We prove an existence and uniqueness result for quasilinear Stochastic PDEs with obstacle (OSPDE in short). Our method is based on analytical technics coming from the parabolic potential theory. The solution is expressed as a pair $(u,\nu)$ where $u$ is a predictable continuous process which takes values in a proper Sobolev space and $\nu$ is a random regular measure satisfying the minimal Skohorod condition.

This paper derives exponential concentration inequalities and polynomial moment inequalities for the spectral norm of a random matrix. The analysis requires a matrix extension of the scalar concentration theory developed by Sourav Chatterjee using Stein’s method of exchangeable pairs. When applied to a sum of independent random matrices, this approach yields matrix generalizations of the classical inequalities due to Hoeffding, Bernstein, Khintchine and Rosenthal. The same technique delivers bounds for sums of dependent random matrices and more general matrix-valued functions of dependent random variables.

The Parisi formula for the free energy in the Sherrington–Kirkpatrick and mixed $p$-spin models for even $p\geq2$ was proved in the seminal work of Michel Talagrand [Ann. of Math. (2) 163 (2006) 221–263]. In this paper we prove the Parisi formula for general mixed $p$-spin models which also include $p$-spin interactions for odd $p$. Most of the ideas used in the paper are well known and can now be combined following a recent proof of the Parisi ultrametricity conjecture in [Ann. of Math. (2) 177 (2013) 383–393].

We consider the boundary case (in the sense of Biggins and Kyprianou [Electron. J. Probab. 10 (2005) 609–631] in a one-dimensional super-critical branching random walk, and study the additive martingale $(W_{n})$. We prove that, upon the system’s survival, $n^{1/2}W_{n}$ converges in probability, but not almost surely, to a positive limit. The limit is identified as a constant multiple of the almost sure limit, discovered by Biggins and Kyprianou [Adv. in Appl. Probab. 36 (2004) 544–581], of the derivative martingale.

A new coupling argument is introduced to establish Driver’s integration by parts formula and shift Harnack inequality. Unlike known coupling methods where two marginal processes with different starting points are constructed to move together as soon as possible, for the new-type coupling the two marginal processes start from the same point but their difference is aimed to reach a fixed quantity at a given time. Besides the integration by parts formula, the new coupling method is also efficient to imply the shift Harnack inequality. Differently from known Harnack inequalities where the values of a reference function at different points are compared, in the shift Harnack inequality the reference function, rather than the initial point, is shifted. A number of applications of the integration by parts and shift Harnack inequality are presented. The general results are illustrated by some concrete models including the stochastic Hamiltonian system where the associated diffusion process can be highly degenerate, delayed SDEs and semi-linear SPDEs.

The structure of Gaussian random fields over high levels is a well researched and well understood area, particularly if the field is smooth. However, the question as to whether or not two or more points which lie in an excursion set belong to the same connected component has constantly eluded analysis. We study this problem from the point of view of large deviations, finding the asymptotic probabilities that two such points are connected by a path laying within the excursion set, and so belong to the same component. In addition, we obtain a characterization and descriptions of the most likely paths, given that one exists.

We consider the standard first passage percolation model in the rescaled graph $\mathbb{Z}^{d}/n$ for $d\geq2$ and a domain $\Omega $ of boundary $\Gamma $ in $\mathbb{R}^{d}$. Let $\Gamma ^{1}$ and $\Gamma ^{2}$ be two disjoint open subsets of $\Gamma $, representing the parts of $\Gamma $ through which some water can enter and escape from $\Omega $. A law of large numbers for the maximal flow from $\Gamma ^{1}$ to $\Gamma ^{2}$ in $\Omega $ is already known. In this paper we investigate the asymptotic behavior of a maximal stream and a minimal cutset. A maximal stream is a vector measure $\vec{\mu}_{n}^{\max}$ that describes how the maximal amount of fluid can cross $\Omega $. Under conditions on the regularity of the domain and on the law of the capacities of the edges, we prove that the sequence $(\vec{\mu}_{n}^{\max})_{n\geq1}$ converges a.s. to the set of the solutions of a continuous deterministic problem of maximal stream in an anisotropic network. A minimal cutset can been seen as the boundary of a set $E_{n}^{\min}$ that separates $\Gamma ^{1}$ from $\Gamma ^{2}$ in $\Omega $ and whose random capacity is minimal. Under the same conditions, we prove that the sequence $(E_{n}^{\min})_{n\geq1}$ converges toward the set of the solutions of a continuous deterministic problem of minimal cutset. We deduce from this a continuous deterministic max-flow min-cut theorem and a new proof of the law of large numbers for the maximal flow. This proof is more natural than the existing one, since it relies on the study of maximal streams and minimal cutsets, which are the pertinent objects to look at.

Let $X,X_{1},X_{2},\ldots$ be i.i.d. mean zero random vectors with values in a separable Banach space $B$, $S_{n}=X_{1}+\cdots+X_{n}$ for $n\ge1$, and assume $\{c_{n}:n\ge1\}$ is a suitably regular sequence of constants. Furthermore, let $S_{(n)}(t)$, $0\le t\le1$ be the corresponding linearly interpolated partial sum processes. We study the cluster sets $A=C(\{S_{n}/c_{n}\})$ and $\mathcal{A}=C(\{S_{(n)}(\cdot)/c_{n}\})$. In particular, $A$ and $\mathcal{A}$ are shown to be nonrandom, and we derive criteria when elements in $B$ and continuous functions $f:[0,1]\to B$ belong to $A$ and $\mathcal{A}$, respectively. When $B=\mathbb{R}^{d}$ we refine our clustering criteria to show both $A$ and $\mathcal{A}$ are compact, symmetric, and star-like, and also obtain both upper and lower bound sets for $\mathcal{A}$. When the coordinates of $X$ in $\mathbb{R}^{d}$ are independent random variables, we are able to represent $\mathcal{A}$ in terms of $A$ and the classical Strassen set $\mathcal{K}$, and, except for degenerate cases, show $\mathcal{A}$ is strictly larger than the lower bound set whenever $d\ge2$. In addition, we show that for any compact, symmetric, star-like subset $A$ of $\mathbb{R}^{d}$, there exists an $X$ such that the corresponding functional cluster set $\mathcal{A}$ is always the lower bound subset. If $d=2$, then additional refinements identify $\mathcal{A}$ as a subset of $\{(x_{1}g_{1},x_{2}g_{2}):(x_{1},x_{2})\in A,g_{1},g_{2}\in\mathcal{K}\}$, which is the functional cluster set obtained when the coordinates are assumed to be independent.

Let $X$ be a matrix sampled uniformly from the set of doubly stochastic matrices of size $n\times n$. We show that the empirical spectral distribution of the normalized matrix $\sqrt{n}(X-{\mathbf{E} }X)$ converges almost surely to the circular law. This confirms a conjecture of Chatterjee, Diaconis and Sly.

In a recent breakthrough work, Chatterjee [Ann. of Math. (2) 177 (2013) 663–697] proved a long standing conjecture that relates the transversal exponent $\xi$ and the fluctuation exponent $\chi$ in first-passage percolation on $\mathbb{Z}^{d}$. The purpose of this paper is to replace the main argument of Chatterjee (2013) and give an alternative proof of this relation. Specifically, we show that under the assumption that exponents defined in Chatterjee (2013) exist, one has the relation $\chi\leq2\xi-1$. One advantage of our argument is that it does not require the “nearly Gamma” assumption of Chatterjee (2013).

We introduce a new disorder regime for directed polymers in dimension $1+1$ that sits between the weak and strong disorder regimes. We call it the intermediate disorder regime. It is accessed by scaling the inverse temperature parameter $\beta$ to zero as the polymer length $n$ tends to infinity. The natural choice of scaling is $\beta_{n}:=\beta n^{-1/4}$. We show that the polymer measure under this scaling has previously unseen behavior. While the fluctuation exponents of the polymer endpoint and the log partition function are identical to those for simple random walk ($\zeta=1/2$, $\chi=0$), the fluctuations themselves are different. These fluctuations are still influenced by the random environment, and there is no self-averaging of the polymer measure. In particular, the random distribution of the polymer endpoint converges in law (under a diffusive scaling of space) to a random absolutely continuous measure on the real line. The randomness of the measure is inherited from a stationary process $A_{\beta}$ that has the recently discovered crossover distributions as its one-point marginals, which for large $\beta$ become the GUE Tracy–Widom distribution. We also prove existence of a limiting law for the four-parameter field of polymer transition probabilities that can be described by the stochastic heat equation.

In particular, in this weak noise limit, we obtain the convergence of the point-to-point free energy fluctuations to the GUE Tracy–Widom distribution. We emphasize that the scaling behaviour obtained is universal and does not depend on the law of the disorder.

We identify stationary distributions of generalized Fleming–Viot processes with jump mechanisms specified by certain beta laws together with a parameter measure. Each of these distributions is obtained from normalized stable random measures after a suitable biased transformation followed by mixing by the law of a Dirichlet random measure with the same parameter measure. The calculations are based primarily on the well-known relationship to measure-valued branching processes with immigration.

We study subcritical two-dimensional oriented percolation seen from its rightmost point on the set of infinite configurations which are bounded above. This a Feller process whose state space is not compact and has no invariant measures. We prove that it converges in distribution to a measure which charges only finite configurations.