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We propose an extended framework for marginalized domain adaptation, aimed at addressing unsupervised, supervised and semi-supervised scenarios. We argue that the denoising principle should be extended to explicitly promote domain-invariant…

Computer Vision and Pattern Recognition · Computer Science 2017-02-21 Gabriela Csurka , Boris Chidlovski , Stephane Clinchant , Sophia Michel

We establish the first extension results for divergence-free (or solenoidal) elements of $\mathrm{L}^{1}$-based function spaces. Here, the key point is to preserve the solenoidality constraint while simultaneously keeping the underlying…

Analysis of PDEs · Mathematics 2024-08-09 Franz Gmeineder , Stefan Schiffer

The method used earlier for analysis of correlated nanoscopic systems is extended to infinite (periodic) s-band like systems described by the Hubbard model and its extensions. The optimized single-particle wave functions contained in the…

Strongly Correlated Electrons · Physics 2009-02-18 Jan Kurzyk , Włodzimierz Wójcik , Jozef Spałek

The boundary integral equation method ascertains explicit relations between localized surface phonon and plasmon polariton resonances and the eigenvalues of its associated electrostatic operator. We show that group-theoretical analysis of…

Mesoscale and Nanoscale Physics · Physics 2017-03-16 R. C. Voicu , T. Sandu

This paper presents an adaptive numerical framework for solving exterior "sound-soft" scattering problems governed by the Helmholtz equation. By interpreting the Method of Fundamental Solutions through the lens of rational approximation, we…

Numerical Analysis · Mathematics 2026-01-28 Stefano Costa

The diffuse domain method for partial differential equations on complicated geometries recently received strong attention in particular from practitioners, but many fundamental issues in the analysis are still widely open. In this paper we…

Numerical Analysis · Mathematics 2014-12-19 Martin Burger , Ole Løseth Elvetun , Matthias Schlottbom

Maximization of submodular functions under various constraints is a fundamental problem that has been studied extensively. A powerful technique that has emerged and has been shown to be extremely effective for such problems is the…

Data Structures and Algorithms · Computer Science 2024-09-24 Niv Buchbinder , Moran Feldman

The process of renormalisation in nonperturbative Hamiltonian Effective Field Theory (HEFT) is examined in the $\Delta$-resonance scattering channel. As an extension of effective field theory incorporating the L\"uscher formalism, HEFT…

High Energy Physics - Lattice · Physics 2022-08-31 Curtis D. Abell , Derek B. Leinweber , Anthony W. Thomas , Jia-Jun Wu

We study expansions near the boundary of solutions to the Dirichlet problem for the constant mean curvature equation in the hyperbolic space. With a characterization of remainders of the expansion by multiple integrals, we establish optimal…

Analysis of PDEs · Mathematics 2016-08-30 Qing Han , Yue Wang

It is well known, by now, that the three-dimensional non-viscous planetary geostrophic model, with vertical hydrostatic balance and horizontal Rayleigh friction, coupled to the heat diffusion and transport, is mathematically ill-posed. This…

Analysis of PDEs · Mathematics 2010-12-30 Chongsheng Cao , Edriss S. Titi

Diffuse domain methods (DDMs) have garnered significant attention for approximating solutions to partial differential equations on complex geometries. These methods implicitly represent the geometry by replacing the sharp boundary interface…

Analysis of PDEs · Mathematics 2025-04-25 Toai Luong , Tadele Mengesha , Steven M. Wise , Ming Hei Wong

This paper investigates the numerical modeling of a time-dependent heat transmission problem by the convolution quadrature boundary element method. It introduces the latest theoretical development into the error analysis of the numerical…

Numerical Analysis · Mathematics 2017-11-08 Tianyu Qiu , Alexander Rieder , Francisco-Javier Sayas , Shougui Zhang

In situ and remotely sensed observations have potential to facilitate data-driven predictive models for oceanography. A suite of machine learning models, including regression, decision tree and deep learning approaches were developed to…

Atmospheric and Oceanic Physics · Physics 2020-06-24 Stefan Wolff , Fearghal O'Donncha , Bei Chen

In this paper, we present a stochastic method for the simulation of Laplace's equation with a mixed boundary condition in planar domains that are polygonal or bounded by circular arcs. We call this method the Reflected Walk-on-Spheres…

Numerical Analysis · Mathematics 2024-07-10 Qiansheng Han , Antti Rasila , Tommi Sottinen

At the ANZIAM conference in Hobart in February, 2018, there were several talks on the solution of Laplace problems in multiply connected domains by means of conformal mapping. It appears to be not widely known that such problems can also be…

Numerical Analysis · Mathematics 2018-03-05 Lloyd N. Trefethen

A new systematic approach to the construction of approximate solutions to a class of nonlinear singularly perturbed feedback control systems using the boundary layer functions especially with regard to the possible occurrence of the…

Optimization and Control · Mathematics 2017-09-07 Robert Vrabel

We develop a stochastic parametrization, based on a `simple' deterministic model for the dynamics of steady longshore currents, that produces ensembles that are statistically consistent with field observations of these currents. Unlike…

Data Analysis, Statistics and Probability · Physics 2016-12-06 Juan M. Restrepo , Shankar C. Venkataramani

We consider a problem relating to magnetic confinement devices known as stellarators. Plasma is confined by magnetic fields generated by current-carrying coils, and here we investigate how closely to the plasma they need to be positioned.…

Analysis of PDEs · Mathematics 2021-08-18 Adam J. Golab , James C. Robinson , José L. Rodrigo

Open-set single-source domain generalization aims to use a single-source domain to learn a robust model that can be generalized to unknown target domains with both domain shifts and label shifts. The scarcity of the source domain and the…

Computer Vision and Pattern Recognition · Computer Science 2024-11-06 Pengkun Jiao , Na Zhao , Jingjing Chen , Yu-Gang Jiang

Temperature scaling is a popular technique for tuning the sharpness of a model distribution. It is used extensively for sampling likely generations and calibrating model uncertainty, and even features as a controllable parameter to many…

Machine Learning · Computer Science 2023-10-03 Andy Shih , Dorsa Sadigh , Stefano Ermon
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