Critical Behavior of the Annealed Ising Model on Random Regular Graphs

Science.gov (United States)

Can, Van Hao

2017-11-01

In Giardinà et al. (ALEA Lat Am J Probab Math Stat 13(1):121-161, 2016), the authors have defined an annealed Ising model on random graphs and proved limit theorems for the magnetization of this model on some random graphs including random 2-regular graphs. Then in Can (Annealed limit theorems for the Ising model on random regular graphs, arXiv:1701.08639, 2017), we generalized their results to the class of all random regular graphs. In this paper, we study the critical behavior of this model. In particular, we determine the critical exponents and prove a non standard limit theorem stating that the magnetization scaled by n^{3/4} converges to a specific random variable, with n the number of vertices of random regular graphs.

Learning Probabilistic Logic Models from Probabilistic Examples.

Science.gov (United States)

Chen, Jianzhong; Muggleton, Stephen; Santos, José

2008-10-01

We revisit an application developed originally using abductive Inductive Logic Programming (ILP) for modeling inhibition in metabolic networks. The example data was derived from studies of the effects of toxins on rats using Nuclear Magnetic Resonance (NMR) time-trace analysis of their biofluids together with background knowledge representing a subset of the Kyoto Encyclopedia of Genes and Genomes (KEGG). We now apply two Probabilistic ILP (PILP) approaches - abductive Stochastic Logic Programs (SLPs) and PRogramming In Statistical modeling (PRISM) to the application. Both approaches support abductive learning and probability predictions. Abductive SLPs are a PILP framework that provides possible worlds semantics to SLPs through abduction. Instead of learning logic models from non-probabilistic examples as done in ILP, the PILP approach applied in this paper is based on a general technique for introducing probability labels within a standard scientific experimental setting involving control and treated data. Our results demonstrate that the PILP approach provides a way of learning probabilistic logic models from probabilistic examples, and the PILP models learned from probabilistic examples lead to a significant decrease in error accompanied by improved insight from the learned results compared with the PILP models learned from non-probabilistic examples.

Mathematical Modeling the Geometric Regularity in Proteus Mirabilis Colonies

Science.gov (United States)

Zhang, Bin; Jiang, Yi; Minsu Kim Collaboration

Proteus Mirabilis colony exhibits striking spatiotemporal regularity, with concentric ring patterns with alternative high and low bacteria density in space, and periodicity for repetition process of growth and swarm in time. We present a simple mathematical model to explain the spatiotemporal regularity of P. Mirabilis colonies. We study a one-dimensional system. Using a reaction-diffusion model with thresholds in cell density and nutrient concentration, we recreated periodic growth and spread patterns, suggesting that the nutrient constraint and cell density regulation might be sufficient to explain the spatiotemporal periodicity in P. Mirabilis colonies. We further verify this result using a cell based model.

Inclusion Professional Development Model and Regular Middle School Educators

Science.gov (United States)

Royster, Otelia; Reglin, Gary L.; Losike-Sedimo, Nonofo

2014-01-01

The purpose of this study was to determine the impact of a professional development model on regular education middle school teachers' knowledge of best practices for teaching inclusive classes and attitudes toward teaching these classes. There were 19 regular education teachers who taught the core subjects. Findings for Research Question 1…

On Regularity Criteria for the Two-Dimensional Generalized Liquid Crystal Model

Directory of Open Access Journals (Sweden)

Yanan Wang

2014-01-01

Full Text Available We establish the regularity criteria for the two-dimensional generalized liquid crystal model. It turns out that the global existence results satisfy our regularity criteria naturally.

Coordinate-invariant regularization

International Nuclear Information System (INIS)

Halpern, M.B.

1987-01-01

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

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

Directory of Open Access Journals (Sweden)

P. V. Bezditko

2009-03-01

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

Chiral Thirring–Wess model with Faddeevian regularization

International Nuclear Information System (INIS)

Rahaman, Anisur

2015-01-01

Replacing vector type of interaction of the Thirring–Wess model by the chiral type a new model is presented which is termed here as chiral Thirring–Wess model. Ambiguity parameters of regularization are so chosen that the model falls into the Faddeevian class. The resulting Faddeevian class of model in general does not possess Lorentz invariance. However we can exploit the arbitrariness admissible in the ambiguity parameters to relate the quantum mechanically generated ambiguity parameters with the classical parameter involved in the masslike term of the gauge field which helps to maintain physical Lorentz invariance instead of the absence of manifestly Lorentz covariance of the model. The phase space structure and the theoretical spectrum of this class of model have been determined through Dirac’s method of quantization of constraint system

Regularized multivariate regression models with skew-t error distributions

KAUST Repository

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

Followee recommendation in microblog using matrix factorization model with structural regularization.

Science.gov (United States)

Yu, Yan; Qiu, Robin G

2014-01-01

Microblog that provides us a new communication and information sharing platform has been growing exponentially since it emerged just a few years ago. To microblog users, recommending followees who can serve as high quality information sources is a competitive service. To address this problem, in this paper we propose a matrix factorization model with structural regularization to improve the accuracy of followee recommendation in microblog. More specifically, we adapt the matrix factorization model in traditional item recommender systems to followee recommendation in microblog and use structural regularization to exploit structure information of social network to constrain matrix factorization model. The experimental analysis on a real-world dataset shows that our proposed model is promising.

Generating Models of Infinite-State Communication Protocols Using Regular Inference with Abstraction

Science.gov (United States)

Aarts, Fides; Jonsson, Bengt; Uijen, Johan

In order to facilitate model-based verification and validation, effort is underway to develop techniques for generating models of communication system components from observations of their external behavior. Most previous such work has employed regular inference techniques which generate modest-size finite-state models. They typically suppress parameters of messages, although these have a significant impact on control flow in many communication protocols. We present a framework, which adapts regular inference to include data parameters in messages and states for generating components with large or infinite message alphabets. A main idea is to adapt the framework of predicate abstraction, successfully used in formal verification. Since we are in a black-box setting, the abstraction must be supplied externally, using information about how the component manages data parameters. We have implemented our techniques by connecting the LearnLib tool for regular inference with the protocol simulator ns-2, and generated a model of the SIP component as implemented in ns-2.

Universal Regularizers For Robust Sparse Coding and Modeling

OpenAIRE

Ramirez, Ignacio; Sapiro, Guillermo

2010-01-01

Sparse data models, where data is assumed to be well represented as a linear combination of a few elements from a dictionary, have gained considerable attention in recent years, and their use has led to state-of-the-art results in many signal and image processing tasks. It is now well understood that the choice of the sparsity regularization term is critical in the success of such models. Based on a codelength minimization interpretation of sparse coding, and using tools from universal coding...

An ILP based Algorithm for Optimal Customer Selection for Demand Response in SmartGrids

Energy Technology Data Exchange (ETDEWEB)

Kuppannagari, Sanmukh R. [Univ. of Southern California, Los Angeles, CA (United States); Kannan, Rajgopal [Louisiana State Univ., Baton Rouge, LA (United States); Prasanna, Viktor K. [Univ. of Southern California, Los Angeles, CA (United States)

2015-12-07

Demand Response (DR) events are initiated by utilities during peak demand periods to curtail consumption. They ensure system reliability and minimize the utility’s expenditure. Selection of the right customers and strategies is critical for a DR event. An effective DR scheduling algorithm minimizes the curtailment error which is the absolute difference between the achieved curtailment value and the target. State-of-the-art heuristics exist for customer selection, however their curtailment errors are unbounded and can be as high as 70%. In this work, we develop an Integer Linear Programming (ILP) formulation for optimally selecting customers and curtailment strategies that minimize the curtailment error during DR events in SmartGrids. We perform experiments on real world data obtained from the University of Southern California’s SmartGrid and show that our algorithm achieves near exact curtailment values with errors in the range of 10^-7 to 10^-5, which are within the range of numerical errors. We compare our results against the state-of-the-art heuristic being deployed in practice in the USC SmartGrid. We show that for the same set of available customer strategy pairs our algorithm performs 103 to 107 times better in terms of the curtailment errors incurred.

Dynamics of coherent states in regular and chaotic regimes of the non-integrable Dicke model

Science.gov (United States)

Lerma-Hernández, S.; Chávez-Carlos, J.; Bastarrachea-Magnani, M. A.; López-del-Carpio, B.; Hirsch, J. G.

2018-04-01

The quantum dynamics of initial coherent states is studied in the Dicke model and correlated with the dynamics, regular or chaotic, of their classical limit. Analytical expressions for the survival probability, i.e. the probability of finding the system in its initial state at time t, are provided in the regular regions of the model. The results for regular regimes are compared with those of the chaotic ones. It is found that initial coherent states in regular regions have a much longer equilibration time than those located in chaotic regions. The properties of the distributions for the initial coherent states in the Hamiltonian eigenbasis are also studied. It is found that for regular states the components with no negligible contribution are organized in sequences of energy levels distributed according to Gaussian functions. In the case of chaotic coherent states, the energy components do not have a simple structure and the number of participating energy levels is larger than in the regular cases.

Regularized integrable version of the one-dimensional quantum sine-Gordon model

International Nuclear Information System (INIS)

Japaridze, G.I.; Nersesyan, A.A.; Wiegmann, P.B.

1983-01-01

The authors derive a regularized exactly solvable version of the one-dimensional quantum sine-Gordon model proceeding from the exact solution of the U(1)-symmetric Thirring model. The ground state and the excitation spectrum are obtained in the region ν 2 < 8π. (Auth.)

Model-based estimation with boundary side information or boundary regularization

International Nuclear Information System (INIS)

Chiao, P.C.; Rogers, W.L.; Fessler, J.A.; Clinthorne, N.H.; Hero, A.O.

1994-01-01

The authors have previously developed a model-based strategy for joint estimation of myocardial perfusion and boundaries using ECT (Emission Computed Tomography). The authors have also reported difficulties with boundary estimation in low contrast and low count rate situations. In this paper, the authors propose using boundary side information (obtainable from high resolution MRI and CT images) or boundary regularization to improve both perfusion and boundary estimation in these situations. To fuse boundary side information into the emission measurements, the authors formulate a joint log-likelihood function to include auxiliary boundary measurements as well as ECT projection measurements. In addition, the authors introduce registration parameters to align auxiliary boundary measurements with ECT measurements and jointly estimate these parameters with other parameters of interest from the composite measurements. In simulated PET O-15 water myocardial perfusion studies using a simplified model, the authors show that the joint estimation improves perfusion estimation performance and gives boundary alignment accuracy of <0.5 mm even at 0.2 million counts. The authors implement boundary regularization through formulating a penalized log-likelihood function. The authors also demonstrate in simulations that simultaneous regularization of the epicardial boundary and myocardial thickness gives comparable perfusion estimation accuracy with the use of boundary side information

Operator regularization in the Weinberg-Salam model

International Nuclear Information System (INIS)

Chowdhury, A.M.; McKeon, D.G.C.

1987-01-01

The technique of operator regularization is applied to the Weinberg-Salam model. By directly regulating operators that arise in the course of evaluating path integrals in the background-field formalism, we preserve all symmetries of the theory. An expansion due to Schwinger is employed to compute amplitudes perturbatively, thereby avoiding Feynman diagrams. No explicitly divergent quantities arise in this approach. The general features of the method are outlined with particular attention paid to the problem of simultaneously regulating functions of an operator A and inverse functions upon which A itself depends. Specific application is made to computation of the one-loop contribution to the muon-photon vertex in the Weinberg-Salam model in the limit of zero momentum transfer to the photon

Implicit Regularization for Reconstructing 3D Building Rooftop Models Using Airborne LiDAR Data

Directory of Open Access Journals (Sweden)

Jaewook Jung

2017-03-01

Full Text Available With rapid urbanization, highly accurate and semantically rich virtualization of building assets in 3D become more critical for supporting various applications, including urban planning, emergency response and location-based services. Many research efforts have been conducted to automatically reconstruct building models at city-scale from remotely sensed data. However, developing a fully-automated photogrammetric computer vision system enabling the massive generation of highly accurate building models still remains a challenging task. One the most challenging task for 3D building model reconstruction is to regularize the noises introduced in the boundary of building object retrieved from a raw data with lack of knowledge on its true shape. This paper proposes a data-driven modeling approach to reconstruct 3D rooftop models at city-scale from airborne laser scanning (ALS data. The focus of the proposed method is to implicitly derive the shape regularity of 3D building rooftops from given noisy information of building boundary in a progressive manner. This study covers a full chain of 3D building modeling from low level processing to realistic 3D building rooftop modeling. In the element clustering step, building-labeled point clouds are clustered into homogeneous groups by applying height similarity and plane similarity. Based on segmented clusters, linear modeling cues including outer boundaries, intersection lines, and step lines are extracted. Topology elements among the modeling cues are recovered by the Binary Space Partitioning (BSP technique. The regularity of the building rooftop model is achieved by an implicit regularization process in the framework of Minimum Description Length (MDL combined with Hypothesize and Test (HAT. The parameters governing the MDL optimization are automatically estimated based on Min-Max optimization and Entropy-based weighting method. The performance of the proposed method is tested over the International

Implicit Regularization for Reconstructing 3D Building Rooftop Models Using Airborne LiDAR Data.

Science.gov (United States)

Jung, Jaewook; Jwa, Yoonseok; Sohn, Gunho

2017-03-19

With rapid urbanization, highly accurate and semantically rich virtualization of building assets in 3D become more critical for supporting various applications, including urban planning, emergency response and location-based services. Many research efforts have been conducted to automatically reconstruct building models at city-scale from remotely sensed data. However, developing a fully-automated photogrammetric computer vision system enabling the massive generation of highly accurate building models still remains a challenging task. One the most challenging task for 3D building model reconstruction is to regularize the noises introduced in the boundary of building object retrieved from a raw data with lack of knowledge on its true shape. This paper proposes a data-driven modeling approach to reconstruct 3D rooftop models at city-scale from airborne laser scanning (ALS) data. The focus of the proposed method is to implicitly derive the shape regularity of 3D building rooftops from given noisy information of building boundary in a progressive manner. This study covers a full chain of 3D building modeling from low level processing to realistic 3D building rooftop modeling. In the element clustering step, building-labeled point clouds are clustered into homogeneous groups by applying height similarity and plane similarity. Based on segmented clusters, linear modeling cues including outer boundaries, intersection lines, and step lines are extracted. Topology elements among the modeling cues are recovered by the Binary Space Partitioning (BSP) technique. The regularity of the building rooftop model is achieved by an implicit regularization process in the framework of Minimum Description Length (MDL) combined with Hypothesize and Test (HAT). The parameters governing the MDL optimization are automatically estimated based on Min-Max optimization and Entropy-based weighting method. The performance of the proposed method is tested over the International Society for

Novel Harmonic Regularization Approach for Variable Selection in Cox’s Proportional Hazards Model

Directory of Open Access Journals (Sweden)

Ge-Jin Chu

2014-01-01

Full Text Available Variable selection is an important issue in regression and a number of variable selection methods have been proposed involving nonconvex penalty functions. In this paper, we investigate a novel harmonic regularization method, which can approximate nonconvex Lq  (1/2regularizations, to select key risk factors in the Cox’s proportional hazards model using microarray gene expression data. The harmonic regularization method can be efficiently solved using our proposed direct path seeking approach, which can produce solutions that closely approximate those for the convex loss function and the nonconvex regularization. Simulation results based on the artificial datasets and four real microarray gene expression datasets, such as real diffuse large B-cell lymphoma (DCBCL, the lung cancer, and the AML datasets, show that the harmonic regularization method can be more accurate for variable selection than existing Lasso series methods.

Analytic supersymmetric regularization for the pure N=1 super-Yang-Mills model

International Nuclear Information System (INIS)

Abdalla, E.; Jasinschi, R.S.

1987-01-01

We calculate for the pure N=1 super-Yang-Mills model the quantum correction to the background field strength up to two loops. In using background field method, analytic regularization and Seeley coefficient expansion we show how these corrections arise. Our method differs from the dimensional regularization via dimensional reduction scheme in various respects, in particular to the origin of the background field strength as appearing in the divergent expressions. (orig.)

Physical model of dimensional regularization

Energy Technology Data Exchange (ETDEWEB)

Schonfeld, Jonathan F.

2016-12-15

We explicitly construct fractals of dimension 4-ε on which dimensional regularization approximates scalar-field-only quantum-field theory amplitudes. The construction does not require fractals to be Lorentz-invariant in any sense, and we argue that there probably is no Lorentz-invariant fractal of dimension greater than 2. We derive dimensional regularization's power-law screening first for fractals obtained by removing voids from 3-dimensional Euclidean space. The derivation applies techniques from elementary dielectric theory. Surprisingly, fractal geometry by itself does not guarantee the appropriate power-law behavior; boundary conditions at fractal voids also play an important role. We then extend the derivation to 4-dimensional Minkowski space. We comment on generalization to non-scalar fields, and speculate about implications for quantum gravity. (orig.)

Boundary regularity of Nevanlinna domains and univalent functions in model subspaces

International Nuclear Information System (INIS)

Baranov, Anton D; Fedorovskiy, Konstantin Yu

2011-01-01

In the paper we study boundary regularity of Nevanlinna domains, which have appeared in problems of uniform approximation by polyanalytic polynomials. A new method for constructing Nevanlinna domains with essentially irregular nonanalytic boundaries is suggested; this method is based on finding appropriate univalent functions in model subspaces, that is, in subspaces of the form K Θ =H 2 ominus ΘH 2 , where Θ is an inner function. To describe the irregularity of the boundaries of the domains obtained, recent results by Dolzhenko about boundary regularity of conformal mappings are used. Bibliography: 18 titles.

Context-Specific Metabolic Model Extraction Based on Regularized Least Squares Optimization.

Directory of Open Access Journals (Sweden)

Semidán Robaina Estévez

Full Text Available Genome-scale metabolic models have proven highly valuable in investigating cell physiology. Recent advances include the development of methods to extract context-specific models capable of describing metabolism under more specific scenarios (e.g., cell types. Yet, none of the existing computational approaches allows for a fully automated model extraction and determination of a flux distribution independent of user-defined parameters. Here we present RegrEx, a fully automated approach that relies solely on context-specific data and ℓ1-norm regularization to extract a context-specific model and to provide a flux distribution that maximizes its correlation to data. Moreover, the publically available implementation of RegrEx was used to extract 11 context-specific human models using publicly available RNAseq expression profiles, Recon1 and also Recon2, the most recent human metabolic model. The comparison of the performance of RegrEx and its contending alternatives demonstrates that the proposed method extracts models for which both the structure, i.e., reactions included, and the flux distributions are in concordance with the employed data. These findings are supported by validation and comparison of method performance on additional data not used in context-specific model extraction. Therefore, our study sets the ground for applications of other regularization techniques in large-scale metabolic modeling.

Dynamic contrast enhanced CT measurement of blood flow during interstitial laser photocoagulation: comparison with an Arrhenius damage model

International Nuclear Information System (INIS)

Purdie, T.J.; Lee, T.J.; Iizuka, M.; Sherar, M.D.

2000-01-01

One effect of heating during interstitial laser photocoagulation (ILP) is to directly destroy the tumour vasculature resulting in a loss of viable blood supply. Therefore, blood flow measured during and after treatment can be a useful indicator of tissue thermal damage. In this study, the effect of ILP treatment on rabbit thigh tumours was investigated by measuring blood flow changes using dynamic contrast enhanced computed tomography (CT). The CT measured changes in blood flow of treated tumour tissue were fitted to an Arrhenius model assuming first order rate kinetics. Our results show that changes in blood flow of tumour tissue distant from surrounding normal tissue are well described by an Arrhenius model. By contrast, the temperature profile of tumour tissue adjacent to normal tissue must be modified to account for heat dissipation by the latter. Finally, the Arrhenius parameters derived in the study are similar to those derived by heating tumour tissue to a lower temperature (<47 deg. C) than the current study. In conclusion, CT can be used to monitor blood flow changes during ILP and these measurements are related to the thermal damage predicted by the Arrhenius model. (author)

Regularized lattice Boltzmann model for immiscible two-phase flows with power-law rheology

Science.gov (United States)

Ba, Yan; Wang, Ningning; Liu, Haihu; Li, Qiang; He, Guoqiang

2018-03-01

In this work, a regularized lattice Boltzmann color-gradient model is developed for the simulation of immiscible two-phase flows with power-law rheology. This model is as simple as the Bhatnagar-Gross-Krook (BGK) color-gradient model except that an additional regularization step is introduced prior to the collision step. In the regularization step, the pseudo-inverse method is adopted as an alternative solution for the nonequilibrium part of the total distribution function, and it can be easily extended to other discrete velocity models no matter whether a forcing term is considered or not. The obtained expressions for the nonequilibrium part are merely related to macroscopic variables and velocity gradients that can be evaluated locally. Several numerical examples, including the single-phase and two-phase layered power-law fluid flows between two parallel plates, and the droplet deformation and breakup in a simple shear flow, are conducted to test the capability and accuracy of the proposed color-gradient model. Results show that the present model is more stable and accurate than the BGK color-gradient model for power-law fluids with a wide range of power-law indices. Compared to its multiple-relaxation-time counterpart, the present model can increase the computing efficiency by around 15%, while keeping the same accuracy and stability. Also, the present model is found to be capable of reasonably predicting the critical capillary number of droplet breakup.

Structure-Based Low-Rank Model With Graph Nuclear Norm Regularization for Noise Removal.

Science.gov (United States)

Ge, Qi; Jing, Xiao-Yuan; Wu, Fei; Wei, Zhi-Hui; Xiao, Liang; Shao, Wen-Ze; Yue, Dong; Li, Hai-Bo

2017-07-01

Nonlocal image representation methods, including group-based sparse coding and block-matching 3-D filtering, have shown their great performance in application to low-level tasks. The nonlocal prior is extracted from each group consisting of patches with similar intensities. Grouping patches based on intensity similarity, however, gives rise to disturbance and inaccuracy in estimation of the true images. To address this problem, we propose a structure-based low-rank model with graph nuclear norm regularization. We exploit the local manifold structure inside a patch and group the patches by the distance metric of manifold structure. With the manifold structure information, a graph nuclear norm regularization is established and incorporated into a low-rank approximation model. We then prove that the graph-based regularization is equivalent to a weighted nuclear norm and the proposed model can be solved by a weighted singular-value thresholding algorithm. Extensive experiments on additive white Gaussian noise removal and mixed noise removal demonstrate that the proposed method achieves a better performance than several state-of-the-art algorithms.

A Multivariate Asymmetric Long Memory Conditional Volatility Model with X, Regularity and Asymptotics

NARCIS (Netherlands)

M. Asai (Manabu); M.J. McAleer (Michael)

2016-01-01

textabstractThe paper derives a Multivariate Asymmetric Long Memory conditional volatility model with Exogenous Variables (X), or the MALMX model, with dynamic conditional correlations, appropriate regularity conditions, and associated asymptotic theory. This enables checking of internal consistency

S-matrix regularities of two-dimensional sigma-models of Stiefel manifolds

International Nuclear Information System (INIS)

Flume-Gorczyca, B.

1980-01-01

The S-matrices of the two-dimensional nonlinear O(n + m)/O(n) and O(n + m)/O(n) x O(m) sigma-models corresponding to Stiefel and Grassmann manifolds, respectively, are compared in leading order in 1/n. It is shown, that after averaging over O(m) labels of the incoming and outgoing particles, the S-matrices of both models become identical. This result explains why commonly expected regularities of the Grassmann models, in particular absence of particle production, are found, modulo an O(m) average, also in Stiefel models. (orig.)

Geometric continuum regularization of quantum field theory

International Nuclear Information System (INIS)

Halpern, M.B.

1989-01-01

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

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

International Nuclear Information System (INIS)

Sussman, Roberto A

2010-01-01

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

Model-based estimation with boundary side information or boundary regularization [cardiac emission CT].

Science.gov (United States)

Chiao, P C; Rogers, W L; Fessler, J A; Clinthorne, N H; Hero, A O

1994-01-01

The authors have previously developed a model-based strategy for joint estimation of myocardial perfusion and boundaries using ECT (emission computed tomography). They have also reported difficulties with boundary estimation in low contrast and low count rate situations. Here they propose using boundary side information (obtainable from high resolution MRI and CT images) or boundary regularization to improve both perfusion and boundary estimation in these situations. To fuse boundary side information into the emission measurements, the authors formulate a joint log-likelihood function to include auxiliary boundary measurements as well as ECT projection measurements. In addition, they introduce registration parameters to align auxiliary boundary measurements with ECT measurements and jointly estimate these parameters with other parameters of interest from the composite measurements. In simulated PET O-15 water myocardial perfusion studies using a simplified model, the authors show that the joint estimation improves perfusion estimation performance and gives boundary alignment accuracy of <0.5 mm even at 0.2 million counts. They implement boundary regularization through formulating a penalized log-likelihood function. They also demonstrate in simulations that simultaneous regularization of the epicardial boundary and myocardial thickness gives comparable perfusion estimation accuracy with the use of boundary side information.

Statistics of the Navier–Stokes-alpha-beta regularization model for fluid turbulence

International Nuclear Information System (INIS)

Hinz, Denis F; Kim, Tae-Yeon; Fried, Eliot

2014-01-01

We explore one-point and two-point statistics of the Navier–Stokes-αβ regularization model at moderate Reynolds number (Re ≈ 200) in homogeneous isotropic turbulence. The results are compared to the limit cases of the Navier–Stokes-α model and the Navier–Stokes-αβ model without subgrid-scale stress, as well as with high-resolution direct numerical simulation. After reviewing spectra of different energy norms of the Navier–Stokes-αβ model, the Navier–Stokes-α model, and Navier–Stokes-αβ model without subgrid-scale stress, we present probability density functions and normalized probability density functions of the filtered and unfiltered velocity increments along with longitudinal velocity structure functions of the regularization models and direct numerical simulation results. We highlight differences in the statistical properties of the unfiltered and filtered velocity fields entering the governing equations of the Navier–Stokes-α and Navier–Stokes-αβ models and discuss the usability of both velocity fields for realistic flow predictions. The influence of the modified viscous term in the Navier–Stokes-αβ model is studied through comparison to the case where the underlying subgrid-scale stress tensor is neglected. Whereas, the filtered velocity field is found to have physically more viable probability density functions and structure functions for the approximation of direct numerical simulation results, the unfiltered velocity field is found to have flatness factors close to direct numerical simulation results. (paper)

Laplacian manifold regularization method for fluorescence molecular tomography

Science.gov (United States)

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

2017-04-01

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

Mixture models with entropy regularization for community detection in networks

Science.gov (United States)

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

2018-04-01

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

Uncertainty modelling and analysis of volume calculations based on a regular grid digital elevation model (DEM)

Science.gov (United States)

Li, Chang; Wang, Qing; Shi, Wenzhong; Zhao, Sisi

2018-05-01

The accuracy of earthwork calculations that compute terrain volume is critical to digital terrain analysis (DTA). The uncertainties in volume calculations (VCs) based on a DEM are primarily related to three factors: 1) model error (ME), which is caused by an adopted algorithm for a VC model, 2) discrete error (DE), which is usually caused by DEM resolution and terrain complexity, and 3) propagation error (PE), which is caused by the variables' error. Based on these factors, the uncertainty modelling and analysis of VCs based on a regular grid DEM are investigated in this paper. Especially, how to quantify the uncertainty of VCs is proposed by a confidence interval based on truncation error (TE). In the experiments, the trapezoidal double rule (TDR) and Simpson's double rule (SDR) were used to calculate volume, where the TE is the major ME, and six simulated regular grid DEMs with different terrain complexity and resolution (i.e. DE) were generated by a Gauss synthetic surface to easily obtain the theoretical true value and eliminate the interference of data errors. For PE, Monte-Carlo simulation techniques and spatial autocorrelation were used to represent DEM uncertainty. This study can enrich uncertainty modelling and analysis-related theories of geographic information science.

Entanglement entropy production in gravitational collapse: covariant regularization and solvable models

Science.gov (United States)

Bianchi, Eugenio; De Lorenzo, Tommaso; Smerlak, Matteo

2015-06-01

We study the dynamics of vacuum entanglement in the process of gravitational collapse and subsequent black hole evaporation. In the first part of the paper, we introduce a covariant regularization of entanglement entropy tailored to curved spacetimes; this regularization allows us to propose precise definitions for the concepts of black hole "exterior entropy" and "radiation entropy." For a Vaidya model of collapse we find results consistent with the standard thermodynamic properties of Hawking radiation. In the second part of the paper, we compute the vacuum entanglement entropy of various spherically-symmetric spacetimes of interest, including the nonsingular black hole model of Bardeen, Hayward, Frolov and Rovelli-Vidotto and the "black hole fireworks" model of Haggard-Rovelli. We discuss specifically the role of event and trapping horizons in connection with the behavior of the radiation entropy at future null infinity. We observe in particular that ( i) in the presence of an event horizon the radiation entropy diverges at the end of the evaporation process, ( ii) in models of nonsingular evaporation (with a trapped region but no event horizon) the generalized second law holds only at early times and is violated in the "purifying" phase, ( iii) at late times the radiation entropy can become negative (i.e. the radiation can be less correlated than the vacuum) before going back to zero leading to an up-down-up behavior for the Page curve of a unitarily evaporating black hole.

Entanglement entropy production in gravitational collapse: covariant regularization and solvable models

International Nuclear Information System (INIS)

Bianchi, Eugenio; Lorenzo, Tommaso De; Smerlak, Matteo

2015-01-01

We study the dynamics of vacuum entanglement in the process of gravitational collapse and subsequent black hole evaporation. In the first part of the paper, we introduce a covariant regularization of entanglement entropy tailored to curved spacetimes; this regularization allows us to propose precise definitions for the concepts of black hole “exterior entropy” and “radiation entropy.” For a Vaidya model of collapse we find results consistent with the standard thermodynamic properties of Hawking radiation. In the second part of the paper, we compute the vacuum entanglement entropy of various spherically-symmetric spacetimes of interest, including the nonsingular black hole model of Bardeen, Hayward, Frolov and Rovelli-Vidotto and the “black hole fireworks” model of Haggard-Rovelli. We discuss specifically the role of event and trapping horizons in connection with the behavior of the radiation entropy at future null infinity. We observe in particular that (i) in the presence of an event horizon the radiation entropy diverges at the end of the evaporation process, (ii) in models of nonsingular evaporation (with a trapped region but no event horizon) the generalized second law holds only at early times and is violated in the “purifying” phase, (iii) at late times the radiation entropy can become negative (i.e. the radiation can be less correlated than the vacuum) before going back to zero leading to an up-down-up behavior for the Page curve of a unitarily evaporating black hole.

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

DEFF Research Database (Denmark)

Lavancier, Frédéric; Møller, Jesper

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

Regularized Biot-Savart Laws for Modeling Magnetic Flux Ropes

Science.gov (United States)

Titov, Viacheslav; Downs, Cooper; Mikic, Zoran; Torok, Tibor; Linker, Jon A.

2017-08-01

Many existing models assume that magnetic flux ropes play a key role in solar flares and coronal mass ejections (CMEs). It is therefore important to develop efficient methods for constructing flux-rope configurations constrained by observed magnetic data and the initial morphology of CMEs. As our new step in this direction, we have derived and implemented a compact analytical form that represents the magnetic field of a thin flux rope with an axis of arbitrary shape and a circular cross-section. This form implies that the flux rope carries axial current I and axial flux F, so that the respective magnetic field is a curl of the sum of toroidal and poloidal vector potentials proportional to I and F, respectively. The vector potentials are expressed in terms of Biot-Savart laws whose kernels are regularized at the rope axis. We regularized them in such a way that for a straight-line axis the form provides a cylindrical force-free flux rope with a parabolic profile of the axial current density. So far, we set the shape of the rope axis by tracking the polarity inversion lines of observed magnetograms and estimating its height and other parameters of the rope from a calculated potential field above these lines. In spite of this heuristic approach, we were able to successfully construct pre-eruption configurations for the 2009 February13 and 2011 October 1 CME events. These applications demonstrate that our regularized Biot-Savart laws are indeed a very flexible and efficient method for energizing initial configurations in MHD simulations of CMEs. We discuss possible ways of optimizing the axis paths and other extensions of the method in order to make it more useful and robust.Research supported by NSF, NASA's HSR and LWS Programs, and AFOSR.

Multiple graph regularized protein domain ranking.

Science.gov (United States)

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

2012-11-19

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

Diverse Regular Employees and Non-regular Employment (Japanese)

OpenAIRE

MORISHIMA Motohiro

2011-01-01

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

Optimization of the Regularization in Background and Foreground Modeling

Directory of Open Access Journals (Sweden)

Si-Qi Wang

2014-01-01

Full Text Available Background and foreground modeling is a typical method in the application of computer vision. The current general “low-rank + sparse” model decomposes the frames from the video sequences into low-rank background and sparse foreground. But the sparse assumption in such a model may not conform with the reality, and the model cannot directly reflect the correlation between the background and foreground either. Thus, we present a novel model to solve this problem by decomposing the arranged data matrix D into low-rank background L and moving foreground M. Here, we only need to give the priori assumption of the background to be low-rank and let the foreground be separated from the background as much as possible. Based on this division, we use a pair of dual norms, nuclear norm and spectral norm, to regularize the foreground and background, respectively. Furthermore, we use a reweighted function instead of the normal norm so as to get a better and faster approximation model. Detailed explanation based on linear algebra about our two models will be presented in this paper. By the observation of the experimental results, we can see that our model can get better background modeling, and even simplified versions of our algorithms perform better than mainstream techniques IALM and GoDec.

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

Science.gov (United States)

Parekh, Ankit

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

Multiple graph regularized protein domain ranking

KAUST Repository

Wang, Jim Jing-Yan

2012-11-19

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

Multiple graph regularized protein domain ranking

KAUST Repository

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

2012-01-01

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

Multiple graph regularized protein domain ranking

Directory of Open Access Journals (Sweden)

Wang Jim

2012-11-01

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

Effects of attitude, social influence, and self-efficacy model factors on regular mammography performance in life-transition aged women in Korea.

Science.gov (United States)

Lee, Chang Hyun; Kim, Young Im

2015-01-01

This study analyzed predictors of regular mammography performance in Korea. In addition, we determined factors affecting regular mammography performance in life-transition aged women by applying an attitude, social influence, and self-efficacy (ASE) model. Data were collected from women aged over 40 years residing in province J in Korea. The 178 enrolled subjects provided informed voluntary consent prior to completing a structural questionnaire. The overall regular mammography performance rate of the subjects was 41.6%. Older age, city residency, high income and part-time job were associated with a high regular mammography performance. Among women who had undergone more breast self-examinations (BSE) or more doctors' physical examinations (PE), there were higher regular mammography performance rates. All three ASE model factors were significantly associated with regular mammography performance. Women with a high level of positive ASE values had a significantly high regular mammography performance rate. Within the ASE model, self-efficacy and social influence were particularly important. Logistic regression analysis explained 34.7% of regular mammography performance and PE experience (β=4.645, p=.003), part- time job (β=4.010, p=.050), self-efficacy (β=1.820, p=.026) and social influence (β=1.509, p=.038) were significant factors. Promotional strategies that could improve self-efficacy, reinforce social influence and reduce geographical, time and financial barriers are needed to increase the regular mammography performance rate in life-transition aged.

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

Science.gov (United States)

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

2018-02-01

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

Low-rank matrix approximation with manifold regularization.

Science.gov (United States)

Zhang, Zhenyue; Zhao, Keke

2013-07-01

This paper proposes a new model of low-rank matrix factorization that incorporates manifold regularization to the matrix factorization. Superior to the graph-regularized nonnegative matrix factorization, this new regularization model has globally optimal and closed-form solutions. A direct algorithm (for data with small number of points) and an alternate iterative algorithm with inexact inner iteration (for large scale data) are proposed to solve the new model. A convergence analysis establishes the global convergence of the iterative algorithm. The efficiency and precision of the algorithm are demonstrated numerically through applications to six real-world datasets on clustering and classification. Performance comparison with existing algorithms shows the effectiveness of the proposed method for low-rank factorization in general.

SU-F-R-41: Regularized PCA Can Model Treatment-Related Changes in Head and Neck Patients Using Daily CBCTs

International Nuclear Information System (INIS)

Chetvertkov, M; Siddiqui, F; Chetty, I; Kumarasiri, A; Liu, C; Gordon, J

2016-01-01

Purpose: To use daily cone beam CTs (CBCTs) to develop regularized principal component analysis (PCA) models of anatomical changes in head and neck (H&N) patients, to guide replanning decisions in adaptive radiation therapy (ART). Methods: Known deformations were applied to planning CT (pCT) images of 10 H&N patients to model several different systematic anatomical changes. A Pinnacle plugin was used to interpolate systematic changes over 35 fractions, generating a set of 35 synthetic CTs for each patient. Deformation vector fields (DVFs) were acquired between the pCT and synthetic CTs and random fraction-to-fraction changes were superimposed on the DVFs. Standard non-regularized and regularized patient-specific PCA models were built using the DVFs. The ability of PCA to extract the known deformations was quantified. PCA models were also generated from clinical CBCTs, for which the deformations and DVFs were not known. It was hypothesized that resulting eigenvectors/eigenfunctions with largest eigenvalues represent the major anatomical deformations during the course of treatment. Results: As demonstrated with quantitative results in the supporting document regularized PCA is more successful than standard PCA at capturing systematic changes early in the treatment. Regularized PCA is able to detect smaller systematic changes against the background of random fraction-to-fraction changes. To be successful at guiding ART, regularized PCA should be coupled with models of when anatomical changes occur: early, late or throughout the treatment course. Conclusion: The leading eigenvector/eigenfunction from the both PCA approaches can tentatively be identified as a major systematic change during radiotherapy course when systematic changes are large enough with respect to random fraction-to-fraction changes. In all cases the regularized PCA approach appears to be more reliable at capturing systematic changes, enabling dosimetric consequences to be projected once trends are

SU-F-R-41: Regularized PCA Can Model Treatment-Related Changes in Head and Neck Patients Using Daily CBCTs

Energy Technology Data Exchange (ETDEWEB)

Chetvertkov, M [Wayne State University, Detroit, MI (United States); Henry Ford Health System, Detroit, MI (United States); Siddiqui, F; Chetty, I; Kumarasiri, A; Liu, C; Gordon, J [Henry Ford Health System, Detroit, MI (United States)

2016-06-15

Purpose: To use daily cone beam CTs (CBCTs) to develop regularized principal component analysis (PCA) models of anatomical changes in head and neck (H&N) patients, to guide replanning decisions in adaptive radiation therapy (ART). Methods: Known deformations were applied to planning CT (pCT) images of 10 H&N patients to model several different systematic anatomical changes. A Pinnacle plugin was used to interpolate systematic changes over 35 fractions, generating a set of 35 synthetic CTs for each patient. Deformation vector fields (DVFs) were acquired between the pCT and synthetic CTs and random fraction-to-fraction changes were superimposed on the DVFs. Standard non-regularized and regularized patient-specific PCA models were built using the DVFs. The ability of PCA to extract the known deformations was quantified. PCA models were also generated from clinical CBCTs, for which the deformations and DVFs were not known. It was hypothesized that resulting eigenvectors/eigenfunctions with largest eigenvalues represent the major anatomical deformations during the course of treatment. Results: As demonstrated with quantitative results in the supporting document regularized PCA is more successful than standard PCA at capturing systematic changes early in the treatment. Regularized PCA is able to detect smaller systematic changes against the background of random fraction-to-fraction changes. To be successful at guiding ART, regularized PCA should be coupled with models of when anatomical changes occur: early, late or throughout the treatment course. Conclusion: The leading eigenvector/eigenfunction from the both PCA approaches can tentatively be identified as a major systematic change during radiotherapy course when systematic changes are large enough with respect to random fraction-to-fraction changes. In all cases the regularized PCA approach appears to be more reliable at capturing systematic changes, enabling dosimetric consequences to be projected once trends are

From recreational to regular drug use

DEFF Research Database (Denmark)

Järvinen, Margaretha; Ravn, Signe

2011-01-01

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

Manifold regularization for sparse unmixing of hyperspectral images.

Science.gov (United States)

Liu, Junmin; Zhang, Chunxia; Zhang, Jiangshe; Li, Huirong; Gao, Yuelin

2016-01-01

Recently, sparse unmixing has been successfully applied to spectral mixture analysis of remotely sensed hyperspectral images. Based on the assumption that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance, unmixing of each mixed pixel in the scene is to find an optimal subset of signatures in a very large spectral library, which is cast into the framework of sparse regression. However, traditional sparse regression models, such as collaborative sparse regression , ignore the intrinsic geometric structure in the hyperspectral data. In this paper, we propose a novel model, called manifold regularized collaborative sparse regression , by introducing a manifold regularization to the collaborative sparse regression model. The manifold regularization utilizes a graph Laplacian to incorporate the locally geometrical structure of the hyperspectral data. An algorithm based on alternating direction method of multipliers has been developed for the manifold regularized collaborative sparse regression model. Experimental results on both the simulated and real hyperspectral data sets have demonstrated the effectiveness of our proposed model.

Manifold Regularized Correlation Object Tracking

OpenAIRE

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

2017-01-01

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

Learning Sparse Visual Representations with Leaky Capped Norm Regularizers

OpenAIRE

Wangni, Jianqiao; Lin, Dahua

2017-01-01

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

Adaptive regularization of noisy linear inverse problems

DEFF Research Database (Denmark)

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

2006-01-01

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

Regular Breakfast and Blood Lead Levels among Preschool Children

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Needleman Herbert

2011-04-01

Full Text Available Abstract Background Previous studies have shown that fasting increases lead absorption in the gastrointestinal tract of adults. Regular meals/snacks are recommended as a nutritional intervention for lead poisoning in children, but epidemiological evidence of links between fasting and blood lead levels (B-Pb is rare. The purpose of this study was to examine the association between eating a regular breakfast and B-Pb among children using data from the China Jintan Child Cohort Study. Methods Parents completed a questionnaire regarding children's breakfast-eating habit (regular or not, demographics, and food frequency. Whole blood samples were collected from 1,344 children for the measurements of B-Pb and micronutrients (iron, copper, zinc, calcium, and magnesium. B-Pb and other measures were compared between children with and without regular breakfast. Linear regression modeling was used to evaluate the association between regular breakfast and log-transformed B-Pb. The association between regular breakfast and risk of lead poisoning (B-Pb≥10 μg/dL was examined using logistic regression modeling. Results Median B-Pb among children who ate breakfast regularly and those who did not eat breakfast regularly were 6.1 μg/dL and 7.2 μg/dL, respectively. Eating breakfast was also associated with greater zinc blood levels. Adjusting for other relevant factors, the linear regression model revealed that eating breakfast regularly was significantly associated with lower B-Pb (beta = -0.10 units of log-transformed B-Pb compared with children who did not eat breakfast regularly, p = 0.02. Conclusion The present study provides some initial human data supporting the notion that eating a regular breakfast might reduce B-Pb in young children. To our knowledge, this is the first human study exploring the association between breakfast frequency and B-Pb in young children.

The L0 Regularized Mumford-Shah Model for Bias Correction and Segmentation of Medical Images.

Science.gov (United States)

Duan, Yuping; Chang, Huibin; Huang, Weimin; Zhou, Jiayin; Lu, Zhongkang; Wu, Chunlin

2015-11-01

We propose a new variant of the Mumford-Shah model for simultaneous bias correction and segmentation of images with intensity inhomogeneity. First, based on the model of images with intensity inhomogeneity, we introduce an L0 gradient regularizer to model the true intensity and a smooth regularizer to model the bias field. In addition, we derive a new data fidelity using the local intensity properties to allow the bias field to be influenced by its neighborhood. Second, we use a two-stage segmentation method, where the fast alternating direction method is implemented in the first stage for the recovery of true intensity and bias field and a simple thresholding is used in the second stage for segmentation. Different from most of the existing methods for simultaneous bias correction and segmentation, we estimate the bias field and true intensity without fixing either the number of the regions or their values in advance. Our method has been validated on medical images of various modalities with intensity inhomogeneity. Compared with the state-of-art approaches and the well-known brain software tools, our model is fast, accurate, and robust with initializations.

Selection of regularization parameter for l1-regularized damage detection

Science.gov (United States)

Hou, Rongrong; Xia, Yong; Bao, Yuequan; Zhou, Xiaoqing

2018-06-01

The l1 regularization technique has been developed for structural health monitoring and damage detection through employing the sparsity condition of structural damage. The regularization parameter, which controls the trade-off between data fidelity and solution size of the regularization problem, exerts a crucial effect on the solution. However, the l1 regularization problem has no closed-form solution, and the regularization parameter is usually selected by experience. This study proposes two strategies of selecting the regularization parameter for the l1-regularized damage detection problem. The first method utilizes the residual and solution norms of the optimization problem and ensures that they are both small. The other method is based on the discrepancy principle, which requires that the variance of the discrepancy between the calculated and measured responses is close to the variance of the measurement noise. The two methods are applied to a cantilever beam and a three-story frame. A range of the regularization parameter, rather than one single value, can be determined. When the regularization parameter in this range is selected, the damage can be accurately identified even for multiple damage scenarios. This range also indicates the sensitivity degree of the damage identification problem to the regularization parameter.

Phase-field modelling of ductile fracture: a variational gradient-extended plasticity-damage theory and its micromorphic regularization.

Science.gov (United States)

Miehe, C; Teichtmeister, S; Aldakheel, F

2016-04-28

This work outlines a novel variational-based theory for the phase-field modelling of ductile fracture in elastic-plastic solids undergoing large strains. The phase-field approach regularizes sharp crack surfaces within a pure continuum setting by a specific gradient damage modelling. It is linked to a formulation of gradient plasticity at finite strains. The framework includes two independent length scales which regularize both the plastic response as well as the crack discontinuities. This ensures that the damage zones of ductile fracture are inside of plastic zones, and guarantees on the computational side a mesh objectivity in post-critical ranges. © 2016 The Author(s).

Regularized forecasting of chaotic dynamical systems

International Nuclear Information System (INIS)

Bollt, Erik M.

2017-01-01

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

Regularized multivariate regression models with skew-t error distributions

KAUST Repository

Chen, Lianfu

2014-06-01

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 the regression coefficient and inverse scale matrices simultaneously. The sparsity is introduced through penalizing the negative log-likelihood by adding L1-penalties on the entries of the two matrices. Taking advantage of the hierarchical representation of skew-t distributions, and using the expectation conditional maximization (ECM) algorithm, we reduce the problem to penalized normal likelihood and develop a procedure to minimize the ensuing objective function. Using a simulation study the performance of the method is assessed, and the methodology is illustrated using a real data set with a 24-dimensional response vector. © 2014 Elsevier B.V.

Using Regularization to Infer Cell Line Specificity in Logical Network Models of Signaling Pathways

Directory of Open Access Journals (Sweden)

Sébastien De Landtsheer

2018-05-01

Full Text Available Understanding the functional properties of cells of different origins is a long-standing challenge of personalized medicine. Especially in cancer, the high heterogeneity observed in patients slows down the development of effective cures. The molecular differences between cell types or between healthy and diseased cellular states are usually determined by the wiring of regulatory networks. Understanding these molecular and cellular differences at the systems level would improve patient stratification and facilitate the design of rational intervention strategies. Models of cellular regulatory networks frequently make weak assumptions about the distribution of model parameters across cell types or patients. These assumptions are usually expressed in the form of regularization of the objective function of the optimization problem. We propose a new method of regularization for network models of signaling pathways based on the local density of the inferred parameter values within the parameter space. Our method reduces the complexity of models by creating groups of cell line-specific parameters which can then be optimized together. We demonstrate the use of our method by recovering the correct topology and inferring accurate values of the parameters of a small synthetic model. To show the value of our method in a realistic setting, we re-analyze a recently published phosphoproteomic dataset from a panel of 14 colon cancer cell lines. We conclude that our method efficiently reduces model complexity and helps recovering context-specific regulatory information.

Critical behavior of the XY-rotor model on regular and small-world networks

Science.gov (United States)

De Nigris, Sarah; Leoncini, Xavier

2013-07-01

We study the XY rotors model on small networks whose number of links scales with the system size Nlinks˜Nγ, where 1≤γ≤2. We first focus on regular one-dimensional rings in the microcanonical ensemble. For γ1.5, the system equilibrium properties are found to be identical to the mean field, which displays a second-order phase transition at a critical energy density ɛ=E/N,ɛc=0.75. Moreover, for γc≃1.5 we find that a nontrivial state emerges, characterized by an infinite susceptibility. We then consider small-world networks, using the Watts-Strogatz mechanism on the regular networks parametrized by γ. We first analyze the topology and find that the small-world regime appears for rewiring probabilities which scale as pSW∝1/Nγ. Then considering the XY-rotors model on these networks, we find that a second-order phase transition occurs at a critical energy ɛc which logarithmically depends on the topological parameters p and γ. We also define a critical probability pMF, corresponding to the probability beyond which the mean field is quantitatively recovered, and we analyze its dependence on γ.

TRANSPOSABLE REGULARIZED COVARIANCE MODELS WITH AN APPLICATION TO MISSING DATA IMPUTATION.

Science.gov (United States)

Allen, Genevera I; Tibshirani, Robert

2010-06-01

Missing data estimation is an important challenge with high-dimensional data arranged in the form of a matrix. Typically this data matrix is transposable , meaning that either the rows, columns or both can be treated as features. To model transposable data, we present a modification of the matrix-variate normal, the mean-restricted matrix-variate normal , in which the rows and columns each have a separate mean vector and covariance matrix. By placing additive penalties on the inverse covariance matrices of the rows and columns, these so called transposable regularized covariance models allow for maximum likelihood estimation of the mean and non-singular covariance matrices. Using these models, we formulate EM-type algorithms for missing data imputation in both the multivariate and transposable frameworks. We present theoretical results exploiting the structure of our transposable models that allow these models and imputation methods to be applied to high-dimensional data. Simulations and results on microarray data and the Netflix data show that these imputation techniques often outperform existing methods and offer a greater degree of flexibility.

Modelling the Flow Stress of Alloy 316L using a Multi-Layered Feed Forward Neural Network with Bayesian Regularization

Science.gov (United States)

Abiriand Bhekisipho Twala, Olufunminiyi

2017-08-01

In this paper, a multilayer feedforward neural network with Bayesian regularization constitutive model is developed for alloy 316L during high strain rate and high temperature plastic deformation. The input variables are strain rate, temperature and strain while the output value is the flow stress of the material. The results show that the use of Bayesian regularized technique reduces the potential of overfitting and overtraining. The prediction quality of the model is thereby improved. The model predictions are in good agreement with experimental measurements. The measurement data used for the network training and model comparison were taken from relevant literature. The developed model is robust as it can be generalized to deformation conditions slightly below or above the training dataset.

Centered Differential Waveform Inversion with Minimum Support Regularization

KAUST Repository

Kazei, Vladimir

2017-05-26

Time-lapse full-waveform inversion has two major challenges. The first one is the reconstruction of a reference model (baseline model for most of approaches). The second is inversion for the time-lapse changes in the parameters. Common model approach is utilizing the information contained in all available data sets to build a better reference model for time lapse inversion. Differential (Double-difference) waveform inversion allows to reduce the artifacts introduced into estimates of time-lapse parameter changes by imperfect inversion for the baseline-reference model. We propose centered differential waveform inversion (CDWI) which combines these two approaches in order to benefit from both of their features. We apply minimum support regularization commonly used with electromagnetic methods of geophysical exploration. We test the CDWI method on synthetic dataset with random noise and show that, with Minimum support regularization, it provides better resolution of velocity changes than with total variation and Tikhonov regularizations in time-lapse full-waveform inversion.

Likelihood ratio decisions in memory: three implied regularities.

Science.gov (United States)

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

2009-06-01

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

Regularities in hadron systematics, Regge trajectories and a string quark model

International Nuclear Information System (INIS)

Chekanov, S.V.; Levchenko, B.B.

2006-08-01

An empirical principle for the construction of a linear relationship between the total angular momentum and squared-mass of baryons is proposed. In order to examine linearity of the trajectories, a rigorous least-squares regression analysis was performed. Unlike the standard Regge-Chew-Frautschi approach, the constructed trajectories do not have non-linear behaviour. A similar regularity may exist for lowest-mass mesons. The linear baryonic trajectories are well described by a semi-classical picture based on a spinning relativistic string with tension. The obtained numerical solution of this model was used to extract the (di)quark masses. (orig.)

Regularized plane-wave least-squares Kirchhoff migration

KAUST Repository

Wang, Xin

2013-09-22

A Kirchhoff least-squares migration (LSM) is developed in the prestack plane-wave domain to increase the quality of migration images. A regularization term is included that accounts for mispositioning of reflectors due to errors in the velocity model. Both synthetic and field results show that: 1) LSM with a reflectivity model common for all the plane-wave gathers provides the best image when the migration velocity model is accurate, but it is more sensitive to the velocity errors, 2) the regularized plane-wave LSM is more robust in the presence of velocity errors, and 3) LSM achieves both computational and IO saving by plane-wave encoding compared to shot-domain LSM for the models tested.

Modelling the Cost Performance of a Given Logistics Network Operating Under Regular and Irregular Conditions

NARCIS (Netherlands)

Janic, M.

2009-01-01

This paper develops an analytical model for the assessment of the cost performance of a given logistics network operating under regular and irregular (disruptive) conditions. In addition, the paper aims to carry out a sensitivity analysis of this cost with respect to changes of the most influencing

Optimal control of a head-of-line processor sharing model with regular and opportunity customers

NARCIS (Netherlands)

Wijk, van A.C.C.

2011-01-01

Motivated by a workload control setting, we study a model where two types of customers are served by a single server according to the head-of-line processor sharing discipline. Regular customers and opportunity customers are arriving to the system according to two independent Poisson processes, each

Manifold Regularized Correlation Object Tracking.

Science.gov (United States)

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

2018-05-01

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

Tidal-induced large-scale regular bed form patterns in a three-dimensional shallow water model

NARCIS (Netherlands)

Hulscher, Suzanne J.M.H.

1996-01-01

The three-dimensional model presented in this paper is used to study how tidal currents form wave-like bottom patterns. Inclusion of vertical flow structure turns out to be necessary to describe the formation, or absence, of all known large-scale regular bottom features. The tide and topography are

On Some Calculations of Effective Action and Fujikawa Regularized Anomaly in the Chiral Schwinger Model

OpenAIRE

Mehrdad, GOSHTASBPOUR; Center for Theoretical Physics and Mathematics, AEOI:Department of Physics, Shahid Beheshti University

1991-01-01

Extended D^†+D-DD^† Fujikawa regularization of anomaly and a method of integration of fermions for the chiral Schwinger model are criticized. On the basis of the corrected integration method, a new extended version of D^2 is obtained, resulting in the Jackiw-Rajaraman effective action.

A biological network-based regularized artificial neural network model for robust phenotype prediction from gene expression data.

Science.gov (United States)

Kang, Tianyu; Ding, Wei; Zhang, Luoyan; Ziemek, Daniel; Zarringhalam, Kourosh

2017-12-19

Stratification of patient subpopulations that respond favorably to treatment or experience and adverse reaction is an essential step toward development of new personalized therapies and diagnostics. It is currently feasible to generate omic-scale biological measurements for all patients in a study, providing an opportunity for machine learning models to identify molecular markers for disease diagnosis and progression. However, the high variability of genetic background in human populations hampers the reproducibility of omic-scale markers. In this paper, we develop a biological network-based regularized artificial neural network model for prediction of phenotype from transcriptomic measurements in clinical trials. To improve model sparsity and the overall reproducibility of the model, we incorporate regularization for simultaneous shrinkage of gene sets based on active upstream regulatory mechanisms into the model. We benchmark our method against various regression, support vector machines and artificial neural network models and demonstrate the ability of our method in predicting the clinical outcomes using clinical trial data on acute rejection in kidney transplantation and response to Infliximab in ulcerative colitis. We show that integration of prior biological knowledge into the classification as developed in this paper, significantly improves the robustness and generalizability of predictions to independent datasets. We provide a Java code of our algorithm along with a parsed version of the STRING DB database. In summary, we present a method for prediction of clinical phenotypes using baseline genome-wide expression data that makes use of prior biological knowledge on gene-regulatory interactions in order to increase robustness and reproducibility of omic-scale markers. The integrated group-wise regularization methods increases the interpretability of biological signatures and gives stable performance estimates across independent test sets.

Fast and compact regular expression matching

DEFF Research Database (Denmark)

Bille, Philip; Farach-Colton, Martin

2008-01-01

We study 4 problems in string matching, namely, regular expression matching, approximate regular expression matching, string edit distance, and subsequence indexing, on a standard word RAM model of computation that allows logarithmic-sized words to be manipulated in constant time. We show how...... to improve the space and/or remove a dependency on the alphabet size for each problem using either an improved tabulation technique of an existing algorithm or by combining known algorithms in a new way....

Constrained Perturbation Regularization Approach for Signal Estimation Using Random Matrix Theory

KAUST Repository

Suliman, Mohamed Abdalla Elhag

2016-10-06

In this work, we propose a new regularization approach for linear least-squares problems with random matrices. In the proposed constrained perturbation regularization approach, an artificial perturbation matrix with a bounded norm is forced into the system model matrix. This perturbation is introduced to improve the singular-value structure of the model matrix and, hence, the solution of the estimation problem. Relying on the randomness of the model matrix, a number of deterministic equivalents from random matrix theory are applied to derive the near-optimum regularizer that minimizes the mean-squared error of the estimator. Simulation results demonstrate that the proposed approach outperforms a set of benchmark regularization methods for various estimated signal characteristics. In addition, simulations show that our approach is robust in the presence of model uncertainty.

Coupling regularizes individual units in noisy populations

International Nuclear Information System (INIS)

Ly Cheng; Ermentrout, G. Bard

2010-01-01

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

Regularity and chaos in cavity QED

International Nuclear Information System (INIS)

Bastarrachea-Magnani, Miguel Angel; López-del-Carpio, Baldemar; Chávez-Carlos, Jorge; Lerma-Hernández, Sergio; Hirsch, Jorge G

2017-01-01

The interaction of a quantized electromagnetic field in a cavity with a set of two-level atoms inside it can be described with algebraic Hamiltonians of increasing complexity, from the Rabi to the Dicke models. Their algebraic character allows, through the use of coherent states, a semiclassical description in phase space, where the non-integrable Dicke model has regions associated with regular and chaotic motion. The appearance of classical chaos can be quantified calculating the largest Lyapunov exponent over the whole available phase space for a given energy. In the quantum regime, employing efficient diagonalization techniques, we are able to perform a detailed quantitative study of the regular and chaotic regions, where the quantum participation ratio (P R ) of coherent states on the eigenenergy basis plays a role equivalent to the Lyapunov exponent. It is noted that, in the thermodynamic limit, dividing the participation ratio by the number of atoms leads to a positive value in chaotic regions, while it tends to zero in the regular ones. (paper)

Regular network model for the sea ice-albedo feedback in the Arctic.

Science.gov (United States)

Müller-Stoffels, Marc; Wackerbauer, Renate

2011-03-01

The Arctic Ocean and sea ice form a feedback system that plays an important role in the global climate. The complexity of highly parameterized global circulation (climate) models makes it very difficult to assess feedback processes in climate without the concurrent use of simple models where the physics is understood. We introduce a two-dimensional energy-based regular network model to investigate feedback processes in an Arctic ice-ocean layer. The model includes the nonlinear aspect of the ice-water phase transition, a nonlinear diffusive energy transport within a heterogeneous ice-ocean lattice, and spatiotemporal atmospheric and oceanic forcing at the surfaces. First results for a horizontally homogeneous ice-ocean layer show bistability and related hysteresis between perennial ice and perennial open water for varying atmospheric heat influx. Seasonal ice cover exists as a transient phenomenon. We also find that ocean heat fluxes are more efficient than atmospheric heat fluxes to melt Arctic sea ice.

A trim-loss minimization in a produce-handling vehicle production plant

Directory of Open Access Journals (Sweden)

Apichai Ritvirool

2007-01-01

Full Text Available How to cut out the required pieces from raw materials by minimizing waste is a trim-loss problem. The integer linear programming (ILP model was developed to solve this problem. In addition, this ILPmodel could be used for planning an order over some future time period. Time horizon of ordering raw material including weekly, monthly, quarterly, and annually could be planned to reduce the trim loss. Thenumerical examples using an industrial case study of a produce-handling vehicle production plant were presented to illustrate how the proposed ILP model could be applied to actual systems and the types ofinformation that was obtained relative to implementation. The results showed that the proposed ILP model can be used as a decision support tool for selecting time horizon of order planning and cutting patterns todecrease material cost and waste from cutting raw material.

Strong self-coupling expansion in the lattice-regularized standard SU(2) Higgs model

International Nuclear Information System (INIS)

Decker, K.; Weisz, P.; Montvay, I.

1985-11-01

Expectation values at an arbitrary point of the 3-dimensional coupling parameter space in the lattice-regularized SU(2) Higgs-model with a doublet scalar field are expressed by a series of expectation values at infinite self-coupling (lambda=infinite). Questions of convergence of this 'strong self-coupling expansion' (SSCE) are investigated. The SSCE is a potentially useful tool for the study of the lambda-dependence at any value (zero or non-zero) of the bare gauge coupling. (orig.)

Strong self-coupling expansion in the lattice-regularized standard SU(2) Higgs model

International Nuclear Information System (INIS)

Decker, K.; Weisz, P.

1986-01-01

Expectation values at an arbitrary point of the 3-dimensional coupling parameter space in the lattice-regularized SU(2) Higgs model with a doublet scalar field are expressed by a series of expectation values at infinite self-coupling (lambda=infinite). Questions of convergence of this ''strong self-coupling expansion'' (SSCE) are investigated. The SSCE is a potentially useful tool for the study of the lambda-dependence at any value (zero or non-zero) of the bare gauge coupling. (orig.)

Global regularity for a family of 3D models of the axi-symmetric Navier–Stokes equations

Science.gov (United States)

Hou, Thomas Y.; Liu, Pengfei; Wang, Fei

2018-05-01

We consider a family of three-dimensional models for the axi-symmetric incompressible Navier–Stokes equations. The models are derived by changing the strength of the convection terms in the axisymmetric Navier–Stokes equations written using a set of transformed variables. We prove the global regularity of the family of models in the case that the strength of convection is slightly stronger than that of the original Navier–Stokes equations, which demonstrates the potential stabilizing effect of convection.

Predicting Factors Associated with Regular Physical Activity among College Students: Applying BASNEF Model

Directory of Open Access Journals (Sweden)

B. Moeini

2011-10-01

Full Text Available Introduction & Objective: One of the important problems in modern society is people's sedentary life style. The aim of this study was to determine factors associated with regular physical activity among college students based on BASNEF model.Materials & Methods: This study was a cross-sectional study carried out on 400 students in Hamadan University of Medical Sciences. Based on the assignment among different schools, classified sampling method was chosen for data gathering using a questionnaire in three parts including: demographic information, constructs of BASNEF model, and standard international physical activity questionnaire (IPAQ. Data were analyzed by SPSS-13, and using appropriate statistical tests (Chi-square, T-test and regression. Results: Based on the results, 271 students(67.8 % had low, 124 (31% moderate ,and 5 (1.2% vigorous physical activity. There was a significant relationship (c2=6.739, df= 1, P= 0.034 between their residence and physical activity and students living in dormitory were reported to have higher level of physical activity. Behavioral intention and enabling factors from the constructs of BASNEF model were the best predictors for having physical activity in students (OR=1.215, P = 0.000 and (OR=1.119, P= 0.000 respectively.Conclusion: With regard to the fact that majority of the students did not engage in enough physical activity and enabling factors were the most effective predictors for having regular physical activity in them, it seems that providing sports facilities can promote physical activity among the students.(Sci J Hamadan Univ Med Sci 2011;18(3:70-76

Optimal behaviour can violate the principle of regularity.

Science.gov (United States)

Trimmer, Pete C

2013-07-22

Understanding decisions is a fundamental aim of behavioural ecology, psychology and economics. The regularity axiom of utility theory holds that a preference between options should be maintained when other options are made available. Empirical studies have shown that animals violate regularity but this has not been understood from a theoretical perspective, such decisions have therefore been labelled as irrational. Here, I use models of state-dependent behaviour to demonstrate that choices can violate regularity even when behavioural strategies are optimal. I also show that the range of conditions over which regularity should be violated can be larger when options do not always persist into the future. Consequently, utility theory--based on axioms, including transitivity, regularity and the independence of irrelevant alternatives--is undermined, because even alternatives that are never chosen by an animal (in its current state) can be relevant to a decision.

Spectrally-consistent regularization modeling of turbulent natural convection flows

International Nuclear Information System (INIS)

Trias, F Xavier; Gorobets, Andrey; Oliva, Assensi; Verstappen, Roel

2012-01-01

The incompressible Navier-Stokes equations constitute an excellent mathematical modelization of turbulence. Unfortunately, attempts at performing direct simulations are limited to relatively low-Reynolds numbers because of the almost numberless small scales produced by the non-linear convective term. Alternatively, a dynamically less complex formulation is proposed here. Namely, regularizations of the Navier-Stokes equations that preserve the symmetry and conservation properties exactly. To do so, both convective and diffusive terms are altered in the same vein. In this way, the convective production of small scales is effectively restrained whereas the modified diffusive term introduces a hyperviscosity effect and consequently enhances the destruction of small scales. In practice, the only additional ingredient is a self-adjoint linear filter whose local filter length is determined from the requirement that vortex-stretching must stop at the smallest grid scale. In the present work, the performance of the above-mentioned recent improvements is assessed through application to turbulent natural convection flows by means of comparison with DNS reference data.

Forcing absoluteness and regularity properties

NARCIS (Netherlands)

Ikegami, D.

2010-01-01

For a large natural class of forcing notions, we prove general equivalence theorems between forcing absoluteness statements, regularity properties, and transcendence properties over L and the core model K. We use our results to answer open questions from set theory of the reals.

Higher order total variation regularization for EIT reconstruction.

Science.gov (United States)

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

2018-01-08

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

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

Science.gov (United States)

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

2011-03-09

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

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

Directory of Open Access Journals (Sweden)

David J Poza

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

Distance-regular graphs

NARCIS (Netherlands)

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

2016-01-01

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

Regular expressions cookbook

CERN Document Server

Goyvaerts, Jan

2009-01-01

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

Regularization parameter selection methods for ill-posed Poisson maximum likelihood estimation

International Nuclear Information System (INIS)

Bardsley, Johnathan M; Goldes, John

2009-01-01

In image processing applications, image intensity is often measured via the counting of incident photons emitted by the object of interest. In such cases, image data noise is accurately modeled by a Poisson distribution. This motivates the use of Poisson maximum likelihood estimation for image reconstruction. However, when the underlying model equation is ill-posed, regularization is needed. Regularized Poisson likelihood estimation has been studied extensively by the authors, though a problem of high importance remains: the choice of the regularization parameter. We will present three statistically motivated methods for choosing the regularization parameter, and numerical examples will be presented to illustrate their effectiveness

LL-regular grammars

NARCIS (Netherlands)

Nijholt, Antinus

1980-01-01

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

Regularized strings with extrinsic curvature

International Nuclear Information System (INIS)

Ambjoern, J.; Durhuus, B.

1987-07-01

We analyze models of discretized string theories, where the path integral over world sheet variables is regularized by summing over triangulated surfaces. The inclusion of curvature in the action is a necessity for the scaling of the string tension. We discuss the physical properties of models with extrinsic curvature terms in the action and show that the string tension vanishes at the critical point where the bare extrinsic curvature coupling tends to infinity. Similar results are derived for models with intrinsic curvature. (orig.)

Processing SPARQL queries with regular expressions in RDF databases

Science.gov (United States)

2011-01-01

Background As the Resource Description Framework (RDF) data model is widely used for modeling and sharing a lot of online bioinformatics resources such as Uniprot (dev.isb-sib.ch/projects/uniprot-rdf) or Bio2RDF (bio2rdf.org), SPARQL - a W3C recommendation query for RDF databases - has become an important query language for querying the bioinformatics knowledge bases. Moreover, due to the diversity of users’ requests for extracting information from the RDF data as well as the lack of users’ knowledge about the exact value of each fact in the RDF databases, it is desirable to use the SPARQL query with regular expression patterns for querying the RDF data. To the best of our knowledge, there is currently no work that efficiently supports regular expression processing in SPARQL over RDF databases. Most of the existing techniques for processing regular expressions are designed for querying a text corpus, or only for supporting the matching over the paths in an RDF graph. Results In this paper, we propose a novel framework for supporting regular expression processing in SPARQL query. Our contributions can be summarized as follows. 1) We propose an efficient framework for processing SPARQL queries with regular expression patterns in RDF databases. 2) We propose a cost model in order to adapt the proposed framework in the existing query optimizers. 3) We build a prototype for the proposed framework in C++ and conduct extensive experiments demonstrating the efficiency and effectiveness of our technique. Conclusions Experiments with a full-blown RDF engine show that our framework outperforms the existing ones by up to two orders of magnitude in processing SPARQL queries with regular expression patterns. PMID:21489225

Processing SPARQL queries with regular expressions in RDF databases.

Science.gov (United States)

Lee, Jinsoo; Pham, Minh-Duc; Lee, Jihwan; Han, Wook-Shin; Cho, Hune; Yu, Hwanjo; Lee, Jeong-Hoon

2011-03-29

As the Resource Description Framework (RDF) data model is widely used for modeling and sharing a lot of online bioinformatics resources such as Uniprot (dev.isb-sib.ch/projects/uniprot-rdf) or Bio2RDF (bio2rdf.org), SPARQL - a W3C recommendation query for RDF databases - has become an important query language for querying the bioinformatics knowledge bases. Moreover, due to the diversity of users' requests for extracting information from the RDF data as well as the lack of users' knowledge about the exact value of each fact in the RDF databases, it is desirable to use the SPARQL query with regular expression patterns for querying the RDF data. To the best of our knowledge, there is currently no work that efficiently supports regular expression processing in SPARQL over RDF databases. Most of the existing techniques for processing regular expressions are designed for querying a text corpus, or only for supporting the matching over the paths in an RDF graph. In this paper, we propose a novel framework for supporting regular expression processing in SPARQL query. Our contributions can be summarized as follows. 1) We propose an efficient framework for processing SPARQL queries with regular expression patterns in RDF databases. 2) We propose a cost model in order to adapt the proposed framework in the existing query optimizers. 3) We build a prototype for the proposed framework in C++ and conduct extensive experiments demonstrating the efficiency and effectiveness of our technique. Experiments with a full-blown RDF engine show that our framework outperforms the existing ones by up to two orders of magnitude in processing SPARQL queries with regular expression patterns.

Effect of ultrasound-guided interstitial laser photocoagulation on benign solitary solid cold thyroid nodules

DEFF Research Database (Denmark)

Døssing, Helle; Bennedbaek, Finn Noe; Hegedüs, Laszlo

2006-01-01

with a cytologically benign solitary solid and scintigraphically cold thyroid nodule causing local discomfort were assigned to one session of ILP (ILP-1) (n = 15) or three monthly ILP sessions (ILP-3) (n = 15) and followed for 6 months. ILP was performed under continuous ultrasound (US)--guidance and with an output...... power of 2.5-3.5 W. Thyroid nodule volume was assessed by US. Pressure and cosmetic complaints were evaluated on a visual analogue scale. MAIN OUTCOME: In the ILP- 1 group, thyroid nodule volume decreased from 10.1 +/- 4.3 mL (mean +/- standard deviation [SD]) to 5.7 +/- 3.2 mL (p = 0...

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

NARCIS (Netherlands)

Hochstenbach, M.E.; Reichel, L.

2010-01-01

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

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

Science.gov (United States)

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

2015-01-05

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

Using multi-relational data mining to discriminate blended therapy efficiency on patients based on log data

Directory of Open Access Journals (Sweden)

Artur Rocha

2018-06-01

Full Text Available Introduction: Clinical trials of blended Internet-based treatments deliver a wealth of data from various sources, such as self-report questionnaires, diagnostic interviews, treatment platform log files and Ecological Momentary Assessments (EMA. Mining these complex data for clinically relevant patterns is a daunting task for which no definitive best method exists. In this paper, we explore the expressive power of the multi-relational Inductive Logic Programming (ILP data mining approach, using combined trial data of the EU E-COMPARED depression trial. Methods: We explored the capability of ILP to handle and combine (implicit multiple relationships in the E-COMPARED data. This data set has the following features that favor ILP analysis: 1 Time reasoning is involved; 2 there is a reasonable amount of explicit useful relations to be analyzed; 3 ILP is capable of building comprehensible models that might be perceived as putative explanations by domain experts; 4 both numerical and statistical models may coexist within ILP models if necessary. In our analyses, we focused on scores of the PHQ-8 self-report questionnaire (which taps depressive symptom severity, and on EMA of mood and various other clinically relevant factors. Both measures were administered during treatment, which lasted between 9 to 16 weeks. Results: E-COMPARED trial data revealed different individual improvement patterns: PHQ-8 scores suggested that some individuals improved quickly during the first weeks of the treatment, while others improved at a (much slower pace, or not at all. Combining self-reported Ecological Momentary Assessments (EMA, PHQ-8 scores and log data about the usage of the ICT4D platform in the context of blended care, we set out to unveil possible causes for these different trajectories. Discussion: This work complements other studies into alternative data mining approaches to E-COMPARED trial data analysis, which are all aimed to identify clinically

Regular Expression Pocket Reference

CERN Document Server

Stubblebine, Tony

2007-01-01

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

Regularized Regression and Density Estimation based on Optimal Transport

KAUST Repository

Burger, M.

2012-03-11

The aim of this paper is to investigate a novel nonparametric approach for estimating and smoothing density functions as well as probability densities from discrete samples based on a variational regularization method with the Wasserstein metric as a data fidelity. The approach allows a unified treatment of discrete and continuous probability measures and is hence attractive for various tasks. In particular, the variational model for special regularization functionals yields a natural method for estimating densities and for preserving edges in the case of total variation regularization. In order to compute solutions of the variational problems, a regularized optimal transport problem needs to be solved, for which we discuss several formulations and provide a detailed analysis. Moreover, we compute special self-similar solutions for standard regularization functionals and we discuss several computational approaches and results. © 2012 The Author(s).

An adaptive regularization parameter choice strategy for multispectral bioluminescence tomography

Energy Technology Data Exchange (ETDEWEB)

Feng Jinchao; Qin Chenghu; Jia Kebin; Han Dong; Liu Kai; Zhu Shouping; Yang Xin; Tian Jie [Medical Image Processing Group, Institute of Automation, Chinese Academy of Sciences, P. O. Box 2728, Beijing 100190 (China); College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124 (China); Medical Image Processing Group, Institute of Automation, Chinese Academy of Sciences, P. O. Box 2728, Beijing 100190 (China); Medical Image Processing Group, Institute of Automation, Chinese Academy of Sciences, P. O. Box 2728, Beijing 100190 (China) and School of Life Sciences and Technology, Xidian University, Xi' an 710071 (China)

2011-11-15

Purpose: Bioluminescence tomography (BLT) provides an effective tool for monitoring physiological and pathological activities in vivo. However, the measured data in bioluminescence imaging are corrupted by noise. Therefore, regularization methods are commonly used to find a regularized solution. Nevertheless, for the quality of the reconstructed bioluminescent source obtained by regularization methods, the choice of the regularization parameters is crucial. To date, the selection of regularization parameters remains challenging. With regards to the above problems, the authors proposed a BLT reconstruction algorithm with an adaptive parameter choice rule. Methods: The proposed reconstruction algorithm uses a diffusion equation for modeling the bioluminescent photon transport. The diffusion equation is solved with a finite element method. Computed tomography (CT) images provide anatomical information regarding the geometry of the small animal and its internal organs. To reduce the ill-posedness of BLT, spectral information and the optimal permissible source region are employed. Then, the relationship between the unknown source distribution and multiview and multispectral boundary measurements is established based on the finite element method and the optimal permissible source region. Since the measured data are noisy, the BLT reconstruction is formulated as l{sub 2} data fidelity and a general regularization term. When choosing the regularization parameters for BLT, an efficient model function approach is proposed, which does not require knowledge of the noise level. This approach only requests the computation of the residual and regularized solution norm. With this knowledge, we construct the model function to approximate the objective function, and the regularization parameter is updated iteratively. Results: First, the micro-CT based mouse phantom was used for simulation verification. Simulation experiments were used to illustrate why multispectral data were used

Processing SPARQL queries with regular expressions in RDF databases

Directory of Open Access Journals (Sweden)

Cho Hune

2011-03-01

Full Text Available Abstract Background As the Resource Description Framework (RDF data model is widely used for modeling and sharing a lot of online bioinformatics resources such as Uniprot (dev.isb-sib.ch/projects/uniprot-rdf or Bio2RDF (bio2rdf.org, SPARQL - a W3C recommendation query for RDF databases - has become an important query language for querying the bioinformatics knowledge bases. Moreover, due to the diversity of users’ requests for extracting information from the RDF data as well as the lack of users’ knowledge about the exact value of each fact in the RDF databases, it is desirable to use the SPARQL query with regular expression patterns for querying the RDF data. To the best of our knowledge, there is currently no work that efficiently supports regular expression processing in SPARQL over RDF databases. Most of the existing techniques for processing regular expressions are designed for querying a text corpus, or only for supporting the matching over the paths in an RDF graph. Results In this paper, we propose a novel framework for supporting regular expression processing in SPARQL query. Our contributions can be summarized as follows. 1 We propose an efficient framework for processing SPARQL queries with regular expression patterns in RDF databases. 2 We propose a cost model in order to adapt the proposed framework in the existing query optimizers. 3 We build a prototype for the proposed framework in C++ and conduct extensive experiments demonstrating the efficiency and effectiveness of our technique. Conclusions Experiments with a full-blown RDF engine show that our framework outperforms the existing ones by up to two orders of magnitude in processing SPARQL queries with regular expression patterns.

Modeling hemispherical and directional radiative fluxes in regular-clumped canopies

International Nuclear Information System (INIS)

Begue, A.

1992-01-01

A model of radiative transfer in regular-clumped canopies is presented. The canopy is approximated by an array of porous cylinders located at the vertices of equilateral triangles. The model is split into two submodels, each describing a different level of structure: 1) The macrostructure submodel is based on Brown and Pandolfo (1969), who applied geometrical optics theory to an array of opaque cylinders. This model is adapted for porous cylinders and is used to derive expressions for directional interception efficiency as a function of height, radius, spacing and porosity of the cylinders. 2) The microstructure submodel makes use of the average canopy transmittance theory, applied to a cylinder, to compute the porosity of the clumps as a function of the leaf area density, the leaf inclination distribution function, the dimensions of the cylinder (height and radius), and the transmittance of green leaves in the appropriate spectral band. It is shown that, in the case of erectophile plant stands, the daily porosity of the cylinder can be approximated by the porosity calculated using the extinction coefficient of diffuse radiation. Directional interception efficiency, geometric conditions (incidence/viewing), and landscape component reflectances are used to compute hemispherical (interception, absorption, and reflectance) and directional (reflectance) radiative fluxes from simple analytical formulae. This model is validated against a data set of biological, radiative (PAR region) and radiometric (SPOT channels) measurements, collected in Niger on pearl millet (Pennisetum typhoides). The model fits the data quite well in terms of hourly and daily single-band or combined (NDVI) radiative fluxes. Close correspondence to measured fluxes, using few parameters, and the possibility of inversion makes the present model a valuable tool for the study of radiative transfer in discontinuous canopies. (author)

Surface-based prostate registration with biomechanical regularization

Science.gov (United States)

van de Ven, Wendy J. M.; Hu, Yipeng; Barentsz, Jelle O.; Karssemeijer, Nico; Barratt, Dean; Huisman, Henkjan J.

2013-03-01

Adding MR-derived information to standard transrectal ultrasound (TRUS) images for guiding prostate biopsy is of substantial clinical interest. A tumor visible on MR images can be projected on ultrasound by using MRUS registration. A common approach is to use surface-based registration. We hypothesize that biomechanical modeling will better control deformation inside the prostate than a regular surface-based registration method. We developed a novel method by extending a surface-based registration with finite element (FE) simulation to better predict internal deformation of the prostate. For each of six patients, a tetrahedral mesh was constructed from the manual prostate segmentation. Next, the internal prostate deformation was simulated using the derived radial surface displacement as boundary condition. The deformation field within the gland was calculated using the predicted FE node displacements and thin-plate spline interpolation. We tested our method on MR guided MR biopsy imaging data, as landmarks can easily be identified on MR images. For evaluation of the registration accuracy we used 45 anatomical landmarks located in all regions of the prostate. Our results show that the median target registration error of a surface-based registration with biomechanical regularization is 1.88 mm, which is significantly different from 2.61 mm without biomechanical regularization. We can conclude that biomechanical FE modeling has the potential to improve the accuracy of multimodal prostate registration when comparing it to regular surface-based registration.

Insulin-like peptide genes in honey bee fat body respond differently to manipulation of social behavioral physiology.

Science.gov (United States)

Nilsen, Kari-Anne; Ihle, Kate E; Frederick, Katy; Fondrk, M Kim; Smedal, Bente; Hartfelder, Klaus; Amdam, Gro V

2011-05-01

Nutrient sensitive insulin-like peptides (ILPs) have profound effects on invertebrate metabolism, nutrient storage, fertility and aging. Many insects transcribe ILPs in specialized neurosecretory cells at changing levels correlated with life history. However, the major site of insect metabolism and nutrient storage is not the brain, but rather the fat body, where functions of ILP expression are rarely studied and poorly understood. Fat body is analogous to mammalian liver and adipose tissue, with nutrient stores that often correlate with behavior. We used the honey bee (Apis mellifera), an insect with complex behavior, to test whether ILP genes in fat body respond to experimentally induced changes of behavioral physiology. Honey bee fat body influences endocrine state and behavior by secreting the yolk protein precursor vitellogenin (Vg), which suppresses lipophilic juvenile hormone and social foraging behavior. In a two-factorial experiment, we used RNA interference (RNAi)-mediated vg gene knockdown and amino acid nutrient enrichment of hemolymph (blood) to perturb this regulatory module. We document factor-specific changes in fat body ilp1 and ilp2 mRNA, the bee's ILP-encoding genes, and confirm that our protocol affects social behavior. We show that ilp1 and ilp2 are regulated independently and differently and diverge in their specific expression-localization between fat body oenocyte and trophocyte cells. Insect ilp functions may be better understood by broadening research to account for expression in fat body and not only brain.

Parameter identification in ODE models with oscillatory dynamics: a Fourier regularization approach

Science.gov (United States)

Chiara D'Autilia, Maria; Sgura, Ivonne; Bozzini, Benedetto

2017-12-01

In this paper we consider a parameter identification problem (PIP) for data oscillating in time, that can be described in terms of the dynamics of some ordinary differential equation (ODE) model, resulting in an optimization problem constrained by the ODEs. In problems with this type of data structure, simple application of the direct method of control theory (discretize-then-optimize) yields a least-squares cost function exhibiting multiple ‘low’ minima. Since in this situation any optimization algorithm is liable to fail in the approximation of a good solution, here we propose a Fourier regularization approach that is able to identify an iso-frequency manifold {{ S}} of codimension-one in the parameter space \

Correction of engineering servicing regularity of transporttechnological machines in operational process

Science.gov (United States)

Makarova, A. N.; Makarov, E. I.; Zakharov, N. S.

2018-03-01

In the article, the issue of correcting engineering servicing regularity on the basis of actual dependability data of cars in operation is considered. The purpose of the conducted research is to increase dependability of transport-technological machines by correcting engineering servicing regularity. The subject of the research is the mechanism of engineering servicing regularity influence on reliability measure. On the basis of the analysis of researches carried out before, a method of nonparametric estimation of car failure measure according to actual time-to-failure data was chosen. A possibility of describing the failure measure dependence on engineering servicing regularity by various mathematical models is considered. It is proven that the exponential model is the most appropriate for that purpose. The obtained results can be used as a separate method of engineering servicing regularity correction with certain operational conditions taken into account, as well as for the technical-economical and economical-stochastic methods improvement. Thus, on the basis of the conducted researches, a method of engineering servicing regularity correction of transport-technological machines in the operational process was developed. The use of that method will allow decreasing the number of failures.

A lattice Boltzmann model for substrates with regularly structured surface roughness

Science.gov (United States)

Yagub, A.; Farhat, H.; Kondaraju, S.; Singh, T.

2015-11-01

Superhydrophobic surface characteristics are important in many industrial applications, ranging from the textile to the military. It was observed that surfaces fabricated with nano/micro roughness can manipulate the droplet contact angle, thus providing an opportunity to control the droplet wetting characteristics. The Shan and Chen (SC) lattice Boltzmann model (LBM) is a good numerical tool, which holds strong potentials to qualify for simulating droplets wettability. This is due to its realistic nature of droplet contact angle (CA) prediction on flat smooth surfaces. But SC-LBM was not able to replicate the CA on rough surfaces because it lacks a real representation of the physics at work under these conditions. By using a correction factor to influence the interfacial tension within the asperities, the physical forces acting on the droplet at its contact lines were mimicked. This approach allowed the model to replicate some experimentally confirmed Wenzel and Cassie wetting cases. Regular roughness structures with different spacing were used to validate the study using the classical Wenzel and Cassie equations. The present work highlights the strength and weakness of the SC model and attempts to qualitatively conform it to the fundamental physics, which causes a change in the droplet apparent contact angle, when placed on nano/micro structured surfaces.

The geometry of continuum regularization

International Nuclear Information System (INIS)

Halpern, M.B.

1987-03-01

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

A Sim(2 invariant dimensional regularization

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J. Alfaro

2017-09-01

Full Text Available We introduce a Sim(2 invariant dimensional regularization of loop integrals. Then we can compute the one loop quantum corrections to the photon self energy, electron self energy and vertex in the Electrodynamics sector of the Very Special Relativity Standard Model (VSRSM.

Regular expression containment

DEFF Research Database (Denmark)

Henglein, Fritz; Nielsen, Lasse

2011-01-01

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

Supersymmetric dimensional regularization

International Nuclear Information System (INIS)

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

1980-01-01

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

Regularization Techniques for Linear Least-Squares Problems

KAUST Repository

Suliman, Mohamed

2016-04-01

Linear estimation is a fundamental branch of signal processing that deals with estimating the values of parameters from a corrupted measured data. Throughout the years, several optimization criteria have been used to achieve this task. The most astonishing attempt among theses is the linear least-squares. Although this criterion enjoyed a wide popularity in many areas due to its attractive properties, it appeared to suffer from some shortcomings. Alternative optimization criteria, as a result, have been proposed. These new criteria allowed, in one way or another, the incorporation of further prior information to the desired problem. Among theses alternative criteria is the regularized least-squares (RLS). In this thesis, we propose two new algorithms to find the regularization parameter for linear least-squares problems. In the constrained perturbation regularization algorithm (COPRA) for random matrices and COPRA for linear discrete ill-posed problems, an artificial perturbation matrix with a bounded norm is forced into the model matrix. This perturbation is introduced to enhance the singular value structure of the matrix. As a result, the new modified model is expected to provide a better stabilize substantial solution when used to estimate the original signal through minimizing the worst-case residual error function. Unlike many other regularization algorithms that go in search of minimizing the estimated data error, the two new proposed algorithms are developed mainly to select the artifcial perturbation bound and the regularization parameter in a way that approximately minimizes the mean-squared error (MSE) between the original signal and its estimate under various conditions. The first proposed COPRA method is developed mainly to estimate the regularization parameter when the measurement matrix is complex Gaussian, with centered unit variance (standard), and independent and identically distributed (i.i.d.) entries. Furthermore, the second proposed COPRA

Synergistic antitumor activity of histamine plus melphalan in isolated limb perfusion: preclinical studies.

Science.gov (United States)

Brunstein, Flavia; Hoving, Saske; Seynhaeve, Ann L B; van Tiel, Sandra T; Guetens, Gunther; de Bruijn, Ernst A; Eggermont, Alexander M M; ten Hagen, Timo L M

2004-11-03

We have previously shown how tumor response of isolated limb perfusion (ILP) with melphalan was improved when tumor necrosis factor alpha (TNF-alpha) was added. Taking into account that other vasoactive drugs could also improve tumor response to ILP, we evaluated histamine (Hi) as an alternative to TNF-alpha. We used a rat ILP model to assess the combined effects of Hi and melphalan (n = 6) on tumor regression, melphalan uptake (n = 6), and tissue histology (n = 2) compared with Hi or melphalan alone. We also evaluated the growth of BN-175 tumor cells as well as apoptosis, necrosis, cell morphology, and paracellular permeability of human umbilical vein endothelial cells (HUVECs) after Hi treatment alone and in combination with melphalan. The antitumor effect of the combination of Hi and melphalan in vivo was synergistic, and Hi-dependent reduction in tumor volume was blocked by H1 and H2 receptor inhibitors. Tumor regression was observed in 66% of the animals treated with Hi and melphalan, compared with 17% after treatment with Hi or melphalan alone. Tumor melphalan uptake increased and vascular integrity in the surrounding tissue was reduced after ILP treatment with Hi and melphalan compared with melphalan alone. In vitro results paralleled in vivo results. BN-175 tumor cells were more sensitive to the cytotoxicity of combined treatment than HUVECs, and Hi treatment increased the permeability of HUVECs. Hi in combination with melphalan in ILP improved response to that of melphalan alone through direct and indirect mechanisms. These results warrant further evaluation in the clinical ILP setting and, importantly, in organ perfusion.

Predictive features of persistent activity emergence in regular spiking and intrinsic bursting model neurons.

Directory of Open Access Journals (Sweden)

Kyriaki Sidiropoulou

Full Text Available Proper functioning of working memory involves the expression of stimulus-selective persistent activity in pyramidal neurons of the prefrontal cortex (PFC, which refers to neural activity that persists for seconds beyond the end of the stimulus. The mechanisms which PFC pyramidal neurons use to discriminate between preferred vs. neutral inputs at the cellular level are largely unknown. Moreover, the presence of pyramidal cell subtypes with different firing patterns, such as regular spiking and intrinsic bursting, raises the question as to what their distinct role might be in persistent firing in the PFC. Here, we use a compartmental modeling approach to search for discriminatory features in the properties of incoming stimuli to a PFC pyramidal neuron and/or its response that signal which of these stimuli will result in persistent activity emergence. Furthermore, we use our modeling approach to study cell-type specific differences in persistent activity properties, via implementing a regular spiking (RS and an intrinsic bursting (IB model neuron. We identify synaptic location within the basal dendrites as a feature of stimulus selectivity. Specifically, persistent activity-inducing stimuli consist of activated synapses that are located more distally from the soma compared to non-inducing stimuli, in both model cells. In addition, the action potential (AP latency and the first few inter-spike-intervals of the neuronal response can be used to reliably detect inducing vs. non-inducing inputs, suggesting a potential mechanism by which downstream neurons can rapidly decode the upcoming emergence of persistent activity. While the two model neurons did not differ in the coding features of persistent activity emergence, the properties of persistent activity, such as the firing pattern and the duration of temporally-restricted persistent activity were distinct. Collectively, our results pinpoint to specific features of the neuronal response to a given

Regularization by External Variables

DEFF Research Database (Denmark)

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

2016-01-01

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

Regular Single Valued Neutrosophic Hypergraphs

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Muhammad Aslam Malik

2016-12-01

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

Salt-body Inversion with Minimum Gradient Support and Sobolev Space Norm Regularizations

KAUST Repository

Kazei, Vladimir

2017-05-26

Full-waveform inversion (FWI) is a technique which solves the ill-posed seismic inversion problem of fitting our model data to the measured ones from the field. FWI is capable of providing high-resolution estimates of the model, and of handling wave propagation of arbitrary complexity (visco-elastic, anisotropic); yet, it often fails to retrieve high-contrast geological structures, such as salt. One of the reasons for the FWI failure is that the updates at earlier iterations are too smooth to capture the sharp edges of the salt boundary. We compare several regularization approaches, which promote sharpness of the edges. Minimum gradient support (MGS) regularization focuses the inversion on blocky models, even more than the total variation (TV) does. However, both approaches try to invert undesirable high wavenumbers in the model too early for a model of complex structure. Therefore, we apply the Sobolev space norm as a regularizing term in order to maintain a balance between sharp and smooth updates in FWI. We demonstrate the application of these regularizations on a Marmousi model, enriched by a chunk of salt. The model turns out to be too complex in some parts to retrieve its full velocity distribution, yet the salt shape and contrast are retrieved.

Reducing errors in the GRACE gravity solutions using regularization

Science.gov (United States)

Save, Himanshu; Bettadpur, Srinivas; Tapley, Byron D.

2012-09-01

The nature of the gravity field inverse problem amplifies the noise in the GRACE data, which creeps into the mid and high degree and order harmonic coefficients of the Earth's monthly gravity fields provided by GRACE. Due to the use of imperfect background models and data noise, these errors are manifested as north-south striping in the monthly global maps of equivalent water heights. In order to reduce these errors, this study investigates the use of the L-curve method with Tikhonov regularization. L-curve is a popular aid for determining a suitable value of the regularization parameter when solving linear discrete ill-posed problems using Tikhonov regularization. However, the computational effort required to determine the L-curve is prohibitively high for a large-scale problem like GRACE. This study implements a parameter-choice method, using Lanczos bidiagonalization which is a computationally inexpensive approximation to L-curve. Lanczos bidiagonalization is implemented with orthogonal transformation in a parallel computing environment and projects a large estimation problem on a problem of the size of about 2 orders of magnitude smaller for computing the regularization parameter. Errors in the GRACE solution time series have certain characteristics that vary depending on the ground track coverage of the solutions. These errors increase with increasing degree and order. In addition, certain resonant and near-resonant harmonic coefficients have higher errors as compared with the other coefficients. Using the knowledge of these characteristics, this study designs a regularization matrix that provides a constraint on the geopotential coefficients as a function of its degree and order. This regularization matrix is then used to compute the appropriate regularization parameter for each monthly solution. A 7-year time-series of the candidate regularized solutions (Mar 2003-Feb 2010) show markedly reduced error stripes compared with the unconstrained GRACE release 4

Regularized Laplace-Fourier-Domain Full Waveform Inversion Using a Weighted l 2 Objective Function

Science.gov (United States)

Jun, Hyunggu; Kwon, Jungmin; Shin, Changsoo; Zhou, Hongbo; Cogan, Mike

2017-03-01

Full waveform inversion (FWI) can be applied to obtain an accurate velocity model that contains important geophysical and geological information. FWI suffers from the local minimum problem when the starting model is not sufficiently close to the true model. Therefore, an accurate macroscale velocity model is essential for successful FWI, and Laplace-Fourier-domain FWI is appropriate for obtaining such a velocity model. However, conventional Laplace-Fourier-domain FWI remains an ill-posed and ill-conditioned problem, meaning that small errors in the data can result in large differences in the inverted model. This approach also suffers from certain limitations related to the logarithmic objective function. To overcome the limitations of conventional Laplace-Fourier-domain FWI, we introduce a weighted l 2 objective function, instead of the logarithmic objective function, as the data-domain objective function, and we also introduce two different model-domain regularizations: first-order Tikhonov regularization and prior model regularization. The weighting matrix for the data-domain objective function is constructed to suitably enhance the far-offset information. Tikhonov regularization smoothes the gradient, and prior model regularization allows reliable prior information to be taken into account. Two hyperparameters are obtained through trial and error and used to control the trade-off and achieve an appropriate balance between the data-domain and model-domain gradients. The application of the proposed regularizations facilitates finding a unique solution via FWI, and the weighted l 2 objective function ensures a more reasonable residual, thereby improving the stability of the gradient calculation. Numerical tests performed using the Marmousi synthetic dataset show that the use of the weighted l 2 objective function and the model-domain regularizations significantly improves the Laplace-Fourier-domain FWI. Because the Laplace-Fourier-domain FWI is improved, the

Scaffold hopping in drug discovery using inductive logic programming.

Science.gov (United States)

Tsunoyama, Kazuhisa; Amini, Ata; Sternberg, Michael J E; Muggleton, Stephen H

2008-05-01

In chemoinformatics, searching for compounds which are structurally diverse and share a biological activity is called scaffold hopping. Scaffold hopping is important since it can be used to obtain alternative structures when the compound under development has unexpected side-effects. Pharmaceutical companies use scaffold hopping when they wish to circumvent prior patents for targets of interest. We propose a new method for scaffold hopping using inductive logic programming (ILP). ILP uses the observed spatial relationships between pharmacophore types in pretested active and inactive compounds and learns human-readable rules describing the diverse structures of active compounds. The ILP-based scaffold hopping method is compared to two previous algorithms (chemically advanced template search, CATS, and CATS3D) on 10 data sets with diverse scaffolds. The comparison shows that the ILP-based method is significantly better than random selection while the other two algorithms are not. In addition, the ILP-based method retrieves new active scaffolds which were not found by CATS and CATS3D. The results show that the ILP-based method is at least as good as the other methods in this study. ILP produces human-readable rules, which makes it possible to identify the three-dimensional features that lead to scaffold hopping. A minor variant of a rule learnt by ILP for scaffold hopping was subsequently found to cover an inhibitor identified by an independent study. This provides a successful result in a blind trial of the effectiveness of ILP to generate rules for scaffold hopping. We conclude that ILP provides a valuable new approach for scaffold hopping.

Efficient multidimensional regularization for Volterra series estimation

Science.gov (United States)

Birpoutsoukis, Georgios; Csurcsia, Péter Zoltán; Schoukens, Johan

2018-05-01

This paper presents an efficient nonparametric time domain nonlinear system identification method. It is shown how truncated Volterra series models can be efficiently estimated without the need of long, transient-free measurements. The method is a novel extension of the regularization methods that have been developed for impulse response estimates of linear time invariant systems. To avoid the excessive memory needs in case of long measurements or large number of estimated parameters, a practical gradient-based estimation method is also provided, leading to the same numerical results as the proposed Volterra estimation method. Moreover, the transient effects in the simulated output are removed by a special regularization method based on the novel ideas of transient removal for Linear Time-Varying (LTV) systems. Combining the proposed methodologies, the nonparametric Volterra models of the cascaded water tanks benchmark are presented in this paper. The results for different scenarios varying from a simple Finite Impulse Response (FIR) model to a 3rd degree Volterra series with and without transient removal are compared and studied. It is clear that the obtained models capture the system dynamics when tested on a validation dataset, and their performance is comparable with the white-box (physical) models.

Geostatistical regularization operators for geophysical inverse problems on irregular meshes

Science.gov (United States)

Jordi, C.; Doetsch, J.; Günther, T.; Schmelzbach, C.; Robertsson, J. OA

2018-05-01

Irregular meshes allow to include complicated subsurface structures into geophysical modelling and inverse problems. The non-uniqueness of these inverse problems requires appropriate regularization that can incorporate a priori information. However, defining regularization operators for irregular discretizations is not trivial. Different schemes for calculating smoothness operators on irregular meshes have been proposed. In contrast to classical regularization constraints that are only defined using the nearest neighbours of a cell, geostatistical operators include a larger neighbourhood around a particular cell. A correlation model defines the extent of the neighbourhood and allows to incorporate information about geological structures. We propose an approach to calculate geostatistical operators for inverse problems on irregular meshes by eigendecomposition of a covariance matrix that contains the a priori geological information. Using our approach, the calculation of the operator matrix becomes tractable for 3-D inverse problems on irregular meshes. We tested the performance of the geostatistical regularization operators and compared them against the results of anisotropic smoothing in inversions of 2-D surface synthetic electrical resistivity tomography (ERT) data as well as in the inversion of a realistic 3-D cross-well synthetic ERT scenario. The inversions of 2-D ERT and seismic traveltime field data with geostatistical regularization provide results that are in good accordance with the expected geology and thus facilitate their interpretation. In particular, for layered structures the geostatistical regularization provides geologically more plausible results compared to the anisotropic smoothness constraints.

Multi-omic data integration enables discovery of hidden biological regularities

DEFF Research Database (Denmark)

Ebrahim, Ali; Brunk, Elizabeth; Tan, Justin

2016-01-01

Rapid growth in size and complexity of biological data sets has led to the 'Big Data to Knowledge' challenge. We develop advanced data integration methods for multi- level analysis of genomic, transcriptomic, ribosomal profiling, proteomic and fluxomic data. First, we show that pairwise integration...... of primary omics data reveals regularities that tie cellular processes together in Escherichia coli: the number of protein molecules made per mRNA transcript and the number of ribosomes required per translated protein molecule. Second, we show that genome- scale models, based on genomic and bibliomic data......, enable quantitative synchronization of disparate data types. Integrating omics data with models enabled the discovery of two novel regularities: condition invariant in vivo turnover rates of enzymes and the correlation of protein structural motifs and translational pausing. These regularities can...

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

DEFF Research Database (Denmark)

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

2009-01-01

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

Regularization methods in Banach spaces

CERN Document Server

Schuster, Thomas; Hofmann, Bernd; Kazimierski, Kamil S

2012-01-01

Regularization methods aimed at finding stable approximate solutions are a necessary tool to tackle inverse and ill-posed problems. Usually the mathematical model of an inverse problem consists of an operator equation of the first kind and often the associated forward operator acts between Hilbert spaces. However, for numerous problems the reasons for using a Hilbert space setting seem to be based rather on conventions than on an approprimate and realistic model choice, so often a Banach space setting would be closer to reality. Furthermore, sparsity constraints using general Lp-norms or the B

Manifold regularized multitask learning for semi-supervised multilabel image classification.

Science.gov (United States)

Luo, Yong; Tao, Dacheng; Geng, Bo; Xu, Chao; Maybank, Stephen J

2013-02-01

It is a significant challenge to classify images with multiple labels by using only a small number of labeled samples. One option is to learn a binary classifier for each label and use manifold regularization to improve the classification performance by exploring the underlying geometric structure of the data distribution. However, such an approach does not perform well in practice when images from multiple concepts are represented by high-dimensional visual features. Thus, manifold regularization is insufficient to control the model complexity. In this paper, we propose a manifold regularized multitask learning (MRMTL) algorithm. MRMTL learns a discriminative subspace shared by multiple classification tasks by exploiting the common structure of these tasks. It effectively controls the model complexity because different tasks limit one another's search volume, and the manifold regularization ensures that the functions in the shared hypothesis space are smooth along the data manifold. We conduct extensive experiments, on the PASCAL VOC'07 dataset with 20 classes and the MIR dataset with 38 classes, by comparing MRMTL with popular image classification algorithms. The results suggest that MRMTL is effective for image classification.

Catalytic micromotor generating self-propelled regular motion through random fluctuation

Science.gov (United States)

Yamamoto, Daigo; Mukai, Atsushi; Okita, Naoaki; Yoshikawa, Kenichi; Shioi, Akihisa

2013-07-01

Most of the current studies on nano/microscale motors to generate regular motion have adapted the strategy to fabricate a composite with different materials. In this paper, we report that a simple object solely made of platinum generates regular motion driven by a catalytic chemical reaction with hydrogen peroxide. Depending on the morphological symmetry of the catalytic particles, a rich variety of random and regular motions are observed. The experimental trend is well reproduced by a simple theoretical model by taking into account of the anisotropic viscous effect on the self-propelled active Brownian fluctuation.

Development and utilization of novel intron length polymorphic markers in foxtail millet (Setaria italica (L.) P. Beauv.).

Science.gov (United States)

Gupta, Sarika; Kumari, Kajal; Das, Jyotirmoy; Lata, Charu; Puranik, Swati; Prasad, Manoj

2011-07-01

Introns are noncoding sequences in a gene that are transcribed to precursor mRNA but spliced out during mRNA maturation and are abundant in eukaryotic genomes. The availability of codominant molecular markers and saturated genetic linkage maps have been limited in foxtail millet (Setaria italica (L.) P. Beauv.). Here, we describe the development of 98 novel intron length polymorphic (ILP) markers in foxtail millet using sequence information of the model plant rice. A total of 575 nonredundant expressed sequence tag (EST) sequences were obtained, of which 327 and 248 unique sequences were from dehydration- and salinity-stressed suppression subtractive hybridization libraries, respectively. The BLAST analysis of 98 EST sequences suggests a nearly defined function for about 64% of them, and they were grouped into 11 different functional categories. All 98 ILP primer pairs showed a high level of cross-species amplification in two millets and two nonmillets species ranging from 90% to 100%, with a mean of ∼97%. The mean observed heterozygosity and Nei's average gene diversity 0.016 and 0.171, respectively, established the efficiency of the ILP markers for distinguishing the foxtail millet accessions. Based on 26 ILP markers, a reasonable dendrogram of 45 foxtail millet accessions was constructed, demonstrating the utility of ILP markers in germplasm characterizations and genomic relationships in millets and nonmillets species.

Stochastic analytic regularization

International Nuclear Information System (INIS)

Alfaro, J.

1984-07-01

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

Developing logistic regression models using purchase attributes and demographics to predict the probability of purchases of regular and specialty eggs.

Science.gov (United States)

Bejaei, M; Wiseman, K; Cheng, K M

2015-01-01

Consumers' interest in specialty eggs appears to be growing in Europe and North America. The objective of this research was to develop logistic regression models that utilise purchaser attributes and demographics to predict the probability of a consumer purchasing a specific type of table egg including regular (white and brown), non-caged (free-run, free-range and organic) or nutrient-enhanced eggs. These purchase prediction models, together with the purchasers' attributes, can be used to assess market opportunities of different egg types specifically in British Columbia (BC). An online survey was used to gather data for the models. A total of 702 completed questionnaires were submitted by BC residents. Selected independent variables included in the logistic regression to develop models for different egg types to predict the probability of a consumer purchasing a specific type of table egg. The variables used in the model accounted for 54% and 49% of variances in the purchase of regular and non-caged eggs, respectively. Research results indicate that consumers of different egg types exhibit a set of unique and statistically significant characteristics and/or demographics. For example, consumers of regular eggs were less educated, older, price sensitive, major chain store buyers, and store flyer users, and had lower awareness about different types of eggs and less concern regarding animal welfare issues. However, most of the non-caged egg consumers were less concerned about price, had higher awareness about different types of table eggs, purchased their eggs from local/organic grocery stores, farm gates or farmers markets, and they were more concerned about care and feeding of hens compared to consumers of other eggs types.

3D first-arrival traveltime tomography with modified total variation regularization

Science.gov (United States)

Jiang, Wenbin; Zhang, Jie

2018-02-01

Three-dimensional (3D) seismic surveys have become a major tool in the exploration and exploitation of hydrocarbons. 3D seismic first-arrival traveltime tomography is a robust method for near-surface velocity estimation. A common approach for stabilizing the ill-posed inverse problem is to apply Tikhonov regularization to the inversion. However, the Tikhonov regularization method recovers smooth local structures while blurring the sharp features in the model solution. We present a 3D first-arrival traveltime tomography method with modified total variation (MTV) regularization to preserve sharp velocity contrasts and improve the accuracy of velocity inversion. To solve the minimization problem of the new traveltime tomography method, we decouple the original optimization problem into two following subproblems: a standard traveltime tomography problem with the traditional Tikhonov regularization and a L2 total variation problem. We apply the conjugate gradient method and split-Bregman iterative method to solve these two subproblems, respectively. Our synthetic examples show that the new method produces higher resolution models than the conventional traveltime tomography with Tikhonov regularization. We apply the technique to field data. The stacking section shows significant improvements with static corrections from the MTV traveltime tomography.

Mathematical Model and Simulation of Particle Flow around Choanoflagellates Using the Method of Regularized Stokeslets

Science.gov (United States)

Nararidh, Niti

2013-11-01

Choanoflagellates are unicellular organisms whose intriguing morphology includes a set of collars/microvilli emanating from the cell body, surrounding the beating flagellum. We investigated the role of the microvilli in the feeding and swimming behavior of the organism using a three-dimensional model based on the method of regularized Stokeslets. This model allows us to examine the velocity generated around the feeding organism tethered in place, as well as to predict the paths of surrounding free flowing particles. In particular, we can depict the effective capture of nutritional particles and bacteria in the fluid, showing the hydrodynamic cooperation between the cell, flagellum, and microvilli of the organism. Funding Source: Murchison Undergraduate Research Fellowship.

Solution path for manifold regularized semisupervised classification.

Science.gov (United States)

Wang, Gang; Wang, Fei; Chen, Tao; Yeung, Dit-Yan; Lochovsky, Frederick H

2012-04-01

Traditional learning algorithms use only labeled data for training. However, labeled examples are often difficult or time consuming to obtain since they require substantial human labeling efforts. On the other hand, unlabeled data are often relatively easy to collect. Semisupervised learning addresses this problem by using large quantities of unlabeled data with labeled data to build better learning algorithms. In this paper, we use the manifold regularization approach to formulate the semisupervised learning problem where a regularization framework which balances a tradeoff between loss and penalty is established. We investigate different implementations of the loss function and identify the methods which have the least computational expense. The regularization hyperparameter, which determines the balance between loss and penalty, is crucial to model selection. Accordingly, we derive an algorithm that can fit the entire path of solutions for every value of the hyperparameter. Its computational complexity after preprocessing is quadratic only in the number of labeled examples rather than the total number of labeled and unlabeled examples.

Stability of negative ionization fronts: Regularization by electric screening?

International Nuclear Information System (INIS)

Arrayas, Manuel; Ebert, Ute

2004-01-01

We recently have proposed that a reduced interfacial model for streamer propagation is able to explain spontaneous branching. Such models require regularization. In the present paper we investigate how transversal Fourier modes of a planar ionization front are regularized by the electric screening length. For a fixed value of the electric field ahead of the front we calculate the dispersion relation numerically. These results guide the derivation of analytical asymptotes for arbitrary fields: for small wave-vector k, the growth rate s(k) grows linearly with k, for large k, it saturates at some positive plateau value. We give a physical interpretation of these results

Viscous Regularization of the Euler Equations and Entropy Principles

KAUST Repository

Guermond, Jean-Luc

2014-03-11

This paper investigates a general class of viscous regularizations of the compressible Euler equations. A unique regularization is identified that is compatible with all the generalized entropies, à la [Harten et al., SIAM J. Numer. Anal., 35 (1998), pp. 2117-2127], and satisfies the minimum entropy principle. A connection with a recently proposed phenomenological model by [H. Brenner, Phys. A, 370 (2006), pp. 190-224] is made. © 2014 Society for Industrial and Applied Mathematics.

Improvements in GRACE Gravity Fields Using Regularization

Science.gov (United States)

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

2008-12-01

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

Optimal Tikhonov Regularization in Finite-Frequency Tomography

Science.gov (United States)

Fang, Y.; Yao, Z.; Zhou, Y.

2017-12-01

The last decade has witnessed a progressive transition in seismic tomography from ray theory to finite-frequency theory which overcomes the resolution limit of the high-frequency approximation in ray theory. In addition to approximations in wave propagation physics, a main difference between ray-theoretical tomography and finite-frequency tomography is the sparseness of the associated sensitivity matrix. It is well known that seismic tomographic problems are ill-posed and regularizations such as damping and smoothing are often applied to analyze the tradeoff between data misfit and model uncertainty. The regularizations depend on the structure of the matrix as well as noise level of the data. Cross-validation has been used to constrain data uncertainties in body-wave finite-frequency inversions when measurements at multiple frequencies are available to invert for a common structure. In this study, we explore an optimal Tikhonov regularization in surface-wave phase-velocity tomography based on minimization of an empirical Bayes risk function using theoretical training datasets. We exploit the structure of the sensitivity matrix in the framework of singular value decomposition (SVD) which also allows for the calculation of complete resolution matrix. We compare the optimal Tikhonov regularization in finite-frequency tomography with traditional tradeo-off analysis using surface wave dispersion measurements from global as well as regional studies.

Phonotactics in inductive logic programming

NARCIS (Netherlands)

Nerbonne, J.; Konstantopoulos, S.; Klopotek, M.A.; Wierzchon, S.T.; Trojanowski, K.

2004-01-01

We examine the results of applying inductive logic programming (ILP) to a relatively simple linguistic task, that of recognizing monosyllables in one language. ILP is suited to linguistic problems given linguists' preference for formulating their theories in discrete rules, and because of ILP's

76 FR 18007 - Intermediary Lending Pilot Program

Science.gov (United States)

2011-04-01

..., which are referenced in the Act's legislative history as bases for the ILP program. Requiring ILP... delinquency by an Eligible Small Business Concern. Finally, ILP Intermediaries will incur some costs of... loan request, lending history, projected lending activity, information regarding current or previous...

Inactivation of Viruses and Bacteriophages as Models for Swine Hepatitis E Virus in Food Matrices.

Science.gov (United States)

Emmoth, Eva; Rovira, Jordi; Rajkovic, Andreja; Corcuera, Elena; Wilches Pérez, Diego; Dergel, Irene; Ottoson, Jakob R; Widén, Frederik

2017-03-01

Hepatitis E virus has been recognised as a food-borne virus hazard in pork products, due to its zoonotic properties. This risk can be reduced by adequate treatment of the food to inactivate food-borne viruses. We used a spectrum of viruses and bacteriophages to evaluate the effect of three food treatments: high pressure processing (HPP), lactic acid (LA) and intense light pulse (ILP) treatments. On swine liver at 400 MPa for 10 min, HPP gave log 10 reductions of ≥4.2, ≥5.0 and 3.4 for feline calicivirus (FCV) 2280, FCV wildtype (wt) and murine norovirus 1 (MNV 1), respectively. Escherichia coli coliphage ϕX174 displayed a lower reduction of 1.1, while Escherichia coli coliphage MS2 was unaffected. For ham at 600 MPa, the corresponding reductions were 4.1, 4.4, 2.9, 1.7 and 1.3 log 10 . LA treatment at 2.2 M gave log 10 reductions in the viral spectrum of 0.29-2.1 for swine liver and 0.87-3.1 for ham, with ϕX174 and MNV 1, respectively, as the most stable microorganisms. The ILP treatment gave log 10 reductions of 1.6-2.8 for swine liver, 0.97-2.2 for ham and 1.3-2.3 for sausage, at 15-60 J cm -2 , with MS2 as the most stable microorganism. The HPP treatment gave significantly (p virus reduction on swine liver than ham for the viruses at equivalent pressure/time combinations. For ILP treatment, reductions on swine liver were significantly (p virus contamination and in advice to food producers. Conservative model indicators for the pathogenic viruses could be suggested.

Effective field theory dimensional regularization

International Nuclear Information System (INIS)

Lehmann, Dirk; Prezeau, Gary

2002-01-01

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

Effective field theory dimensional regularization

Science.gov (United States)

Lehmann, Dirk; Prézeau, Gary

2002-01-01

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

Shakeout: A New Approach to Regularized Deep Neural Network Training.

Science.gov (United States)

Kang, Guoliang; Li, Jun; Tao, Dacheng

2018-05-01

Recent years have witnessed the success of deep neural networks in dealing with a plenty of practical problems. Dropout has played an essential role in many successful deep neural networks, by inducing regularization in the model training. In this paper, we present a new regularized training approach: Shakeout. Instead of randomly discarding units as Dropout does at the training stage, Shakeout randomly chooses to enhance or reverse each unit's contribution to the next layer. This minor modification of Dropout has the statistical trait: the regularizer induced by Shakeout adaptively combines , and regularization terms. Our classification experiments with representative deep architectures on image datasets MNIST, CIFAR-10 and ImageNet show that Shakeout deals with over-fitting effectively and outperforms Dropout. We empirically demonstrate that Shakeout leads to sparser weights under both unsupervised and supervised settings. Shakeout also leads to the grouping effect of the input units in a layer. Considering the weights in reflecting the importance of connections, Shakeout is superior to Dropout, which is valuable for the deep model compression. Moreover, we demonstrate that Shakeout can effectively reduce the instability of the training process of the deep architecture.

An analysis of electrical impedance tomography with applications to Tikhonov regularization

KAUST Repository

Jin, Bangti

2012-01-16

This paper analyzes the continuum model/complete electrode model in the electrical impedance tomography inverse problem of determining the conductivity parameter from boundary measurements. The continuity and differentiability of the forward operator with respect to the conductivity parameter in L p-norms are proved. These analytical results are applied to several popular regularization formulations, which incorporate a priori information of smoothness/sparsity on the inhomogeneity through Tikhonov regularization, for both linearized and nonlinear models. Some important properties, e.g., existence, stability, consistency and convergence rates, are established. This provides some theoretical justifications of their practical usage. © EDP Sciences, SMAI, 2012.

An analysis of electrical impedance tomography with applications to Tikhonov regularization

KAUST Repository

Jin, Bangti; Maass, Peter

2012-01-01

This paper analyzes the continuum model/complete electrode model in the electrical impedance tomography inverse problem of determining the conductivity parameter from boundary measurements. The continuity and differentiability of the forward operator with respect to the conductivity parameter in L p-norms are proved. These analytical results are applied to several popular regularization formulations, which incorporate a priori information of smoothness/sparsity on the inhomogeneity through Tikhonov regularization, for both linearized and nonlinear models. Some important properties, e.g., existence, stability, consistency and convergence rates, are established. This provides some theoretical justifications of their practical usage. © EDP Sciences, SMAI, 2012.

Contour Propagation With Riemannian Elasticity Regularization

DEFF Research Database (Denmark)

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

2011-01-01

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

Regularized lattice Bhatnagar-Gross-Krook model for two- and three-dimensional cavity flow simulations.

Science.gov (United States)

Montessori, A; Falcucci, G; Prestininzi, P; La Rocca, M; Succi, S

2014-05-01

We investigate the accuracy and performance of the regularized version of the single-relaxation-time lattice Boltzmann equation for the case of two- and three-dimensional lid-driven cavities. The regularized version is shown to provide a significant gain in stability over the standard single-relaxation time, at a moderate computational overhead.

Hierarchical regular small-world networks

International Nuclear Information System (INIS)

Boettcher, Stefan; Goncalves, Bruno; Guclu, Hasan

2008-01-01

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

Maximum mutual information regularized classification

KAUST Repository

Wang, Jim Jing-Yan

2014-09-07

In this paper, a novel pattern classification approach is proposed by regularizing the classifier learning to maximize mutual information between the classification response and the true class label. We argue that, with the learned classifier, the uncertainty of the true class label of a data sample should be reduced by knowing its classification response as much as possible. The reduced uncertainty is measured by the mutual information between the classification response and the true class label. To this end, when learning a linear classifier, we propose to maximize the mutual information between classification responses and true class labels of training samples, besides minimizing the classification error and reducing the classifier complexity. An objective function is constructed by modeling mutual information with entropy estimation, and it is optimized by a gradient descend method in an iterative algorithm. Experiments on two real world pattern classification problems show the significant improvements achieved by maximum mutual information regularization.

Maximum mutual information regularized classification

KAUST Repository

Wang, Jim Jing-Yan; Wang, Yi; Zhao, Shiguang; Gao, Xin

2014-01-01

In this paper, a novel pattern classification approach is proposed by regularizing the classifier learning to maximize mutual information between the classification response and the true class label. We argue that, with the learned classifier, the uncertainty of the true class label of a data sample should be reduced by knowing its classification response as much as possible. The reduced uncertainty is measured by the mutual information between the classification response and the true class label. To this end, when learning a linear classifier, we propose to maximize the mutual information between classification responses and true class labels of training samples, besides minimizing the classification error and reducing the classifier complexity. An objective function is constructed by modeling mutual information with entropy estimation, and it is optimized by a gradient descend method in an iterative algorithm. Experiments on two real world pattern classification problems show the significant improvements achieved by maximum mutual information regularization.

75 FR 76006 - Regular Meeting

Science.gov (United States)

2010-12-07

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

Combining kernel matrix optimization and regularization to improve particle size distribution retrieval

Science.gov (United States)

Ma, Qian; Xia, Houping; Xu, Qiang; Zhao, Lei

2018-05-01

A new method combining Tikhonov regularization and kernel matrix optimization by multi-wavelength incidence is proposed for retrieving particle size distribution (PSD) in an independent model with improved accuracy and stability. In comparison to individual regularization or multi-wavelength least squares, the proposed method exhibited better anti-noise capability, higher accuracy and stability. While standard regularization typically makes use of the unit matrix, it is not universal for different PSDs, particularly for Junge distributions. Thus, a suitable regularization matrix was chosen by numerical simulation, with the second-order differential matrix found to be appropriate for most PSD types.

General inverse problems for regular variation

DEFF Research Database (Denmark)

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

2014-01-01

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

Parameter optimization in the regularized kernel minimum noise fraction transformation

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg; Vestergaard, Jacob Schack

2012-01-01

Based on the original, linear minimum noise fraction (MNF) transformation and kernel principal component analysis, a kernel version of the MNF transformation was recently introduced. Inspired by we here give a simple method for finding optimal parameters in a regularized version of kernel MNF...... analysis. We consider the model signal-to-noise ratio (SNR) as a function of the kernel parameters and the regularization parameter. In 2-4 steps of increasingly refined grid searches we find the parameters that maximize the model SNR. An example based on data from the DLR 3K camera system is given....

Regular-chaos transition of the energy spectrum and electromagnetic transition intensities in 44V nucleus using the framework of the nuclear shell model

International Nuclear Information System (INIS)

Hamoudi, A.K.; Abdul Majeed Al-Rahmani, A.

2012-01-01

The spectral fluctuations and the statistics of electromagnetic transition intensities and electromagnetic moments in 44 V nucleus are studied by the framework of the interacting shell model, using the FPD6 as a realistic effective interaction in the isospin formalism for 4 particles move in the fp-model space with a 40 Ca core. To look for a regular-chaos transition in 44 V nucleus, we perform shell model calculations using various interaction strengths β to the off-diagonal matrix elements of the FPD6. The nearest-neighbors level spacing distribution P(s) and the distribution of electromagnetic transition intensities [such as, B(M1) and B(E2) transitions] are found to have a regular dynamic at β=0, a chaotic dynamic at β⩾0.3 and an intermediate situation at 0 3 statistic we have found a regular dynamic at β=0, a chaotic dynamic at β⩾0.4 and an intermediate situation at 0<β<0.4. It is also found that the statistics of the squares of M1 and E2 moments, which are consistent with a Porter-Thomas distribution, have no dependence on the interaction strength β.

Image super-resolution reconstruction based on regularization technique and guided filter

Science.gov (United States)

Huang, De-tian; Huang, Wei-qin; Gu, Pei-ting; Liu, Pei-zhong; Luo, Yan-min

2017-06-01

In order to improve the accuracy of sparse representation coefficients and the quality of reconstructed images, an improved image super-resolution algorithm based on sparse representation is presented. In the sparse coding stage, the autoregressive (AR) regularization and the non-local (NL) similarity regularization are introduced to improve the sparse coding objective function. A group of AR models which describe the image local structures are pre-learned from the training samples, and one or several suitable AR models can be adaptively selected for each image patch to regularize the solution space. Then, the image non-local redundancy is obtained by the NL similarity regularization to preserve edges. In the process of computing the sparse representation coefficients, the feature-sign search algorithm is utilized instead of the conventional orthogonal matching pursuit algorithm to improve the accuracy of the sparse coefficients. To restore image details further, a global error compensation model based on weighted guided filter is proposed to realize error compensation for the reconstructed images. Experimental results demonstrate that compared with Bicubic, L1SR, SISR, GR, ANR, NE + LS, NE + NNLS, NE + LLE and A + (16 atoms) methods, the proposed approach has remarkable improvement in peak signal-to-noise ratio, structural similarity and subjective visual perception.

Describing chaotic attractors: Regular and perpetual points

Science.gov (United States)

Dudkowski, Dawid; Prasad, Awadhesh; Kapitaniak, Tomasz

2018-03-01

We study the concepts of regular and perpetual points for describing the behavior of chaotic attractors in dynamical systems. The idea of these points, which have been recently introduced to theoretical investigations, is thoroughly discussed and extended into new types of models. We analyze the correlation between regular and perpetual points, as well as their relation with phase space, showing the potential usefulness of both types of points in the qualitative description of co-existing states. The ability of perpetual points in finding attractors is indicated, along with its potential cause. The location of chaotic trajectories and sets of considered points is investigated and the study on the stability of systems is shown. The statistical analysis of the observing desired states is performed. We focus on various types of dynamical systems, i.e., chaotic flows with self-excited and hidden attractors, forced mechanical models, and semiconductor superlattices, exhibiting the universality of appearance of the observed patterns and relations.

Dimensional versus lattice regularization within Luescher's Yang Mills theory

International Nuclear Information System (INIS)

Diekmann, B.; Langer, M.; Schuette, D.

1993-01-01

It is pointed out that the coefficients of Luescher's effective model space Hamiltonian, which is based upon dimensional regularization techniques, can be reproduced by applying folded diagram perturbation theory to the Kogut Susskind Hamiltonian and by performing a lattice continuum limit (keeping the volume fixed). Alternative cutoff regularizations of the Hamiltonian are in general inconsistent, the critical point beeing the correct prediction for Luescher's tadpole coefficient which is formally quadratically divergent and which has to become a well defined (negative) number. (orig.)

Continuum-regularized quantum gravity

International Nuclear Information System (INIS)

Chan Huesum; Halpern, M.B.

1987-01-01

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

Online co-regularized algorithms

NARCIS (Netherlands)

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

2012-01-01

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

Regularized Discriminant Analysis: A Large Dimensional Study

KAUST Repository

Yang, Xiaoke

2018-04-28

In this thesis, we focus on studying the performance of general regularized discriminant analysis (RDA) classifiers. The data used for analysis is assumed to follow Gaussian mixture model with different means and covariances. RDA offers a rich class of regularization options, covering as special cases the regularized linear discriminant analysis (RLDA) and the regularized quadratic discriminant analysis (RQDA) classi ers. We analyze RDA under the double asymptotic regime where the data dimension and the training size both increase in a proportional way. This double asymptotic regime allows for application of fundamental results from random matrix theory. Under the double asymptotic regime and some mild assumptions, we show that the asymptotic classification error converges to a deterministic quantity that only depends on the data statistical parameters and dimensions. This result not only implicates some mathematical relations between the misclassification error and the class statistics, but also can be leveraged to select the optimal parameters that minimize the classification error, thus yielding the optimal classifier. Validation results on the synthetic data show a good accuracy of our theoretical findings. We also construct a general consistent estimator to approximate the true classification error in consideration of the unknown previous statistics. We benchmark the performance of our proposed consistent estimator against classical estimator on synthetic data. The observations demonstrate that the general estimator outperforms others in terms of mean squared error (MSE).

Substructural Regularization With Data-Sensitive Granularity for Sequence Transfer Learning.

Science.gov (United States)

Sun, Shichang; Liu, Hongbo; Meng, Jiana; Chen, C L Philip; Yang, Yu

2018-06-01

Sequence transfer learning is of interest in both academia and industry with the emergence of numerous new text domains from Twitter and other social media tools. In this paper, we put forward the data-sensitive granularity for transfer learning, and then, a novel substructural regularization transfer learning model (STLM) is proposed to preserve target domain features at substructural granularity in the light of the condition of labeled data set size. Our model is underpinned by hidden Markov model and regularization theory, where the substructural representation can be integrated as a penalty after measuring the dissimilarity of substructures between target domain and STLM with relative entropy. STLM can achieve the competing goals of preserving the target domain substructure and utilizing the observations from both the target and source domains simultaneously. The estimation of STLM is very efficient since an analytical solution can be derived as a necessary and sufficient condition. The relative usability of substructures to act as regularization parameters and the time complexity of STLM are also analyzed and discussed. Comprehensive experiments of part-of-speech tagging with both Brown and Twitter corpora fully justify that our model can make improvements on all the combinations of source and target domains.

A regularized, model-based approach to phase-based conductivity mapping using MRI.

Science.gov (United States)

Ropella, Kathleen M; Noll, Douglas C

2017-11-01

To develop a novel regularized, model-based approach to phase-based conductivity mapping that uses structural information to improve the accuracy of conductivity maps. The inverse of the three-dimensional Laplacian operator is used to model the relationship between measured phase maps and the object conductivity in a penalized weighted least-squares optimization problem. Spatial masks based on structural information are incorporated into the problem to preserve data near boundaries. The proposed Inverse Laplacian method was compared against a restricted Gaussian filter in simulation, phantom, and human experiments. The Inverse Laplacian method resulted in lower reconstruction bias and error due to noise in simulations than the Gaussian filter. The Inverse Laplacian method also produced conductivity maps closer to the measured values in a phantom and with reduced noise in the human brain, as compared to the Gaussian filter. The Inverse Laplacian method calculates conductivity maps with less noise and more accurate values near boundaries. Improving the accuracy of conductivity maps is integral for advancing the applications of conductivity mapping. Magn Reson Med 78:2011-2021, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Regularity dimension of sequences and its application to phylogenetic tree reconstruction

International Nuclear Information System (INIS)

Pham, Tuan D.

2012-01-01

The concept of dimension is a central development of chaos theory for studying nonlinear dynamical systems. Different types of dimensions have been derived to interpret different geometrical or physical observations. Approximate entropy and its modified methods have been introduced for studying regularity and complexity of time-series data in physiology and biology. Here, the concept of power laws and entropy measure are adopted to develop the regularity dimension of sequences to model a mathematical relationship between the frequency with which information about signal regularity changes in various scales. The proposed regularity dimension is applied to reconstruct phylogenetic trees using mitochondrial DNA (mtDNA) sequences for the family Hominidae, which can be validated according to the hypothesized evolutionary relationships between organisms.

Analysis of Logic Programs Using Regular Tree Languages

DEFF Research Database (Denmark)

Gallagher, John Patrick

2012-01-01

The eld of nite tree automata provides fundamental notations and tools for reasoning about set of terms called regular or recognizable tree languages. We consider two kinds of analysis using regular tree languages, applied to logic programs. The rst approach is to try to discover automatically...... a tree automaton from a logic program, approximating its minimal Herbrand model. In this case the input for the analysis is a program, and the output is a tree automaton. The second approach is to expose or check properties of the program that can be expressed by a given tree automaton. The input...... to the analysis is a program and a tree automaton, and the output is an abstract model of the program. These two contrasting abstract interpretations can be used in a wide range of analysis and verication problems....

Using Tikhonov Regularization for Spatial Projections from CSR Regularized Spherical Harmonic GRACE Solutions

Science.gov (United States)

Save, H.; Bettadpur, S. V.

2013-12-01

It has been demonstrated before that using Tikhonov regularization produces spherical harmonic solutions from GRACE that have very little residual stripes while capturing all the signal observed by GRACE within the noise level. This paper demonstrates a two-step process and uses Tikhonov regularization to remove the residual stripes in the CSR regularized spherical harmonic coefficients when computing the spatial projections. We discuss methods to produce mass anomaly grids that have no stripe features while satisfying the necessary condition of capturing all observed signal within the GRACE noise level.

Regularized maximum correntropy machine

KAUST Repository

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

2015-01-01

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

Regularized maximum correntropy machine

KAUST Repository

Wang, Jim Jing-Yan

2015-02-12

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

Multiple graph regularized nonnegative matrix factorization

KAUST Repository

Wang, Jim Jing-Yan

2013-10-01

Non-negative matrix factorization (NMF) has been widely used as a data representation method based on components. To overcome the disadvantage of NMF in failing to consider the manifold structure of a data set, graph regularized NMF (GrNMF) has been proposed by Cai et al. by constructing an affinity graph and searching for a matrix factorization that respects graph structure. Selecting a graph model and its corresponding parameters is critical for this strategy. This process is usually carried out by cross-validation or discrete grid search, which are time consuming and prone to overfitting. In this paper, we propose a GrNMF, called MultiGrNMF, in which the intrinsic manifold is approximated by a linear combination of several graphs with different models and parameters inspired by ensemble manifold regularization. Factorization metrics and linear combination coefficients of graphs are determined simultaneously within a unified object function. They are alternately optimized in an iterative algorithm, thus resulting in a novel data representation algorithm. Extensive experiments on a protein subcellular localization task and an Alzheimer\\'s disease diagnosis task demonstrate the effectiveness of the proposed algorithm. © 2013 Elsevier Ltd. All rights reserved.

Can Individualized Learning Plans in an advanced clinical experience course for fourth year medical students foster Self-Directed Learning?

Science.gov (United States)

Chitkara, Maribeth B; Satnick, Daniel; Lu, Wei-Hsin; Fleit, Howard; Go, Roderick A; Chandran, Latha

2016-09-01

Residency programs have utilized Individualized Learning Plans (ILPs) to customize resident education while undergraduate medical education has not done so in a meaningful way. We discuss the use of ILPs within a fourth year medical school course to facilitate self-directed learning (SDL). At Stony Brook University School of Medicine, an ILP component was added to the Advanced Clinical Experience (ACE) course for fourth year students. Each completed an ILP outlining personal learning goals and strategies to achieve them. An adaptation of the Motivated Strategies for Learning Questionnaire (MSLQ) (Duncan T and McKeachie W, Educ Psych 40(2):117-128, 2005 and Cook DA et al., Med Ed 45:1230-1240, 2011) was used to measure success of ILPs in improving SDL. Qualitative data analysis was conducted on the ILPs and self-reflections. Forty-eight students participated. Two of the four SDL sub-domains identified on the MSLQ showed improvement; self-efficacy (p = .001) and self-regulation (p = .002). 'Medical Knowledge' was the competency most frequently identified as an area of concentration (90 %) and professionalism was selected least frequently (4 %). A higher percentage (83 %) of students who reported complete achievement of their ILP goals also reported feeling better prepared for entering residency. ILPs improve SDL strategies among medical students and may serve as useful tools to help shape future learning goals as they transition to residency training.

Quasi-regular impurity distribution driven by charge-density wave

International Nuclear Information System (INIS)

Baldea, I.; Badescu, M.

1991-09-01

The displacive motion of the impurity distribution immersed into the one-dimensional system has recently been studied in detail as one kind of quasi-regularity driven by CDW. As a further investigation of this problem we develop here a microscopical model for a different kind of quasi-regular impurity distribution driven by CDW, consisting of the modulation in the probability of occupied sites. The dependence on impurity concentration and temperature of relevant CDW quantities is obtained. Data reported in the quasi-1D materials NbSe 3 and Ta 2 NiSe 7 (particularly, thermal hysteresis effects at CDW transition) are interpreted in the framework of the present model. Possible similarities to other physical systems are also suggested. (author). 38 refs, 7 figs

Graph Regularized Auto-Encoders for Image Representation.

Science.gov (United States)

Yiyi Liao; Yue Wang; Yong Liu

2017-06-01

Image representation has been intensively explored in the domain of computer vision for its significant influence on the relative tasks such as image clustering and classification. It is valuable to learn a low-dimensional representation of an image which preserves its inherent information from the original image space. At the perspective of manifold learning, this is implemented with the local invariant idea to capture the intrinsic low-dimensional manifold embedded in the high-dimensional input space. Inspired by the recent successes of deep architectures, we propose a local invariant deep nonlinear mapping algorithm, called graph regularized auto-encoder (GAE). With the graph regularization, the proposed method preserves the local connectivity from the original image space to the representation space, while the stacked auto-encoders provide explicit encoding model for fast inference and powerful expressive capacity for complex modeling. Theoretical analysis shows that the graph regularizer penalizes the weighted Frobenius norm of the Jacobian matrix of the encoder mapping, where the weight matrix captures the local property in the input space. Furthermore, the underlying effects on the hidden representation space are revealed, providing insightful explanation to the advantage of the proposed method. Finally, the experimental results on both clustering and classification tasks demonstrate the effectiveness of our GAE as well as the correctness of the proposed theoretical analysis, and it also suggests that GAE is a superior solution to the current deep representation learning techniques comparing with variant auto-encoders and existing local invariant methods.

Enhanced manifold regularization for semi-supervised classification.

Science.gov (United States)

Gan, Haitao; Luo, Zhizeng; Fan, Yingle; Sang, Nong

2016-06-01

Manifold regularization (MR) has become one of the most widely used approaches in the semi-supervised learning field. It has shown superiority by exploiting the local manifold structure of both labeled and unlabeled data. The manifold structure is modeled by constructing a Laplacian graph and then incorporated in learning through a smoothness regularization term. Hence the labels of labeled and unlabeled data vary smoothly along the geodesics on the manifold. However, MR has ignored the discriminative ability of the labeled and unlabeled data. To address the problem, we propose an enhanced MR framework for semi-supervised classification in which the local discriminative information of the labeled and unlabeled data is explicitly exploited. To make full use of labeled data, we firstly employ a semi-supervised clustering method to discover the underlying data space structure of the whole dataset. Then we construct a local discrimination graph to model the discriminative information of labeled and unlabeled data according to the discovered intrinsic structure. Therefore, the data points that may be from different clusters, though similar on the manifold, are enforced far away from each other. Finally, the discrimination graph is incorporated into the MR framework. In particular, we utilize semi-supervised fuzzy c-means and Laplacian regularized Kernel minimum squared error for semi-supervised clustering and classification, respectively. Experimental results on several benchmark datasets and face recognition demonstrate the effectiveness of our proposed method.

Image deblurring using a perturbation-basec regularization approach

KAUST Repository

Alanazi, Abdulrahman

2017-11-02

The image restoration problem deals with images in which information has been degraded by blur or noise. In this work, we present a new method for image deblurring by solving a regularized linear least-squares problem. In the proposed method, a synthetic perturbation matrix with a bounded norm is forced into the discrete ill-conditioned model matrix. This perturbation is added to enhance the singular-value structure of the matrix and hence to provide an improved solution. A method is proposed to find a near-optimal value of the regularization parameter for the proposed approach. To reduce the computational complexity, we present a technique based on the bootstrapping method to estimate the regularization parameter for both low and high-resolution images. Experimental results on the image deblurring problem are presented. Comparisons are made with three benchmark methods and the results demonstrate that the proposed method clearly outperforms the other methods in terms of both the output PSNR and SSIM values.

Image deblurring using a perturbation-basec regularization approach

KAUST Repository

Alanazi, Abdulrahman; Ballal, Tarig; Masood, Mudassir; Al-Naffouri, Tareq Y.

2017-01-01

The image restoration problem deals with images in which information has been degraded by blur or noise. In this work, we present a new method for image deblurring by solving a regularized linear least-squares problem. In the proposed method, a synthetic perturbation matrix with a bounded norm is forced into the discrete ill-conditioned model matrix. This perturbation is added to enhance the singular-value structure of the matrix and hence to provide an improved solution. A method is proposed to find a near-optimal value of the regularization parameter for the proposed approach. To reduce the computational complexity, we present a technique based on the bootstrapping method to estimate the regularization parameter for both low and high-resolution images. Experimental results on the image deblurring problem are presented. Comparisons are made with three benchmark methods and the results demonstrate that the proposed method clearly outperforms the other methods in terms of both the output PSNR and SSIM values.

Regularities of Multifractal Measures

Indian Academy of Sciences (India)

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

Validity and Reliability of Revised Inventory of Learning Processes.

Science.gov (United States)

Gadzella, B. M.; And Others

The Inventory of Learning Processes (ILP) was developed by Schmeck, Ribich, and Ramanaiah in 1977 as a self-report inventory to assess learning style through a behavioral-oriented approach. The ILP was revised by Schmeck in 1983. The Revised ILP contains six scales: (1) Deep Processing; (2) Elaborative Processing; (3) Shallow Processing; (4)…

Adaptive Regularization of Neural Classifiers

DEFF Research Database (Denmark)

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

1997-01-01

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

Analysis of regularized inversion of data corrupted by white Gaussian noise

International Nuclear Information System (INIS)

Kekkonen, Hanne; Lassas, Matti; Siltanen, Samuli

2014-01-01

Tikhonov regularization is studied in the case of linear pseudodifferential operator as the forward map and additive white Gaussian noise as the measurement error. The measurement model for an unknown function u(x) is m(x) = Au(x) + δ ε (x), where δ > 0 is the noise magnitude. If ε was an L 2 -function, Tikhonov regularization gives an estimate T α (m) = u∈H r arg min { ||Au-m|| L 2 2 + α||u|| H r 2 } for u where α = α(δ) is the regularization parameter. Here penalization of the Sobolev norm ||u|| H r covers the cases of standard Tikhonov regularization (r = 0) and first derivative penalty (r = 1). Realizations of white Gaussian noise are almost never in L 2 , but do belong to H s with probability one if s < 0 is small enough. A modification of Tikhonov regularization theory is presented, covering the case of white Gaussian measurement noise. Furthermore, the convergence of regularized reconstructions to the correct solution as δ → 0 is proven in appropriate function spaces using microlocal analysis. The convergence of the related finite-dimensional problems to the infinite-dimensional problem is also analysed. (paper)

Joint leaf chlorophyll content and leaf area index retrieval from Landsat data using a regularized model inversion system (REGFLEC)

KAUST Repository

Houborg, Rasmus

2015-01-19

Leaf area index (LAI) and leaf chlorophyll content (Chll) represent key biophysical and biochemical controls on water, energy and carbon exchange processes in the terrestrial biosphere. In combination, LAI and Chll provide critical information on vegetation density, vitality and photosynthetic potentials. However, simultaneous retrieval of LAI and Chll from space observations is extremely challenging. Regularization strategies are required to increase the robustness and accuracy of retrieved properties and enable more reliable separation of soil, leaf and canopy parameters. To address these challenges, the REGularized canopy reFLECtance model (REGFLEC) inversion system was refined to incorporate enhanced techniques for exploiting ancillary LAI and temporal information derived from multiple satellite scenes. In this current analysis, REGFLEC is applied to a time-series of Landsat data.A novel aspect of the REGFLEC approach is the fact that no site-specific data are required to calibrate the model, which may be run in a largely automated fashion using information extracted entirely from image-based and other widely available datasets. Validation results, based upon in-situ LAI and Chll observations collected over maize and soybean fields in central Nebraska for the period 2001-2005, demonstrate Chll retrieval with a relative root-mean-square-deviation (RMSD) on the order of 19% (RMSD=8.42μgcm-2). While Chll retrievals were clearly influenced by the version of the leaf optical properties model used (PROSPECT), the application of spatio-temporal regularization constraints was shown to be critical for estimating Chll with sufficient accuracy. REGFLEC also reproduced the dynamics of in-situ measured LAI well (r2 =0.85), but estimates were biased low, particularly over maize (LAI was underestimated by ~36 %). This disparity may be attributed to differences between effective and true LAI caused by significant foliage clumping not being properly accounted for in the canopy

Condition Number Regularized Covariance Estimation.

Science.gov (United States)

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

2013-06-01

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

Object Tracking via 2DPCA and l2-Regularization

Directory of Open Access Journals (Sweden)

Haijun Wang

2016-01-01

Full Text Available We present a fast and robust object tracking algorithm by using 2DPCA and l2-regularization in a Bayesian inference framework. Firstly, we model the challenging appearance of the tracked object using 2DPCA bases, which exploit the strength of subspace representation. Secondly, we adopt the l2-regularization to solve the proposed presentation model and remove the trivial templates from the sparse tracking method which can provide a more fast tracking performance. Finally, we present a novel likelihood function that considers the reconstruction error, which is concluded from the orthogonal left-projection matrix and the orthogonal right-projection matrix. Experimental results on several challenging image sequences demonstrate that the proposed method can achieve more favorable performance against state-of-the-art tracking algorithms.

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

DEFF Research Database (Denmark)

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

2014-01-01

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

Discriminative Elastic-Net Regularized Linear Regression.

Science.gov (United States)

Zhang, Zheng; Lai, Zhihui; Xu, Yong; Shao, Ling; Wu, Jian; Xie, Guo-Sen

2017-03-01

In this paper, we aim at learning compact and discriminative linear regression models. Linear regression has been widely used in different problems. However, most of the existing linear regression methods exploit the conventional zero-one matrix as the regression targets, which greatly narrows the flexibility of the regression model. Another major limitation of these methods is that the learned projection matrix fails to precisely project the image features to the target space due to their weak discriminative capability. To this end, we present an elastic-net regularized linear regression (ENLR) framework, and develop two robust linear regression models which possess the following special characteristics. First, our methods exploit two particular strategies to enlarge the margins of different classes by relaxing the strict binary targets into a more feasible variable matrix. Second, a robust elastic-net regularization of singular values is introduced to enhance the compactness and effectiveness of the learned projection matrix. Third, the resulting optimization problem of ENLR has a closed-form solution in each iteration, which can be solved efficiently. Finally, rather than directly exploiting the projection matrix for recognition, our methods employ the transformed features as the new discriminate representations to make final image classification. Compared with the traditional linear regression model and some of its variants, our method is much more accurate in image classification. Extensive experiments conducted on publicly available data sets well demonstrate that the proposed framework can outperform the state-of-the-art methods. The MATLAB codes of our methods can be available at http://www.yongxu.org/lunwen.html.

A Variance Minimization Criterion to Feature Selection Using Laplacian Regularization.

Science.gov (United States)

He, Xiaofei; Ji, Ming; Zhang, Chiyuan; Bao, Hujun

2011-10-01

In many information processing tasks, one is often confronted with very high-dimensional data. Feature selection techniques are designed to find the meaningful feature subset of the original features which can facilitate clustering, classification, and retrieval. In this paper, we consider the feature selection problem in unsupervised learning scenarios, which is particularly difficult due to the absence of class labels that would guide the search for relevant information. Based on Laplacian regularized least squares, which finds a smooth function on the data manifold and minimizes the empirical loss, we propose two novel feature selection algorithms which aim to minimize the expected prediction error of the regularized regression model. Specifically, we select those features such that the size of the parameter covariance matrix of the regularized regression model is minimized. Motivated from experimental design, we use trace and determinant operators to measure the size of the covariance matrix. Efficient computational schemes are also introduced to solve the corresponding optimization problems. Extensive experimental results over various real-life data sets have demonstrated the superiority of the proposed algorithms.

A discriminative method for family-based protein remote homology detection that combines inductive logic programming and propositional models.

Science.gov (United States)

Bernardes, Juliana S; Carbone, Alessandra; Zaverucha, Gerson

2011-03-23

Remote homology detection is a hard computational problem. Most approaches have trained computational models by using either full protein sequences or multiple sequence alignments (MSA), including all positions. However, when we deal with proteins in the "twilight zone" we can observe that only some segments of sequences (motifs) are conserved. We introduce a novel logical representation that allows us to represent physico-chemical properties of sequences, conserved amino acid positions and conserved physico-chemical positions in the MSA. From this, Inductive Logic Programming (ILP) finds the most frequent patterns (motifs) and uses them to train propositional models, such as decision trees and support vector machines (SVM). We use the SCOP database to perform our experiments by evaluating protein recognition within the same superfamily. Our results show that our methodology when using SVM performs significantly better than some of the state of the art methods, and comparable to other. However, our method provides a comprehensible set of logical rules that can help to understand what determines a protein function. The strategy of selecting only the most frequent patterns is effective for the remote homology detection. This is possible through a suitable first-order logical representation of homologous properties, and through a set of frequent patterns, found by an ILP system, that summarizes essential features of protein functions.

On the MSE Performance and Optimization of Regularized Problems

KAUST Repository

Alrashdi, Ayed

2016-11-01

The amount of data that has been measured, transmitted/received, and stored in the recent years has dramatically increased. So, today, we are in the world of big data. Fortunately, in many applications, we can take advantages of possible structures and patterns in the data to overcome the curse of dimensionality. The most well known structures include sparsity, low-rankness, block sparsity. This includes a wide range of applications such as machine learning, medical imaging, signal processing, social networks and computer vision. This also led to a specific interest in recovering signals from noisy compressed measurements (Compressed Sensing (CS) problem). Such problems are generally ill-posed unless the signal is structured. The structure can be captured by a regularizer function. This gives rise to a potential interest in regularized inverse problems, where the process of reconstructing the structured signal can be modeled as a regularized problem. This thesis particularly focuses on finding the optimal regularization parameter for such problems, such as ridge regression, LASSO, square-root LASSO and low-rank Generalized LASSO. Our goal is to optimally tune the regularizer to minimize the mean-squared error (MSE) of the solution when the noise variance or structure parameters are unknown. The analysis is based on the framework of the Convex Gaussian Min-max Theorem (CGMT) that has been used recently to precisely predict performance errors.

Regularizations: different recipes for identical situations

International Nuclear Information System (INIS)

Gambin, E.; Lobo, C.O.; Battistel, O.A.

2004-03-01

We present a discussion where the choice of the regularization procedure and the routing for the internal lines momenta are put at the same level of arbitrariness in the analysis of Ward identities involving simple and well-known problems in QFT. They are the complex self-interacting scalar field and two simple models where the SVV and AVV process are pertinent. We show that, in all these problems, the conditions to symmetry relations preservation are put in terms of the same combination of divergent Feynman integrals, which are evaluated in the context of a very general calculational strategy, concerning the manipulations and calculations involving divergences. Within the adopted strategy, all the arbitrariness intrinsic to the problem are still maintained in the final results and, consequently, a perfect map can be obtained with the corresponding results of the traditional regularization techniques. We show that, when we require an universal interpretation for the arbitrariness involved, in order to get consistency with all stated physical constraints, a strong condition is imposed for regularizations which automatically eliminates the ambiguities associated to the routing of the internal lines momenta of loops. The conclusion is clean and sound: the association between ambiguities and unavoidable symmetry violations in Ward identities cannot be maintained if an unique recipe is required for identical situations in the evaluation of divergent physical amplitudes. (author)

EIT image reconstruction with four dimensional regularization.

Science.gov (United States)

Dai, Tao; Soleimani, Manuchehr; Adler, Andy

2008-09-01

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

Borderline personality disorder and regularly drinking alcohol before sex.

Science.gov (United States)

Thompson, Ronald G; Eaton, Nicholas R; Hu, Mei-Chen; Hasin, Deborah S

2017-07-01

Drinking alcohol before sex increases the likelihood of engaging in unprotected intercourse, having multiple sexual partners and becoming infected with sexually transmitted infections. Borderline personality disorder (BPD), a complex psychiatric disorder characterised by pervasive instability in emotional regulation, self-image, interpersonal relationships and impulse control, is associated with substance use disorders and sexual risk behaviours. However, no study has examined the relationship between BPD and drinking alcohol before sex in the USA. This study examined the association between BPD and regularly drinking before sex in a nationally representative adult sample. Participants were 17 491 sexually active drinkers from Wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions. Logistic regression models estimated effects of BPD diagnosis, specific borderline diagnostic criteria and BPD criterion count on the likelihood of regularly (mostly or always) drinking alcohol before sex, adjusted for controls. Borderline personality disorder diagnosis doubled the odds of regularly drinking before sex [adjusted odds ratio (AOR) = 2.26; confidence interval (CI) = 1.63, 3.14]. Of nine diagnostic criteria, impulsivity in areas that are self-damaging remained a significant predictor of regularly drinking before sex (AOR = 1.82; CI = 1.42, 2.35). The odds of regularly drinking before sex increased by 20% for each endorsed criterion (AOR = 1.20; CI = 1.14, 1.27) DISCUSSION AND CONCLUSIONS: This is the first study to examine the relationship between BPD and regularly drinking alcohol before sex in the USA. Substance misuse treatment should assess regularly drinking before sex, particularly among patients with BPD, and BPD treatment should assess risk at the intersection of impulsivity, sexual behaviour and substance use. [Thompson Jr RG, Eaton NR, Hu M-C, Hasin DS Borderline personality disorder and regularly drinking alcohol

Randomized prospective study comparing a single radioiodine dose and a single laser therapy session in autonomously functioning thyroid nodules

DEFF Research Database (Denmark)

Døssing, Helle; Bennedbaek, Finn Noe; Bonnema, Steen Joop

2007-01-01

at inclusion and at 1, 3 and 6 months after treatment. RESULTS: Normalization of serum TSH was achieved in 7 out of 14 patients in the ILP group and in all 15 patients in the (131)I group (P=0.0025). In the ILP group, mean thyroid nodule volume reduction was 44+/-5% (s.e.m.; P... 47+/-8% (Phypothyroidism but no major side...... hypothyroidism. Using the present design, ILP seems inferior to (131)I therapy in normalization of serum TSH. The potential value of ILP as a non-surgical alternative to (131)I needs further investigation...

Effect of ultrasound-guided interstitial laser photocoagulation on benign solitary solid cold thyroid nodules - a randomised study

DEFF Research Database (Denmark)

Døssing, Helle; Bennedbaek, Finn Noe; Hegedüs, Laszlo

2005-01-01

AIM: To evaluate the efficacy of ultrasound (US)-guided interstitial laser photocoagulation (ILP) on thyroid function, nodule size and patient satisfaction in benign solitary solid cold thyroid nodules by comparing one ILP session with no treatment in a prospective randomised study. MATERIALS...... and thyroid function was determined by routine assays before and during follow-up. Pressure and cosmetic complaints before and at 6 months were evaluated on a visual analogue scale. ILP was performed under US guidance and with an output power of 2.5-3.5 W. RESULTS: In the ILP group, the nodule volume...

Bio-inspired Artificial Intelligence: А Generalized Net Model of the Regularization Process in MLP

Directory of Open Access Journals (Sweden)

Stanimir Surchev

2013-10-01

Full Text Available Many objects and processes inspired by the nature have been recreated by the scientists. The inspiration to create a Multilayer Neural Network came from human brain as member of the group. It possesses complicated structure and it is difficult to recreate, because of the existence of too many processes that require different solving methods. The aim of the following paper is to describe one of the methods that improve learning process of Artificial Neural Network. The proposed generalized net method presents Regularization process in Multilayer Neural Network. The purpose of verification is to protect the neural network from overfitting. The regularization is commonly used in neural network training process. Many methods of verification are present, the subject of interest is the one known as Regularization. It contains function in order to set weights and biases with smaller values to protect from overfitting.

Automated identification of protein-ligand interaction features using Inductive Logic Programming: a hexose binding case study.

Science.gov (United States)

A Santos, Jose C; Nassif, Houssam; Page, David; Muggleton, Stephen H; E Sternberg, Michael J

2012-07-11

There is a need for automated methods to learn general features of the interactions of a ligand class with its diverse set of protein receptors. An appropriate machine learning approach is Inductive Logic Programming (ILP), which automatically generates comprehensible rules in addition to prediction. The development of ILP systems which can learn rules of the complexity required for studies on protein structure remains a challenge. In this work we use a new ILP system, ProGolem, and demonstrate its performance on learning features of hexose-protein interactions. The rules induced by ProGolem detect interactions mediated by aromatics and by planar-polar residues, in addition to less common features such as the aromatic sandwich. The rules also reveal a previously unreported dependency for residues cys and leu. They also specify interactions involving aromatic and hydrogen bonding residues. This paper shows that Inductive Logic Programming implemented in ProGolem can derive rules giving structural features of protein/ligand interactions. Several of these rules are consistent with descriptions in the literature. In addition to confirming literature results, ProGolem's model has a 10-fold cross-validated predictive accuracy that is superior, at the 95% confidence level, to another ILP system previously used to study protein/hexose interactions and is comparable with state-of-the-art statistical learners.

A Regularized Linear Dynamical System Framework for Multivariate Time Series Analysis.

Science.gov (United States)

Liu, Zitao; Hauskrecht, Milos

2015-01-01

Linear Dynamical System (LDS) is an elegant mathematical framework for modeling and learning Multivariate Time Series (MTS). However, in general, it is difficult to set the dimension of an LDS's hidden state space. A small number of hidden states may not be able to model the complexities of a MTS, while a large number of hidden states can lead to overfitting. In this paper, we study learning methods that impose various regularization penalties on the transition matrix of the LDS model and propose a regularized LDS learning framework (rLDS) which aims to (1) automatically shut down LDSs' spurious and unnecessary dimensions, and consequently, address the problem of choosing the optimal number of hidden states; (2) prevent the overfitting problem given a small amount of MTS data; and (3) support accurate MTS forecasting. To learn the regularized LDS from data we incorporate a second order cone program and a generalized gradient descent method into the Maximum a Posteriori framework and use Expectation Maximization to obtain a low-rank transition matrix of the LDS model. We propose two priors for modeling the matrix which lead to two instances of our rLDS. We show that our rLDS is able to recover well the intrinsic dimensionality of the time series dynamics and it improves the predictive performance when compared to baselines on both synthetic and real-world MTS datasets.

Exact Solution of the Gyration Radius of an Individual's Trajectory for a Simplified Human Regular Mobility Model

Science.gov (United States)

Yan, Xiao-Yong; Han, Xiao-Pu; Zhou, Tao; Wang, Bing-Hong

2011-12-01

We propose a simplified human regular mobility model to simulate an individual's daily travel with three sequential activities: commuting to workplace, going to do leisure activities and returning home. With the assumption that the individual has a constant travel speed and inferior limit of time at home and in work, we prove that the daily moving area of an individual is an ellipse, and finally obtain an exact solution of the gyration radius. The analytical solution captures the empirical observation well.

Condition Number Regularized Covariance Estimation*

Science.gov (United States)

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

2012-01-01

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

p53- and ERK7-dependent ribosome surveillance response regulates Drosophila insulin-like peptide secretion.

Directory of Open Access Journals (Sweden)

Kiran Hasygar

2014-11-01

Full Text Available Insulin-like signalling is a conserved mechanism that coordinates animal growth and metabolism with nutrient status. In Drosophila, insulin-producing median neurosecretory cells (IPCs regulate larval growth by secreting insulin-like peptides (dILPs in a diet-dependent manner. Previous studies have shown that nutrition affects dILP secretion through humoral signals derived from the fat body. Here we uncover a novel mechanism that operates cell autonomously in the IPCs to regulate dILP secretion. We observed that impairment of ribosome biogenesis specifically in the IPCs strongly inhibits dILP secretion, which consequently leads to reduced body size and a delay in larval development. This response is dependent on p53, a known surveillance factor for ribosome biogenesis. A downstream effector of this growth inhibitory response is an atypical MAP kinase ERK7 (ERK8/MAPK15, which is upregulated in the IPCs following impaired ribosome biogenesis as well as starvation. We show that ERK7 is sufficient and essential to inhibit dILP secretion upon impaired ribosome biogenesis, and it acts epistatically to p53. Moreover, we provide evidence that p53 and ERK7 contribute to the inhibition of dILP secretion upon starvation. Thus, we conclude that a cell autonomous ribosome surveillance response, which leads to upregulation of ERK7, inhibits dILP secretion to impede tissue growth under limiting dietary conditions.

Differential regularization of a non-relativistic anyon model

International Nuclear Information System (INIS)

Freedman, D.Z.; Rius, N.

1993-07-01

Differential regularization is applied to a field theory of a non-relativistic charged boson field φ with λ(φ * φ) 2 self-interaction and coupling to a statistics-changing 0(1) Chern-Simons gauge field. Renormalized configuration-space amplitudes for all diagrams contributing to the φ * φ * φφ 4-point function, which is the only primitively divergent Green's function, are obtained up to 3-loop order. The renormalization group equations are explicitly checked, and the scheme dependence of the β-function is investigated. If the renormalization scheme is fixed to agree with a previous 1-loop calculation, the 2- and 3-loop contributions to β(λ, e) vanish, and β(λ, ε) itself vanishes when the ''self-dual'' condition relating λ to the gauge coupling e is imposed. (author). 12 refs, 1 fig

Regular and chaotic behaviors of plasma oscillations modeled by a modified Duffing equation

International Nuclear Information System (INIS)

Enjieu Kadji, H.G.; Chabi Orou, J.B.; Woafo, P.; Abdus Salam International Centre for Theoretical Physics, Trieste

2005-07-01

The regular and chaotic behavior of plasma oscillations governed by a modified Duffing equation is studied. The plasma oscillations are described by a nonlinear differential equation of the form x + w 0 2 x + βx 2 + αx 3 = 0 which is similar to a Duffing equation. By focusing on the quadratic term, which is mainly the term modifying the Duffing equation, the harmonic balance method and the fourth order Runge-Kutta algorithm are used to derive regular and chaotic motions respectively. A strong chaotic behavior exhibited by the system in that event when the system is subjected to an external periodic forcing oscillation is reported as β varies. (author)

Variational regularization of 3D data experiments with Matlab

CERN Document Server

Montegranario, Hebert

2014-01-01

Variational Regularization of 3D Data provides an introduction to variational methods for data modelling and its application in computer vision. In this book, the authors identify interpolation as an inverse problem that can be solved by Tikhonov regularization. The proposed solutions are generalizations of one-dimensional splines, applicable to n-dimensional data and the central idea is that these splines can be obtained by regularization theory using a trade-off between the fidelity of the data and smoothness properties.As a foundation, the authors present a comprehensive guide to the necessary fundamentals of functional analysis and variational calculus, as well as splines. The implementation and numerical experiments are illustrated using MATLAB®. The book also includes the necessary theoretical background for approximation methods and some details of the computer implementation of the algorithms. A working knowledge of multivariable calculus and basic vector and matrix methods should serve as an adequat...

Structural characterization of the packings of granular regular polygons.

Science.gov (United States)

Wang, Chuncheng; Dong, Kejun; Yu, Aibing

2015-12-01

By using a recently developed method for discrete modeling of nonspherical particles, we simulate the random packings of granular regular polygons with three to 11 edges under gravity. The effects of shape and friction on the packing structures are investigated by various structural parameters, including packing fraction, the radial distribution function, coordination number, Voronoi tessellation, and bond-orientational order. We find that packing fraction is generally higher for geometrically nonfrustrated regular polygons, and can be increased by the increase of edge number and decrease of friction. The changes of packing fraction are linked with those of the microstructures, such as the variations of the translational and orientational orders and local configurations. In particular, the free areas of Voronoi tessellations (which are related to local packing fractions) can be described by log-normal distributions for all polygons. The quantitative analyses establish a clearer picture for the packings of regular polygons.

Human visual system automatically encodes sequential regularities of discrete events.

Science.gov (United States)

Kimura, Motohiro; Schröger, Erich; Czigler, István; Ohira, Hideki

2010-06-01

regularities. In combination with a wide range of auditory MMN studies, the present study highlights the critical role of sensory systems in automatically encoding sequential regularities when modeling the world.

Subcortical processing of speech regularities underlies reading and music aptitude in children

Science.gov (United States)

2011-01-01

Background Neural sensitivity to acoustic regularities supports fundamental human behaviors such as hearing in noise and reading. Although the failure to encode acoustic regularities in ongoing speech has been associated with language and literacy deficits, how auditory expertise, such as the expertise that is associated with musical skill, relates to the brainstem processing of speech regularities is unknown. An association between musical skill and neural sensitivity to acoustic regularities would not be surprising given the importance of repetition and regularity in music. Here, we aimed to define relationships between the subcortical processing of speech regularities, music aptitude, and reading abilities in children with and without reading impairment. We hypothesized that, in combination with auditory cognitive abilities, neural sensitivity to regularities in ongoing speech provides a common biological mechanism underlying the development of music and reading abilities. Methods We assessed auditory working memory and attention, music aptitude, reading ability, and neural sensitivity to acoustic regularities in 42 school-aged children with a wide range of reading ability. Neural sensitivity to acoustic regularities was assessed by recording brainstem responses to the same speech sound presented in predictable and variable speech streams. Results Through correlation analyses and structural equation modeling, we reveal that music aptitude and literacy both relate to the extent of subcortical adaptation to regularities in ongoing speech as well as with auditory working memory and attention. Relationships between music and speech processing are specifically driven by performance on a musical rhythm task, underscoring the importance of rhythmic regularity for both language and music. Conclusions These data indicate common brain mechanisms underlying reading and music abilities that relate to how the nervous system responds to regularities in auditory input

Subcortical processing of speech regularities underlies reading and music aptitude in children.

Science.gov (United States)

Strait, Dana L; Hornickel, Jane; Kraus, Nina

2011-10-17

Neural sensitivity to acoustic regularities supports fundamental human behaviors such as hearing in noise and reading. Although the failure to encode acoustic regularities in ongoing speech has been associated with language and literacy deficits, how auditory expertise, such as the expertise that is associated with musical skill, relates to the brainstem processing of speech regularities is unknown. An association between musical skill and neural sensitivity to acoustic regularities would not be surprising given the importance of repetition and regularity in music. Here, we aimed to define relationships between the subcortical processing of speech regularities, music aptitude, and reading abilities in children with and without reading impairment. We hypothesized that, in combination with auditory cognitive abilities, neural sensitivity to regularities in ongoing speech provides a common biological mechanism underlying the development of music and reading abilities. We assessed auditory working memory and attention, music aptitude, reading ability, and neural sensitivity to acoustic regularities in 42 school-aged children with a wide range of reading ability. Neural sensitivity to acoustic regularities was assessed by recording brainstem responses to the same speech sound presented in predictable and variable speech streams. Through correlation analyses and structural equation modeling, we reveal that music aptitude and literacy both relate to the extent of subcortical adaptation to regularities in ongoing speech as well as with auditory working memory and attention. Relationships between music and speech processing are specifically driven by performance on a musical rhythm task, underscoring the importance of rhythmic regularity for both language and music. These data indicate common brain mechanisms underlying reading and music abilities that relate to how the nervous system responds to regularities in auditory input. Definition of common biological underpinnings

Subcortical processing of speech regularities underlies reading and music aptitude in children

Directory of Open Access Journals (Sweden)

Strait Dana L

2011-10-01

Full Text Available Abstract Background Neural sensitivity to acoustic regularities supports fundamental human behaviors such as hearing in noise and reading. Although the failure to encode acoustic regularities in ongoing speech has been associated with language and literacy deficits, how auditory expertise, such as the expertise that is associated with musical skill, relates to the brainstem processing of speech regularities is unknown. An association between musical skill and neural sensitivity to acoustic regularities would not be surprising given the importance of repetition and regularity in music. Here, we aimed to define relationships between the subcortical processing of speech regularities, music aptitude, and reading abilities in children with and without reading impairment. We hypothesized that, in combination with auditory cognitive abilities, neural sensitivity to regularities in ongoing speech provides a common biological mechanism underlying the development of music and reading abilities. Methods We assessed auditory working memory and attention, music aptitude, reading ability, and neural sensitivity to acoustic regularities in 42 school-aged children with a wide range of reading ability. Neural sensitivity to acoustic regularities was assessed by recording brainstem responses to the same speech sound presented in predictable and variable speech streams. Results Through correlation analyses and structural equation modeling, we reveal that music aptitude and literacy both relate to the extent of subcortical adaptation to regularities in ongoing speech as well as with auditory working memory and attention. Relationships between music and speech processing are specifically driven by performance on a musical rhythm task, underscoring the importance of rhythmic regularity for both language and music. Conclusions These data indicate common brain mechanisms underlying reading and music abilities that relate to how the nervous system responds to

Quasi-brittle damage modeling based on incremental energy relaxation combined with a viscous-type regularization

Science.gov (United States)

Langenfeld, K.; Junker, P.; Mosler, J.

2018-05-01

This paper deals with a constitutive model suitable for the analysis of quasi-brittle damage in structures. The model is based on incremental energy relaxation combined with a viscous-type regularization. A similar approach—which also represents the inspiration for the improved model presented in this paper—was recently proposed in Junker et al. (Contin Mech Thermodyn 29(1):291-310, 2017). Within this work, the model introduced in Junker et al. (2017) is critically analyzed first. This analysis leads to an improved model which shows the same features as that in Junker et al. (2017), but which (i) eliminates unnecessary model parameters, (ii) can be better interpreted from a physics point of view, (iii) can capture a fully softened state (zero stresses), and (iv) is characterized by a very simple evolution equation. In contrast to the cited work, this evolution equation is (v) integrated fully implicitly and (vi) the resulting time-discrete evolution equation can be solved analytically providing a numerically efficient closed-form solution. It is shown that the final model is indeed well-posed (i.e., its tangent is positive definite). Explicit conditions guaranteeing this well-posedness are derived. Furthermore, by additively decomposing the stress rate into deformation- and purely time-dependent terms, the functionality of the model is explained. Illustrative numerical examples confirm the theoretical findings.

Sparse regularization for force identification using dictionaries

Science.gov (United States)

Qiao, Baijie; Zhang, Xingwu; Wang, Chenxi; Zhang, Hang; Chen, Xuefeng

2016-04-01

The classical function expansion method based on minimizing l2-norm of the response residual employs various basis functions to represent the unknown force. Its difficulty lies in determining the optimum number of basis functions. Considering the sparsity of force in the time domain or in other basis space, we develop a general sparse regularization method based on minimizing l1-norm of the coefficient vector of basis functions. The number of basis functions is adaptively determined by minimizing the number of nonzero components in the coefficient vector during the sparse regularization process. First, according to the profile of the unknown force, the dictionary composed of basis functions is determined. Second, a sparsity convex optimization model for force identification is constructed. Third, given the transfer function and the operational response, Sparse reconstruction by separable approximation (SpaRSA) is developed to solve the sparse regularization problem of force identification. Finally, experiments including identification of impact and harmonic forces are conducted on a cantilever thin plate structure to illustrate the effectiveness and applicability of SpaRSA. Besides the Dirac dictionary, other three sparse dictionaries including Db6 wavelets, Sym4 wavelets and cubic B-spline functions can also accurately identify both the single and double impact forces from highly noisy responses in a sparse representation frame. The discrete cosine functions can also successfully reconstruct the harmonic forces including the sinusoidal, square and triangular forces. Conversely, the traditional Tikhonov regularization method with the L-curve criterion fails to identify both the impact and harmonic forces in these cases.

The Evolution of Frequency Distributions: Relating Regularization to Inductive Biases through Iterated Learning

Science.gov (United States)

Reali, Florencia; Griffiths, Thomas L.

2009-01-01

The regularization of linguistic structures by learners has played a key role in arguments for strong innate constraints on language acquisition, and has important implications for language evolution. However, relating the inductive biases of learners to regularization behavior in laboratory tasks can be challenging without a formal model. In this…

Regularized Biot–Savart Laws for Modeling Magnetic Flux Ropes

Science.gov (United States)

Titov, Viacheslav S.; Downs, Cooper; Mikić, Zoran; Török, Tibor; Linker, Jon A.; Caplan, Ronald M.

2018-01-01

Many existing models assume that magnetic flux ropes play a key role in solar flares and coronal mass ejections (CMEs). It is therefore important to develop efficient methods for constructing flux-rope configurations constrained by observed magnetic data and the morphology of the pre-eruptive source region. For this purpose, we have derived and implemented a compact analytical form that represents the magnetic field of a thin flux rope with an axis of arbitrary shape and circular cross-sections. This form implies that the flux rope carries axial current I and axial flux F, so that the respective magnetic field is the curl of the sum of axial and azimuthal vector potentials proportional to I and F, respectively. We expressed the vector potentials in terms of modified Biot–Savart laws, whose kernels are regularized at the axis in such a way that, when the axis is straight, these laws define a cylindrical force-free flux rope with a parabolic profile for the axial current density. For the cases we have studied so far, we determined the shape of the rope axis by following the polarity inversion line of the eruptions’ source region, using observed magnetograms. The height variation along the axis and other flux-rope parameters are estimated by means of potential-field extrapolations. Using this heuristic approach, we were able to construct pre-eruption configurations for the 2009 February 13 and 2011 October 1 CME events. These applications demonstrate the flexibility and efficiency of our new method for energizing pre-eruptive configurations in simulations of CMEs.

Decentralized formation of random regular graphs for robust multi-agent networks

KAUST Repository

Yazicioglu, A. Yasin

2014-12-15

Multi-agent networks are often modeled via interaction graphs, where the nodes represent the agents and the edges denote direct interactions between the corresponding agents. Interaction graphs have significant impact on the robustness of networked systems. One family of robust graphs is the random regular graphs. In this paper, we present a locally applicable reconfiguration scheme to build random regular graphs through self-organization. For any connected initial graph, the proposed scheme maintains connectivity and the average degree while minimizing the degree differences and randomizing the links. As such, if the average degree of the initial graph is an integer, then connected regular graphs are realized uniformly at random as time goes to infinity.

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

Directory of Open Access Journals (Sweden)

Dustin Kai Yan Lau

2014-03-01

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

Verbal working memory is related to the acquisition of cross-linguistic phonological regularities

NARCIS (Netherlands)

Bosma, E.; Heeringa, W.; Hoekstra, E.; Versloot, A.; Blom, E.

Closely related languages share cross-linguistic phonological regularities, such as Frisian -âld [c:t] and Dutch -oud [hut], as in the cognate pairs kâld [kc:t] - koud [khut] 'cold' and wâld [wc:t] - woud [whut] 'forest'. Within Bybee's (1995, 2001, 2008, 2010) network model, these regularities are,

Regularity effect in prospective memory during aging

Directory of Open Access Journals (Sweden)

Geoffrey Blondelle

2016-10-01

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

J-regular rings with injectivities

OpenAIRE

Shen, Liang

2010-01-01

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

Regular exercise and related factors in patients with Parkinson's disease: Applying zero-inflated negative binomial modeling of exercise count data.

Science.gov (United States)

Lee, JuHee; Park, Chang Gi; Choi, Moonki

2016-05-01

This study was conducted to identify risk factors that influence regular exercise among patients with Parkinson's disease in Korea. Parkinson's disease is prevalent in the elderly, and may lead to a sedentary lifestyle. Exercise can enhance physical and psychological health. However, patients with Parkinson's disease are less likely to exercise than are other populations due to physical disability. A secondary data analysis and cross-sectional descriptive study were conducted. A convenience sample of 106 patients with Parkinson's disease was recruited at an outpatient neurology clinic of a tertiary hospital in Korea. Demographic characteristics, disease-related characteristics (including disease duration and motor symptoms), self-efficacy for exercise, balance, and exercise level were investigated. Negative binomial regression and zero-inflated negative binomial regression for exercise count data were utilized to determine factors involved in exercise. The mean age of participants was 65.85 ± 8.77 years, and the mean duration of Parkinson's disease was 7.23 ± 6.02 years. Most participants indicated that they engaged in regular exercise (80.19%). Approximately half of participants exercised at least 5 days per week for 30 min, as recommended (51.9%). Motor symptoms were a significant predictor of exercise in the count model, and self-efficacy for exercise was a significant predictor of exercise in the zero model. Severity of motor symptoms was related to frequency of exercise. Self-efficacy contributed to the probability of exercise. Symptom management and improvement of self-efficacy for exercise are important to encourage regular exercise in patients with Parkinson's disease. Copyright © 2015 Elsevier Inc. All rights reserved.

"Plug-and-play" edge-preserving regularization

DEFF Research Database (Denmark)

Chen, Donghui; Kilmer, Misha E.; Hansen, Per Christian

2014-01-01

In many inverse problems it is essential to use regularization methods that preserve edges in the reconstructions, and many reconstruction models have been developed for this task, such as the Total Variation (TV) approach. The associated algorithms are complex and require a good knowledge of large...... cosine transform.hence the term "plug-and-play" . We do not attempt to improve on TV reconstructions, but rather provide an easy-to-use approach to computing reconstructions with similar properties....

Asymptotic performance of regularized quadratic discriminant analysis based classifiers

KAUST Repository

Elkhalil, Khalil

2017-12-13

This paper carries out a large dimensional analysis of the standard regularized quadratic discriminant analysis (QDA) classifier designed on the assumption that data arise from a Gaussian mixture model. The analysis relies on fundamental results from random matrix theory (RMT) when both the number of features and the cardinality of the training data within each class grow large at the same pace. Under some mild assumptions, we show that the asymptotic classification error converges to a deterministic quantity that depends only on the covariances and means associated with each class as well as the problem dimensions. Such a result permits a better understanding of the performance of regularized QDA and can be used to determine the optimal regularization parameter that minimizes the misclassification error probability. Despite being valid only for Gaussian data, our theoretical findings are shown to yield a high accuracy in predicting the performances achieved with real data sets drawn from popular real data bases, thereby making an interesting connection between theory and practice.

New regularities in mass spectra of hadrons

International Nuclear Information System (INIS)

Kajdalov, A.B.

1989-01-01

The properties of bosonic and baryonic Regge trajectories for hadrons composed of light quarks are considered. Experimental data agree with an existence of daughter trajectories consistent with string models. It is pointed out that the parity doubling for baryonic trajectories, observed experimentally, is not understood in the existing quark models. Mass spectrum of bosons and baryons indicates to an approximate supersymmetry in the mass region M>1 GeV. These regularities indicates to a high degree of symmetry for the dynamics in the confinement region. 8 refs.; 5 figs

Effective action for scalar fields and generalized zeta-function regularization

International Nuclear Information System (INIS)

Cognola, Guido; Zerbini, Sergio

2004-01-01

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

Nonlocal Regularized Algebraic Reconstruction Techniques for MRI: An Experimental Study

Directory of Open Access Journals (Sweden)

Xin Li

2013-01-01

Full Text Available We attempt to revitalize researchers' interest in algebraic reconstruction techniques (ART by expanding their capabilities and demonstrating their potential in speeding up the process of MRI acquisition. Using a continuous-to-discrete model, we experimentally study the application of ART into MRI reconstruction which unifies previous nonuniform-fast-Fourier-transform- (NUFFT- based and gridding-based approaches. Under the framework of ART, we advocate the use of nonlocal regularization techniques which are leveraged from our previous research on modeling photographic images. It is experimentally shown that nonlocal regularization ART (NR-ART can often outperform their local counterparts in terms of both subjective and objective qualities of reconstructed images. On one real-world k-space data set, we find that nonlocal regularization can achieve satisfactory reconstruction from as few as one-third of samples. We also address an issue related to image reconstruction from real-world k-space data but overlooked in the open literature: the consistency of reconstructed images across different resolutions. A resolution-consistent extension of NR-ART is developed and shown to effectively suppress the artifacts arising from frequency extrapolation. Both source codes and experimental results of this work are made fully reproducible.

Iterative Regularization with Minimum-Residual Methods

DEFF Research Database (Denmark)

Jensen, Toke Koldborg; Hansen, Per Christian

2007-01-01

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

Iterative regularization with minimum-residual methods

DEFF Research Database (Denmark)

Jensen, Toke Koldborg; Hansen, Per Christian

2006-01-01

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

Thermodynamics Prediction of Wax Precipitation in Black Oil Using Regular Solution Model and Plus Fraction Characterization

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Wei Wang

2013-01-01

Full Text Available The precipitation of wax/solid paraffin during production, transportation, and processing of crude oil is a serious problem. It is essential to have a reliable model to predict the wax appearance temperature and the amount of solid precipitated at different conditions. This paper presents a work to predict the solid precipitation based on solid-liquid equilibrium with regular solution-molecular thermodynamic theory and characterization of the crude oil plus fraction. Due to the differences of solubility characteristics between solid and liquid phase, the solubility parameters of liquid and solid phase are calculated by a modified model. The heat capacity change between solid and liquid phase is considered and estimated in the thermodynamic model. An activity coefficient based thermodynamic method combined with two characteristic methods to calculate wax precipitation in crude oil, especially heavy oil, has been tested with experimental data. The results show that the wax appearance temperature and the amount of weight precipitated can be predicted well with the experimental data.

Asymptotic analysis of a pile-up of regular edge dislocation walls

KAUST Repository

Hall, Cameron L.

2011-12-01

The idealised problem of a pile-up of regular dislocation walls (that is, of planes each containing an infinite number of parallel, identical and equally spaced dislocations) was presented by Roy et al. [A. Roy, R.H.J. Peerlings, M.G.D. Geers, Y. Kasyanyuk, Materials Science and Engineering A 486 (2008) 653-661] as a prototype for understanding the importance of discrete dislocation interactions in dislocation-based plasticity models. They noted that analytic solutions for the dislocation wall density are available for a pile-up of regular screw dislocation walls, but that numerical methods seem to be necessary for investigating regular edge dislocation walls. In this paper, we use the techniques of discrete-to-continuum asymptotic analysis to obtain a detailed description of a pile-up of regular edge dislocation walls. To leading order, we find that the dislocation wall density is governed by a simple differential equation and that boundary layers are present at both ends of the pile-up. © 2011 Elsevier B.V.

Asymptotic analysis of a pile-up of regular edge dislocation walls

KAUST Repository

Hall, Cameron L.

2011-01-01

The idealised problem of a pile-up of regular dislocation walls (that is, of planes each containing an infinite number of parallel, identical and equally spaced dislocations) was presented by Roy et al. [A. Roy, R.H.J. Peerlings, M.G.D. Geers, Y. Kasyanyuk, Materials Science and Engineering A 486 (2008) 653-661] as a prototype for understanding the importance of discrete dislocation interactions in dislocation-based plasticity models. They noted that analytic solutions for the dislocation wall density are available for a pile-up of regular screw dislocation walls, but that numerical methods seem to be necessary for investigating regular edge dislocation walls. In this paper, we use the techniques of discrete-to-continuum asymptotic analysis to obtain a detailed description of a pile-up of regular edge dislocation walls. To leading order, we find that the dislocation wall density is governed by a simple differential equation and that boundary layers are present at both ends of the pile-up. © 2011 Elsevier B.V.

The Evolution of Reputation-Based Cooperation in Regular Networks

Directory of Open Access Journals (Sweden)

Tatsuya Sasaki

2017-01-01

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

Interstitial laser photocoagulation for benign thyroid nodules: time to treat large nodules.

Science.gov (United States)

Amabile, Gerardo; Rotondi, Mario; Pirali, Barbara; Dionisio, Rosa; Agozzino, Lucio; Lanza, Michele; Buonanno, Luciano; Di Filippo, Bruno; Fonte, Rodolfo; Chiovato, Luca

2011-09-01

Interstitial laser photocoagulation (ILP) is a new therapeutic option for the ablation of non-functioning and hyper-functioning benign thyroid nodules. Amelioration of the ablation procedure currently allows treating large nodules. Aim of this study was to evaluate the therapeutic efficacy of ILP, performed according to a modified protocol of ablation, in patients with large functioning and non-functioning thyroid nodules and to identify the best parameters for predicting successful outcome in hyperthyroid patients. Fifty-one patients with non-functioning thyroid nodules (group 1) and 26 patients with hyperfunctioning thyroid nodules (group 2) were enrolled. All patients had a nodular volume ≥40 ml. Patients were addressed to 1-3 cycles of ILP. A cycle consisted of three ILP sessions, each lasting 5-10 minutes repeated at an interval of 1 month. After each cycle of ILP patients underwent thyroid evaluation. A nodule volume reduction, expressed as percentage of the basal volume, significantly occurred in both groups (F = 190.4; P nodule volume; (iii) total amount of energy delivered expressed in Joule. ROC curves identified the percentage of volume reduction as the best parameter predicting a normalized serum TSH (area under the curve 0.962; P thyroid nodules, both in terms of nodule size reduction and cure of hyperthyroidism (87% of cured patients after the last ILP cycle). ILP should not be limited to patients refusing or being ineligible for surgery and/or radioiodine. Copyright © 2011 Wiley-Liss, Inc.

Weighted regularized statistical shape space projection for breast 3D model reconstruction.

Science.gov (United States)

Ruiz, Guillermo; Ramon, Eduard; García, Jaime; Sukno, Federico M; Ballester, Miguel A González

2018-05-02

The use of 3D imaging has increased as a practical and useful tool for plastic and aesthetic surgery planning. Specifically, the possibility of representing the patient breast anatomy in a 3D shape and simulate aesthetic or plastic procedures is a great tool for communication between surgeon and patient during surgery planning. For the purpose of obtaining the specific 3D model of the breast of a patient, model-based reconstruction methods can be used. In particular, 3D morphable models (3DMM) are a robust and widely used method to perform 3D reconstruction. However, if additional prior information (i.e., known landmarks) is combined with the 3DMM statistical model, shape constraints can be imposed to improve the 3DMM fitting accuracy. In this paper, we present a framework to fit a 3DMM of the breast to two possible inputs: 2D photos and 3D point clouds (scans). Our method consists in a Weighted Regularized (WR) projection into the shape space. The contribution of each point in the 3DMM shape is weighted allowing to assign more relevance to those points that we want to impose as constraints. Our method is applied at multiple stages of the 3D reconstruction process. Firstly, it can be used to obtain a 3DMM initialization from a sparse set of 3D points. Additionally, we embed our method in the 3DMM fitting process in which more reliable or already known 3D points or regions of points, can be weighted in order to preserve their shape information. The proposed method has been tested in two different input settings: scans and 2D pictures assessing both reconstruction frameworks with very positive results. Copyright © 2018 Elsevier B.V. All rights reserved.

The Jump Set under Geometric Regularization. Part 1: Basic Technique and First-Order Denoising

KAUST Repository

Valkonen, Tuomo

2015-01-01

© 2015 Society for Industrial and Applied Mathematics. Let u ∈ BV(Ω) solve the total variation (TV) denoising problem with L²-squared fidelity and data f. Caselles, Chambolle, and Novaga [Multiscale Model. Simul., 6 (2008), pp. 879-894] have shown the containment H^m-1 (Ju \\\\Jf) = 0 of the jump set Ju of u in that of f. Their proof unfortunately depends heavily on the co-area formula, as do many results in this area, and as such is not directly extensible to higher-order, curvature-based, and other advanced geometric regularizers, such as total generalized variation and Euler\\'s elastica. These have received increased attention in recent times due to their better practical regularization properties compared to conventional TV or wavelets. We prove analogous jump set containment properties for a general class of regularizers. We do this with novel Lipschitz transformation techniques and do not require the co-area formula. In the present Part 1 we demonstrate the general technique on first-order regularizers, while in Part 2 we will extend it to higher-order regularizers. In particular, we concentrate in this part on TV and, as a novelty, Huber-regularized TV. We also demonstrate that the technique would apply to nonconvex TV models as well as the Perona-Malik anisotropic diffusion, if these approaches were well-posed to begin with.

Higher derivative regularization and chiral anomaly

International Nuclear Information System (INIS)

Nagahama, Yoshinori.

1985-02-01

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

Multilinear Graph Embedding: Representation and Regularization for Images.

Science.gov (United States)

Chen, Yi-Lei; Hsu, Chiou-Ting

2014-02-01

Given a set of images, finding a compact and discriminative representation is still a big challenge especially when multiple latent factors are hidden in the way of data generation. To represent multifactor images, although multilinear models are widely used to parameterize the data, most methods are based on high-order singular value decomposition (HOSVD), which preserves global statistics but interprets local variations inadequately. To this end, we propose a novel method, called multilinear graph embedding (MGE), as well as its kernelization MKGE to leverage the manifold learning techniques into multilinear models. Our method theoretically links the linear, nonlinear, and multilinear dimensionality reduction. We also show that the supervised MGE encodes informative image priors for image regularization, provided that an image is represented as a high-order tensor. From our experiments on face and gait recognition, the superior performance demonstrates that MGE better represents multifactor images than classic methods, including HOSVD and its variants. In addition, the significant improvement in image (or tensor) completion validates the potential of MGE for image regularization.

Insulin-like peptide response to nutritional input in honey bee workers.

Science.gov (United States)

Ihle, Kate E; Baker, Nicholas A; Amdam, Gro V

2014-10-01

The rise in metabolic disorders in the past decades has heightened focus on achieving a healthy dietary balance in humans. This is also an increasingly important issue in the management of honey bees (Apis mellifera) where poor nutrition has negative effects on health and productivity in agriculture, and nutrition is suggested as a contributing factor in the recent global declines in honey bee populations. As in other organisms, the insulin/insulin-like signaling (IIS) pathway is likely involved in maintaining nutrient homeostasis in honey bees. Honey bees have two insulin-like peptides (Ilps) with differing spatial expression patterns in the fat body suggesting that AmIlp1 potentially functions in lipid metabolism while AmIlp2 is a more general indicator of nutritional status. We fed caged worker bees artificial diets high in carbohydrates, proteins or lipids and measured expression of AmIlp1, AmIlp2, and the insulin receptor substrate (IRS) to test their responses to dietary macronutrients. We also measured lifespan, worker weight and gustatory sensitivity to sugar as measures of individual physical condition. We found that expression of AmIlp1 was affected by diet composition and was highest on a diet high in protein. Expression of AmIlp2 and AmIRS were not affected by diet. Workers lived longest on a diet high in carbohydrates and low in protein and lipids. However, bees fed this diet weighed less than those that received a diet high in protein and low in carbohydrates and lipids. Bees fed the high carbohydrates diet were also more responsive to sugar, potentially indicating greater levels of hunger. These results support a role for AmIlp1 in nutritional homeostasis and provide new insight into how unbalanced diets impact individual honey bee health. Copyright © 2014 Elsevier Ltd. All rights reserved.

Transition from regular to irregular reflection of cylindrical converging shock waves over convex obstacles

Science.gov (United States)

Vignati, F.; Guardone, A.

2017-11-01

An analytical model for the evolution of regular reflections of cylindrical converging shock waves over circular-arc obstacles is proposed. The model based on the new (local) parameter, the perceived wedge angle, which substitutes the (global) wedge angle of planar surfaces and accounts for the time-dependent curvature of both the shock and the obstacle at the reflection point, is introduced. The new model compares fairly well with numerical results. Results from numerical simulations of the regular to Mach transition—eventually occurring further downstream along the obstacle—point to the perceived wedge angle as the most significant parameter to identify regular to Mach transitions. Indeed, at the transition point, the value of the perceived wedge angle is between 39° and 42° for all investigated configurations, whereas, e.g., the absolute local wedge angle varies in between 10° and 45° in the same conditions.

Perturbation-Based Regularization for Signal Estimation in Linear Discrete Ill-posed Problems

KAUST Repository

Suliman, Mohamed Abdalla Elhag; Ballal, Tarig; Al-Naffouri, Tareq Y.

2016-01-01

Estimating the values of unknown parameters from corrupted measured data faces a lot of challenges in ill-posed problems. In such problems, many fundamental estimation methods fail to provide a meaningful stabilized solution. In this work, we propose a new regularization approach and a new regularization parameter selection approach for linear least-squares discrete ill-posed problems. The proposed approach is based on enhancing the singular-value structure of the ill-posed model matrix to acquire a better solution. Unlike many other regularization algorithms that seek to minimize the estimated data error, the proposed approach is developed to minimize the mean-squared error of the estimator which is the objective in many typical estimation scenarios. The performance of the proposed approach is demonstrated by applying it to a large set of real-world discrete ill-posed problems. Simulation results demonstrate that the proposed approach outperforms a set of benchmark regularization methods in most cases. In addition, the approach also enjoys the lowest runtime and offers the highest level of robustness amongst all the tested benchmark regularization methods.

Perturbation-Based Regularization for Signal Estimation in Linear Discrete Ill-posed Problems

KAUST Repository

Suliman, Mohamed Abdalla Elhag

2016-11-29

Estimating the values of unknown parameters from corrupted measured data faces a lot of challenges in ill-posed problems. In such problems, many fundamental estimation methods fail to provide a meaningful stabilized solution. In this work, we propose a new regularization approach and a new regularization parameter selection approach for linear least-squares discrete ill-posed problems. The proposed approach is based on enhancing the singular-value structure of the ill-posed model matrix to acquire a better solution. Unlike many other regularization algorithms that seek to minimize the estimated data error, the proposed approach is developed to minimize the mean-squared error of the estimator which is the objective in many typical estimation scenarios. The performance of the proposed approach is demonstrated by applying it to a large set of real-world discrete ill-posed problems. Simulation results demonstrate that the proposed approach outperforms a set of benchmark regularization methods in most cases. In addition, the approach also enjoys the lowest runtime and offers the highest level of robustness amongst all the tested benchmark regularization methods.

75 FR 53966 - Regular Meeting

Science.gov (United States)

2010-09-02

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

Application of Health Belief Model\\'s constructs for predicting regular consumption of folic acid supplements in pregnant women referred to Borazjan’s health centers in 2014-15

Directory of Open Access Journals (Sweden)

Zohre kafaee

2016-04-01

Full Text Available Background: One of the most common disorders in pregnancy is Folic Acid deficiency and its complications. The aim of this study was to examine the predictors of regular use of folic acid supplements based on HBM in pregnant women referred to Borazjan’s health centers. Material and Methods: In this cross-sectional study, 228 pregnant women or women with planning of pregnancy referred to health centers of Borazjan with random sampling method evaluated. Data was collected with questionnaire in 4 parts included demographic characteristics, knowledge, health belief model constructs and questions about folic acid supplement use. Data was analyzed by SPSS software with using appropriate statistical tests. Results: The mean age of samples was 27.4±5.41. 144 patients (63.2% consumed Folic Acid pills regularly, and 84 patients (36.8% had irregular use. The awareness of folic acid in 22.8% of women was good, 59.6%  and 17.5% of samples had intermediate and poor awareness, respectively. The perceived barriers (P<0.001, perceived benefits (P=0.002 and self-efficacy (P<0.001 had relation with consumption of folic acid and among demographic variables, only education level (P=0.04 had relation with the consumption of pills. In logistic regression perceived barriers was only predictor. Age and educational level had indirect effect in regular consume pill. Conclusion: Perceived barriers was strongest predictors of folic acid use, therefore intervention based on health belief model, with emphasis on reducing barriers is necessary for improving the use of this medicine during pregnancy.

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

Science.gov (United States)

Seto, Masako; Morimoto, Kanehisa; Maruyama, Soichiro

2006-05-01

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

Parameter identification for continuous point emission source based on Tikhonov regularization method coupled with particle swarm optimization algorithm.

Science.gov (United States)

Ma, Denglong; Tan, Wei; Zhang, Zaoxiao; Hu, Jun

2017-03-05

In order to identify the parameters of hazardous gas emission source in atmosphere with less previous information and reliable probability estimation, a hybrid algorithm coupling Tikhonov regularization with particle swarm optimization (PSO) was proposed. When the source location is known, the source strength can be estimated successfully by common Tikhonov regularization method, but it is invalid when the information about both source strength and location is absent. Therefore, a hybrid method combining linear Tikhonov regularization and PSO algorithm was designed. With this method, the nonlinear inverse dispersion model was transformed to a linear form under some assumptions, and the source parameters including source strength and location were identified simultaneously by linear Tikhonov-PSO regularization method. The regularization parameters were selected by L-curve method. The estimation results with different regularization matrixes showed that the confidence interval with high-order regularization matrix is narrower than that with zero-order regularization matrix. But the estimation results of different source parameters are close to each other with different regularization matrixes. A nonlinear Tikhonov-PSO hybrid regularization was also designed with primary nonlinear dispersion model to estimate the source parameters. The comparison results of simulation and experiment case showed that the linear Tikhonov-PSO method with transformed linear inverse model has higher computation efficiency than nonlinear Tikhonov-PSO method. The confidence intervals from linear Tikhonov-PSO are more reasonable than that from nonlinear method. The estimation results from linear Tikhonov-PSO method are similar to that from single PSO algorithm, and a reasonable confidence interval with some probability levels can be additionally given by Tikhonov-PSO method. Therefore, the presented linear Tikhonov-PSO regularization method is a good potential method for hazardous emission

Regularized finite element modeling of progressive failure in soils within nonlocal softening plasticity

Science.gov (United States)

Huang, Maosong; Qu, Xie; Lü, Xilin

2017-11-01

By solving a nonlinear complementarity problem for the consistency condition, an improved implicit stress return iterative algorithm for a generalized over-nonlocal strain softening plasticity was proposed, and the consistent tangent matrix was obtained. The proposed algorithm was embodied into existing finite element codes, and it enables the nonlocal regularization of ill-posed boundary value problem caused by the pressure independent and dependent strain softening plasticity. The algorithm was verified by the numerical modeling of strain localization in a plane strain compression test. The results showed that a fast convergence can be achieved and the mesh-dependency caused by strain softening can be effectively eliminated. The influences of hardening modulus and material characteristic length on the simulation were obtained. The proposed algorithm was further used in the simulations of the bearing capacity of a strip footing; the results are mesh-independent, and the progressive failure process of the soil was well captured.

Incremental projection approach of regularization for inverse problems

Energy Technology Data Exchange (ETDEWEB)

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

2016-10-15

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

Geometric regularizations and dual conifold transitions

International Nuclear Information System (INIS)

Landsteiner, Karl; Lazaroiu, Calin I.

2003-01-01

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

Existence and regularity of solutions of a phase field model for solidification with convection of pure materials in two dimensions

Directory of Open Access Journals (Sweden)

Jose Luiz Boldrini

2003-11-01

Full Text Available We study the existence and regularity of weak solutions of a phase field type model for pure material solidification in presence of natural convection. We assume that the non-stationary solidification process occurs in a two dimensional bounded domain. The governing equations of the model are the phase field equation coupled with a nonlinear heat equation and a modified Navier-Stokes equation. These equations include buoyancy forces modelled by Boussinesq approximation and a Carman-Koseny term to model the flow in mushy regions. Since these modified Navier-Stokes equations only hold in the non-solid regions, which are not known a priori, we have a free boundary-value problem.

Lessons from a “Failed” Experiment: Zinc Silicates with Complex Morphology by Reaction of Zinc Acetate, the Ionic Liquid Precursor (ILP Tetrabutylammonium Hydroxide (TBAH, and Glass

Directory of Open Access Journals (Sweden)

Andreas Taubert

2008-08-01

Full Text Available At elevated temperatures, the ionic liquid precursor (ILP tetrabutylammonium hydroxide reacts with zinc acetate and the glass wall of the reaction vessel. While the reaction of OH- with the glass wall is not surprising as such and could be considered a failed experiment, the resulting materials are interesting for a variety of applications. If done on purpose and under controlled conditions, the reaction with the glass wall results in uniform, well-defined hemimorphite Zn4Si2O7(OH2·nH2O and willemite Zn2SiO4 microcrystals and films. Their morphology can be adjusted by variation of the reaction time and reaction temperature. The hemimorphite can be transformed to Zn2SiO4 via calcination. The process is therefore a viable approach for the fabrication of porous films on glass surfaces with potential applications as catalyst support, among others.

Determining the flexibility of regular and chaotic attractors

International Nuclear Information System (INIS)

Marhl, Marko; Perc, Matjaz

2006-01-01

We present an overview of measures that are appropriate for determining the flexibility of regular and chaotic attractors. In particular, we focus on those system properties that constitute its responses to external perturbations. We deploy a systematic approach, first introducing the simplest measure given by the local divergence of the system along the attractor, and then develop more rigorous mathematical tools for estimating the flexibility of the system's dynamics. The presented measures are tested on the regular Brusselator and chaotic Hindmarsh-Rose model of an excitable neuron with equal success, thus indicating the overall effectiveness and wide applicability range of the proposed theory. Since responses of dynamical systems to external signals are crucial in several scientific disciplines, and especially in natural sciences, we discuss several important aspects and biological implications of obtained results

Adaptive regularization

DEFF Research Database (Denmark)

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

1994-01-01

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

Regularizing portfolio optimization

International Nuclear Information System (INIS)

Still, Susanne; Kondor, Imre

2010-01-01

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

Regularizing portfolio optimization

Science.gov (United States)

Still, Susanne; Kondor, Imre

2010-07-01

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

Automated identification of protein-ligand interaction features using Inductive Logic Programming: a hexose binding case study

Directory of Open Access Journals (Sweden)

A Santos Jose C

2012-07-01

Full Text Available Abstract Background There is a need for automated methods to learn general features of the interactions of a ligand class with its diverse set of protein receptors. An appropriate machine learning approach is Inductive Logic Programming (ILP, which automatically generates comprehensible rules in addition to prediction. The development of ILP systems which can learn rules of the complexity required for studies on protein structure remains a challenge. In this work we use a new ILP system, ProGolem, and demonstrate its performance on learning features of hexose-protein interactions. Results The rules induced by ProGolem detect interactions mediated by aromatics and by planar-polar residues, in addition to less common features such as the aromatic sandwich. The rules also reveal a previously unreported dependency for residues cys and leu. They also specify interactions involving aromatic and hydrogen bonding residues. This paper shows that Inductive Logic Programming implemented in ProGolem can derive rules giving structural features of protein/ligand interactions. Several of these rules are consistent with descriptions in the literature. Conclusions In addition to confirming literature results, ProGolem’s model has a 10-fold cross-validated predictive accuracy that is superior, at the 95% confidence level, to another ILP system previously used to study protein/hexose interactions and is comparable with state-of-the-art statistical learners.

Regular and promotional sales in new product life-cycle: A competitive approach

OpenAIRE

Guidolin, Mariangela; Guseo, Renato; Mortarino, Cinzia

2016-01-01

In this paper, we consider the application of the Lotka-Volterra model with churn effects, LVch, (Guidolin and Guseo, 2015) to the case of a confectionary product produced in Italy and recently commercialized in a European country. Weekly time series, referring separately to quantities of regular and promotional sales, are available. Their joint inspection highlighted the presence of compensatory dynamics suggesting the study with the LVch to estimate whether competition between regular and p...

Tessellating the Sphere with Regular Polygons

Science.gov (United States)

Soto-Johnson, Hortensia; Bechthold, Dawn

2004-01-01

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

Supporting Regularized Logistic Regression Privately and Efficiently

Science.gov (United States)

Li, Wenfa; Liu, Hongzhe; Yang, Peng; Xie, Wei

2016-01-01

As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc. PMID:27271738

Supporting Regularized Logistic Regression Privately and Efficiently.

Science.gov (United States)

Li, Wenfa; Liu, Hongzhe; Yang, Peng; Xie, Wei

2016-01-01

As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc.

Supporting Regularized Logistic Regression Privately and Efficiently.

Directory of Open Access Journals (Sweden)

Wenfa Li

Full Text Available As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc.

Accelerating Large Data Analysis By Exploiting Regularities

Science.gov (United States)

Moran, Patrick J.; Ellsworth, David

2003-01-01

We present techniques for discovering and exploiting regularity in large curvilinear data sets. The data can be based on a single mesh or a mesh composed of multiple submeshes (also known as zones). Multi-zone data are typical to Computational Fluid Dynamics (CFD) simulations. Regularities include axis-aligned rectilinear and cylindrical meshes as well as cases where one zone is equivalent to a rigid-body transformation of another. Our algorithms can also discover rigid-body motion of meshes in time-series data. Next, we describe a data model where we can utilize the results from the discovery process in order to accelerate large data visualizations. Where possible, we replace general curvilinear zones with rectilinear or cylindrical zones. In rigid-body motion cases we replace a time-series of meshes with a transformed mesh object where a reference mesh is dynamically transformed based on a given time value in order to satisfy geometry requests, on demand. The data model enables us to make these substitutions and dynamic transformations transparently with respect to the visualization algorithms. We present results with large data sets where we combine our mesh replacement and transformation techniques with out-of-core paging in order to achieve significant speed-ups in analysis.

Regularizing Feynman path integrals using the generalized Kontsevich-Vishik trace

Science.gov (United States)

Hartung, Tobias

2017-12-01

A fully regulated definition of Feynman's path integral is presented here. The proposed re-formulation of the path integral coincides with the familiar formulation whenever the path integral is well defined. In particular, it is consistent with respect to lattice formulations and Wick rotations, i.e., it can be used in Euclidean and Minkowski space-time. The path integral regularization is introduced through the generalized Kontsevich-Vishik trace, that is, the extension of the classical trace to Fourier integral operators. Physically, we are replacing the time-evolution semi-group by a holomorphic family of operators such that the corresponding path integrals are well defined in some half space of C . The regularized path integral is, thus, defined through analytic continuation. This regularization can be performed by means of stationary phase approximation or computed analytically depending only on the Hamiltonian and the observable (i.e., known a priori). In either case, the computational effort to evaluate path integrals or expectations of observables reduces to the evaluation of integrals over spheres. Furthermore, computations can be performed directly in the continuum and applications (analytic computations and their implementations) to a number of models including the non-trivial cases of the massive Schwinger model and a φ4 theory.

Business structures and sustainable regional legal practice: the use of incorporated legal practices by regional, rural and remote legal practitioners

OpenAIRE

Caroline Lydia Hart

2012-01-01

Since 2007 the Legal Profession Act 2007 (Qld) has offered legal practitioners a wider choice of business structure other than sole practitioner or partnership, to include incorporated legal practice ('ILP') or multidisciplinary partnership. In particular the use of ILPs offers legal practitioners a range of benefits in terms of operating a law firm consistent with business management practices. The status of ILP however comes at a cost of putting in place 'appropriate management systems'. ...

The Sensory Quality of Meat, Game, Poultry, Seafood and Meat Products as Affected by Intense Light Pulses: A Systematic Review

OpenAIRE

Tomasevic, Igor; Rajkovic, Andreja

2015-01-01

The effect of intense light pulses (ILP) on sensory quality of 16 different varieties of meat, meat products, game, poultry and seafood are reviewed. Changes induced by ILP are animal species, type of meat product and fluences applied dependent. ILP significantly deteriorates sensory quality of cooked meat products. It causes less change in the sensory properties of dry cured than cooked meat products while fermented sausage is least affected. The higher fluence applied significantly changes ...

Accretion onto some well-known regular black holes

International Nuclear Information System (INIS)

Jawad, Abdul; Shahzad, M.U.

2016-01-01

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

Accretion onto some well-known regular black holes

Energy Technology Data Exchange (ETDEWEB)

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

2016-03-15

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

Accretion onto some well-known regular black holes

Science.gov (United States)

Jawad, Abdul; Shahzad, M. Umair

2016-03-01

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

Class of regular bouncing cosmologies

Science.gov (United States)

Vasilić, Milovan

2017-06-01

In this paper, I construct a class of everywhere regular geometric sigma models that possess bouncing solutions. Precisely, I show that every bouncing metric can be made a solution of such a model. My previous attempt to do so by employing one scalar field has failed due to the appearance of harmful singularities near the bounce. In this work, I use four scalar fields to construct a class of geometric sigma models which are free of singularities. The models within the class are parametrized by their background geometries. I prove that, whatever background is chosen, the dynamics of its small perturbations is classically stable on the whole time axis. Contrary to what one expects from the structure of the initial Lagrangian, the physics of background fluctuations is found to carry two tensor, two vector, and two scalar degrees of freedom. The graviton mass, which naturally appears in these models, is shown to be several orders of magnitude smaller than its experimental bound. I provide three simple examples to demonstrate how this is done in practice. In particular, I show that graviton mass can be made arbitrarily small.

A Large Dimensional Analysis of Regularized Discriminant Analysis Classifiers

KAUST Repository

Elkhalil, Khalil

2017-11-01

This article carries out a large dimensional analysis of standard regularized discriminant analysis classifiers designed on the assumption that data arise from a Gaussian mixture model with different means and covariances. The analysis relies on fundamental results from random matrix theory (RMT) when both the number of features and the cardinality of the training data within each class grow large at the same pace. Under mild assumptions, we show that the asymptotic classification error approaches a deterministic quantity that depends only on the means and covariances associated with each class as well as the problem dimensions. Such a result permits a better understanding of the performance of regularized discriminant analsysis, in practical large but finite dimensions, and can be used to determine and pre-estimate the optimal regularization parameter that minimizes the misclassification error probability. Despite being theoretically valid only for Gaussian data, our findings are shown to yield a high accuracy in predicting the performances achieved with real data sets drawn from the popular USPS data base, thereby making an interesting connection between theory and practice.

Knockout mutations of insulin-like peptide genes enhance sexual receptivity in Drosophila virgin females.

Science.gov (United States)

Watanabe, Kazuki; Sakai, Takaomi

2016-01-01

In the fruitfly Drosophila melanogaster, females take the initiative to mate successfully because they decide whether to mate or not. However, little is known about the molecular and neuronal mechanisms regulating sexual receptivity in virgin females. Genetic tools available in Drosophila are useful for identifying molecules and neural circuits involved in the regulation of sexual receptivity. We previously demonstrated that insulin-producing cells (IPCs) in the female brain are critical to the regulation of female sexual receptivity. Ablation and inactivation of IPCs enhance female sexual receptivity, suggesting that neurosecretion from IPCs inhibits female sexual receptivity. IPCs produce and release insulin-like peptides (Ilps) that modulate various biological processes such as metabolism, growth, lifespan and behaviors. Here, we report a novel role of the Ilps in sexual behavior in Drosophila virgin females. Compared with wild-type females, females with knockout mutations of Ilps showed a high mating success rate toward wild-type males, whereas wild-type males courted wild-type and Ilp-knockout females to the same extent. Wild-type receptive females retard their movement during male courtship and this reduced female mobility allows males to copulate. Thus, it was anticipated that knockout mutations of Ilps would reduce general locomotion. However, the locomotor activity in Ilp-knockout females was significantly higher than that in wild-type females. Thus, our findings indicate that the high mating success rate in Ilp-knockout females is caused by their enhanced sexual receptivity, but not by improvement of their sex appeal or by general sluggishness.

Diagrammatic methods in phase-space regularization

International Nuclear Information System (INIS)

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

1987-11-01

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

Metric regularity and subdifferential calculus

International Nuclear Information System (INIS)

Ioffe, A D

2000-01-01

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

Verbal Working Memory Is Related to the Acquisition of Cross-Linguistic Phonological Regularities

NARCIS (Netherlands)

Bosma, E.; Heeringa, Wilbert; Hoekstra, E.; Versloot, A.P.; Blom, W.B.T.

2017-01-01

Closely related languages share cross-linguistic phonological regularities, such as Frisian -âld [ͻ:t] and Dutch -oud [ʱut], as in the cognate pairs kâld [kͻ:t] – koud [kʱut] ‘cold’ and wâld [wͻ:t] – woud [wʱut] ‘forest’. Within Bybee’s (1995, 2001, 2008, 2010) network model, these regularities are,

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

Science.gov (United States)

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

2017-12-01

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

Robust regularized singular value decomposition with application to mortality data

KAUST Repository

Zhang, Lingsong; Shen, Haipeng; Huang, Jianhua Z.

2013-01-01

We develop a robust regularized singular value decomposition (RobRSVD) method for analyzing two-way functional data. The research is motivated by the application of modeling human mortality as a smooth two-way function of age group and year. The Rob

Temporal regularity of the environment drives time perception

OpenAIRE

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

2016-01-01

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

Quadratic contributions of softly broken supersymmetry in the light of loop regularization

Energy Technology Data Exchange (ETDEWEB)

Bai, Dong [Chinese Academy of Sciences, Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Beijing (China); University of Chinese Academy of Sciences, School of Physical Sciences, Beijing (China); Wu, Yue-Liang [Chinese Academy of Sciences, Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Beijing (China); International Centre for Theoretical Physics Asia-Pacific (ICTP-AP), Beijing (China); University of Chinese Academy of Sciences, School of Physical Sciences, Beijing (China)

2017-09-15

Loop regularization (LORE) is a novel regularization scheme in modern quantum field theories. It makes no change to the spacetime structure and respects both gauge symmetries and supersymmetry. As a result, LORE should be useful in calculating loop corrections in supersymmetry phenomenology. To further demonstrate its power, in this article we revisit in the light of LORE the old issue of the absence of quadratic contributions (quadratic divergences) in softly broken supersymmetric field theories. It is shown explicitly by Feynman diagrammatic calculations that up to two loops the Wess-Zumino model with soft supersymmetry breaking terms (WZ' model), one of the simplest models with the explicit supersymmetry breaking, is free of quadratic contributions. All the quadratic contributions cancel with each other perfectly, which is consistent with results dictated by the supergraph techniques. (orig.)

SQED two-loop beta function in the context of Implicit regularization

International Nuclear Information System (INIS)

Cherchiglia, Adriano Lana; Sampaio, Marcos; Nemes, Maria Carolina

2013-01-01

Full text: In this work we present the state-of-art for Implicit Regularization (IReg) in the context of supersymmetric theories. IReg is a four-dimensional regularization technique in momentum space which disentangles, in a consistent way at arbitrary order, the divergencies, regularization dependent and finite parts of any Feynman amplitude. Since it does not resort to modifications on the physical space-time dimensions of the underlying quantum field theoretical model, it can be consistently applied to supersymmetric theories. First we describe the technique and present previous results for supersymmetric models: the two-loop beta function for the Wess-Zumino model (both in the component and superfield formalism); the two-loop beta function for Super Yang-Mills (in the superfield formalism using the background field technique). After, we present our calculation of the two-loop beta function for massless and massive SQED using the superfield formalism with and without resorting to the background field technique. We find that only in the second case the two-loop divergence cancels out. We argue it is due to an anomalous Jacobian under the rescaling of the fields in the path-integral which is necessary for the application of the supersymmetric background field technique. We find, however, that in both cases the two-loop coefficients of beta function are non-null. Finally we briefly discuss the anomaly puzzle in the context of our technique. (author)

MT-ADRES: multi-threading on coarse-grained reconfigurable architecture

DEFF Research Database (Denmark)

Wu, Kehuai; Kanstein, Andreas; Madsen, Jan

2008-01-01

The coarse-grained reconfigurable architecture ADRES (architecture for dynamically reconfigurable embedded systems) and its compiler offer high instruction-level parallelism (ILP) to applications by means of a sparsely interconnected array of functional units and register files. As high-ILP archi......The coarse-grained reconfigurable architecture ADRES (architecture for dynamically reconfigurable embedded systems) and its compiler offer high instruction-level parallelism (ILP) to applications by means of a sparsely interconnected array of functional units and register files. As high......-ILP architectures achieve only low parallelism when executing partially sequential code segments, which is also known as Amdahl's law, this article proposes to extend ADRES to MT-ADRES (multi-threaded ADRES) to also exploit thread-level parallelism. On MT-ADRES architectures, the array can be partitioned...

MT-ADRES: Multithreading on Coarse-Grained Reconfigurable Architecture

DEFF Research Database (Denmark)

Wu, Kehuai; Kanstein, Andreas; Madsen, Jan

2007-01-01

The coarse-grained reconfigurable architecture ADRES (Architecture for Dynamically Reconfigurable Embedded Systems) and its compiler offer high instruction-level parallelism (ILP) to applications by means of a sparsely interconnected array of functional units and register files. As high-ILP archi......The coarse-grained reconfigurable architecture ADRES (Architecture for Dynamically Reconfigurable Embedded Systems) and its compiler offer high instruction-level parallelism (ILP) to applications by means of a sparsely interconnected array of functional units and register files. As high......-ILP architectures achieve only low parallelism when executing partially sequential code segments, which is also known as Amdahl’s law, this paper proposes to extend ADRES to MT-ADRES (Multi-Threaded ADRES) to also exploit thread-level parallelism. On MT-ADRES architectures, the array can be partitioned in multiple...

Bardeen regular black hole with an electric source

Science.gov (United States)

Rodrigues, Manuel E.; Silva, Marcos V. de S.

2018-06-01

If some energy conditions on the stress-energy tensor are violated, is possible construct regular black holes in General Relativity and in alternative theories of gravity. This type of solution has horizons but does not present singularities. The first regular black hole was presented by Bardeen and can be obtained from Einstein equations in the presence of an electromagnetic field. E. Ayon-Beato and A. Garcia reinterpreted the Bardeen metric as a magnetic solution of General Relativity coupled to a nonlinear electrodynamics. In this work, we show that the Bardeen model may also be interpreted as a solution of Einstein equations in the presence of an electric source, whose electric field does not behave as a Coulomb field. We analyzed the asymptotic forms of the Lagrangian for the electric case and also analyzed the energy conditions.

The uniqueness of the regularization procedure

International Nuclear Information System (INIS)

Brzezowski, S.

1981-01-01

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

Learning regularization parameters for general-form Tikhonov

International Nuclear Information System (INIS)

Chung, Julianne; Español, Malena I

2017-01-01

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

5 CFR 551.421 - Regular working hours.

Science.gov (United States)

2010-01-01

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

Regular extensions of some classes of grammars

NARCIS (Netherlands)

Nijholt, Antinus

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

The effect of intense light pulses on the sensory quality and instrumental color of meat from different animal breeds

OpenAIRE

Tomašević I.

2015-01-01

Intense light pulses (ILP) are an emerging processing technology, which has a potential to decontaminate food products. The light generated by ILP lamps consists of a continuum broadband spectrum from deep UV to the infrared, especially rich in UV range below 400 nm, which is germicidal. Evaluation of the effect of intense light pulses (ILP) on sensory quality of meat, game and poultry was performed using two kinds of red meat (beef and pork), two kinds of ...

Regular non-twisting S-branes

International Nuclear Information System (INIS)

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

2004-01-01

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

Linear Optimization of Frequency Spectrum Assignments Across System

Science.gov (United States)

2016-03-01

selection tools, frequency allocation, transmission optimization, electromagnetic maneuver warfare, electronic protection, assignment model 15. NUMBER ...Characteristics Modeled ...............................................................29 Table 10.   Antenna Systems Modeled , Number of Systems and...surveillance EW early warning GAMS general algebraic modeling system GHz gigahertz IDE integrated development environment ILP integer linear program

Total variation regularization in measurement and image space for PET reconstruction

KAUST Repository

Burger, M

2014-09-18

© 2014 IOP Publishing Ltd. The aim of this paper is to test and analyse a novel technique for image reconstruction in positron emission tomography, which is based on (total variation) regularization on both the image space and the projection space. We formulate our variational problem considering both total variation penalty terms on the image and on an idealized sinogram to be reconstructed from a given Poisson distributed noisy sinogram. We prove existence, uniqueness and stability results for the proposed model and provide some analytical insight into the structures favoured by joint regularization. For the numerical solution of the corresponding discretized problem we employ the split Bregman algorithm and extensively test the approach in comparison to standard total variation regularization on the image. The numerical results show that an additional penalty on the sinogram performs better on reconstructing images with thin structures.

One-loop regularization of the Polyakov string functional

International Nuclear Information System (INIS)

Cohen, E.; Kluberg-Stern, H.; Peschanski, R.

1989-01-01

The divergences of the vacuum amplitude for the bosonic Polyakov string are studied at the one-loop level in a modular invariant regularization scheme, characterized by a dimensional cutoff analogous to proper time. As a result, the singular behaviour in the cutoff is not uniform in the range of the modulus variable and this yields a control on the singularities induced by the tachyon and the dilaton. The divergences are those of a sigma model, but the coefficients of the sigma-model counter-terms are different for the sphere and the flat torus. (orig.)

Near-Regular Structure Discovery Using Linear Programming

KAUST Repository

Huang, Qixing

2014-06-02

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

Regular Expression Matching and Operational Semantics

Directory of Open Access Journals (Sweden)

Asiri Rathnayake

2011-08-01

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

Tetravalent one-regular graphs of order 4p2

DEFF Research Database (Denmark)

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

2014-01-01

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

Understanding of increased diffuse scattering in regular arrays of fluctuating resonant particles

DEFF Research Database (Denmark)

Andryieuski, Andrei; Petrov, Mihail; Lavrinenko, Andrei

2015-01-01

In this presentation we will discuss the analytical and numerical approaches to modeling electromagnetic properties of geometrically regular subwavelength 2D arrays of random resonant plasmonic particles. Amorphous metamaterials and metasurfaces attract interest of the scientific community due...... with regular periodic arrangements of resonant nanoparticles of random polarizability/size/material at normal plane-wave incidence. We show that randomness of the polarizability is related to increase in diffused scattering and we relate this phenomenon to a modification of the dipoles’ interaction constant...

Mapping the N-Z plane: residual mass regularities

International Nuclear Information System (INIS)

Hirsch, J.G.; Frank, A.; Velazquez, V.

2004-01-01

A new development in the study of the deviations between experimental nuclear masses and those calculated in the framework of the Finite Range Droplet Model is introduced. Some frequencies are isolated and used in a simple fit to reduce significantly the error width. The presence of this regular residual correlations suggests that the Strutinsky method of including microscopic fluctuations in nuclear masses could be improved. (Author)

Regularization and error assignment to unfolded distributions

CERN Document Server

Zech, Gunter

2011-01-01

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

Expression of multidrug resistance genes MVP, MDR1, and MRP1 determined sequentially before, during, and after hyperthermic isolated limb perfusion of soft tissue sarcoma and melanoma patients.

Science.gov (United States)

Stein, Ulrike; Jürchott, Karsten; Schläfke, Matthias; Hohenberger, Peter

2002-08-01

Isolated, hyperthermic limb perfusion (ILP) with recombinant human tumor necrosis factor alpha and melphalan is a highly effective treatment for advanced soft tissue sarcoma (STS) and locoregional metastatic malignant melanoma. Multidrug resistance (MDR)-associated genes are known to be inducible by heat and drugs; expression levels of the major vault protein (MVP), MDR1, and MDR-associated protein 1 (MRP1) were determined sequentially before, during, and after ILP of patients. Twenty-one STS or malignant melanoma patients were treated by ILP. Tumor tissue temperatures were recorded continuously and ranged from 33.4 degrees C initially to peak values of 40.4 degrees C during ILP. Serial true-cut biopsy specimens from tumor tissues were routinely microdissected. Expression analyses for MDR genes were performed by real-time reverse transcriptase polymerase chain reaction and immunohistochemistry. In 83% of the patients, MVP expression was induced during hyperthermic ILP. MVP-mRNA inductions often paralleled the increase in temperature during ILP. Increased MVP protein expressions either were observed simultaneously with the MVP-mRNA induction or were delayed until after the induction at the transcriptional level. Inductions of MDR1 and MRP1 were observed in only 13% and 27% of the specimens analyzed. Temperatures and drugs applied preferentially led to an induction of MVP and were not sufficient to induce MDR1 and MRP1 in the majority of tumors. This study is the first to analyze the expression of MDR-associated genes sequentially during ILP of patients and demonstrates that treatment might lead to increased levels of MVP, whereas enhanced levels of MDR1 and MRP1 remain rare events.

Representation of molecular structure using quantum topology with inductive logic programming in structure-activity relationships.

Science.gov (United States)

Buttingsrud, Bård; Ryeng, Einar; King, Ross D; Alsberg, Bjørn K

2006-06-01

The requirement of aligning each individual molecule in a data set severely limits the type of molecules which can be analysed with traditional structure activity relationship (SAR) methods. A method which solves this problem by using relations between objects is inductive logic programming (ILP). Another advantage of this methodology is its ability to include background knowledge as 1st-order logic. However, previous molecular ILP representations have not been effective in describing the electronic structure of molecules. We present a more unified and comprehensive representation based on Richard Bader's quantum topological atoms in molecules (AIM) theory where critical points in the electron density are connected through a network. AIM theory provides a wealth of chemical information about individual atoms and their bond connections enabling a more flexible and chemically relevant representation. To obtain even more relevant rules with higher coverage, we apply manual postprocessing and interpretation of ILP rules. We have tested the usefulness of the new representation in SAR modelling on classifying compounds of low/high mutagenicity and on a set of factor Xa inhibitors of high and low affinity.

Application of Turchin's method of statistical regularization

Science.gov (United States)

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

2018-04-01

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

HIERARCHICAL REGULARIZATION OF POLYGONS FOR PHOTOGRAMMETRIC POINT CLOUDS OF OBLIQUE IMAGES

Directory of Open Access Journals (Sweden)

L. Xie

2017-05-01

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

Consistent momentum space regularization/renormalization of supersymmetric quantum field theories: the three-loop β-function for the Wess-Zumino model

International Nuclear Information System (INIS)

Carneiro, David; Sampaio, Marcos; Nemes, Maria Carolina; Scarpelli, Antonio Paulo Baeta

2003-01-01

We compute the three loop β function of the Wess-Zumino model to motivate implicit regularization (IR) as a consistent and practical momentum-space framework to study supersymmetric quantum field theories. In this framework which works essentially in the physical dimension of the theory we show that ultraviolet are clearly disentangled from infrared divergences. We obtain consistent results which motivate the method as a good choice to study supersymmetry anomalies in quantum field theories. (author)

On the regularized fermionic projector of the vacuum

Science.gov (United States)

Finster, Felix

2008-03-01

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

On the regularized fermionic projector of the vacuum

International Nuclear Information System (INIS)

Finster, Felix

2008-01-01

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

Spatially-Variant Tikhonov Regularization for Double-Difference Waveform Inversion

Energy Technology Data Exchange (ETDEWEB)

Lin, Youzuo [Los Alamos National Laboratory; Huang, Lianjie [Los Alamos National Laboratory; Zhang, Zhigang [Los Alamos National Laboratory

2011-01-01

Double-difference waveform inversion is a potential tool for quantitative monitoring for geologic carbon storage. It jointly inverts time-lapse seismic data for changes in reservoir geophysical properties. Due to the ill-posedness of waveform inversion, it is a great challenge to obtain reservoir changes accurately and efficiently, particularly when using time-lapse seismic reflection data. Regularization techniques can be utilized to address the issue of ill-posedness. The regularization parameter controls the smoothness of inversion results. A constant regularization parameter is normally used in waveform inversion, and an optimal regularization parameter has to be selected. The resulting inversion results are a trade off among regions with different smoothness or noise levels; therefore the images are either over regularized in some regions while under regularized in the others. In this paper, we employ a spatially-variant parameter in the Tikhonov regularization scheme used in double-difference waveform tomography to improve the inversion accuracy and robustness. We compare the results obtained using a spatially-variant parameter with those obtained using a constant regularization parameter and those produced without any regularization. We observe that, utilizing a spatially-variant regularization scheme, the target regions are well reconstructed while the noise is reduced in the other regions. We show that the spatially-variant regularization scheme provides the flexibility to regularize local regions based on the a priori information without increasing computational costs and the computer memory requirement.

Stark widths regularities within spectral series of sodium isoelectronic sequence

Science.gov (United States)

Trklja, Nora; Tapalaga, Irinel; Dojčinović, Ivan P.; Purić, Jagoš

2018-02-01

Stark widths within spectral series of sodium isoelectronic sequence have been studied. This is a unique approach that includes both neutrals and ions. Two levels of problem are considered: if the required atomic parameters are known, Stark widths can be calculated by some of the known methods (in present paper modified semiempirical formula has been used), but if there is a lack of parameters, regularities enable determination of Stark broadening data. In the framework of regularity research, Stark broadening dependence on environmental conditions and certain atomic parameters has been investigated. The aim of this work is to give a simple model, with minimum of required parameters, which can be used for calculation of Stark broadening data for any chosen transitions within sodium like emitters. Obtained relations were used for predictions of Stark widths for transitions that have not been measured or calculated yet. This system enables fast data processing by using of proposed theoretical model and it provides quality control and verification of obtained results.

SU-E-J-67: Evaluation of Breathing Patterns for Respiratory-Gated Radiation Therapy Using Respiration Regularity Index

Energy Technology Data Exchange (ETDEWEB)

Cheong, K; Lee, M; Kang, S; Yoon, J; Park, S; Hwang, T; Kim, H; Kim, K; Han, T; Bae, H [Hallym University College of Medicine, Anyang (Korea, Republic of)

2014-06-01

Purpose: Despite the importance of accurately estimating the respiration regularity of a patient in motion compensation treatment, an effective and simply applicable method has rarely been reported. The authors propose a simple respiration regularity index based on parameters derived from a correspondingly simplified respiration model. Methods: In order to simplify a patient's breathing pattern while preserving the data's intrinsic properties, we defined a respiration model as a power of cosine form with a baseline drift. According to this respiration formula, breathing-pattern fluctuation could be explained using four factors: sample standard deviation of respiration period, sample standard deviation of amplitude and the results of simple regression of the baseline drift (slope and standard deviation of residuals of a respiration signal. Overall irregularity (δ) was defined as a Euclidean norm of newly derived variable using principal component analysis (PCA) for the four fluctuation parameters. Finally, the proposed respiration regularity index was defined as ρ=ln(1+(1/ δ))/2, a higher ρ indicating a more regular breathing pattern. Subsequently, we applied it to simulated and clinical respiration signals from real-time position management (RPM; Varian Medical Systems, Palo Alto, CA) and investigated respiration regularity. Moreover, correlations between the regularity of the first session and the remaining fractions were investigated using Pearson's correlation coefficient. Results: The respiration regularity was determined based on ρ; patients with ρ<0.3 showed worse regularity than the others, whereas ρ>0.7 was suitable for respiratory-gated radiation therapy (RGRT). Fluctuations in breathing cycle and amplitude were especially determinative of ρ. If the respiration regularity of a patient's first session was known, it could be estimated through subsequent sessions. Conclusions: Respiration regularity could be objectively determined

Comparison of Subcutaneous Regular Insulin and Lispro Insulin in Diabetics Receiving Continuous Nutrition

Science.gov (United States)

Stull, Mamie C.; Strilka, Richard J.; Clemens, Michael S.; Armen, Scott B.

2015-01-01

Background: Optimal management of non–critically ill patients with diabetes maintained on continuous enteral feeding (CEN) is poorly defined. Subcutaneous (SQ) lispro and SQ regular insulin were compared in a simulated type 1 and type 2 diabetic patient receiving CEN. Method: A glucose-insulin feedback mathematical model was employed to simulate type 1 and type 2 diabetic patients on CEN. Each patient received 25 SQ injections of regular insulin or insulin lispro, ranging from 0-6 U. Primary endpoints were the change in mean glucose concentration (MGC) and change in glucose variability (GV); hypoglycemic episodes were also reported. The model was first validated against patient data. Results: Both SQ insulin preparations linearly decreased MGC, however, SQ regular insulin decreased GV whereas SQ lispro tended to increase GV. Hourly glucose concentration measurements were needed to capture the increase in GV. In the type 2 diabetic patient, “rebound hyperglycemia” occurred after SQ lispro was rapidly metabolized. Although neither SQ insulin preparation caused hypoglycemia, SQ lispro significantly lowered MGC compared to SQ regular insulin. Thus, it may be more likely to cause hypoglycemia. Analyses of the detailed glucose concentration versus time data suggest that the inferior performance of lispro resulted from its shorter duration of action. Finally, the effects of both insulin preparations persisted beyond their duration of actions in the type 2 diabetic patient. Conclusions: Subcutaneous regular insulin may be the short-acting insulin preparation of choice for this subset of diabetic patients. Clinical trial is required before a definitive recommendation can be made. PMID:26134836

Verbal Working Memory Is Related to the Acquisition of Cross-Linguistic Phonological Regularities.

Science.gov (United States)

Bosma, Evelyn; Heeringa, Wilbert; Hoekstra, Eric; Versloot, Arjen; Blom, Elma

2017-01-01

Closely related languages share cross-linguistic phonological regularities, such as Frisian -âld [ͻ:t] and Dutch -oud [ʱut], as in the cognate pairs kâld [kͻ:t] - koud [kʱut] 'cold' and wâld [wͻ:t] - woud [wʱut] 'forest'. Within Bybee's (1995, 2001, 2008, 2010) network model, these regularities are, just like grammatical rules within a language, generalizations that emerge from schemas of phonologically and semantically related words. Previous research has shown that verbal working memory is related to the acquisition of grammar, but not vocabulary. This suggests that verbal working memory supports the acquisition of linguistic regularities. In order to test this hypothesis we investigated whether verbal working memory is also related to the acquisition of cross-linguistic phonological regularities. For three consecutive years, 5- to 8-year-old Frisian-Dutch bilingual children ( n = 120) were tested annually on verbal working memory and a Frisian receptive vocabulary task that comprised four cognate categories: (1) identical cognates, (2) non-identical cognates that either do or (3) do not exhibit a phonological regularity between Frisian and Dutch, and (4) non-cognates. The results showed that verbal working memory had a significantly stronger effect on cognate category (2) than on the other three cognate categories. This suggests that verbal working memory is related to the acquisition of cross-linguistic phonological regularities. More generally, it confirms the hypothesis that verbal working memory plays a role in the acquisition of linguistic regularities.

Regular variation on measure chains

Czech Academy of Sciences Publication Activity Database

Řehák, Pavel; Vitovec, J.

2010-01-01

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

New regular black hole solutions

International Nuclear Information System (INIS)

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

2011-01-01

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

On geodesics in low regularity

Science.gov (United States)

Sämann, Clemens; Steinbauer, Roland

2018-02-01

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

A Scheme to Optimize Flow Routing and Polling Switch Selection of Software Defined Networks.

Directory of Open Access Journals (Sweden)

Huan Chen

Full Text Available This paper aims at minimizing the communication cost for collecting flow information in Software Defined Networks (SDN. Since flow-based information collecting method requires too much communication cost, and switch-based method proposed recently cannot benefit from controlling flow routing, jointly optimize flow routing and polling switch selection is proposed to reduce the communication cost. To this end, joint optimization problem is formulated as an Integer Linear Programming (ILP model firstly. Since the ILP model is intractable in large size network, we also design an optimal algorithm for the multi-rooted tree topology and an efficient heuristic algorithm for general topology. According to extensive simulations, it is found that our method can save up to 55.76% communication cost compared with the state-of-the-art switch-based scheme.

A Scheme to Optimize Flow Routing and Polling Switch Selection of Software Defined Networks.

Science.gov (United States)

Chen, Huan; Li, Lemin; Ren, Jing; Wang, Yang; Zhao, Yangming; Wang, Xiong; Wang, Sheng; Xu, Shizhong

2015-01-01

This paper aims at minimizing the communication cost for collecting flow information in Software Defined Networks (SDN). Since flow-based information collecting method requires too much communication cost, and switch-based method proposed recently cannot benefit from controlling flow routing, jointly optimize flow routing and polling switch selection is proposed to reduce the communication cost. To this end, joint optimization problem is formulated as an Integer Linear Programming (ILP) model firstly. Since the ILP model is intractable in large size network, we also design an optimal algorithm for the multi-rooted tree topology and an efficient heuristic algorithm for general topology. According to extensive simulations, it is found that our method can save up to 55.76% communication cost compared with the state-of-the-art switch-based scheme.

Discharge regularity in the turtle posterior crista: comparisons between experiment and theory.

Science.gov (United States)

Goldberg, Jay M; Holt, Joseph C

2013-12-01

Intra-axonal recordings were made from bouton fibers near their termination in the turtle posterior crista. Spike discharge, miniature excitatory postsynaptic potentials (mEPSPs), and afterhyperpolarizations (AHPs) were monitored during resting activity in both regularly and irregularly discharging units. Quantal size (qsize) and quantal rate (qrate) were estimated by shot-noise theory. Theoretically, the ratio, σV/(dμV/dt), between synaptic noise (σV) and the slope of the mean voltage trajectory (dμV/dt) near threshold crossing should determine discharge regularity. AHPs are deeper and more prolonged in regular units; as a result, dμV/dt is larger, the more regular the discharge. The qsize is larger and qrate smaller in irregular units; these oppositely directed trends lead to little variation in σV with discharge regularity. Of the two variables, dμV/dt is much more influential than the nearly constant σV in determining regularity. Sinusoidal canal-duct indentations at 0.3 Hz led to modulations in spike discharge and synaptic voltage. Gain, the ratio between the amplitudes of the two modulations, and phase leads re indentation of both modulations are larger in irregular units. Gain variations parallel the sensitivity of the postsynaptic spike encoder, the set of conductances that converts synaptic input into spike discharge. Phase variations reflect both synaptic inputs to the encoder and postsynaptic processes. Experimental data were interpreted using a stochastic integrate-and-fire model. Advantages of an irregular discharge include an enhanced encoder gain and the prevention of nonlinear phase locking. Regular and irregular units are more efficient, respectively, in the encoding of low- and high-frequency head rotations, respectively.

Manifold Regularized Reinforcement Learning.

Science.gov (United States)

Li, Hongliang; Liu, Derong; Wang, Ding

2018-04-01

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

SAR image regularization with fast approximate discrete minimization.

Science.gov (United States)

Denis, Loïc; Tupin, Florence; Darbon, Jérôme; Sigelle, Marc

2009-07-01

Synthetic aperture radar (SAR) images, like other coherent imaging modalities, suffer from speckle noise. The presence of this noise makes the automatic interpretation of images a challenging task and noise reduction is often a prerequisite for successful use of classical image processing algorithms. Numerous approaches have been proposed to filter speckle noise. Markov random field (MRF) modelization provides a convenient way to express both data fidelity constraints and desirable properties of the filtered image. In this context, total variation minimization has been extensively used to constrain the oscillations in the regularized image while preserving its edges. Speckle noise follows heavy-tailed distributions, and the MRF formulation leads to a minimization problem involving nonconvex log-likelihood terms. Such a minimization can be performed efficiently by computing minimum cuts on weighted graphs. Due to memory constraints, exact minimization, although theoretically possible, is not achievable on large images required by remote sensing applications. The computational burden of the state-of-the-art algorithm for approximate minimization (namely the alpha -expansion) is too heavy specially when considering joint regularization of several images. We show that a satisfying solution can be reached, in few iterations, by performing a graph-cut-based combinatorial exploration of large trial moves. This algorithm is applied to joint regularization of the amplitude and interferometric phase in urban area SAR images.

Traveling waves of the regularized short pulse equation

International Nuclear Information System (INIS)

Shen, Y; Horikis, T P; Kevrekidis, P G; Frantzeskakis, D J

2014-01-01

The properties of the so-called regularized short pulse equation (RSPE) are explored with a particular focus on the traveling wave solutions of this model. We theoretically analyze and numerically evolve two sets of such solutions. First, using a fixed point iteration scheme, we numerically integrate the equation to find solitary waves. It is found that these solutions are well approximated by a finite sum of hyperbolic secants powers. The dependence of the soliton's parameters (height, width, etc) to the parameters of the equation is also investigated. Second, by developing a multiple scale reduction of the RSPE to the nonlinear Schrödinger equation, we are able to construct (both standing and traveling) envelope wave breather type solutions of the former, based on the solitary wave structures of the latter. Both the regular and the breathing traveling wave solutions identified are found to be robust and should thus be amenable to observations in the form of few optical cycle pulses. (paper)

Exclusion of children with intellectual disabilities from regular ...

African Journals Online (AJOL)

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

SU-E-J-67: Evaluation of Breathing Patterns for Respiratory-Gated Radiation Therapy Using Respiration Regularity Index

International Nuclear Information System (INIS)

Cheong, K; Lee, M; Kang, S; Yoon, J; Park, S; Hwang, T; Kim, H; Kim, K; Han, T; Bae, H

2014-01-01

Purpose: Despite the importance of accurately estimating the respiration regularity of a patient in motion compensation treatment, an effective and simply applicable method has rarely been reported. The authors propose a simple respiration regularity index based on parameters derived from a correspondingly simplified respiration model. Methods: In order to simplify a patient's breathing pattern while preserving the data's intrinsic properties, we defined a respiration model as a power of cosine form with a baseline drift. According to this respiration formula, breathing-pattern fluctuation could be explained using four factors: sample standard deviation of respiration period, sample standard deviation of amplitude and the results of simple regression of the baseline drift (slope and standard deviation of residuals of a respiration signal. Overall irregularity (δ) was defined as a Euclidean norm of newly derived variable using principal component analysis (PCA) for the four fluctuation parameters. Finally, the proposed respiration regularity index was defined as ρ=ln(1+(1/ δ))/2, a higher ρ indicating a more regular breathing pattern. Subsequently, we applied it to simulated and clinical respiration signals from real-time position management (RPM; Varian Medical Systems, Palo Alto, CA) and investigated respiration regularity. Moreover, correlations between the regularity of the first session and the remaining fractions were investigated using Pearson's correlation coefficient. Results: The respiration regularity was determined based on ρ; patients with ρ 0.7 was suitable for respiratory-gated radiation therapy (RGRT). Fluctuations in breathing cycle and amplitude were especially determinative of ρ. If the respiration regularity of a patient's first session was known, it could be estimated through subsequent sessions. Conclusions: Respiration regularity could be objectively determined using a respiration regularity index, ρ. Such single-index testing of

On infinite regular and chiral maps

OpenAIRE

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

2015-01-01

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

29 CFR 779.18 - Regular rate.

Science.gov (United States)

2010-07-01

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

Continuum regularized Yang-Mills theory

International Nuclear Information System (INIS)

Sadun, L.A.

1987-01-01

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

Differences in rheological profile of regular diesel and bio-diesel fuel

Directory of Open Access Journals (Sweden)

Jiří Čupera

2010-01-01

Full Text Available Biodiesel represents a promising alternative to regular fossil diesel. Fuel viscosity markedly influences injection, spraying and combustion, viscosity is thus critical factor to be evaluated and monitored. This work is focused on quantifying the differences in temperature dependent kinematic viscosity regular diesel fuel and B30 biodiesel fuel. The samples were assumed to be Newtonian fluids. Vis­co­si­ty was measured on a digital rotary viscometer in a range of 0 to 80 °C. More significant difference between minimum and maximum values was found in case of diesel fuel in comparison with biodiesel fuel. Temperature dependence of both fuels was modeled using several mathematical models – polynomial, power and Gaussian equation. The Gaussian fit offers the best match between experimental and computed data. Description of viscosity behavior of fuels is critically important, e.g. when considering or calculating running efficiency and performance of combustion engines. The models proposed in this work may be used as a tool for precise prediction of rheological behavior of diesel-type fuels.

Reference: 453 [Arabidopsis Phenome Database[Archive

Lifescience Database Archive (English)

Full Text Available ctor. We demonstrate that this protein functions as a transcriptional repressor in vivo. The express...ion of all members of the CYCLINA2 (CYCA2) family was reduced in an ILP1 overexpressing l...ine, and the mouse (Mus musculus) homolog of ILP1 repressed cyclin A2 expression in mouse NIH3T3 cells. T-DN...A insertion mutants of ILP1 showed reduced polyploidy and upregulated all CYCA2 express...ion. Furthermore, loss of CYCA2;1 expression induces an increase in polyploidy in Arabidopsis. We demo

Resource Loading by Branch-and-Price Techniques

NARCIS (Netherlands)

Hans, Elias W.

2001-01-01

The main contribution of this thesis is twofold. First, we propose a modeling approach that offers a generic framework to formulate various types of resource loading and RCCP problems as ILP models. Second, we propose various algorithms that can solve problems of reasonable size, i.e., typically

Verbal Working Memory Is Related to the Acquisition of Cross-Linguistic Phonological Regularities

Directory of Open Access Journals (Sweden)

Evelyn Bosma

2017-09-01

Full Text Available Closely related languages share cross-linguistic phonological regularities, such as Frisian -âld [ͻ:t] and Dutch -oud [ʱut], as in the cognate pairs kâld [kͻ:t] – koud [kʱut] ‘cold’ and wâld [wͻ:t] – woud [wʱut] ‘forest’. Within Bybee’s (1995, 2001, 2008, 2010 network model, these regularities are, just like grammatical rules within a language, generalizations that emerge from schemas of phonologically and semantically related words. Previous research has shown that verbal working memory is related to the acquisition of grammar, but not vocabulary. This suggests that verbal working memory supports the acquisition of linguistic regularities. In order to test this hypothesis we investigated whether verbal working memory is also related to the acquisition of cross-linguistic phonological regularities. For three consecutive years, 5- to 8-year-old Frisian-Dutch bilingual children (n = 120 were tested annually on verbal working memory and a Frisian receptive vocabulary task that comprised four cognate categories: (1 identical cognates, (2 non-identical cognates that either do or (3 do not exhibit a phonological regularity between Frisian and Dutch, and (4 non-cognates. The results showed that verbal working memory had a significantly stronger effect on cognate category (2 than on the other three cognate categories. This suggests that verbal working memory is related to the acquisition of cross-linguistic phonological regularities. More generally, it confirms the hypothesis that verbal working memory plays a role in the acquisition of linguistic regularities.

An Inductive Logic Programming Approach to Validate Hexose Binding Biochemical Knowledge.

Science.gov (United States)

Nassif, Houssam; Al-Ali, Hassan; Khuri, Sawsan; Keirouz, Walid; Page, David

2010-01-01

Hexoses are simple sugars that play a key role in many cellular pathways, and in the regulation of development and disease mechanisms. Current protein-sugar computational models are based, at least partially, on prior biochemical findings and knowledge. They incorporate different parts of these findings in predictive black-box models. We investigate the empirical support for biochemical findings by comparing Inductive Logic Programming (ILP) induced rules to actual biochemical results. We mine the Protein Data Bank for a representative data set of hexose binding sites, non-hexose binding sites and surface grooves. We build an ILP model of hexose-binding sites and evaluate our results against several baseline machine learning classifiers. Our method achieves an accuracy similar to that of other black-box classifiers while providing insight into the discriminating process. In addition, it confirms wet-lab findings and reveals a previously unreported Trp-Glu amino acids dependency.

Factors Associated with Regular Physical Activity for the Prevention of Osteoporosis in Female Employees Alborz University of Medical Sciences: Application of Health Belief Model

Directory of Open Access Journals (Sweden)

E. Hatefnia

2016-05-01

Full Text Available Background: Osteoporosis is a metabolic bone disease and a growing global health problem that causes bones to thin and fragile. It is estimated that about two million people suffer from osteoporosis. According to the World Health Organization recommends regular physical activity is effective in preventing and while the results of some studies show about 65% of working women in Iran; do not get enough physical activity. This study aimed to determine factors associated with regular physical activity behavior for the prevention of osteoporosis in female employees Alborz University of Medical Sciences and was designed by HBM Methods: This study is a cross-sectional study involving 217 female university employees, all of whom were studied with the consent of the census. Tools for data collection questionnaire that included demographic questions, knowledge and questions based on health belief model structures that had done Validity and reliability. Data were analyzed using spss Edition19 and descriptive analytical statistics tests. Findings: The results show that regular physical activity was 37/8%. Idependent t-test showed a significant difference (P< 0/001 knowledge and self-efficacy between the two groups (with and without regular physical activity. Logistic regression analysis showed that knowledge and self-efficacy are significant predictor of Physical activity behavior. In this study, a significant association was found between the income and physical activity And the other factors such relationship wasnot found for physical activity. Conclusion: According to lack of regular physical activity and considering the relationship between knowledge and self-efficacy with physical activity, the need to addressing this issue through educational programming based on related factors. 

Regularity and predictability of human mobility in personal space.

Directory of Open Access Journals (Sweden)

Daniel Austin

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

Regularity effect in prospective memory during aging

OpenAIRE

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

2016-01-01

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

20 CFR 226.14 - Employee regular annuity rate.

Science.gov (United States)

2010-04-01

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

Regular algebra and finite machines

CERN Document Server

Conway, John Horton

2012-01-01

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

39 CFR 6.1 - Regular meetings, annual meeting.

Science.gov (United States)

2010-07-01

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

Regular black holes from semi-classical down to Planckian size

Science.gov (United States)

Spallucci, Euro; Smailagic, Anais

In this paper, we review various models of curvature singularity free black holes (BHs). In the first part of the review, we describe semi-classical solutions of the Einstein equations which, however, contains a “quantum” input through the matter source. We start by reviewing the early model by Bardeen where the metric is regularized by-hand through a short-distance cutoff, which is justified in terms of nonlinear electro-dynamical effects. This toy-model is useful to point-out the common features shared by all regular semi-classical black holes. Then, we solve Einstein equations with a Gaussian source encoding the quantum spread of an elementary particle. We identify, the a priori arbitrary, Gaussian width with the Compton wavelength of the quantum particle. This Compton-Gauss model leads to the estimate of a terminal density that a gravitationally collapsed object can achieve. We identify this density to be the Planck density, and reformulate the Gaussian model assuming this as its peak density. All these models, are physically reliable as long as the BH mass is big enough with respect to the Planck mass. In the truly Planckian regime, the semi-classical approximation breaks down. In this case, a fully quantum BH description is needed. In the last part of this paper, we propose a nongeometrical quantum model of Planckian BHs implementing the Holographic Principle and realizing the “classicalization” scenario recently introduced by Dvali and collaborators. The classical relation between the mass and radius of the BH emerges only in the classical limit, far away from the Planck scale.

Thermal expansion and transformation behavior of cerium and plutonium alloys: an application of the Aptekar-Ponyatovsky regular solution model.

Science.gov (United States)

Lawson, A C; Lashley, J C

2011-09-14

In this paper we apply the Aptekar-Ponyatovsky (AP) regular solution thermodynamic model to the analysis of experimental data for the coefficient of thermal expansion (CTE) and determine the AP model parameters for unalloyed cerium metal, Ce-Th-La alloys, and Pu-Ga alloys. We find that the high temperature CTE of cerium metal follows the predictions of the AP model based on low temperature, high pressure data. For Ce-Th-La alloys we use the AP parameters to track the suppression of the first-order γ-α cerium transition. We show the AP model accounts for the negative CTE observed for Pu-Ga alloys and is equivalent to an earlier invar model. Finally, we apply the AP parameters obtained for Pu-Ga alloys to rationalize the observed δ-α transformation pressures of these alloys. We show that the anomalous values of the Grüneisen and Grüneisen-Anderson parameters are important features of the thermal properties of plutonium. A strong analogy between the properties of plutonium and cerium is confirmed.

NON-RADIAL OSCILLATIONS IN M-GIANT SEMI-REGULAR VARIABLES: STELLAR MODELS AND KEPLER OBSERVATIONS

Energy Technology Data Exchange (ETDEWEB)

Stello, Dennis; Compton, Douglas L.; Bedding, Timothy R.; Kiss, Laszlo L.; Bellamy, Beau [Sydney Institute for Astronomy (SIfA), School of Physics, University of Sydney, NSW 2006 (Australia); Christensen-Dalsgaard, Jørgen; Kjeldsen, Hans [Stellar Astrophysics Centre, Department of Physics and Astronomy, Aarhus University, Ny Munkegade 120, DK-8000 Aarhus C (Denmark); García, Rafael A. [Laboratoire AIM, CEA/DSM-CNRS, Université Paris 7 Diderot, IRFU/SAp, Centre de Saclay, F-91191 Gif-sur-Yvette (France); Mathur, Savita, E-mail: stello@physics.usyd.edu.au [Space Science Institute, 4750 Walnut Street, Suite 205, Boulder, CO 80301 (United States)

2014-06-10

The success of asteroseismology relies heavily on our ability to identify the frequency patterns of stellar oscillation modes. For stars like the Sun this is relatively easy because the mode frequencies follow a regular pattern described by a well-founded asymptotic relation. When a solar-like star evolves off the main sequence and onto the red giant branch its structure changes dramatically, resulting in changes in the frequency pattern of the modes. We follow the evolution of the adiabatic frequency pattern from the main sequence to near the tip of the red giant branch for a series of models. We find a significant departure from the asymptotic relation for the non-radial modes near the red giant branch tip, resulting in a triplet frequency pattern. To support our investigation we analyze almost four years of Kepler data of the most luminous stars in the field (late K and early M type) and find that their frequency spectra indeed show a triplet pattern dominated by dipole modes even for the most luminous stars in our sample. Our identification explains previous results from ground-based observations reporting fine structure in the Petersen diagram and sub-ridges in the period-luminosity diagram. Finally, we find ''new ridges'' of non-radial modes with frequencies below the fundamental mode in our model calculations, and we speculate they are related to f modes.

A regularized vortex-particle mesh method for large eddy simulation

Science.gov (United States)

Spietz, H. J.; Walther, J. H.; Hejlesen, M. M.

2017-11-01

We present recent developments of the remeshed vortex particle-mesh method for simulating incompressible fluid flow. The presented method relies on a parallel higher-order FFT based solver for the Poisson equation. Arbitrary high order is achieved through regularization of singular Green's function solutions to the Poisson equation and recently we have derived novel high order solutions for a mixture of open and periodic domains. With this approach the simulated variables may formally be viewed as the approximate solution to the filtered Navier Stokes equations, hence we use the method for Large Eddy Simulation by including a dynamic subfilter-scale model based on test-filters compatible with the aforementioned regularization functions. Further the subfilter-scale model uses Lagrangian averaging, which is a natural candidate in light of the Lagrangian nature of vortex particle methods. A multiresolution variation of the method is applied to simulate the benchmark problem of the flow past a square cylinder at Re = 22000 and the obtained results are compared to results from the literature.

A regularized stationary mean-field game

KAUST Repository

Yang, Xianjin

2016-01-01

In the thesis, we discuss the existence and numerical approximations of solutions of a regularized mean-field game with a low-order regularization. In the first part, we prove a priori estimates and use the continuation method to obtain the existence of a solution with a positive density. Finally, we introduce the monotone flow method and solve the system numerically.

A regularized stationary mean-field game

KAUST Repository

Yang, Xianjin

2016-04-19

In the thesis, we discuss the existence and numerical approximations of solutions of a regularized mean-field game with a low-order regularization. In the first part, we prove a priori estimates and use the continuation method to obtain the existence of a solution with a positive density. Finally, we introduce the monotone flow method and solve the system numerically.

Automating InDesign with Regular Expressions

CERN Document Server

Kahrel, Peter

2006-01-01

If you need to make automated changes to InDesign documents beyond what basic search and replace can handle, you need regular expressions, and a bit of scripting to make them work. This Short Cut explains both how to write regular expressions, so you can find and replace the right things, and how to use them in InDesign specifically.

Stochastic methods of data modeling: application to the reconstruction of non-regular data

International Nuclear Information System (INIS)

Buslig, Leticia

2014-01-01

This research thesis addresses two issues or applications related to IRSN studies. The first one deals with the mapping of measurement data (the IRSN must regularly control the radioactivity level in France and, for this purpose, uses a network of sensors distributed among the French territory). The objective is then to predict, by means of reconstruction model which used observations, maps which will be used to inform the population. The second application deals with the taking of uncertainties into account in complex computation codes (the IRSN must perform safety studies to assess the risks of loss of integrity of a nuclear reactor in case of hypothetical accidents, and for this purpose, codes are used which simulate physical phenomena occurring within an installation). Some input parameters are not precisely known, and the author therefore tries to assess the impact of some uncertainties on simulated values. She notably aims at seeing whether variations of input parameters may push the system towards a behaviour which is very different from that obtained with parameters having a reference value, or even towards a state in which safety conditions are not met. The precise objective of this second part is then to a reconstruction model which is not costly (in terms of computation time) and to perform simulation in relevant areas (strong gradient areas, threshold overrun areas, so on). Two issues are then important: the choice of the approximation model and the construction of the experiment plan. The model is based on a kriging-type stochastic approach, and an important part of the work addresses the development of new numerical techniques of experiment planning. The first part proposes a generic criterion of adaptive planning, and reports its analysis and implementation. In the second part, an alternative to error variance addition is developed. Methodological developments are tested on analytic functions, and then applied to the cases of measurement mapping and

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

International Nuclear Information System (INIS)

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

2012-01-01

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

Post-Modern Perspectives on Orthodox Positivism

NARCIS (Netherlands)

Venzke, I.

2013-01-01

This contribution explains the travails of international legal positivism (ILP) from post-modern perspectives. It identifies conventional precepts of orthodox ILP and shows how variants of post-modern thinking unravel them. The focus rests on three main such precepts and their critique: first,

Dimensional regularization in configuration space

International Nuclear Information System (INIS)

Bollini, C.G.; Giambiagi, J.J.

1995-09-01

Dimensional regularization is introduced in configuration space by Fourier transforming in D-dimensions the perturbative momentum space Green functions. For this transformation, Bochner theorem is used, no extra parameters, such as those of Feynman or Bogoliubov-Shirkov are needed for convolutions. The regularized causal functions in x-space have ν-dependent moderated singularities at the origin. They can be multiplied together and Fourier transformed (Bochner) without divergence problems. The usual ultraviolet divergences appear as poles of the resultant functions of ν. Several example are discussed. (author). 9 refs

Matrix regularization of 4-manifolds

OpenAIRE

Trzetrzelewski, M.

2012-01-01

We consider products of two 2-manifolds such as S^2 x S^2, embedded in Euclidean space and show that the corresponding 4-volume preserving diffeomorphism algebra can be approximated by a tensor product SU(N)xSU(N) i.e. functions on a manifold are approximated by the Kronecker product of two SU(N) matrices. A regularization of the 4-sphere is also performed by constructing N^2 x N^2 matrix representations of the 4-algebra (and as a byproduct of the 3-algebra which makes the regularization of S...

Prevalence and Correlates of Having a Regular Physician among Women Presenting for Induced Abortion.

Science.gov (United States)

Chor, Julie; Hebert, Luciana E; Hasselbacher, Lee A; Whitaker, Amy K

2016-01-01

To determine the prevalence and correlates of having a regular physician among women presenting for induced abortion. We conducted a retrospective review of women presenting to an urban, university-based family planning clinic for abortion between January 2008 and September 2011. We conducted bivariate analyses, comparing women with and without a regular physician, and multivariable regression modeling, to identify factors associated with not having a regular physician. Of 834 women, 521 (62.5%) had a regular physician and 313 (37.5%) did not. Women with a prior pregnancy, live birth, or spontaneous abortion were more likely than women without these experiences to have a regular physician. Women with a prior induced abortion were not more likely than women who had never had a prior induced abortion to have a regular physician. Compared with women younger than 18 years, women aged 18 to 26 years were less likely to have a physician (adjusted odds ratio [aOR], 0.25; 95% confidence interval [CI], 0.10-0.62). Women with a prior live birth had increased odds of having a regular physician compared with women without a prior pregnancy (aOR, 1.89; 95% CI, 1.13-3.16). Women without medical/fetal indications and who had not been victims of sexual assault (self-indicated) were less likely to report having a regular physician compared with women with medical/fetal indications (aOR, 0.55; 95% CI, 0.17-0.82). The abortion visit is a point of contact with a large number of women without a regular physician and therefore provides an opportunity to integrate women into health care. Copyright © 2016 Jacobs Institute of Women's Health. Published by Elsevier Inc. All rights reserved.

On the equivalence of different regularization methods

International Nuclear Information System (INIS)

Brzezowski, S.

1985-01-01

The R-circunflex-operation preceded by the regularization procedure is discussed. Some arguments are given, according to which the results may depend on the method of regularization, introduced in order to avoid divergences in perturbation calculations. 10 refs. (author)

Accreting fluids onto regular black holes via Hamiltonian approach

Energy Technology Data Exchange (ETDEWEB)

Jawad, Abdul [COMSATS Institute of Information Technology, Department of Mathematics, Lahore (Pakistan); Shahzad, M.U. [COMSATS Institute of Information Technology, Department of Mathematics, Lahore (Pakistan); University of Central Punjab, CAMS, UCP Business School, Lahore (Pakistan)

2017-08-15

We investigate the accretion of test fluids onto regular black holes such as Kehagias-Sfetsos black holes and regular black holes with Dagum distribution function. We analyze the accretion process when different test fluids are falling onto these regular black holes. The accreting fluid is being classified through the equation of state according to the features of regular black holes. The behavior of fluid flow and the existence of sonic points is being checked for these regular black holes. It is noted that the three-velocity depends on critical points and the equation of state parameter on phase space. (orig.)

Automated design system for a rotor with an ellipse lobe profile

International Nuclear Information System (INIS)

Jung, Sung Yuen; Kim Chul; Han, Seung Moo; Cho, Hae Yong

2009-01-01

An internal lobe pump (ILP) is suitable for machine tool oil hydraulics, automotive engines, compressors, and various other devices. In particular, the ILP is an essential component of an automotive engine, used to feed lubricant oil through the system. The main components of an ILP are its rotors. The outer rotor is typically characterized by lobes with an elliptical shape, and the inner rotor profile is a conjugate to the outer profile. This paper describes a theoretical analysis of an ILP and the development of an integrated automated system for rotor design. This system is composed of three main modules and has been developed using AutoLISP for the AutoCAD program. The system generates a new lobe profile and automatically calculates flow rate and flow rate irregularity according to the lobe profile generated. Results obtained from the analysis can enable oil pump designers and manufacturers to become more efficient

Automated design system for a rotor with an ellipse lobe profile

Energy Technology Data Exchange (ETDEWEB)

Jung, Sung Yuen; Kim Chul [Pusan National University, Busan (Korea, Republic of); Han, Seung Moo [Kyung Hee University, Seoul (Korea, Republic of); Cho, Hae Yong [Chungbuk National University, Cheongju (Korea, Republic of)

2009-11-15

An internal lobe pump (ILP) is suitable for machine tool oil hydraulics, automotive engines, compressors, and various other devices. In particular, the ILP is an essential component of an automotive engine, used to feed lubricant oil through the system. The main components of an ILP are its rotors. The outer rotor is typically characterized by lobes with an elliptical shape, and the inner rotor profile is a conjugate to the outer profile. This paper describes a theoretical analysis of an ILP and the development of an integrated automated system for rotor design. This system is composed of three main modules and has been developed using AutoLISP for the AutoCAD program. The system generates a new lobe profile and automatically calculates flow rate and flow rate irregularity according to the lobe profile generated. Results obtained from the analysis can enable oil pump designers and manufacturers to become more efficient

Pauli-Villars regularization in nonperturbative Hamiltonian approach on the light front

Energy Technology Data Exchange (ETDEWEB)

Malyshev, M. Yu., E-mail: mimalysh@yandex.ru; Paston, S. A.; Prokhvatilov, E. V.; Zubov, R. A.; Franke, V. A. [Saint Petersburg State University, Saint Petersburg (Russian Federation)

2016-01-22

The advantage of Pauli-Villars regularization in quantum field theory quantized on the light front is explained. Simple examples of scalar λφ{sup 4} field theory and Yukawa-type model are used. We give also an example of nonperturbative calculation in the theory with Pauli-Villars fields, using for that a model of anharmonic oscillator modified by inclusion of ghost variables playing the role similar to Pauli-Villars fields.

Benefit of adaptive FEC in shared backup path protected elastic optical network.

Science.gov (United States)

Guo, Hong; Dai, Hua; Wang, Chao; Li, Yongcheng; Bose, Sanjay K; Shen, Gangxiang

2015-07-27

We apply an adaptive forward error correction (FEC) allocation strategy to an Elastic Optical Network (EON) operated with shared backup path protection (SBPP). To maximize the protected network capacity that can be carried, an Integer Linear Programing (ILP) model and a spectrum window plane (SWP)-based heuristic algorithm are developed. Simulation results show that the FEC coding overhead required by the adaptive FEC scheme is significantly lower than that needed by a fixed FEC allocation strategy resulting in higher network capacity for the adaptive strategy. The adaptive FEC allocation strategy can also significantly outperform the fixed FEC allocation strategy both in terms of the spare capacity redundancy and the average FEC coding overhead needed per optical channel. The proposed heuristic algorithm is efficient and not only performs closer to the ILP model but also does much better than the shortest-path algorithm.

Regularized inversion of controlled source and earthquake data

International Nuclear Information System (INIS)

Ramachandran, Kumar

2012-01-01

Estimation of the seismic velocity structure of the Earth's crust and upper mantle from travel-time data has advanced greatly in recent years. Forward modelling trial-and-error methods have been superseded by tomographic methods which allow more objective analysis of large two-dimensional and three-dimensional refraction and/or reflection data sets. The fundamental purpose of travel-time tomography is to determine the velocity structure of a medium by analysing the time it takes for a wave generated at a source point within the medium to arrive at a distribution of receiver points. Tomographic inversion of first-arrival travel-time data is a nonlinear problem since both the velocity of the medium and ray paths in the medium are unknown. The solution for such a problem is typically obtained by repeated application of linearized inversion. Regularization of the nonlinear problem reduces the ill posedness inherent in the tomographic inversion due to the under-determined nature of the problem and the inconsistencies in the observed data. This paper discusses the theory of regularized inversion for joint inversion of controlled source and earthquake data, and results from synthetic data testing and application to real data. The results obtained from tomographic inversion of synthetic data and real data from the northern Cascadia subduction zone show that the velocity model and hypocentral parameters can be efficiently estimated using this approach. (paper)

From inactive to regular jogger

DEFF Research Database (Denmark)

Lund-Cramer, Pernille; Brinkmann Løite, Vibeke; Bredahl, Thomas Viskum Gjelstrup

study was conducted using individual semi-structured interviews on how a successful long-term behavior change had been achieved. Ten informants were purposely selected from participants in the DANO-RUN research project (7 men, 3 women, average age 41.5). Interviews were performed on the basis of Theory...... of Planned Behavior (TPB) and The Transtheoretical Model (TTM). Coding and analysis of interviews were performed using NVivo 10 software. Results TPB: During the behavior change process, the intention to jogging shifted from a focus on weight loss and improved fitness to both physical health, psychological......Title From inactive to regular jogger - a qualitative study of achieved behavioral change among recreational joggers Authors Pernille Lund-Cramer & Vibeke Brinkmann Løite Purpose Despite extensive knowledge of barriers to physical activity, most interventions promoting physical activity have proven...

Major earthquakes occur regularly on an isolated plate boundary fault.

Science.gov (United States)

Berryman, Kelvin R; Cochran, Ursula A; Clark, Kate J; Biasi, Glenn P; Langridge, Robert M; Villamor, Pilar

2012-06-29

The scarcity of long geological records of major earthquakes, on different types of faults, makes testing hypotheses of regular versus random or clustered earthquake recurrence behavior difficult. We provide a fault-proximal major earthquake record spanning 8000 years on the strike-slip Alpine Fault in New Zealand. Cyclic stratigraphy at Hokuri Creek suggests that the fault ruptured to the surface 24 times, and event ages yield a 0.33 coefficient of variation in recurrence interval. We associate this near-regular earthquake recurrence with a geometrically simple strike-slip fault, with high slip rate, accommodating a high proportion of plate boundary motion that works in isolation from other faults. We propose that it is valid to apply time-dependent earthquake recurrence models for seismic hazard estimation to similar faults worldwide.

Bounded Perturbation Regularization for Linear Least Squares Estimation

KAUST Repository

Ballal, Tarig

2017-10-18

This paper addresses the problem of selecting the regularization parameter for linear least-squares estimation. We propose a new technique called bounded perturbation regularization (BPR). In the proposed BPR method, a perturbation with a bounded norm is allowed into the linear transformation matrix to improve the singular-value structure. Following this, the problem is formulated as a min-max optimization problem. Next, the min-max problem is converted to an equivalent minimization problem to estimate the unknown vector quantity. The solution of the minimization problem is shown to converge to that of the ℓ2 -regularized least squares problem, with the unknown regularizer related to the norm bound of the introduced perturbation through a nonlinear constraint. A procedure is proposed that combines the constraint equation with the mean squared error (MSE) criterion to develop an approximately optimal regularization parameter selection algorithm. Both direct and indirect applications of the proposed method are considered. Comparisons with different Tikhonov regularization parameter selection methods, as well as with other relevant methods, are carried out. Numerical results demonstrate that the proposed method provides significant improvement over state-of-the-art methods.

Multiview Hessian regularization for image annotation.

Science.gov (United States)

Liu, Weifeng; Tao, Dacheng

2013-07-01

The rapid development of computer hardware and Internet technology makes large scale data dependent models computationally tractable, and opens a bright avenue for annotating images through innovative machine learning algorithms. Semisupervised learning (SSL) therefore received intensive attention in recent years and was successfully deployed in image annotation. One representative work in SSL is Laplacian regularization (LR), which smoothes the conditional distribution for classification along the manifold encoded in the graph Laplacian, however, it is observed that LR biases the classification function toward a constant function that possibly results in poor generalization. In addition, LR is developed to handle uniformly distributed data (or single-view data), although instances or objects, such as images and videos, are usually represented by multiview features, such as color, shape, and texture. In this paper, we present multiview Hessian regularization (mHR) to address the above two problems in LR-based image annotation. In particular, mHR optimally combines multiple HR, each of which is obtained from a particular view of instances, and steers the classification function that varies linearly along the data manifold. We apply mHR to kernel least squares and support vector machines as two examples for image annotation. Extensive experiments on the PASCAL VOC'07 dataset validate the effectiveness of mHR by comparing it with baseline algorithms, including LR and HR.

Major amputation for intractable extremity melanoma after failure of isolated limb perfusion

NARCIS (Netherlands)

Kapma, M. R.; Vrouenraets, B. C.; Nieweg, O. E.; van Geel, A. N.; Noorda, E. M.; Eggermont, A. M. M.; Kroon, B. B. R.

2005-01-01

AIM: The aim of this study was to analyse indications and results of amputation for intractable extremity melanoma after failure of isolated limb perfusion (ILP). METHODS: Between 1978 and 2001, 451 patients with loco-regional advanced extremity melanoma underwent 505 ILPs. Amputation of the

MRI reconstruction with joint global regularization and transform learning.

Science.gov (United States)

Tanc, A Korhan; Eksioglu, Ender M

2016-10-01

Sparsity based regularization has been a popular approach to remedy the measurement scarcity in image reconstruction. Recently, sparsifying transforms learned from image patches have been utilized as an effective regularizer for the Magnetic Resonance Imaging (MRI) reconstruction. Here, we infuse additional global regularization terms to the patch-based transform learning. We develop an algorithm to solve the resulting novel cost function, which includes both patchwise and global regularization terms. Extensive simulation results indicate that the introduced mixed approach has improved MRI reconstruction performance, when compared to the algorithms which use either of the patchwise transform learning or global regularization terms alone. Copyright © 2016 Elsevier Ltd. All rights reserved.

Self-calibration for lab-μCT using space-time regularized projection-based DVC and model reduction

Science.gov (United States)

Jailin, C.; Buljac, A.; Bouterf, A.; Poncelet, M.; Hild, F.; Roux, S.

2018-02-01

An online calibration procedure for x-ray lab-CT is developed using projection-based digital volume correlation. An initial reconstruction of the sample is positioned in the 3D space for every angle so that its projection matches the initial one. This procedure allows a space-time displacement field to be estimated for the scanned sample, which is regularized with (i) rigid body motions in space and (ii) modal time shape functions computed using model reduction techniques (i.e. proper generalized decomposition). The result is an accurate identification of the position of the sample adapted for each angle, which may deviate from the desired perfect rotation required for standard reconstructions. An application of this procedure to a 4D in situ mechanical test is shown. The proposed correction leads to a much improved tomographic reconstruction quality.

Beneficial effect of combined aspiration and interstitial laser therapy in patients with benign cystic thyroid nodules

DEFF Research Database (Denmark)

Døssing, H; Bennedbaek, F N; Hegedüs, L

2006-01-01

nodule causing local discomfort were assigned to cyst aspiration followed by ultrasound-guided ILP and followed for 12 months. The ILP was performed under continuous ultrasound-guidance and with an output power of 2.5-3.5 W. The volume of the nodules was assessed by means of ultrasound and determination...... part. Both pressure symptoms and cosmetic complaints were significantly reduced. The only side effect was mild pain or tenderness for a few days. Our study suggests that complete cyst aspiration and subsequent ultrasound-guided ILP of benign cystic thyroid nodules is a feasible and safe technique...

Tumour response after hyperthermic isolated limb perfusion for locally advanced melanoma

DEFF Research Database (Denmark)

Paulsen, Ida Felbo; Chakera, A H; Drejøe, Jennifer Berg

2014-01-01

. Time from ILP to recurrence was a median of seven months (range 1-37 months) for patients with CR or PR. Survival was longer for patients with CR or PR than for patients showing NC or progression. Several patients had mild or moderate local toxicity reactions, two patients developed severe local...... toxicity. CONCLUSION: ILP induces tumour regression in the vast majority of patients. One patient, i.e. 1% of the group, died from surgical complications. Otherwise, ILP treatment had an acceptable morbidity in this group of very sick patients. We are convinced that the treatment should be offered...

Use of individualized learning plans among fourth-year sub-interns in pediatrics and internal medicine.

Science.gov (United States)

Shepard, Michelle E; Sastre, Elizabeth A; Davidson, Mario A; Fleming, Amy E

2012-01-01

Individualized Learning Plans (ILPs) are an effective tool for promoting self-directed learning among residents. However, no literature details ILP use among medical students. Fifty fourth-year sub-interns in pediatrics and internal medicine created ILPs, including a self-assessment of strengths and weaknesses based on ACGME core competencies and the setting of learning objectives. During weekly follow-up meetings with faculty mentors and peers, students discussed challenges and revised goals. Upon completion of the rotation, students completed a survey of Likert-scale questions addressing satisfaction with and perceived utility of ILP components. Students most often self-identified strengths in the areas of Professionalism and Interpersonal and Communication Skills and weaknesses in Patient Care and Systems-Based Practice. Eighty-two percent set at least one learning objective in an identified area of weakness. Students expressed high confidence in their abilities to create achievable learning objectives and to generate strategies to meet those objectives. Students agreed that discussions during group meetings were meaningful, and they identified the setting learning objectives and weekly meetings as the most important elements of the exercise. Fourth-year sub-interns reported that ILPs helped them to accomplish rotation goals, with the setting of learning objectives and weekly discussions being the most useful elements.

A dynamical regularization algorithm for solving inverse source problems of elliptic partial differential equations

Science.gov (United States)

Zhang, Ye; Gong, Rongfang; Cheng, Xiaoliang; Gulliksson, Mårten

2018-06-01

This study considers the inverse source problem for elliptic partial differential equations with both Dirichlet and Neumann boundary data. The unknown source term is to be determined by additional boundary conditions. Unlike the existing methods found in the literature, which usually employ the first-order in time gradient-like system (such as the steepest descent methods) for numerically solving the regularized optimization problem with a fixed regularization parameter, we propose a novel method with a second-order in time dissipative gradient-like system and a dynamical selected regularization parameter. A damped symplectic scheme is proposed for the numerical solution. Theoretical analysis is given for both the continuous model and the numerical algorithm. Several numerical examples are provided to show the robustness of the proposed algorithm.

Sparsity-regularized HMAX for visual recognition.

Directory of Open Access Journals (Sweden)

Xiaolin Hu

Full Text Available About ten years ago, HMAX was proposed as a simple and biologically feasible model for object recognition, based on how the visual cortex processes information. However, the model does not encompass sparse firing, which is a hallmark of neurons at all stages of the visual pathway. The current paper presents an improved model, called sparse HMAX, which integrates sparse firing. This model is able to learn higher-level features of objects on unlabeled training images. Unlike most other deep learning models that explicitly address global structure of images in every layer, sparse HMAX addresses local to global structure gradually along the hierarchy by applying patch-based learning to the output of the previous layer. As a consequence, the learning method can be standard sparse coding (SSC or independent component analysis (ICA, two techniques deeply rooted in neuroscience. What makes SSC and ICA applicable at higher levels is the introduction of linear higher-order statistical regularities by max pooling. After training, high-level units display sparse, invariant selectivity for particular individuals or for image categories like those observed in human inferior temporal cortex (ITC and medial temporal lobe (MTL. Finally, on an image classification benchmark, sparse HMAX outperforms the original HMAX by a large margin, suggesting its great potential for computer vision.

Strictly-regular number system and data structures

DEFF Research Database (Denmark)

Elmasry, Amr Ahmed Abd Elmoneim; Jensen, Claus; Katajainen, Jyrki

2010-01-01

We introduce a new number system that we call the strictly-regular system, which efficiently supports the operations: digit-increment, digit-decrement, cut, concatenate, and add. Compared to other number systems, the strictly-regular system has distinguishable properties. It is superior to the re...

Analysis of regularized Navier-Stokes equations, 2

Science.gov (United States)

Ou, Yuh-Roung; Sritharan, S. S.

1989-01-01

A practically important regularization of the Navier-Stokes equations was analyzed. As a continuation of the previous work, the structure of the attractors characterizing the solutins was studied. Local as well as global invariant manifolds were found. Regularity properties of these manifolds are analyzed.

Regularities, Natural Patterns and Laws of Nature

Directory of Open Access Journals (Sweden)

Stathis Psillos

2014-02-01

Full Text Available  The goal of this paper is to sketch an empiricist metaphysics of laws of nature. The key idea is that there are regularities without regularity-enforcers. Differently put, there are natural laws without law-makers of a distinct metaphysical kind. This sketch will rely on the concept of a natural pattern and more significantly on the existence of a network of natural patterns in nature. The relation between a regularity and a pattern will be analysed in terms of mereology.  Here is the road map. In section 2, I will briefly discuss the relation between empiricism and metaphysics, aiming to show that an empiricist metaphysics is possible. In section 3, I will offer arguments against stronger metaphysical views of laws. Then, in section 4 I will motivate nomic objectivism. In section 5, I will address the question ‘what is a regularity?’ and will develop a novel answer to it, based on the notion of a natural pattern. In section 6, I will raise the question: ‘what is a law of nature?’, the answer to which will be: a law of nature is a regularity that is characterised by the unity of a natural pattern.

Limited angle CT reconstruction by simultaneous spatial and Radon domain regularization based on TV and data-driven tight frame

Science.gov (United States)

Zhang, Wenkun; Zhang, Hanming; Wang, Linyuan; Cai, Ailong; Li, Lei; Yan, Bin

2018-02-01

Limited angle computed tomography (CT) reconstruction is widely performed in medical diagnosis and industrial testing because of the size of objects, engine/armor inspection requirements, and limited scan flexibility. Limited angle reconstruction necessitates usage of optimization-based methods that utilize additional sparse priors. However, most of conventional methods solely exploit sparsity priors of spatial domains. When CT projection suffers from serious data deficiency or various noises, obtaining reconstruction images that meet the requirement of quality becomes difficult and challenging. To solve this problem, this paper developed an adaptive reconstruction method for limited angle CT problem. The proposed method simultaneously uses spatial and Radon domain regularization model based on total variation (TV) and data-driven tight frame. Data-driven tight frame being derived from wavelet transformation aims at exploiting sparsity priors of sinogram in Radon domain. Unlike existing works that utilize pre-constructed sparse transformation, the framelets of the data-driven regularization model can be adaptively learned from the latest projection data in the process of iterative reconstruction to provide optimal sparse approximations for given sinogram. At the same time, an effective alternating direction method is designed to solve the simultaneous spatial and Radon domain regularization model. The experiments for both simulation and real data demonstrate that the proposed algorithm shows better performance in artifacts depression and details preservation than the algorithms solely using regularization model of spatial domain. Quantitative evaluations for the results also indicate that the proposed algorithm applying learning strategy performs better than the dual domains algorithms without learning regularization model

Combined sphere-spheroid particle model for the retrieval of the microphysical aerosol parameters via regularized inversion of lidar data

Science.gov (United States)

Samaras, Stefanos; Böckmann, Christine; Nicolae, Doina

2016-06-01

In this work we propose a two-step advancement of the Mie spherical-particle model accounting for particle non-sphericity. First, a naturally two-dimensional (2D) generalized model (GM) is made, which further triggers analogous 2D re-definitions of microphysical parameters. We consider a spheroidal-particle approach where the size distribution is additionally dependent on aspect ratio. Second, we incorporate the notion of a sphere-spheroid particle mixture (PM) weighted by a non-sphericity percentage. The efficiency of these two models is investigated running synthetic data retrievals with two different regularization methods to account for the inherent instability of the inversion procedure. Our preliminary studies show that a retrieval with the PM model improves the fitting errors and the microphysical parameter retrieval and it has at least the same efficiency as the GM. While the general trend of the initial size distributions is captured in our numerical experiments, the reconstructions are subject to artifacts. Finally, our approach is applied to a measurement case yielding acceptable results.

Simple regular black hole with logarithmic entropy correction

Energy Technology Data Exchange (ETDEWEB)

Morales-Duran, Nicolas; Vargas, Andres F.; Hoyos-Restrepo, Paulina; Bargueno, Pedro [Universidad de los Andes, Departamento de Fisica, Bogota, Distrito Capital (Colombia)

2016-10-15

A simple regular black hole solution satisfying the weak energy condition is obtained within Einstein-non-linear electrodynamics theory. We have computed the thermodynamic properties of this black hole by a careful analysis of the horizons and we have found that the usual Bekenstein-Hawking entropy gets corrected by a logarithmic term. Therefore, in this sense our model realises some quantum gravity predictions which add this kind of correction to the black hole entropy. In particular, we have established some similitudes between our model and a quadratic generalised uncertainty principle. This similitude has been confirmed by the existence of a remnant, which prevents complete evaporation, in agreement with the quadratic generalised uncertainty principle case. (orig.)

Regularization of the Boundary-Saddle-Node Bifurcation

Directory of Open Access Journals (Sweden)

Xia Liu

2018-01-01

Full Text Available In this paper we treat a particular class of planar Filippov systems which consist of two smooth systems that are separated by a discontinuity boundary. In such systems one vector field undergoes a saddle-node bifurcation while the other vector field is transversal to the boundary. The boundary-saddle-node (BSN bifurcation occurs at a critical value when the saddle-node point is located on the discontinuity boundary. We derive a local topological normal form for the BSN bifurcation and study its local dynamics by applying the classical Filippov’s convex method and a novel regularization approach. In fact, by the regularization approach a given Filippov system is approximated by a piecewise-smooth continuous system. Moreover, the regularization process produces a singular perturbation problem where the original discontinuous set becomes a center manifold. Thus, the regularization enables us to make use of the established theories for continuous systems and slow-fast systems to study the local behavior around the BSN bifurcation.

Low-Complexity Regularization Algorithms for Image Deblurring

KAUST Repository

Alanazi, Abdulrahman

2016-11-01

Image restoration problems deal with images in which information has been degraded by blur or noise. In practice, the blur is usually caused by atmospheric turbulence, motion, camera shake, and several other mechanical or physical processes. In this study, we present two regularization algorithms for the image deblurring problem. We first present a new method based on solving a regularized least-squares (RLS) problem. This method is proposed to find a near-optimal value of the regularization parameter in the RLS problems. Experimental results on the non-blind image deblurring problem are presented. In all experiments, comparisons are made with three benchmark methods. The results demonstrate that the proposed method clearly outperforms the other methods in terms of both the output PSNR and structural similarity, as well as the visual quality of the deblurred images. To reduce the complexity of the proposed algorithm, we propose a technique based on the bootstrap method to estimate the regularization parameter in low and high-resolution images. Numerical results show that the proposed technique can effectively reduce the computational complexity of the proposed algorithms. In addition, for some cases where the point spread function (PSF) is separable, we propose using a Kronecker product so as to reduce the computations. Furthermore, in the case where the image is smooth, it is always desirable to replace the regularization term in the RLS problems by a total variation term. Therefore, we propose a novel method for adaptively selecting the regularization parameter in a so-called square root regularized total variation (SRTV). Experimental results demonstrate that our proposed method outperforms the other benchmark methods when applied to smooth images in terms of PSNR, SSIM and the restored image quality. In this thesis, we focus on the non-blind image deblurring problem, where the blur kernel is assumed to be known. However, we developed algorithms that also work

Progressive image denoising through hybrid graph Laplacian regularization: a unified framework.

Science.gov (United States)

Liu, Xianming; Zhai, Deming; Zhao, Debin; Zhai, Guangtao; Gao, Wen

2014-04-01

Recovering images from corrupted observations is necessary for many real-world applications. In this paper, we propose a unified framework to perform progressive image recovery based on hybrid graph Laplacian regularized regression. We first construct a multiscale representation of the target image by Laplacian pyramid, then progressively recover the degraded image in the scale space from coarse to fine so that the sharp edges and texture can be eventually recovered. On one hand, within each scale, a graph Laplacian regularization model represented by implicit kernel is learned, which simultaneously minimizes the least square error on the measured samples and preserves the geometrical structure of the image data space. In this procedure, the intrinsic manifold structure is explicitly considered using both measured and unmeasured samples, and the nonlocal self-similarity property is utilized as a fruitful resource for abstracting a priori knowledge of the images. On the other hand, between two successive scales, the proposed model is extended to a projected high-dimensional feature space through explicit kernel mapping to describe the interscale correlation, in which the local structure regularity is learned and propagated from coarser to finer scales. In this way, the proposed algorithm gradually recovers more and more image details and edges, which could not been recovered in previous scale. We test our algorithm on one typical image recovery task: impulse noise removal. Experimental results on benchmark test images demonstrate that the proposed method achieves better performance than state-of-the-art algorithms.

Local regularity analysis of strata heterogeneities from sonic logs

Directory of Open Access Journals (Sweden)

S. Gaci

2010-09-01

Full Text Available Borehole logs provide geological information about the rocks crossed by the wells. Several properties of rocks can be interpreted in terms of lithology, type and quantity of the fluid filling the pores and fractures.

Here, the logs are assumed to be nonhomogeneous Brownian motions (nhBms which are generalized fractional Brownian motions (fBms indexed by depth-dependent Hurst parameters H(z. Three techniques, the local wavelet approach (LWA, the average-local wavelet approach (ALWA, and Peltier Algorithm (PA, are suggested to estimate the Hurst functions (or the regularity profiles from the logs.

First, two synthetic sonic logs with different parameters, shaped by the successive random additions (SRA algorithm, are used to demonstrate the potential of the proposed methods. The obtained Hurst functions are close to the theoretical Hurst functions. Besides, the transitions between the modeled layers are marked by Hurst values discontinuities. It is also shown that PA leads to the best Hurst value estimations.

Second, we investigate the multifractional property of sonic logs data recorded at two scientific deep boreholes: the pilot hole VB and the ultra deep main hole HB, drilled for the German Continental Deep Drilling Program (KTB. All the regularity profiles independently obtained for the logs provide a clear correlation with lithology, and from each regularity profile, we derive a similar segmentation in terms of lithological units. The lithological discontinuities (strata' bounds and faults contacts are located at the local extrema of the Hurst functions. Moreover, the regularity profiles are compared with the KTB estimated porosity logs, showing a significant relation between the local extrema of the Hurst functions and the fluid-filled fractures. The Hurst function may then constitute a tool to characterize underground heterogeneities.

Adapting a regularized canopy reflectance model (REGFLEC) for the retrieval challenges of dryland agricultural systems

KAUST Repository

Houborg, Rasmus

2016-08-20

A regularized canopy reflectance model (REGFLEC) is applied over a dryland irrigated agricultural system in Saudi Arabia for the purpose of retrieving leaf area index (LAI) and leaf chlorophyll content (Chll). To improve the robustness of the retrieved properties, REGFLEC was modified to 1) correct for aerosol and adjacency effects, 2) consider foliar dust effects on modeled canopy reflectances, 3) include spectral information in the red-edge wavelength region, and 4) exploit empirical LAI estimates in the model inversion. Using multi-spectral RapidEye imagery allowed Chll to be retrieved with a Mean Absolute Deviation (MAD) of 7.9 μg cm− 2 (16%), based upon in-situ measurements conducted in fields of alfalfa, Rhodes grass and maize over the course of a growing season. LAI and Chll compensation effects on canopy reflectance were largely avoided by informing the inversion process with ancillary LAI inputs established empirically on the basis of a statistical machine learning technique. As a result, LAI was reproduced with good accuracy, with an overall MAD of 0.42 m2 m− 2 (12.5%). Results highlighted the considerable challenges associated with the translation of at-sensor radiance observations to surface bidirectional reflectances in dryland environments, where issues such as high aerosol loadings and large spatial gradients in surface reflectance from bright desert soils to dark vegetated fields are often present. Indeed, surface reflectances in the visible bands were reduced by up to 60% after correction for such adjacency effects. In addition, dust deposition on leaves required explicit modification of the reflectance sub-model to account for its influence. By implementing these model refinements, REGFLEC demonstrated its utility for within-field characterization of vegetation conditions over the challenging landscapes typical of dryland agricultural regions, offering a means through which improvements can be made in the management of these globally

Automatic inference of indexing rules for MEDLINE

Directory of Open Access Journals (Sweden)

Shooshan Sonya E

2008-11-01

Full Text Available Abstract Background: Indexing is a crucial step in any information retrieval system. In MEDLINE, a widely used database of the biomedical literature, the indexing process involves the selection of Medical Subject Headings in order to describe the subject matter of articles. The need for automatic tools to assist MEDLINE indexers in this task is growing with the increasing number of publications being added to MEDLINE. Methods: In this paper, we describe the use and the customization of Inductive Logic Programming (ILP to infer indexing rules that may be used to produce automatic indexing recommendations for MEDLINE indexers. Results: Our results show that this original ILP-based approach outperforms manual rules when they exist. In addition, the use of ILP rules also improves the overall performance of the Medical Text Indexer (MTI, a system producing automatic indexing recommendations for MEDLINE. Conclusion: We expect the sets of ILP rules obtained in this experiment to be integrated into MTI.

Discovering H-bonding rules in crystals with inductive logic programming.

Science.gov (United States)

Ando, Howard Y; Dehaspe, Luc; Luyten, Walter; Van Craenenbroeck, Elke; Vandecasteele, Henk; Van Meervelt, Luc

2006-01-01

In the domain of crystal engineering, various schemes have been proposed for the classification of hydrogen bonding (H-bonding) patterns observed in 3D crystal structures. In this study, the aim is to complement these schemes with rules that predict H-bonding in crystals from 2D structural information only. Modern computational power and the advances in inductive logic programming (ILP) can now provide computational chemistry with the opportunity for extracting structure-specific rules from large databases that can be incorporated into expert systems. ILP technology is here applied to H-bonding in crystals to develop a self-extracting expert system utilizing data in the Cambridge Structural Database of small molecule crystal structures. A clear increase in performance was observed when the ILP system DMax was allowed to refer to the local structural environment of the possible H-bond donor/acceptor pairs. This ability distinguishes ILP from more traditional approaches that build rules on the basis of global molecular properties.

Statistical regularities in the rank-citation profile of scientists.

Science.gov (United States)

Petersen, Alexander M; Stanley, H Eugene; Succi, Sauro

2011-01-01

Recent science of science research shows that scientific impact measures for journals and individual articles have quantifiable regularities across both time and discipline. However, little is known about the scientific impact distribution at the scale of an individual scientist. We analyze the aggregate production and impact using the rank-citation profile c(i)(r) of 200 distinguished professors and 100 assistant professors. For the entire range of paper rank r, we fit each c(i)(r) to a common distribution function. Since two scientists with equivalent Hirsch h-index can have significantly different c(i)(r) profiles, our results demonstrate the utility of the β(i) scaling parameter in conjunction with h(i) for quantifying individual publication impact. We show that the total number of citations C(i) tallied from a scientist's N(i) papers scales as [Formula: see text]. Such statistical regularities in the input-output patterns of scientists can be used as benchmarks for theoretical models of career progress.

Deterministic automata for extended regular expressions

Directory of Open Access Journals (Sweden)

Syzdykov Mirzakhmet

2017-12-01

Full Text Available In this work we present the algorithms to produce deterministic finite automaton (DFA for extended operators in regular expressions like intersection, subtraction and complement. The method like “overriding” of the source NFA(NFA not defined with subset construction rules is used. The past work described only the algorithm for AND-operator (or intersection of regular languages; in this paper the construction for the MINUS-operator (and complement is shown.

Regularities of intermediate adsorption complex relaxation

International Nuclear Information System (INIS)

Manukova, L.A.

1982-01-01

The experimental data, characterizing the regularities of intermediate adsorption complex relaxation in the polycrystalline Mo-N 2 system at 77 K are given. The method of molecular beam has been used in the investigation. The analytical expressions of change regularity in the relaxation process of full and specific rates - of transition from intermediate state into ''non-reversible'', of desorption into the gas phase and accumUlation of the particles in the intermediate state are obtained

Regular and platform switching: bone stress analysis varying implant type.

Science.gov (United States)

Gurgel-Juarez, Nália Cecília; de Almeida, Erika Oliveira; Rocha, Eduardo Passos; Freitas, Amílcar Chagas; Anchieta, Rodolfo Bruniera; de Vargas, Luis Carlos Merçon; Kina, Sidney; França, Fabiana Mantovani Gomes

2012-04-01

This study aimed to evaluate stress distribution on peri-implant bone simulating the influence of platform switching in external and internal hexagon implants using three-dimensional finite element analysis. Four mathematical models of a central incisor supported by an implant were created: External Regular model (ER) with 5.0 mm × 11.5 mm external hexagon implant and 5.0 mm abutment (0% abutment shifting), Internal Regular model (IR) with 4.5 mm × 11.5 mm internal hexagon implant and 4.5 mm abutment (0% abutment shifting), External Switching model (ES) with 5.0 mm × 11.5 mm external hexagon implant and 4.1 mm abutment (18% abutment shifting), and Internal Switching model (IS) with 4.5 mm × 11.5 mm internal hexagon implant and 3.8 mm abutment (15% abutment shifting). The models were created by SolidWorks software. The numerical analysis was performed using ANSYS Workbench. Oblique forces (100 N) were applied to the palatal surface of the central incisor. The maximum (σ(max)) and minimum (σ(min)) principal stress, equivalent von Mises stress (σ(vM)), and maximum principal elastic strain (ε(max)) values were evaluated for the cortical and trabecular bone. For cortical bone, the highest stress values (σ(max) and σ(vm) ) (MPa) were observed in IR (87.4 and 82.3), followed by IS (83.3 and 72.4), ER (82 and 65.1), and ES (56.7 and 51.6). For ε(max), IR showed the highest stress (5.46e-003), followed by IS (5.23e-003), ER (5.22e-003), and ES (3.67e-003). For the trabecular bone, the highest stress values (σ(max)) (MPa) were observed in ER (12.5), followed by IS (12), ES (11.9), and IR (4.95). For σ(vM), the highest stress values (MPa) were observed in IS (9.65), followed by ER (9.3), ES (8.61), and IR (5.62). For ε(max) , ER showed the highest stress (5.5e-003), followed by ES (5.43e-003), IS (3.75e-003), and IR (3.15e-003). The influence of platform switching was more evident for cortical bone than for trabecular bone, mainly for the external hexagon

Sparse structure regularized ranking

KAUST Repository

Wang, Jim Jing-Yan; Sun, Yijun; Gao, Xin

2014-01-01

Learning ranking scores is critical for the multimedia database retrieval problem. In this paper, we propose a novel ranking score learning algorithm by exploring the sparse structure and using it to regularize ranking scores. To explore the sparse

Noninvasive technique for measurement of heartbeat regularity in zebrafish (Danio rerio embryos

Directory of Open Access Journals (Sweden)

Cheng Shuk

2009-02-01

Full Text Available Abstract Background Zebrafish (Danio rerio, due to its optical accessibility and similarity to human, has emerged as model organism for cardiac research. Although various methods have been developed to assess cardiac functions in zebrafish embryos, there lacks a method to assess heartbeat regularity in blood vessels. Heartbeat regularity is an important parameter for cardiac function and is associated with cardiotoxicity in human being. Using stereomicroscope and digital video camera, we have developed a simple, noninvasive method to measure the heart rate and heartbeat regularity in peripheral blood vessels. Anesthetized embryos were mounted laterally in agarose on a slide and the caudal blood circulation of zebrafish embryo was video-recorded under stereomicroscope and the data was analyzed by custom-made software. The heart rate was determined by digital motion analysis and power spectral analysis through extraction of frequency characteristics of the cardiac rhythm. The heartbeat regularity, defined as the rhythmicity index, was determined by short-time Fourier Transform analysis. Results The heart rate measured by this noninvasive method in zebrafish embryos at 52 hour post-fertilization was similar to that determined by direct visual counting of ventricle beating (p > 0.05. In addition, the method was validated by a known cardiotoxic drug, terfenadine, which affects heartbeat regularity in humans and induces bradycardia and atrioventricular blockage in zebrafish. A significant decrease in heart rate was found by our method in treated embryos (p p Conclusion The data support and validate this rapid, simple, noninvasive method, which includes video image analysis and frequency analysis. This method is capable of measuring the heart rate and heartbeat regularity simultaneously via the analysis of caudal blood flow in zebrafish embryos. With the advantages of rapid sample preparation procedures, automatic image analysis and data analysis, this

Propagation of spiking regularity and double coherence resonance in feedforward networks.

Science.gov (United States)

Men, Cong; Wang, Jiang; Qin, Ying-Mei; Deng, Bin; Tsang, Kai-Ming; Chan, Wai-Lok

2012-03-01

We investigate the propagation of spiking regularity in noisy feedforward networks (FFNs) based on FitzHugh-Nagumo neuron model systematically. It is found that noise could modulate the transmission of firing rate and spiking regularity. Noise-induced synchronization and synfire-enhanced coherence resonance are also observed when signals propagate in noisy multilayer networks. It is interesting that double coherence resonance (DCR) with the combination of synaptic input correlation and noise intensity is finally attained after the processing layer by layer in FFNs. Furthermore, inhibitory connections also play essential roles in shaping DCR phenomena. Several properties of the neuronal network such as noise intensity, correlation of synaptic inputs, and inhibitory connections can serve as control parameters in modulating both rate coding and the order of temporal coding.

Regular behaviors in SU(2) Yang-Mills classical mechanics

International Nuclear Information System (INIS)

Xu Xiaoming

1997-01-01

In order to study regular behaviors in high-energy nucleon-nucleon collisions, a representation of the vector potential A i a is defined with respect to the (a,i)-dependence in the SU(2) Yang-Mills classical mechanics. Equations of the classical infrared field as well as effective potentials are derived for the elastic or inelastic collision of two plane wave in a three-mode model and the decay of an excited spherically-symmetric field

Sparse structure regularized ranking

KAUST Repository

Wang, Jim Jing-Yan

2014-04-17

Learning ranking scores is critical for the multimedia database retrieval problem. In this paper, we propose a novel ranking score learning algorithm by exploring the sparse structure and using it to regularize ranking scores. To explore the sparse structure, we assume that each multimedia object could be represented as a sparse linear combination of all other objects, and combination coefficients are regarded as a similarity measure between objects and used to regularize their ranking scores. Moreover, we propose to learn the sparse combination coefficients and the ranking scores simultaneously. A unified objective function is constructed with regard to both the combination coefficients and the ranking scores, and is optimized by an iterative algorithm. Experiments on two multimedia database retrieval data sets demonstrate the significant improvements of the propose algorithm over state-of-the-art ranking score learning algorithms.

20 CFR 226.35 - Deductions from regular annuity rate.

Science.gov (United States)

2010-04-01

... 20 Employees' Benefits 1 2010-04-01 2010-04-01 false Deductions from regular annuity rate. 226.35... COMPUTING EMPLOYEE, SPOUSE, AND DIVORCED SPOUSE ANNUITIES Computing a Spouse or Divorced Spouse Annuity § 226.35 Deductions from regular annuity rate. The regular annuity rate of the spouse and divorced...

Regularization theory for ill-posed problems selected topics

CERN Document Server

Lu, Shuai

2013-01-01

Thismonograph is a valuable contribution to thehighly topical and extremly productive field ofregularisationmethods for inverse and ill-posed problems. The author is an internationally outstanding and acceptedmathematicianin this field. In his book he offers a well-balanced mixtureof basic and innovative aspects.He demonstrates new,differentiatedviewpoints, and important examples for applications. The bookdemontrates thecurrent developments inthe field of regularization theory,such as multiparameter regularization and regularization in learning theory. The book is written for graduate and PhDs

Decomposed process mining with DivideAndConquer

NARCIS (Netherlands)

Verbeek, H.M.W.; Limonad, L.; Weber, B.

2014-01-01

Many known process mining techniques scale badly in the number of activities in an event log. Examples of such techniques include the ILP Miner and the standard replay, which also uses ILP techniques. To alleviate the problems these techniques face, we can decompose a large problem (with many

20 CFR 226.34 - Divorced spouse regular annuity rate.

Science.gov (United States)

2010-04-01

... 20 Employees' Benefits 1 2010-04-01 2010-04-01 false Divorced spouse regular annuity rate. 226.34... COMPUTING EMPLOYEE, SPOUSE, AND DIVORCED SPOUSE ANNUITIES Computing a Spouse or Divorced Spouse Annuity § 226.34 Divorced spouse regular annuity rate. The regular annuity rate of a divorced spouse is equal to...

Chimeric mitochondrial peptides from contiguous regular and swinger RNA.

Science.gov (United States)

Seligmann, Hervé

2016-01-01

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

Regularization parameter estimation for underdetermined problems by the χ 2 principle with application to 2D focusing gravity inversion

International Nuclear Information System (INIS)

Vatankhah, Saeed; Ardestani, Vahid E; Renaut, Rosemary A

2014-01-01

The χ 2 principle generalizes the Morozov discrepancy principle to the augmented residual of the Tikhonov regularized least squares problem. For weighting of the data fidelity by a known Gaussian noise distribution on the measured data, when the stabilizing, or regularization, term is considered to be weighted by unknown inverse covariance information on the model parameters, the minimum of the Tikhonov functional becomes a random variable that follows a χ 2 -distribution with m+p−n degrees of freedom for the model matrix G of size m×n, m⩾n, and regularizer L of size p × n. Then, a Newton root-finding algorithm, employing the generalized singular value decomposition, or singular value decomposition when L = I, can be used to find the regularization parameter α. Here the result and algorithm are extended to the underdetermined case, m 2 algorithms when m 2 and unbiased predictive risk estimator of the regularization parameter are used for the first time in this context. For a simulated underdetermined data set with noise, these regularization parameter estimation methods, as well as the generalized cross validation method, are contrasted with the use of the L-curve and the Morozov discrepancy principle. Experiments demonstrate the efficiency and robustness of the χ 2 principle and unbiased predictive risk estimator, moreover showing that the L-curve and Morozov discrepancy principle are outperformed in general by the other three techniques. Furthermore, the minimum support stabilizer is of general use for the χ 2 principle when implemented without the desirable knowledge of the mean value of the model. (paper)

A delay differential equation model for dengue transmission with regular visits to a mosquito breeding site

Science.gov (United States)

Yaacob, Y.; Yeak, S. H.; Lim, R. S.; Soewono, E.

2015-03-01

Dengue disease has been known as one of widely transmitted vector-borne diseases which potentially affects millions of people throughout the world especially in tropical and sub-tropical countries. One of the main factors contributing in the complication of the transmission process is the mobility of people in which people may get infection in the places far from their home. Here we construct a delay differential equation model for dengue transmission in a closed population where regular visits of people to a mosquito breeding site out of their residency such as traditional market take place daily. Basic reproductive ratio of the system is obtained and depends on the ratio between the outgoing rates of susceptible human and infective human. It is shown that the increase of mobility with different variation of mobility rates may contribute to different level of basic reproductive ratio as well as different level of outbreaks.

Use of regularized principal component analysis to model anatomical changes during head and neck radiation therapy for treatment adaptation and response assessment

International Nuclear Information System (INIS)

Chetvertkov, Mikhail A.; Siddiqui, Farzan; Chetty, Indrin; Kumarasiri, Akila; Liu, Chang; Gordon, J. James; Kim, Jinkoo

2016-01-01

Purpose: To develop standard (SPCA) and regularized (RPCA) principal component analysis models of anatomical changes from daily cone beam CTs (CBCTs) of head and neck (H&N) patients and assess their potential use in adaptive radiation therapy, and for extracting quantitative information for treatment response assessment. Methods: Planning CT images of ten H&N patients were artificially deformed to create “digital phantom” images, which modeled systematic anatomical changes during radiation therapy. Artificial deformations closely mirrored patients’ actual deformations and were interpolated to generate 35 synthetic CBCTs, representing evolving anatomy over 35 fractions. Deformation vector fields (DVFs) were acquired between pCT and synthetic CBCTs (i.e., digital phantoms) and between pCT and clinical CBCTs. Patient-specific SPCA and RPCA models were built from these synthetic and clinical DVF sets. EigenDVFs (EDVFs) having the largest eigenvalues were hypothesized to capture the major anatomical deformations during treatment. Results: Principal component analysis (PCA) models achieve variable results, depending on the size and location of anatomical change. Random changes prevent or degrade PCA’s ability to detect underlying systematic change. RPCA is able to detect smaller systematic changes against the background of random fraction-to-fraction changes and is therefore more successful than SPCA at capturing systematic changes early in treatment. SPCA models were less successful at modeling systematic changes in clinical patient images, which contain a wider range of random motion than synthetic CBCTs, while the regularized approach was able to extract major modes of motion. Conclusions: Leading EDVFs from the both PCA approaches have the potential to capture systematic anatomical change during H&N radiotherapy when systematic changes are large enough with respect to random fraction-to-fraction changes. In all cases the RPCA approach appears to be more

Use of regularized principal component analysis to model anatomical changes during head and neck radiation therapy for treatment adaptation and response assessment

Energy Technology Data Exchange (ETDEWEB)

Chetvertkov, Mikhail A., E-mail: chetvertkov@wayne.edu [Department of Radiation Oncology, Wayne State University School of Medicine, Detroit, Michigan 48201 and Department of Radiation Oncology, Henry Ford Health System, Detroit, Michigan 48202 (United States); Siddiqui, Farzan; Chetty, Indrin; Kumarasiri, Akila; Liu, Chang; Gordon, J. James [Department of Radiation Oncology, Henry Ford Health System, Detroit, Michigan 48202 (United States); Kim, Jinkoo [Department of Radiation Oncology, Stony Brook University Hospital, Stony Brook, New York 11794 (United States)

2016-10-15

Purpose: To develop standard (SPCA) and regularized (RPCA) principal component analysis models of anatomical changes from daily cone beam CTs (CBCTs) of head and neck (H&N) patients and assess their potential use in adaptive radiation therapy, and for extracting quantitative information for treatment response assessment. Methods: Planning CT images of ten H&N patients were artificially deformed to create “digital phantom” images, which modeled systematic anatomical changes during radiation therapy. Artificial deformations closely mirrored patients’ actual deformations and were interpolated to generate 35 synthetic CBCTs, representing evolving anatomy over 35 fractions. Deformation vector fields (DVFs) were acquired between pCT and synthetic CBCTs (i.e., digital phantoms) and between pCT and clinical CBCTs. Patient-specific SPCA and RPCA models were built from these synthetic and clinical DVF sets. EigenDVFs (EDVFs) having the largest eigenvalues were hypothesized to capture the major anatomical deformations during treatment. Results: Principal component analysis (PCA) models achieve variable results, depending on the size and location of anatomical change. Random changes prevent or degrade PCA’s ability to detect underlying systematic change. RPCA is able to detect smaller systematic changes against the background of random fraction-to-fraction changes and is therefore more successful than SPCA at capturing systematic changes early in treatment. SPCA models were less successful at modeling systematic changes in clinical patient images, which contain a wider range of random motion than synthetic CBCTs, while the regularized approach was able to extract major modes of motion. Conclusions: Leading EDVFs from the both PCA approaches have the potential to capture systematic anatomical change during H&N radiotherapy when systematic changes are large enough with respect to random fraction-to-fraction changes. In all cases the RPCA approach appears to be more

Dimensional regularization and analytical continuation at finite temperature

International Nuclear Information System (INIS)

Chen Xiangjun; Liu Lianshou

1998-01-01

The relationship between dimensional regularization and analytical continuation of infrared divergent integrals at finite temperature is discussed and a method of regularization of infrared divergent integrals and infrared divergent sums is given

TNFa-based isolated limb perfusion in the rat : development of a model and analysis of efficacy determining factors

NARCIS (Netherlands)

E.R. Manusama (Eric)

1998-01-01

textabstractIsolated limb perfusion (lLP) with high dose TNFa in combination with IFNr and melphalan in patients with melanoma in transit metastases confined to the limb has recently been reported to result in much higher complete tumor response rates than after the standard therapy of ILP with

Regular breakfast consumption is associated with increased IQ in kindergarten children.

Science.gov (United States)

Liu, Jianghong; Hwang, Wei-Ting; Dickerman, Barbra; Compher, Charlene

2013-04-01

Studies have documented a positive relationship between regular breakfast consumption and cognitive outcomes in youth. However, most of these studies have emphasized specific measures of cognition rather than cognitive performance as a broad construct (e.g., IQ test scores) and have been limited to Western samples of school-age children and adolescents. This study aims to extend the literature on breakfast consumption and cognition by examining these constructs in a sample of Chinese kindergarten-age children. This cross-sectional study consisted of a sample of 1269 children (697 boys and 572 girls) aged 6 years from the Chinese city of Jintan. Cognition was assessed with the Chinese version of the Wechsler preschool and primary scale of intelligence-revised. Breakfast habits were assessed through parental questionnaire. Analyses of variance and linear regression models were used to analyze the association between breakfast habits and IQ. Socioeconomic and parental psychosocial variables related to intelligence were controlled for. Findings showed that children who regularly have breakfast on a near-daily basis had significantly higher full scale, verbal, and performance IQ test scores (all pbreakfast. This relationship persisted for VIQ (verbal IQ) and FIQ (full IQ) even after adjusting for gender, current living location, parental education, parental occupation, and primary child caregiver. Findings may reflect nutritional as well as social benefits of regular breakfast consumption on cognition, and regular breakfast consumption should be encouraged among young children. Copyright © 2013 Elsevier Ltd. All rights reserved.

Regular and conformal regular cores for static and rotating solutions

Energy Technology Data Exchange (ETDEWEB)

Azreg-Aïnou, Mustapha

2014-03-07

Using a new metric for generating rotating solutions, we derive in a general fashion the solution of an imperfect fluid and that of its conformal homolog. We discuss the conditions that the stress–energy tensors and invariant scalars be regular. On classical physical grounds, it is stressed that conformal fluids used as cores for static or rotating solutions are exempt from any malicious behavior in that they are finite and defined everywhere.

Regular and conformal regular cores for static and rotating solutions

International Nuclear Information System (INIS)

Azreg-Aïnou, Mustapha

2014-01-01

Using a new metric for generating rotating solutions, we derive in a general fashion the solution of an imperfect fluid and that of its conformal homolog. We discuss the conditions that the stress–energy tensors and invariant scalars be regular. On classical physical grounds, it is stressed that conformal fluids used as cores for static or rotating solutions are exempt from any malicious behavior in that they are finite and defined everywhere.

Image degradation characteristics and restoration based on regularization for diffractive imaging

Science.gov (United States)

Zhi, Xiyang; Jiang, Shikai; Zhang, Wei; Wang, Dawei; Li, Yun

2017-11-01

The diffractive membrane optical imaging system is an important development trend of ultra large aperture and lightweight space camera. However, related investigations on physics-based diffractive imaging degradation characteristics and corresponding image restoration methods are less studied. In this paper, the model of image quality degradation for the diffraction imaging system is first deduced mathematically based on diffraction theory and then the degradation characteristics are analyzed. On this basis, a novel regularization model of image restoration that contains multiple prior constraints is established. After that, the solving approach of the equation with the multi-norm coexistence and multi-regularization parameters (prior's parameters) is presented. Subsequently, the space-variant PSF image restoration method for large aperture diffractive imaging system is proposed combined with block idea of isoplanatic region. Experimentally, the proposed algorithm demonstrates its capacity to achieve multi-objective improvement including MTF enhancing, dispersion correcting, noise and artifact suppressing as well as image's detail preserving, and produce satisfactory visual quality. This can provide scientific basis for applications and possesses potential application prospects on future space applications of diffractive membrane imaging technology.

Joint Segmentation and Shape Regularization with a Generalized Forward Backward Algorithm.

Science.gov (United States)

Stefanoiu, Anca; Weinmann, Andreas; Storath, Martin; Navab, Nassir; Baust, Maximilian

2016-05-11

This paper presents a method for the simultaneous segmentation and regularization of a series of shapes from a corresponding sequence of images. Such series arise as time series of 2D images when considering video data, or as stacks of 2D images obtained by slicewise tomographic reconstruction. We first derive a model where the regularization of the shape signal is achieved by a total variation prior on the shape manifold. The method employs a modified Kendall shape space to facilitate explicit computations together with the concept of Sobolev gradients. For the proposed model, we derive an efficient and computationally accessible splitting scheme. Using a generalized forward-backward approach, our algorithm treats the total variation atoms of the splitting via proximal mappings, whereas the data terms are dealt with by gradient descent. The potential of the proposed method is demonstrated on various application examples dealing with 3D data. We explain how to extend the proposed combined approach to shape fields which, for instance, arise in the context of 3D+t imaging modalities, and show an application in this setup as well.

An integer programming model for gate assignment problem at airline terminals

Science.gov (United States)

Chun, Chong Kok; Nordin, Syarifah Zyurina

2015-05-01

In this paper, we concentrate on a gate assignment problem (GAP) at the airlines terminal. Our problem is to assign an arrival plane to a suitable gate. There are two considerations needed to take. One of its is passenger walking distance from arrival gate to departure gate while another consideration is the transport baggage distance from one gate to another. Our objective is to minimize the total distance between the gates that related to assign the arrival plane to the suitable gates. An integer linear programming (ILP) model is proposed to solve this gate assignment problem. We also conduct a computational experiment using CPLEX 12.1 solver in AIMMS 3.10 software to analyze the performance of the model. Results of the computational experiments are presented. The efficiency of flights assignment is depends on the ratio of the weight for both total passenger traveling distances and total baggage transport distances.

Modelling the implications of regular increases in tobacco taxation in the tobacco endgame.

Science.gov (United States)

Cobiac, Linda J; Ikeda, Tak; Nghiem, Nhung; Blakely, Tony; Wilson, Nick

2015-06-01

We examine the potential role for taxation in the tobacco endgame in New Zealand, where the goal is to become 'smokefree' (less than 5% smoking prevalence) by 2025. Modelling study using a dynamic population model. New Zealand, Māori and non-Māori men and women. Annual increases in tobacco excise tax of 5%, 10%, 15% and 20% (with 10% reflecting the annual increase recently legislated by the New Zealand Government to 2016). With a continued commitment to annual 10% increases in tobacco excise tax, in addition to on-going Quitline and cessation support, New Zealand's smoking prevalence is projected to fall from 15.1% in 2013 to 8.7% (95% uncertainty interval 8.6% to 8.9%) by 2025. This is compared to 9.9% without any further tax rises. With annual tax increases of 20%, the prevalence is projected to fall to 7.6% (7.5% to 7.7%) by 2025. The potential reductions in smoking prevalence are substantial for both Māori and non-Māori populations, although annual tax increases as high as 20% will still only see Māori smoking prevalence in 2025 approaching the non-Māori smoking levels for 2013. Scenario analyses did not suggest that growth of the illicit tobacco market would substantively undermine the impact of tobacco tax rises. Nevertheless, unknown factors such as the gradual denormalisation of smoking and changes to the 'nicotine market' may influence sensitivity to changes in tobacco prices in the future. Regular increases in tobacco taxation could play an important role in helping to achieve tobacco endgames. However, this modelling in New Zealand suggests that a wider range of tobacco endgame strategies will be needed to achieve a smoke-free goal of less than 5% prevalence for all social groups--a conclusion that could also apply in other countries. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Regularity criteria for incompressible magnetohydrodynamics equations in three dimensions

International Nuclear Information System (INIS)

Lin, Hongxia; Du, Lili

2013-01-01

In this paper, we give some new global regularity criteria for three-dimensional incompressible magnetohydrodynamics (MHD) equations. More precisely, we provide some sufficient conditions in terms of the derivatives of the velocity or pressure, for the global regularity of strong solutions to 3D incompressible MHD equations in the whole space, as well as for periodic boundary conditions. Moreover, the regularity criterion involving three of the nine components of the velocity gradient tensor is also obtained. The main results generalize the recent work by Cao and Wu (2010 Two regularity criteria for the 3D MHD equations J. Diff. Eqns 248 2263–74) and the analysis in part is based on the works by Cao C and Titi E (2008 Regularity criteria for the three-dimensional Navier–Stokes equations Indiana Univ. Math. J. 57 2643–61; 2011 Gobal regularity criterion for the 3D Navier–Stokes equations involving one entry of the velocity gradient tensor Arch. Rational Mech. Anal. 202 919–32) for 3D incompressible Navier–Stokes equations. (paper)

Long-term outcome following interstitial laser photocoagulation of benign cold thyroid nodules

DEFF Research Database (Denmark)

Døssing, Helle; Bennedbæk, Finn Noe; Hegedüs, Laszlo

2011-01-01

discomfort were assigned to ILP. ILP (using one laser fiber) was performed under continuous ultrasound (US) guidance and with an output power of 1.5-3.5 W. Thyroid nodule volume was assessed by US and thyroid function determined by routine assays, before and during follow-up. Pressure symptoms and cosmetic...... complaints were evaluated on a visual analogue scale (0-10 cm). Of the total patients, six had thyroid surgery 6 months after ILP and three were lost to follow-up. The median follow-up for the remaining 69 patients was 67 months (range 12-114). Results The overall median nodule volume decreased from 8.2 ml...

Soil potassium content in an integrated crop-livestock system under no-tillage with different grazing intensities

OpenAIRE

Ferreira, Eric Victor de Oliveira; Anghinoni, Ibanor; Carvalho, Paulo César de Faccio; Costa, Sergio Ely Valadão Gigante de Andrade; Cao, Eduardo Giacomelli

2009-01-01

A dinâmica de K em sistemas de integração lavoura-pecuária (ILP) diverge daquelas de outros sistemas de manejo, porque os sistemas ILP são mais complexos e envolvem, além das práticas relacionadas à cultura de interesse econômico, a introdução do animal. Objetivou-se, neste trabalho, avaliar as concentrações do K do solo em um sistema ILP, em plantio direto, com diferentes intensidades de pastejo (aveia-preta + azevém) de bovinos no inverno e a cultura da soja cultivada no verão. O experiment...

cGMP and NHR signaling co-regulate expression of insulin-like peptides and developmental activation of infective larvae in Strongyloides stercoralis.

Directory of Open Access Journals (Sweden)

Jonathan D Stoltzfus

2014-07-01

Full Text Available The infectious form of the parasitic nematode Strongyloides stercoralis is a developmentally arrested third-stage larva (L3i, which is morphologically similar to the developmentally arrested dauer larva in the free-living nematode Caenorhabditis elegans. We hypothesize that the molecular pathways regulating C. elegans dauer development also control L3i arrest and activation in S. stercoralis. This study aimed to determine the factors that regulate L3i activation, with a focus on G protein-coupled receptor-mediated regulation of cyclic guanosine monophosphate (cGMP pathway signaling, including its modulation of the insulin/IGF-1-like signaling (IIS pathway. We found that application of the membrane-permeable cGMP analog 8-bromo-cGMP potently activated development of S. stercoralis L3i, as measured by resumption of feeding, with 85.1 ± 2.2% of L3i feeding in 200 µM 8-bromo-cGMP in comparison to 0.6 ± 0.3% in the buffer diluent. Utilizing RNAseq, we examined L3i stimulated with DMEM, 8-bromo-cGMP, or the DAF-12 nuclear hormone receptor (NHR ligand Δ7-dafachronic acid (DA--a signaling pathway downstream of IIS in C. elegans. L3i stimulated with 8-bromo-cGMP up-regulated transcripts of the putative agonistic insulin-like peptide (ILP -encoding genes Ss-ilp-1 (20-fold and Ss-ilp-6 (11-fold in comparison to controls without stimulation. Surprisingly, we found that Δ7-DA similarly modulated transcript levels of ILP-encoding genes. Using the phosphatidylinositol-4,5-bisphosphate 3-kinase inhibitor LY294002, we demonstrated that 400 nM Δ7-DA-mediated activation (93.3 ± 1.1% L3i feeding can be blocked using this IIS inhibitor at 100 µM (7.6 ± 1.6% L3i feeding. To determine the tissues where promoters of ILP-encoding genes are active, we expressed promoter::egfp reporter constructs in transgenic S. stercoralis post-free-living larvae. Ss-ilp-1 and Ss-ilp-6 promoters are active in the hypodermis and neurons and the Ss-ilp-7 promoter is active in the

Regular-fat dairy and human health

DEFF Research Database (Denmark)

Astrup, Arne; Bradley, Beth H Rice; Brenna, J Thomas

2016-01-01

In recent history, some dietary recommendations have treated dairy fat as an unnecessary source of calories and saturated fat in the human diet. These assumptions, however, have recently been brought into question by current research on regular fat dairy products and human health. In an effort to......, cheese and yogurt, can be important components of an overall healthy dietary pattern. Systematic examination of the effects of dietary patterns that include regular-fat milk, cheese and yogurt on human health is warranted....

Bounded Perturbation Regularization for Linear Least Squares Estimation

KAUST Repository

Ballal, Tarig; Suliman, Mohamed Abdalla Elhag; Al-Naffouri, Tareq Y.

2017-01-01

This paper addresses the problem of selecting the regularization parameter for linear least-squares estimation. We propose a new technique called bounded perturbation regularization (BPR). In the proposed BPR method, a perturbation with a bounded

Recognition Memory for Novel Stimuli: The Structural Regularity Hypothesis

Science.gov (United States)

Cleary, Anne M.; Morris, Alison L.; Langley, Moses M.

2007-01-01

Early studies of human memory suggest that adherence to a known structural regularity (e.g., orthographic regularity) benefits memory for an otherwise novel stimulus (e.g., G. A. Miller, 1958). However, a more recent study suggests that structural regularity can lead to an increase in false-positive responses on recognition memory tests (B. W. A.…

Stochastic-based resource expansion planning for a grid-connected microgrid using interval linear programming

International Nuclear Information System (INIS)

Shaban Boloukat, Mohammad Hadi; Akbari Foroud, Asghar

2016-01-01

This paper represents a stochastic approach for long-term optimal resource expansion planning of a grid-connected microgrid (MG) containing different technologies as intermittent renewable energy resources, energy storage systems and thermal resources. Maximizing profit and reliability, along with minimizing investment and operation costs, are major objectives which have been considered in this model. Also, the impacts of intermittency and uncertainty in renewable energy resources were investigated. The interval linear programming (ILP) was applied for modelling inherent stochastic nature of the renewable energy resources. ILP presents some superiority in modelling of uncertainties in MG planning. The problem was formulated as a mixed-integer linear programming. It has been demonstrated previously that the benders decomposition (BD) served as an effective tool for solving such problems. BD divides the original problem into a master (investment) problem and operation and reliability subproblems. In this paper a multiperiod MG planning is presented, considering life time, maximum penetration limit of each technology, interest rate, capital recovery factor and investment fund. Real-time energy exchange with the utility is covered, with a consideration of variable tariffs at different load blocks. The presented approach can help MG planners to adopt best decision under various uncertainty levels based on their budgetary policies. - Highlights: • Considering uncertain nature of the renewable resources with applying ILP. • Considering the effect of intermittency of renewable in MG planning. • Multiobjective MG planning problem which covers cost, profit and reliability. • Multiperiod approach for MG planning considering life time and MPL of technologies. • Presenting real-time energy exchange with the utility considering variable tariffs.

Energy functions for regularization algorithms

Science.gov (United States)

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

1991-01-01

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

Three regularities of recognition memory: the role of bias.

Science.gov (United States)

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

2015-12-01

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

Biased Dropout and Crossmap Dropout: Learning towards effective Dropout regularization in convolutional neural network.

Science.gov (United States)

Poernomo, Alvin; Kang, Dae-Ki

2018-08-01

Training a deep neural network with a large number of parameters often leads to overfitting problem. Recently, Dropout has been introduced as a simple, yet effective regularization approach to combat overfitting in such models. Although Dropout has shown remarkable results on many deep neural network cases, its actual effect on CNN has not been thoroughly explored. Moreover, training a Dropout model will significantly increase the training time as it takes longer time to converge than a non-Dropout model with the same architecture. To deal with these issues, we address Biased Dropout and Crossmap Dropout, two novel approaches of Dropout extension based on the behavior of hidden units in CNN model. Biased Dropout divides the hidden units in a certain layer into two groups based on their magnitude and applies different Dropout rate to each group appropriately. Hidden units with higher activation value, which give more contributions to the network final performance, will be retained by a lower Dropout rate, while units with lower activation value will be exposed to a higher Dropout rate to compensate the previous part. The second approach is Crossmap Dropout, which is an extension of the regular Dropout in convolution layer. Each feature map in a convolution layer has a strong correlation between each other, particularly in every identical pixel location in each feature map. Crossmap Dropout tries to maintain this important correlation yet at the same time break the correlation between each adjacent pixel with respect to all feature maps by applying the same Dropout mask to all feature maps, so that all pixels or units in equivalent positions in each feature map will be either dropped or active during training. Our experiment with various benchmark datasets shows that our approaches provide better generalization than the regular Dropout. Moreover, our Biased Dropout takes faster time to converge during training phase, suggesting that assigning noise appropriately in

An algorithmic framework for Mumford–Shah regularization of inverse problems in imaging

International Nuclear Information System (INIS)

Hohm, Kilian; Weinmann, Andreas; Storath, Martin

2015-01-01

The Mumford–Shah model is a very powerful variational approach for edge preserving regularization of image reconstruction processes. However, it is algorithmically challenging because one has to deal with a non-smooth and non-convex functional. In this paper, we propose a new efficient algorithmic framework for Mumford–Shah regularization of inverse problems in imaging. It is based on a splitting into specific subproblems that can be solved exactly. We derive fast solvers for the subproblems which are key for an efficient overall algorithm. Our method neither requires a priori knowledge of the gray or color levels nor of the shape of the discontinuity set. We demonstrate the wide applicability of the method for different modalities. In particular, we consider the reconstruction from Radon data, inpainting, and deconvolution. Our method can be easily adapted to many further imaging setups. The relevant condition is that the proximal mapping of the data fidelity can be evaluated a within reasonable time. In other words, it can be used whenever classical Tikhonov regularization is possible. (paper)

Method of transferring regular shaped vessel into cell

International Nuclear Information System (INIS)

Murai, Tsunehiko.

1997-01-01

The present invention concerns a method of transferring regular shaped vessels from a non-contaminated area to a contaminated cell. A passage hole for allowing the regular shaped vessels to pass in the longitudinal direction is formed to a partitioning wall at the bottom of the contaminated cell. A plurality of regular shaped vessel are stacked in multiple stages in a vertical direction from the non-contaminated area present below the passage hole, allowed to pass while being urged and transferred successively into the contaminated cell. As a result, since they are transferred while substantially closing the passage hole by the regular shaped vessels, radiation rays or contaminated materials are prevented from discharging from the contaminated cell to the non-contaminated area. Since there is no requirement to open/close an isolation door frequently, the workability upon transfer can be improved remarkably. In addition, the sealing member for sealing the gap between the regular shaped vessel passing through the passage hole and the partitioning wall of the bottom is disposed to the passage hole, the contaminated materials in the contaminated cells can be prevented from discharging from the gap to the non-contaminated area. (N.H.)

Automatic Constraint Detection for 2D Layout Regularization.

Science.gov (United States)

Jiang, Haiyong; Nan, Liangliang; Yan, Dong-Ming; Dong, Weiming; Zhang, Xiaopeng; Wonka, Peter

2016-08-01

In this paper, we address the problem of constraint detection for layout regularization. The layout we consider is a set of two-dimensional elements where each element is represented by its bounding box. Layout regularization is important in digitizing plans or images, such as floor plans and facade images, and in the improvement of user-created contents, such as architectural drawings and slide layouts. To regularize a layout, we aim to improve the input by detecting and subsequently enforcing alignment, size, and distance constraints between layout elements. Similar to previous work, we formulate layout regularization as a quadratic programming problem. In addition, we propose a novel optimization algorithm that automatically detects constraints. We evaluate the proposed framework using a variety of input layouts from different applications. Our results demonstrate that our method has superior performance to the state of the art.

Automatic Constraint Detection for 2D Layout Regularization

KAUST Repository

Jiang, Haiyong

2015-09-18

In this paper, we address the problem of constraint detection for layout regularization. As layout we consider a set of two-dimensional elements where each element is represented by its bounding box. Layout regularization is important for digitizing plans or images, such as floor plans and facade images, and for the improvement of user created contents, such as architectural drawings and slide layouts. To regularize a layout, we aim to improve the input by detecting and subsequently enforcing alignment, size, and distance constraints between layout elements. Similar to previous work, we formulate the layout regularization as a quadratic programming problem. In addition, we propose a novel optimization algorithm to automatically detect constraints. In our results, we evaluate the proposed framework on a variety of input layouts from different applications, which demonstrates our method has superior performance to the state of the art.

Lavrentiev regularization method for nonlinear ill-posed problems

International Nuclear Information System (INIS)

Kinh, Nguyen Van

2002-10-01

In this paper we shall be concerned with Lavientiev regularization method to reconstruct solutions x 0 of non ill-posed problems F(x)=y o , where instead of y 0 noisy data y δ is an element of X with absolut(y δ -y 0 ) ≤ δ are given and F:X→X is an accretive nonlinear operator from a real reflexive Banach space X into itself. In this regularization method solutions x α δ are obtained by solving the singularly perturbed nonlinear operator equation F(x)+α(x-x*)=y δ with some initial guess x*. Assuming certain conditions concerning the operator F and the smoothness of the element x*-x 0 we derive stability estimates which show that the accuracy of the regularized solutions is order optimal provided that the regularization parameter α has been chosen properly. (author)

Mathematic Model of Digital Control System with PID Regulator and Regular Step of Quantization with Information Transfer via the Channel of Plural Access

Science.gov (United States)

Abramov, G. V.; Emeljanov, A. E.; Ivashin, A. L.

Theoretical bases for modeling a digital control system with information transfer via the channel of plural access and a regular quantization cycle are submitted. The theory of dynamic systems with random changes of the structure including elements of the Markov random processes theory is used for a mathematical description of a network control system. The characteristics of similar control systems are received. Experimental research of the given control systems is carried out.

Online Manifold Regularization by Dual Ascending Procedure

Directory of Open Access Journals (Sweden)

Boliang Sun

2013-01-01

Full Text Available We propose a novel online manifold regularization framework based on the notion of duality in constrained optimization. The Fenchel conjugate of hinge functions is a key to transfer manifold regularization from offline to online in this paper. Our algorithms are derived by gradient ascent in the dual function. For practical purpose, we propose two buffering strategies and two sparse approximations to reduce the computational complexity. Detailed experiments verify the utility of our approaches. An important conclusion is that our online MR algorithms can handle the settings where the target hypothesis is not fixed but drifts with the sequence of examples. We also recap and draw connections to earlier works. This paper paves a way to the design and analysis of online manifold regularization algorithms.

Graph Regularized Meta-path Based Transductive Regression in Heterogeneous Information Network.

Science.gov (United States)

Wan, Mengting; Ouyang, Yunbo; Kaplan, Lance; Han, Jiawei

2015-01-01

A number of real-world networks are heterogeneous information networks, which are composed of different types of nodes and links. Numerical prediction in heterogeneous information networks is a challenging but significant area because network based information for unlabeled objects is usually limited to make precise estimations. In this paper, we consider a graph regularized meta-path based transductive regression model ( Grempt ), which combines the principal philosophies of typical graph-based transductive classification methods and transductive regression models designed for homogeneous networks. The computation of our method is time and space efficient and the precision of our model can be verified by numerical experiments.

Regularized Biot-Savart Laws for Modeling Magnetic Configurations with Flux Ropes

Science.gov (United States)

Titov, V. S.; Downs, C.; Mikic, Z.; Torok, T.; Linker, J.

2017-12-01

Many existing models assume that magnetic flux ropes play a key role in solar flares and coronal mass ejections (CMEs). It is therefore important to develop efficient methods for constructing flux-rope configurations constrained by observed magnetic data and the initial morphology of CMEs. For this purpose, we have derived and implemented a compact analytical form that represents the magnetic field of a thin flux rope with an axis of arbitrary shape and a circular cross-section. This form implies that the flux rope carries axial current I and axial flux F, so that the respective magnetic field is the curl of the sum of toroidal and poloidal vector potentials proportional to I and F, respectively. We expressed the vector potentials in terms of modified Biot-Savart laws whose kernels are regularized at the axis in such a way that these laws define a cylindrical force-free flux rope with a parabolic profile of the axial current density, when the axis is straight. For the cases we have studied so far, we determined the shape of the rope axis by following the polarity inversion line of the eruptions' source region, using observed magnetograms. The height variation along the axis and other flux-rope parameters are estimated by means of potential field extrapolations. Using this heuristic approach, we were able to construct pre-eruption configurations for the 2009 February13 and 2011 October 1 CME events. These applications demonstrate the flexibility and efficiency of our new method for energizing pre-eruptive configurations in MHD simulations of CMEs. We discuss possible ways of optimizing the axis paths and other extensions of the method in order to make it more useful and robust. Research supported by NSF, NASA's HSR and LWS Programs, and AFOSR.

Reactor building 3D-model for evaluating the pressures on concrete regularization and foundation waterproofing membrane

Energy Technology Data Exchange (ETDEWEB)

Mello Junior, Glauco J.T.; Cardoso, Tarcisio de F.; Prates, Carlos L.M. [Eletrobras Termonuclear S.A. - ELETRONUCLEAR, Rio de Janeiro, RJ (Brazil). Gerencia de Analise de Tensoes GAN.T], e-mail: glauco@eletronuclear.gov.br, e-mail: tarci@eletronuclear.gov.br, e-mail: prates@eletronuclear.gov.br

2009-07-01

Angra dos Reis site in Brazil has already 2 operating PWR NPPs. Unit 3, with identical design to Unit 2, also a 1350 MW PWR, is expected to have its construction started in 2009. This new plant shall be founded directly on sound rock. The first step is to prepare this rock surface with a concrete regularization and a foundation waterproofing membrane. This study presents a 3D model approach of the corresponding reactor building to verify the maximum pressure acting on this surface. The 3D model permits to show a more realistic pressure distribution at every foundation specific detail. A static analysis is performed using ANSYS Mechanical Release 11.1. Dead weight, permanent and live loads, Safe Shutdown Earthquake (SSE) combined with Burst Pressure Wave (BPW) from the Feedwater Tank (SSB=SSE+BPW) and differences of temperature are taken into account. Considering all foundation nodes , the pressure distribution on the waterproofing membrane for each load case is obtained for vertical and horizontal directions, which corresponds to compression and tangential reaction loads. The maximum values occur in distinct positions for each load case. The maximum results are obtained according to DIN 25449 (2008) load combination criteria. The results are compared to a simplified analysis performed before, showing a good agreement in global values. (author)

Reactor building 3D-model for evaluating the pressures on concrete regularization and foundation waterproofing membrane

International Nuclear Information System (INIS)

Mello Junior, Glauco J.T.; Cardoso, Tarcisio de F.; Prates, Carlos L.M.

2009-01-01

Angra dos Reis site in Brazil has already 2 operating PWR NPPs. Unit 3, with identical design to Unit 2, also a 1350 MW PWR, is expected to have its construction started in 2009. This new plant shall be founded directly on sound rock. The first step is to prepare this rock surface with a concrete regularization and a foundation waterproofing membrane. This study presents a 3D model approach of the corresponding reactor building to verify the maximum pressure acting on this surface. The 3D model permits to show a more realistic pressure distribution at every foundation specific detail. A static analysis is performed using ANSYS Mechanical Release 11.1. Dead weight, permanent and live loads, Safe Shutdown Earthquake (SSE) combined with Burst Pressure Wave (BPW) from the Feedwater Tank (SSB=SSE+BPW) and differences of temperature are taken into account. Considering all foundation nodes , the pressure distribution on the waterproofing membrane for each load case is obtained for vertical and horizontal directions, which corresponds to compression and tangential reaction loads. The maximum values occur in distinct positions for each load case. The maximum results are obtained according to DIN 25449 (2008) load combination criteria. The results are compared to a simplified analysis performed before, showing a good agreement in global values. (author)

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

DEFF Research Database (Denmark)

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

2014-01-01

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

UNIVERSAL REGULAR AUTONOMOUS ASYNCHRONOUS SYSTEMS: ω-LIMIT SETS, INVARIANCE AND BASINS OF ATTRACTION

Directory of Open Access Journals (Sweden)

Serban Vlad

2011-07-01

Full Text Available The asynchronous systems are the non-deterministic real timebinarymodels of the asynchronous circuits from electrical engineering.Autonomy means that the circuits and their models have no input.Regularity means analogies with the dynamical systems, thus such systems may be considered to be real time dynamical systems with a’vector field’, Universality refers to the case when the state space of the system is the greatest possible in the sense of theinclusion. The purpose of this paper is that of defining, by analogy with the dynamical systems theory, the omega-limit sets, the invariance and the basins of attraction of the universal regular autonomous asynchronous systems.

Integral equations of the first kind, inverse problems and regularization: a crash course

International Nuclear Information System (INIS)

Groetsch, C W

2007-01-01

This paper is an expository survey of the basic theory of regularization for Fredholm integral equations of the first kind and related background material on inverse problems. We begin with an historical introduction to the field of integral equations of the first kind, with special emphasis on model inverse problems that lead to such equations. The basic theory of linear Fredholm equations of the first kind, paying particular attention to E. Schmidt's singular function analysis, Picard's existence criterion, and the Moore-Penrose theory of generalized inverses is outlined. The fundamentals of the theory of Tikhonov regularization are then treated and a collection of exercises and a bibliography are provided

Regular Network Class Features Enhancement Using an Evolutionary Synthesis Algorithm

Directory of Open Access Journals (Sweden)

O. G. Monahov

2014-01-01

Full Text Available This paper investigates a solution of the optimization problem concerning the construction of diameter-optimal regular networks (graphs. Regular networks are of practical interest as the graph-theoretical models of reliable communication networks of parallel supercomputer systems, as a basis of the structure in a model of small world in optical and neural networks. It presents a new class of parametrically described regular networks - hypercirculant networks (graphs. An approach that uses evolutionary algorithms for the automatic generation of parametric descriptions of optimal hypercirculant networks is developed. Synthesis of optimal hypercirculant networks is based on the optimal circulant networks with smaller degree of nodes. To construct optimal hypercirculant networks is used a template of circulant network from the known optimal families of circulant networks with desired number of nodes and with smaller degree of nodes. Thus, a generating set of the circulant network is used as a generating subset of the hypercirculant network, and the missing generators are synthesized by means of the evolutionary algorithm, which is carrying out minimization of diameter (average diameter of networks. A comparative analysis of the structural characteristics of hypercirculant, toroidal, and circulant networks is conducted. The advantage hypercirculant networks under such structural characteristics, as diameter, average diameter, and the width of bisection, with comparable costs of the number of nodes and the number of connections is demonstrated. It should be noted the advantage of hypercirculant networks of dimension three over four higher-dimensional tori. Thus, the optimization of hypercirculant networks of dimension three is more efficient than the introduction of an additional dimension for the corresponding toroidal structures. The paper also notes the best structural parameters of hypercirculant networks in comparison with iBT-networks previously

Regular graph construction for semi-supervised learning

International Nuclear Information System (INIS)

Vega-Oliveros, Didier A; Berton, Lilian; Eberle, Andre Mantini; Lopes, Alneu de Andrade; Zhao, Liang

2014-01-01

Semi-supervised learning (SSL) stands out for using a small amount of labeled points for data clustering and classification. In this scenario graph-based methods allow the analysis of local and global characteristics of the available data by identifying classes or groups regardless data distribution and representing submanifold in Euclidean space. Most of methods used in literature for SSL classification do not worry about graph construction. However, regular graphs can obtain better classification accuracy compared to traditional methods such as k-nearest neighbor (kNN), since kNN benefits the generation of hubs and it is not appropriate for high-dimensionality data. Nevertheless, methods commonly used for generating regular graphs have high computational cost. We tackle this problem introducing an alternative method for generation of regular graphs with better runtime performance compared to methods usually find in the area. Our technique is based on the preferential selection of vertices according some topological measures, like closeness, generating at the end of the process a regular graph. Experiments using the global and local consistency method for label propagation show that our method provides better or equal classification rate in comparison with kNN

Pricing and hedging derivative securities with neural networks: Bayesian regularization, early stopping, and bagging.

Science.gov (United States)

Gençay, R; Qi, M

2001-01-01

We study the effectiveness of cross validation, Bayesian regularization, early stopping, and bagging to mitigate overfitting and improving generalization for pricing and hedging derivative securities with daily S&P 500 index daily call options from January 1988 to December 1993. Our results indicate that Bayesian regularization can generate significantly smaller pricing and delta-hedging errors than the baseline neural-network (NN) model and the Black-Scholes model for some years. While early stopping does not affect the pricing errors, it significantly reduces the hedging error (HE) in four of the six years we investigated. Although computationally most demanding, bagging seems to provide the most accurate pricing and delta hedging. Furthermore, the standard deviation of the MSPE of bagging is far less than that of the baseline model in all six years, and the standard deviation of the average HE of bagging is far less than that of the baseline model in five out of six years. We conclude that they be used at least in cases when no appropriate hints are available.

Mathematical model of parking space unit for triangular parking area

Science.gov (United States)

Syahrini, Intan; Sundari, Teti; Iskandar, Taufiq; Halfiani, Vera; Munzir, Said; Ramli, Marwan

2018-01-01

Parking space unit (PSU) is an effective measure for the area size of a vehicle, including the free space and the width of the door opening of the vehicle (car). This article discusses a mathematical model for parking space of vehicles in triangular shape area. An optimization model for triangular parking lot is developed. Integer Linear Programming (ILP) method is used to determine the maximum number of the PSU. The triangular parking lot is in isosceles and equilateral triangles shape and implements four possible rows and five possible angles for each field. The vehicles which are considered are cars and motorcycles. The results show that the isosceles triangular parking area has 218 units of optimal PSU, which are 84 units of PSU for cars and 134 units of PSU for motorcycles. Equilateral triangular parking area has 688 units of optimal PSU, which are 175 units of PSU for cars and 513 units of PSU for motorcycles.

Comparison of Subcutaneous Regular Insulin and Lispro Insulin in Diabetics Receiving Continuous Nutrition: A Numerical Study.

Science.gov (United States)

Stull, Mamie C; Strilka, Richard J; Clemens, Michael S; Armen, Scott B

2015-06-30

Optimal management of non-critically ill patients with diabetes maintained on continuous enteral feeding (CEN) is poorly defined. Subcutaneous (SQ) lispro and SQ regular insulin were compared in a simulated type 1 and type 2 diabetic patient receiving CEN. A glucose-insulin feedback mathematical model was employed to simulate type 1 and type 2 diabetic patients on CEN. Each patient received 25 SQ injections of regular insulin or insulin lispro, ranging from 0-6 U. Primary endpoints were the change in mean glucose concentration (MGC) and change in glucose variability (GV); hypoglycemic episodes were also reported. The model was first validated against patient data. Both SQ insulin preparations linearly decreased MGC, however, SQ regular insulin decreased GV whereas SQ lispro tended to increase GV. Hourly glucose concentration measurements were needed to capture the increase in GV. In the type 2 diabetic patient, "rebound hyperglycemia" occurred after SQ lispro was rapidly metabolized. Although neither SQ insulin preparation caused hypoglycemia, SQ lispro significantly lowered MGC compared to SQ regular insulin. Thus, it may be more likely to cause hypoglycemia. Analyses of the detailed glucose concentration versus time data suggest that the inferior performance of lispro resulted from its shorter duration of action. Finally, the effects of both insulin preparations persisted beyond their duration of actions in the type 2 diabetic patient. Subcutaneous regular insulin may be the short-acting insulin preparation of choice for this subset of diabetic patients. Clinical trial is required before a definitive recommendation can be made. © 2015 Diabetes Technology Society.

Time-Homogeneous Parabolic Wick-Anderson Model in One Space Dimension: Regularity of Solution

OpenAIRE

Kim, Hyun-Jung; Lototsky, Sergey V

2017-01-01

Even though the heat equation with random potential is a well-studied object, the particular case of time-independent Gaussian white noise in one space dimension has yet to receive the attention it deserves. The paper investigates the stochastic heat equation with space-only Gaussian white noise on a bounded interval. The main result is that the space-time regularity of the solution is the same for additive noise and for multiplicative noise in the Wick-It\\^o-Skorokhod interpretation.

Minimization and parameter estimation for seminorm regularization models with I-divergence constraints

International Nuclear Information System (INIS)

Teuber, T; Steidl, G; Chan, R H

2013-01-01

In this paper, we analyze the minimization of seminorms ‖L · ‖ on R n under the constraint of a bounded I-divergence D(b, H · ) for rather general linear operators H and L. The I-divergence is also known as Kullback–Leibler divergence and appears in many models in imaging science, in particular when dealing with Poisson data but also in the case of multiplicative Gamma noise. Often H represents, e.g., a linear blur operator and L is some discrete derivative or frame analysis operator. A central part of this paper consists in proving relations between the parameters of I-divergence constrained and penalized problems. To solve the I-divergence constrained problem, we consider various first-order primal–dual algorithms which reduce the problem to the solution of certain proximal minimization problems in each iteration step. One of these proximation problems is an I-divergence constrained least-squares problem which can be solved based on Morozov’s discrepancy principle by a Newton method. We prove that these algorithms produce not only a sequence of vectors which converges to a minimizer of the constrained problem but also a sequence of parameters which converges to a regularization parameter so that the corresponding penalized problem has the same solution. Furthermore, we derive a rule for automatically setting the constraint parameter for data corrupted by multiplicative Gamma noise. The performance of the various algorithms is finally demonstrated for different image restoration tasks both for images corrupted by Poisson noise and multiplicative Gamma noise. (paper)

Frações lábeis e recalcitrantes da matéria orgânica em solos sob integração lavoura-pecuária

Directory of Open Access Journals (Sweden)

Eulene Francisco da Silva

2011-10-01

Full Text Available O objetivo deste trabalho foi avaliar o estoque de carbono orgânico total (COT e nitrogênio total (NT, C e N nas frações lábeis e recalcitrantes da matéria orgânica do solo (MOS e o índice de manejo de C, em solos sob sistema de integração lavoura-pecuária (ILP, após quatro (ILP4 e oito anos (ILP8 de implantação. Para comparação, foram utilizados outros sistemas de manejo: pastagem e lavoura em plantio direto (PD, além de vegetação nativa. O experimento foi realizado em delineamento inteiramente casualizado, com cinco repetições. A integração lavoura-pecuária, estabelecida num período de oito anos, é capaz de alcançar um novo estado estável equivalente ao sistema sob plantio direto com 23 anos de implantação. Houve acúmulo no estoque de C total de 101,0 (ILP8 e 104,2 Mg ha-1 (PD e N total de 5,5 (ILP8 e 5,8 Mg ha-1 (PD, na camada 0-30 cm, bem como aumento no estoque de C e N nas frações lábeis e recalcitrantes da MOS. Para o ILP com oito anos, o índice de manejo de C de 88 foi superior ao dos demais sistemas de manejo e não diferiu da vegetação nativa na camada 0-10 cm, todavia, foi similar ao PD na análise de todo o perfil (0-30 cm.

76 FR 20799 - Intermediary Lending Pilot Program Meeting

Science.gov (United States)

2011-04-13

... Pilot (ILP) program established by the Small Business Jobs Act of 2010. The meetings will be open to the... holding open meetings to discuss the ILP program established in the Small Business Jobs Act of 2010 (Pub... program public meeting, please contact: 1. San Francisco--Steve Bangs, (415) 744-6792, fax (415) 744-6812...

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

Science.gov (United States)

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

2014-08-01

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

Fluctuations of quantum fields via zeta function regularization

International Nuclear Information System (INIS)

Cognola, Guido; Zerbini, Sergio; Elizalde, Emilio

2002-01-01

Explicit expressions for the expectation values and the variances of some observables, which are bilinear quantities in the quantum fields on a D-dimensional manifold, are derived making use of zeta function regularization. It is found that the variance, related to the second functional variation of the effective action, requires a further regularization and that the relative regularized variance turns out to be 2/N, where N is the number of the fields, thus being independent of the dimension D. Some illustrating examples are worked through. The issue of the stress tensor is also briefly addressed

X-ray computed tomography using curvelet sparse regularization.

Science.gov (United States)

Wieczorek, Matthias; Frikel, Jürgen; Vogel, Jakob; Eggl, Elena; Kopp, Felix; Noël, Peter B; Pfeiffer, Franz; Demaret, Laurent; Lasser, Tobias

2015-04-01

Reconstruction of x-ray computed tomography (CT) data remains a mathematically challenging problem in medical imaging. Complementing the standard analytical reconstruction methods, sparse regularization is growing in importance, as it allows inclusion of prior knowledge. The paper presents a method for sparse regularization based on the curvelet frame for the application to iterative reconstruction in x-ray computed tomography. In this work, the authors present an iterative reconstruction approach based on the alternating direction method of multipliers using curvelet sparse regularization. Evaluation of the method is performed on a specifically crafted numerical phantom dataset to highlight the method's strengths. Additional evaluation is performed on two real datasets from commercial scanners with different noise characteristics, a clinical bone sample acquired in a micro-CT and a human abdomen scanned in a diagnostic CT. The results clearly illustrate that curvelet sparse regularization has characteristic strengths. In particular, it improves the restoration and resolution of highly directional, high contrast features with smooth contrast variations. The authors also compare this approach to the popular technique of total variation and to traditional filtered backprojection. The authors conclude that curvelet sparse regularization is able to improve reconstruction quality by reducing noise while preserving highly directional features.

Synchronisation phenomenon in three blades rotor driven by regular or chaotic oscillations

Directory of Open Access Journals (Sweden)

Szmit Zofia

2018-01-01

Full Text Available The goal of the paper is to analysed the influence of the different types of excitation on the synchronisation phenomenon in case of the rotating system composed of a rigid hub and three flexible composite beams. In the model is assumed that two blades, due to structural differences, are de-tuned. Numerical calculation are divided on two parts, firstly the rotating system is exited by a torque given by regular harmonic function, than in the second part the torque is produced by chaotic Duffing oscillator. The synchronisation phenomenon between the beams is analysed both either for regular or chaotic motions. Partial differential equations of motion are solved numerically and resonance curves, time series and Poincaré maps are presented for selected excitation torques.

Regularization of Instantaneous Frequency Attribute Computations

Science.gov (United States)

Yedlin, M. J.; Margrave, G. F.; Van Vorst, D. G.; Ben Horin, Y.

2014-12-01

We compare two different methods of computation of a temporally local frequency:1) A stabilized instantaneous frequency using the theory of the analytic signal.2) A temporally variant centroid (or dominant) frequency estimated from a time-frequency decomposition.The first method derives from Taner et al (1979) as modified by Fomel (2007) and utilizes the derivative of the instantaneous phase of the analytic signal. The second method computes the power centroid (Cohen, 1995) of the time-frequency spectrum, obtained using either the Gabor or Stockwell Transform. Common to both methods is the necessity of division by a diagonal matrix, which requires appropriate regularization.We modify Fomel's (2007) method by explicitly penalizing the roughness of the estimate. Following Farquharson and Oldenburg (2004), we employ both the L curve and GCV methods to obtain the smoothest model that fits the data in the L2 norm.Using synthetic data, quarry blast, earthquakes and the DPRK tests, our results suggest that the optimal method depends on the data. One of the main applications for this work is the discrimination between blast events and earthquakesFomel, Sergey. " Local seismic attributes." , Geophysics, 72.3 (2007): A29-A33.Cohen, Leon. " Time frequency analysis theory and applications." USA: Prentice Hall, (1995).Farquharson, Colin G., and Douglas W. Oldenburg. "A comparison of automatic techniques for estimating the regularization parameter in non-linear inverse problems." Geophysical Journal International 156.3 (2004): 411-425.Taner, M. Turhan, Fulton Koehler, and R. E. Sheriff. " Complex seismic trace analysis." Geophysics, 44.6 (1979): 1041-1063.

Semisupervised Support Vector Machines With Tangent Space Intrinsic Manifold Regularization.

Science.gov (United States)

Sun, Shiliang; Xie, Xijiong

2016-09-01

Semisupervised learning has been an active research topic in machine learning and data mining. One main reason is that labeling examples is expensive and time-consuming, while there are large numbers of unlabeled examples available in many practical problems. So far, Laplacian regularization has been widely used in semisupervised learning. In this paper, we propose a new regularization method called tangent space intrinsic manifold regularization. It is intrinsic to data manifold and favors linear functions on the manifold. Fundamental elements involved in the formulation of the regularization are local tangent space representations, which are estimated by local principal component analysis, and the connections that relate adjacent tangent spaces. Simultaneously, we explore its application to semisupervised classification and propose two new learning algorithms called tangent space intrinsic manifold regularized support vector machines (TiSVMs) and tangent space intrinsic manifold regularized twin SVMs (TiTSVMs). They effectively integrate the tangent space intrinsic manifold regularization consideration. The optimization of TiSVMs can be solved by a standard quadratic programming, while the optimization of TiTSVMs can be solved by a pair of standard quadratic programmings. The experimental results of semisupervised classification problems show the effectiveness of the proposed semisupervised learning algorithms.

Self- and rater-assessed effectiveness of "thinking-aloud" and "regular" morning report to intensify young physicians' clinical skills.

Science.gov (United States)

Hsu, Hui-Chi; Lee, Fa-Yauh; Yang, Ying-Ying; Tsao, Yen-Po; Lee, Wen-Shin; Chuang, Chiao-Lin; Chang, Ching-Chih; Huang, Chia-Chang; Huang, Chin-Chou; Ho, Shung-Tai

2015-09-01

This study compared the effects of the "thinking aloud" (TA) morning report (MR), which is characterized by sequential and interactive case discussion by all participants, with "regular" MR for clinical skill training of young physicians. Between February 2011 and February 2014, young physicians [including postgraduate year-1 (PGY1) residents, interns, and clerks) from our hospital were sequentially enrolled and followed for 3 months. The self- and rater-assessed educational values of two MR models for building up clinical skills of young physicians were compared. The junior (intern and clerk) attendees had higher self-assessed educational values scores and reported post-training application frequency of skills trained by TA MR compared with the senior (PGY1 resident) attendees. Higher average and percentage of increased overall rater-assessed OSCE scores were noted among the regular MR senior attendees and TA MR junior attendees than in their corresponding control groups (regular MR junior attendees and TA MR senior attendees). Interestingly, regular MRs provided additional beneficial effects for establishing the "professionalism, consulting skills and organization efficiency" aspects of clinical skills of senior/junior attendees. Moreover, senior and junior attendees benefited the most by participating in seven sessions of regular MR and TA MR each month, respectively. TA MR effectively trains junior attendees in basic clinical skills, whereas regular MR enhances senior attendees' "work reports, professionalism, organizational efficiency, skills in dealing with controversial and professional issues." Undoubtedly, all elements of the two MR models should be integrated together to ensure patient safety and good discipline among young physicians. Copyright © 2015. Published by Elsevier Taiwan.