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We compute the singular support and the characteristic cycle of a rank 1 sheaf on a smooth variety in codimension 2 using ramification theory, when the ramification of the sheaf is clean. We develop a general theory, called the partially…

Algebraic Geometry · Mathematics 2022-06-08 Yuri Yatagawa

Monotone determined spaces are natural topological extensions of dcpo. Its main purpose is to build an extended framework for domain theory. In this paper, we study the one-step closure and ideal convergence on monotone determined space.…

General Topology · Mathematics 2022-12-14 Wu Wang

In this paper we present a new semidefinite programming hierarchy for covering problems in compact metric spaces. Over the last years, these kind of hierarchies were developed primarily for geometric packing and for energy minimization…

Optimization and Control · Mathematics 2026-02-12 Cordian Riener , Jan Rolfes , Frank Vallentin

The K-means one-step dimensionality reduction clustering method has made some progress in addressing the curse of dimensionality in clustering tasks. However, it combines the K-means clustering and dimensionality reduction processes for…

Machine Learning · Computer Science 2024-10-31 Fangfang Li , Quanxue Gao , Cheng Deng , Wei Xia

We develop a unified framework for iterated symmetric extensions with countable support and, more generally, with $<\kappa$-support. Set-length iterations are treated uniformly, and when the iteration template is first-order definable over…

Logic · Mathematics 2026-01-26 Frank Gilson

We isolate the limit-stage filter construction needed for countable-support symmetric iterations built from standard successor-step symmetric systems. At successor stages we take the $\omega_1$-completion of the usual successor-stage…

Logic · Mathematics 2026-03-10 Frank Gilson

This paper investigates the problem of safety certification for black-box discrete-time stochastic systems, where both the system dynamics and disturbance distributions are unknown, and only sampled data are available. Under such limited…

Systems and Control · Electrical Eng. & Systems 2026-02-17 Taoran Wu , Dominik Wagner , Jingduo Pan , Luke Ong , Arvind Easwaran , Bai Xue

We put the theory of interacting topological crystalline phases on a systematic footing. These are topological phases protected by space-group symmetries. Our central tool is an elucidation of what it means to "gauge" such symmetries. We…

Strongly Correlated Electrons · Physics 2018-03-21 Ryan Thorngren , Dominic V. Else

We investigate the parameterized complexity of Binary CSP parameterized by the vertex cover number and the treedepth of the constraint graph, as well as by a selection of related modulator-based parameters. The main findings are as follows:…

Discrete Mathematics · Computer Science 2023-09-22 Hans L. Bodlaender , Carla Groenland , Michał Pilipczuk

Persistent homology tracks topological features across geometric scales, encoding birth and death of cycles as barcodes. We develop a complementary theory where the filtration parameter is algebraic precision rather than geometric scale.…

Algebraic Topology · Mathematics 2025-11-04 Robert Ghrist , Cassie Ding

We announce a generalization of Zimmer's cocycle superrigidity theorem proven using harmonic map techniques. This allows us to generalize many results concerning higher rank lattices to all lattices in semisimple groups with property $(T)$.…

Differential Geometry · Mathematics 2007-05-23 David Fisher , Theron Hitchman

The local structure of disordered jammed packings of monodisperse spheres without friction, generated by the Lubachevsky-Stillinger algorithm, is studied for packing fractions above and below 64%. The structural similarity of the particle…

Soft Condensed Matter · Physics 2015-06-04 Sebastian C. Kapfer , Walter Mickel , Klaus Mecke , Gerd E. Schröder-Turk

Factorizing low-rank matrices is a problem with many applications in machine learning and statistics, ranging from sparse PCA to community detection and sub-matrix localization. For probabilistic models in the Bayes optimal setting, general…

Information Theory · Computer Science 2018-12-07 Jean Barbier , Mohamad Dia , Nicolas Macris , Florent Krzakala , Lenka Zdeborová

The Bures--Wasserstein geometry of covariance matrices provides a canonical distance on the statistical manifold of centred Gaussian measures and lies at the intersection of information geometry, quantum information, and optimal transport.…

Differential Geometry · Mathematics 2026-03-31 Leonardo Marconi

It has been shown that univalent circle packings filling in the complex plane $\bold C$ are unique up to similarities of $\bold C$. Here we prove that bounded degree branched circle packings properly covering $\bold C$ are uniquely…

Metric Geometry · Mathematics 2016-09-06 Tomasz Dubejko

We propose a one-step procedure to estimate the latent positions in random dot product graphs efficiently. Unlike the classical spectral-based methods such as the adjacency and Laplacian spectral embedding, the proposed one-step procedure…

Statistics Theory · Mathematics 2020-11-16 Fangzheng Xie , Yanxun Xu

Pseudo-Riemannian manifolds with parallel Weyl tensor that are not conformally flat or locally symmetric, also known as ECS manifolds, have a natural local invariant, the rank, which equals 1 or 2, and is the dimension of a certain…

Differential Geometry · Mathematics 2023-11-03 Andrzej Derdzinski , Ivo Terek

Let $\mathrm{Sym}_q(m)$ be the space of symmetric matrices in $\mathbb{F}_q^{m\times m}$. A subspace of $\mathrm{Sym}_q(m)$ equipped with the rank distance is called a symmetric rank-metric code. In this paper we study the covering…

Information Theory · Computer Science 2024-06-19 Usman Mushrraf , Ferdinando Zullo

We make a systematic study of symmetry-protected topological gapped phases of quantum spin chains in the presence of the frieze space groups in one dimension using matrix product states. Here, the spatial symmetries of the one-dimensional…

Strongly Correlated Electrons · Physics 2023-03-29 Bram Vancraeynest-De Cuiper , Jacob C. Bridgeman , Nicolas Dewolf , Jutho Haegeman , Frank Verstraete

In a previous paper it was shown that a machine learning regression problem can be solved within the framework of random function theory, with the optimal kernel analytically derived from symmetry and indifference principles and coinciding…

Machine Learning · Computer Science 2025-12-19 Yuriy N. Bakhvalov
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