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Related papers: Stochastic higher spin six vertex model and q-TASE…

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We introduce difference operators on the space of symmetric functions which are a natural generalization of the $(q,t)$-Macdonald operators. In the $t\to\infty$ limit, they satisfy the $A_{N-1}$ quantum $Q$-system. We identify the elements…

Mathematical Physics · Physics 2017-07-07 Philippe Di Francesco , Rinat Kedem

In this work, we conduct a systematic study of stochastic saddle point problems (SSP) and stochastic variational inequalities (SVI) under the constraint of $(\epsilon,\delta)$-differential privacy (DP) in both Euclidean and non-Euclidean…

Machine Learning · Computer Science 2024-11-11 Raef Bassily , Cristóbal Guzmán , Michael Menart

We study the probability distribution of entanglement in the Quantum Symmetric Simple Exclusion Process, a model of fermions hopping with random Brownian amplitudes between neighboring sites. We consider a protocol where the system is…

Statistical Mechanics · Physics 2021-07-30 Denis Bernard , Lorenzo Piroli

We present the Bethe ansatz solution for the discrete time zero range and asymmetric exclusion processes with fully parallel dynamics. The model depends on two parameters: $p$, the probability of single particle hopping, and $q$, the…

Statistical Mechanics · Physics 2007-05-23 A. M. Povolotsky , J. F. F. Mendes

We study online statistical inference for the solutions of stochastic optimization problems with equality and inequality constraints. Such problems are prevalent in statistics and machine learning, encompassing constrained $M$-estimation,…

Machine Learning · Statistics 2025-12-11 Yihang Gao , Michael K. Ng , Michael W. Mahoney , Sen Na

We show that the multi-species higher spin stochastic vertex model, also called the U_q(A_n^{(1)}) vertex model, satisfies a duality where the indicator function has the form {\eta^x_{[1,n]} \geq \xi^x_{[1,n]} }. In other words, for every…

Probability · Mathematics 2019-11-05 Jeffrey Kuan

We study homogeneous nucleation in the two-dimensional $q-$state Potts model for $q=3,5,10,20$ and ferromagnetic couplings $J_{ij} \propto \Theta (R - |i-j|)$, by means of Monte Carlo simulations employing heat bath dynamics. Metastability…

Statistical Mechanics · Physics 2021-07-21 Giuseppe Gagliardi , Francesco Macheda

We present an application of the theory of stochastic processes to model and categorize non-equilibrium physical phenomena. The concepts of uniformly continuous probability measures and modular evolution lead to a systematic hierarchical…

Mathematical Physics · Physics 2009-08-18 Enrique Hernandez-Lemus , Jesus K. Estrada-Gil

Using the Boltzmann weights of classical Statistical Mechanics vertex models we define a new class of Tensor Product Ansatzs for 2D quantum lattice systems, characterized by a strong anisotropy, which gives rise to stripe like structures.…

Strongly Correlated Electrons · Physics 2009-11-07 M. A. Martin-Delgado , M. Roncaglia , G. Sierra

Top-antitop pairs produced at hadron colliders are largely unpolarized, but their spins are highly correlated. The structure of these correlations varies significantly over top production phase space, allowing very detailed tests of the…

High Energy Physics - Phenomenology · Physics 2015-06-12 Matthew Baumgart , Brock Tweedie

Trajectory optimization and posture generation are hard problems in robot locomotion, which can be non-convex and have multiple local optima. Progress on these problems is further hindered by a lack of open benchmarks, since comparisons of…

Robotics · Computer Science 2017-10-10 Martim Brandao , Kenji Hashimoto , Atsuo Takanishi

We show that unconverged stochastic gradient descent can be interpreted as a procedure that samples from a nonparametric variational approximate posterior distribution. This distribution is implicitly defined as the transformation of an…

Machine Learning · Statistics 2015-04-07 Dougal Maclaurin , David Duvenaud , Ryan P. Adams

A recent paper \cite{KMMO} introduced the stochastic U_q(A_n^{(1)}) vertex model. The stochastic S-matrix is related to the R-matrix of the quantum group U_q(A_n^{(1)}) by a gauge transformation. We will show that a certain function…

Probability · Mathematics 2018-04-06 Jeffrey Kuan

We investigate a stochastic approach to non-equilibrium quantum spin systems based on recent insights linking quantum and classical dynamics. Exploiting a sequence of exact transformations, quantum expectation values can be recast as…

Statistical Mechanics · Physics 2019-01-31 S. De Nicola , B. Doyon , M. J. Bhaseen

The totally asymmetric simple exclusion process (TASEP) on the one-dimensional lattice with the Bernoulli \rho measure as initial conditions, 0<\rho<1, is stationary in space and time. Let N_t(j) be the number of particles which have…

Mathematical Physics · Physics 2013-02-07 Patrik L. Ferrari , Herbert Spohn

We investigate the asymptotic behavior of perfect matchings on rail-yard graphs with doubly free boundary conditions and Jack weights. While a special case of this model reduces to the half space Macdonald process with Jack weights…

Probability · Mathematics 2025-05-27 Zhongyang Li , Kaili Shi

This paper introduces the Trimmed Functional Empirical Process (TFEP) as a robust framework for statistical inference when dealing with heavy-tailed or skewed distributions, where classical moments such as the mean or variance may be…

Methodology · Statistics 2025-12-09 Abdoulaye Camara , Saliou Diouf , Moumouni Diallo , Gane Samb Lo

We propose an extragradient method with stepsizes bounded away from zero for stochastic variational inequalities requiring only pseudo-monotonicity. We provide convergence and complexity analysis, allowing for an unbounded feasible set,…

Optimization and Control · Mathematics 2017-03-02 Alfredo Iusem , Alejandro Jofré , Roberto I. Oliveira , Philip Thompson

Recently James Martin introduced multiline queues, and used them to give a combinatorial formula for the stationary distribution of the multispecies asymmetric simple exclusion exclusion process (ASEP) on a circle. The ASEP is a model of…

Combinatorics · Mathematics 2021-10-07 Sylvie Corteel , Olya Mandelshtam , Lauren Williams

Despite the practical success of deep neural networks, a comprehensive theoretical framework that can predict practically relevant scores, such as the test accuracy, from knowledge of the training data is currently lacking. Huge…

Disordered Systems and Neural Networks · Physics 2024-06-27 R. Pacelli , S. Ariosto , M. Pastore , F. Ginelli , M. Gherardi , P. Rotondo