A smooth solution $\{ \Gamma(t)\}_{t \in[0,T]}\subset \R^d $ of a parabolic geometric flow is characterized as the reachability set of a stochastic target problem. In this control problem the controller tries to steer the state process into a given deterministic set $\Tc$ with probability one. The reachability set, $V(t)$, for the target problem is the set of all initial data x from which the state process $\xx(t) \in \Tc$ for some control process $\nu$. This representation is proved by studying the squared distance function to $\Gamma(t)$. For the codimension k mean curvature flow, the state process is $dX(t)= \sqrt{2} P \,dW(t)$, where $W(t)$ is a d-dimensional Brownian motion, and the control P is any projection matrix onto a $(d-k)$-dimensional plane. Smooth solutions of the inverse mean curvature flow and a discussion of non smooth solutions are also given.

Three different results are established which turn out to be closely connected so that the first one implies the second one which in turn implies the third one. The first one states the smoothness of an invariant diffusion density with respect to a parameter. The second establishes a similar smoothness of the solution of the Poisson equation in $\mathbb{R}^d$. The third one states a diffusion approximation result, or in other words an averaging of singularly perturbed diffusion for "fully coupled SDE systems'' or "SDE systems with complete dependence.''

In this paper we consider drift and entropy growth for symmetric finitary random walks on finitely generated groups. We construct examples of various intermediate asymptotics of the drift for such random walks. We establish general inequalities which connect drift, entropy and exponential growth rate of groups. Then we apply these inequalities to get estimates for entropy in particular examples.

We prove in a direct fashion that a multidimensional probability measure $\mu$ is determinate if the higher-dimensional analogue of Carleman's condition is satisfied. In that case, the polynomials, as well as certain proper subspaces of the trigonometric functions, are dense in all associated $L_p$-spaces for $1\leq p<\infty$. In particular these three statements hold if the reciprocal of a quasi-analytic weight has finite integral under $\mu$. We give practical examples of such weights, based on their classification.

As in the one-dimensional case, the results on determinacy of measures supported on $\Rn$ lead to sufficient conditions for determinacy of measures supported in a positive convex cone, that is, the higher-dimensional analogue of determinacy in the sense of Stieltjes.

Let $X, X_1, X_2,\ldots$ be a sequence of nondegenerate i.i.d. random variables with zero means. In this paper we show that a self-normalized version of Donsker's theorem holds only under the assumption that X belongs to the domain of attraction of the normal law. A thus resulting extension of the arc sine law is also discussed. We also establish that a weak invariance principle holds true for self-normalized, self-randomized partial sums processes of independent random variables that are assumed to be symmetric around mean zero, if and only if $\max_{1\le j\le n}|X_j|/V_n\to_P 0$, as $n\to\infty$, where $V_n^2=\sum_{j=1}^{n}X_j^2$.

We study the following problem for critical site percolation on the triangular lattice. Let A and B be sites on a horizontal line e separated by distance n. Consider, in the half-plane above e, the lowest occupied crossing $R_n$ from the half-line left of A to the half-line right of B. We show that the probability that $R_n$ has a site at distance smaller than m from $\mathit{AB}$ is of order $(\log (n/m))^{-1}$, uniformly in $1 \leq m \leq n/2$. Much of our analysis can be carried out for other two-dimensional lattices as well.

This work is involved with the short-time asymptotics of diffusion semigroups in a general setting. A generalization of Fang's version of Varadhan's formula is proven for general Dirichlet spaces that are local and conservative. The intrinsic metric appearing in the formula is characterized by pointwise distance for canonical Dirichlet spaces on loop groups.

Let G be a Gaussian vector taking its values in a separable Fréchet space E. We denote by $\gamma$ its law and by $(H,\Vert\!\cdot\!\Vert)$ its reproducing Hilbert space. Moreover, let X be an E-valued random vector of law $\mu$. In the first section, we prove that if $\mu$ is absolutely continuous relative to $\gamma$, then there exist necessarily a Gaussian vector $G'$ of law $\gamma$ and an H-valued random vector Z such that $G' + Z$ has the law $\mu$ of X. This fact is a direct consequence of concentration properties of Gaussian vectors and, in some sense, it is an unexpected achievement of a part of the Cameron--Martin theorem.

In the second section, using the classical Cameron--Martin theorem and rotation invariance properties of Gaussian probabilities, we show that, in many situations, such a condition is sufficient for $\mu$ being absolutely continuous relative to $\gamma$.

We discuss angular convergence of Riemannian Brownian motion on a Cartan--Hadamard manifold and show that the Dirichlet problem at infinity for such a manifold is uniquely solvable under the curvature conditions $-Ce^{(2-\eta) ar(x)}\le K_M(x)\le-a^2$\vspace*{0.5pt} ($\eta>0$) and $-Cr(x)^{2\beta} \le K_M(x)\le - \alpha (\alpha-1)/r(x)^2$ ($\alpha>\beta+2>2$), respectively.

We will introduce a class of m-times integrated Brownian motions. The exact asymptotic expansions for the $L_2$-small ball probabilities will be discussed for members of this class, of which the usual m-times integrated Brownian motion is an example. Another example will be what we call an Euler-integrated Brownian motion. We will also find very sharp estimates for the asymptotics of the eigenvalues of the covariance operator of integrated Brownian motions and will, therefore, obtain exact, not just logarithmic, asymptotics.

Let $\Phi = \{\phi(x)\dvtx x\in \mathbb{R}^2\}$ be a Gaussian random field on the plane. For $A \subset \R^2$, we investigate the relationship between the $\sigma$-field ${\mathcal F}(\Phi, A) = \sigma \{ \phi(x)\dvtx x \in A \} $ and the infinitesimal or germ $\sigma$-field $\,\bigcap_{\varepsilon >0} {\mathcal F} (\Phi, A_{\varepsilon }),$ where $A_{\varepsilon}$ is an $\varepsilon$-neighborhood of A. General analytic conditions are developed giving necessary and sufficient conditions for the equality of these two $\sigma$-fields. These conditions are potential theoretic in nature and are formulated in terms of the reproducing kernel Hilbert space associated with $\Phi $. The Bessel fields $\Phi_{\beta}$\vspace*{-1pt} satisfying the pseudo-partial differential equation $(I-\Delta)^{\beta/2}\phi(x)=\dot W(x)$, $\beta>1$, for which the reproducing kernel Hilbert spaces are identified as spaces of Bessel potentials ${\mathcal L}^{\beta, 2}$, are studied in detail and the conditions for equality are conditions for spectral synthesis in ${\mathcal L}^{\beta,2}$. The case $\beta = 2$ is of special interest, and we deduce sharp conditions for the sharp Markov property to hold here, complementing the work of Dalang and Walsh on the Brownian sheet.

We study the global properties of transition semigroups $(p_t^{\nu , \Psi , A})$ of $(A, \Psi )$-superprocesses over compact type spaces with possibly nonzero immigration $\nu$ in various function spaces. In particular, we compare the different rates of convergence of $(p_t^{\nu ,\Psi ,A})$ to equilibrium. Our analysis is based on an explicit formula for the Gateaux derivative of $p_t^{\nu ,\Psi , A} F$.

This paper establishes the continuity of the density of $(1+\beta)$-stable super-Brownian motion $(0<\beta<1)$ for fixed times in $d=1$, and local unboundedness of the density in all higher dimensions where it exists. We also prove local unboundedness of the density in time for a fixed spatial parameter in any dimension where the density exists, and local unboundedness of the occupation density (the local time) in the spatial parameter for dimensions $d\geq2$ where the local time exists.

The point of view of the particle is an approach that has proven very powerful in the study of many models of random motions in random media. We provide a new use of this approach to prove the law of large numbers in the case of one or higher-dimensional, finite range, transient random walks in mixing random environments. One of the advantages of this method over what has been used so far is that it is not restricted to i.i.d. environments.

We study the regularity of the sample paths of certain Brownian motions on the infinite dimensional torus ${\mathbb T}^\infty$ and other compact connected groups in terms of the associated intrinsic distance. For each $\lambda\in (0,1)$, we give examples where the intrinsic distance $d$ is continuous and defines the topology of ${\mathbb T}^\infty$ and where the sample paths satisfy \[ 0<\liminf_{t\ra 0} \frac{d(X_0,X_t)}{t^{(1-\lambda)/2}}\le \limsup_{t\ra 0} \frac{d(X_0,X_t)}{t^{(1-\lambda)/2}}<\infty \] and \[ 0<\lim_{\varepsilon\to 0} \sup_{0<t<s<1 \atop t-s\le \varepsilon}\frac{d(X_s,X_t)}{(t-s )^{(1-\lambda)/2}} <\infty. \]

We investigate a scaling limit of gradient stochastic dynamics associated with Gibbs states in classical continuous systems on ${\mathbb R}^d$, $d \ge 1$. The aim is to derive macroscopic quantities from a given microscopic or mesoscopic system. The scaling we consider has been investigated by Brox (in 1980), Rost (in 1981), Spohn (in 1986) and Guo and Papanicolaou (in 1985), under the assumption that the underlying potential is in $C^3_0$ and positive. We prove that the Dirichlet forms of the scaled stochastic dynamics converge on a core of functions to the Dirichlet form of a generalized Ornstein--Uhlenbeck process. The proof is based on the analysis and geometry on the configuration space which was developed by Albeverio, Kondratiev and Röckner (in 1998), and works for general Gibbs measures of Ruelle type. Hence, the underlying potential may have a singularity at the origin, only has to be bounded from below and may not be compactly supported. Therefore, singular interactions of physical interest are covered, as, for example, the one given by the Lennard--Jones potential, which is studied in the theory of fluids. Furthermore, using the Lyons--Zheng decomposition we give a simple proof for the tightness of the scaled processes. We also prove that the corresponding generators, however, do not converge in the $L^2$-sense. This settles a conjecture formulated by Brox, by Rost and by Spohn.

We construct and study a family of probability measures on the configuration space over countable discrete space associated with nonnegative definite symmetric operators via determinants. Under a mild condition they turn out unique Gibbs measures. Also some ergodic properties, including the entropy positivity, are discussed in the lattice case.

Given a bounded class of functions, we introduce a combinatorial quantity (related to the idea of Vapnik--Chervonenkis classes) that is much more explicit than the Koltchinskii--Pollard entropy, but is proved to be essentially of the same order.

We investigate a new methodology, worked out by Ledoux and Massart, to prove concentration-of-measure inequalities. The method is based on certain modified logarithmic Sobolev inequalities. We provide some very simple and general ready-to-use inequalities. One of these inequalities may be considered as an exponential version of the Efron--Stein inequality. The main purpose of this paper is to point out the simplicity and the generality of the approach. We show how the new method can recover many of Talagrand's revolutionary inequalities and provide new applications in a variety of problems including Rademacher averages, Rademacher chaos, the number of certain small subgraphs in a random graph, and the minimum of the empirical risk in some statistical estimation problems.

Using martingale techniques and comparison with the generalized Urn scheme, it is shown that the edge reinforced random walk on a graph of bounded degree, with the weight function $W(k) = k^\rho,\,\rho > 1 $, traverses (crosses) a random attracting edge at all large times. If the graph is a triangle, the above result is in agreement with a conjecture of Sellke.

In this paper, we show a strong relation between the depth first processes associated to Galton--Watson trees with finite variance, conditioned by the total progeny: the depth first walk, the depth first queue process, the height process; a consequence is that these processes (suitably normalized) converge to the same Brownian excursion. This provides an alternative proof of Aldous' one of the convergence of the depth first walk to the Brownian excursion which does not use the existence of a limit tree. The methods that we introduce allow one to compute some functionals of trees or discrete excursions; for example, we compute the limit law of the process of the height of nodes with a given out-degree, and the process of the height of nodes, root of a given subtree.

This paper considers some measure-valued processes $\{X_t\dvtx t \in [0,T]\}$ based on an underlying critical branching particle structure with random branching rates. In the case of constant branching these processes are Dawson--Watanabe processes. Sufficient conditions on functionals $\Phi$ of the process are given that imply that the expectations $E(\Phi(X_T))$ are comparable to the constant branching case. Applications to hitting estimates and regularity of solutions are discussed. The result is established via the martingale optimality principle of stochastic control theory. Key steps, which are of independent interest, are the proof of a version of Itô's lemma for $\Phi(X_t)$, suitable for a large class of functions of measures (Theorem 3) and the proof of various smoothing properties of the Dawson--Watanabe transition semigroup (Section 3).