arXiv subjects

Mathew Joseph

Publications and source records attributed to Mathew Joseph.

At least 19 recordsLinked to original sources

arXiv2026

The ergodic theory of SPDEs in a weak-noise regime

Consider a parabolic SPDE \[ \partial_t u = \Delta u + \sigma(u)\eta, \] on $(0\,,\infty)\times\mathbb{R}^d$, where $\eta$ is a centered, generalized Gaussian noise with $\text{Cov}[\eta(t\,,x)\,,\eta(s\,,y)]=\delta_0(t-s)\Lambda(x-y)$ for a tempered Borel measure $\Lambda$ that is positive definite and satisfies a mild weak-noise. The existence of invariant measures of versions of these types of SPDEs has been studied at great length, particularly in the ``weak-noise regime''; see for example Assing and Manthey \cite{AssingManthey2003}, Chen and Eisenberg \cite{ChenEisenberg2024}, Chen, Ouyang, Tindel, and Xia \cite{ChenOuyangTindelXia2024}, Eckmann and Hairer \cite{EckmannHairer2001}, Misiats and Stanzhytskyi \cite{MSY2020}, Yu Gu and Jiawei Li \cite{GuLi2020}, and Tessitore and Zabczyk \cite{TessitoreZabczyk1998}. Here, we characterize all annealed, ergodic, invariant measures for the above SPDE in the weak-noise regime.

math.PR↗

arXiv2026

Survival of a long random string among hard Poisson traps

In [AJM26], we gave large-time asymptotic bounds on the annealed survival probability of a moving polymer taking values in ${\mathbb R}^d, d \geq 1$. This polymer is a solution of a stochastic heat equation driven by additive spacetime white noise on $[0,T] \times [0,J]$, in an environment of Poisson traps. For fixed $J$, the annealed survivial probability decays exponentially with rate proportional to $T^{d/(d+2)}$. In this work we examine the large $J$ asymptotics of the annealed survival probability for any fixed time $T>0$. We prove upper and lower bounds for the annealed survival probability in the cases of hard obstacles. Our bounds decay exponentially with rate proportional to $J^{d/(d+2)}$. The exponents also depend on time $T >0$.

math.PR↗

arXiv2025

Blowup for the multiplicative stochastic heat equation with superlinear drift

We consider the stochastic heat equation with multiplicative white noise: $\partial_t u =\partial_x^2u + b(u) +\sigma(u) \dot W$, both on $[0,1]$ and $\mathbf{R}$. In the case of $[0,1]$ we show that the finite Osgood criterion on $b$ is a necessary and sufficient condition for finite-time blowup, under fairly general conditions on $\sigma$. In the case of $\mathbf{R}$ we show instantaneous explosion when we start with initial profile $u_0\equiv 1$, extending the work of [10] which dealt with bounded $\sigma$. The second result follows from the first by a comparison result which shows that the solution on $\mathbf{R}$ stays above the corresponding solution on $[0,1]$ with Dirichlet boundary conditions.

math.PR↗

arXiv2025

LiteCast: A Lightweight Forecaster for Carbon Optimizations

Over recent decades, electricity demand has experienced sustained growth through widespread electrification of transportation and the accelerated expansion of Artificial Intelligence (AI). Grids have managed the resulting surges by scaling generation capacity, incorporating additional resources such as solar and wind, and implementing demand-response mechanisms. Altogether, these policies influence a region's carbon intensity by affecting its energy mix. To mitigate the environmental impacts of consumption, carbon-aware optimizations often rely on long-horizon, high-accuracy forecasts of the grid's carbon intensity that typically use compute intensive models with extensive historical energy mix data. In addition to limiting scalability, accuracy improvements do not necessarily translate into proportional increases in savings. Highlighting the need for more efficient forecasting strategies, we argue that carbon forecasting solutions can achieve the majority of savings without requiring highly precise and complex predictions. Instead, it is the preservation of the ranking of forecasts relative to the ground-truth that drives realized savings. In this paper, we present LiteCast, a lightweight time series forecasting method capable of quickly modeling a region's energy mix to estimate its carbon intensity. LiteCast requires only a few days of historical energy and weather data, delivering fast forecasts that can quickly adapt to sudden changes in the electrical grid. Our evaluation in 50 worldwide regions under various real-world workloads shows that LiteCast outperforms state-of-the-art forecasters, delivering 20% higher savings with near-optimal performance, achieving 97% of the maximum attainable average savings, while remaining lightweight, efficient to run, and adaptive to new data.

cs.DC↗

arXiv2025

Instantaneous blowup for interacting SDEs with superlinear drift

We consider a system of interacting SDEs on the integer lattice with multiplicative noise and a drift satisfying the finite Osgood's condition. We show instantaneous everywhere blowup for initial profiles decaying slower than $\exp \left( -\sqrt{\big|\log |x|\big|}\right)$. We employ the splitting-up method to compare the interacting system to a one-dimensional SDE which blows up.

math.PR↗

arXiv2022

Sausage Volume of the Random String and Survival in a medium of Poisson Traps

We provide asymptotic bounds on the survival probability of a moving polymer in an environment of Poisson traps. Our model for the polymer is the vector-valued solution of a stochastic heat equation driven by additive spacetime white noise; solutions take values in ${\mathbb R}^d, d \geq 1$. We give upper and lower bounds for the survival probability in the cases of hard and soft obstacles. Our bounds decay exponentially with rate proportional to $T^{d/(d+2)}$, the same exponent that occurs in the case of Brownian motion. The exponents also depend on the length $J$ of the polymer, but here our upper and lower bounds involve different powers of $J$. Secondly, our main theorems imply upper and lower bounds for the growth of the Wiener sausage around our string. The Wiener sausage is the union of balls of a given radius centered at points of our random string, with time less than or equal to a given value.

math.PR↗

arXiv2021

Small ball probability estimates for the H\"older semi-norm of the stochastic heat equation

We consider the stochastic heat equation on $[0,\,1]$ with periodic boundary conditions and driven by space-time white noise. Under various natural conditions, we study small ball probabilities for the H\"older semi-norms of the solutions, and provide near optimal bounds on these probabilities. As an application, we prove a support theorem in these H\"older semi-norms.

math.PR↗

arXiv2020

Small ball probabilities and a support theorem for the stochastic heat equation

We consider the following stochastic partial differential equation on $t \geq 0, x\in[0,J], J \geq 1$ where we consider $[0,J]$ to be the circle with end points identified: \begin{equation*} \partial_t{\mathbf u}(t,x) =\frac{1}{2}\,\partial_x^2 {\mathbf u}(t,x) + {\mathbf g}(t,x,\mathbf u) + {\mathbf \sigma}(t,x, {\mathbf u})\dot {\mathbf W}(t,x) , \end{equation*} and $\dot {\mathbf W }(t,x)$ is 2-parameter $d$-dimensional vector valued white noise and ${\mathbf \sigma}$ is function from ${\mathbb R}_+\times {\mathbb R} \times {\mathbb R}^d \rightarrow {\mathbb R}^d$ to space of symmetric $d\times d$ matrices which is Lipschitz in $\mathbf u$. We assume that $\sigma$ is uniformly elliptic and that $\mathbf g$ is uniformly bounded. Assuming that ${\mathbf u}(0,x) \equiv \mathbf 0$, we prove small-ball probabilities for the solution $\mathbf u$. We also prove a support theorem for solutions, when ${\mathbf u}(0,x)$ is not necessarily zero.

math.PR↗

arXiv2019

Edit Based Grading of SQL Queries

Grading student SQL queries manually is a tedious and error-prone process. Earlier work on testing correctness of student SQL queries, such as the XData system, can be used to test correctness of a student query. However, in case a student query is found to be incorrect there is currently no way to automatically assign partial marks. Partial marking is important so that small errors are penalized less than large errors. Manually awarding partial marks is not scalable for classes with large number of students, especially MOOCs, and is also prone to human errors. In this paper, we discuss techniques to find a minimum cost set of edits to a student query that would make it correct, which can help assign partial marks, and to help students understand exactly where they went wrong. Given the limitations of current formal methods for checking equivalence, our approach is based on finding nearest query, from a set of instructor provided correct queries, that is found to be equivalent based on query canonicalization. We show that exhaustive techniques are expensive, and propose a greedy heuristic approach that works well both in terms of runtime and accuracy on queries in real-world datasets. Our system can also be used in a learning mode where query edits can be suggested as feedback to students to guide them towards a correct query. Our partial marking system has been successfully used in courses at IIT Bombay and IIT Dharwad.

cs.DB↗

arXiv2018

Longest increasing path within the critical strip

A Poisson point process of unit intensity is placed in the square $[0,n]^2$. An increasing path is a curve connecting $(0,0)$ with $(n,n)$ which is non-decreasing in each coordinate. Its length is the number of points of the Poisson process which it passes through. Baik, Deift and Johansson proved that the maximal length of an increasing path has expectation $2n-n^{1/3}(c_1+o(1))$, variance $n^{2/3}(c_2+o(1))$ and that it converges to the Tracy-Widom distribution after suitable scaling. Johansson further showed that all maximal paths have a displacement of $n^{\frac23+o(1)}$ from the diagonal with probability tending to one as $n\to \infty$. Here we prove that the maximal length of an increasing path restricted to lie within a strip of width $n^{\gamma}, \gamma<\frac23$, around the diagonal has expectation $2n-n^{1-\gamma+o(1)}$, variance $n^{1 - \frac{\gamma}{2}+o(1)}$ and that it converges to the Gaussian distribution after suitable scaling.

math.PR↗

arXiv2017

An invariance principle for the stochastic heat equation

We approximate the white-noise driven stochastic heat equation by replacing the fractional Laplacian by the generator of a discrete time random walk on the one dimensional lattice, and approximating white noise by a collection of i.i.d. mean zero random variables. As a consequence, we give an alternative proof of the weak convergence of the scaled partition function of directed polymers in the intermediate disorder regime, to the stochastic heat equation; an advantage of the proof is that it gives the convergence of all moments.

math.PR↗

arXiv2016

An approximation result for a class of stochastic heat equations with colored noise

We show that a large class of stochastic heat equations can be approximated by systems of interacting stochastic differential equations. As a consequence, we prove various comparison principles extending earlier results. Among other things, our results enable us to obtain sharp estimates on the moments of the solution. A main technical ingredient of our method is a local limit theorem which is of independent interest.

math.PR↗

arXiv2015

Query Answering over Contextualized RDF/OWL Knowledge with Forall-Existential Bridge Rules: Decidable Finite Extension Classes (Post Print)

The proliferation of contextualized knowledge in the Semantic Web (SW) has led to the popularity of knowledge formats such as \emph{quads} in the SW community. A quad is an extension of an RDF triple with contextual information of the triple. In this paper, we study the problem of query answering over quads augmented with forall-existential bridge rules that enable interoperability of reasoning between triples in various contexts. We call a set of quads together with such expressive bridge rules, a quad-system. Query answering over quad-systems is undecidable, in general. We derive decidable classes of quad-systems, for which query answering can be done using forward chaining. Sound, complete and terminating procedures, which are adaptations of the well known chase algorithm, are provided for these classes for deciding query entailment. Safe, msafe, and csafe class of quad-systems restrict the structure of blank nodes generated during the chase computation process to be directed acyclic graphs (DAGs) of bounded depth. RR and restricted RR classes do not allow the generation of blank nodes during the chase computation process. Both data and combined complexity of query entailment has been established for the classes derived. We further show that quad-systems are equivalent to forall-existential rules whose predicates are restricted to ternary arity, modulo polynomial time translations. We subsequently show that the technique of safety, strictly subsumes in expressivity, some of the well known and expressive techniques, such as joint acyclicity and model faithful acyclicity, used for decidability guarantees in the realm of forall-existential rules.

cs.DB↗

arXiv2014

Query Answering over Contextualized RDF/OWL Knowledge with Forall-Existential Bridge Rules: Attaining Decidability using Acyclicity (full version)

The recent outburst of context-dependent knowledge on the Semantic Web (SW) has led to the realization of the importance of the quads in the SW community. Quads, which extend a standard RDF triple, by adding a new parameter of the `context' of an RDF triple, thus informs a reasoner to distinguish between the knowledge in various contexts. Although this distinction separates the triples in an RDF graph into various contexts, and allows the reasoning to be decoupled across various contexts, bridge rules need to be provided for inter-operating the knowledge across these contexts. We call a set of quads together with the bridge rules, a quad-system. In this paper, we discuss the problem of query answering over quad-systems with expressive forall-existential bridge rules. It turns out the query answering over quad-systems is undecidable, in general. We derive a decidable class of quad-systems, namely context-acyclic quad-systems, for which query answering can be done using forward chaining. Tight bounds for data and combined complexity of query entailment has been established for the derived class.

cs.LO↗

arXiv2014

Strong invariance and noise-comparison principles for some parabolic stochastic PDEs

We consider a system of interacting diffusions on the integer lattice. By letting the mesh size go to zero and by using a suitable scaling, we show that the system converges (in a strong sense) to a solution of the stochastic heat equation on the real line. As a consequence, we obtain comparison inequalities for product moments of the stochastic heat equation with different nonlinearities.

math.PR↗

arXiv2014

Remarks on non-linear noise excitability of some stochastic heat equations

We consider nonlinear parabolic SPDEs of the form $\partial_t u=Δu + λσ(u)\dot w$ on the interval $(0, L)$, where $\dot w$ denotes space-time white noise, $σ$ is Lipschitz continuous. Under Dirichlet boundary conditions and a linear growth condition on $σ$, we show that the expected $L^2$-energy is of order $\exp[\text{const}\timesλ^4]$ as $λ\rightarrow \infty$. This significantly improves a recent result of Khoshnevisan and Kim. Our method is very different from theirs and it allows us to arrive at the same conclusion for the same equation but with Neumann boundary condition. This improves over another result of Khoshnevisan and Kim.

math.PR↗

arXiv2013

Semi-discrete semi-linear parabolic SPDEs

Consider an infinite system \[\partial_tu_t(x)=(\mathscr{L}u_t)(x)+ \sigma\bigl(u_t(x)\bigr)\partial_tB_t(x)\] of interacting It\^{o} diffusions, started at a nonnegative deterministic bounded initial profile. We study local and global features of the solution under standard regularity assumptions on the nonlinearity $\sigma$. We will show that, locally in time, the solution behaves as a collection of independent diffusions. We prove also that the $k$th moment Lyapunov exponent is frequently of sharp order $k^2$, in contrast to the continuous-space stochastic heat equation whose $k$th moment Lyapunov exponent can be of sharp order $k^3$. When the underlying walk is transient and the noise level is sufficiently low, we prove also that the solution is a.s. uniformly dissipative provided that the initial profile is in $\ell^1(\mathbf {Z}^d)$.

math.PR↗

arXiv2013

On the chaotic character of the stochastic heat equation, before the onset of intermitttency

We consider a nonlinear stochastic heat equation $\partial_tu=\frac{1}{2}\partial_{xx}u+σ(u)\partial_{xt}W$, where $\partial_{xt}W$ denotes space-time white noise and $σ:\mathbf {R}\to \mathbf {R}$ is Lipschitz continuous. We establish that, at every fixed time $t>0$, the global behavior of the solution depends in a critical manner on the structure of the initial function $u_0$: under suitable conditions on $u_0$ and $σ$, $\sup_{x\in \mathbf {R}}u_t(x)$ is a.s. finite when $u_0$ has compact support, whereas with probability one, $\limsup_{|x|\to\infty}u_t(x)/({\log}|x|)^{1/6}>0$ when $u_0$ is bounded uniformly away from zero. This sensitivity to the initial data of the stochastic heat equation is a way to state that the solution to the stochastic heat equation is chaotic at fixed times, well before the onset of intermittency.

math.PR↗