Results 251 to 260 of about 1,649,451 (289)

Quasi-support hyperplanes in asymmetric normed spaces

Computational and Applied Mathematics
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jianrong Wu, Hua Duan, Zhenyu Jin
openaire   +1 more source

The Unseen Bias: How Norm Discrepancy in Pre-Norm MLLMs Leads to Visual Information Loss

arXiv.org
Multimodal Large Language Models (MLLMs), which couple pre-trained vision encoders and language models, have shown remarkable capabilities. However, their reliance on the ubiquitous Pre-Norm architecture introduces a subtle yet critical flaw: a severe ...
Bozhou Li, Xinda Xue, Sihan Yang, Yang Shi, Xinlong Chen, Yushuo Guan, Yuanxing Zhang, Wentao Zhang   +7 more
semanticscholar   +1 more source

Chebyshev sets composed of subspaces in asymmetric normed spaces

Izvestiya: Mathematics
By definition, a Chebyshev set is a set of existence and uniqueness, that is, any point has a unique best approximant from this set. We study properties of Chebyshev sets composed of finitely or infinitely many planes (closed affine subspaces, possibly ...
A. Alimov, I. G. Tsar'kov
semanticscholar   +1 more source

Assessing asymmetric selection of responsible airlines based on CSR, normative, and emotional factors: a complexity approach with SEM, fsQCA, and NCA

Asia Pacific Journal of Tourism Research
This study comprehensively explored the roles of corporate social responsibility (CSR), normative, and emotional factors in the formation of the intention to select a responsible airline, based on the Theory of Planned Behavior (TPB), the Norm Activation
Heesup Han, A. Wong, S. Kim, Xin Cheng, Xiaoting Chi   +4 more
semanticscholar   +1 more source

The Dual Space of an Asymmetric Normed Linear Space

Quaestiones Mathematicae, 2003
Given an asymmetric normed linear space ( X , q ), we construct and study its dual space ( X *, q *). In particular, we show that ( x *, q *) is a biBanach semilinear space and prove that ( X , q ) can be identified as a subspace of its bidual by an isometric isomorphism.
L.M. García-Raffi, Romaguera Sanchez-Pérez   +1 more
openaire   +3 more sources

Best approximation in asymmetric normed linear spaces

International Conference on Information Science and Technology, 2011
In this paper we show that the set of right K-Lipschitz mappings from an asymmetric normed linear space (X,p) to another asymmetric normed linear space (Y,q), which vanish at a fixed point x 0 ∈ X can be endowed with the structure of an asymmetric normed cone.
null Wen Li, null Du Zou, null Deyi Li, null Zhaoyuan Zhang   +3 more
openaire   +1 more source

Multilinear operators between asymmetric normed spaces

Colloquium Mathematicum, 2020
The authors prove some fundamental results for multilinear operators between asymmetric normed spaces (see [\textit{S. Cobzaş}, Functional analysis in asymmetric normed spaces. Basel: Birkhäuser (2013; Zbl 1266.46001)]). Among other results, they give criteria for the continuity of multilinear operators, Banach-Steinhaus type theorems, and a closed ...
Latreche, Faiz, Dahia, Elhadj
openaire   +2 more sources

Asymmetric Mixing Matrix Optimization for Faster Average Consensus in Wireless Sensor Networks

IEEE Internet of Things Journal
Achieving fast and accurate average consensus is pivotal for numerous collaborative tasks in wireless sensor networks (WSNs). Toward this end, this article explores the design of asymmetric mixing matrices for achieving faster average consensus rate in ...
Miao Jiang, Yiqing Li
semanticscholar   +1 more source

Guarantees of a Preconditioned Subgradient Algorithm for Overparameterized Asymmetric Low-rank Matrix Recovery

International Conference on Machine Learning
In this paper, we focus on a matrix factorization-based approach to recover low-rank {\it asymmetric} matrices from corrupted measurements. We propose an {\it Overparameterized Preconditioned Subgradient Algorithm (OPSA)} and provide, for the first time ...
Paris Giampouras, Hanqin Cai, René Vidal   +2 more
semanticscholar   +1 more source

Learning Analysis of Kernel Ridgeless Regression with Asymmetric Kernel Learning

arXiv.org
Ridgeless regression has garnered attention among researchers, particularly in light of the ``Benign Overfitting'' phenomenon, where models interpolating noisy samples demonstrate robust generalization.
Fan He, Mingzhe He, Lei Shi, Xiaolin Huang, J. Suykens   +4 more
semanticscholar   +1 more source