vestfold College, Maritime Dept., Toensberg (Norway)
1998-12-31
In Norway, Vestfold College, Maritime Department presents new opportunities for non-invasive, on- or off-line acoustic monitoring of rotating machinery such as off-shore pumps and diesel engines. New developments within acoustic sensor technology coupled with chemometric data analysis of complex signals now allow condition monitoring of hitherto unavailable flexibility and diagnostic specificity. Chemometrics paired with existing knowledge yields a new and powerful tool for condition monitoring. By the use of multivariate techniques and acoustics it is possible to quantify wear and tear as well as predict the performance of working components in complex machinery. This presentation describes the AMCM method and one result of a feasibility study conducted onboard the LPG/C `Norgas Mariner` owned by Norwegian Gas Carriers as (NGC), Oslo. (orig.) 6 refs.
Acoustic multivariate condition monitoring - AMCM
Energy Technology Data Exchange (ETDEWEB)
Rosenhave, P.E. [Vestfold College, Maritime Dept., Toensberg (Norway)
1997-12-31
In Norway, Vestfold College, Maritime Department presents new opportunities for non-invasive, on- or off-line acoustic monitoring of rotating machinery such as off-shore pumps and diesel engines. New developments within acoustic sensor technology coupled with chemometric data analysis of complex signals now allow condition monitoring of hitherto unavailable flexibility and diagnostic specificity. Chemometrics paired with existing knowledge yields a new and powerful tool for condition monitoring. By the use of multivariate techniques and acoustics it is possible to quantify wear and tear as well as predict the performance of working components in complex machinery. This presentation describes the AMCM method and one result of a feasibility study conducted onboard the LPG/C `Norgas Mariner` owned by Norwegian Gas Carriers as (NGC), Oslo. (orig.) 6 refs.
DEFF Research Database (Denmark)
Barndorff-Nielsen, Ole Eiler; Stelzer, Robert
Univariate superpositions of Ornstein-Uhlenbeck (OU) type processes, called supOU processes, provide a class of continuous time processes capable of exhibiting long memory behaviour. This paper introduces multivariate supOU processes and gives conditions for their existence and finiteness...... of moments. Moreover, the second order moment structure is explicitly calculated, and examples exhibit the possibility of long range dependence. Our supOU processes are defined via homogeneous and factorisable Lévy bases. We show that the behaviour of supOU processes is particularly nice when the mean...... reversion parameter is restricted to normal matrices and especially to strictly negative definite ones.For finite variation Lévy bases we are able to give conditions for supOU processes to have locally bounded càdlàg paths of finite variation and to show an analogue of the stochastic differential equation...
DEFF Research Database (Denmark)
Barndorff-Nielsen, Ole Eiler; Stelzer, Robert
2011-01-01
Univariate superpositions of Ornstein–Uhlenbeck-type processes (OU), called supOU processes, provide a class of continuous time processes capable of exhibiting long memory behavior. This paper introduces multivariate supOU processes and gives conditions for their existence and finiteness of moments....... Moreover, the second-order moment structure is explicitly calculated, and examples exhibit the possibility of long-range dependence. Our supOU processes are defined via homogeneous and factorizable Lévy bases. We show that the behavior of supOU processes is particularly nice when the mean reversion...... parameter is restricted to normal matrices and especially to strictly negative definite ones. For finite variation Lévy bases we are able to give conditions for supOU processes to have locally bounded càdlàg paths of finite variation and to show an analogue of the stochastic differential equation of OU...
An Exact Confidence Region in Multivariate Calibration
Mathew, Thomas; Kasala, Subramanyam
1994-01-01
In the multivariate calibration problem using a multivariate linear model, an exact confidence region is constructed. It is shown that the region is always nonempty and is invariant under nonsingular transformations.
A Newton Algorithm for Multivariate Total Least Squares Problems
Directory of Open Access Journals (Sweden)
WANG Leyang
2016-04-01
Full Text Available In order to improve calculation efficiency of parameter estimation, an algorithm for multivariate weighted total least squares adjustment based on Newton method is derived. The relationship between the solution of this algorithm and that of multivariate weighted total least squares adjustment based on Lagrange multipliers method is analyzed. According to propagation of cofactor, 16 computational formulae of cofactor matrices of multivariate total least squares adjustment are also listed. The new algorithm could solve adjustment problems containing correlation between observation matrix and coefficient matrix. And it can also deal with their stochastic elements and deterministic elements with only one cofactor matrix. The results illustrate that the Newton algorithm for multivariate total least squares problems could be practiced and have higher convergence rate.
Asymptotics of Multivariate Regression with Consecutively Added Dependent Varibles
Raats, V.M.; van der Genugten, B.B.; Moors, J.J.A.
2004-01-01
We consider multivariate regression where new dependent variables are consecutively added during the experiment (or in time).So, viewed at the end of the experiment, the number of observations decreases with each added variable. The explanatory variables are observed throughout.In a previous paper
Multivariate strategies in functional magnetic resonance imaging
DEFF Research Database (Denmark)
Hansen, Lars Kai
2007-01-01
We discuss aspects of multivariate fMRI modeling, including the statistical evaluation of multivariate models and means for dimensional reduction. In a case study we analyze linear and non-linear dimensional reduction tools in the context of a `mind reading' predictive multivariate fMRI model....
Boosted Multivariate Trees for Longitudinal Data
Pande, Amol; Li, Liang; Rajeswaran, Jeevanantham; Ehrlinger, John; Kogalur, Udaya B.; Blackstone, Eugene H.; Ishwaran, Hemant
2017-01-01
Machine learning methods provide a powerful approach for analyzing longitudinal data in which repeated measurements are observed for a subject over time. We boost multivariate trees to fit a novel flexible semi-nonparametric marginal model for longitudinal data. In this model, features are assumed to be nonparametric, while feature-time interactions are modeled semi-nonparametrically utilizing P-splines with estimated smoothing parameter. In order to avoid overfitting, we describe a relatively simple in sample cross-validation method which can be used to estimate the optimal boosting iteration and which has the surprising added benefit of stabilizing certain parameter estimates. Our new multivariate tree boosting method is shown to be highly flexible, robust to covariance misspecification and unbalanced designs, and resistant to overfitting in high dimensions. Feature selection can be used to identify important features and feature-time interactions. An application to longitudinal data of forced 1-second lung expiratory volume (FEV1) for lung transplant patients identifies an important feature-time interaction and illustrates the ease with which our method can find complex relationships in longitudinal data. PMID:29249866
Physics of partially ionized plasmas
Krishan, Vinod
2016-01-01
Plasma is one of the four fundamental states of matter; the other three being solid, liquid and gas. Several components, such as molecular clouds, diffuse interstellar gas, the solar atmosphere, the Earth's ionosphere and laboratory plasmas, including fusion plasmas, constitute the partially ionized plasmas. This book discusses different aspects of partially ionized plasmas including multi-fluid description, equilibrium and types of waves. The discussion goes on to cover the reionization phase of the universe, along with a brief description of high discharge plasmas, tokomak plasmas and laser plasmas. Various elastic and inelastic collisions amongst the three particle species are also presented. In addition, the author demonstrates the novelty of partially ionized plasmas using many examples; for instance, in partially ionized plasma the magnetic induction is subjected to the ambipolar diffusion and the Hall effect, as well as the usual resistive dissipation. Also included is an observation of kinematic dynam...
Validation of models with multivariate output
International Nuclear Information System (INIS)
Rebba, Ramesh; Mahadevan, Sankaran
2006-01-01
This paper develops metrics for validating computational models with experimental data, considering uncertainties in both. A computational model may generate multiple response quantities and the validation experiment might yield corresponding measured values. Alternatively, a single response quantity may be predicted and observed at different spatial and temporal points. Model validation in such cases involves comparison of multiple correlated quantities. Multiple univariate comparisons may give conflicting inferences. Therefore, aggregate validation metrics are developed in this paper. Both classical and Bayesian hypothesis testing are investigated for this purpose, using multivariate analysis. Since, commonly used statistical significance tests are based on normality assumptions, appropriate transformations are investigated in the case of non-normal data. The methodology is implemented to validate an empirical model for energy dissipation in lap joints under dynamic loading
Multivariate modelling with 1H NMR of pleural effusion in murine cerebral malaria
Directory of Open Access Journals (Sweden)
Ghosh Soumita
2011-11-01
Full Text Available Abstract Background Cerebral malaria is a clinical manifestation of Plasmodium falciparum infection. Although brain damage is the predominant pathophysiological complication of cerebral malaria (CM, respiratory distress, acute lung injury, hydrothorax/pleural effusion are also observed in several cases. Immunological parameters have been assessed in pleural fluid in murine models; however there are no reports of characterization of metabolites present in pleural effusion. Methods 1H NMR of the sera and the pleural effusion of cerebral malaria infected mice were analyzed using principal component analysis, orthogonal partial least square analysis, multiway principal component analysis, and multivariate curve resolution. Results It has been observed that there was 100% occurrence of pleural effusion (PE in the mice affected with CM, as opposed to those are non-cerebral and succumbing to hyperparasitaemia (NCM/HP. An analysis of 1H NMR and SDS-PAGE profile of PE and serum samples of each of the CM mice exhibited a similar profile in terms of constituents. Multivariate analysis on these two classes of biofluids was performed and significant differences were detected in concentrations of metabolites. Glucose, creatine and glutamine contents were high in the PE and lipids being high in the sera. Multivariate curve resolution between sera and pleural effusion showed that changes in PE co-varied with that of serum in CM mice. The increase of glucose in PE is negatively correlated to the glucose in serum in CM as obtained from the result of multiway principal component analysis. Conclusions This study reports for the first time, the characterization of metabolites in pleural effusion formed during murine cerebral malaria. The study indicates that the origin of PE metabolites in murine CM may be the serum. The loss of the components like glucose, glutamine and creatine into the PE may worsen the situation of patients, in conjunction with the enhanced
Multivariate pluvial flood damage models
International Nuclear Information System (INIS)
Van Ootegem, Luc; Verhofstadt, Elsy; Van Herck, Kristine; Creten, Tom
2015-01-01
Depth–damage-functions, relating the monetary flood damage to the depth of the inundation, are commonly used in the case of fluvial floods (floods caused by a river overflowing). We construct four multivariate damage models for pluvial floods (caused by extreme rainfall) by differentiating on the one hand between ground floor floods and basement floods and on the other hand between damage to residential buildings and damage to housing contents. We do not only take into account the effect of flood-depth on damage, but also incorporate the effects of non-hazard indicators (building characteristics, behavioural indicators and socio-economic variables). By using a Tobit-estimation technique on identified victims of pluvial floods in Flanders (Belgium), we take into account the effect of cases of reported zero damage. Our results show that the flood depth is an important predictor of damage, but with a diverging impact between ground floor floods and basement floods. Also non-hazard indicators are important. For example being aware of the risk just before the water enters the building reduces content damage considerably, underlining the importance of warning systems and policy in this case of pluvial floods. - Highlights: • Prediction of damage of pluvial floods using also non-hazard information • We include ‘no damage cases’ using a Tobit model. • The damage of flood depth is stronger for ground floor than for basement floods. • Non-hazard indicators are especially important for content damage. • Potential gain of policies that increase awareness of flood risks
Multivariate pluvial flood damage models
Energy Technology Data Exchange (ETDEWEB)
Van Ootegem, Luc [HIVA — University of Louvain (Belgium); SHERPPA — Ghent University (Belgium); Verhofstadt, Elsy [SHERPPA — Ghent University (Belgium); Van Herck, Kristine; Creten, Tom [HIVA — University of Louvain (Belgium)
2015-09-15
Depth–damage-functions, relating the monetary flood damage to the depth of the inundation, are commonly used in the case of fluvial floods (floods caused by a river overflowing). We construct four multivariate damage models for pluvial floods (caused by extreme rainfall) by differentiating on the one hand between ground floor floods and basement floods and on the other hand between damage to residential buildings and damage to housing contents. We do not only take into account the effect of flood-depth on damage, but also incorporate the effects of non-hazard indicators (building characteristics, behavioural indicators and socio-economic variables). By using a Tobit-estimation technique on identified victims of pluvial floods in Flanders (Belgium), we take into account the effect of cases of reported zero damage. Our results show that the flood depth is an important predictor of damage, but with a diverging impact between ground floor floods and basement floods. Also non-hazard indicators are important. For example being aware of the risk just before the water enters the building reduces content damage considerably, underlining the importance of warning systems and policy in this case of pluvial floods. - Highlights: • Prediction of damage of pluvial floods using also non-hazard information • We include ‘no damage cases’ using a Tobit model. • The damage of flood depth is stronger for ground floor than for basement floods. • Non-hazard indicators are especially important for content damage. • Potential gain of policies that increase awareness of flood risks.
Vranish, John M. (Inventor)
2010-01-01
A partial gear bearing including an upper half, comprising peak partial teeth, and a lower, or bottom, half, comprising valley partial teeth. The upper half also has an integrated roller section between each of the peak partial teeth with a radius equal to the gear pitch radius of the radially outwardly extending peak partial teeth. Conversely, the lower half has an integrated roller section between each of the valley half teeth with a radius also equal to the gear pitch radius of the peak partial teeth. The valley partial teeth extend radially inwardly from its roller section. The peak and valley partial teeth are exactly out of phase with each other, as are the roller sections of the upper and lower halves. Essentially, the end roller bearing of the typical gear bearing has been integrated into the normal gear tooth pattern.
Multivariate refined composite multiscale entropy analysis
International Nuclear Information System (INIS)
Humeau-Heurtier, Anne
2016-01-01
Multiscale entropy (MSE) has become a prevailing method to quantify signals complexity. MSE relies on sample entropy. However, MSE may yield imprecise complexity estimation at large scales, because sample entropy does not give precise estimation of entropy when short signals are processed. A refined composite multiscale entropy (RCMSE) has therefore recently been proposed. Nevertheless, RCMSE is for univariate signals only. The simultaneous analysis of multi-channel (multivariate) data often over-performs studies based on univariate signals. We therefore introduce an extension of RCMSE to multivariate data. Applications of multivariate RCMSE to simulated processes reveal its better performances over the standard multivariate MSE. - Highlights: • Multiscale entropy quantifies data complexity but may be inaccurate at large scale. • A refined composite multiscale entropy (RCMSE) has therefore recently been proposed. • Nevertheless, RCMSE is adapted to univariate time series only. • We herein introduce an extension of RCMSE to multivariate data. • It shows better performances than the standard multivariate multiscale entropy.
Matos, Larissa A.; Bandyopadhyay, Dipankar; Castro, Luis M.; Lachos, Victor H.
2015-01-01
In biomedical studies on HIV RNA dynamics, viral loads generate repeated measures that are often subjected to upper and lower detection limits, and hence these responses are either left- or right-censored. Linear and non-linear mixed-effects censored (LMEC/NLMEC) models are routinely used to analyse these longitudinal data, with normality assumptions for the random effects and residual errors. However, the derived inference may not be robust when these underlying normality assumptions are questionable, especially the presence of outliers and thick-tails. Motivated by this, Matos et al. (2013b) recently proposed an exact EM-type algorithm for LMEC/NLMEC models using a multivariate Student’s-t distribution, with closed-form expressions at the E-step. In this paper, we develop influence diagnostics for LMEC/NLMEC models using the multivariate Student’s-t density, based on the conditional expectation of the complete data log-likelihood. This partially eliminates the complexity associated with the approach of Cook (1977, 1986) for censored mixed-effects models. The new methodology is illustrated via an application to a longitudinal HIV dataset. In addition, a simulation study explores the accuracy of the proposed measures in detecting possible influential observations for heavy-tailed censored data under different perturbation and censoring schemes. PMID:26190871
Kantarci, T.
2012-01-01
The five essays in this dissertation address a range of topics in the micro-economic literature on partial retirement. The focus is on the labor market behavior of older age groups. The essays examine the economic and non-economic determinants of partial retirement behavior, the effect of partial
Multivariate Regression Analysis and Slaughter Livestock,
AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY
Highly Efficient Estimators of Multivariate Location with High Breakdown Point
Lopuhaa, H.P.
1991-01-01
We propose an affine equivariant estimator of multivariate location that combines a high breakdown point and a bounded influence function with high asymptotic efficiency. This proposal is basically a location $M$-estimator based on the observations obtained after scaling with an affine equivariant
Displaying an Outlier in Multivariate Data | Gordor | Journal of ...
African Journals Online (AJOL)
... a multivariate data set is proposed. The technique involves the projection of the multidimensional data onto a single dimension called the outlier displaying component. When the observations are plotted on this component the outlier is appreciably revealed. Journal of Applied Science and Technology (JAST), Vol. 4, Nos.
Extending a scatterplot for displaying group structure in multivariate ...
African Journals Online (AJOL)
... when regarded as extensions of ordinary scatterplots to describe variation and group structure in multivariate observations, is demonstrated by presenting a case study from the South African wood pulp industry. It is shown how multidimensional standards specified by users of a product may be added to the biplot in the ...
Karam, Jose A; Devine, Catherine E; Fellman, Bryan M; Urbauer, Diana L; Abel, E Jason; Allaf, Mohamad E; Bex, Axel; Lane, Brian R; Thompson, R Houston; Wood, Christopher G
2016-04-01
To evaluate how many patients could have undergone partial nephrectomy (PN) rather than radical nephrectomy (RN) before and after neoadjuvant axitinib therapy, as assessed by five independent urological oncologists, and to study the variability of inter-observer agreement. Pre- and post-systemic treatment computed tomography scans from 22 patients with clear cell renal cell carcinoma in a phase II neoadjuvant axitinib trial were reviewed by five independent urological oncologists. R.E.N.A.L. nephrometry score and κ statistics were calculated. The median R.E.N.A.L. nephrometry score changed from 11 before treatment to 10 after treatment (P = 0.002). Five tumours with moderate complexity before axitinib treatment remained moderate complexity after treatment. Of 17 tumours with high complexity before axitinib treatment, three became moderate complexity after treatment. The overall κ statistic was 0.611. Moderate-complexity κ was 0.611 vs a high-complexity κ of 0.428. Before axitinib treatment the κ was 0.550 vs 0.609 after treatment. After treatment with axitinib, all five reviewers agreed that only five patients required RN (instead of eight before treatment) and that 10 patients could now undergo PN (instead of three before treatment). The odds of PN feasibility were 22.8-times higher after treatment with axitinib. There is considerable variability in inter-observer agreement on the feasibility of PN in patients treated with neoadjuvant targeted therapy. Although more patients were candidates for PN after neoadjuvant axitinib therapy, it remains difficult to identify these patients a priori. © 2015 The Authors BJU International © 2015 BJU International Published by John Wiley & Sons Ltd.
Multivariate Analysis of Ladle Vibration
Yenus, Jaefer; Brooks, Geoffrey; Dunn, Michelle
2016-08-01
The homogeneity of composition and uniformity of temperature of the steel melt before it is transferred to the tundish are crucial in making high-quality steel product. The homogenization process is performed by stirring the melt using inert gas in ladles. Continuous monitoring of this process is important to make sure the action of stirring is constant throughout the ladle. Currently, the stirring process is monitored by process operators who largely rely on visual and acoustic phenomena from the ladle. However, due to lack of measurable signals, the accuracy and suitability of this manual monitoring are problematic. The actual flow of argon gas to the ladle may not be same as the flow gage reading due to leakage along the gas line components. As a result, the actual degree of stirring may not be correctly known. Various researchers have used one-dimensional vibration, and sound and image signals measured from the ladle to predict the degree of stirring inside. They developed online sensors which are indeed to monitor the online stirring phenomena. In this investigation, triaxial vibration signals have been measured from a cold water model which is a model of an industrial ladle. Three flow rate ranges and varying bath heights were used to collect vibration signals. The Fast Fourier Transform was applied to the dataset before it has been analyzed using principal component analysis (PCA) and partial least squares (PLS). PCA was used to unveil the structure in the experimental data. PLS was mainly applied to predict the stirring from the vibration response. It was found that for each flow rate range considered in this study, the informative signals reside in different frequency ranges. The first latent variables in these frequency ranges explain more than 95 pct of the variation in the stirring process for the entire single layer and the double layer data collected from the cold model. PLS analysis in these identified frequency ranges demonstrated that the latent
Directory of Open Access Journals (Sweden)
Francine Blanchet-Sadri
2011-08-01
Full Text Available Partial words are sequences over a finite alphabet that may contain wildcard symbols, called holes, which match or are compatible with all letters; partial words without holes are said to be full words (or simply words. Given an infinite partial word w, the number of distinct full words over the alphabet that are compatible with factors of w of length n, called subwords of w, refers to a measure of complexity of infinite partial words so-called subword complexity. This measure is of particular interest because we can construct partial words with subword complexities not achievable by full words. In this paper, we consider the notion of recurrence over infinite partial words, that is, we study whether all of the finite subwords of a given infinite partial word appear infinitely often, and we establish connections between subword complexity and recurrence in this more general framework.
Multivariate Bioclimatic Ecosystem Change Approaches
2015-02-06
course the sandy soils of the Sandhills will not migrate. This observation suggests that a new nomenclature for ecosystems must be developed if...Coast Sandhills. At that time period, not only will the climate be similar, but the soil character will also be similar. Therefore about the year 2115...Disaggregation of global circulation model outputs decision and policy analysis. Working Paper No. 2. Cali, Colombia : International Centre for Tropical
Multivariate Marshall and Olkin Exponential Minification Process ...
African Journals Online (AJOL)
A stationary bivariate minification process with bivariate Marshall-Olkin exponential distribution that was earlier studied by Miroslav et al [15]is in this paper extended to multivariate minification process with multivariate Marshall and Olkin exponential distribution as its stationary marginal distribution. The innovation and the ...
Multivariate multiscale entropy of financial markets
Lu, Yunfan; Wang, Jun
2017-11-01
In current process of quantifying the dynamical properties of the complex phenomena in financial market system, the multivariate financial time series are widely concerned. In this work, considering the shortcomings and limitations of univariate multiscale entropy in analyzing the multivariate time series, the multivariate multiscale sample entropy (MMSE), which can evaluate the complexity in multiple data channels over different timescales, is applied to quantify the complexity of financial markets. Its effectiveness and advantages have been detected with numerical simulations with two well-known synthetic noise signals. For the first time, the complexity of four generated trivariate return series for each stock trading hour in China stock markets is quantified thanks to the interdisciplinary application of this method. We find that the complexity of trivariate return series in each hour show a significant decreasing trend with the stock trading time progressing. Further, the shuffled multivariate return series and the absolute multivariate return series are also analyzed. As another new attempt, quantifying the complexity of global stock markets (Asia, Europe and America) is carried out by analyzing the multivariate returns from them. Finally we utilize the multivariate multiscale entropy to assess the relative complexity of normalized multivariate return volatility series with different degrees.
Lasso and probabilistic inequalities for multivariate point processes
DEFF Research Database (Denmark)
Hansen, Niels Richard; Reynaud-Bouret, Patricia; Rivoirard, Vincent
2015-01-01
Due to its low computational cost, Lasso is an attractive regularization method for high-dimensional statistical settings. In this paper, we consider multivariate counting processes depending on an unknown function parameter to be estimated by linear combinations of a fixed dictionary. To select...... for multivariate Hawkes processes are proven, which allows us to check these assumptions by considering general dictionaries based on histograms, Fourier or wavelet bases. Motivated by problems of neuronal activity inference, we finally carry out a simulation study for multivariate Hawkes processes and compare our...... methodology with the adaptive Lasso procedure proposed by Zou in (J. Amer. Statist. Assoc. 101 (2006) 1418–1429). We observe an excellent behavior of our procedure. We rely on theoretical aspects for the essential question of tuning our methodology. Unlike adaptive Lasso of (J. Amer. Statist. Assoc. 101 (2006...
Constructing ordinal partition transition networks from multivariate time series.
Zhang, Jiayang; Zhou, Jie; Tang, Ming; Guo, Heng; Small, Michael; Zou, Yong
2017-08-10
A growing number of algorithms have been proposed to map a scalar time series into ordinal partition transition networks. However, most observable phenomena in the empirical sciences are of a multivariate nature. We construct ordinal partition transition networks for multivariate time series. This approach yields weighted directed networks representing the pattern transition properties of time series in velocity space, which hence provides dynamic insights of the underling system. Furthermore, we propose a measure of entropy to characterize ordinal partition transition dynamics, which is sensitive to capturing the possible local geometric changes of phase space trajectories. We demonstrate the applicability of pattern transition networks to capture phase coherence to non-coherence transitions, and to characterize paths to phase synchronizations. Therefore, we conclude that the ordinal partition transition network approach provides complementary insight to the traditional symbolic analysis of nonlinear multivariate time series.
Multivariable controller for a 600 MWe CANDU nuclear power plant
International Nuclear Information System (INIS)
Mensah, S.
1982-11-01
The problems of designing a multivariable regulator for a nuclear power station of the Gentilly-2 type are studied. A reduced model, G2LDM, linearized around steady state operating conditions, is derived from the non-linear model G2SIM. The resulting linear model is described by state-space equations. Good agreement is demonstrated between the transient responses of both models. Properties of G2LDM are assessed by performing controllability and observability tests, cyclicity and rank tests, and eigenanalysis. A comprehensive set of application-orinented algorithms which allow multivariable controller design with closed-loop pole-assignment techniques are implemented in a computer-aided design package via several modules. A general scheme for the implementation of a multivariable controller in G2SIM is designed, and simulation tests show satisfactory performance of the controller [fr
Multivariate Analysis and Prediction of Dioxin-Furan ...
Peer Review Draft of Regional Methods Initiative Final Report Dioxins, which are bioaccumulative and environmentally persistent, pose an ongoing risk to human and ecosystem health. Fish constitute a significant source of dioxin exposure for humans and fish-eating wildlife. Current dioxin analytical methods are costly, time-consuming, and produce hazardous by-products. A Danish team developed a novel, multivariate statistical methodology based on the covariance of dioxin-furan congener Toxic Equivalences (TEQs) and fatty acid methyl esters (FAMEs) and applied it to North Atlantic Ocean fishmeal samples. The goal of the current study was to attempt to extend this Danish methodology to 77 whole and composite fish samples from three trophic groups: predator (whole largemouth bass), benthic (whole flathead and channel catfish) and forage fish (composite bluegill, pumpkinseed and green sunfish) from two dioxin contaminated rivers (Pocatalico R. and Kanawha R.) in West Virginia, USA. Multivariate statistical analyses, including, Principal Components Analysis (PCA), Hierarchical Clustering, and Partial Least Squares Regression (PLS), were used to assess the relationship between the FAMEs and TEQs in these dioxin contaminated freshwater fish from the Kanawha and Pocatalico Rivers. These three multivariate statistical methods all confirm that the pattern of Fatty Acid Methyl Esters (FAMEs) in these freshwater fish covaries with and is predictive of the WHO TE
Probing beer aging chemistry by nuclear magnetic resonance and multivariate analysis
International Nuclear Information System (INIS)
Rodrigues, J.A.; Barros, A.S.; Carvalho, B.; Brandao, T.; Gil, Ana M.
2011-01-01
Graphical abstract: The use of nuclear magnetic resonance (NMR) metabonomics for monitoring the chemical changes occurring in beer exposed to forced aging (at 45 deg. C for up to 18 days) is described. Both principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were applied to the NMR spectra of beer recorded as a function of aging and an aging trend was observed. Inspection of PLS-DA loadings and peak integration revealed the importance of well known markers (e.g. 5-HMF) as well as of other compounds: amino acids, higher alcohols, organic acids, dextrins and some still unassigned spin systems. 2D correlation analysis enabled relevant compound variations to be confirmed and inter-compound correlations to be assessed, thus offering improved insight into the chemical aspects of beer aging. Highlights: · Use of NMR metabonomics for monitoring the chemical changes occurring in beer exposed to forced aging. · Compositional variations evaluated by principal component analysis and partial least squares-discriminant analysis. · Results reveal importance of known markers and other compounds: amino and organic acids, higher alcohols, dextrins. · 2D correlation analysis reveals inter-compound relationships, offering insight into beer aging chemistry. - Abstract: This paper describes the use of nuclear magnetic resonance (NMR) spectroscopy, in tandem with multivariate analysis (MVA), for monitoring the chemical changes occurring in a lager beer exposed to forced aging (at 45 deg. C for up to 18 days). To evaluate the resulting compositional variations, both principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were applied to the NMR spectra of beer recorded as a function of aging and a clear aging trend was observed. Inspection of PLS-DA loadings and peak integration enabled the changing compounds to be identified, revealing the importance of well known markers such as 5-hydroxymethylfurfural (5
Probing beer aging chemistry by nuclear magnetic resonance and multivariate analysis
Energy Technology Data Exchange (ETDEWEB)
Rodrigues, J.A. [CICECO-Department of Chemistry, University of Aveiro, Campus de Santiago, 3810-193 Aveiro (Portugal); Barros, A.S. [QOPNA-Department of Chemistry, University of Aveiro, Campus de Santiago, 3810-193 Aveiro (Portugal); Carvalho, B.; Brandao, T. [UNICER, Bebidas de Portugal, Leca do Balio, 4466-955, S. Mamede de Infesta (Portugal); Gil, Ana M., E-mail: agil@ua.pt [CICECO-Department of Chemistry, University of Aveiro, Campus de Santiago, 3810-193 Aveiro (Portugal)
2011-09-30
Graphical abstract: The use of nuclear magnetic resonance (NMR) metabonomics for monitoring the chemical changes occurring in beer exposed to forced aging (at 45 deg. C for up to 18 days) is described. Both principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were applied to the NMR spectra of beer recorded as a function of aging and an aging trend was observed. Inspection of PLS-DA loadings and peak integration revealed the importance of well known markers (e.g. 5-HMF) as well as of other compounds: amino acids, higher alcohols, organic acids, dextrins and some still unassigned spin systems. 2D correlation analysis enabled relevant compound variations to be confirmed and inter-compound correlations to be assessed, thus offering improved insight into the chemical aspects of beer aging. Highlights: {center_dot} Use of NMR metabonomics for monitoring the chemical changes occurring in beer exposed to forced aging. {center_dot} Compositional variations evaluated by principal component analysis and partial least squares-discriminant analysis. {center_dot} Results reveal importance of known markers and other compounds: amino and organic acids, higher alcohols, dextrins. {center_dot} 2D correlation analysis reveals inter-compound relationships, offering insight into beer aging chemistry. - Abstract: This paper describes the use of nuclear magnetic resonance (NMR) spectroscopy, in tandem with multivariate analysis (MVA), for monitoring the chemical changes occurring in a lager beer exposed to forced aging (at 45 deg. C for up to 18 days). To evaluate the resulting compositional variations, both principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were applied to the NMR spectra of beer recorded as a function of aging and a clear aging trend was observed. Inspection of PLS-DA loadings and peak integration enabled the changing compounds to be identified, revealing the importance of well known
Landsliding in partially saturated materials
Godt, J.W.; Baum, R.L.; Lu, N.
2009-01-01
[1] Rainfall-induced landslides are pervasive in hillslope environments around the world and among the most costly and deadly natural hazards. However, capturing their occurrence with scientific instrumentation in a natural setting is extremely rare. The prevailing thinking on landslide initiation, particularly for those landslides that occur under intense precipitation, is that the failure surface is saturated and has positive pore-water pressures acting on it. Most analytic methods used for landslide hazard assessment are based on the above perception and assume that the failure surface is located beneath a water table. By monitoring the pore water and soil suction response to rainfall, we observed shallow landslide occurrence under partially saturated conditions for the first time in a natural setting. We show that the partially saturated shallow landslide at this site is predictable using measured soil suction and water content and a novel unified effective stress concept for partially saturated earth materials. Copyright 2009 by the American Geophysical Union.
PCC/SRC, PCC and SRC Calculation from Multivariate Input for Sensitivity Analysis
International Nuclear Information System (INIS)
Iman, R.L.; Shortencarier, M.J.; Johnson, J.D.
1995-01-01
1 - Description of program or function: PCC/SRC is designed for use in conjunction with sensitivity analyses of complex computer models. PCC/SRC calculates the partial correlation coefficients (PCC) and the standardized regression coefficients (SRC) from the multivariate input to, and output from, a computer model. 2 - Method of solution: PCC/SRC calculates the coefficients on either the original observations or on the ranks of the original observations. These coefficients provide alternative measures of the relative contribution (importance) of each of the various input variables to the observed variations in output. Relationships between the coefficients and differences in their interpretations are identified. If the computer model output has an associated time or spatial history, PCC/SRC will generate a graph of the coefficients over time or space for each input-variable, output- variable combination of interest, indicating the importance of each input value over time or space. 3 - Restrictions on the complexity of the problem - Maxima of: 100 observations, 100 different time steps or intervals between successive dependent variable readings, 50 independent variables (model input), 20 dependent variables (model output). 10 ordered triples specifying intervals between dependent variable readings
Partial Evaluation of the Euclidian Algorithm
DEFF Research Database (Denmark)
Danvy, Olivier; Goldberg, Mayer
1997-01-01
-like behavior. Each of them presents a challenge for partial evaluation. The Euclidian algorithm is one of them, and in this article, we make it amenable to partial evaluation. We observe that the number of iterations in the Euclidian algorithm is bounded by a number that can be computed given either of the two...... arguments. We thus rephrase this algorithm using bounded recursion. The resulting program is better suited for automatic unfolding and thus for partial evaluation. Its specialization is efficient....
Partial rectangular metric spaces and fixed point theorems.
Shukla, Satish
2014-01-01
The purpose of this paper is to introduce the concept of partial rectangular metric spaces as a generalization of rectangular metric and partial metric spaces. Some properties of partial rectangular metric spaces and some fixed point results for quasitype contraction in partial rectangular metric spaces are proved. Some examples are given to illustrate the observed results.
Supremum Norm Posterior Contraction and Credible Sets for Nonparametric Multivariate Regression
Yoo, W.W.; Ghosal, S
2016-01-01
In the setting of nonparametric multivariate regression with unknown error variance, we study asymptotic properties of a Bayesian method for estimating a regression function f and its mixed partial derivatives. We use a random series of tensor product of B-splines with normal basis coefficients as a
A New Iteration Multivariate Pad e´ Approximation Technique for ...
African Journals Online (AJOL)
In this paper, the Laplace transform, the New iteration method and the Multivariate Pade´ approximation technique are employed to solve nonlinear fractional partial differential equations whose fractional derivatives are described in the sense of Caputo. The Laplace transform is used to ”fully” determine the initial iteration ...
Directory of Open Access Journals (Sweden)
Cláudio Roberto Rosário
2012-07-01
Full Text Available The purpose of this research is to improve the practice on customer satisfaction analysis The article presents an analysis model to analyze the answers of a customer satisfaction evaluation in a systematic way with the aid of multivariate statistical techniques, specifically, exploratory analysis with PCA – Partial Components Analysis with HCA - Hierarchical Cluster Analysis. It was tried to evaluate the applicability of the model to be used by the issue company as a tool to assist itself on identifying the value chain perceived by the customer when applied the questionnaire of customer satisfaction. It was found with the assistance of multivariate statistical analysis that it was observed similar behavior among customers. It also allowed the company to conduct reviews on questions of the questionnaires, using analysis of the degree of correlation between the questions that was not a company’s practice before this research.
Cross-covariance functions for multivariate geostatistics
Genton, Marc G.
2015-05-01
Continuously indexed datasets with multiple variables have become ubiquitous in the geophysical, ecological, environmental and climate sciences, and pose substantial analysis challenges to scientists and statisticians. For many years, scientists developed models that aimed at capturing the spatial behavior for an individual process; only within the last few decades has it become commonplace to model multiple processes jointly. The key difficulty is in specifying the cross-covariance function, that is, the function responsible for the relationship between distinct variables. Indeed, these cross-covariance functions must be chosen to be consistent with marginal covariance functions in such a way that the second-order structure always yields a nonnegative definite covariance matrix. We review the main approaches to building cross-covariance models, including the linear model of coregionalization, convolution methods, the multivariate Matérn and nonstationary and space-time extensions of these among others. We additionally cover specialized constructions, including those designed for asymmetry, compact support and spherical domains, with a review of physics-constrained models. We illustrate select models on a bivariate regional climate model output example for temperature and pressure, along with a bivariate minimum and maximum temperature observational dataset; we compare models by likelihood value as well as via cross-validation co-kriging studies. The article closes with a discussion of unsolved problems. © Institute of Mathematical Statistics, 2015.
Multivariate Welch t-test on distances.
Alekseyenko, Alexander V
2016-12-01
Permutational non-Euclidean analysis of variance, PERMANOVA, is routinely used in exploratory analysis of multivariate datasets to draw conclusions about the significance of patterns visualized through dimension reduction. This method recognizes that pairwise distance matrix between observations is sufficient to compute within and between group sums of squares necessary to form the (pseudo) F statistic. Moreover, not only Euclidean, but arbitrary distances can be used. This method, however, suffers from loss of power and type I error inflation in the presence of heteroscedasticity and sample size imbalances. We develop a solution in the form of a distance-based Welch t-test, [Formula: see text], for two sample potentially unbalanced and heteroscedastic data. We demonstrate empirically the desirable type I error and power characteristics of the new test. We compare the performance of PERMANOVA and [Formula: see text] in reanalysis of two existing microbiome datasets, where the methodology has originated. The source code for methods and analysis of this article is available at https://github.com/alekseyenko/Tw2 Further guidance on application of these methods can be obtained from the author. alekseye@musc.edu. © The Author 2016. Published by Oxford University Press.
Cross-covariance functions for multivariate geostatistics
Genton, Marc G.; Kleiber, William
2015-01-01
Continuously indexed datasets with multiple variables have become ubiquitous in the geophysical, ecological, environmental and climate sciences, and pose substantial analysis challenges to scientists and statisticians. For many years, scientists developed models that aimed at capturing the spatial behavior for an individual process; only within the last few decades has it become commonplace to model multiple processes jointly. The key difficulty is in specifying the cross-covariance function, that is, the function responsible for the relationship between distinct variables. Indeed, these cross-covariance functions must be chosen to be consistent with marginal covariance functions in such a way that the second-order structure always yields a nonnegative definite covariance matrix. We review the main approaches to building cross-covariance models, including the linear model of coregionalization, convolution methods, the multivariate Matérn and nonstationary and space-time extensions of these among others. We additionally cover specialized constructions, including those designed for asymmetry, compact support and spherical domains, with a review of physics-constrained models. We illustrate select models on a bivariate regional climate model output example for temperature and pressure, along with a bivariate minimum and maximum temperature observational dataset; we compare models by likelihood value as well as via cross-validation co-kriging studies. The article closes with a discussion of unsolved problems. © Institute of Mathematical Statistics, 2015.
Multivariate meta-analysis: Potential and promise
Jackson, Dan; Riley, Richard; White, Ian R
2011-01-01
The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day ‘Multivariate meta-analysis’ event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd. PMID:21268052
A review of multivariate analyses in imaging genetics
Directory of Open Access Journals (Sweden)
Jingyu eLiu
2014-03-01
Full Text Available Recent advances in neuroimaging technology and molecular genetics provide the unique opportunity to investigate genetic influence on the variation of brain attributes. Since the year 2000, when the initial publication on brain imaging and genetics was released, imaging genetics has been a rapidly growing research approach with increasing publications every year. Several reviews have been offered to the research community focusing on various study designs. In addition to study design, analytic tools and their proper implementation are also critical to the success of a study. In this review, we survey recent publications using data from neuroimaging and genetics, focusing on methods capturing multivariate effects accommodating the large number of variables from both imaging data and genetic data. We group the analyses of genetic or genomic data into either a prior driven or data driven approach, including gene-set enrichment analysis, multifactor dimensionality reduction, principal component analysis, independent component analysis (ICA, and clustering. For the analyses of imaging data, ICA and extensions of ICA are the most widely used multivariate methods. Given detailed reviews of multivariate analyses of imaging data available elsewhere, we provide a brief summary here that includes a recently proposed method known as independent vector analysis. Finally, we review methods focused on bridging the imaging and genetic data by establishing multivariate and multiple genotype-phenotype associations, including sparse partial least squares, sparse canonical correlation analysis, sparse reduced rank regression and parallel ICA. These methods are designed to extract latent variables from both genetic and imaging data, which become new genotypes and phenotypes, and the links between the new genotype-phenotype pairs are maximized using different cost functions. The relationship between these methods along with their assumptions, advantages, and
Multivariate statistical methods a first course
Marcoulides, George A
2014-01-01
Multivariate statistics refer to an assortment of statistical methods that have been developed to handle situations in which multiple variables or measures are involved. Any analysis of more than two variables or measures can loosely be considered a multivariate statistical analysis. An introductory text for students learning multivariate statistical methods for the first time, this book keeps mathematical details to a minimum while conveying the basic principles. One of the principal strategies used throughout the book--in addition to the presentation of actual data analyses--is poin
Exploratory multivariate analysis by example using R
Husson, Francois; Pages, Jerome
2010-01-01
Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, and hierarchical cluster analysis.The authors take a geometric point of view that provides a unified vision for exploring multivariate data tables. Within this framework, they present the prin
Multivariable control in nuclear power stations
International Nuclear Information System (INIS)
Parent, M.; McMorran, P.D.
1982-11-01
Multivariable methods have the potential to improve the control of large systems such as nuclear power stations. Linear-quadratic optimal control is a multivariable method based on the minimization of a cost function. A related technique leads to the Kalman filter for estimation of plant state from noisy measurements. A design program for optimal control and Kalman filtering has been developed as part of a computer-aided design package for multivariable control systems. The method is demonstrated on a model of a nuclear steam generator, and simulated results are presented
Directional outlyingness for multivariate functional data
Dai, Wenlin; Genton, Marc G.
2018-01-01
The direction of outlyingness is crucial to describing the centrality of multivariate functional data. Motivated by this idea, classical depth is generalized to directional outlyingness for functional data. Theoretical properties of functional
The value of multivariate model sophistication
DEFF Research Database (Denmark)
Rombouts, Jeroen; Stentoft, Lars; Violante, Francesco
2014-01-01
We assess the predictive accuracies of a large number of multivariate volatility models in terms of pricing options on the Dow Jones Industrial Average. We measure the value of model sophistication in terms of dollar losses by considering a set of 444 multivariate models that differ in their spec....... In addition to investigating the value of model sophistication in terms of dollar losses directly, we also use the model confidence set approach to statistically infer the set of models that delivers the best pricing performances.......We assess the predictive accuracies of a large number of multivariate volatility models in terms of pricing options on the Dow Jones Industrial Average. We measure the value of model sophistication in terms of dollar losses by considering a set of 444 multivariate models that differ...
Multivariate survival analysis and competing risks
Crowder, Martin J
2012-01-01
Multivariate Survival Analysis and Competing Risks introduces univariate survival analysis and extends it to the multivariate case. It covers competing risks and counting processes and provides many real-world examples, exercises, and R code. The text discusses survival data, survival distributions, frailty models, parametric methods, multivariate data and distributions, copulas, continuous failure, parametric likelihood inference, and non- and semi-parametric methods. There are many books covering survival analysis, but very few that cover the multivariate case in any depth. Written for a graduate-level audience in statistics/biostatistics, this book includes practical exercises and R code for the examples. The author is renowned for his clear writing style, and this book continues that trend. It is an excellent reference for graduate students and researchers looking for grounding in this burgeoning field of research.
Simplicial band depth for multivariate functional data
Ló pez-Pintado, Sara; Sun, Ying; Lin, Juan K.; Genton, Marc G.
2014-01-01
sample of curves. Based on these depths, a sample of multivariate curves can be ordered from the center outward and order statistics can be defined. Properties of the proposed depths, such as invariance and consistency, can be established. A simulation
Ellipsoidal prediction regions for multivariate uncertainty characterization
DEFF Research Database (Denmark)
Golestaneh, Faranak; Pinson, Pierre; Azizipanah-Abarghooee, Rasoul
2018-01-01
, for classes of decision-making problems based on robust, interval chance-constrained optimization, necessary inputs take the form of multivariate prediction regions rather than scenarios. The current literature is at very primitive stage of characterizing multivariate prediction regions to be employed...... in these classes of optimization problems. To address this issue, we introduce a new class of multivariate forecasts which form as multivariate ellipsoids for non-Gaussian variables. We propose a data-driven systematic framework to readily generate and evaluate ellipsoidal prediction regions, with predeﬁned...... probability guarantees and minimum conservativeness. A skill score is proposed for quantitative assessment of the quality of prediction ellipsoids. A set of experiments is used to illustrate the discrimination ability of the proposed scoring rule for potential misspeciﬁcation of ellipsoidal prediction regions...
An Introduction to Applied Multivariate Analysis
Raykov, Tenko
2008-01-01
Focuses on the core multivariate statistics topics which are of fundamental relevance for its understanding. This book emphasis on the topics that are critical to those in the behavioral, social, and educational sciences.
Multivariable Feedback Control of Nuclear Reactors
Directory of Open Access Journals (Sweden)
Rune Moen
1982-07-01
Full Text Available Multivariable feedback control has been adapted for optimal control of the spatial power distribution in nuclear reactor cores. Two design techniques, based on the theory of automatic control, were developed: the State Variable Feedback (SVF is an application of the linear optimal control theory, and the Multivariable Frequency Response (MFR is based on a generalization of the traditional frequency response approach to control system design.
Application of multivariate splines to discrete mathematics
Xu, Zhiqiang
2005-01-01
Using methods developed in multivariate splines, we present an explicit formula for discrete truncated powers, which are defined as the number of non-negative integer solutions of linear Diophantine equations. We further use the formula to study some classical problems in discrete mathematics as follows. First, we extend the partition function of integers in number theory. Second, we exploit the relation between the relative volume of convex polytopes and multivariate truncated powers and giv...
A joint model for multivariate hierarchical semicontinuous data with replications.
Kassahun-Yimer, Wondwosen; Albert, Paul S; Lipsky, Leah M; Nansel, Tonja R; Liu, Aiyi
2017-01-01
Longitudinal data are often collected in biomedical applications in such a way that measurements on more than one response are taken from a given subject repeatedly overtime. For some problems, these multiple profiles need to be modeled jointly to get insight on the joint evolution and/or association of these responses over time. In practice, such longitudinal outcomes may have many zeros that need to be accounted for in the analysis. For example, in dietary intake studies, as we focus on in this paper, some food components are eaten daily by almost all subjects, while others are consumed episodically, where individuals have time periods where they do not eat these components followed by periods where they do. These episodically consumed foods need to be adequately modeled to account for the many zeros that are encountered. In this paper, we propose a joint model to analyze multivariate hierarchical semicontinuous data characterized by many zeros and more than one replicate observations at each measurement occasion. This approach allows for different probability mechanisms for describing the zero behavior as compared with the mean intake given that the individual consumes the food. To deal with the potentially large number of multivariate profiles, we use a pairwise model fitting approach that was developed in the context of multivariate Gaussian random effects models with large number of multivariate components. The novelty of the proposed approach is that it incorporates: (1) multivariate, possibly correlated, response variables; (2) within subject correlation resulting from repeated measurements taken from each subject; (3) many zero observations; (4) overdispersion; and (5) replicate measurements at each visit time.
Measures of dependence for multivariate Lévy distributions
Boland, J.; Hurd, T. R.; Pivato, M.; Seco, L.
2001-02-01
Recent statistical analysis of a number of financial databases is summarized. Increasing agreement is found that logarithmic equity returns show a certain type of asymptotic behavior of the largest events, namely that the probability density functions have power law tails with an exponent α≈3.0. This behavior does not vary much over different stock exchanges or over time, despite large variations in trading environments. The present paper proposes a class of multivariate distributions which generalizes the observed qualities of univariate time series. A new consequence of the proposed class is the "spectral measure" which completely characterizes the multivariate dependences of the extreme tails of the distribution. This measure on the unit sphere in M-dimensions, in principle completely general, can be determined empirically by looking at extreme events. If it can be observed and determined, it will prove to be of importance for scenario generation in portfolio risk management.
Hyperbolic partial differential equations
Witten, Matthew
1986-01-01
Hyperbolic Partial Differential Equations III is a refereed journal issue that explores the applications, theory, and/or applied methods related to hyperbolic partial differential equations, or problems arising out of hyperbolic partial differential equations, in any area of research. This journal issue is interested in all types of articles in terms of review, mini-monograph, standard study, or short communication. Some studies presented in this journal include discretization of ideal fluid dynamics in the Eulerian representation; a Riemann problem in gas dynamics with bifurcation; periodic M
Successful removable partial dentures.
Lynch, Christopher D
2012-03-01
Removable partial dentures (RPDs) remain a mainstay of prosthodontic care for partially dentate patients. Appropriately designed, they can restore masticatory efficiency, improve aesthetics and speech, and help secure overall oral health. However, challenges remain in providing such treatments, including maintaining adequate plaque control, achieving adequate retention, and facilitating patient tolerance. The aim of this paper is to review the successful provision of RPDs. Removable partial dentures are a successful form of treatment for replacing missing teeth, and can be successfully provided with appropriate design and fabrication concepts in mind.
Beginning partial differential equations
O'Neil, Peter V
2011-01-01
A rigorous, yet accessible, introduction to partial differential equations-updated in a valuable new edition Beginning Partial Differential Equations, Second Edition provides a comprehensive introduction to partial differential equations (PDEs) with a special focus on the significance of characteristics, solutions by Fourier series, integrals and transforms, properties and physical interpretations of solutions, and a transition to the modern function space approach to PDEs. With its breadth of coverage, this new edition continues to present a broad introduction to the field, while also addres
Partial knee replacement - slideshow
... page: //medlineplus.gov/ency/presentations/100225.htm Partial knee replacement - series—Normal anatomy To use the sharing ... A.M. Editorial team. Related MedlinePlus Health Topics Knee Replacement A.D.A.M., Inc. is accredited ...
Beginning partial differential equations
O'Neil, Peter V
2014-01-01
A broad introduction to PDEs with an emphasis on specialized topics and applications occurring in a variety of fields Featuring a thoroughly revised presentation of topics, Beginning Partial Differential Equations, Third Edition provides a challenging, yet accessible,combination of techniques, applications, and introductory theory on the subjectof partial differential equations. The new edition offers nonstandard coverageon material including Burger's equation, the telegraph equation, damped wavemotion, and the use of characteristics to solve nonhomogeneous problems. The Third Edition is or
Fitting and Testing Conditional Multinormal Partial Credit Models
Hessen, David J.
2012-01-01
A multinormal partial credit model for factor analysis of polytomously scored items with ordered response categories is derived using an extension of the Dutch Identity (Holland in "Psychometrika" 55:5-18, 1990). In the model, latent variables are assumed to have a multivariate normal distribution conditional on unweighted sums of item…
An architecture for implementation of multivariable controllers
DEFF Research Database (Denmark)
Niemann, Hans Henrik; Stoustrup, Jakob
1999-01-01
Browse > Conferences> American Control Conference, Prev | Back to Results | Next » An architecture for implementation of multivariable controllers 786292 searchabstract Niemann, H. ; Stoustrup, J. ; Dept. of Autom., Tech. Univ., Lyngby This paper appears in: American Control Conference, 1999....... Proceedings of the 1999 Issue Date : 1999 Volume : 6 On page(s): 4029 - 4033 vol.6 Location: San Diego, CA Meeting Date : 02 Jun 1999-04 Jun 1999 Print ISBN: 0-7803-4990-3 References Cited: 7 INSPEC Accession Number: 6403075 Digital Object Identifier : 10.1109/ACC.1999.786292 Date of Current Version : 06...... august 2002 Abstract An architecture for implementation of multivariable controllers is presented in this paper. The architecture is based on the Youla-Jabr-Bongiorno-Kucera parameterization of all stabilizing controllers. By using this architecture for implementation of multivariable controllers...
A MULTIVARIATE ANALYSIS OF CROATIAN COUNTIES ENTREPRENEURSHIP
Directory of Open Access Journals (Sweden)
Elza Jurun
2012-12-01
Full Text Available In the focus of this paper is a multivariate analysis of Croatian Counties entrepreneurship. Complete data base available by official statistic institutions at national and regional level is used. Modern econometric methodology starting from a comparative analysis via multiple regression to multivariate cluster analysis is carried out as well as the analysis of successful or inefficacious entrepreneurship measured by indicators of efficiency, profitability and productivity. Time horizons of the comparative analysis are in 2004 and 2010. Accelerators of socio-economic development - number of entrepreneur investors, investment in fixed assets and current assets ratio in multiple regression model are analytically filtered between twenty-six independent variables as variables of the dominant influence on GDP per capita in 2010 as dependent variable. Results of multivariate cluster analysis of twentyone Croatian Counties are interpreted also in the sense of three Croatian NUTS 2 regions according to European nomenclature of regional territorial division of Croatia.
Simplicial band depth for multivariate functional data
López-Pintado, Sara
2014-03-05
We propose notions of simplicial band depth for multivariate functional data that extend the univariate functional band depth. The proposed simplicial band depths provide simple and natural criteria to measure the centrality of a trajectory within a sample of curves. Based on these depths, a sample of multivariate curves can be ordered from the center outward and order statistics can be defined. Properties of the proposed depths, such as invariance and consistency, can be established. A simulation study shows the robustness of this new definition of depth and the advantages of using a multivariate depth versus the marginal depths for detecting outliers. Real data examples from growth curves and signature data are used to illustrate the performance and usefulness of the proposed depths. © 2014 Springer-Verlag Berlin Heidelberg.
Multivariate generalized linear mixed models using R
Berridge, Damon Mark
2011-01-01
Multivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound models for analyzing large and complex data sets, enabling readers to answer increasingly complex research questions. The book applies the principles of modeling to longitudinal data from panel and related studies via the Sabre software package in R. A Unified Framework for a Broad Class of Models The authors first discuss members of the family of generalized linear models, gradually adding complexity to the modeling framework by incorporating random effects. After reviewing the generalized linear model notation, they illustrate a range of random effects models, including three-level, multivariate, endpoint, event history, and state dependence models. They estimate the multivariate generalized linear mixed models (MGLMMs) using either standard or adaptive Gaussian quadrature. The authors also compare two-level fixed and random effects linear models. The appendices contain additional information on quadrature, model...
A MATLAB companion for multivariable calculus
Cooper, Jeffery
2001-01-01
Offering a concise collection of MatLab programs and exercises to accompany a third semester course in multivariable calculus, A MatLab Companion for Multivariable Calculus introduces simple numerical procedures such as numerical differentiation, numerical integration and Newton''s method in several variables, thereby allowing students to tackle realistic problems. The many examples show students how to use MatLab effectively and easily in many contexts. Numerous exercises in mathematics and applications areas are presented, graded from routine to more demanding projects requiring some programming. Matlab M-files are provided on the Harcourt/Academic Press web site at http://www.harcourt-ap.com/matlab.html.* Computer-oriented material that complements the essential topics in multivariable calculus* Main ideas presented with examples of computations and graphics displays using MATLAB * Numerous examples of short code in the text, which can be modified for use with the exercises* MATLAB files are used to implem...
Classification of Specialized Farms Applying Multivariate Statistical Methods
Directory of Open Access Journals (Sweden)
Zuzana Hloušková
2017-01-01
Full Text Available Classification of specialized farms applying multivariate statistical methods The paper is aimed at application of advanced multivariate statistical methods when classifying cattle breeding farming enterprises by their economic size. Advantage of the model is its ability to use a few selected indicators compared to the complex methodology of current classification model that requires knowledge of detailed structure of the herd turnover and structure of cultivated crops. Output of the paper is intended to be applied within farm structure research focused on future development of Czech agriculture. As data source, the farming enterprises database for 2014 has been used, from the FADN CZ system. The predictive model proposed exploits knowledge of actual size classes of the farms tested. Outcomes of the linear discriminatory analysis multifactor classification method have supported the chance of filing farming enterprises in the group of Small farms (98 % filed correctly, and the Large and Very Large enterprises (100 % filed correctly. The Medium Size farms have been correctly filed at 58.11 % only. Partial shortages of the process presented have been found when discriminating Medium and Small farms.
Multivariable nonlinear analysis of foreign exchange rates
Suzuki, Tomoya; Ikeguchi, Tohru; Suzuki, Masuo
2003-05-01
We analyze the multivariable time series of foreign exchange rates. These are price movements that have often been analyzed, and dealing time intervals and spreads between bid and ask prices. Considering dealing time intervals as event timing such as neurons’ firings, we use raster plots (RPs) and peri-stimulus time histograms (PSTHs) which are popular methods in the field of neurophysiology. Introducing special processings to obtaining RPs and PSTHs time histograms for analyzing exchange rates time series, we discover that there exists dynamical interaction among three variables. We also find that adopting multivariables leads to improvements of prediction accuracy.
Partial scram incident in FBTR
International Nuclear Information System (INIS)
Usha, S.; Pillai, C.P.; Muralikrishna, G.
1989-01-01
Evaluation of a partial scram incident occurred at the Fast Breeder Test Reactor at Kalpakkam was carried out. Based on the observations of the experiments it was ascertained that the nonpersistant order was due to superimposed noise component on the channel that was close to the threshold and had resulted in intermittent supply to electro-magnetic (EM) coils. Owing to a larger discharge time and a smaller charge time, the EM coils got progressively discharged. It was confirmed that during the incident, partial scram took place since the charging and discharging patterns of the EM coils are dissimilar and EM coils of rods A, E and F had discharged faster than others for noise component of a particular duty cycle. However, nonlatching of scram order was because of the fact that noise pulse duration was less than latching time. (author)
Multivariate stability of force-reflecting teleoperation: Structures of finite and infinite zeros
International Nuclear Information System (INIS)
Daniel, R.W.; McAree, P.R.
2000-01-01
This paper presents a stability analysis of force-position teleoperation under general end-effector contact. The analysis is based on the finite and infinite zero structure of the multivariable root-locus resulting from modulation of the environment stiffness. The starting point is an analysis of the stability of robot force control, motivated by the observation that the human-operator in a force reflection loop acts as a force servo, generating position commands in response to reflected force. Asymptotic root loci properties are used to establish passivity conditions on force feedback to give root locus interpretations of the well-known results that (1) feedback via the inverse joint Jacobian can lead to (kinematic) instability and that (2) passivity is preserved by kinematically proper force feedback through the transpose of the joint angle Jacobian. It is demonstrated that a fully constrained force-position teleoperation loop has an identical infinite zero structure to that of a slave manipulation under kinematically proper force control and that the dominant vibration modes of a force-position loop are fully described by a multivariable analogue of the single-input single-output pseudo-system investigated in a study by Daniel and McAree. Extension of the analysis to cover partial end-effector constraint provides a design tool for teleoperation control and serves to aid selection of teleoperation slate-arms. The paper concludes by giving a passivity condition for multiple-input multiple-output force-position teleoperation for stable contact against all environments
DEFF Research Database (Denmark)
Mikosch, Thomas Valentin; Wintenberger, Olivier
2014-01-01
We introduce the cluster index of a multivariate stationary sequence and characterize the index in terms of the spectral tail process. This index plays a major role in limit theory for partial sums of sequences. We illustrate the use of the cluster index by characterizing infinite variance stable...... limit distributions and precise large deviation results for sums of multivariate functions acting on a stationary Markov chain under a drift condition....
A Cyber-Attack Detection Model Based on Multivariate Analyses
Sakai, Yuto; Rinsaka, Koichiro; Dohi, Tadashi
In the present paper, we propose a novel cyber-attack detection model based on two multivariate-analysis methods to the audit data observed on a host machine. The statistical techniques used here are the well-known Hayashi's quantification method IV and cluster analysis method. We quantify the observed qualitative audit event sequence via the quantification method IV, and collect similar audit event sequence in the same groups based on the cluster analysis. It is shown in simulation experiments that our model can improve the cyber-attack detection accuracy in some realistic cases where both normal and attack activities are intermingled.
Algorithm of Dynamic Model Structural Identification of the Multivariable Plant
Directory of Open Access Journals (Sweden)
Л.М. Блохін
2004-02-01
Full Text Available The new algorithm of dynamic model structural identification of the multivariable stabilized plant with observable and unobservable disturbances in the regular operating modes is offered in this paper. With the help of the offered algorithm it is possible to define the “perturbed” models of dynamics not only of the plant, but also the dynamics characteristics of observable and unobservable casual disturbances taking into account the absence of correlation between themselves and control inputs with the unobservable perturbations.
Calculus of multivariate functions: it's application in business | Awen ...
African Journals Online (AJOL)
Multivariate functions can be applied to situations in business organizations like ... of capital invested in the plant, the size of the labour force and the cost of raw ... of multivariate functions and has considered types of multivariate differentiation ...
Modelling the Covariance Structure in Marginal Multivariate Count Models
DEFF Research Database (Denmark)
Bonat, W. H.; Olivero, J.; Grande-Vega, M.
2017-01-01
The main goal of this article is to present a flexible statistical modelling framework to deal with multivariate count data along with longitudinal and repeated measures structures. The covariance structure for each response variable is defined in terms of a covariance link function combined...... be used to indicate whether there was statistical evidence of a decline in blue duikers and other species hunted during the study period. Determining whether observed drops in the number of animals hunted are indeed true is crucial to assess whether species depletion effects are taking place in exploited...... with a matrix linear predictor involving known matrices. In order to specify the joint covariance matrix for the multivariate response vector, the generalized Kronecker product is employed. We take into account the count nature of the data by means of the power dispersion function associated with the Poisson...
Multivariate Analysis of Industrial Scale Fermentation Data
DEFF Research Database (Denmark)
Mears, Lisa; Nørregård, Rasmus; Stocks, Stuart M.
2015-01-01
Multivariate analysis allows process understanding to be gained from the vast and complex datasets recorded from fermentation processes, however the application of such techniques to this field can be limited by the data pre-processing requirements and data handling. In this work many iterations...
Multivariate Option Pricing Using Dynamic Copula Models
van den Goorbergh, R.W.J.; Genest, C.; Werker, B.J.M.
2003-01-01
This paper examines the behavior of multivariate option prices in the presence of association between the underlying assets.Parametric families of copulas offering various alternatives to the normal dependence structure are used to model this association, which is explicitly assumed to vary over
Fully conditional specification in multivariate imputation
van Buuren, S.; Brand, J. P.L.; Groothuis-Oudshoorn, C. G.M.; Rubin, D. B.
2006-01-01
The use of the Gibbs sampler with fully conditionally specified models, where the distribution of each variable given the other variables is the starting point, has become a popular method to create imputations in incomplete multivariate data. The theoretical weakness of this approach is that the
Multivariate ordination statistics workshop with R slides
Strack, Michael
2015-01-01
2-hour workshop given at Macquarie University Department of Biological Sciences, 4 November 2015. Workshop was an introduction to the family of techniques falling under multivariate ordination, using the R language and drawing heavily from the book "Numerical Ecology with R" by Borcard et. al (2012).
Multivariate Analysis of Schools and Educational Policy.
Kiesling, Herbert J.
This report describes a multivariate analysis technique that approaches the problems of educational production function analysis by (1) using comparable measures of output across large experiments, (2) accounting systematically for differences in socioeconomic background, and (3) treating the school as a complete system in which different…
Multivariate Discrete First Order Stochastic Dominance
DEFF Research Database (Denmark)
Tarp, Finn; Østerdal, Lars Peter
This paper characterizes the principle of first order stochastic dominance in a multivariate discrete setting. We show that a distribution f first order stochastic dominates distribution g if and only if f can be obtained from g by iteratively shifting density from one outcome to another...
Multivariate Time Series Decomposition into Oscillation Components.
Matsuda, Takeru; Komaki, Fumiyasu
2017-08-01
Many time series are considered to be a superposition of several oscillation components. We have proposed a method for decomposing univariate time series into oscillation components and estimating their phases (Matsuda & Komaki, 2017 ). In this study, we extend that method to multivariate time series. We assume that several oscillators underlie the given multivariate time series and that each variable corresponds to a superposition of the projections of the oscillators. Thus, the oscillators superpose on each variable with amplitude and phase modulation. Based on this idea, we develop gaussian linear state-space models and use them to decompose the given multivariate time series. The model parameters are estimated from data using the empirical Bayes method, and the number of oscillators is determined using the Akaike information criterion. Therefore, the proposed method extracts underlying oscillators in a data-driven manner and enables investigation of phase dynamics in a given multivariate time series. Numerical results show the effectiveness of the proposed method. From monthly mean north-south sunspot number data, the proposed method reveals an interesting phase relationship.
Ranking multivariate GARCH models by problem dimension
M. Caporin (Massimiliano); M.J. McAleer (Michael)
2010-01-01
textabstractIn the last 15 years, several Multivariate GARCH (MGARCH) models have appeared in the literature. The two most widely known and used are the Scalar BEKK model of Engle and Kroner (1995) and Ding and Engle (2001), and the DCC model of Engle (2002). Some recent research has begun to
Multivariate and multiscale data assimilation in terrestrial systems: a review.
Montzka, Carsten; Pauwels, Valentijn R N; Franssen, Harrie-Jan Hendricks; Han, Xujun; Vereecken, Harry
2012-11-26
More and more terrestrial observational networks are being established to monitor climatic, hydrological and land-use changes in different regions of the World. In these networks, time series of states and fluxes are recorded in an automated manner, often with a high temporal resolution. These data are important for the understanding of water, energy, and/or matter fluxes, as well as their biological and physical drivers and interactions with and within the terrestrial system. Similarly, the number and accuracy of variables, which can be observed by spaceborne sensors, are increasing. Data assimilation (DA) methods utilize these observations in terrestrial models in order to increase process knowledge as well as to improve forecasts for the system being studied. The widely implemented automation in observing environmental states and fluxes makes an operational computation more and more feasible, and it opens the perspective of short-time forecasts of the state of terrestrial systems. In this paper, we review the state of the art with respect to DA focusing on the joint assimilation of observational data precedents from different spatial scales and different data types. An introduction is given to different DA methods, such as the Ensemble Kalman Filter (EnKF), Particle Filter (PF) and variational methods (3/4D-VAR). In this review, we distinguish between four major DA approaches: (1) univariate single-scale DA (UVSS), which is the approach used in the majority of published DA applications, (2) univariate multiscale DA (UVMS) referring to a methodology which acknowledges that at least some of the assimilated data are measured at a different scale than the computational grid scale, (3) multivariate single-scale DA (MVSS) dealing with the assimilation of at least two different data types, and (4) combined multivariate multiscale DA (MVMS). Finally, we conclude with a discussion on the advantages and disadvantages of the assimilation of multiple data types in a
Multivariate and Multiscale Data Assimilation in Terrestrial Systems: A Review
Directory of Open Access Journals (Sweden)
Harry Vereecken
2012-11-01
Full Text Available More and more terrestrial observational networks are being established to monitor climatic, hydrological and land-use changes in different regions of the World. In these networks, time series of states and fluxes are recorded in an automated manner, often with a high temporal resolution. These data are important for the understanding of water, energy, and/or matter fluxes, as well as their biological and physical drivers and interactions with and within the terrestrial system. Similarly, the number and accuracy of variables, which can be observed by spaceborne sensors, are increasing. Data assimilation (DA methods utilize these observations in terrestrial models in order to increase process knowledge as well as to improve forecasts for the system being studied. The widely implemented automation in observing environmental states and fluxes makes an operational computation more and more feasible, and it opens the perspective of short-time forecasts of the state of terrestrial systems. In this paper, we review the state of the art with respect to DA focusing on the joint assimilation of observational data precedents from different spatial scales and different data types. An introduction is given to different DA methods, such as the Ensemble Kalman Filter (EnKF, Particle Filter (PF and variational methods (3/4D-VAR. In this review, we distinguish between four major DA approaches: (1 univariate single-scale DA (UVSS, which is the approach used in the majority of published DA applications, (2 univariate multiscale DA (UVMS referring to a methodology which acknowledges that at least some of the assimilated data are measured at a different scale than the computational grid scale, (3 multivariate single-scale DA (MVSS dealing with the assimilation of at least two different data types, and (4 combined multivariate multiscale DA (MVMS. Finally, we conclude with a discussion on the advantages and disadvantages of the assimilation of multiple data types in a
Partial differential equations
Evans, Lawrence C
2010-01-01
This text gives a comprehensive survey of modern techniques in the theoretical study of partial differential equations (PDEs) with particular emphasis on nonlinear equations. The exposition is divided into three parts: representation formulas for solutions; theory for linear partial differential equations; and theory for nonlinear partial differential equations. Included are complete treatments of the method of characteristics; energy methods within Sobolev spaces; regularity for second-order elliptic, parabolic, and hyperbolic equations; maximum principles; the multidimensional calculus of variations; viscosity solutions of Hamilton-Jacobi equations; shock waves and entropy criteria for conservation laws; and, much more.The author summarizes the relevant mathematics required to understand current research in PDEs, especially nonlinear PDEs. While he has reworked and simplified much of the classical theory (particularly the method of characteristics), he primarily emphasizes the modern interplay between funct...
Optimization of partial search
International Nuclear Information System (INIS)
Korepin, Vladimir E
2005-01-01
A quantum Grover search algorithm can find a target item in a database faster than any classical algorithm. One can trade accuracy for speed and find a part of the database (a block) containing the target item even faster; this is partial search. A partial search algorithm was recently suggested by Grover and Radhakrishnan. Here we optimize it. Efficiency of the search algorithm is measured by the number of queries to the oracle. The author suggests a new version of the Grover-Radhakrishnan algorithm which uses a minimal number of such queries. The algorithm can run on the same hardware that is used for the usual Grover algorithm. (letter to the editor)
Auxiliary partial liver transplantation
C.B. Reuvers (Cornelis Bastiaan)
1986-01-01
textabstractIn this thesis studies on auxiliary partial liver transplantation in the dog and the pig are reported. The motive to perform this study was the fact that patients with acute hepatic failure or end-stage chronic liver disease are often considered to form too great a risk for successful
DEFF Research Database (Denmark)
Andersen, Marie Louise Max; Hougaard, Philip; Pörksen, Sven
2014-01-01
OBJECTIVE: To validate the partial remission (PR) definition based on insulin dose-adjusted HbA1c (IDAA1c). SUBJECTS AND METHODS: The IDAA1c was developed using data in 251 children from the European Hvidoere cohort. For validation, 129 children from a Danish cohort were followed from the onset...
Fundamental partial compositeness
DEFF Research Database (Denmark)
Sannino, Francesco; Strumia, Alessandro; Tesi, Andrea
2016-01-01
We construct renormalizable Standard Model extensions, valid up to the Planck scale, that give a composite Higgs from a new fundamental strong force acting on fermions and scalars. Yukawa interactions of these particles with Standard Model fermions realize the partial compositeness scenario. Unde...
Fernandez, R.; Deveaux, V.
2010-01-01
We provide a formal definition and study the basic properties of partially ordered chains (POC). These systems were proposed to model textures in image processing and to represent independence relations between random variables in statistics (in the later case they are known as Bayesian networks).
W. van der Hoek (Wiebe); J.O.M. Jaspars; E. Thijsse
1995-01-01
textabstractWe propose an epistemic logic in which knowledge is fully introspective and implies truth, although truth need not imply epistemic possibility. The logic is presented in sequential format and is interpreted in a natural class of partial models, called balloon models. We examine the
Data classification and MTBF prediction with a multivariate analysis approach
International Nuclear Information System (INIS)
Braglia, Marcello; Carmignani, Gionata; Frosolini, Marco; Zammori, Francesco
2012-01-01
The paper presents a multivariate statistical approach that supports the classification of mechanical components, subjected to specific operating conditions, in terms of the Mean Time Between Failure (MTBF). Assessing the influence of working conditions and/or environmental factors on the MTBF is a prerequisite for the development of an effective preventive maintenance plan. However, this task may be demanding and it is generally performed with ad-hoc experimental methods, lacking of statistical rigor. To solve this common problem, a step by step multivariate data classification technique is proposed. Specifically, a set of structured failure data are classified in a meaningful way by means of: (i) cluster analysis, (ii) multivariate analysis of variance, (iii) feature extraction and (iv) predictive discriminant analysis. This makes it possible not only to define the MTBF of the analyzed components, but also to identify the working parameters that explain most of the variability of the observed data. The approach is finally demonstrated on 126 centrifugal pumps installed in an oil refinery plant; obtained results demonstrate the quality of the final discrimination, in terms of data classification and failure prediction.
Extracting bb Higgs Decay Signals using Multivariate Techniques
Energy Technology Data Exchange (ETDEWEB)
Smith, W Clarke; /George Washington U. /SLAC
2012-08-28
For low-mass Higgs boson production at ATLAS at {radical}s = 7 TeV, the hard subprocess gg {yields} h{sup 0} {yields} b{bar b} dominates but is in turn drowned out by background. We seek to exploit the intrinsic few-MeV mass width of the Higgs boson to observe it above the background in b{bar b}-dijet mass plots. The mass resolution of existing mass-reconstruction algorithms is insufficient for this purpose due to jet combinatorics, that is, the algorithms cannot identify every jet that results from b{bar b} Higgs decay. We combine these algorithms using the neural net (NN) and boosted regression tree (BDT) multivariate methods in attempt to improve the mass resolution. Events involving gg {yields} h{sup 0} {yields} b{bar b} are generated using Monte Carlo methods with Pythia and then the Toolkit for Multivariate Analysis (TMVA) is used to train and test NNs and BDTs. For a 120 GeV Standard Model Higgs boson, the m{sub h{sup 0}}-reconstruction width is reduced from 8.6 to 6.5 GeV. Most importantly, however, the methods used here allow for more advanced m{sub h{sup 0}}-reconstructions to be created in the future using multivariate methods.
Multi-instance dictionary learning via multivariate performance measure optimization
Wang, Jim Jing-Yan
2016-12-29
The multi-instance dictionary plays a critical role in multi-instance data representation. Meanwhile, different multi-instance learning applications are evaluated by specific multivariate performance measures. For example, multi-instance ranking reports the precision and recall. It is not difficult to see that to obtain different optimal performance measures, different dictionaries are needed. This observation motives us to learn performance-optimal dictionaries for this problem. In this paper, we propose a novel joint framework for learning the multi-instance dictionary and the classifier to optimize a given multivariate performance measure, such as the F1 score and precision at rank k. We propose to represent the bags as bag-level features via the bag-instance similarity, and learn a classifier in the bag-level feature space to optimize the given performance measure. We propose to minimize the upper bound of a multivariate loss corresponding to the performance measure, the complexity of the classifier, and the complexity of the dictionary, simultaneously, with regard to both the dictionary and the classifier parameters. In this way, the dictionary learning is regularized by the performance optimization, and a performance-optimal dictionary is obtained. We develop an iterative algorithm to solve this minimization problem efficiently using a cutting-plane algorithm and a coordinate descent method. Experiments on multi-instance benchmark data sets show its advantage over both traditional multi-instance learning and performance optimization methods.
Multivariable control in nuclear power stations -survey of design methods
International Nuclear Information System (INIS)
Mcmorran, P.D.
1979-12-01
The development of larger nuclear generating stations increases the importance of dynamic interaction between controllers, because each control action may affect several plant outputs. Multivariable control provides the techniques to design controllers which perform well under these conditions. This report is a foundation for further work on the application of multivariable control in AECL. It covers the requirements of control and the fundamental mathematics used, then reviews the most important linear methods, based on both state-space and frequency-response concepts. State-space methods are derived from analysis of the system differential equations, while frequency-response methods use the input-output transfer function. State-space methods covered include linear-quadratic optimal control, pole shifting, and the theory of state observers and estimators. Frequency-response methods include the inverse Nyquist array method, and classical non-interactive techniques. Transfer-function methods are particularly emphasized since they can incorporate ill-defined design criteria. The underlying concepts, and the application strengths and weaknesses of each design method are presented. A review of significant applications is also given. It is concluded that the inverse Nyquist array method, a frequency-response technique based on inverse transfer-function matrices, is preferred for the design of multivariable controllers for nuclear power plants. This method may be supplemented by information obtained from a modal analysis of the plant model. (auth)
Various forms of indexing HDMR for modelling multivariate classification problems
Energy Technology Data Exchange (ETDEWEB)
Aksu, Çağrı [Bahçeşehir University, Information Technologies Master Program, Beşiktaş, 34349 İstanbul (Turkey); Tunga, M. Alper [Bahçeşehir University, Software Engineering Department, Beşiktaş, 34349 İstanbul (Turkey)
2014-12-10
The Indexing HDMR method was recently developed for modelling multivariate interpolation problems. The method uses the Plain HDMR philosophy in partitioning the given multivariate data set into less variate data sets and then constructing an analytical structure through these partitioned data sets to represent the given multidimensional problem. Indexing HDMR makes HDMR be applicable to classification problems having real world data. Mostly, we do not know all possible class values in the domain of the given problem, that is, we have a non-orthogonal data structure. However, Plain HDMR needs an orthogonal data structure in the given problem to be modelled. In this sense, the main idea of this work is to offer various forms of Indexing HDMR to successfully model these real life classification problems. To test these different forms, several well-known multivariate classification problems given in UCI Machine Learning Repository were used and it was observed that the accuracy results lie between 80% and 95% which are very satisfactory.
Multi-instance dictionary learning via multivariate performance measure optimization
Wang, Jim Jing-Yan; Tsang, Ivor Wai-Hung; Cui, Xuefeng; Lu, Zhiwu; Gao, Xin
2016-01-01
The multi-instance dictionary plays a critical role in multi-instance data representation. Meanwhile, different multi-instance learning applications are evaluated by specific multivariate performance measures. For example, multi-instance ranking reports the precision and recall. It is not difficult to see that to obtain different optimal performance measures, different dictionaries are needed. This observation motives us to learn performance-optimal dictionaries for this problem. In this paper, we propose a novel joint framework for learning the multi-instance dictionary and the classifier to optimize a given multivariate performance measure, such as the F1 score and precision at rank k. We propose to represent the bags as bag-level features via the bag-instance similarity, and learn a classifier in the bag-level feature space to optimize the given performance measure. We propose to minimize the upper bound of a multivariate loss corresponding to the performance measure, the complexity of the classifier, and the complexity of the dictionary, simultaneously, with regard to both the dictionary and the classifier parameters. In this way, the dictionary learning is regularized by the performance optimization, and a performance-optimal dictionary is obtained. We develop an iterative algorithm to solve this minimization problem efficiently using a cutting-plane algorithm and a coordinate descent method. Experiments on multi-instance benchmark data sets show its advantage over both traditional multi-instance learning and performance optimization methods.
Ayoko, Godwin A.; Singh, Kirpal; Balerea, Steven; Kokot, Serge
2007-03-01
SummaryPhysico-chemical properties of surface water and groundwater samples from some developing countries have been subjected to multivariate analyses by the non-parametric multi-criteria decision-making methods, PROMETHEE and GAIA. Complete ranking information necessary to select one source of water in preference to all others was obtained, and this enabled relationships between the physico-chemical properties and water quality to be assessed. Thus, the ranking of the quality of the water bodies was found to be strongly dependent on the total dissolved solid, phosphate, sulfate, ammonia-nitrogen, calcium, iron, chloride, magnesium, zinc, nitrate and fluoride contents of the waters. However, potassium, manganese and zinc composition showed the least influence in differentiating the water bodies. To model and predict the water quality influencing parameters, partial least squares analyses were carried out on a matrix made up of the results of water quality assessment studies carried out in Nigeria, Papua New Guinea, Egypt, Thailand and India/Pakistan. The results showed that the total dissolved solid, calcium, sulfate, sodium and chloride contents can be used to predict a wide range of physico-chemical characteristics of water. The potential implications of these observations on the financial and opportunity costs associated with elaborate water quality monitoring are discussed.
Alberto Moldes, Carlos; Fontão de Lima Filho, Oscar; Manuel Camiña, José; Gabriela Kiriachek, Soraya; Lia Molas, María; Mui Tsai, Siu
2013-11-27
Silicon has been extensively researched in relation to the response of plants to biotic and abiotic stress, as an element triggering defense mechanisms which activate the antioxidant system. Furthermore, in some species, adding silicon to unstressed plants modifies the activity of certain antioxidant enzymes participating in detoxifying processes. Thus, in this study, we analyzed the activity of antioxidant enzymes in leaves and roots of unstressed cotton plants fertilized with silicon (Si). Cotton plants were grown in hydroponic culture and added with increasing doses of potassium silicate; then, the enzymatic activity of catalase (CAT), guaiacol peroxidase (GPOX), ascorbate peroxidase (APX), and lipid peroxidation were determined. Using multivariate analysis, we found that silicon altered the activity of GPOX, APX, and CAT in roots and leaves of unstressed cotton plants, whereas lipid peroxidation was not affected. The analysis of these four variables in concert showed a clear differentiation among Si treatments. We observed that enzymatic activities in leaves and roots changed as silicon concentration increased, to stabilize at 100 and 200 mg Si L(-1) treatments in leaves and roots, respectively. Those alterations would allow a new biochemical status that could be partially responsible for the beneficial effects of silicon. This study might contribute to adjust the silicon application doses for optimal fertilization, preventing potential toxic effects and unnecessary cost.
Algebraic partial Boolean algebras
International Nuclear Information System (INIS)
Smith, Derek
2003-01-01
Partial Boolean algebras, first studied by Kochen and Specker in the 1960s, provide the structure for Bell-Kochen-Specker theorems which deny the existence of non-contextual hidden variable theories. In this paper, we study partial Boolean algebras which are 'algebraic' in the sense that their elements have coordinates in an algebraic number field. Several of these algebras have been discussed recently in a debate on the validity of Bell-Kochen-Specker theorems in the context of finite precision measurements. The main result of this paper is that every algebraic finitely-generated partial Boolean algebra B(T) is finite when the underlying space H is three-dimensional, answering a question of Kochen and showing that Conway and Kochen's infinite algebraic partial Boolean algebra has minimum dimension. This result contrasts the existence of an infinite (non-algebraic) B(T) generated by eight elements in an abstract orthomodular lattice of height 3. We then initiate a study of higher-dimensional algebraic partial Boolean algebras. First, we describe a restriction on the determinants of the elements of B(T) that are generated by a given set T. We then show that when the generating set T consists of the rays spanning the minimal vectors in a real irreducible root lattice, B(T) is infinite just if that root lattice has an A 5 sublattice. Finally, we characterize the rays of B(T) when T consists of the rays spanning the minimal vectors of the root lattice E 8
Power Estimation in Multivariate Analysis of Variance
Directory of Open Access Journals (Sweden)
Jean François Allaire
2007-09-01
Full Text Available Power is often overlooked in designing multivariate studies for the simple reason that it is believed to be too complicated. In this paper, it is shown that power estimation in multivariate analysis of variance (MANOVA can be approximated using a F distribution for the three popular statistics (Hotelling-Lawley trace, Pillai-Bartlett trace, Wilk`s likelihood ratio. Consequently, the same procedure, as in any statistical test, can be used: computation of the critical F value, computation of the noncentral parameter (as a function of the effect size and finally estimation of power using a noncentral F distribution. Various numerical examples are provided which help to understand and to apply the method. Problems related to post hoc power estimation are discussed.
Directional outlyingness for multivariate functional data
Dai, Wenlin
2018-04-07
The direction of outlyingness is crucial to describing the centrality of multivariate functional data. Motivated by this idea, classical depth is generalized to directional outlyingness for functional data. Theoretical properties of functional directional outlyingness are investigated and the total outlyingness can be naturally decomposed into two parts: magnitude outlyingness and shape outlyingness which represent the centrality of a curve for magnitude and shape, respectively. This decomposition serves as a visualization tool for the centrality of curves. Furthermore, an outlier detection procedure is proposed based on functional directional outlyingness. This criterion applies to both univariate and multivariate curves and simulation studies show that it outperforms competing methods. Weather and electrocardiogram data demonstrate the practical application of our proposed framework.
Multivariate max-stable spatial processes
Genton, Marc G.; Padoan, S. A.; Sang, H.
2015-01-01
Max-stable processes allow the spatial dependence of extremes to be modelled and quantified, so they are widely adopted in applications. For a better understanding of extremes, it may be useful to study several variables simultaneously. To this end, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. We define a Poisson process construction and introduce multivariate versions of the Smith Gaussian extreme-value, the Schlather extremal-Gaussian and extremal-t, and the Brown–Resnick models. We develop inference for the models based on composite likelihoods. We present results of Monte Carlo simulations and an application to daily maximum wind speed and wind gust.
Multivariate Process Control with Autocorrelated Data
DEFF Research Database (Denmark)
Kulahci, Murat
2011-01-01
As sensor and computer technology continues to improve, it becomes a normal occurrence that we confront with high dimensional data sets. As in many areas of industrial statistics, this brings forth various challenges in statistical process control and monitoring. This new high dimensional data...... often exhibit not only cross-‐correlation among the quality characteristics of interest but also serial dependence as a consequence of high sampling frequency and system dynamics. In practice, the most common method of monitoring multivariate data is through what is called the Hotelling’s T2 statistic....... In this paper, we discuss the effect of autocorrelation (when it is ignored) on multivariate control charts based on these methods and provide some practical suggestions and remedies to overcome this problem....
Multivariate max-stable spatial processes
Genton, Marc G.
2015-02-11
Max-stable processes allow the spatial dependence of extremes to be modelled and quantified, so they are widely adopted in applications. For a better understanding of extremes, it may be useful to study several variables simultaneously. To this end, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. We define a Poisson process construction and introduce multivariate versions of the Smith Gaussian extreme-value, the Schlather extremal-Gaussian and extremal-t, and the Brown–Resnick models. We develop inference for the models based on composite likelihoods. We present results of Monte Carlo simulations and an application to daily maximum wind speed and wind gust.
Multivariate Approaches to Classification in Extragalactic Astronomy
Directory of Open Access Journals (Sweden)
Didier eFraix-Burnet
2015-08-01
Full Text Available Clustering objects into synthetic groups is a natural activity of any science. Astrophysics is not an exception and is now facing a deluge of data. For galaxies, the one-century old Hubble classification and the Hubble tuning fork are still largely in use, together with numerous mono- or bivariate classifications most often made by eye. However, a classification must be driven by the data, and sophisticated multivariate statistical tools are used more and more often. In this paper we review these different approaches in order to situate them in the general context of unsupervised and supervised learning. We insist on the astrophysical outcomes of these studies to show that multivariate analyses provide an obvious path toward a renewal of our classification of galaxies and are invaluable tools to investigate the physics and evolution of galaxies.
Regression Models For Multivariate Count Data.
Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei
2017-01-01
Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data.
A short note on multivariate dependence modeling
Czech Academy of Sciences Publication Activity Database
Bína, V.; Jiroušek, Radim
2013-01-01
Roč. 49, č. 3 (2013), s. 420-432 ISSN 0023-5954 Grant - others:GA ČR(CZ) GAP403/12/2175 Program:GA Institutional support: RVO:67985556 Keywords : multivariate distribution * dependence * copula Subject RIV: IN - Informatics, Computer Science Impact factor: 0.563, year: 2013 http://library.utia.cas.cz/separaty/2014/MTR/jirousek-0427848.pdf
Multivariate fractional Poisson processes and compound sums
Beghin, Luisa; Macci, Claudio
2015-01-01
In this paper we present multivariate space-time fractional Poisson processes by considering common random time-changes of a (finite-dimensional) vector of independent classical (non-fractional) Poisson processes. In some cases we also consider compound processes. We obtain some equations in terms of some suitable fractional derivatives and fractional difference operators, which provides the extension of known equations for the univariate processes.
On Multivariate Methods in Robust Econometrics
Czech Academy of Sciences Publication Activity Database
Kalina, Jan
2012-01-01
Roč. 21, č. 1 (2012), s. 69-82 ISSN 1210-0455 R&D Projects: GA MŠk(CZ) 1M06014 Institutional research plan: CEZ:AV0Z10300504 Keywords : least weighted squares * heteroscedasticity * multivariate statistics * model selection * diagnostics * computational aspects Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.561, year: 2012 http://www.vse.cz/pep/abstrakt.php?IDcl=411
Precision Index in the Multivariate Context
Czech Academy of Sciences Publication Activity Database
Šiman, Miroslav
2014-01-01
Roč. 43, č. 2 (2014), s. 377-387 ISSN 0361-0926 R&D Projects: GA MŠk(CZ) 1M06047 Institutional support: RVO:67985556 Keywords : data depth * multivariate quantile * process capability index * precision index * regression quantile Subject RIV: BA - General Mathematics Impact factor: 0.274, year: 2014 http://library.utia.cas.cz/separaty/2014/SI/siman-0425059.pdf
The evolution of multivariate maternal effects.
Directory of Open Access Journals (Sweden)
Bram Kuijper
2014-04-01
Full Text Available There is a growing interest in predicting the social and ecological contexts that favor the evolution of maternal effects. Most predictions focus, however, on maternal effects that affect only a single character, whereas the evolution of maternal effects is poorly understood in the presence of suites of interacting traits. To overcome this, we simulate the evolution of multivariate maternal effects (captured by the matrix M in a fluctuating environment. We find that the rate of environmental fluctuations has a substantial effect on the properties of M: in slowly changing environments, offspring are selected to have a multivariate phenotype roughly similar to the maternal phenotype, so that M is characterized by positive dominant eigenvalues; by contrast, rapidly changing environments favor Ms with dominant eigenvalues that are negative, as offspring favor a phenotype which substantially differs from the maternal phenotype. Moreover, when fluctuating selection on one maternal character is temporally delayed relative to selection on other traits, we find a striking pattern of cross-trait maternal effects in which maternal characters influence not only the same character in offspring, but also other offspring characters. Additionally, when selection on one character contains more stochastic noise relative to selection on other traits, large cross-trait maternal effects evolve from those maternal traits that experience the smallest amounts of noise. The presence of these cross-trait maternal effects shows that individual maternal effects cannot be studied in isolation, and that their study in a multivariate context may provide important insights about the nature of past selection. Our results call for more studies that measure multivariate maternal effects in wild populations.
The evolution of multivariate maternal effects.
Kuijper, Bram; Johnstone, Rufus A; Townley, Stuart
2014-04-01
There is a growing interest in predicting the social and ecological contexts that favor the evolution of maternal effects. Most predictions focus, however, on maternal effects that affect only a single character, whereas the evolution of maternal effects is poorly understood in the presence of suites of interacting traits. To overcome this, we simulate the evolution of multivariate maternal effects (captured by the matrix M) in a fluctuating environment. We find that the rate of environmental fluctuations has a substantial effect on the properties of M: in slowly changing environments, offspring are selected to have a multivariate phenotype roughly similar to the maternal phenotype, so that M is characterized by positive dominant eigenvalues; by contrast, rapidly changing environments favor Ms with dominant eigenvalues that are negative, as offspring favor a phenotype which substantially differs from the maternal phenotype. Moreover, when fluctuating selection on one maternal character is temporally delayed relative to selection on other traits, we find a striking pattern of cross-trait maternal effects in which maternal characters influence not only the same character in offspring, but also other offspring characters. Additionally, when selection on one character contains more stochastic noise relative to selection on other traits, large cross-trait maternal effects evolve from those maternal traits that experience the smallest amounts of noise. The presence of these cross-trait maternal effects shows that individual maternal effects cannot be studied in isolation, and that their study in a multivariate context may provide important insights about the nature of past selection. Our results call for more studies that measure multivariate maternal effects in wild populations.
Multivariate statistical assessment of coal properties
Czech Academy of Sciences Publication Activity Database
Klika, Z.; Serenčíšová, J.; Kožušníková, Alena; Kolomazník, I.; Študentová, S.; Vontorová, J.
2014-01-01
Roč. 128, č. 128 (2014), s. 119-127 ISSN 0378-3820 R&D Projects: GA MŠk ED2.1.00/03.0082 Institutional support: RVO:68145535 Keywords : coal properties * structural,chemical and petrographical properties * multivariate statistics Subject RIV: DH - Mining, incl. Coal Mining Impact factor: 3.352, year: 2014 http://dx.doi.org/10.1016/j.fuproc.2014.06.029
Preliminary Multivariable Cost Model for Space Telescopes
Stahl, H. Philip
2010-01-01
Parametric cost models are routinely used to plan missions, compare concepts and justify technology investments. Previously, the authors published two single variable cost models based on 19 flight missions. The current paper presents the development of a multi-variable space telescopes cost model. The validity of previously published models are tested. Cost estimating relationships which are and are not significant cost drivers are identified. And, interrelationships between variables are explored
Modeling Covariance Breakdowns in Multivariate GARCH
Jin, Xin; Maheu, John M
2014-01-01
This paper proposes a flexible way of modeling dynamic heterogeneous covariance breakdowns in multivariate GARCH (MGARCH) models. During periods of normal market activity, volatility dynamics are governed by an MGARCH specification. A covariance breakdown is any significant temporary deviation of the conditional covariance matrix from its implied MGARCH dynamics. This is captured through a flexible stochastic component that allows for changes in the conditional variances, covariances and impl...
Multivariate NIR studies of seed-water interaction in Scots Pine Seeds (Pinus sylvestris L.)
Lestander, Torbjörn
2003-01-01
This thesis describes seed-water interaction using near infrared (NIR) spectroscopy, multivariate regression models and Scots pine seeds. The presented research covers classification of seed viability, prediction of seed moisture content, selection of NIR wavelengths and interpretation of seed-water interaction modelled and analysed by principal component analysis, ordinary least squares (OLS), partial least squares (PLS), bi-orthogonal least squares (BPLS) and genetic algorithms. The potenti...
Bayesian Inference of a Multivariate Regression Model
Directory of Open Access Journals (Sweden)
Marick S. Sinay
2014-01-01
Full Text Available We explore Bayesian inference of a multivariate linear regression model with use of a flexible prior for the covariance structure. The commonly adopted Bayesian setup involves the conjugate prior, multivariate normal distribution for the regression coefficients and inverse Wishart specification for the covariance matrix. Here we depart from this approach and propose a novel Bayesian estimator for the covariance. A multivariate normal prior for the unique elements of the matrix logarithm of the covariance matrix is considered. Such structure allows for a richer class of prior distributions for the covariance, with respect to strength of beliefs in prior location hyperparameters, as well as the added ability, to model potential correlation amongst the covariance structure. The posterior moments of all relevant parameters of interest are calculated based upon numerical results via a Markov chain Monte Carlo procedure. The Metropolis-Hastings-within-Gibbs algorithm is invoked to account for the construction of a proposal density that closely matches the shape of the target posterior distribution. As an application of the proposed technique, we investigate a multiple regression based upon the 1980 High School and Beyond Survey.
DEFF Research Database (Denmark)
Bladt, Mogens; Nielsen, Bo Friis
2012-01-01
Laplace transform. In a longer perspective stochastic and statistical analysis for MVME will in particular apply to any of the previously defined distributions. Multivariate gamma distributions have been used in a variety of fields like hydrology, [11], [10], [6], space (wind modeling) [9] reliability [3......Numerous definitions of multivariate exponential and gamma distributions can be retrieved from the literature [4]. These distribtuions belong to the class of Multivariate Matrix-- Exponetial Distributions (MVME) whenever their joint Laplace transform is a rational function. The majority...... of these distributions further belongs to an important subclass of MVME distributions [5, 1] where the multivariate random vector can be interpreted as a number of simultaneously collected rewards during sojourns in a the states of a Markov chain with one absorbing state, the rest of the states being transient. We...
AN APPLICATION OF FUNCTIONAL MULTIVARIATE REGRESSION MODEL TO MULTICLASS CLASSIFICATION
Krzyśko, Mirosław; Smaga, Łukasz
2017-01-01
In this paper, the scale response functional multivariate regression model is considered. By using the basis functions representation of functional predictors and regression coefficients, this model is rewritten as a multivariate regression model. This representation of the functional multivariate regression model is used for multiclass classification for multivariate functional data. Computational experiments performed on real labelled data sets demonstrate the effectiveness of the proposed ...
Kim, Yang-Soo; Kim, Sung-Eun; Bae, Sung-Ho; Lee, Hyo-Jin; Jee, Won-Hee; Park, Chang Kyun
2017-07-01
The purpose of this study was to analyse the natural course of symptomatic full-thickness and partial-thickness rotator cuff tears treated non-operatively and to identify risk factors affecting tear enlargement. One hundred and twenty-two patients who received non-surgical treatment for a partial- or full-thickness supraspinatus tear were included in this study. All rotator cuff tears were diagnosed with magnetic resonance imaging (MRI), and the same modality was used for follow-up studies. Follow-up MRI was performed after at least a 6-month interval. We evaluated the correlation between tear enlargement and follow-up duration. Eleven risk factors were analysed by both univariate and multivariate analyses to identify factors that affect enlargement of rotator cuff tears. The mean follow-up period was 24.4 ± 19.5 months. Out of 122 patients, 34 (27.9%) patients had an initial full-thickness tear and 88 (72.1%) patients had a partial-thickness tear. Considering all patients together, tear size increased in 51/122 (41.8%) patients, was unchanged in 65/122 (53.3%) patients, and decreased in 6/122 (4.9%) patients. Tear size increased for 28/34 (82.4%) patients with full-thickness tears and 23/88 (26.1%) patients with partial-thickness tears. From the two groups which were followed over 12 months, a higher rate of enlargement was observed in full-thickness tears than in partial-thickness tears (6-12 months, n.s.; 12-24 months, P = 0.002; over 24 months, P rotator cuff tears and 23/88 (26.1%) of symptomatic partial-thickness tears increased in size over a follow-up period of 6-100 months. Full-thickness tears showed a higher rate of enlargement than partial-thickness tears regardless of the follow-up duration. Univariate and multivariate analyses suggested that full-thickness tear was the most reliable risk factor for tear enlargement. The clinical relevance of these observations is that full-thickness rotator cuff tears treated conservatively should be
Aggregation-cokriging for highly multivariate spatial data
Furrer, R.; Genton, M. G.
2011-01-01
Best linear unbiased prediction of spatially correlated multivariate random processes, often called cokriging in geostatistics, requires the solution of a large linear system based on the covariance and cross-covariance matrix of the observations. For many problems of practical interest, it is impossible to solve the linear system with direct methods. We propose an efficient linear unbiased predictor based on a linear aggregation of the covariables. The primary variable together with this single meta-covariable is used to perform cokriging. We discuss the optimality of the approach under different covariance structures, and use it to create reanalysis type high-resolution historical temperature fields. © 2011 Biometrika Trust.
Aggregation-cokriging for highly multivariate spatial data
Furrer, R.
2011-08-26
Best linear unbiased prediction of spatially correlated multivariate random processes, often called cokriging in geostatistics, requires the solution of a large linear system based on the covariance and cross-covariance matrix of the observations. For many problems of practical interest, it is impossible to solve the linear system with direct methods. We propose an efficient linear unbiased predictor based on a linear aggregation of the covariables. The primary variable together with this single meta-covariable is used to perform cokriging. We discuss the optimality of the approach under different covariance structures, and use it to create reanalysis type high-resolution historical temperature fields. © 2011 Biometrika Trust.
Partially composite Higgs models
DEFF Research Database (Denmark)
Alanne, Tommi; Buarque Franzosi, Diogo; Frandsen, Mads T.
2018-01-01
We study the phenomenology of partially composite-Higgs models where electroweak symmetry breaking is dynamically induced, and the Higgs is a mixture of a composite and an elementary state. The models considered have explicit realizations in terms of gauge-Yukawa theories with new strongly...... interacting fermions coupled to elementary scalars and allow for a very SM-like Higgs state. We study constraints on their parameter spaces from vacuum stability and perturbativity as well as from LHC results and find that requiring vacuum stability up to the compositeness scale already imposes relevant...... constraints. A small part of parameter space around the classically conformal limit is stable up to the Planck scale. This is however already strongly disfavored by LHC results. in different limits, the models realize both (partially) composite-Higgs and (bosonic) technicolor models and a dynamical extension...
Empirical Bayes Approaches to Multivariate Fuzzy Partitions.
Woodbury, Max A.; Manton, Kenneth G.
1991-01-01
An empirical Bayes-maximum likelihood estimation procedure is presented for the application of fuzzy partition models in describing high dimensional discrete response data. The model describes individuals in terms of partial membership in multiple latent categories that represent bounded discrete spaces. (SLD)
Hennessy, M J; Binnie, C D
2000-01-01
To establish the incidence and symptoms of partial seizures in a cohort of patients investigated on account of known sensitivity to intermittent photic stimulation and/or precipitation of seizures by environmental visual stimuli such as television (TV) screens or computer monitors. We report 43 consecutive patients with epilepsy, who had exhibited a significant EEG photoparoxysmal response or who had seizures precipitated by environmental visual stimuli and underwent detailed assessment of their photosensitivity in the EEG laboratory, during which all were questioned concerning their ictal symptoms. All patients were considered on clinical grounds to have an idiopathic epilepsy syndrome. Twenty-eight (65%) patients reported visually precipitated attacks occurring initially with maintained consciousness, in some instances evolving to a period of confusion or to a secondarily generalized seizure. Visual symptoms were most commonly reported and included positive symptoms such as coloured circles or spots, but also blindness and subjective symptoms such as "eyes going funny." Other symptoms described included nonspecific cephalic sensations, deja-vu, auditory hallucinations, nausea, and vomiting. No patient reported any clear spontaneous partial seizures, and there were no grounds for supposing that any had partial epilepsy excepting the ictal phenomenology of some or all of the visually induced attacks. These findings provide clinical support for the physiological studies that indicate that the trigger mechanism for human photosensitivity involves binocularly innervated cells located in the visual cortex. Thus the visual cortex is the seat of the primary epileptogenic process, and the photically triggered discharges and seizures may be regarded as partial with secondary generalization.
Arthroscopic partial medial meniscectomy
Directory of Open Access Journals (Sweden)
Dašić Žarko
2011-01-01
Full Text Available Background/Aim. Meniscal injuries are common in professional or recreational sports as well as in daily activities. If meniscal lesions lead to physical impairment they usually require surgical treatment. Arthroscopic treatment of meniscal injuries is one of the most often performed orthopedic operative procedures. Methods. The study analyzed the results of arthroscopic partial medial meniscectomy in 213 patients in a 24-month period, from 2006, to 2008. Results. In our series of arthroscopically treated medial meniscus tears we noted 78 (36.62% vertical complete bucket handle lesions, 19 (8.92% vertical incomplete lesions, 18 (8.45% longitudinal tears, 35 (16.43% oblique tears, 18 (8.45% complex degenerative lesions, 17 (7.98% radial lesions and 28 (13.14% horisontal lesions. Mean preoperative International Knee Documentation Committee (IKDC score was 49.81%, 1 month after the arthroscopic partial medial meniscectomy the mean IKDC score was 84.08%, and 6 months after mean IKDC score was 90.36%. Six months after the procedure 197 (92.49% of patients had good or excellent subjective postoperative clinical outcomes, while 14 (6.57% patients subjectively did not notice a significant improvement after the intervention, and 2 (0.93% patients had no subjective improvement after the partial medial meniscectomy at all. Conclusion. Arthroscopic partial medial meniscetomy is minimally invasive diagnostic and therapeutic procedure and in well selected cases is a method of choice for treatment of medial meniscus injuries when repair techniques are not a viable option. It has small rate of complications, low morbidity and fast rehabilitation.
Hierarchical partial order ranking
International Nuclear Information System (INIS)
Carlsen, Lars
2008-01-01
Assessing the potential impact on environmental and human health from the production and use of chemicals or from polluted sites involves a multi-criteria evaluation scheme. A priori several parameters are to address, e.g., production tonnage, specific release scenarios, geographical and site-specific factors in addition to various substance dependent parameters. Further socio-economic factors may be taken into consideration. The number of parameters to be included may well appear to be prohibitive for developing a sensible model. The study introduces hierarchical partial order ranking (HPOR) that remedies this problem. By HPOR the original parameters are initially grouped based on their mutual connection and a set of meta-descriptors is derived representing the ranking corresponding to the single groups of descriptors, respectively. A second partial order ranking is carried out based on the meta-descriptors, the final ranking being disclosed though average ranks. An illustrative example on the prioritisation of polluted sites is given. - Hierarchical partial order ranking of polluted sites has been developed for prioritization based on a large number of parameters
Nogawa, Toshifumi; Takayama, Yoshiyuki; Ishida, Keita; Yokoyama, Atsuro
The aim of this study was to compare masticatory performance, occlusal force, and oral health-related quality of life (OHRQoL) in patients with mandibular distal-extension edentulism between those with implant-supported fixed prostheses (ISFPs) and those with removable partial dentures (RPDs), and to evaluate relationships among them. Subjects were recruited from patients using ISFPs or RPDs for mandibular distal-extension edentulism. Masticatory performance was evaluated based on the glucose extracted from chewed gummy jelly. Occlusal force was measured with a pressure-sensitive sheet, and data were subjected to computer analysis. The Japanese version of the Oral Health Impact Profile (OHIP-J) was used to evaluate OHRQoL. The masticatory performance, occlusal force, and OHIP-J scores of the ISFP and RPD groups were compared using the Wilcoxon rank-sum test. The relationships among the variables were analyzed using the Spearman rank correlation coefficient test. Multivariate logistic regression analysis was employed with the OHIP-J score as a dependent variable. Nineteen patients with ISFPs and 25 patients with RPDs participated in this study. No significant difference was observed between the two groups with regard to masticatory performance and occlusal force. The OHIP-J score was significantly lower in the ISFP group than in the RPD group. The OHIP-J score had no significant correlation with masticatory performance, but was significantly correlated with occlusal force and the prosthetic method. Multivariate logistic regression analysis showed that younger age, RPDs, and lower occlusal force were significantly associated with a higher OHIP-J summary score. The present results suggest that the difference in masticatory performance and occlusal force between ISFPs and RPDs is small, but ISFPs are superior to RPDs with regard to OHRQoL in patients with mandibular distal-extension edentulism. In addition, there appears to be a slight correlation between the OHIP
Time varying, multivariate volume data reduction
Energy Technology Data Exchange (ETDEWEB)
Ahrens, James P [Los Alamos National Laboratory; Fout, Nathaniel [UC DAVIS; Ma, Kwan - Liu [UC DAVIS
2010-01-01
Large-scale supercomputing is revolutionizing the way science is conducted. A growing challenge, however, is understanding the massive quantities of data produced by large-scale simulations. The data, typically time-varying, multivariate, and volumetric, can occupy from hundreds of gigabytes to several terabytes of storage space. Transferring and processing volume data of such sizes is prohibitively expensive and resource intensive. Although it may not be possible to entirely alleviate these problems, data compression should be considered as part of a viable solution, especially when the primary means of data analysis is volume rendering. In this paper we present our study of multivariate compression, which exploits correlations among related variables, for volume rendering. Two configurations for multidimensional compression based on vector quantization are examined. We emphasize quality reconstruction and interactive rendering, which leads us to a solution using graphics hardware to perform on-the-fly decompression during rendering. In this paper we present a solution which addresses the need for data reduction in large supercomputing environments where data resulting from simulations occupies tremendous amounts of storage. Our solution employs a lossy encoding scheme to acrueve data reduction with several options in terms of rate-distortion behavior. We focus on encoding of multiple variables together, with optional compression in space and time. The compressed volumes can be rendered directly with commodity graphics cards at interactive frame rates and rendering quality similar to that of static volume renderers. Compression results using a multivariate time-varying data set indicate that encoding multiple variables results in acceptable performance in the case of spatial and temporal encoding as compared to independent compression of variables. The relative performance of spatial vs. temporal compression is data dependent, although temporal compression has the
Multivariate linear models and repeated measurements revisited
DEFF Research Database (Denmark)
Dalgaard, Peter
2009-01-01
Methods for generalized analysis of variance based on multivariate normal theory have been known for many years. In a repeated measurements context, it is most often of interest to consider transformed responses, typically within-subject contrasts or averages. Efficiency considerations leads...... to sphericity assumptions, use of F tests and the Greenhouse-Geisser and Huynh-Feldt adjustments to compensate for deviations from sphericity. During a recent implementation of such methods in the R language, the general structure of such transformations was reconsidered, leading to a flexible specification...
New multivariable capabilities of the INCA program
Bauer, Frank H.; Downing, John P.; Thorpe, Christopher J.
1989-01-01
The INteractive Controls Analysis (INCA) program was developed at NASA's Goddard Space Flight Center to provide a user friendly, efficient environment for the design and analysis of control systems, specifically spacecraft control systems. Since its inception, INCA has found extensive use in the design, development, and analysis of control systems for spacecraft, instruments, robotics, and pointing systems. The (INCA) program was initially developed as a comprehensive classical design analysis tool for small and large order control systems. The latest version of INCA, expected to be released in February of 1990, was expanded to include the capability to perform multivariable controls analysis and design.
Multivariate Analysis for the Processing of Signals
Directory of Open Access Journals (Sweden)
Beattie J.R.
2014-01-01
Full Text Available Real-world experiments are becoming increasingly more complex, needing techniques capable of tracking this complexity. Signal based measurements are often used to capture this complexity, where a signal is a record of a sample’s response to a parameter (e.g. time, displacement, voltage, wavelength that is varied over a range of values. In signals the responses at each value of the varied parameter are related to each other, depending on the composition or state sample being measured. Since signals contain multiple information points, they have rich information content but are generally complex to comprehend. Multivariate Analysis (MA has profoundly transformed their analysis by allowing gross simplification of the tangled web of variation. In addition MA has also provided the advantage of being much more robust to the influence of noise than univariate methods of analysis. In recent years, there has been a growing awareness that the nature of the multivariate methods allows exploitation of its benefits for purposes other than data analysis, such as pre-processing of signals with the aim of eliminating irrelevant variations prior to analysis of the signal of interest. It has been shown that exploiting multivariate data reduction in an appropriate way can allow high fidelity denoising (removal of irreproducible non-signals, consistent and reproducible noise-insensitive correction of baseline distortions (removal of reproducible non-signals, accurate elimination of interfering signals (removal of reproducible but unwanted signals and the standardisation of signal amplitude fluctuations. At present, the field is relatively small but the possibilities for much wider application are considerable. Where signal properties are suitable for MA (such as the signal being stationary along the x-axis, these signal based corrections have the potential to be highly reproducible, and highly adaptable and are applicable in situations where the data is noisy or
Multivariable adaptive control of bio process
Energy Technology Data Exchange (ETDEWEB)
Maher, M.; Bahhou, B.; Roux, G. [Centre National de la Recherche Scientifique (CNRS), 31 - Toulouse (France); Maher, M. [Faculte des Sciences, Rabat (Morocco). Lab. de Physique
1995-12-31
This paper presents a multivariable adaptive control of a continuous-flow fermentation process for the alcohol production. The linear quadratic control strategy is used for the regulation of substrate and ethanol concentrations in the bioreactor. The control inputs are the dilution rate and the influent substrate concentration. A robust identification algorithm is used for the on-line estimation of linear MIMO model`s parameters. Experimental results of a pilot-plant fermenter application are reported and show the control performances. (authors) 8 refs.
Topics in multivariate approximation and interpolation
Jetter, Kurt
2005-01-01
This book is a collection of eleven articles, written by leading experts and dealing with special topics in Multivariate Approximation and Interpolation. The material discussed here has far-reaching applications in many areas of Applied Mathematics, such as in Computer Aided Geometric Design, in Mathematical Modelling, in Signal and Image Processing and in Machine Learning, to mention a few. The book aims at giving a comprehensive information leading the reader from the fundamental notions and results of each field to the forefront of research. It is an ideal and up-to-date introduction for gr
Partial Least Squares tutorial for analyzing neuroimaging data
Directory of Open Access Journals (Sweden)
Patricia Van Roon
2014-09-01
Full Text Available Partial least squares (PLS has become a respected and meaningful soft modeling analysis technique that can be applied to very large datasets where the number of factors or variables is greater than the number of observations. Current biometric studies (e.g., eye movements, EKG, body movements, EEG are often of this nature. PLS eliminates the multiple linear regression issues of over-fitting data by finding a few underlying or latent variables (factors that account for most of the variation in the data. In real-world applications, where linear models do not always apply, PLS can model the non-linear relationship well. This tutorial introduces two PLS methods, PLS Correlation (PLSC and PLS Regression (PLSR and their applications in data analysis which are illustrated with neuroimaging examples. Both methods provide straightforward and comprehensible techniques for determining and modeling relationships between two multivariate data blocks by finding latent variables that best describes the relationships. In the examples, the PLSC will analyze the relationship between neuroimaging data such as Event-Related Potential (ERP amplitude averages from different locations on the scalp with their corresponding behavioural data. Using the same data, the PLSR will be used to model the relationship between neuroimaging and behavioural data. This model will be able to predict future behaviour solely from available neuroimaging data. To find latent variables, Singular Value Decomposition (SVD for PLSC and Non-linear Iterative PArtial Least Squares (NIPALS for PLSR are implemented in this tutorial. SVD decomposes the large data block into three manageable matrices containing a diagonal set of singular values, as well as left and right singular vectors. For PLSR, NIPALS algorithms are used because it provides amore precise estimation of the latent variables. Mathematica notebooks are provided for each PLS method with clearly labeled sections and subsections. The
Multivariate and Spatial Visualisation of Archaeological Assemblages
Directory of Open Access Journals (Sweden)
Martin Sterry
2018-05-01
Full Text Available Multivariate analyses, in particular correspondence analysis (CA, have become a standard exploratory tool for analysing and interpreting variance in archaeological assemblages. While they have greatly helped analysts, they unfortunately remain abstract to the viewer, all the more so if the viewer has little or no experience with multivariate statistics. A second issue with these analyses can arise from the detachment of archaeological material from its geo-referenced location and typically considered only in terms of arbitrary classifications (e.g. North Europe, Central Europe, South Europe instead of the full range of local conditions (e.g. proximity to other assemblages, relationships with other spatial phenomena. This article addresses these issues by presenting a novel method for spatially visualising CA so that these analyses can be interpreted intuitively. The method works by transforming the resultant bi-plots of the CA into colour maps using the HSV colour model, in which the similarity and difference between assemblages directly corresponds to the similarity and difference of the colours used to display them. Utilising two datasets – ceramics from the excavations of the Roman fortress of Vetera I, and terra sigillata forms collected as part of 'The Samian Project' – the article demonstrates how the method is applied and how it can be used to draw out spatial and temporal trends.
Particulate characterization by PIXE multivariate spectral analysis
International Nuclear Information System (INIS)
Antolak, Arlyn J.; Morse, Daniel H.; Grant, Patrick G.; Kotula, Paul G.; Doyle, Barney L.; Richardson, Charles B.
2007-01-01
Obtaining particulate compositional maps from scanned PIXE (proton-induced X-ray emission) measurements is extremely difficult due to the complexity of analyzing spectroscopic data collected with low signal-to-noise at each scan point (pixel). Multivariate spectral analysis has the potential to analyze such data sets by reducing the PIXE data to a limited number of physically realizable and easily interpretable components (that include both spectral and image information). We have adapted the AXSIA (automated expert spectral image analysis) program, originally developed by Sandia National Laboratories to quantify electron-excited X-ray spectroscopy data, for this purpose. Samples consisting of particulates with known compositions and sizes were loaded onto Mylar and paper filter substrates and analyzed by scanned micro-PIXE. The data sets were processed by AXSIA and the associated principal component spectral data were quantified by converting the weighting images into concentration maps. The results indicate automated, nonbiased, multivariate statistical analysis is useful for converting very large amounts of data into a smaller, more manageable number of compositional components needed for locating individual particles-of-interest on large area collection media
Peramalan Multivariate untuk Menentukan Harga Emas Global
Directory of Open Access Journals (Sweden)
David Christian
2016-12-01
Full Text Available Gold is one of the most enticing commodities and a very popular way of investing. Gold’s price is allegedly influenced by another factors such as US Dollar, oil’s price, inflation rate, and stock exchange so that its model is not only affected by its value. The aim of this research is to determine the best forecasting model and influencing factors to gold’s price. This research reviews the univariate modeling as a benchmark and comparison to the multivariate one. Univariate time series is modeled using the ARIMA model which indicates that the fluctuation of the gold prices are following the white noise. Gold’s multivariate modeling is built using the Vector Error Correction Model with oil’s price, US Dollar and Dow Jones indices, and inflation rate as its predictors. Research’s result shows that the VECM model has been able to model the gold’s price well and all factors investigated are influencing gold’s price. US Dollar and oil’s price are negatively correlated with gold’s price as the inflation rate is positively correlated. Dow Jones Index is positively correlated with gold’s price only at its first two periods
Network structure of multivariate time series.
Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito
2015-10-21
Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.
Crane cabins' interior space multivariate anthropometric modeling.
Essdai, Ahmed; Spasojević Brkić, Vesna K; Golubović, Tamara; Brkić, Aleksandar; Popović, Vladimir
2018-01-01
Previous research has shown that today's crane cabins fail to meet the needs of a large proportion of operators. Performance and financial losses and effects on safety should not be overlooked as well. The first aim of this survey is to model the crane cabin interior space using up-to-date crane operator anthropometric data and to compare the multivariate and univariate method anthropometric models. The second aim of the paper is to define the crane cabin interior space dimensions that enable anthropometric convenience. To facilitate the cabin design, the anthropometric dimensions of 64 crane operators in the first sample and 19 more in the second sample were collected in Serbia. The multivariate anthropometric models, spanning 95% of the population on the basis of a set of 8 anthropometric dimensions, have been developed. The percentile method was also used on the same set of data. The dimensions of the interior space, necessary for the accommodation of the crane operator, are 1174×1080×1865 mm. The percentiles results for the 5th and 95th model are within the obtained dimensions. The results of this study may prove useful to crane cabin designers in eliminating anthropometric inconsistencies and improving the health of operators, but can also aid in improving the safety, performance and financial results of the companies where crane cabins operate.
MULTIVARIATE ACCOUNTING IN INTERNATIONAL FINANCIAL REPORTING STANDARDS
Directory of Open Access Journals (Sweden)
V. V. IEVDOKYMOV
2017-03-01
Full Text Available The necessity of the research on the basis of the positivist model of scientific knowledge is proved. The value of the conceptual framework in the process of bookkeeping selection is analyzed. The differences of the accounting selection adjustment procedure in US GAAP and IFRS are considered. The role and importance of the qualitative characteristics of financial reporting in the implementation of accounting selection are substantiated. The structure of the qualitative characteristics of financial reporting and their limitations under the Conceptual Framework for the preparation and presentation of financial statements are examined. The correlation between the accounting rules and alternatives adopted in US GAAP and IAS / IFRS is analyzed. The necessity to discuss the issue of the feasibility of «rule-oriented» or «principle-oriented» accounting model in the context of multivariate concept is studied. The authors prove the necessity of the application of institutional theory to solve the problems of accounting opportunism that arises when using the concept of multivariate accounting in International Financial Reporting Standards.
Fuzzy multivariable control of domestic heat pumps
International Nuclear Information System (INIS)
Underwood, C.P.
2015-01-01
Poor control has been identified as one of the reasons why recent field trials of domestic heat pumps in the UK have produced disappointing results. Most of the technology in use today uses a thermostatically-controlled fixed speed compressor with a mechanical expansion device. This article investigates improved control of these heat pumps through the design and evaluation of a new multivariable fuzzy logic control system utilising a variable speed compressor drive with capacity control linked through to evaporator superheat control. A new dynamic thermal model of a domestic heat pump validated using experimental data forms the basis of the work. The proposed control system is evaluated using median and extreme daily heating demand profiles for a typical UK house compared with a basic thermostatically-controlled alternative. Results show good tracking of the heating temperature and superheat control variables, reduced cycling and an improvement in performance averaging 20%. - Highlights: • A new dynamic model of a domestic heat pump is developed and validated. • A new multivariable fuzzy logic heat pump control system is developed/reported. • The fuzzy controller regulates both plant capacity and evaporator superheat degree. • Thermal buffer storage is also considered as well as compressor cycling. • The new controller shows good variable tracking and a reduction in energy of 20%.
Estimating uncertainty in multivariate responses to selection.
Stinchcombe, John R; Simonsen, Anna K; Blows, Mark W
2014-04-01
Predicting the responses to natural selection is one of the key goals of evolutionary biology. Two of the challenges in fulfilling this goal have been the realization that many estimates of natural selection might be highly biased by environmentally induced covariances between traits and fitness, and that many estimated responses to selection do not incorporate or report uncertainty in the estimates. Here we describe the application of a framework that blends the merits of the Robertson-Price Identity approach and the multivariate breeder's equation to address these challenges. The approach allows genetic covariance matrices, selection differentials, selection gradients, and responses to selection to be estimated without environmentally induced bias, direct and indirect selection and responses to selection to be distinguished, and if implemented in a Bayesian-MCMC framework, statistically robust estimates of uncertainty on all of these parameters to be made. We illustrate our approach with a worked example of previously published data. More generally, we suggest that applying both the Robertson-Price Identity and the multivariate breeder's equation will facilitate hypothesis testing about natural selection, genetic constraints, and evolutionary responses. © 2013 The Author(s). Evolution © 2013 The Society for the Study of Evolution.
Partially ordered algebraic systems
Fuchs, Laszlo
2011-01-01
Originally published in an important series of books on pure and applied mathematics, this monograph by a distinguished mathematician explores a high-level area in algebra. It constitutes the first systematic summary of research concerning partially ordered groups, semigroups, rings, and fields. The self-contained treatment features numerous problems, complete proofs, a detailed bibliography, and indexes. It presumes some knowledge of abstract algebra, providing necessary background and references where appropriate. This inexpensive edition of a hard-to-find systematic survey will fill a gap i
International Nuclear Information System (INIS)
Sprung, D.W.L.
1975-01-01
This paper is a brief review of those aspects of the effective interaction problem that can be grouped under the heading of infinite partial summations of the perturbation series. After a brief mention of the classic examples of infinite summations, the author turns to the effective interaction problem for two extra core particles. Their direct interaction is summed to produce the G matrix, while their indirect interaction through the core is summed in a variety of ways under the heading of core polarization. (orig./WL) [de
Chen, Herman Z. Q.; Kitaev, Sergey; Mütze, Torsten; Sun, Brian Y.
2016-01-01
A universal word for a finite alphabet $A$ and some integer $n\\geq 1$ is a word over $A$ such that every word in $A^n$ appears exactly once as a subword (cyclically or linearly). It is well-known and easy to prove that universal words exist for any $A$ and $n$. In this work we initiate the systematic study of universal partial words. These are words that in addition to the letters from $A$ may contain an arbitrary number of occurrences of a special `joker' symbol $\\Diamond\
Partial differential equations
Agranovich, M S
2002-01-01
Mark Vishik's Partial Differential Equations seminar held at Moscow State University was one of the world's leading seminars in PDEs for over 40 years. This book celebrates Vishik's eightieth birthday. It comprises new results and survey papers written by many renowned specialists who actively participated over the years in Vishik's seminars. Contributions include original developments and methods in PDEs and related fields, such as mathematical physics, tomography, and symplectic geometry. Papers discuss linear and nonlinear equations, particularly linear elliptic problems in angles and gener
Partial differential equations
Levine, Harold
1997-01-01
The subject matter, partial differential equations (PDEs), has a long history (dating from the 18th century) and an active contemporary phase. An early phase (with a separate focus on taut string vibrations and heat flow through solid bodies) stimulated developments of great importance for mathematical analysis, such as a wider concept of functions and integration and the existence of trigonometric or Fourier series representations. The direct relevance of PDEs to all manner of mathematical, physical and technical problems continues. This book presents a reasonably broad introductory account of the subject, with due regard for analytical detail, applications and historical matters.
Partial differential equations
Sloan, D; Süli, E
2001-01-01
/homepage/sac/cam/na2000/index.html7-Volume Set now available at special set price ! Over the second half of the 20th century the subject area loosely referred to as numerical analysis of partial differential equations (PDEs) has undergone unprecedented development. At its practical end, the vigorous growth and steady diversification of the field were stimulated by the demand for accurate and reliable tools for computational modelling in physical sciences and engineering, and by the rapid development of computer hardware and architecture. At the more theoretical end, the analytical insight in
Elliptic partial differential equations
Han, Qing
2011-01-01
Elliptic Partial Differential Equations by Qing Han and FangHua Lin is one of the best textbooks I know. It is the perfect introduction to PDE. In 150 pages or so it covers an amazing amount of wonderful and extraordinary useful material. I have used it as a textbook at both graduate and undergraduate levels which is possible since it only requires very little background material yet it covers an enormous amount of material. In my opinion it is a must read for all interested in analysis and geometry, and for all of my own PhD students it is indeed just that. I cannot say enough good things abo
DEFF Research Database (Denmark)
Darkner, Sune; Sporring, Jon
2011-01-01
Mutual Information (MI) and normalized mutual information (NMI) are popular choices as similarity measure for multimodal image registration. Presently, one of two approaches is often used for estimating these measures: The Parzen Window (PW) and the Generalized Partial Volume (GPV). Their theoret...... of view as well as w.r.t. computational complexity. Finally, we present algorithms for both approaches for NMI which is comparable in speed to Sum of Squared Differences (SSD), and we illustrate the differences between PW and GPV on a number of registration examples....
Unilateral removable partial dentures.
Goodall, W A; Greer, A C; Martin, N
2017-01-27
Removable partial dentures (RPDs) are widely used to replace missing teeth in order to restore both function and aesthetics for the partially dentate patient. Conventional RPD design is frequently bilateral and consists of a major connector that bridges both sides of the arch. Some patients cannot and will not tolerate such an extensive appliance. For these patients, bridgework may not be a predictable option and it is not always possible to provide implant-retained restorations. This article presents unilateral RPDs as a potential treatment modality for such patients and explores indications and contraindications for their use, including factors relating to patient history, clinical presentation and patient wishes. Through case examples, design, material and fabrication considerations will be discussed. While their use is not widespread, there are a number of patients who benefit from the provision of unilateral RPDs. They are a useful treatment to have in the clinician's armamentarium, but a highly-skilled dental team and a specific patient presentation is required in order for them to be a reasonable and predictable prosthetic option.
Salami, Alireza; Rieckmann, Anna; Fischer, Håkan; Bäckman, Lars
2014-02-01
Functional neuroimaging studies demonstrate age-related differences in recruitment of a large-scale attentional network during interference resolution, especially within dorsolateral prefrontal cortex (DLPFC) and anterior cingulate cortex (ACC). These alterations in functional responses have been frequently observed despite equivalent task performance, suggesting age-related reallocation of neural resources, although direct evidence for a facilitating effect in aging is sparse. We used the multi-source interference task and multivariate partial-least-squares to investigate age-related differences in the neuronal signature of conflict resolution, and their behavioral implications in younger and older adults. There were interference-related increases in activity, involving fronto-parietal and basal ganglia networks that generalized across age. In addition an age-by-task interaction was observed within a distributed network, including DLPFC and ACC, with greater activity during interference in the old. Next, we combined brain-behavior and functional connectivity analyses to investigate whether compensatory brain changes were present in older adults, using DLPFC and ACC as regions of interest (i.e. seed regions). This analysis revealed two networks differentially related to performance across age groups. A structural analysis revealed age-related gray-matter losses in regions facilitating performance in the young, suggesting that functional reorganization may partly reflect structural alterations in aging. Collectively, these findings suggest that age-related structural changes contribute to reductions in the efficient recruitment of a youth-like interference network, which cascades into instantiation of a different network facilitating conflict resolution in elderly people. © 2013. Published by Elsevier Inc. All rights reserved.
Oxidation management of white wines using cyclic voltammetry and multivariate process monitoring.
Martins, Rui C; Oliveira, Raquel; Bento, Fatima; Geraldo, Dulce; Lopes, Vitor V; Guedes de Pinho, Paula; Oliveira, Carla M; Silva Ferreira, Antonio C
2008-12-24
The development of a fingerprinting strategy capable to evaluate the "oxidation status" of white wines based on cyclic voltammetry is proposed here. It is known that the levels of specific antioxidants and redox mechanisms may be evaluated by cyclic voltammetry. This electrochemical technique was applied on two sets of samples. One group was composed of normal aged white wines and a second group obtained from a white wine forced aging protocol with different oxygen, SO(2), pH, and temperature regimens. A study of antioxidant additions, namely ascorbic acid, was also made in order to establish a statistical link between voltammogram fingerprints and chemical antioxidant substances. It was observed that the oxidation curve presented typical features, which enables sample discrimination according to age, oxygen consumption, and antioxidant additions. In fact, it was possible to place the results into four significant orthogonal directions, compressing 99.8% of nonrandom features. Attempts were made to make voltammogram fingerprinting a tool for monitoring oxidation management. For this purpose, a supervised multivariate control chart was developed using a control sample as reference. When white wines are plotted onto the chart, it is possible to monitor the oxidation status and to diagnose the effects of oxygen regimes and antioxidant activity. Finally, quantification of substances implicated in the oxidation process as reagents (antioxidants) and products (off-flavors) was tried using a supervised algorithmic the partial least square regression analysis. Good correlations (r > 0.93) were observed for ascorbic acid, Folin-Ciocalteu index, total SO(2), methional, and phenylacetaldehyde. These results show that cyclic voltammetry fingerprinting can be used to monitor and diagnose the effects of wine oxidation.
DEFF Research Database (Denmark)
Hansen, T.
The purpose of this Ph.D. thesis is twofold: The first purpose is to devise a new method for application of multivariable controllers in boiler control systems in which they act as optional process optimizing extensions to conventional control systems and in such a way that the safety measures...... mentioned, the concept is applicable to new as well as existing plants. The seccond purpose is to suggest specific methods for experimental modelling and multivariable controller design which are possible to use under the conceptual framework, implement them and test them in a boiler application....
Temperature uniformity control in RTP using multivariable adaptive control
Energy Technology Data Exchange (ETDEWEB)
Morales, S.; Dahhou, B.; Dilhac, J.M. [Centre National de la Recherche Scientifique (CNRS), 31 - Toulouse (France); Morales, S.
1995-12-31
In Rapid Thermal Processing (RTP) control of the wafer temperature during all processing to get good trajectory following, together with spatial temperature uniformity, is essential. It is well know as RTP process is nonlinear, classical control laws are not very efficient. In this work, the authors aim at studying the applicability of MIMO (Multiple Inputs Multiple Outputs) adaptive techniques to solve the temperature control problems in RTP. A multivariable linear discrete time CARIMA (Controlled Auto Regressive Integrating Moving Average) model of the highly non-linear process is identified on-line using a robust identification technique. The identified model is used to compute an infinite time LQ (Linear Quadratic) based control law, with a partial state reference model. This reference model smooths the original setpoint sequence, and at the same time gives a tracking capability to the LQ control law. After an experimental open-loop investigation, the results of the application of the adaptive control law are presented. Finally, some comments on the future difficulties and developments of the application of adaptive control in RTP are given. (author) 13 refs.
Models and Inference for Multivariate Spatial Extremes
Vettori, Sabrina
2017-12-07
The development of flexible and interpretable statistical methods is necessary in order to provide appropriate risk assessment measures for extreme events and natural disasters. In this thesis, we address this challenge by contributing to the developing research field of Extreme-Value Theory. We initially study the performance of existing parametric and non-parametric estimators of extremal dependence for multivariate maxima. As the dimensionality increases, non-parametric estimators are more flexible than parametric methods but present some loss in efficiency that we quantify under various scenarios. We introduce a statistical tool which imposes the required shape constraints on non-parametric estimators in high dimensions, significantly improving their performance. Furthermore, by embedding the tree-based max-stable nested logistic distribution in the Bayesian framework, we develop a statistical algorithm that identifies the most likely tree structures representing the data\\'s extremal dependence using the reversible jump Monte Carlo Markov Chain method. A mixture of these trees is then used for uncertainty assessment in prediction through Bayesian model averaging. The computational complexity of full likelihood inference is significantly decreased by deriving a recursive formula for the nested logistic model likelihood. The algorithm performance is verified through simulation experiments which also compare different likelihood procedures. Finally, we extend the nested logistic representation to the spatial framework in order to jointly model multivariate variables collected across a spatial region. This situation emerges often in environmental applications but is not often considered in the current literature. Simulation experiments show that the new class of multivariate max-stable processes is able to detect both the cross and inner spatial dependence of a number of extreme variables at a relatively low computational cost, thanks to its Bayesian hierarchical
MIDAS: Regionally linear multivariate discriminative statistical mapping.
Varol, Erdem; Sotiras, Aristeidis; Davatzikos, Christos
2018-07-01
Statistical parametric maps formed via voxel-wise mass-univariate tests, such as the general linear model, are commonly used to test hypotheses about regionally specific effects in neuroimaging cross-sectional studies where each subject is represented by a single image. Despite being informative, these techniques remain limited as they ignore multivariate relationships in the data. Most importantly, the commonly employed local Gaussian smoothing, which is important for accounting for registration errors and making the data follow Gaussian distributions, is usually chosen in an ad hoc fashion. Thus, it is often suboptimal for the task of detecting group differences and correlations with non-imaging variables. Information mapping techniques, such as searchlight, which use pattern classifiers to exploit multivariate information and obtain more powerful statistical maps, have become increasingly popular in recent years. However, existing methods may lead to important interpretation errors in practice (i.e., misidentifying a cluster as informative, or failing to detect truly informative voxels), while often being computationally expensive. To address these issues, we introduce a novel efficient multivariate statistical framework for cross-sectional studies, termed MIDAS, seeking highly sensitive and specific voxel-wise brain maps, while leveraging the power of regional discriminant analysis. In MIDAS, locally linear discriminative learning is applied to estimate the pattern that best discriminates between two groups, or predicts a variable of interest. This pattern is equivalent to local filtering by an optimal kernel whose coefficients are the weights of the linear discriminant. By composing information from all neighborhoods that contain a given voxel, MIDAS produces a statistic that collectively reflects the contribution of the voxel to the regional classifiers as well as the discriminative power of the classifiers. Critically, MIDAS efficiently assesses the
Tutorial on Online Partial Evaluation
Directory of Open Access Journals (Sweden)
William R. Cook
2011-09-01
Full Text Available This paper is a short tutorial introduction to online partial evaluation. We show how to write a simple online partial evaluator for a simple, pure, first-order, functional programming language. In particular, we show that the partial evaluator can be derived as a variation on a compositionally defined interpreter. We demonstrate the use of the resulting partial evaluator for program optimization in the context of model-driven development.
Multivariate Non-Symmetric Stochastic Models for Spatial Dependence Models
Haslauer, C. P.; Bárdossy, A.
2017-12-01
A copula based multivariate framework allows more flexibility to describe different kind of dependences than what is possible using models relying on the confining assumption of symmetric Gaussian models: different quantiles can be modelled with a different degree of dependence; it will be demonstrated how this can be expected given process understanding. maximum likelihood based multivariate quantitative parameter estimation yields stable and reliable results; not only improved results in cross-validation based measures of uncertainty are obtained but also a more realistic spatial structure of uncertainty compared to second order models of dependence; as much information as is available is included in the parameter estimation: incorporation of censored measurements (e.g., below detection limit, or ones that are above the sensitive range of the measurement device) yield to more realistic spatial models; the proportion of true zeros can be jointly estimated with and distinguished from censored measurements which allow estimates about the age of a contaminant in the system; secondary information (categorical and on the rational scale) has been used to improve the estimation of the primary variable; These copula based multivariate statistical techniques are demonstrated based on hydraulic conductivity observations at the Borden (Canada) site, the MADE site (USA), and a large regional groundwater quality data-set in south-west Germany. Fields of spatially distributed K were simulated with identical marginal simulation, identical second order spatial moments, yet substantially differing solute transport characteristics when numerical tracer tests were performed. A statistical methodology is shown that allows the delineation of a boundary layer separating homogenous parts of a spatial data-set. The effects of this boundary layer (macro structure) and the spatial dependence of K (micro structure) on solute transport behaviour is shown.
Type-Directed Partial Evaluation
DEFF Research Database (Denmark)
Danvy, Olivier
1998-01-01
Type-directed partial evaluation uses a normalization function to achieve partial evaluation. These lecture notes review its background, foundations, practice, and applications. Of specific interest is the modular technique of offline and online type-directed partial evaluation in Standard ML...
Type-Directed Partial Evaluation
DEFF Research Database (Denmark)
Danvy, Olivier
1998-01-01
Type-directed partial evaluation uses a normalization function to achieve partial evaluation. These lecture notes review its background, foundations, practice, and applications. Of specific interest is the modular technique of offline and online type-directed partial evaluation in Standard ML of ...
Classification of adulterated honeys by multivariate analysis.
Amiry, Saber; Esmaiili, Mohsen; Alizadeh, Mohammad
2017-06-01
In this research, honey samples were adulterated with date syrup (DS) and invert sugar syrup (IS) at three concentrations (7%, 15% and 30%). 102 adulterated samples were prepared in six batches with 17 replications for each batch. For each sample, 32 parameters including color indices, rheological, physical, and chemical parameters were determined. To classify the samples, based on type and concentrations of adulterant, a multivariate analysis was applied using principal component analysis (PCA) followed by a linear discriminant analysis (LDA). Then, 21 principal components (PCs) were selected in five sets. Approximately two-thirds were identified correctly using color indices (62.75%) or rheological properties (67.65%). A power discrimination was obtained using physical properties (97.06%), and the best separations were achieved using two sets of chemical properties (set 1: lactone, diastase activity, sucrose - 100%) (set 2: free acidity, HMF, ash - 95%). Copyright © 2016 Elsevier Ltd. All rights reserved.
Multivariate Functional Data Visualization and Outlier Detection
Dai, Wenlin; Genton, Marc G.
2017-01-01
This article proposes a new graphical tool, the magnitude-shape (MS) plot, for visualizing both the magnitude and shape outlyingness of multivariate functional data. The proposed tool builds on the recent notion of functional directional outlyingness, which measures the centrality of functional data by simultaneously considering the level and the direction of their deviation from the central region. The MS-plot intuitively presents not only levels but also directions of magnitude outlyingness on the horizontal axis or plane, and demonstrates shape outlyingness on the vertical axis. A dividing curve or surface is provided to separate non-outlying data from the outliers. Both the simulated data and the practical examples confirm that the MS-plot is superior to existing tools for visualizing centrality and detecting outliers for functional data.
Multivariate Functional Data Visualization and Outlier Detection
Dai, Wenlin
2017-03-19
This article proposes a new graphical tool, the magnitude-shape (MS) plot, for visualizing both the magnitude and shape outlyingness of multivariate functional data. The proposed tool builds on the recent notion of functional directional outlyingness, which measures the centrality of functional data by simultaneously considering the level and the direction of their deviation from the central region. The MS-plot intuitively presents not only levels but also directions of magnitude outlyingness on the horizontal axis or plane, and demonstrates shape outlyingness on the vertical axis. A dividing curve or surface is provided to separate non-outlying data from the outliers. Both the simulated data and the practical examples confirm that the MS-plot is superior to existing tools for visualizing centrality and detecting outliers for functional data.
Lectures in feedback design for multivariable systems
Isidori, Alberto
2017-01-01
This book focuses on methods that relate, in one form or another, to the “small-gain theorem”. It is aimed at readers who are interested in learning methods for the design of feedback laws for linear and nonlinear multivariable systems in the presence of model uncertainties. With worked examples throughout, it includes both introductory material and more advanced topics. Divided into two parts, the first covers relevant aspects of linear-systems theory, the second, nonlinear theory. In order to deepen readers’ understanding, simpler single-input–single-output systems generally precede treatment of more complex multi-input–multi-output (MIMO) systems and linear systems precede nonlinear systems. This approach is used throughout, including in the final chapters, which explain the latest advanced ideas governing the stabilization, regulation, and tracking of nonlinear MIMO systems. Two major design problems are considered, both in the presence of model uncertainties: asymptotic stabilization with a “...
Multivariate approach to matrimonial mobility in Catalonia.
Calafell, F; Hernández, M
1993-10-01
Matrimonial mobility in Catalonia was studied using 1986 census data. Comarca (a geographic division) of birth was used as the population unit, and a measure of affinity (a statistical distance) between comarques in spouse geographic origin was defined. This distance was analyzed with multivariate methods drawn from numerical taxonomy to detect any discontinuities in matrimonial mobility and gene flow between comarques. Results show a three-level pattern of gene flow in Catalonia: (1) a strong endogamy within comarques; (2) a 100-km matrimonial circle around every comarca; and (3) the capital, Barcelona, which attracts migrants from all over Catalonia. The regionalization in matrimonial mobility follows the geographically clear-cut groups of comarques almost exactly.
Nonparametric Bayes Modeling of Multivariate Categorical Data.
Dunson, David B; Xing, Chuanhua
2012-01-01
Modeling of multivariate unordered categorical (nominal) data is a challenging problem, particularly in high dimensions and cases in which one wishes to avoid strong assumptions about the dependence structure. Commonly used approaches rely on the incorporation of latent Gaussian random variables or parametric latent class models. The goal of this article is to develop a nonparametric Bayes approach, which defines a prior with full support on the space of distributions for multiple unordered categorical variables. This support condition ensures that we are not restricting the dependence structure a priori. We show this can be accomplished through a Dirichlet process mixture of product multinomial distributions, which is also a convenient form for posterior computation. Methods for nonparametric testing of violations of independence are proposed, and the methods are applied to model positional dependence within transcription factor binding motifs.
Some developments in multivariate image analysis
DEFF Research Database (Denmark)
Kucheryavskiy, Sergey
be up to several million. The main MIA tool for exploratory analysis is score density plot – all pixels are projected into principal component space and on the corresponding scores plots are colorized according to their density (how many pixels are crowded in the unit area of the plot). Looking...... for and analyzing patterns on these plots and the original image allow to do interactive analysis, to get some hidden information, build a supervised classification model, and much more. In the present work several alternative methods to original principal component analysis (PCA) for building the projection......Multivariate image analysis (MIA), one of the successful chemometric applications, now is used widely in different areas of science and industry. Introduced in late 80s it has became very popular with hyperspectral imaging, where MIA is one of the most efficient tools for exploratory analysis...
Multivariate analysis of data in sensory science
Naes, T; Risvik, E
1996-01-01
The state-of-the-art of multivariate analysis in sensory science is described in this volume. Both methods for aggregated and individual sensory profiles are discussed. Processes and results are presented in such a way that they can be understood not only by statisticians but also by experienced sensory panel leaders and users of sensory analysis. The techniques presented are focused on examples and interpretation rather than on the technical aspects, with an emphasis on new and important methods which are possibly not so well known to scientists in the field. Important features of the book are discussions on the relationship among the methods with a strong accent on the connection between problems and methods. All procedures presented are described in relation to sensory data and not as completely general statistical techniques. Sensory scientists, applied statisticians, chemometricians, those working in consumer science, food scientists and agronomers will find this book of value.
FACT. Multivariate extraction of muon ring images
Energy Technology Data Exchange (ETDEWEB)
Noethe, Maximilian; Temme, Fabian; Buss, Jens [Experimentelle Physik 5b, TU Dortmund, Dortmund (Germany); Collaboration: FACT-Collaboration
2016-07-01
In ground-based gamma-ray astronomy, muon ring images are an important event class for instrument calibration and monitoring of its properties. In this talk, a multivariate approach will be presented, that is well suited for real time extraction of muons from data streams of Imaging Atmospheric Cherenkov Telescopes (IACT). FACT, the First G-APD Cherenkov Telescope is located on the Canary Island of La Palma and is the first IACT to use Silicon Photomultipliers for detecting the Cherenkov photons of extensive air showers. In case of FACT, the extracted muon events are used to calculate the time resolution of the camera. In addition, the effect of the mirror alignment in May 2014 on properties of detected muons is investigated. Muon candidates are identified with a random forest classification algorithm. The performance of the classifier is evaluated for different sets of image parameters in order to compare the gain in performance with the computational costs of their calculation.
Advanced event reweighting using multivariate analysis
International Nuclear Information System (INIS)
Martschei, D; Feindt, M; Honc, S; Wagner-Kuhr, J
2012-01-01
Multivariate analysis (MVA) methods, especially discrimination techniques such as neural networks, are key ingredients in modern data analysis and play an important role in high energy physics. They are usually trained on simulated Monte Carlo (MC) samples to discriminate so called 'signal' from 'background' events and are then applied to data to select real events of signal type. We here address procedures that improve this work flow. This will be the enhancement of data / MC agreement by reweighting MC samples on a per event basis. Then training MVAs on real data using the sPlot technique will be discussed. Finally we will address the construction of MVAs whose discriminator is independent of a certain control variable, i.e. cuts on this variable will not change the discriminator shape.
Multivariate Markov chain modeling for stock markets
Maskawa, Jun-ichi
2003-06-01
We study a multivariate Markov chain model as a stochastic model of the price changes of portfolios in the framework of the mean field approximation. The time series of price changes are coded into the sequences of up and down spins according to their signs. We start with the discussion for small portfolios consisting of two stock issues. The generalization of our model to arbitrary size of portfolio is constructed by a recurrence relation. The resultant form of the joint probability of the stationary state coincides with Gibbs measure assigned to each configuration of spin glass model. Through the analysis of actual portfolios, it has been shown that the synchronization of the direction of the price changes is well described by the model.
Derivatives of Multivariate Bernstein Operators and Smoothness with Jacobi Weights
Directory of Open Access Journals (Sweden)
Jianjun Wang
2012-01-01
Full Text Available Using the modulus of smoothness, directional derivatives of multivariate Bernstein operators with weights are characterized. The obtained results partly generalize the corresponding ones for multivariate Bernstein operators without weights.
Regularized multivariate regression models with skew-t error distributions
Chen, Lianfu; Pourahmadi, Mohsen; Maadooliat, Mehdi
2014-01-01
We consider regularization of the parameters in multivariate linear regression models with the errors having a multivariate skew-t distribution. An iterative penalized likelihood procedure is proposed for constructing sparse estimators of both
Graphics for the multivariate two-sample problem
International Nuclear Information System (INIS)
Friedman, J.H.; Rafsky, L.C.
1981-01-01
Some graphical methods for comparing multivariate samples are presented. These methods are based on minimal spanning tree techniques developed for multivariate two-sample tests. The utility of these methods is illustrated through examples using both real and artificial data
Multivariate Receptor Models for Spatially Correlated Multipollutant Data
Jun, Mikyoung; Park, Eun Sug
2013-01-01
The goal of multivariate receptor modeling is to estimate the profiles of major pollution sources and quantify their impacts based on ambient measurements of pollutants. Traditionally, multivariate receptor modeling has been applied to multiple air
Marshall-Olkin multivariate semi-logistic distribution and minification ...
African Journals Online (AJOL)
Olkin multivariate logistic distribution (MO-ML) are introduced and studied. Various characterizations properties of Marshall-Olkin multivariate semi-logistic distribution are investigated and studied. First order autoregressive minification processes ...
Scale and shape mixtures of multivariate skew-normal distributions
Arellano-Valle, Reinaldo B.; Ferreira, Clé cio S.; Genton, Marc G.
2018-01-01
We introduce a broad and flexible class of multivariate distributions obtained by both scale and shape mixtures of multivariate skew-normal distributions. We present the probabilistic properties of this family of distributions in detail and lay down
Applied partial differential equations
Logan, J David
2004-01-01
This primer on elementary partial differential equations presents the standard material usually covered in a one-semester, undergraduate course on boundary value problems and PDEs. What makes this book unique is that it is a brief treatment, yet it covers all the major ideas: the wave equation, the diffusion equation, the Laplace equation, and the advection equation on bounded and unbounded domains. Methods include eigenfunction expansions, integral transforms, and characteristics. Mathematical ideas are motivated from physical problems, and the exposition is presented in a concise style accessible to science and engineering students; emphasis is on motivation, concepts, methods, and interpretation, rather than formal theory. This second edition contains new and additional exercises, and it includes a new chapter on the applications of PDEs to biology: age structured models, pattern formation; epidemic wave fronts, and advection-diffusion processes. The student who reads through this book and solves many of t...
Paul, Clayton R
2010-01-01
"Inductance is an unprecedented text, thoroughly discussing "loop" inductance as well as the increasingly important "partial" inductance. These concepts and their proper calculation are crucial in designing modern high-speed digital systems. World-renowned leader in electromagnetics Clayton Paul provides the knowledge and tools necessary to understand and calculate inductance." "With the present and increasing emphasis on high-speed digital systems and high-frequency analog systems, it is imperative that system designers develop an intimate understanding of the concepts and methods in this book. Inductance is a much-needed textbook designed for senior and graduate-level engineering students, as well as a hands-on guide for working engineers and professionals engaged in the design of high-speed digital and high-frequency analog systems."--Jacket.
Fundamental partial compositeness
Sannino, Francesco
2016-11-07
We construct renormalizable Standard Model extensions, valid up to the Planck scale, that give a composite Higgs from a new fundamental strong force acting on fermions and scalars. Yukawa interactions of these particles with Standard Model fermions realize the partial compositeness scenario. Successful models exist because gauge quantum numbers of Standard Model fermions admit a minimal enough 'square root'. Furthermore, right-handed SM fermions have an SU(2)$_R$-like structure, yielding a custodially-protected composite Higgs. Baryon and lepton numbers arise accidentally. Standard Model fermions acquire mass at tree level, while the Higgs potential and flavor violations are generated by quantum corrections. We further discuss accidental symmetries and other dynamical features stemming from the new strongly interacting scalars. If the same phenomenology can be obtained from models without our elementary scalars, they would reappear as composite states.
Fundamental partial compositeness
International Nuclear Information System (INIS)
Sannino, Francesco; Strumia, Alessandro; Tesi, Andrea; Vigiani, Elena
2016-01-01
We construct renormalizable Standard Model extensions, valid up to the Planck scale, that give a composite Higgs from a new fundamental strong force acting on fermions and scalars. Yukawa interactions of these particles with Standard Model fermions realize the partial compositeness scenario. Under certain assumptions on the dynamics of the scalars, successful models exist because gauge quantum numbers of Standard Model fermions admit a minimal enough ‘square root’. Furthermore, right-handed SM fermions have an SU(2)_R-like structure, yielding a custodially-protected composite Higgs. Baryon and lepton numbers arise accidentally. Standard Model fermions acquire mass at tree level, while the Higgs potential and flavor violations are generated by quantum corrections. We further discuss accidental symmetries and other dynamical features stemming from the new strongly interacting scalars. If the same phenomenology can be obtained from models without our elementary scalars, they would reappear as composite states.
International Nuclear Information System (INIS)
Najjar, M.S.
1987-01-01
A process is described for the production of gaseous mixtures comprising H/sub 2/+CO by the partial oxidation of a fuel feedstock comprising a heavy liquid hydrocarbonaceous fuel having a nickel, iron, and vanadium-containing ash or petroleum coke having a nickel, iron, and vanadium-containing ash, or mixtures thereof. The feedstock includes a minimum of 0.5 wt. % of sulfur and the ash includes a minimum of 5.0 wt. % vanadium, a minimum of 0.5 ppm nickel, and a minimum of 0.5 ppm iron. The process comprises: (1) mixing together a copper-containing additive with the fuel feedstock; wherein the weight ratio of copper-containing additive to ash in the fuel feedstock is in the range of about 1.0-10.0, and there is at least 10 parts by weight of copper for each part by weight of vanadium; (2) reacting the mixture from (1) at a temperature in the range of 2200 0 F to 2900 0 F and a pressure in the range of about 5 to 250 atmospheres in a free-flow refactory lined partial oxidation reaction zone with a free-oxygen containing gas in the presence of a temperature moderator and in a reducing atmosphere to produce a hot raw effluent gas stream comprising H/sub 2/+CO and entrained molten slag; and where in the reaction zone and the copper-containing additive combines with at least a portion of the nickel and iron constituents and sulfur found in the feedstock to produce a liquid phase washing agent that collects and transports at least a portion of the vanadium-containing oxide laths and spinels and other ash components and refractory out of the reaction zone; and (3) separating nongaseous materials from the hot raw effluent gas stream
Multivariate Pareto Minification Processes | Umar | Journal of the ...
African Journals Online (AJOL)
Autoregressive (AR) and autoregressive moving average (ARMA) processes with multivariate exponential (ME) distribution are presented and discussed. The theory of positive dependence is used to show that in many cases, multivariate exponential autoregressive (MEAR) and multivariate autoregressive moving average ...
Multivariate semi-logistic distribution and processes | Umar | Journal ...
African Journals Online (AJOL)
Multivariate semi-logistic distribution is introduced and studied. Some characterizations properties of multivariate semi-logistic distribution are presented. First order autoregressive minification processes and its generalization to kth order autoregressive minification processes with multivariate semi-logistic distribution as ...
Multivariable biorthogonal continuous--discrete Wilson and Racah polynomials
International Nuclear Information System (INIS)
Tratnik, M.V.
1990-01-01
Several families of multivariable, biorthogonal, partly continuous and partly discrete, Wilson polynomials are presented. These yield limit cases that are purely continuous in some of the variables and purely discrete in the others, or purely discrete in all the variables. The latter are referred to as the multivariable biorthogonal Racah polynomials. Interesting further limit cases include the multivariable biorthogonal Hahn and dual Hahn polynomials
Local Strategy Combined with a Wavelength Selection Method for Multivariate Calibration
Directory of Open Access Journals (Sweden)
Haitao Chang
2016-06-01
Full Text Available One of the essential factors influencing the prediction accuracy of multivariate calibration models is the quality of the calibration data. A local regression strategy, together with a wavelength selection approach, is proposed to build the multivariate calibration models based on partial least squares regression. The local algorithm is applied to create a calibration set of spectra similar to the spectrum of an unknown sample; the synthetic degree of grey relation coefficient is used to evaluate the similarity. A wavelength selection method based on simple-to-use interactive self-modeling mixture analysis minimizes the influence of noisy variables, and the most informative variables of the most similar samples are selected to build the multivariate calibration model based on partial least squares regression. To validate the performance of the proposed method, ultraviolet-visible absorbance spectra of mixed solutions of food coloring analytes in a concentration range of 20–200 µg/mL is measured. Experimental results show that the proposed method can not only enhance the prediction accuracy of the calibration model, but also greatly reduce its complexity.
Multivariate Risk-Return Decision Making Within Dynamic Estimation
Directory of Open Access Journals (Sweden)
Josip Arnerić
2008-10-01
Full Text Available Risk management in this paper is focused on multivariate risk-return decision making assuming time-varying estimation. Empirical research in risk management showed that the static "mean-variance" methodology in portfolio optimization is very restrictive with unrealistic assumptions. The objective of this paper is estimation of time-varying portfolio stocks weights by constraints on risk measure. Hence, risk measure dynamic estimation is used in risk controlling. By risk control manager makes free supplementary capital for new investments.Univariate modeling approach is not appropriate, even when portfolio returns are treated as one variable. Portfolio weights are time-varying, and therefore it is necessary to reestimate whole model over time. Using assumption of bivariate Student´s t-distribution, in multivariate GARCH(p,q models, it becomes possible to forecast time-varying portfolio risk much more precisely. The complete procedure of analysis is established from Zagreb Stock Exchange using daily observations of Pliva and Podravka stocks.
A multivariate analysis of Antarctic sea ice since 1979
Energy Technology Data Exchange (ETDEWEB)
Magalhaes Neto, Newton de; Evangelista, Heitor [Universidade do Estado do Rio de Janeiro (Uerj), LARAMG - Laboratorio de Radioecologia e Mudancas Globais, Maracana, Rio de Janeiro, RJ (Brazil); Tanizaki-Fonseca, Kenny [Universidade do Estado do Rio de Janeiro (Uerj), LARAMG - Laboratorio de Radioecologia e Mudancas Globais, Maracana, Rio de Janeiro, RJ (Brazil); Universidade Federal Fluminense (UFF), Dept. Analise Geoambiental, Inst. de Geociencias, Niteroi, RJ (Brazil); Penello Meirelles, Margareth Simoes [Universidade do Estado do Rio de Janeiro (UERJ)/Geomatica, Maracana, Rio de Janeiro, RJ (Brazil); Garcia, Carlos Eiras [Universidade Federal do Rio Grande (FURG), Laboratorio de Oceanografia Fisica, Rio Grande, RS (Brazil)
2012-03-15
Recent satellite observations have shown an increase in the total extent of Antarctic sea ice, during periods when the atmosphere and oceans tend to be warmer surrounding a significant part of the continent. Despite an increase in total sea ice, regional analyses depict negative trends in the Bellingshausen-Amundsen Sea and positive trends in the Ross Sea. Although several climate parameters are believed to drive the formation of Antarctic sea ice and the local atmosphere, a descriptive mechanism that could trigger such differences in trends are still unknown. In this study we employed a multivariate analysis in order to identify the response of the Antarctic sea ice with respect to commonly utilized climate forcings/parameters, as follows: (1) The global air surface temperature, (2) The global sea surface temperature, (3) The atmospheric CO{sub 2} concentration, (4) The South Annular Mode, (5) The Nino 3, (6) The Nino (3 + 4, 7) The Nino 4, (8) The Southern Oscillation Index, (9) The Multivariate ENSO Index, (10) the Total Solar Irradiance, (11) The maximum O{sub 3} depletion area, and (12) The minimum O{sub 3} concentration over Antarctica. Our results indicate that western Antarctic sea ice is simultaneously impacted by several parameters; and that the minimum, mean, and maximum sea ice extent may respond to a separate set of climatic/geochemical parameters. (orig.)
Visualization of diversity in large multivariate data sets.
Pham, Tuan; Hess, Rob; Ju, Crystal; Zhang, Eugene; Metoyer, Ronald
2010-01-01
Understanding the diversity of a set of multivariate objects is an important problem in many domains, including ecology, college admissions, investing, machine learning, and others. However, to date, very little work has been done to help users achieve this kind of understanding. Visual representation is especially appealing for this task because it offers the potential to allow users to efficiently observe the objects of interest in a direct and holistic way. Thus, in this paper, we attempt to formalize the problem of visualizing the diversity of a large (more than 1000 objects), multivariate (more than 5 attributes) data set as one worth deeper investigation by the information visualization community. In doing so, we contribute a precise definition of diversity, a set of requirements for diversity visualizations based on this definition, and a formal user study design intended to evaluate the capacity of a visual representation for communicating diversity information. Our primary contribution, however, is a visual representation, called the Diversity Map, for visualizing diversity. An evaluation of the Diversity Map using our study design shows that users can judge elements of diversity consistently and as or more accurately than when using the only other representation specifically designed to visualize diversity.
Partial oxidation of 2-propanol on perovskites
Energy Technology Data Exchange (ETDEWEB)
Sumathi, R.; Viswanathan, B.; Varadarajan, T.K. [Indian Inst. of Tech., Madras (India). Dept. of Chemistry
1998-12-31
Partial oxidation of 2-propanol was carried out on AB{sub 1-x}B`{sub x}O{sub 3} (A=Ba, B=Pb, Ce, Ti; B`=Bi, Sb and Cu) type perovskite oxides. Acetone was the major product observed on all the catalysts. All the catalysts underwent partial reduction during the reaction depending on the composition of the reactant, nature of the B site cation and the extent of substitution at B site. The catalytic activity has been correlated with the reducibility of the perovskite oxides determined from Temperature Programmed Reduction (TPR) studies. (orig.)
Generalized solutions of nonlinear partial differential equations
Rosinger, EE
1987-01-01
During the last few years, several fairly systematic nonlinear theories of generalized solutions of rather arbitrary nonlinear partial differential equations have emerged. The aim of this volume is to offer the reader a sufficiently detailed introduction to two of these recent nonlinear theories which have so far contributed most to the study of generalized solutions of nonlinear partial differential equations, bringing the reader to the level of ongoing research.The essence of the two nonlinear theories presented in this volume is the observation that much of the mathematics concernin
Multivariate data analysis approach to understand magnetic properties of perovskite manganese oxides
International Nuclear Information System (INIS)
Imamura, N.; Mizoguchi, T.; Yamauchi, H.; Karppinen, M.
2008-01-01
Here we apply statistical multivariate data analysis techniques to obtain some insights into the complex structure-property relations in antiferromagnetic (AFM) and ferromagnetic (FM) manganese perovskite systems, AMnO 3 . The 131 samples included in the present analyses are described by 21 crystal-structure or crystal-chemical (CS/CC) parameters. Principal component analysis (PCA), carried out separately for the AFM and FM compounds, is used to model and evaluate the various relationships among the magnetic properties and the various CS/CC parameters. Moreover, for the AFM compounds, PLS (partial least squares projections to latent structures) analysis is performed so as to predict the magnitude of the Neel temperature on the bases of the CS/CC parameters. Finally, so-called PLS-DA (PLS discriminant analysis) method is employed to find out the most influential/characteristic CS/CC parameters that differentiate the two classes of compounds from each other. - Graphical abstract: Statistical multivariate data analysis techniques are applied to detect structure-property relations in antiferromagnetic (AFM) and ferromagnetic (FM) manganese perovskites. For AFM compounds, partial least squares projections to latent structures analysis predict the magnitude of the Neel temperature on the bases of structural parameters only. Moreover, AFM and FM compounds are well separated by means of so-called partial least squares discriminant analysis method
Directory of Open Access Journals (Sweden)
Lassi Rieppo
Full Text Available Fourier Transform Infrared (FT-IR spectroscopic imaging has been earlier applied for the spatial estimation of the collagen and the proteoglycan (PG contents of articular cartilage (AC. However, earlier studies have been limited to the use of univariate analysis techniques. Current analysis methods lack the needed specificity for collagen and PGs. The aim of the present study was to evaluate the suitability of partial least squares regression (PLSR and principal component regression (PCR methods for the analysis of the PG content of AC. Multivariate regression models were compared with earlier used univariate methods and tested with a sample material consisting of healthy and enzymatically degraded steer AC. Chondroitinase ABC enzyme was used to increase the variation in PG content levels as compared to intact AC. Digital densitometric measurements of Safranin O-stained sections provided the reference for PG content. The results showed that multivariate regression models predict PG content of AC significantly better than earlier used absorbance spectrum (i.e. the area of carbohydrate region with or without amide I normalization or second derivative spectrum univariate parameters. Increased molecular specificity favours the use of multivariate regression models, but they require more knowledge of chemometric analysis and extended laboratory resources for gathering reference data for establishing the models. When true molecular specificity is required, the multivariate models should be used.
Multivariate sensitivity to voice during auditory categorization.
Lee, Yune Sang; Peelle, Jonathan E; Kraemer, David; Lloyd, Samuel; Granger, Richard
2015-09-01
Past neuroimaging studies have documented discrete regions of human temporal cortex that are more strongly activated by conspecific voice sounds than by nonvoice sounds. However, the mechanisms underlying this voice sensitivity remain unclear. In the present functional MRI study, we took a novel approach to examining voice sensitivity, in which we applied a signal detection paradigm to the assessment of multivariate pattern classification among several living and nonliving categories of auditory stimuli. Within this framework, voice sensitivity can be interpreted as a distinct neural representation of brain activity that correctly distinguishes human vocalizations from other auditory object categories. Across a series of auditory categorization tests, we found that bilateral superior and middle temporal cortex consistently exhibited robust sensitivity to human vocal sounds. Although the strongest categorization was in distinguishing human voice from other categories, subsets of these regions were also able to distinguish reliably between nonhuman categories, suggesting a general role in auditory object categorization. Our findings complement the current evidence of cortical sensitivity to human vocal sounds by revealing that the greatest sensitivity during categorization tasks is devoted to distinguishing voice from nonvoice categories within human temporal cortex. Copyright © 2015 the American Physiological Society.
Multivariate volume visualization through dynamic projections
Energy Technology Data Exchange (ETDEWEB)
Liu, Shusen [Univ. of Utah, Salt Lake City, UT (United States); Wang, Bei [Univ. of Utah, Salt Lake City, UT (United States); Thiagarajan, Jayaraman J. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Bremer, Peer -Timo [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Pascucci, Valerio [Univ. of Utah, Salt Lake City, UT (United States)
2014-11-01
We propose a multivariate volume visualization framework that tightly couples dynamic projections with a high-dimensional transfer function design for interactive volume visualization. We assume that the complex, high-dimensional data in the attribute space can be well-represented through a collection of low-dimensional linear subspaces, and embed the data points in a variety of 2D views created as projections onto these subspaces. Through dynamic projections, we present animated transitions between different views to help the user navigate and explore the attribute space for effective transfer function design. Our framework not only provides a more intuitive understanding of the attribute space but also allows the design of the transfer function under multiple dynamic views, which is more flexible than being restricted to a single static view of the data. For large volumetric datasets, we maintain interactivity during the transfer function design via intelligent sampling and scalable clustering. As a result, using examples in combustion and climate simulations, we demonstrate how our framework can be used to visualize interesting structures in the volumetric space.
Scattering amplitudes from multivariate polynomial division
Energy Technology Data Exchange (ETDEWEB)
Mastrolia, Pierpaolo, E-mail: pierpaolo.mastrolia@cern.ch [Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Muenchen (Germany); Dipartimento di Fisica e Astronomia, Universita di Padova, Padova (Italy); INFN Sezione di Padova, via Marzolo 8, 35131 Padova (Italy); Mirabella, Edoardo, E-mail: mirabell@mppmu.mpg.de [Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Muenchen (Germany); Ossola, Giovanni, E-mail: GOssola@citytech.cuny.edu [New York City College of Technology, City University of New York, 300 Jay Street, Brooklyn, NY 11201 (United States); Graduate School and University Center, City University of New York, 365 Fifth Avenue, New York, NY 10016 (United States); Peraro, Tiziano, E-mail: peraro@mppmu.mpg.de [Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Muenchen (Germany)
2012-11-15
We show that the evaluation of scattering amplitudes can be formulated as a problem of multivariate polynomial division, with the components of the integration-momenta as indeterminates. We present a recurrence relation which, independently of the number of loops, leads to the multi-particle pole decomposition of the integrands of the scattering amplitudes. The recursive algorithm is based on the weak Nullstellensatz theorem and on the division modulo the Groebner basis associated to all possible multi-particle cuts. We apply it to dimensionally regulated one-loop amplitudes, recovering the well-known integrand-decomposition formula. Finally, we focus on the maximum-cut, defined as a system of on-shell conditions constraining the components of all the integration-momenta. By means of the Finiteness Theorem and of the Shape Lemma, we prove that the residue at the maximum-cut is parametrized by a number of coefficients equal to the number of solutions of the cut itself.
Multivariate Heteroscedasticity Models for Functional Brain Connectivity
Directory of Open Access Journals (Sweden)
Christof Seiler
2017-12-01
Full Text Available Functional brain connectivity is the co-occurrence of brain activity in different areas during resting and while doing tasks. The data of interest are multivariate timeseries measured simultaneously across brain parcels using resting-state fMRI (rfMRI. We analyze functional connectivity using two heteroscedasticity models. Our first model is low-dimensional and scales linearly in the number of brain parcels. Our second model scales quadratically. We apply both models to data from the Human Connectome Project (HCP comparing connectivity between short and conventional sleepers. We find stronger functional connectivity in short than conventional sleepers in brain areas consistent with previous findings. This might be due to subjects falling asleep in the scanner. Consequently, we recommend the inclusion of average sleep duration as a covariate to remove unwanted variation in rfMRI studies. A power analysis using the HCP data shows that a sample size of 40 detects 50% of the connectivity at a false discovery rate of 20%. We provide implementations using R and the probabilistic programming language Stan.
Hierarchical multivariate covariance analysis of metabolic connectivity.
Carbonell, Felix; Charil, Arnaud; Zijdenbos, Alex P; Evans, Alan C; Bedell, Barry J
2014-12-01
Conventional brain connectivity analysis is typically based on the assessment of interregional correlations. Given that correlation coefficients are derived from both covariance and variance, group differences in covariance may be obscured by differences in the variance terms. To facilitate a comprehensive assessment of connectivity, we propose a unified statistical framework that interrogates the individual terms of the correlation coefficient. We have evaluated the utility of this method for metabolic connectivity analysis using [18F]2-fluoro-2-deoxyglucose (FDG) positron emission tomography (PET) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. As an illustrative example of the utility of this approach, we examined metabolic connectivity in angular gyrus and precuneus seed regions of mild cognitive impairment (MCI) subjects with low and high β-amyloid burdens. This new multivariate method allowed us to identify alterations in the metabolic connectome, which would not have been detected using classic seed-based correlation analysis. Ultimately, this novel approach should be extensible to brain network analysis and broadly applicable to other imaging modalities, such as functional magnetic resonance imaging (MRI).
Multivariate survivorship analysis using two cross-sectional samples.
Hill, M E
1999-11-01
As an alternative to survival analysis with longitudinal data, I introduce a method that can be applied when one observes the same cohort in two cross-sectional samples collected at different points in time. The method allows for the estimation of log-probability survivorship models that estimate the influence of multiple time-invariant factors on survival over a time interval separating two samples. This approach can be used whenever the survival process can be adequately conceptualized as an irreversible single-decrement process (e.g., mortality, the transition to first marriage among a cohort of never-married individuals). Using data from the Integrated Public Use Microdata Series (Ruggles and Sobek 1997), I illustrate the multivariate method through an investigation of the effects of race, parity, and educational attainment on the survival of older women in the United States.
[On the recurrence risk with partial larynx resections (author's transl)].
Flach, M
1978-06-01
28 cases of recurrences after 127 vertical and horizontal partial larynx resections (22 per cent) were analysed. Unfavourable preconditions for partial resections are the affliction of the ary cartilage and arrest of vocal chord movility. The hemiresections according to Hautant have the heighest recidivation percentage (40 per cent) judging from the observations available. Radical operations after failured partial resections are uncertain as to their prognoses.
Li, Yanming; Nan, Bin; Zhu, Ji
2015-06-01
We propose a multivariate sparse group lasso variable selection and estimation method for data with high-dimensional predictors as well as high-dimensional response variables. The method is carried out through a penalized multivariate multiple linear regression model with an arbitrary group structure for the regression coefficient matrix. It suits many biology studies well in detecting associations between multiple traits and multiple predictors, with each trait and each predictor embedded in some biological functional groups such as genes, pathways or brain regions. The method is able to effectively remove unimportant groups as well as unimportant individual coefficients within important groups, particularly for large p small n problems, and is flexible in handling various complex group structures such as overlapping or nested or multilevel hierarchical structures. The method is evaluated through extensive simulations with comparisons to the conventional lasso and group lasso methods, and is applied to an eQTL association study. © 2015, The International Biometric Society.
DEFF Research Database (Denmark)
Kjærsgård, Inger Vibeke Holst; Nørrelykke, M.R.; Jessen, Flemming
2006-01-01
Multivariate data analysis has been combined with proteomics to enhance the recovery of information from 2-DE of cod muscle proteins during different storage conditions. Proteins were extracted according to 11 different storage conditions and samples were resolved by 2-DE. Data generated by 2-DE...... was subjected to principal component analysis (PCA) and discriminant partial least squares regression (DPLSR). Applying PCA to 2-DE data revealed the samples to form groups according to frozen storage time, whereas differences due to different storage temperatures or chilled storage in modified atmosphere...... light chain 1, 2 and 3, triose-phosphate isomerase, glyceraldehyde-3-phosphate dehydrogenase, aldolase A and two ?-actin fragments, and a nuclease diphosphate kinase B fragment to change in concentration, during frozen storage. Application of proteomics, multivariate data analysis and MS/MS to analyse...
Soft sensor design by multivariate fusion of image features and process measurements
DEFF Research Database (Denmark)
Lin, Bao; Jørgensen, Sten Bay
2011-01-01
This paper presents a multivariate data fusion procedure for design of dynamic soft sensors where suitably selected image features are combined with traditional process measurements to enhance the performance of data-driven soft sensors. A key issue of fusing multiple sensor data, i.e. to determine...... with a multivariate analysis technique from RGB pictures. The color information is also transformed to hue, saturation and intensity components. Both sets of image features are combined with traditional process measurements to obtain an inferential model by partial least squares (PLS) regression. A dynamic PLS model...... oxides (NOx) emission of cement kilns. On-site tests demonstrate improved performance over soft sensors based on conventional process measurements only....
Partial Actions and Power Sets
Directory of Open Access Journals (Sweden)
Jesús Ávila
2013-01-01
Full Text Available We consider a partial action (X,α with enveloping action (T,β. In this work we extend α to a partial action on the ring (P(X,Δ,∩ and find its enveloping action (E,β. Finally, we introduce the concept of partial action of finite type to investigate the relationship between (E,β and (P(T,β.
Algorithms over partially ordered sets
DEFF Research Database (Denmark)
Baer, Robert M.; Østerby, Ole
1969-01-01
in partially ordered sets, answer the combinatorial question of how many maximal chains might exist in a partially ordered set withn elements, and we give an algorithm for enumerating all maximal chains. We give (in § 3) algorithms which decide whether a partially ordered set is a (lower or upper) semi......-lattice, and whether a lattice has distributive, modular, and Boolean properties. Finally (in § 4) we give Algol realizations of the various algorithms....
Anatomic partial nephrectomy: technique evolution.
Azhar, Raed A; Metcalfe, Charles; Gill, Inderbir S
2015-03-01
Partial nephrectomy provides equivalent long-term oncologic and superior functional outcomes as radical nephrectomy for T1a renal masses. Herein, we review the various vascular clamping techniques employed during minimally invasive partial nephrectomy, describe the evolution of our partial nephrectomy technique and provide an update on contemporary thinking about the impact of ischemia on renal function. Recently, partial nephrectomy surgical technique has shifted away from main artery clamping and towards minimizing/eliminating global renal ischemia during partial nephrectomy. Supported by high-fidelity three-dimensional imaging, novel anatomic-based partial nephrectomy techniques have recently been developed, wherein partial nephrectomy can now be performed with segmental, minimal or zero global ischemia to the renal remnant. Sequential innovations have included early unclamping, segmental clamping, super-selective clamping and now culminating in anatomic zero-ischemia surgery. By eliminating 'under-the-gun' time pressure of ischemia for the surgeon, these techniques allow an unhurried, tightly contoured tumour excision with point-specific sutured haemostasis. Recent data indicate that zero-ischemia partial nephrectomy may provide better functional outcomes by minimizing/eliminating global ischemia and preserving greater vascularized kidney volume. Contemporary partial nephrectomy includes a spectrum of surgical techniques ranging from conventional-clamped to novel zero-ischemia approaches. Technique selection should be tailored to each individual case on the basis of tumour characteristics, surgical feasibility, surgeon experience, patient demographics and baseline renal function.
Partial order infinitary term rewriting
DEFF Research Database (Denmark)
Bahr, Patrick
2014-01-01
We study an alternative model of infinitary term rewriting. Instead of a metric on terms, a partial order on partial terms is employed to formalise convergence of reductions. We consider both a weak and a strong notion of convergence and show that the metric model of convergence coincides with th...... to the metric setting -- orthogonal systems are both infinitarily confluent and infinitarily normalising in the partial order setting. The unique infinitary normal forms that the partial order model admits are Böhm trees....
Multivariate statistical analysis of wildfires in Portugal
Costa, Ricardo; Caramelo, Liliana; Pereira, Mário
2013-04-01
Several studies demonstrate that wildfires in Portugal present high temporal and spatial variability as well as cluster behavior (Pereira et al., 2005, 2011). This study aims to contribute to the characterization of the fire regime in Portugal with the multivariate statistical analysis of the time series of number of fires and area burned in Portugal during the 1980 - 2009 period. The data used in the analysis is an extended version of the Rural Fire Portuguese Database (PRFD) (Pereira et al, 2011), provided by the National Forest Authority (Autoridade Florestal Nacional, AFN), the Portuguese Forest Service, which includes information for more than 500,000 fire records. There are many multiple advanced techniques for examining the relationships among multiple time series at the same time (e.g., canonical correlation analysis, principal components analysis, factor analysis, path analysis, multiple analyses of variance, clustering systems). This study compares and discusses the results obtained with these different techniques. Pereira, M.G., Trigo, R.M., DaCamara, C.C., Pereira, J.M.C., Leite, S.M., 2005: "Synoptic patterns associated with large summer forest fires in Portugal". Agricultural and Forest Meteorology. 129, 11-25. Pereira, M. G., Malamud, B. D., Trigo, R. M., and Alves, P. I.: The history and characteristics of the 1980-2005 Portuguese rural fire database, Nat. Hazards Earth Syst. Sci., 11, 3343-3358, doi:10.5194/nhess-11-3343-2011, 2011 This work is supported by European Union Funds (FEDER/COMPETE - Operational Competitiveness Programme) and by national funds (FCT - Portuguese Foundation for Science and Technology) under the project FCOMP-01-0124-FEDER-022692, the project FLAIR (PTDC/AAC-AMB/104702/2008) and the EU 7th Framework Program through FUME (contract number 243888).
Models and analysis for multivariate failure time data
Shih, Joanna Huang
The goal of this research is to develop and investigate models and analytic methods for multivariate failure time data. We compare models in terms of direct modeling of the margins, flexibility of dependency structure, local vs. global measures of association, and ease of implementation. In particular, we study copula models, and models produced by right neutral cumulative hazard functions and right neutral hazard functions. We examine the changes of association over time for families of bivariate distributions induced from these models by displaying their density contour plots, conditional density plots, correlation curves of Doksum et al, and local cross ratios of Oakes. We know that bivariate distributions with same margins might exhibit quite different dependency structures. In addition to modeling, we study estimation procedures. For copula models, we investigate three estimation procedures. the first procedure is full maximum likelihood. The second procedure is two-stage maximum likelihood. At stage 1, we estimate the parameters in the margins by maximizing the marginal likelihood. At stage 2, we estimate the dependency structure by fixing the margins at the estimated ones. The third procedure is two-stage partially parametric maximum likelihood. It is similar to the second procedure, but we estimate the margins by the Kaplan-Meier estimate. We derive asymptotic properties for these three estimation procedures and compare their efficiency by Monte-Carlo simulations and direct computations. For models produced by right neutral cumulative hazards and right neutral hazards, we derive the likelihood and investigate the properties of the maximum likelihood estimates. Finally, we develop goodness of fit tests for the dependency structure in the copula models. We derive a test statistic and its asymptotic properties based on the test of homogeneity of Zelterman and Chen (1988), and a graphical diagnostic procedure based on the empirical Bayes approach. We study the
Observing participating observation
DEFF Research Database (Denmark)
Keiding, Tina Bering
2011-01-01
Current methodology concerning participating observation in general leaves the act of observation unobserved. Approaching participating observation from systems theory offers fundamental new insights into the topic. Observation is always participation. There is no way to escape becoming...
Observing participating observation
DEFF Research Database (Denmark)
Keiding, Tina Bering
2010-01-01
Current methodology concerning participating observation in general leaves the act of observation unobserved. Approaching participating observation from systems theory offers fundamental new insights into the topic. Observation is always participation. There is no way to escape becoming...
Partial purification and characterization of alkaline proteases from ...
African Journals Online (AJOL)
Alkaline proteases from the digestive tract of anchovy were partially purified by ammonium sulfate fractionation, dialysis and Sephadex G-75 gel filtration. The purification fold and yield were 6.23 and 4.49%, respectively. The optimum activities of partially purified alkaline proteases were observed at 60°C and at pH 11.0.
On Degenerate Partial Differential Equations
Chen, Gui-Qiang G.
2010-01-01
Some of recent developments, including recent results, ideas, techniques, and approaches, in the study of degenerate partial differential equations are surveyed and analyzed. Several examples of nonlinear degenerate, even mixed, partial differential equations, are presented, which arise naturally in some longstanding, fundamental problems in fluid mechanics and differential geometry. The solution to these fundamental problems greatly requires a deep understanding of nonlinear degenerate parti...
[Acrylic resin removable partial dentures
Baat, C. de; Witter, D.J.; Creugers, N.H.J.
2011-01-01
An acrylic resin removable partial denture is distinguished from other types of removable partial dentures by an all-acrylic resin base which is, in principle, solely supported by the edentulous regions of the tooth arch and in the maxilla also by the hard palate. When compared to the other types of
Partial Epilepsy with Auditory Features
Directory of Open Access Journals (Sweden)
J Gordon Millichap
2004-07-01
Full Text Available The clinical characteristics of 53 sporadic (S cases of idiopathic partial epilepsy with auditory features (IPEAF were analyzed and compared to previously reported familial (F cases of autosomal dominant partial epilepsy with auditory features (ADPEAF in a study at the University of Bologna, Italy.
Unsupervised classification of multivariate geostatistical data: Two algorithms
Romary, Thomas; Ors, Fabien; Rivoirard, Jacques; Deraisme, Jacques
2015-12-01
With the increasing development of remote sensing platforms and the evolution of sampling facilities in mining and oil industry, spatial datasets are becoming increasingly large, inform a growing number of variables and cover wider and wider areas. Therefore, it is often necessary to split the domain of study to account for radically different behaviors of the natural phenomenon over the domain and to simplify the subsequent modeling step. The definition of these areas can be seen as a problem of unsupervised classification, or clustering, where we try to divide the domain into homogeneous domains with respect to the values taken by the variables in hand. The application of classical clustering methods, designed for independent observations, does not ensure the spatial coherence of the resulting classes. Image segmentation methods, based on e.g. Markov random fields, are not adapted to irregularly sampled data. Other existing approaches, based on mixtures of Gaussian random functions estimated via the expectation-maximization algorithm, are limited to reasonable sample sizes and a small number of variables. In this work, we propose two algorithms based on adaptations of classical algorithms to multivariate geostatistical data. Both algorithms are model free and can handle large volumes of multivariate, irregularly spaced data. The first one proceeds by agglomerative hierarchical clustering. The spatial coherence is ensured by a proximity condition imposed for two clusters to merge. This proximity condition relies on a graph organizing the data in the coordinates space. The hierarchical algorithm can then be seen as a graph-partitioning algorithm. Following this interpretation, a spatial version of the spectral clustering algorithm is also proposed. The performances of both algorithms are assessed on toy examples and a mining dataset.
Games for Synthesis of Controllers with Partial Observation
DEFF Research Database (Denmark)
Arnold, André; Vincent, Aymeric; Walukiewicz, Igor
2003-01-01
it is equivalent to automata with loop testing. The problems such as emptiness testing and elimination of alternation are solvable for such automata.The method proposed in this paper to solve a control problem consists in transforming this problem into a problem of satisfiability of a µ-calculus formula so...... that the set of models of this formula is exactly the set of controllers that solve the problem. This transformation relies on a simple construction of the quotient of automata with loop testing by a deterministic transition system. This is enough to deal with centralized control problems. The solution...... of decentralized control problems uses a more involved construction of the quotient of two automata.This work extends the framework of Ramadge and Wonham in two directions. We consider infinite behaviours and arbitrary regular specifications, while the standard framework deals only with specifications on the set...
Observations on saliva osmolality during progressive dehydration and partial rehydration.
Taylor, Nigel A S; van den Heuvel, Anne M J; Kerry, Pete; McGhee, Sheena; Peoples, Gregory E; Brown, Marc A; Patterson, Mark J
2012-09-01
A need exists to identify dehydrated individuals under stressful settings beyond the laboratory. A predictive index based on changes in saliva osmolality has been proposed, and its efficacy and sensitivity was appraised across mass (water) losses from 1 to 7%. Twelve euhydrated males [serum osmolality: 286.1 mOsm kg(-1) H(2)O (SD 4.3)] completed three exercise- and heat-induced dehydration trials (35.6°C, 56% relative humidity): 7% dehydration (6.15 h), 3% dehydration (with 60% fluid replacement: 2.37 h), repeat 7% dehydration (5.27 h). Expectorated saliva osmolality, measured at baseline and at each 1% mass change, was used to predict instantaneous hydration state relative to mass losses of 3 and 6%. Saliva osmolality increased linearly with dehydration, although its basal osmolality and its rate of change varied among and within subjects across trials. Receiver operating characteristic curves indicated a good predictive power for saliva osmolality when used with two, single-threshold cutoffs to differentiate between hydrated and dehydrated individuals (area under curve: 3% cutoff = 0.868, 6% cutoff = 0.831). However, when analysed using a double-threshold detection technique (3 and 6%), as might be used in a field-based monitor, <50% of the osmolality data could correctly identify individuals who exceeded 3% dehydration. Indeed, within the 3-6% dehydration range, its sensitivity was 64%, while beyond 6% dehydration, this fell to 42%. Therefore, while expectorated saliva osmolality tracked mass losses within individuals, its large intra- and inter-individual variability limited its predictive power and sensitivity, rendering its utility questionable within a universal dehydration monitor.
Partial twisting for scalar mesons
International Nuclear Information System (INIS)
Agadjanov, Dimitri; Meißner, Ulf-G.; Rusetsky, Akaki
2014-01-01
The possibility of imposing partially twisted boundary conditions is investigated for the scalar sector of lattice QCD. According to the commonly shared belief, the presence of quark-antiquark annihilation diagrams in the intermediate state generally hinders the use of the partial twisting. Using effective field theory techniques in a finite volume, and studying the scalar sector of QCD with total isospin I=1, we however demonstrate that partial twisting can still be performed, despite the fact that annihilation diagrams are present. The reason for this are delicate cancellations, which emerge due to the graded symmetry in partially quenched QCD with valence, sea and ghost quarks. The modified Lüscher equation in case of partial twisting is given
Multivariate Bonferroni-type inequalities theory and applications
Chen, John
2014-01-01
Multivariate Bonferroni-Type Inequalities: Theory and Applications presents a systematic account of research discoveries on multivariate Bonferroni-type inequalities published in the past decade. The emergence of new bounding approaches pushes the conventional definitions of optimal inequalities and demands new insights into linear and Fréchet optimality. The book explores these advances in bounding techniques with corresponding innovative applications. It presents the method of linear programming for multivariate bounds, multivariate hybrid bounds, sub-Markovian bounds, and bounds using Hamil
Multivariate methods in nuclear waste remediation: Needs and applications
International Nuclear Information System (INIS)
Pulsipher, B.A.
1992-05-01
The United States Department of Energy (DOE) has developed a strategy for nuclear waste remediation and environmental restoration at several major sites across the country. Nuclear and hazardous wastes are found in underground storage tanks, containment drums, soils, and facilities. Due to the many possible contaminants and complexities of sampling and analysis, multivariate methods are directly applicable. However, effective application of multivariate methods will require greater ability to communicate methods and results to a non-statistician community. Moreover, more flexible multivariate methods may be required to accommodate inherent sampling and analysis limitations. This paper outlines multivariate applications in the context of select DOE environmental restoration activities and identifies several perceived needs
Multivariate statistics high-dimensional and large-sample approximations
Fujikoshi, Yasunori; Shimizu, Ryoichi
2010-01-01
A comprehensive examination of high-dimensional analysis of multivariate methods and their real-world applications Multivariate Statistics: High-Dimensional and Large-Sample Approximations is the first book of its kind to explore how classical multivariate methods can be revised and used in place of conventional statistical tools. Written by prominent researchers in the field, the book focuses on high-dimensional and large-scale approximations and details the many basic multivariate methods used to achieve high levels of accuracy. The authors begin with a fundamental presentation of the basic
The Dirichet-Multinomial model for multivariate randomized response data and small samples
Avetisyan, Marianna; Fox, Gerardus J.A.
2012-01-01
In survey sampling the randomized response (RR) technique can be used to obtain truthful answers to sensitive questions. Although the individual answers are masked due to the RR technique, individual (sensitive) response rates can be estimated when observing multivariate response data. The
The Dirichlet-Multinomial Model for Multivariate Randomized Response Data and Small Samples
Avetisyan, Marianna; Fox, Jean-Paul
2012-01-01
In survey sampling the randomized response (RR) technique can be used to obtain truthful answers to sensitive questions. Although the individual answers are masked due to the RR technique, individual (sensitive) response rates can be estimated when observing multivariate response data. The beta-binomial model for binary RR data will be generalized…
Martens, Edwin P; de Boer, Anthonius; Pestman, Wiebe R; Belitser, Svetlana V; Stricker, Bruno H Ch; Klungel, Olaf H
PURPOSE: To compare adjusted effects of drug treatment for hypertension on the risk of stroke from propensity score (PS) methods with a multivariable Cox proportional hazards (Cox PH) regression in an observational study with censored data. METHODS: From two prospective population-based cohort
Dong, Jian-Jun; Li, Qing-Liang; Yin, Hua; Zhong, Cheng; Hao, Jun-Guang; Yang, Pan-Fei; Tian, Yu-Hong; Jia, Shi-Ru
2014-10-15
Sensory evaluation is regarded as a necessary procedure to ensure a reproducible quality of beer. Meanwhile, high-throughput analytical methods provide a powerful tool to analyse various flavour compounds, such as higher alcohol and ester. In this study, the relationship between flavour compounds and sensory evaluation was established by non-linear models such as partial least squares (PLS), genetic algorithm back-propagation neural network (GA-BP), support vector machine (SVM). It was shown that SVM with a Radial Basis Function (RBF) had a better performance of prediction accuracy for both calibration set (94.3%) and validation set (96.2%) than other models. Relatively lower prediction abilities were observed for GA-BP (52.1%) and PLS (31.7%). In addition, the kernel function of SVM played an essential role of model training when the prediction accuracy of SVM with polynomial kernel function was 32.9%. As a powerful multivariate statistics method, SVM holds great potential to assess beer quality. Copyright © 2014 Elsevier Ltd. All rights reserved.
Online Graph Completion: Multivariate Signal Recovery in Computer Vision.
Kim, Won Hwa; Jalal, Mona; Hwang, Seongjae; Johnson, Sterling C; Singh, Vikas
2017-07-01
The adoption of "human-in-the-loop" paradigms in computer vision and machine learning is leading to various applications where the actual data acquisition (e.g., human supervision) and the underlying inference algorithms are closely interwined. While classical work in active learning provides effective solutions when the learning module involves classification and regression tasks, many practical issues such as partially observed measurements, financial constraints and even additional distributional or structural aspects of the data typically fall outside the scope of this treatment. For instance, with sequential acquisition of partial measurements of data that manifest as a matrix (or tensor), novel strategies for completion (or collaborative filtering) of the remaining entries have only been studied recently. Motivated by vision problems where we seek to annotate a large dataset of images via a crowdsourced platform or alternatively, complement results from a state-of-the-art object detector using human feedback, we study the "completion" problem defined on graphs, where requests for additional measurements must be made sequentially. We design the optimization model in the Fourier domain of the graph describing how ideas based on adaptive submodularity provide algorithms that work well in practice. On a large set of images collected from Imgur, we see promising results on images that are otherwise difficult to categorize. We also show applications to an experimental design problem in neuroimaging.
Directory of Open Access Journals (Sweden)
2016-12-01
Full Text Available This paper is on data analysis strategy in a complex, multidimensional, and dynamic domain. The focus is on the use of data mining techniques to explore the importance of multivariate structures; using climate variables which influences climate change. Techniques involved in data mining exercise vary according to the data structures. The multivariate analysis strategy considered here involved choosing an appropriate tool to analyze a process. Factor analysis is introduced into data mining technique in order to reveal the influencing impacts of factors involved as well as solving for multicolinearity effect among the variables. The temporal nature and multidimensionality of the target variables is revealed in the model using multidimensional regression estimates. The strategy of integrating the method of several statistical techniques, using climate variables in Nigeria was employed. R2 of 0.518 was obtained from the ordinary least square regression analysis carried out and the test was not significant at 5% level of significance. However, factor analysis regression strategy gave a good fit with R2 of 0.811 and the test was significant at 5% level of significance. Based on this study, model building should go beyond the usual confirmatory data analysis (CDA, rather it should be complemented with exploratory data analysis (EDA in order to achieve a desired result.
Ellipsoidal prediction regions for multivariate uncertainty characterization
DEFF Research Database (Denmark)
Golestaneh, Faranak; Pinson, Pierre; Azizipanah-Abarghooee, Rasoul
2018-01-01
While substantial advances are observed in probabilistic forecasting for power system operation and electricity market applications, most approaches are still developed in a univariate framework. This prevents from informing about the interdependence structure among locations, lead times and vari......While substantial advances are observed in probabilistic forecasting for power system operation and electricity market applications, most approaches are still developed in a univariate framework. This prevents from informing about the interdependence structure among locations, lead times...... probability guarantees and minimum conservativeness. A skill score is proposed for quantitative assessment of the quality of prediction ellipsoids. A set of experiments is used to illustrate the discrimination ability of the proposed scoring rule for potential misspeciﬁcation of ellipsoidal prediction regions...
Synthetic environmental indicators: A conceptual approach from the multivariate statistics
International Nuclear Information System (INIS)
Escobar J, Luis A
2008-01-01
This paper presents a general description of multivariate statistical analysis and shows two methodologies: analysis of principal components and analysis of distance, DP2. Both methods use techniques of multivariate analysis to define the true dimension of data, which is useful to estimate indicators of environmental quality.
Simulations of full multivariate Tweedie with flexible dependence structure
DEFF Research Database (Denmark)
Cuenin, Johann; Jørgensen, Bent; Kokonendji, Célestin C.
2016-01-01
The paper introduces a variables-in-common method for constructing and simulating multivariate Tweedie distribution, based on linear combinations of independent univariate Tweedie variables. The method is facilitated by the convolution and scaling properties of the Tweedie distributions, using....... The method allows simulation of multivariate distributions from many known, including the Gaussian, Poisson, non-central gamma, gamma and inverse Gaussian distributions....
Optimal non-periodic inspection for a multivariate degradation model
Barker, C.T.; Newby, M.J.
2009-01-01
We address the problem of determining inspection and maintenance strategy for a system whose state is described by a multivariate stochastic process. We relax and extend the usual approaches. The system state is a multivariate stochastic process, decisions are based on a performance measure defined
Multivariate missing data in hydrology - Review and applications
Ben Aissia, Mohamed-Aymen; Chebana, Fateh; Ouarda, Taha B. M. J.
2017-12-01
Water resources planning and management require complete data sets of a number of hydrological variables, such as flood peaks and volumes. However, hydrologists are often faced with the problem of missing data (MD) in hydrological databases. Several methods are used to deal with the imputation of MD. During the last decade, multivariate approaches have gained popularity in the field of hydrology, especially in hydrological frequency analysis (HFA). However, treating the MD remains neglected in the multivariate HFA literature whereas the focus has been mainly on the modeling component. For a complete analysis and in order to optimize the use of data, MD should also be treated in the multivariate setting prior to modeling and inference. Imputation of MD in the multivariate hydrological framework can have direct implications on the quality of the estimation. Indeed, the dependence between the series represents important additional information that can be included in the imputation process. The objective of the present paper is to highlight the importance of treating MD in multivariate hydrological frequency analysis by reviewing and applying multivariate imputation methods and by comparing univariate and multivariate imputation methods. An application is carried out for multiple flood attributes on three sites in order to evaluate the performance of the different methods based on the leave-one-out procedure. The results indicate that, the performance of imputation methods can be improved by adopting the multivariate setting, compared to mean substitution and interpolation methods, especially when using the copula-based approach.
I - Multivariate Classification and Machine Learning in HEP
CERN. Geneva
2016-01-01
Traditional multivariate methods for classification (Stochastic Gradient Boosted Decision Trees and Multi-Layer Perceptrons) are explained in theory and practise using examples from HEP. General aspects of multivariate classification are discussed, in particular different regularisation techniques. Afterwards, data-driven techniques are introduced and compared to MC-based methods.
The analysis of multivariate group differences using common principal components
Bechger, T.M.; Blanca, M.J.; Maris, G.
2014-01-01
Although it is simple to determine whether multivariate group differences are statistically significant or not, such differences are often difficult to interpret. This article is about common principal components analysis as a tool for the exploratory investigation of multivariate group differences
Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models
Price, Larry R.
2012-01-01
The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…
Multivariate Analysis and Machine Learning in Cerebral Palsy Research
Directory of Open Access Journals (Sweden)
Jing Zhang
2017-12-01
Full Text Available Cerebral palsy (CP, a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP.
Multivariate Analysis and Machine Learning in Cerebral Palsy Research.
Zhang, Jing
2017-01-01
Cerebral palsy (CP), a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML) approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP.
A Range-Based Multivariate Model for Exchange Rate Volatility
B. Tims (Ben); R.J. Mahieu (Ronald)
2003-01-01
textabstractIn this paper we present a parsimonious multivariate model for exchange rate volatilities based on logarithmic high-low ranges of daily exchange rates. The multivariate stochastic volatility model divides the log range of each exchange rate into two independent latent factors, which are
A remark on partial linear spaces of girth 5 with an application to strongly regular graphs
Brouwer, A.E.; Neumaier, A.
1988-01-01
We derive a lower bound on the number of points of a partial linear space of girth 5. As an application, certain strongly regular graphs with=2 are ruled out by observing that the first subconstituents are partial linear spaces.
Directory of Open Access Journals (Sweden)
Shikha Awasthi
2017-06-01
Full Text Available Analysis of emission from laser-induced plasma has a unique capability for quantifying the major and minor elements present in any type of samples under optimal analysis conditions. Chemometric techniques are very effective and reliable tools for quantification of multiple components in complex matrices. The feasibility of laser-induced breakdown spectroscopy (LIBS in combination with multivariate analysis was investigated for the analysis of environmental reference materials (RMs. In the present work, different (Certified/Standard Reference Materials of soil and plant origin were analyzed using LIBS and the presence of Al, Ca, Mg, Fe, K, Mn and Si were identified in the LIBS spectra of these materials. Multivariate statistical methods (Partial Least Square Regression and Partial Least Square Discriminant Analysis were employed for quantitative analysis of the constituent elements using the LIBS spectral data. Calibration models were used to predict the concentrations of the different elements of test samples and subsequently, the concentrations were compared with certified concentrations to check the authenticity of models. The non-destructive analytical method namely Instrumental Neutron Activation Analysis (INAA using high flux reactor neutrons and high resolution gamma-ray spectrometry was also used for intercomparison of results of two RMs by LIBS.
Geladi, Paul; Nelson, Andrew; Lindholm-Sethson, Britta
2007-07-09
Electrical impedance gives multivariate complex number data as results. Two examples of multivariate electrical impedance data measured on lipid monolayers in different solutions give rise to matrices (16x50 and 38x50) of complex numbers. Multivariate data analysis by principal component analysis (PCA) or singular value decomposition (SVD) can be used for complex data and the necessary equations are given. The scores and loadings obtained are vectors of complex numbers. It is shown that the complex number PCA and SVD are better at concentrating information in a few components than the naïve juxtaposition method and that Argand diagrams can replace score and loading plots. Different concentrations of Magainin and Gramicidin A give different responses and also the role of the electrolyte medium can be studied. An interaction of Gramicidin A in the solution with the monolayer over time can be observed.
Fractional and multivariable calculus model building and optimization problems
Mathai, A M
2017-01-01
This textbook presents a rigorous approach to multivariable calculus in the context of model building and optimization problems. This comprehensive overview is based on lectures given at five SERC Schools from 2008 to 2012 and covers a broad range of topics that will enable readers to understand and create deterministic and nondeterministic models. Researchers, advanced undergraduate, and graduate students in mathematics, statistics, physics, engineering, and biological sciences will find this book to be a valuable resource for finding appropriate models to describe real-life situations. The first chapter begins with an introduction to fractional calculus moving on to discuss fractional integrals, fractional derivatives, fractional differential equations and their solutions. Multivariable calculus is covered in the second chapter and introduces the fundamentals of multivariable calculus (multivariable functions, limits and continuity, differentiability, directional derivatives and expansions of multivariable ...
International Nuclear Information System (INIS)
Tapper, U.A.S.; Malmqvist, K.G.; Loevestam, N.E.G.; Swietlicki, E.; Salford, L.G.
1991-01-01
The importance of statistical evaluation of multielemental data is illustrated using the data collected in a macro- and micro-PIXE analysis of human brain tumours. By employing a multivariate statistical classification methodology (SIMCA) it was shown that the total information collected from each specimen separates three types of tissue: High malignant, less malignant and normal brain tissue. This makes a classification of a given specimen possible based on the elemental concentrations. Partial least squares regression (PLS), a multivariate regression method, made it possible to study the relative importance of the examined nine trace elements, the dry/wet weight ratio and the age of the patient in predicting the survival time after operation for patients with the high malignant form, astrocytomas grade III-IV. The elemental maps from a microprobe analysis were also subjected to multivariate analysis. This showed that the six elements sorted into maps could be presented in three maps containing all the relevant information. The intensity in these maps is proportional to the value (score) of the actual pixel along the calculated principal components. (orig.)
Chung, Jae Ho; Cho, Seok Hyun; Jeong, Jin Hyeok; Park, Chul Won; Lee, Seung Hwan
2015-09-01
To evaluate clinical characteristics and possible associated factors of idiopathic sudden sensorineural hearing loss (ISSNHL) in children using univariate and multivariate analyses. A retrospective case series with comparisons. From January 2007 to December 2013, medical records of 37 pediatric ISSNHL patients were reviewed to assess hearing recovery rate and examine factors associated with prognosis (gender; side of hearing loss; opposite side hearing loss; treatment onset; presence of vertigo, tinnitus, and ear fullness; initial hearing threshold), using univariate and multivariate analysis, and compare them with 276 adult ISSNHL patients. Pediatric patients comprised only 6.6% of pediatric/adult cases of ISSNHL, and those below 10 years old were only 0.7%. The overall recovery rates (complete and partial) of the pediatric and adult patients were 57.4% and 47.2%, respectively. The complete recovery rate of the pediatric group (46.6%) was higher than that of the adult group (30.8%, P = .040). According to multivariate analysis, absence of tinnitus, later onset of treatment, and higher hearing threshold at initial presentation were associated with a poor prognosis in pediatric ISSNHL. The recovery rate of ISSNHL in pediatric patients is higher than in adults, and the presence of tinnitus and earlier treatment onset is associated with favorable outcomes. 4. © 2015 The American Laryngological, Rhinological and Otological Society, Inc.
Ensemble preprocessing of near-infrared (NIR) spectra for multivariate calibration
International Nuclear Information System (INIS)
Xu Lu; Zhou Yanping; Tang Lijuan; Wu Hailong; Jiang Jianhui; Shen Guoli; Yu Ruqin
2008-01-01
Preprocessing of raw near-infrared (NIR) spectral data is indispensable in multivariate calibration when the measured spectra are subject to significant noises, baselines and other undesirable factors. However, due to the lack of sufficient prior information and an incomplete knowledge of the raw data, NIR spectra preprocessing in multivariate calibration is still trial and error. How to select a proper method depends largely on both the nature of the data and the expertise and experience of the practitioners. This might limit the applications of multivariate calibration in many fields, where researchers are not very familiar with the characteristics of many preprocessing methods unique in chemometrics and have difficulties to select the most suitable methods. Another problem is many preprocessing methods, when used alone, might degrade the data in certain aspects or lose some useful information while improving certain qualities of the data. In order to tackle these problems, this paper proposes a new concept of data preprocessing, ensemble preprocessing method, where partial least squares (PLSs) models built on differently preprocessed data are combined by Monte Carlo cross validation (MCCV) stacked regression. Little or no prior information of the data and expertise are required. Moreover, fusion of complementary information obtained by different preprocessing methods often leads to a more stable and accurate calibration model. The investigation of two real data sets has demonstrated the advantages of the proposed method
Partially massless fields during inflation
Baumann, Daniel; Goon, Garrett; Lee, Hayden; Pimentel, Guilherme L.
2018-04-01
The representation theory of de Sitter space allows for a category of partially massless particles which have no flat space analog, but could have existed during inflation. We study the couplings of these exotic particles to inflationary perturbations and determine the resulting signatures in cosmological correlators. When inflationary perturbations interact through the exchange of these fields, their correlation functions inherit scalings that cannot be mimicked by extra massive fields. We discuss in detail the squeezed limit of the tensor-scalar-scalar bispectrum, and show that certain partially massless fields can violate the tensor consistency relation of single-field inflation. We also consider the collapsed limit of the scalar trispectrum, and find that the exchange of partially massless fields enhances its magnitude, while giving no contribution to the scalar bispectrum. These characteristic signatures provide clean detection channels for partially massless fields during inflation.
Introduction to partial differential equations
Greenspan, Donald
2000-01-01
Designed for use in a one-semester course by seniors and beginning graduate students, this rigorous presentation explores practical methods of solving differential equations, plus the unifying theory underlying the mathematical superstructure. Topics include basic concepts, Fourier series, second-order partial differential equations, wave equation, potential equation, heat equation, approximate solution of partial differential equations, and more. Exercises appear at the ends of most chapters. 1961 edition.
[Acrylic resin removable partial dentures].
de Baat, C; Witter, D J; Creugers, N H J
2011-01-01
An acrylic resin removable partial denture is distinguished from other types of removable partial dentures by an all-acrylic resin base which is, in principle, solely supported by the edentulous regions of the tooth arch and in the maxilla also by the hard palate. When compared to the other types of removable partial dentures, the acrylic resin removable partial denture has 3 favourable aspects: the economic aspect, its aesthetic quality and the ease with which it can be extended and adjusted. Disadvantages are an increased risk of caries developing, gingivitis, periodontal disease, denture stomatitis, alveolar bone reduction, tooth migration, triggering of the gag reflex and damage to the acrylic resin base. Present-day indications are ofa temporary or palliative nature or are motivated by economic factors. Special varieties of the acrylic resin removable partial denture are the spoon denture, the flexible denture fabricated of non-rigid acrylic resin, and the two-piece sectional denture. Furthermore, acrylic resin removable partial dentures can be supplied with clasps or reinforced by fibers or metal wires.
Treatment planning: implant-supported partial overdentures.
Chee, Winston W L
2005-04-01
When multiple anterior teeth are missing, many options of replacement are available. Traditionally, the choice was between a fixed or removable prostheses. Today, with the predictability of dental implants, the options of tooth replacement range from removable partial dentures to implant-supported fixed prostheses. The choice of which restoration that will best provide occlusion and esthetics depends on multiple factors including the number and location of missing teeth, the residual ridge form in relation to the replacement teeth, the relationship of the maxillary and mandibular anterior teeth, the condition of teeth adjacent to the edentulous span, the amount of bone available for implant placement, the patients "smile line" and display of teeth, lip support, and financial constraints. When there is minimal loss of the ridge contour, restorations that emerge from the ridge are the most functional and esthetic restorations, adhesive-type fixed partial dentures, conventional fixed partial dentures, and implant-supported restorations can be indicated with the choice of restoration dependent on a risk benefit and cost benefit analysis. When there is a loss of ridge contour due to residual ridge resorption or trauma, the decision becomes more complex as not only does the tooth structure need to be replaced, the ridge form also has to be replaced. (Figures 1 and 2). This can be assessed clinically as illustrated by Figures 1 and 2 where a dis crepancy in arch form and ridge form in relation to the adjacent teeth and/or opposing arch can be observed. Other considerations are lip support and display of the teeth when smiling. This article presents a case and rationale for implant-supported par tial overdentures. Many authors have written on the merits of com plete overdentures. The complete overdenture has proven to be an improvement over conventional complete prostheses with respect to chewing efficiency, patient comfort and satisfaction. In partial edentulism, the
Monotherapy for partial epilepsy: focus on levetiracetam
Directory of Open Access Journals (Sweden)
Antonio Gambardella
2008-03-01
Full Text Available Antonio Gambardella1,2, Angelo Labate1,2, Eleonora Colosimo1, Roberta Ambrosio1, Aldo Quattrone1,21Institute of Neurology, University Magna Græcia, Catanzaro, Italy; 2Institute of Neurological Sciences, National Research Council, Piano Lago di Mangone, Cosenza, ItalyAbstract: Levetiracetam (LEV, the S-enantiomer of alpha-ethyl-2-oxo-1-pyrollidine acetamide, is a recently licensed antiepileptic drug (AED for adjunctive therapy of partial seizures. Its mechanism of action is uncertain but it exhibits a unique profile of anticonvulsant activity in models of chronic epilepsy. Five randomized, double-blind, placebo-controlled trials enrolling adult or pediatric patients with refractory partial epilepsy have demonstrated the efficacy of LEV as adjunctive therapy, with a responder rate (≥50% reduction in seizure frequency of 28%–45%. Long-term efficacy studies suggest retention rates of 60% after one year, with 13% of patients seizure-free for 6 months of the study and 8% seizure-free for 1 year. More recent studies illustrated successful conversion to monotherapy in patients with refractory epilepsy, and its effectiveness as a single agent in partial epilepsy. LEV has also efficacy in generalized epilepsies. Adverse effects of LEV, including somnolence, lethargy, and dizziness, are generally mild and their occurrence rate seems to be not significantly different from that observed in placebo groups. LEV also has no clinically significant pharmacokinetic interactions with other AEDs, or with commonly prescribed medications. The combination of effective antiepileptic properties with a relatively mild adverse effect profile makes LEV an attractive therapy for partial seizures.Keywords: levetiracetam, partial epilepsy, antiepileptic drugs
Antioxidant activity of Costa Rican propolis: a multivariate analysis approach
International Nuclear Information System (INIS)
Umana Rojas, Eduardo; Solado, Godofredo; Tamayo-Castillo, Giselle
2013-01-01
Propolis is produced by Apis mellifera bees from resins of plants that are found around the apiary. The chemical composition is highly variable and Costa Rica has reported without studies of characterization to define the types of propolis in the country. 119 samples were collected from beekeeping areas of the country. The spectrum of 1 H-NMR and its antioxidant activity against DPPH radical were measured. The spectra have been divided into 243 blocks of 0,04 ppm and processed with the Minitab software for multivariate analysis. 99 of the samples collected were used for construction of models for the valuation of the predictive ability of the model have been used coefficients of determination (R 2 ) of prediction by the software and the remaining 20 samples. The existence of three types of propolis with chemically different metabolomes were determined by principal component analysis (PCA). A prediction model was constructed by analysis of partial least squares (PLS). The prediction model has allowed to classify a propolis according to the level of antioxidant activity (AAO), high (type I and II) or low (type III) from the spectrum of 1 H-NMR. The R 2 has been 0.88 and R 2 prediction of 0, 718 for new samples. The nconiferyl benzoate of group I and nemorosone of the group II as two discriminated antioxidants among the groups I and II were isolated and high concentration levels of these compounds have been differentiated with respect to type III. This has allowed the construction of a linear discriminant model with a success rate of 100% for the samples used for formulation and 92,9 for the prediction of different samples. The classification systems could be applied to the standardization of the quality of propolis from Costa Rica for future medicinal or cosmetic applications that take advantage of its antioxidant properties. Also, the methylated derivative has isolated and identified of the nconiferyl benzoate thereof propolis than was obtained his counterpart
Partial avascular necrosis after talar neck fracture.
Babu, Nina; Schuberth, John M
2010-09-01
Recently, it has been shown that avascular necrosis of the talus can occur in only a portion of the talar body. There is little information regarding the geographic location of the avascular segment and the clinical significance of an incomplete avascular process. Seven patients with partial avascular necrosis after Hawkins type II or III fracture dislocations were evaluated with magnetic resonance scans. The precise anatomic location of the avascular segment was determined and assigned to a specific quadrant of the talar body. The operative exposure, incidence of collapse, and time to operative intervention was recorded. The avascular segment of the talar body was located predominantly in the anterior lateral and superior portion in six of the seven patients. Collapse occurred in three of the patients in the area of avascular process. There were no observable trends with regard to operative exposure, Hawkins classification, incidence of collapse, or time to operative intervention to the location of the avascular segment. Partial avascular necrosis can occur after fracture dislocation of the talus. The predominant location of the avascular segment was the anterior lateral and superior portion of the talar body. This observation corresponds to regional damage to the blood supply of the talus and may help clarify the pathogenesis of partial avascular process.
Does partial occlusion promote normal binocular function?
Li, Jingrong; Thompson, Benjamin; Ding, Zhaofeng; Chan, Lily Y L; Chen, Xiang; Yu, Minbin; Deng, Daming; Hess, Robert F
2012-10-03
There is growing evidence that abnormal binocular interactions play a key role in the amblyopia syndrome and represent a viable target for treatment interventions. In this context the use of partial occlusion using optical devices such as Bangerter filters as an alternative to complete occlusion is of particular interest. The aims of this study were to understand why Bangerter filters do not result in improved binocular outcomes compared to complete occlusion, and to compare the effects of Bangerter filters, optical blur and neutral density (ND) filters on normal binocular function. The effects of four strengths of Bangerter filters (0.8, 0.6, 0.4, 0.2) on letter and vernier acuity, contrast sensitivity, stereoacuity, and interocular suppression were measured in 21 observers with normal vision. In a subset of 14 observers, the partial occlusion effects of Bangerter filters, ND filters and plus lenses on stereopsis and interocular suppression were compared. Bangerter filters did not have graded effect on vision and induced significant disruption to binocular function. This disruption was greater than that of monocular defocus but weaker than that of ND filters. The effect of the Bangerter filters on stereopsis was more pronounced than their effect on monocular acuity, and the induced monocular acuity deficits did not predict the induced deficits in stereopsis. Bangerter filters appear to be particularly disruptive to binocular function. Other interventions, such as optical defocus and those employing computer generated dichoptic stimulus presentation, may be more appropriate than partial occlusion for targeting binocular function during amblyopia treatment.
Response of horizontal magnetic field variation to partial counter electrojet
International Nuclear Information System (INIS)
Mukunda Rao, M.; Alamelu, V.
1985-01-01
The partial counter electrojet events seen through backscatter observations are examined in terms of the parameter ΔH'sub(CEJ)=(Hsub(equator)-Hsub(low lat.))sub(CEJday)-(Hsub(equator)-Hsub(low lat.))sub(mean 1Qday). It is found that the parameter indicates most of the changes in the electrojet observed through the backscatter observations. (author)
A Multivariate Approach to Functional Neuro Modeling
DEFF Research Database (Denmark)
Mørch, Niels J.S.
1998-01-01
by the application of linear and more flexible, nonlinear microscopic regression models to a real-world dataset. The dependency of model performance, as quantified by generalization error, on model flexibility and training set size is demonstrated, leading to the important realization that no uniformly optimal model......, provides the basis for a generalization theoretical framework relating model performance to model complexity and dataset size. Briefly summarized the major topics discussed in the thesis include: - An introduction of the representation of functional datasets by pairs of neuronal activity patterns...... exists. - Model visualization and interpretation techniques. The simplicity of this task for linear models contrasts the difficulties involved when dealing with nonlinear models. Finally, a visualization technique for nonlinear models is proposed. A single observation emerges from the thesis...
Local cerebral metabolism during partial seizures
International Nuclear Information System (INIS)
Engel, J. Jr.; Kuhl, D.E.; Phelps, M.E.; Rausch, R.; Nuwer, M.
1983-01-01
Interictal and ictal fluorodeoxyglucose scans were obtained with positron CT from four patients with spontaneous recurrent partial seizures, one with epilepsia partialis continua, and one with a single partial seizure induced by electrical stimulation of the hippocampus. Ictal metabolic patterns were different for each patient studied. Focal and generalized increased and decreased metabolism were observed. Ictal hypermetabolism may exceed six times the interictal rate and could represent activation of excitatory or inhibitory synapses in the epileptogenic region and its projection fields. Hypometabolism seen on ictal scans most likely reflects postictal depression and may indicate projection fields of inhibited neurons. No quantitative relationship between alterations in metabolism and EEG or behavioral measurements of ictal events could be demonstrated
Local cerebral metabolism during partial seizures
Energy Technology Data Exchange (ETDEWEB)
Engel, J. Jr.; Kuhl, D.E.; Phelps, M.E.; Rausch, R.; Nuwer, M.
1983-04-01
Interictal and ictal fluorodeoxyglucose scans were obtained with positron CT from four patients with spontaneous recurrent partial seizures, one with epilepsia partialis continua, and one with a single partial seizure induced by electrical stimulation of the hippocampus. Ictal metabolic patterns were different for each patient studied. Focal and generalized increased and decreased metabolism were observed. Ictal hypermetabolism may exceed six times the interictal rate and could represent activation of excitatory or inhibitory synapses in the epileptogenic region and its projection fields. Hypometabolism seen on ictal scans most likely reflects postictal depression and may indicate projection fields of inhibited neurons. No quantitative relationship between alterations in metabolism and EEG or behavioral measurements of ictal events could be demonstrated.
Partial cross sections in H- photodetachment
International Nuclear Information System (INIS)
Halka, M.
1993-04-01
This dissertation reports experimental measurements of partial decay cross sections in the H - photodetachment spectrum. Observed decays of the 1 P 0 H -** (n) doubly-excitedresonances to the H(N=2) continuum are reported for n=2,3, and 4 from 1990 runs in which the author participated. A recent analysis of 1989 data revealing effects of static electric fields on the partial decay spectrum above 13.5 eV is also presented. The experiments were performed at the High Resolution Atomic Beam Facility. the Los Alamos Meson Physics Facility, with a relativistic H - beam (β=0.842)intersecting a ND:YAG laser. Variation of the intersection angle amounts to Doppler-shifting the photon energy, allowing continuous tuning of the laser energy as viewed from the moving ions' frame
Multivariate Local Polynomial Regression with Application to Shenzhen Component Index
Directory of Open Access Journals (Sweden)
Liyun Su
2011-01-01
Full Text Available This study attempts to characterize and predict stock index series in Shenzhen stock market using the concepts of multivariate local polynomial regression. Based on nonlinearity and chaos of the stock index time series, multivariate local polynomial prediction methods and univariate local polynomial prediction method, all of which use the concept of phase space reconstruction according to Takens' Theorem, are considered. To fit the stock index series, the single series changes into bivariate series. To evaluate the results, the multivariate predictor for bivariate time series based on multivariate local polynomial model is compared with univariate predictor with the same Shenzhen stock index data. The numerical results obtained by Shenzhen component index show that the prediction mean squared error of the multivariate predictor is much smaller than the univariate one and is much better than the existed three methods. Even if the last half of the training data are used in the multivariate predictor, the prediction mean squared error is smaller than the univariate predictor. Multivariate local polynomial prediction model for nonsingle time series is a useful tool for stock market price prediction.
Adjustment of geochemical background by robust multivariate statistics
Zhou, D.
1985-01-01
Conventional analyses of exploration geochemical data assume that the background is a constant or slowly changing value, equivalent to a plane or a smoothly curved surface. However, it is better to regard the geochemical background as a rugged surface, varying with changes in geology and environment. This rugged surface can be estimated from observed geological, geochemical and environmental properties by using multivariate statistics. A method of background adjustment was developed and applied to groundwater and stream sediment reconnaissance data collected from the Hot Springs Quadrangle, South Dakota, as part of the National Uranium Resource Evaluation (NURE) program. Source-rock lithology appears to be a dominant factor controlling the chemical composition of groundwater or stream sediments. The most efficacious adjustment procedure is to regress uranium concentration on selected geochemical and environmental variables for each lithologic unit, and then to delineate anomalies by a common threshold set as a multiple of the standard deviation of the combined residuals. Robust versions of regression and RQ-mode principal components analysis techniques were used rather than ordinary techniques to guard against distortion caused by outliers Anomalies delineated by this background adjustment procedure correspond with uranium prospects much better than do anomalies delineated by conventional procedures. The procedure should be applicable to geochemical exploration at different scales for other metals. ?? 1985.
HORIZONTAL BRANCH MORPHOLOGY OF GLOBULAR CLUSTERS: A MULTIVARIATE STATISTICAL ANALYSIS
International Nuclear Information System (INIS)
Jogesh Babu, G.; Chattopadhyay, Tanuka; Chattopadhyay, Asis Kumar; Mondal, Saptarshi
2009-01-01
The proper interpretation of horizontal branch (HB) morphology is crucial to the understanding of the formation history of stellar populations. In the present study a multivariate analysis is used (principal component analysis) for the selection of appropriate HB morphology parameter, which, in our case, is the logarithm of effective temperature extent of the HB (log T effHB ). Then this parameter is expressed in terms of the most significant observed independent parameters of Galactic globular clusters (GGCs) separately for coherent groups, obtained in a previous work, through a stepwise multiple regression technique. It is found that, metallicity ([Fe/H]), central surface brightness (μ v ), and core radius (r c ) are the significant parameters to explain most of the variations in HB morphology (multiple R 2 ∼ 0.86) for GGC elonging to the bulge/disk while metallicity ([Fe/H]) and absolute magnitude (M v ) are responsible for GGC belonging to the inner halo (multiple R 2 ∼ 0.52). The robustness is tested by taking 1000 bootstrap samples. A cluster analysis is performed for the red giant branch (RGB) stars of the GGC belonging to Galactic inner halo (Cluster 2). A multi-episodic star formation is preferred for RGB stars of GGC belonging to this group. It supports the asymptotic giant branch (AGB) model in three episodes instead of two as suggested by Carretta et al. for halo GGC while AGB model is suggested to be revisited for bulge/disk GGC.
Air Quality Pattern Assessment in Malaysia Using Multivariate Techniques
International Nuclear Information System (INIS)
Hamza Ahmad Isiyaka; Azman Azid
2015-01-01
This study aims to investigate the spatial characteristics in the pattern of air quality monitoring sites, identify the most discriminating parameters contributing to air pollution, and predict the level of air pollution index (API) in Malaysia using multivariate techniques. Five parameters observed for five years (2000-2004) were used. Hierarchical agglomerative cluster analysis classified the five air quality monitoring sites into two independent groups based on the characteristics of activities in the monitoring stations. Discriminate analysis for standard, backward stepwise and forward stepwise mode gave a correct assignation of more than 87 % in the confusion matrix. This result indicates that only three parameters (PM_1_0, SO_2 and NO_2) with a p<0.0001 discriminate best in polluting the air. The major possible sources of air pollution were identified using principal component analysis that account for more than 58 % and 60 % in the total variance. Based on the findings, anthropogenic activities (vehicular emission, industrial activities, construction sites, bush burning) have a strong influence in the source of air pollution. Furthermore, artificial neural network (ANN) was used to predict the level of air pollution index at R"2 = 0.8493 and RMSE = 5.9184. This indicates that ANN can predict more than 84 % of the API. (author)
A general, multivariate definition of causal effects in epidemiology.
Flanders, W Dana; Klein, Mitchel
2015-07-01
Population causal effects are often defined as contrasts of average individual-level counterfactual outcomes, comparing different exposure levels. Common examples include causal risk difference and risk ratios. These and most other examples emphasize effects on disease onset, a reflection of the usual epidemiological interest in disease occurrence. Exposure effects on other health characteristics, such as prevalence or conditional risk of a particular disability, can be important as well, but contrasts involving these other measures may often be dismissed as non-causal. For example, an observed prevalence ratio might often viewed as an estimator of a causal incidence ratio and hence subject to bias. In this manuscript, we provide and evaluate a definition of causal effects that generalizes those previously available. A key part of the generalization is that contrasts used in the definition can involve multivariate, counterfactual outcomes, rather than only univariate outcomes. An important consequence of our generalization is that, using it, one can properly define causal effects based on a wide variety of additional measures. Examples include causal prevalence ratios and differences and causal conditional risk ratios and differences. We illustrate how these additional measures can be useful, natural, easily estimated, and of public health importance. Furthermore, we discuss conditions for valid estimation of each type of causal effect, and how improper interpretation or inferences for the wrong target population can be sources of bias.
An extended multivariate framework for drought monitoring in Mexico
Real-Rangel, Roberto; Pedrozo-Acuña, Adrián; Breña-Naranjo, Agustín; Alcocer-Yamanaka, Víctor
2017-04-01
Around the world, monitoring natural hazards, such as droughts, represents a critical task in risk assessment and management plans. A reliable drought monitoring system allows to identify regions affected by these phenomena so that early response measures can be implemented. In Mexico, this activity is performed using Mexico's Drought Monitor, which is based on a similar methodology as the United States Drought Monitor and the North American Drought Monitor. The main feature of these monitoring systems is the combination of ground-based and remote sensing observations that is ultimately validated by local experts. However, in Mexico in situ records of variables such as precipitation and streamflow are often scarce, or even null, in many regions of the country. Another issue that adds uncertainty in drought monitoring is the arbitrary weight given to each analyzed variable. This study aims at providing an operational framework for drought monitoring in Mexico, based on univariate and multivariate nonparametric standardized indexes proposed in recent studies. Furthermore, the framework has been extended by taking into account the Enhanced Vegetation Index (EVI) for the drought severity assessment. The analyzed variables used for computing the drought indexes are mainly derived from remote sensing (MODIS) and land surface models datasets (NASA MERRA-2). A qualitative evaluation of the results shows that the indexes used are capable of adequately describes the intensity and spatial distribution of past drought documented events.
Effective network inference through multivariate information transfer estimation
Dahlqvist, Carl-Henrik; Gnabo, Jean-Yves
2018-06-01
Network representation has steadily gained in popularity over the past decades. In many disciplines such as finance, genetics, neuroscience or human travel to cite a few, the network may not directly be observable and needs to be inferred from time-series data, leading to the issue of separating direct interactions between two entities forming the network from indirect interactions coming through its remaining part. Drawing on recent contributions proposing strategies to deal with this problem such as the so-called "global silencing" approach of Barzel and Barabasi or "network deconvolution" of Feizi et al. (2013), we propose a novel methodology to infer an effective network structure from multivariate conditional information transfers. Its core principal is to test the information transfer between two nodes through a step-wise approach by conditioning the transfer for each pair on a specific set of relevant nodes as identified by our algorithm from the rest of the network. The methodology is model free and can be applied to high-dimensional networks with both inter-lag and intra-lag relationships. It outperforms state-of-the-art approaches for eliminating the redundancies and more generally retrieving simulated artificial networks in our Monte-Carlo experiments. We apply the method to stock market data at different frequencies (15 min, 1 h, 1 day) to retrieve the network of US largest financial institutions and then document how bank's centrality measurements relate to bank's systemic vulnerability.
Multivariable Dynamic Ankle Mechanical Impedance With Active Muscles
Lee, Hyunglae; Krebs, Hermano Igo; Hogan, Neville
2015-01-01
Multivariable dynamic ankle mechanical impedance in two coupled degrees-of-freedom (DOFs) was quantified when muscles were active. Measurements were performed at five different target activation levels of tibialis anterior and soleus, from 10% to 30% of maximum voluntary contraction (MVC) with increments of 5% MVC. Interestingly, several ankle behaviors characterized in our previous study of the relaxed ankle were observed with muscles active: ankle mechanical impedance in joint coordinates showed responses largely consistent with a second-order system consisting of inertia, viscosity, and stiffness; stiffness was greater in the sagittal plane than in the frontal plane at all activation conditions for all subjects; and the coupling between dorsiflexion–plantarflexion and inversion–eversion was small—the two DOF measurements were well explained by a strictly diagonal impedance matrix. In general, ankle stiffness increased linearly with muscle activation in all directions in the 2-D space formed by the sagittal and frontal planes, but more in the sagittal than in the frontal plane, resulting in an accentuated “peanut shape.” This characterization of young healthy subjects’ ankle mechanical impedance with active muscles will serve as a baseline to investigate pathophysiological ankle behaviors of biomechanically and/or neurologically impaired patients. PMID:25203497
Representations of Lie algebras and partial differential equations
Xu, Xiaoping
2017-01-01
This book provides explicit representations of finite-dimensional simple Lie algebras, related partial differential equations, linear orthogonal algebraic codes, combinatorics and algebraic varieties, summarizing the author’s works and his joint works with his former students. Further, it presents various oscillator generalizations of the classical representation theorem on harmonic polynomials, and highlights new functors from the representation category of a simple Lie algebra to that of another simple Lie algebra. Partial differential equations play a key role in solving certain representation problems. The weight matrices of the minimal and adjoint representations over the simple Lie algebras of types E and F are proved to generate ternary orthogonal linear codes with large minimal distances. New multi-variable hypergeometric functions related to the root systems of simple Lie algebras are introduced in connection with quantum many-body systems in one dimension. In addition, the book identifies certai...
Multivariate analysis for selecting apple mutants
International Nuclear Information System (INIS)
Faedi, W.; Bagnara, G.L.; Rosati, P.; Cecchini, M.
1992-01-01
The mutlivariate analysis of four year records on several vegetative and productive traits of twenty-one apple mutants (3 of 'Jonathan', 3 of 'Ozark Gold', 14 of 'Mollie's Delicious', 1 of 'Neipling's Early Stayman)' induced by gamma radiations showed that observation of some traits of one-year-old shoots is the most efficient way to reveal compact growing apple mutants. In particular, basal cross-section area, total length and leaf area resulted the most appropriate parameters, while internode length together with conopy height and width are less appropriate. The most interesting mutants we found are: one of 'Mollie's Delicious for the best balance among tree and fruit traits and for high skin color; one of 'Neipling's Early Stayman' with an earlier and more extensively red colored apple than the original clone. (author)
Multivariate localization methods for ensemble Kalman filtering
Roh, S.
2015-12-03
In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (element-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables that exist at the same locations has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.
Multivariate localization methods for ensemble Kalman filtering
Roh, S.
2015-05-08
In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (entry-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.
Multivariate localization methods for ensemble Kalman filtering
Roh, S.; Jun, M.; Szunyogh, I.; Genton, M. G.
2015-12-01
In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (element-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables that exist at the same locations has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.
Multivariate localization methods for ensemble Kalman filtering
Roh, S.; Jun, M.; Szunyogh, I.; Genton, Marc G.
2015-01-01
In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (entry-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.
Multivariate analysis of 2-DE protein patterns - Practical approaches
DEFF Research Database (Denmark)
Jacobsen, Charlotte; Jacobsen, Susanne; Grove, H.
2007-01-01
Practical approaches to the use of multivariate data analysis of 2-DE protein patterns are demonstrated by three independent strategies for the image analysis and the multivariate analysis on the same set of 2-DE data. Four wheat varieties were selected on the basis of their baking quality. Two...... of the varieties were of strong baking quality and hard wheat kernel and two were of weak baking quality and soft kernel. Gliadins at different stages of grain development were analyzed by the application of multivariate data analysis on images of 2-DEs. Patterns related to the wheat varieties, harvest times...
Alternating multivariate trigonometric functions and corresponding Fourier transforms
International Nuclear Information System (INIS)
Klimyk, A U; Patera, J
2008-01-01
We define and study multivariate sine and cosine functions, symmetric with respect to the alternating group A n , which is a subgroup of the permutation (symmetric) group S n . These functions are eigenfunctions of the Laplace operator. They determine Fourier-type transforms. There exist three types of such transforms: expansions into corresponding sine-Fourier and cosine-Fourier series, integral sine-Fourier and cosine-Fourier transforms, and multivariate finite sine and cosine transforms. In all these transforms, alternating multivariate sine and cosine functions are used as a kernel
Scale and shape mixtures of multivariate skew-normal distributions
Arellano-Valle, Reinaldo B.
2018-02-26
We introduce a broad and flexible class of multivariate distributions obtained by both scale and shape mixtures of multivariate skew-normal distributions. We present the probabilistic properties of this family of distributions in detail and lay down the theoretical foundations for subsequent inference with this model. In particular, we study linear transformations, marginal distributions, selection representations, stochastic representations and hierarchical representations. We also describe an EM-type algorithm for maximum likelihood estimation of the parameters of the model and demonstrate its implementation on a wind dataset. Our family of multivariate distributions unifies and extends many existing models of the literature that can be seen as submodels of our proposal.
(Anti)symmetric multivariate exponential functions and corresponding Fourier transforms
International Nuclear Information System (INIS)
Klimyk, A U; Patera, J
2007-01-01
We define and study symmetrized and antisymmetrized multivariate exponential functions. They are defined as determinants and antideterminants of matrices whose entries are exponential functions of one variable. These functions are eigenfunctions of the Laplace operator on the corresponding fundamental domains satisfying certain boundary conditions. To symmetric and antisymmetric multivariate exponential functions there correspond Fourier transforms. There are three types of such Fourier transforms: expansions into the corresponding Fourier series, integral Fourier transforms and multivariate finite Fourier transforms. Eigenfunctions of the integral Fourier transforms are found
Parachute technique for partial penectomy
Directory of Open Access Journals (Sweden)
Fernando Korkes
2010-04-01
Full Text Available PURPOSE: Penile carcinoma is a rare but mutilating malignancy. In this context, partial penectomy is the most commonly applied approach for best oncological results. We herein propose a simple modification of the classic technique of partial penectomy, for better cosmetic and functional results. TECHNIQUE: If partial penectomy is indicated, the present technique can bring additional benefits. Different from classical technique, the urethra is spatulated only ventrally. An inverted "V" skin flap with 0.5 cm of extension is sectioned ventrally. The suture is performed with vicryl 4-0 in a "parachute" fashion, beginning from the ventral portion of the urethra and the "V" flap, followed by the "V" flap angles and than by the dorsal portion of the penis. After completion of the suture, a Foley catheter and light dressing are placed for 24 hours. CONCLUSIONS: Several complex reconstructive techniques have been previously proposed, but normally require specific surgical abilities, adequate patient selection and staged procedures. We believe that these reconstructive techniques are very useful in some specific subsets of patients. However, the technique herein proposed is a simple alternative that can be applied to all men after a partial penectomy, and takes the same amount of time as that in the classic technique. In conclusion, the "parachute" technique for penile reconstruction after partial amputation not only improves the appearance of the penis, but also maintains an adequate function.
Yang, Zhong; Li, Kang; Zhang, Maomao; Xin, Donglin; Zhang, Junhua
2016-01-01
During conversion of bamboo into biofuels and chemicals, it is necessary to efficiently predict the chemical composition and digestibility of biomass. However, traditional methods for determination of lignocellulosic biomass composition are expensive and time consuming. In this work, a novel and fast method for quantitative and qualitative analysis of chemical composition and enzymatic digestibilities of juvenile bamboo and mature bamboo fractions (bamboo green, bamboo timber, bamboo yellow, bamboo node, and bamboo branch) using visible-near infrared spectra was evaluated. The developed partial least squares models yielded coefficients of determination in calibration of 0.88, 0.94, and 0.96, for cellulose, xylan, and lignin of bamboo fractions in raw spectra, respectively. After visible-near infrared spectra being pretreated, the corresponding coefficients of determination in calibration yielded by the developed partial least squares models are 0.994, 0.990, and 0.996, respectively. The score plots of principal component analysis of mature bamboo, juvenile bamboo, and different fractions of mature bamboo were obviously distinguished in raw spectra. Based on partial least squares discriminant analysis, the classification accuracies of mature bamboo, juvenile bamboo, and different fractions of bamboo (bamboo green, bamboo timber, bamboo yellow, and bamboo branch) all reached 100 %. In addition, high accuracies of evaluation of the enzymatic digestibilities of bamboo fractions after pretreatment with aqueous ammonia were also observed. The results showed the potential of visible-near infrared spectroscopy in combination with multivariate analysis in efficiently analyzing the chemical composition and hydrolysabilities of lignocellulosic biomass, such as bamboo fractions.
Partial Transposition on Bipartite System
International Nuclear Information System (INIS)
Xi-Jun, Ren; Yong-Jian, Han; Yu-Chun, Wu; Guang-Can, Guo
2008-01-01
Many properties of partial transposition are unclear as yet. Here we carefully consider the number of the negative eigenvalues of ρ T (ρ's partial transposition) when ρ is a two-partite state. There is strong evidence to show that the number of negative eigenvalues of ρ T is N(N − 1)/2 at most when ρ is a state in Hilbert space C N C N . For the special case, the 2 × 2 system, we use this result to give a partial proof of the conjecture |ρ T | T ≥ 0. We find that this conjecture is strongly connected with the entanglement of the state corresponding to the negative eigenvalue of ρ T or the negative entropy of ρ
International Nuclear Information System (INIS)
Maeda, Munehiro; Yoshiya, Kazuhiko; Suzuki, Eiji
1989-01-01
According to the direction and the thickness of the imaging slice in tomography, the border between the tissues becomes unclear (partial volume effect). In the present MRI experiment, we examined border area between fat and water components using phantom in order to investigate the partial volume effect in MRI. In spin echo sequences, the intensity of the border area showed a linear relationship with composition of fat and water. Whereas, in inversion recovery and field echo sequences, we found the parameters to produce an extremely low intensity area at the border region between fat and water. This low intensity area was explained by cancellation of NMR signals from fat and water due to the difference in the direction of magnetic vectors. Clinically, partial volume effect can cause of mis-evaluation of walls, small nodules, tumor capsules and the tumor invasion in the use of inversion recovery and field echo sequences. (author)
Partially composite Goldstone Higgs boson
DEFF Research Database (Denmark)
Alanne, Tommi; Franzosi, Diogo Buarque; Frandsen, Mads T.
2017-01-01
We consider a model of dynamical electroweak symmetry breaking with a partially composite Goldstone Higgs boson. The model is based on a strongly interacting fermionic sector coupled to a fundamental scalar sector via Yukawa interactions. The SU(4)×SU(4) global symmetry of these two sectors...... is broken to a single SU(4) via Yukawa interactions. Electroweak symmetry breaking is dynamically induced by condensation due to the strong interactions in the new fermionic sector which further breaks the global symmetry SU(4)→Sp(4). The Higgs boson arises as a partially composite state which is an exact...... Goldstone boson in the limit where SM interactions are turned off. Terms breaking the SU(4) global symmetry explicitly generate a mass for the Goldstone Higgs boson. The model realizes in different limits both (partially) composite Higgs and (bosonic) technicolor models, thereby providing a convenient...
Disturbance Error Reduction in Multivariable Optimal Control Systems
Directory of Open Access Journals (Sweden)
Ole A. Solheim
1983-01-01
Full Text Available The paper deals with the design of optimal multivariable controllers, using a modified LQR approach. All controllers discussed contain proportional feedback and, in addition, there may be feedforward, integral action or state estimation.
Classifying hot water chemistry: Application of MULTIVARIATE STATISTICS
Sumintadireja, Prihadi; Irawan, Dasapta Erwin; Rezky, Yuanno; Gio, Prana Ugiana; Agustin, Anggita
2016-01-01
This file is the dataset for the following paper "Classifying hot water chemistry: Application of MULTIVARIATE STATISTICS". Authors: Prihadi Sumintadireja1, Dasapta Erwin Irawan1, Yuano Rezky2, Prana Ugiana Gio3, Anggita Agustin1
Ultrawide Bandwidth Receiver Based on a Multivariate Generalized Gaussian Distribution
Ahmed, Qasim Zeeshan
2015-04-01
Multivariate generalized Gaussian density (MGGD) is used to approximate the multiple access interference (MAI) and additive white Gaussian noise in pulse-based ultrawide bandwidth (UWB) system. The MGGD probability density function (pdf) is shown to be a better approximation of a UWB system as compared to multivariate Gaussian, multivariate Laplacian and multivariate Gaussian-Laplacian mixture (GLM). The similarity between the simulated and the approximated pdf is measured with the help of modified Kullback-Leibler distance (KLD). It is also shown that MGGD has the smallest KLD as compared to Gaussian, Laplacian and GLM densities. A receiver based on the principles of minimum bit error rate is designed for the MGGD pdf. As the requirement is stringent, the adaptive implementation of the receiver is also carried out in this paper. Training sequence of the desired user is the only requirement when implementing the detector adaptively. © 2002-2012 IEEE.
Micro-Raman Imaging for Biology with Multivariate Spectral Analysis
Malvaso, Federica
2015-01-01
. The aim of the following thesis work is to analyze Raman maps related to three pairs of different cells, highlighting differences and similarities through multivariate algorithms. The first pair of analyzed cells are human embryonic stem cells (h
Sunspot Cycle Prediction Using Multivariate Regression and Binary ...
Indian Academy of Sciences (India)
49
Multivariate regression model has been derived based on the available cycles 1 .... The flare index correlates well with various parameters of the solar activity. ...... 32) Sabarinath A and Anilkumar A K 2011 A stochastic prediction model for the.
Methods for Analyzing Multivariate Phenotypes in Genetic Association Studies
Directory of Open Access Journals (Sweden)
Qiong Yang
2012-01-01
Full Text Available Multivariate phenotypes are frequently encountered in genetic association studies. The purpose of analyzing multivariate phenotypes usually includes discovery of novel genetic variants of pleiotropy effects, that is, affecting multiple phenotypes, and the ultimate goal of uncovering the underlying genetic mechanism. In recent years, there have been new method development and application of existing statistical methods to such phenotypes. In this paper, we provide a review of the available methods for analyzing association between a single marker and a multivariate phenotype consisting of the same type of components (e.g., all continuous or all categorical or different types of components (e.g., some are continuous and others are categorical. We also reviewed causal inference methods designed to test whether the detected association with the multivariate phenotype is truly pleiotropy or the genetic marker exerts its effects on some phenotypes through affecting the others.
Fast and Flexible Multivariate Time Series Subsequence Search
National Aeronautics and Space Administration — Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical...
Matrix-based introduction to multivariate data analysis
Adachi, Kohei
2016-01-01
This book enables readers who may not be familiar with matrices to understand a variety of multivariate analysis procedures in matrix forms. Another feature of the book is that it emphasizes what model underlies a procedure and what objective function is optimized for fitting the model to data. The author believes that the matrix-based learning of such models and objective functions is the fastest way to comprehend multivariate data analysis. The text is arranged so that readers can intuitively capture the purposes for which multivariate analysis procedures are utilized: plain explanations of the purposes with numerical examples precede mathematical descriptions in almost every chapter. This volume is appropriate for undergraduate students who already have studied introductory statistics. Graduate students and researchers who are not familiar with matrix-intensive formulations of multivariate data analysis will also find the book useful, as it is based on modern matrix formulations with a special emphasis on ...
Local subdifferentials and multivariational inequalities in Banach and Frechet spaces
Directory of Open Access Journals (Sweden)
Pavlo O. Kasyanov
2008-01-01
Full Text Available Some functional-topological concepts of subdifferential and locally subdifferential maps in Frechet spaces are established. Multivariational inequalities with an operator of the pseudo-monotone type, connected with subdifferential maps, are considered.
Multivariate Cryptography Based on Clipped Hopfield Neural Network.
Wang, Jia; Cheng, Lee-Ming; Su, Tong
2018-02-01
Designing secure and efficient multivariate public key cryptosystems [multivariate cryptography (MVC)] to strengthen the security of RSA and ECC in conventional and quantum computational environment continues to be a challenging research in recent years. In this paper, we will describe multivariate public key cryptosystems based on extended Clipped Hopfield Neural Network (CHNN) and implement it using the MVC (CHNN-MVC) framework operated in space. The Diffie-Hellman key exchange algorithm is extended into the matrix field, which illustrates the feasibility of its new applications in both classic and postquantum cryptography. The efficiency and security of our proposed new public key cryptosystem CHNN-MVC are simulated and found to be NP-hard. The proposed algorithm will strengthen multivariate public key cryptosystems and allows hardware realization practicality.
A simplified parsimonious higher order multivariate Markov chain model
Wang, Chao; Yang, Chuan-sheng
2017-09-01
In this paper, a simplified parsimonious higher-order multivariate Markov chain model (SPHOMMCM) is presented. Moreover, parameter estimation method of TPHOMMCM is give. Numerical experiments shows the effectiveness of TPHOMMCM.