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Automatic Grading of Individual Knee Osteoarthritis Features in Plain Radiographs using Deep Convolutional Neural NetworksJul 18 2019Knee osteoarthritis (OA) is the most common musculoskeletal disease in the world. In primary healthcare, knee OA is diagnosed using clinical examination and radiographic assessment. Osteoarthritis Research Society International (OARSI) atlas of OA radiographic ... More

Deep-Learning for Tidemark Segmentation in Human Osteochondral Tissues Imaged with Micro-computed TomographyJul 11 2019Three-dimensional (3D) semi-quantitative grading of pathological features in articular cartilage (AC) offers significant improvements in basic research of osteoarthritis (OA). We have earlier developed the 3D protocol for imaging of AC and its structures ... More

Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical DataApr 12 2019May 06 2019Knee osteoarthritis (OA) is the most common musculoskeletal disease without a cure, and current treatment options are limited to symptomatic relief. Prediction of OA progression is a very challenging and timely issue, and it could, if resolved, accelerate ... More

Bone Density and Texture from Minimally Post-Processed Knee Radiographs in Subjects with Knee OsteoarthritisFeb 06 2019Plain radiography is the most common modality to assess the stage of osteoarthritis. Our aims were to assess the relationship of radiography-based bone density and texture between radiographs with minimal and clinical post-processing, and to compare the ... More

Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based ApproachOct 29 2017Knee osteoarthritis (OA) is the most common musculoskeletal disorder. OA diagnosis is currently conducted by assessing symptoms and evaluating plain radiographs, but this process suffers from subjectivity. In this study, we present a new transparent computer-aided ... More

A novel method for automatic localization of joint area on knee plain radiographsJan 31 2017Apr 05 2017Osteoarthritis (OA) is a common musculoskeletal condition typically diagnosed from radiographic assessment after clinical examination. However, a visual evaluation made by a practitioner suffers from subjectivity and is highly dependent on the experience. ... More

The Use of Gaussian Processes in System IdentificationJul 13 2019Gaussian processes are used in machine learning to learn input-output mappings from observed data. Gaussian process regression is based on imposing a Gaussian process prior on the unknown regressor function and statistically conditioning it on the observed ... More

An automatic regularization method: An application for 3D X-ray micro-CT reconstruction using sparse dataAug 04 2017Apr 06 2018X-ray tomography is a reliable tool for determining the inner structure of 3D object with penetrating X-rays. However, traditional reconstruction methods such as FDK require dense angular sampling in the data acquisition phase leading to long measurement ... More

Hilbert Space Methods for Reduced-Rank Gaussian Process RegressionJan 21 2014This paper proposes a novel scheme for reduced-rank Gaussian process regression. The method is based on an approximate series expansion of the covariance function in terms of an eigenfunction expansion of the Laplace operator in a compact subset of $\mathbb{R}^d$. ... More

Hilbert Space Methods for Reduced-Rank Gaussian Process RegressionJan 21 2014Jun 07 2018This paper proposes a novel scheme for reduced-rank Gaussian process regression. The method is based on an approximate series expansion of the covariance function in terms of an eigenfunction expansion of the Laplace operator in a compact subset of $\mathbb{R}^d$. ... More

Understanding Human-Centric Images: From Geometry to FashionDec 14 2015Understanding humans from photographs has always been a fundamental goal of computer vision. In this thesis we have developed a hierarchy of tools that cover a wide range of topics with the objective of understanding humans from monocular RGB image: from ... More

Sequential Inference for Latent Force ModelsFeb 14 2012Latent force models (LFMs) are hybrid models combining mechanistic principles with non-parametric components. In this article, we shall show how LFMs can be equivalently formulated and solved using the state variable approach. We shall also show how the ... More

Application of Girsanov Theorem to Particle Filtering of Discretely Observed Continuous-Time Non-Linear SystemsMay 11 2007Apr 29 2008This article considers the application of particle filtering to continuous-discrete optimal filtering problems, where the system model is a stochastic differential equation, and noisy measurements of the system are obtained at discrete instances of time. ... More

Computer Assisted Proof for Normally Hyperbolic Invariant ManifoldsMay 06 2011We present a topological proof of the existence of a normally hyperbolic invariant manifold for maps. In our approach we do not require that the map is a perturbation of some other map for which we already have an invariant manifold. But a non-rigorous, ... More

Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical DataApr 12 2019Knee osteoarthritis (OA) is the most common musculoskeletal disease without a cure, and current treatment options are limited to symptomatic relief. Prediction of OA progression is a very challenging and timely issue, and it could, if resolved, accelerate ... More

Combining Particle MCMC with Rao-Blackwellized Monte Carlo Data Association for Parameter Estimation in Multiple Target TrackingSep 30 2014Feb 20 2015We consider state and parameter estimation in multiple target tracking problems with data association uncertainties and unknown number of targets. We show how the problem can be recast into a conditionally linear Gaussian state-space model with unknown ... More

Infinite-dimensional Bayesian filtering for detection of quasi-periodic phenomena in spatio-temporal dataMar 11 2013Sep 03 2013This paper introduces a spatio-temporal resonator model and an inference method for detection and estimation of nearly periodic temporal phenomena in spatio-temporal data. The model is derived as a spatial extension of a stochastic harmonic resonator ... More

Worst-case optimal approximation with increasingly flat Gaussian kernelsJun 05 2019We study worst-case optimal approximation of positive linear functionals in reproducing kernel Hilbert spaces (RKHSs) induced by increasingly flat Gaussian kernels. This provides a new perspective and some generalisations to the problem of interpolation ... More

Variational Bayesian Adaptation of Noise Covariances in Non-Linear Kalman FilteringFeb 04 2013This paper is considered with joint estimation of state and time-varying noise covariance matrices in non-linear stochastic state space models. We present a variational Bayes and Gaussian filtering based algorithm for efficient computation of the approximate ... More

Big Data: Opportunities and Privacy ChallengesFeb 03 2015Recent advances in data collection and computational statistics coupled with increases in computer processing power, along with the plunging costs of storage are making technologies to effectively analyze large sets of heterogeneous data ubiquitous. Applying ... More

A Simple Determination of the Thermodynamical Characteristics of the Weakly Charged, Very Thin Black RingOct 29 2008In our previous work we suggested a very simple, approximate formalism for description of some basic (especially thermodynamical) characteristics of a non-charged, rotating, very thin black ring. Here, in our new work, generalizing our previous results, ... More

Gaussian kernel quadrature at scaled Gauss-Hermite nodesMar 26 2018May 02 2019This article derives an accurate, explicit, and numerically stable approximation to the kernel quadrature weights in one dimension and on tensor product grids when the kernel and integration measure are Gaussian. The approximation is based on use of scaled ... More

A Simple Determination of the Thermodynamical Characteristics of a Very Thin Black RingAug 05 2008In this work we suggest a very simple, approximate formalism for description of some basic (especially thermodynamical) characteristics of a rotating, very thin black ring. (In fact, our formalism is not theoretically dubious, since, at it is not hard ... More

Gaussian kernel quadrature at scaled Gauss-Hermite nodesMar 26 2018Aug 24 2018This article derives an accurate, numerically stable, and explicit approximation to the kernel quadrature weights in one dimension and on tensor product grids when the kernel and integration measure are Gaussian. The approximation is based on use of scaled ... More

Fully symmetric kernel quadratureMar 18 2017Jan 06 2018Kernel quadratures and other kernel-based approximation methods typically suffer from prohibitive cubic time and quadratic space complexity in the number of function evaluations. The problem arises because a system of linear equations needs to be solved. ... More

Iterative Statistical Linear Regression for Gaussian Smoothing in Continuous-Time Non-linear Stochastic Dynamic SystemsMay 29 2018Jan 18 2019This paper considers approximate smoothing for discretely observed non-linear stochastic differential equations. The problem is tackled by developing methods for linearising stochastic differential equations with respect to an arbitrary Gaussian process. ... More

Numerical Integration as a Finite Matrix Approximation to Multiplication OperatorNov 21 2017Dec 17 2018In this article, numerical integration is formulated as evaluation of a matrix function of a matrix that is obtained as a projection of the multiplication operator on a finite-dimensional basis. The idea is to approximate the continuous spectral representation ... More

MYRIAD: A new N-body code for simulations of Star ClustersJun 16 2010Aug 19 2010We present a new C++ code for collisional N-body simulations of star clusters. The code uses the Hermite fourth-order scheme with block time steps, for advancing the particles in time, while the forces and neighboring particles are computed using the ... More

Parallelizable sparse inverse formulation Gaussian processes (SpInGP)Oct 25 2016We propose a parallelizable sparse inverse formulation Gaussian process (SpInGP) al- gorithm for temporal Gaussian process mod- els. It uses a sparse precision GP formulation and sparse matrix routines to speed up the computations. Due to the state-space ... More

Sigma-Point Filtering and Smoothing Based Parameter Estimation in Nonlinear Dynamic SystemsApr 23 2015Nov 02 2015We consider approximate maximum likelihood parameter estimation in nonlinear state-space models. We discuss both direct optimization of the likelihood and expectation--maximization (EM). For EM, we also give closed-form expressions for the maximization ... More

Iterated Extended Kalman Smoother-based Variable Splitting for $L_1$-Regularized State EstimationMar 20 2019In this paper, we propose a new framework for solving state estimation problems with an additional sparsity-promoting $L_1$-regularizer term. We first formulate such problems as minimization of the sum of linear or nonlinear quadratic error terms and ... More

Thermodynamical Characteristics of "Crystal Lattice" of Many Interacting Kerr Black Holes in Touching LimitOct 06 2008In this work, starting by simple, approximate (quasi-classical) methods presented in our previous works, we reproduce effectively and generalize final results of Herdeiro and Rebelo on the basic thermodynamical characteristics (entropy and temperature) ... More

Software Engineering for Intelligent and Autonomous Systems: Report from the GI Dagstuhl Seminar 18343Apr 02 2019Apr 03 2019Software systems are increasingly used in application domains characterised by uncertain environments, evolving requirements and unexpected failures; sudden system malfunctioning raises serious issues of security, safety, loss of comfort or revenue. During ... More

Moment Conditions for Convergence of Particle Filters with Unbounded Importance WeightsMar 26 2014Aug 18 2014In this paper, we derive moment conditions for particle filter importance weights, which ensure that the particle filter estimates of the expectations of bounded Borel functions converge in mean square and $L^4$ sense, and that the empirical measure of ... More

A probabilistic model for the numerical solution of initial value problemsOct 17 2016Like many numerical methods, solvers for initial value problems (IVPs) on ordinary differential equations estimate an analytically intractable quantity, using the results of tractable computations as inputs. This structure is closely connected to the ... More

Can Cosmological Constant be a Forbidden Zone (GAP) in Quantum VacuumApr 15 2008In this work we suggest, without detailed mathematical analysis, a hypothesis on the physical meaning of cosmological constant. It is primarily based on a conceptual analogy with energy characteristics of the crystal lattice structure, i.e. energy zones ... More

Software Engineering for Intelligent and Autonomous Systems: Report from the GI Dagstuhl Seminar 18343Apr 02 2019Software systems are increasingly used in application domains characterised by uncertain environments, evolving requirements and unexpected failures; sudden system malfunctioning raises serious issues of security, safety, loss of comfort or revenue. During ... More

CT Data of a Pen-Spring: Application to Under-Sampled Dynamic X-ray TomographyJul 03 2019This is the documentation of Computed Tomography (CT) data of a pen-spring. The open data set is available at https://zenodo.org/record/3266936#.XRyMdCZS9oA and can be freely used for scientific purposes with appropriate references to the data and to ... More

A Simple Determination of the (LOGARITHMIC) Corrections of Black Hole Entropy "without Knowing the Details of Quantum Gravity"Oct 06 2008In this work, starting by simple, approximate (quasi-classical) methods presented in our previous works, we suggest a simple determination of the (logarithmic) corrections of (Schwarzschild) black hole entropy "without knowing the details of quantum gravity"(Fursaev). ... More

Series Expansion Approximations of Brownian Motion for Non-Linear Kalman Filtering of Diffusion ProcessesFeb 21 2013Feb 18 2014In this paper, we describe a novel application of sigma-point methods to continuous-discrete filtering. In principle, the nonlinear continuous- discrete filtering problem can be solved exactly. In practice, the solution contains terms that are computationally ... More

A Bayesian Particle Filtering Method For Brain Source LocalisationNov 21 2014Jun 17 2015In this paper, we explore the multiple source localisation problem in the cerebral cortex using magnetoencephalography (MEG) data. We model neural currents as point-wise dipolar sources which dynamically evolve over time, then model dipole dynamics using ... More

Mastering Sketching: Adversarial Augmentation for Structured PredictionMar 27 2017We present an integral framework for training sketch simplification networks that convert challenging rough sketches into clean line drawings. Our approach augments a simplification network with a discriminator network, training both networks jointly ... More

Correlating Pedestrian Flows and Search Engine QueriesJun 19 2012An important challenge for ubiquitous computing is the development of techniques that can characterize a location vis-a-vis the richness and diversity of urban settings. In this paper we report our work on correlating urban pedestrian flows with Google ... More

DeepFault: Fault Localization for Deep Neural NetworksFeb 15 2019Deep Neural Networks (DNNs) are increasingly deployed in safety-critical applications including autonomous vehicles and medical diagnostics. To reduce the residual risk for unexpected DNN behaviour and provide evidence for their trustworthy operation, ... More

On the positivity and magnitudes of Bayesian quadrature weightsDec 20 2018This article reviews and studies the properties of Bayesian quadrature weights, which strongly affect stability and robustness of the quadrature rule. Specifically, we investigate conditions that are needed to guarantee that the weights are positive or ... More

Tests for validity of the semiparametric heteroskedastic transformation modelJan 09 2019There exist a number of tests for assessing the nonparametric heteroscedastic location-scale assumption. Here we consider a goodness-of-fit test for the more general hypothesis of the validity of this model under a parametric functional transformation ... More

Parallelizable sparse inverse formulation Gaussian processes (SpInGP)Oct 25 2016Oct 26 2016We propose a parallelizable sparse inverse formulation Gaussian process (SpInGP) al- gorithm for temporal Gaussian process mod- els. It uses a sparse precision GP formulation and sparse matrix routines to speed up the computations. Due to the state-space ... More

Oscillations of simple networksSep 14 2011Oct 24 2012To describe the flow of a miscible quantity on a network, we introduce the graph wave equation where the standard continuous Laplacian is replaced by the graph Laplacian. This is a natural description of an array of inductances and capacities, of fluid ... More

A probabilistic model for the numerical solution of initial value problemsOct 17 2016Aug 10 2017Like many numerical methods, solvers for initial value problems (IVPs) on ordinary differential equations estimate an analytically intractable quantity, using the results of tractable computations as inputs. This structure is closely connected to the ... More

The Simplest Determination of the Thermodynamical Characteristics of Kerr-Newman Black HoleApr 15 2008In this work, generalizing our previous results, we determine in an original and the simplest way three most important thermodynamical characteristics (Bekenstein-Hawking entropy, Bekenstein quantization of the entropy or (outer) horizon surface area ... More

Study of the derivative expansions for the nuclear structure functionsJul 31 2008We study the convergence of the series expansions sometimes used in the analysis of the nuclear effects in Deep Inelastic Scattering (DIS) proccesses induced by leptons. The recent advances in statistics and quality of the data, in particular for neutrinos ... More

A Bayes-Sard Cubature MethodApr 09 2018May 18 2018This paper focusses on the formulation of numerical integration as an inferential task. To date, research effort has largely focussed on the development of Bayesian cubature, whose distributional output provides uncertainty quantification for the integral. ... More

Kalman-based Spectro-Temporal ECG Analysis using Deep Convolutional Networks for Atrial Fibrillation DetectionDec 12 2018In this article, we propose a novel ECG classification framework for atrial fibrillation (AF) detection using spectro-temporal representation (i.e., time varying spectrum) and deep convolutional networks. In the first step we use a Bayesian spectro-temporal ... More

Parallelizable sparse inverse formulation Gaussian processes (SpInGP)Oct 25 2016Sep 28 2017We propose a parallelizable sparse inverse formulation Gaussian process (SpInGP) for temporal models. It uses a sparse precision GP formulation and sparse matrix routines to speed up the computations. Due to the state-space formulation used in the algorithm, ... More

Data Transformations and Goodness-of-Fit Tests for Type-II Right Censored SamplesDec 11 2013We suggest several goodness-of-fit methods which are appropriate with Type-II right censored data. Our strategy is to transform the original observations from a censored sample into an approximately i.i.d. sample of normal variates and then perform a ... More

State-Space Inference for Non-Linear Latent Force Models with Application to Satellite Orbit PredictionJun 18 2012Latent force models (LFMs) are flexible models that combine mechanistic modelling principles (i.e., physical models) with non-parametric data-driven components. Several key applications of LFMs need non-linearities, which results in analytically intractable ... More

Gaussian filtering and variational approximations for Bayesian smoothing in continuous-discrete stochastic dynamic systemsJul 22 2014Jul 23 2014The Bayesian smoothing equations are generally intractable for systems described by nonlinear stochastic differential equations and discrete-time measurements. Gaussian approximations are a computationally efficient way to approximate the true smoothing ... More

A computationally efficient method to solve the Takagi-Taupin equations for a large deformed crystalJan 27 2016Jun 02 2016We present a treatise on solving the Takagi-Taupin equations in the case of a strain field with an additional, spatially slowly varying component (owing to \emph{e.g.}~heat expansion or angular compression). We show that the presence of such a component ... More

Symmetry Exploits for Bayesian Cubature MethodsSep 26 2018Jan 26 2019Bayesian cubature provides a flexible framework for numerical integration, in which a priori knowledge on the integrand can be encoded and exploited. This additional flexibility, compared to many classical cubature methods, comes at a computational cost ... More

Iterated Extended Kalman Smoother-based Variable Splitting for $L_1$-Regularized State EstimationMar 20 2019Jun 27 2019In this paper, we propose a new framework for solving state estimation problems with an additional sparsity-promoting $L_1$-regularizer term. We first formulate such problems as minimization of the sum of linear or nonlinear quadratic error terms and ... More

Non-Isothermal Electrokinetics: Energetic Variational ApproachOct 22 2017Nov 08 2017Fluid dynamics accompanies with the entropy production thus increases the local temperature, which plays an important role in charged systems such as the ion channel in biological environment and electrodiffusion in capacitors/batteries. In this article, ... More

Temporal Parallelization of Bayesian Filters and SmoothersMay 30 2019This paper presents algorithms for the temporal parallelization of Bayesian filters and smoothers. We define the elements and the operators to pose these problems as the solutions to all-prefix-sums operations for which efficient parallel scan-algorithms ... More

Tests for Time Series of Counts Based on the Probability Generating FunctionOct 22 2014We propose testing procedures for the hypothesis that a given set of discrete observations may be formulated as a particular time series of counts with a specific conditional law. The new test statistics incorporate the empirical probability generating ... More

Weak production of strange $Ξ$ baryons off the nucleonJun 17 2019The charged current Cabibbo-supressed associated $K \Xi$ production off the nucleon induced by anti-neutrinos is studied at low and intermediate energies. The non-resonant terms are obtained using a microscopical model based on the SU(3) chiral Lagrangian. ... More

Inertial-Based Scale Estimation for Structure from Motion on Mobile DevicesNov 29 2016Structure from motion algorithms have an inherent limitation that the reconstruction can only be determined up to the unknown scale factor. Modern mobile devices are equipped with an inertial measurement unit (IMU), which can be used for estimating the ... More

Probabilistic Solutions To Ordinary Differential Equations As Non-Linear Bayesian Filtering: A New PerspectiveOct 08 2018Feb 22 2019We formulate probabilistic numerical approximations to solutions of ordinary differential equations (ODEs) as problems in Gaussian process (GP) regression with non-linear measurement functions. This is achieved by defining the measurement sequence to ... More

Gyroscope-Aided Motion Deblurring with Deep NetworksOct 01 2018Nov 23 2018We propose a deblurring method that incorporates gyroscope measurements into a convolutional neural network (CNN). With the help of such measurements, it can handle extremely strong and spatially-variant motion blur. At the same time, the image data is ... More

Sparse approximations of fractional Matérn fieldsOct 08 2014We consider a fast approximation method for a solution of a certain stochastic non-local pseudodifferential equation. This equation defines a Mat\'ern class random field. The approximation method is based on the spectral compactness of the solution. We ... More

Quantum effects on Lagrangian points and displaced periodic orbits in the Earth-Moon systemJan 12 2015Mar 31 2015Recent work in the literature has shown that the one-loop long distance quantum corrections to the Newtonian potential imply tiny but observable effects in the restricted three-body problem of celestial mechanics, i.e., at the Lagrangian libration points ... More

Phase Lag Sensitivity Analysis for Numerical IntegrationJul 18 2008In the field of numerical integration, methods specially tuned on oscillating functions, are of great practical importance. Such methods are needed in various branches of natural sciences, particularly in physics, since a lot of physical phenomena exhibit ... More

A New Methodology for the Development of Numerical Methods for the Numerical Solution of the Schrödinger EquationNov 15 2008In the present paper we introduce a new methodology for the construction of numerical methods for the approximate solution of the one-dimensional Schr\"odinger equation. The new methodology is based on the requirement of vanishing the phase-lag and its ... More

Two Optimized Symmetric Eight-Step Implicit Methods for Initial-Value Problems with Oscillating SolutionsNov 15 2008In this paper we present two optimized eight-step symmetric implicit methods with phase-lag order ten and infinite (phase-fitted). The methods are constructed to solve numerically the radial time-independent Schr\"odinger equation with the use of the ... More

A Family of Runge-Kutta Methods with Zero Phase-Lag and Derivatives for the Numerical Solution of the Schrödinger Equation and Related ProblemsNov 15 2008We construct a family of two new optimized explicit Runge-Kutta methods with zero phase-lag and derivatives for the numerical solution of the time-independent radial Schr\"odinger equation and related ordinary differential equations with oscillating solutions. ... More

Gaussian process classification using posterior linearisationSep 13 2018Apr 02 2019This paper proposes a new algorithm for Gaussian process classification based on posterior linearisation (PL). In PL, a Gaussian approximation to the posterior density is obtained iteratively using the best possible linearisation of the conditional mean ... More

Inertial-Based Scale Estimation for Structure from Motion on Mobile DevicesNov 29 2016Aug 11 2017Structure from motion algorithms have an inherent limitation that the reconstruction can only be determined up to the unknown scale factor. Modern mobile devices are equipped with an inertial measurement unit (IMU), which can be used for estimating the ... More

Fast Motion Deblurring for Feature Detection and Matching Using Inertial MeasurementsMay 22 2018Many computer vision and image processing applications rely on local features. It is well-known that motion blur decreases the performance of traditional feature detectors and descriptors. We propose an inertial-based deblurring method for improving the ... More

Scaling violation and relativistic effective mass from quasielastic electron scattering: implications for neutrino reactionsMay 20 2015Oct 27 2015The experimental data from quasielastic electron scattering from $^{12}$C are reanalyzed in terms of a new scaling variable suggested by the interacting relativistic Fermi gas with scalar and vector interactions, which is known to generate a relativistic ... More

Quantum Effects on all Lagrangian Points and Prospects to Measure Them in the Earth-Moon SystemJun 16 2015The one-loop long distance quantum corrections to the Newtonian potential imply tiny but observable effects in the restricted three-body problem of celestial mechanics, i.e., both at the Lagrangian points of stable equilibrium and at those of unstable ... More

Probabilistic Solutions To Ordinary Differential Equations As Non-Linear Bayesian Filtering: A New PerspectiveOct 08 2018Apr 24 2019We formulate probabilistic numerical approximations to solutions of ordinary differential equations (ODEs) as problems in Gaussian process (GP) regression with non-linear measurement functions. This is achieved by defining the measurement sequence to ... More

Random versus deterministic exponents in a rich family of diffeomorphismsOct 16 2002We study, both numerically and theoretically, the relationship between the random Lyapunov exponent of a family of area preserving diffeomorphisms of the 2-sphere and the mean of the Lyapunov exponents of the individual members.

Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical SystemsSep 15 2017Aug 13 2018This article is concerned with learning and stochastic control in physical systems which contain unknown input signals. These unknown signals are modeled as Gaussian processes (GP) with certain parametrized covariance structures. The resulting latent ... More

Student-t Process Quadratures for Filtering of Non-Linear Systems with Heavy-Tailed NoiseMar 15 2017Mar 16 2017The aim of this article is to design a moment transformation for Student- t distributed random variables, which is able to account for the error in the numerically computed mean. We employ Student-t process quadrature, an instance of Bayesian quadrature, ... More

A thermo-mechanical explanation for the topology of crack patterns observed on the surface of charred wood and particle fibreboardApr 04 2016In the assessment of wood charring, it was believed for a long time that physicochemical processes were responsible for the creation of cracking patterns on the wood surface. This implied no possibility of rigorous explanations for this topology. In this ... More

A thermo-mechanical explanation for the topology of crack patterns observed on the surface of charred wood and particle fibreboardApr 04 2016Nov 04 2016In the assessment of wood charring, it was believed for a long time that physicochemical processes were responsible for the creation of cracking patterns on the charring wood surface. This implied no possibility to rigorously explain the crack topology. ... More

On the relation between Gaussian process quadratures and sigma-point methodsApr 22 2015This article is concerned with Gaussian process quadratures, which are numerical integration methods based on Gaussian process regression methods, and sigma-point methods, which are used in advanced non-linear Kalman filtering and smoothing algorithms. ... More

Gaussian process classification using posterior linearisationSep 13 2018This paper proposes a new algorithm for Gaussian process classification based on posterior linearisation (PL). In PL, a Gaussian approximation to the posterior density is obtained iteratively using the best possible linearisation of the conditional mean ... More

Gaussian process classification using posterior linearisationSep 13 2018Apr 18 2019This paper proposes a new algorithm for Gaussian process classification based on posterior linearisation (PL). In PL, a Gaussian approximation to the posterior density is obtained iteratively using the best possible linearisation of the conditional mean ... More

On stability of a class of filters for non-linear stochastic systemsSep 15 2018This article considers stability properties of a broad class of commonly used filters, including the extended and unscented Kalman filters, for discrete and continuous-time stochastic dynamic systems with non-linear state dynamics and linear measurements. ... More

A Phase-Fitted Runge-Kutta-Nyström method for the Numerical Solution of Initial Value Problems with Oscillating SolutionsNov 15 2008A new Runge-Kutta-Nystr\"om method, with phase-lag of order infinity, for the integration of second-order periodic initial-value problems is developed in this paper. The new method is based on the Dormand and Prince Runge-Kutta-Nystr\"om method of algebraic ... More

On the stability of tetrahedral relative equilibria in the positively curved 4-body problemApr 25 2012We consider the motion of point masses given by a natural extension of Newtonian gravitation to spaces of constant positive curvature. Our goal is to explore the spectral stability of tetrahedral orbits of the corresponding 4-body problem in the 2-dimensional ... More

Zero Dispersion and Zero Dissipation Implicit Runge-Kutta Methods for the Numerical Solution of Oscillating IVPsNov 15 2008In this paper we present two new methods based on an implicit Runge-Kutta method Gauss which is of algebraic order fourth and has two stages: the first one has zero dispersion and the second one has zero dispersion and zero dissipation. The efficiency ... More

High Order Phase Fitted Multistep Integrators for the Schrödinger Equation with Improved Frequency ToleranceNov 15 2008In this work we introduce a new family of 14-steps linear multistep methods for the integration of the Schr\"odinger equation. The new methods are phase fitted but they are designed in order to improve the frequency tolerance. This is achieved by eliminating ... More

Synchronization in Complex Systems Following the Decision Based Queuing Process: The Rhythmic Applause as a Test CaseJan 11 2008Living communities can be considered as complex systems, thus a fertile ground for studies related to their statistics and dynamics. In this study we revisit the case of the rhythmic applause by utilizing the model proposed by V\'azquez et al. [A. V\'azquez ... More

The nucleon axial mass and the MiniBooNE CCQE neutrino-nucleus dataOct 06 2011Dec 02 2011We analyze the MiniBooNE CCQE $d\sigma/dT_\mu d\cos\theta_\mu$ data using a theoretical model that has proved to be quite successful in the analysis of nuclear reactions with electron, photon and pion probes. We find that RPA and multinucleon knockout ... More

A-dependence of weak nuclear structure functionsMar 24 2013Effect of nuclear medium on the weak structure functions $F_2^A(x,Q^2)$ and $F_3^A(x,Q^2)$ have been studied using charged current (anti)neutrino deep inelastic scattering on various nuclear targets. Relativistic nuclear spectral function which incorporate ... More

Inclusive Charged--Current Neutrino--Nucleus ReactionsFeb 14 2011We present a model for weak CC induced nuclear reactions at energies of interest for current and future neutrino oscillation experiments. This model is a natural extension of the work of Refs.[1,2], where the QE contribution to the inclusive electron ... More

Two Particle-Hole Excitations in Charged Current Quasielastic Antineutrino--Nucleus ScatteringFeb 04 2013We evaluate the quasielastic and multinucleon contributions to the antineutrino nucleus scattering cross section and compare our results with the recent MiniBooNE data. We use a local Fermi gas model that includes RPA correlations and gets the multinucleon ... More

Superscaling analysis of quasielastic electron scattering with relativistic effective massJan 19 2017We provide a parametrization of a new phenomenological scaling function obtained from a chi-square fit to a selected set of (e,e') cross section data expanding a band centered around the quasielastic peak. We start from a re-analysis of quasielastic electron ... More

Adaptive Metropolis Algorithm Using Variational Bayesian Adaptive Kalman FilterAug 27 2013Oct 01 2014Markov chain Monte Carlo (MCMC) methods are powerful computational tools for analysis of complex statistical problems. However, their computational efficiency is highly dependent on the chosen proposal distribution, which is generally difficult to find. ... More

A Simple Theoretical Prediction of the Data Corresponding to Observationally Estimated Value of Cosmological ConstantDec 24 2007In this work a satisfactory, simple theoretical prediction of the data corresponding to observationally (by fine tuning condition) estimated value of the cosmological constant is given. It is supposed (in conceptually analogy with holographic principle) ... More