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A continuous approximation framework for non-linear stochastic as well as deterministic discrete maps is developed. For the stochastic map with uncorelated Gaussian noise, by successively applying the It\^o lemma, we obtain a Langevin type…

Statistical Mechanics · Physics 2017-10-25 David A. Kessler , Stanislav Burov

Using the language of finite element exterior calculus, we define two families of $H^1$-conforming finite element spaces over pyramids with a parallelogram base. The first family has matching polynomial traces with tensor product elements…

Numerical Analysis · Mathematics 2016-09-13 Andrew Gillette

We introduce and study tame homeomorphisms of surfaces of infinite type. These are maps for which curves under iterations do not accumulate onto geodesic laminations with non-proper leaves, but rather just a union of possibly intersecting…

Geometric Topology · Mathematics 2023-10-19 Mladen Bestvina , Federica Fanoni , Jing Tao

Gradient-flow analyses show that simplified linear transformers can learn the in-context linear-regression algorithm, but they do not explain the finite-step behavior of gradient descent at large learning rates. Motivated by empirical work…

Machine Learning · Statistics 2026-05-21 Krishnakumar Balasubramanian

One dimensional intermittent maps with stretched exponential separation of nearby trajectories are considered. When time goes infinity the standard Lyapunov exponent is zero. We investigate the distribution of $\lambda_{\alpha}=…

Chaotic Dynamics · Physics 2015-05-19 Nickolay Korabel , Eli Barkai

We analyze a class of piecewise linear parabolic maps on the torus, namely those obtained by considering a linear map with double eigenvalue one and taking modulo one in each component. We show that within this two parameter family of maps,…

chao-dyn · Physics 2009-10-31 Peter Ashwin , Xin-Chu Fu , Takashi Nishikawa , Karol Zyczkowski

In this paper, first we introduce a new mapping for finding a common fixed point of an infinite family of nonexpansive mappings then we consider iterative method for finding a common element of the set of fixed points of an infinite family…

Functional Analysis · Mathematics 2015-02-18 Vahid Darvish , S. M. Vaezpour

A map $p:E\to X$ has the \emph{unique path lifting} property if every path in $X$, after a choice of an initial point, lifts uniquely to a path in $E$. We prove that if a group $G$ acts on an $\mathbb R$-tree $T$ such that the quotient map…

Algebraic Topology · Mathematics 2026-05-27 Jeremy Brazas , Gregory R. Conner , Paul Fabel , Curtis Kent

We prove the strong completeness for a class of non-degenerate SDEs, whose coefficients are not necessarily uniformly elliptic nor locally Lipschitz continuous nor bounded. Moreover, for each $t$, the solution flow $F_t$ is weakly…

Probability · Mathematics 2016-05-09 Xin Chen , Xue-Mei Li

We prove that the unique entropy solution to a scalar nonlinear conservation law with strictly monotone velocity and nonnegative initial condition can be rigorously obtained as the large particle limit of a microscopic follow-the-leader…

Analysis of PDEs · Mathematics 2015-06-19 Marco Di Francesco , Massimiliano D. Rosini

We obtain some new bifurcation criteria for solutions of general boundary value problems for nonlinear elliptic systems of partial differential equations. The results are of different nature from the ones that can be obtained via the…

Analysis of PDEs · Mathematics 2012-01-31 Jacobo Pejsachowicz

A linear mapping $T$ on a JB$^*$-triple is called triple derivable at orthogonal pairs if for every $a,b,c\in E$ with $a\perp b$ we have $$0 = \{T(a), b,c\} + \{a,T(b),c\}+\{a,b,T(c)\}.$$ We prove that for each bounded linear mapping $T$ on…

Operator Algebras · Mathematics 2020-09-23 Ahlem Ben Ali Essaleh , Antonio M. Peralta

For a semisimple Lie group $G$ satisfying the equal rank condition, the most basic family of unitary irreducible representations is the discrete series found by Harish-Chandra. In this paper, we study some of the branching laws for discrete…

Representation Theory · Mathematics 2020-12-23 Bent Orsted , Jorge A. Vargas

To any solution of a linear system of differential equations, we associate a kernel, correlators satisfying a set of loop equations, and in presence of isomonodromic parameters, a Tau function. We then study their semiclassical expansion…

Mathematical Physics · Physics 2016-10-12 Michel Bergère , Gaëtan Borot , Bertrand Eynard

We consider inverse problems for $p$-Laplace type equations under monotonicity assumptions. In two dimensions, we show that any two conductivities satisfying $\sigma_1 \geq \sigma_2$ and having the same nonlinear Dirichlet-to-Neumann map…

Analysis of PDEs · Mathematics 2016-03-15 Chang-Yu Guo , Manas Kar , Mikko Salo

We give a Hopf boundary point lemma for weak solutions of linear divergence form uniformly elliptic equations, with H$\ddot{\text{o}}$lder continuous top-order coefficients and lower-order coefficients in a Morrey space.

Analysis of PDEs · Mathematics 2018-06-20 Leobardo Rosales

Dimensionality reduction is an effective method for learning high-dimensional data, which can provide better understanding of decision boundaries in human-readable low-dimensional subspace. Linear methods, such as principal component…

Machine Learning · Computer Science 2020-07-09 Koji Maruhashi , Heewon Park , Rui Yamaguchi , Satoru Miyano

Systems whose organization displays causal asymmetry constraints, from evolutionary trees to river basins or transport networks, can be often described in terms of directed paths (causal flows) on a discrete state space. Such a set of paths…

Disordered Systems and Neural Networks · Physics 2010-07-13 Bernat Corominas-Murtra , Carlos Rodríguez-Caso , Joaquín Goñi , Ricard Solé

We present a method to linearize, without approximation, a specific class of eigenvalue problems with eigenvector nonlinearities (NEPv), where the nonlinearities are expressed by scalar functions that are defined by a quotient of linear…

Numerical Analysis · Mathematics 2021-05-24 Rob Claes , Elias Jarlebring , Karl Meerbergen , Parikshit Upadhyaya

We consider linear models for stochastic dynamics. To any such model can be associated a network (namely a directed graph) describing which degrees of freedom interact under the dynamics. We tackle the problem of learning such a network…

Statistics Theory · Mathematics 2011-03-01 José Bento , Morteza Ibrahimi , Andrea Montanari