Research
On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

Trend · papers per month

61122182243 · Jun 202019922001200920172026
48 results for starshaped domains

Proves a generalized Minkowski inequality for starshaped domains.

problem Proving a generalized Minkowski inequality for smooth, (k1)(k-1)-convex starshaped domains.
method Solvability of the degenerate kk-Hessian equation on the exterior domain RnΩ\mathbb R^n\setminusΩ.
result Generalized Minkowski inequality holds for smooth, (k1)(k-1)-convex, starshaped domains.

Paper extends isoperimetric inequalities for non-starshaped hypersurfaces.

problem Isoperimetric inequalities for non-starshaped hypersurfaces.
method Volume preserving and area decreasing mean curvature flow with conformal Killing vector fields.
result Established isoperimetric inequalities for a broader class of hypersurfaces.

The study finds starshaped compact hypersurfaces in warped products with curvature estimates.

problem Finding starshaped compact hypersurfaces in warped product manifolds.
method Deriving global curvature estimates and interior second order a priori estimates for solutions to associated equations.
result Existence of starshaped compact hypersurfaces in warped product manifolds.

New findings on hypersurfaces with specific curvature properties in space forms.

problem Characterizing hypersurfaces with almost constant curvature in space forms.
method Analyzing starshaped hypersurfaces with various curvature conditions.
result Closed starshaped hypersurfaces with almost constant mean curvature or higher order mean curvature are close to geodesic spheres.

Let Wn\mathcal{W}^{n} be the class of CC^{\infty } complete simply connected nn-dimensional manifolds without conjugate points. The hyperbolic space as well as Euclidean space are good examples of such manifolds. Let % W\in \mathcal{W}^{n} and let AA be a subset of WW. This article aims at characterization and bu…

2013-11-03abs ↗pdf ↗

We study inverse mean curvature flows of starshaped, mean convex hypersurfaces in warped product manifolds with a positive warping factor φ(r)\varphi(r). If φ(r)>0\varphi'(r)>0 and φ(r)0\varphi''(r)\geq 0, we show that these flows exist for all times, remain starshaped and mean convex. Plus the positivity of φ(r)\varphi''(r) and …

2016-09-30abs ↗pdf ↗

We prove that, if ΩRnΩ\subset \mathbb{R}^n is an open bounded starshaped domain of class C2C^2, the constancy over Ω\partial Ω of the function φ(y)=0λ(y)j=1n1[1tκj(y)]dt\varphi(y) = \int_0^{λ(y)} \prod_{j=1}^{n-1}[1-t κ_j(y)]\, dt implies that ΩΩ is a ball. Here kj(y)k_j(y) and λ(y)λ(y) denote respectively the principal curvatures and the cut v…

2012-07-26abs ↗pdf ↗

The paper studies a modified scalar curvature flow and proves convergence to a sphere.

problem Analyzing the convergence of a modified scalar curvature flow.
method Flow of starshaped hypersurfaces with a specific speed function, proving existence and convergence.
result The flow converges exponentially fast to a sphere, except for α<2α<2.

The paper proves Michael-Simon inequalities in hyperbolic space using novel curvature flows.

problem Proving the sharp Michael-Simon inequality for mean curvature in hyperbolic space.
method Developed new locally constrained curvature flows for proving the inequality.
result Sharp Michael-Simon inequalities for mean and k-th mean curvatures in starshaped hypersurfaces in hyperbolic space.

The study examines hypersurfaces in warped products and their properties.

problem Characterizing hypersurfaces in warped products satisfying a specific curvature condition.
method Analyzes hypersurfaces in warped products with a Weingarten condition and proves stability results.
result Hypersurfaces in space forms are geodesic spheres under certain curvature conditions.

We study the evolution of strictly mean-convex entire graphs over RnR^n by Inverse Mean Curvature flow. First we establish the global existence of starshaped entire graphs with superlinear growth at infinity. The main result in this work concerns the critical case of asymptotically conical entire convex graphs. In this…

2017-09-19abs ↗pdf ↗

In this article, we prove an eigenvalue pinching theorem for the first eigenvalue of the Laplacian on compact hypersurfaces in a sphere. Let (Mn,g)(M^n,g) be a closed, connected and oriented Riemannian manifold isometrically immersed by φφ into §n+1§^{n+1}. Let q>nq>n and A>0A>0 be some real numbers satisfying $|M|^\frac{1}{n…

2015-08-27abs ↗pdf ↗

In this note, we observe that if BB is a ball in a Euclidean space with dimension nn, n3n\geq3, then a stable CMC hypersurface ΣΣ with free boundary in BB satisfies \[ nA\leq L\leq nA\left( \frac{1+\sqrt{1+4(n+1)H^2}}{2} \right)\,, \] where LL, AA and HH denote the length of Σ\partial Σ, the area of ΣΣ and the…

2016-06-30abs ↗pdf ↗

Method generates intermediate domains to align source and target domains.

problem Challenges of domain adaptation with significant domain divergence.
method Progressive domain augmentation via domain interpolation and multiple subspace alignment.
result Achieves state-of-the-art performance on multiple domain adaptation tasks.

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

problem Acquiring knowledge in unsupervised continual domain shift learning.
method Complementary Domain Adaptation and Generalization (CoDAG) framework.
result CoDAG outperforms state-of-the-art models in all datasets and evaluation metrics.

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

problem Domain shifts change acoustic characteristics, affecting ASD performance.
method Domain generalization techniques to detect anomalies across unknown domains.
result Two types of domain generalization techniques were identified and analyzed.

Extends polydisk theorem to Hartogs domains over symmetric domains.

problem Rigidity phenomena in Riemannian manifolds.
method Extension of polydisk theorem to Hartogs domains over arbitrary symmetric domains.
result Dual of a Hartogs domain over a bounded symmetric domain admits no totally geodesic immersion into any compact Riemannian manifold.

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

problem Different domains have varying complexity, leading to inefficient model training.
method Proposes adaptive parameterization to reduce model complexity without sacrificing performance.
result Efficient multi-domain learning solutions with far fewer parameters.

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…

2018-12-15abs ↗pdf ↗

Proposes novel losses for fine-grained categorical domain adaptation.

problem Fine-grained alignment of categories across domains in unsupervised domain adaptation.
method Joint category-domain classifier with adversarial training losses for both domain and category levels, and vicinal domain adaptation.
result Achieves state-of-the-art performance on benchmark datasets.

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…

2018-07-09abs ↗pdf ↗

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

problem Learning discriminative class boundaries across different domains.
method Distance metric guided feature alignment (MetFA) for domain-invariant and discriminative feature extraction.
result MetFA outperforms state-of-the-art methods in 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…

2019-09-08abs ↗pdf ↗

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

problem Training models to generalize across unseen domains.
method CSD decomposes the model into a common and specific component, discarding the latter.
result CSD outperforms state-of-the-art domain generalization methods.

A new method uses normalizing flows for gradual domain adaptation.

problem Difficulty in domain adaptation when source and target domains have a large gap.
method Proposes using normalizing flows to learn a transformation from target to Gaussian mixture distribution.
result Improves classification performance and mitigates the problem of gradual self-training failure.

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…

2018-07-09abs ↗pdf ↗

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

problem How many domains are needed for domain generalization?
method Introduced a new combinatorial measure called the domain shattering dimension to model domain sample complexity.
result Established a tight quantitative relationship between domain shattering dimension and classic VC dimension.

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

problem Developing models robust to adversarial attacks across diverse domains.
method Derives a new generalization bound and proposes TAROT algorithm.
result TAROT outperforms state-of-the-art methods in accuracy and robustness.

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

problem Imputing missing values in time series data across domains with domain shifts and high missing rates.
method A diffusion-based imputation model that integrates shared spectral components and domain-specific temporal structures, with cross-domain consistency alignment.
result Our model effectively handles missing values and domain shifts, outperforming existing methods.

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…

2004-08-31abs ↗pdf ↗