Stochastic Concurrent Constraint Programming and Differential Equations

We tackle the problem of relating models of systems (mainly biological systems) based on stochastic process algebras (SPA) with models based on differential equations. We define a syntactic procedure that translates programs written in stochastic Concurrent Constraint Programming (sCCP) into a set of Ordinary Differential Equations (ODE), and also the inverse procedure translating ODE's into sCCP programs. For the class of biochemical reactions, we show that the translation is correct w.r.t. the intended rate semantics of the models. Finally, we show that the translation does not generally preserve the dynamical behavior, giving a list of open research problems in this direction.     

Surface Reconstruction Using Alpha Shapes

We describe a method for reconstructing an unknown surface from a set of data points. The basic approach is to extract the surface as a polygon mesh from an α-shape. Even though alpha shapes are generalized polytopes having complicated internal structures, we show that manifold surfaces, with or without boundaries, can be efficiently generated, and these surfaces completely describe the α-shapes to the extent that they are visible from outside. Unlike the original α-shapes, the polygonal surfaces can be easily simplified to yield compact models suitable for a variety of geometric modeling applications such as surface fitting.     

An Estimation Problem for Quasilinear Stochastic Partial Differential Equations

A problem of estimating a functional parameter (x) and functionals () based on observation of a solution u (t, x) of the stochastic partial differential equation is considered. The asymptotic problem setting, as the noise intensity 0, is investigated.     

Streaming Surface Reconstruction Using Wavelets

We present a streaming method for reconstructing surfaces from large data sets generated by a laser range scanner using wavelets. Wavelets provide a localized, multiresolution representation of functions and this makes them ideal candidates for streaming surface reconstruction algorithms. We show how wavelets can be used to reconstruct the indicator function of a shape from a cloud of points with associated normals. Our method proceeds in several steps. We first compute a low‐resolution approximation of the indicator function using an octree followed by a second pass that incrementally adds fine resolution details. The indicator function is then smoothed using a modified octree convolution step and contoured to produce the final surface. Due to the local, multiresolution nature of wavelets, our approach results in an algorithm over 10 times faster than previous methods and can process extremely large data sets in the order of several hundred million points in only an hour.     

Razumikhin-Type Theorems on Stability of Neutral Stochastic Functional Differential Equations

Recently, Mao initiated the study of exponential stability of neutral stochastic functional differential equations (NSFDEs) and employed the Razumikhin techniques to investigated the stability problem of NSFDEs. However, the importance of general asymptotic stability has not been considered. This paper studies Razumikhin-type theorems on general $p$th moment asymptotic stability of NSFDEs. The theorems established in this paper work for some cases to which the existing results cannot be applied.      

Differential Equations with Small Stochastic Additions Under Poisson Approximation Conditions

The methods proposed in the paper allow us to investigate the model of stochastic evolution, which includes Markov switchings, and to identify big jumps of disturbing process in the limiting equation. Big jumps of this type may describe rare catastrophic events in different applied problems. We consider the case where system disturbance is defined by impulse process in nonclassical approximation scheme. Particular attention is paid to the asymptotic behavior of the generator of the evolutionary system under examination.     

Building Blocks for Computer Vision with Stochastic Partial Differential Equations

We discuss the basic concepts of computer vision with stochastic partial differential equations (SPDEs). In typical approaches based on partial differential equations (PDEs), the end result in the best case is usually one value per pixel, the “expected” value. Error estimates or even full probability density functions PDFs are usually not available. This paper provides a framework allowing one to derive such PDFs, rendering computer vision approaches into measurements fulfilling scientific standards due to full error propagation. We identify the image data with random fields in order to model images and image sequences which carry uncertainty in their gray values, e.g. due to noise in the acquisition process. The noisy behaviors of gray values is modeled as stochastic processes which are approximated with the method of generalized polynomial chaos (Wiener-Askey-Chaos). The Wiener-Askey polynomial chaos is combined with a standard spatial approximation based upon piecewise multi-linear finite elements. We present the basic building blocks needed for computer vision and image processing in this stochastic setting, i.e. we discuss the computation of stochastic moments, projections, gradient magnitudes, edge indicators, structure tensors, etc. Finally we show applications of our framework to derive stochastic analogs of well known PDEs for de-noising and optical flow extraction. These models are discretized with the stochastic Galerkin method. Our selection of SPDE models allows us to draw connections to the classical deterministic models as well as to stochastic image processing not based on PDEs. Several examples guide the reader through the presentation and show the usefulness of the framework.     

基于偏微分方程的隐式曲面光顺方法

提出隐式曲面的光顺问题．针对该问题,给出刻画隐式曲面光顺程度的能量模型,并将能量解释为关于隐函数的泛函．基于变分原理,构造出隐函数关于时间的偏微分方程。通过求解该方程得到隐函数序列,使得光顺能量逐渐变小,从而达到光顺隐式曲面的目的．另外．针对光顺问题提出的其它约束条件,如尽可能保持面积不变,保持原有的形状特征等,对模型进行修正．最后,给出方程的实用解法及实验结果。并作简单讨论．实验结果表明该方法通用、灵活、有效,而且程序易于实现．     

3D Surface Reconstruction Using Occluding Contours

This paper addresses the problem of 3D surface reconstruction using image sequences. It has been shown that shape recovery from three or more occluding contours of the surface is possible given a known camera motion. Several algorithms, which have been recently proposed, allow such a reconstruction under the assumption of a linear camera motion. A new approach is presented which deals with the reconstruction problem directly from a discrete point of view. First, a theoretical study of the epipolar correspondence between occluding contours is achieved. A correct depth formulation is then derived from a local approximation of the surface up to order two. This allows the local shape to be estimated, given three consecutive contours, without any constraints on the camera motion. Experimental results are presented for both synthetic and real data.     

Using Chebyshev Polynomials to Approximate Partial Differential Equations

This paper suggests a simple method based on a Chebyshev approximation at Chebyshev nodes to approximate partial differential equations (PDEs). It consists in determining the value function by using a set of nodes and basis functions. We provide two examples: pricing a European option and determining the best policy for shutting down a machine. The suggested method is flexible, easy to programme and efficient. It is also applicable in other fields, providing efficient solutions to complex systems of PDEs.     

pTH Moment Exponential Stability of Stochastic Partial Differential Equations with Poisson Jumps

In this paper, we study a class of stochastic partial differential equations with Poisson jumps, which is more realistic for establishing mathematical models since it has been widely applied in many fields. Under a reasonable condition, we not only establish the existence and uniqueness of the mild solution for the investigated system but also prove that it is pth moment exponentially stable by using the fixed point theory. Then, based on the well‐known Borel‐Cantelli lemma, further, we prove that the mild solution is almost surely pth moment exponentially stable. Our results improve and generalize those given in the previous literature, in particular, the Lyapunov direct method and successive approximation method. Finally, we give an example to illustrate the effectiveness of the obtained results.     

G~1连续几何偏微分方程B样条曲面的构造

为了在曲面拼接和自由形式曲面设计中生成G1光滑的曲面,提出一种使用四阶几何偏微分方程构造B样条曲面的方法.该方法基于切梯度算子、第二切算子、Laplace-Beltrami算子和Giaquinta-Hildebrandt算子在四边形网格上的离散化及收敛性分析,在G1边界光滑约束条件下使用一般形式的四阶几何偏微分方程构造四边B样条曲面片.数值实验结果表明该方法是有效的,确能产生满足G1光滑边界条件的曲面.     

散乱数据点的细分曲面重建算法及实现

提出一种对海量散乱数据根据给定精度拟合出无需裁剪和拼接的、反映细节特征的、分片光滑的细分曲面算法．该算法的核心是基于细分的局部特性,通过对有特征的细分控制网格极限位置分析,按照拟合曲面与数据点的距离误差最小原则,对细分曲面控制网格循环进行调整、优化、特征识别、白适应细分等过程,使得细分曲面不断地逼近原始数据．实例表明：该算法不仅具有高效性、稳定性,同时构造出的细分曲面还较好地反映了原始数据的细节特征。     

Semi-linear Stochastic Difference Equations

We consider in this paper a class of vector valued processes that have the form Y _n + 1 = A _n ( Y _n ) + B _n . B _n is assumed to be stationary ergodic and A _n is assumed to have a divisibility property. This class includes linear stochastic difference equations as well as multi-type branching processes (with a discrete or with a continuous state space). We derive explicit expressions for the probability distribution as well as for the two first moments of state vectors at the stationary regime. We then apply this approach to derive two formalisms to describe the infinite server queue. The first is based on a branching process approach adapted to phase type service time distributions. The second is based on a linear stochastic difference equation and is adapted to independent and generally distributed service times with bounded support. In both cases we allow for generally distributed arrival process (not necessarily i.i.d. nor Markovian).

Discontinuity-Preserving Vector Smoothing of Multivariate MR Images Using Vector Mean Field Annealing

We consider PD-, T1-, and T2-weighted magnetic resonance images jointly as a vector-valued image and use the angle of this vector field to formulate maximum a posteriori restoration as a global optimization problem. We use Mean Field Annealing (MFA) to find restorations that are superior to those obtained by previous multivariate approaches when shading artifacts near the MRI antenna are significant. Local homogeneity of the vector field as well as the angle between the components or the ratio of the components of the field are shown to have potential use for improving segmentation.     

Smooth Surface Reconstruction Using Tensor Fields as Structuring Elements

We propose a new strategy to estimate surface normal information from highly noisy sparse data. Our approach is based on a tensor field morphologically adapted to infer normals. It acts as a three‐dimensional structuring element of smooth surfaces. Robust orientation inference for all input elements is performed by morphological operations using the tensor field. A general normal estimator is defined by combining the inferred normals, their confidences and the tensor field. This estimator can be used to directly reconstruct the surface or give input normals to other reconstruction methods. We present qualitative and quantitative results to show the behavior of the original methods and ours. A comparative discussion of these results shows the efficiency of our propositions.     

A Surface Reconstruction Method Using Global Graph Cut Optimization

Surface reconstruction from multiple calibrated images has been mainly approached using local methods, either as a continuous optimization problem driven by level sets, or by discrete volumetric methods such as space carving. We propose a direct surface reconstruction approach which starts from a continuous geometric functional that is minimized up to a discretization by a global graph-cut algorithm operating on a 3D embedded graph. The method is related to the stereo disparity computation based on graph-cut formulation, but fundamentally different in two aspects. First, existing stereo disparity methods are only interested in obtaining layers of constant disparity, while we focus on high resolution surface geometry. Second, most of the existing graph-cut algorithms only reach approximate solutions, while we guarantee a global minimum. The whole procedure is consistently incorporated into a voxel representation that handles both occlusions and discontinuities. We demonstrate our algorithm on real sequences, yielding remarkably detailed surface geometry up to 1/10th of a pixel. Author has worked on this project during his Ph. D. at ARTIS     

Mean Square Stability of the Composite Milstein Method for Nonlinear Stochastic Differential Delay Equations

In this paper, we construct a composite Milstein method for nonlinear stochastic differential delay equations. Then we analyze the mean square stability for this method and obtain the step size condition under which the composite Milstein method is mean square stable. Moreover, we get the step size condition under which the composite Milstein method is global mean square stable. A nonlinear test stochastic differential delay equation is given for numerical tests. The results of numerical tests verify the theoretical results proposed.     

几何偏微分方程和离散曲面设计

使用若干个几何本质的曲率驱动的偏微分方程来构造符合指定C0或C1边界条件的三边曲面片和四边曲面片,这些方程的数值解由所涉及的微分几何算子的离散化来得到,微分几何算子的离散化则源于参数逼近.所构造的曲面片满足某些特定的几何偏微分方程,故具有理想的形状,将这些曲面片组装起来便构造出复杂的几何模型.通过反复的子分和演化,得到几何模型的多尺度表示.