arXiv research

Proves rotational symmetry for Serrin-type problems in doubly connected domains.

The concept of a conformal deformation has two natural extensions: quasiconformal and harmonic mappings. Both classes do not preserve the conformal type of the domain, however they cannot change it in an arbitrary way. Doubly connected domains are where one first observes nontrivial conformal invariants. Herbert Groetz…

Let $N=(Ω,σ)$ and $M=(Ω^*,ρ)$ be doubly connected Riemann surfaces and assume that $ρ$ is a smooth metric with bounded Gauss curvature $\mathcal{K}$ and finite area. The paper establishes the existence of homeomorphisms between $Ω$ and $Ω^*$ that minimize the Dirichlet energy. In the class of all homeomorphisms $f \col…

Formula for squeezing function on annuli disproves conjecture.

Commentary on Teichmüller's 1938 paper on conformal and quasiconformal mappings.

We prove the solvability of a Dirichlet problem for flat hermitian metrics on Hilbert bundles over compact Riemann surfaces with boundary. We also prove a factorization result for flat hermitian metrics on doubly connected domains.

We consider two eigenvalue problems for Laplacian on some specific doubly connected domain. In particular, we study the following two eigenvalue problems. Let $B_1$ be an open ball in $\mathbb{R}^n$ and $B_0$ be a ball contained in $B_1$. Let $ν$ be the outward unit normal on $\partial B_1$. Then the first eigenvalue o…

Lipschitz mappings found between Riemann surfaces with specific properties.

Let $B_1$ be a ball of radius $r_1$ in $S^n(\Hy^n)$, and let $B_0$ be a smaller ball of radius $r_0$ such that $\bar{B_0}\subset B_1$. For $S^n$ we consider $r_1< π$. Let $u$ be a solution of the problem $-\La u =1$ in $\Om := B_1\setminus \bar{B_0}$ vanishing on the boundary. It is shown that the associated functional…

The study finds lower bounds for the first eigenvalue of the Laplacian in planar domains with magnetic fields.

In this paper we establish a connection between free boundary minimal surfaces in a ball in $\mathbb{R}^3$ and free boundary cones arising in a one-phase problem. We prove that a doubly connected minimal surface with free boundary in a ball is a catenoid.

Our goal is to provide a novel method of representing 2D shapes, where each shape will be assigned a unique fingerprint - a computable approximation to a conformal map of the given shape to a canonical shape in 2D or 3D space (see page 22 for a few examples). In this paper, we make the first significant step in this pr…

Let $A \subset \mathbb{R} ^2$ be a smooth doubly connected domain. We consider the Dirichlet energy $E(u)=\int_{A} |\nabla u|^2$, where $u:A \rightarrow \mathbb{C}$, and look for critical points of this energy with prescribed modulus $|u|=1$ on $\partial A$ and with prescribed degrees on the two connected components o…

Unified approach to conformal and modular invariants on surfaces.

We investigate the dynamics of semigroups generated by a family of polynomial maps on the Riemann sphere such that the postcritical set in the complex plane is bounded. The Julia set of such a semigroup may not be connected in general. We show that for such a polynomial semigroup, if $A$ and $B$ are two connected compo…

Method generates intermediate domains to align source and target domains.

Aims to eliminate domain bias in authentication without domain labels.

CoDAG combines domain adaptation and generalization for unsupervised continual domain shift learning.

DCASE 2022 Task 2 tackles domain shifts in ASD for machine condition monitoring.

Extends polydisk theorem to Hartogs domains over symmetric domains.

Adaptive multi-domain learning reduces parameter count for efficient deep learning.

Recently multi-domain recommender systems have received much attention from researchers because they can solve cold-start problem as well as support for cross-selling. However, when applying into multi-domain items, although algorithms specifically addressing a single domain have many difficulties in capturing the spec…

Proposes novel losses for fine-grained categorical domain adaptation.

Improves unsupervised domain adaptation by mixing source and target domains.

We address the problem of domain generalization where a decision function is learned from the data of several related domains, and the goal is to apply it on an unseen domain successfully. It is assumed that there is plenty of labeled data available in source domains (also called as training domain), but no labeled dat…

MetFA aligns source and target domains for cross-device image classification.

In this paper, we propose a simple model referred as Contradistinguisher (CTDR) for unsupervised domain adaptation whose objective is to jointly learn to contradistinguish on unlabeled target domain in a fully unsupervised manner along with prior knowledge acquired by supervised learning on an entirely different domain…

CSD learns a common component for domain generalization, outperforming existing methods.

Method learns domain-specific representations without supervision.

A new method uses normalizing flows for gradual domain adaptation.

We propose a method to infer domain-specific models such as classifiers for unseen domains, from which no data are given in the training phase, without domain semantic descriptors. When training and test distributions are different, standard supervised learning methods perform poorly. Zero-shot domain adaptation attemp…

Study shows how many domains are needed for generalization, using a new measure called domain shattering dimension.

TAROT enhances robustness and domain adaptability with domain-invariant features.

A new model for imputing missing values in time series data across domains.

We present CROSSGRAD, a method to use multi-domain training data to learn a classifier that generalizes to new domains. CROSSGRAD does not need an adaptation phase via labeled or unlabeled data, or domain features in the new domain. Most existing domain adaptation methods attempt to erase domain signals using technique…

We define self-adjoint extensions of the Hodge Laplacian on Lipschitz domains in Riemannian manifolds, corresponding to either the absolute or the relative boundary condition, and examine regularity properties of these operators' domains and form domains. We obtain results valid for general Lipschitz domains, and stron…

Graph-Relational Domain Adaptation (GRDA) adapts domains based on their graph structure.

A cost-effective framework for gradual domain adaptation using multifidelity.

Proposes first method for continuously indexed domain adaptation.

Dynamic residual adapters improve performance across multiple latent domains without domain labels.

New method removes pseudo-label bias for unsupervised domain adaptation.

Paper proposes MDAT to stabilize domain alignment in label-scarce settings.

JSCN improves cross-domain recommendation by learning domain-invariant user representations.

Paper tackles robust domain adaptation without target domain data.

Interventional domain adaptation improves feature transferability by removing spurious correlations.

Supervised learning algorithms trained on medical images will often fail to generalize across changes in acquisition parameters. Recent work in domain adaptation addresses this challenge and successfully leverages labeled data in a source domain to perform well on an unlabeled target domain. Inspired by recent work in …

A typical domain adaptation approach is to adapt models trained on the annotated data in a source domain (e.g., sunny weather) for achieving high performance on the test data in a target domain (e.g., rainy weather). Whether the target contains a single homogeneous domain or multiple heterogeneous domains, existing wor…

Adaptive object detection method synthesizes target domain images from source domain images.