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Related papers: Crossing Kernels for Boundary and Crosscap CFTs

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Point clouds are sets of points in two or three dimensions. Most kernel methods for learning on sets of points have not yet dealt with the specific geometrical invariances and practical constraints associated with point clouds in computer…

Machine Learning · Computer Science 2007-12-21 Francis Bach

Multi-dimensional constrained covariant density functional theories were developed recently. In these theories, all shape degrees of freedom \beta_{\lambda\mu} deformations with even \mu are allowed, e.g., \beta_{20}, \beta_{22},…

Nuclear Theory · Physics 2013-03-05 Bing-Nan Lu , Jie Zhao , En-Guang Zhao , Shan-Gui Zhou

The systematic investigation of the ground state and fission properties of even-even actinides and superheavy nuclei with $Z=90-120$ from the two-proton up to two-neutron drip lines with proper assessment of systematic theoretical…

Nuclear Theory · Physics 2021-03-09 A. Taninah , S. E. Agbemava , A. V. Afanasjev

Representations by linear integral operators on $L_p$ spaces over measure spaces are investigated for the polynomial covariance type commutation relations and more general two-sided generalizations of covariance commutation relations…

Functional Analysis · Mathematics 2023-05-18 Domingos Djinja , Sergei Silvestrov , Alex Behakanira Tumwesigye

The notion of a (polynomial) kernelization from parameterized complexity is a well-studied model for efficient preprocessing for hard computational problems. By now, it is quite well understood which parameterized problems do or…

Data Structures and Algorithms · Computer Science 2025-04-28 Leonid Antipov , Stefan Kratsch

The Computable Cross Norm (CCNR) was recently discussed in Ref.~\cite{Yin:2022toc} as a measure of multipartite entanglement in a condensed matter context. In this short note, we point out that it is closely related to the $(2,n)$-R\'enyi…

High Energy Physics - Theory · Physics 2024-12-18 Alexey Milekhin , Pratik Rath , Wayne Weng

In this paper, we propose Complex Diffusion Maps (CDM), a novel diffusion mapping framework that aims to reveal the dominant complex harmonics of high-dimensional data. Inspired by the local Gaussian kernel relevant to the heat equation and…

Machine Learning · Computer Science 2026-05-05 Tongzhen Dang , Weiyang Ding , Michael K. Ng

We consider hilbert spaces of holomorphic functions in Cartan domains (in particular in ball and polydisk) and operator of restriction of holomorphic function to a submanifold in Shilov boundary. We discuss conditions when this operator…

funct-an · Mathematics 2013-01-15 Yurii A. Neretin

We propose a new holographic dual of conformal field theory defined on a manifold with boundaries, i.e. BCFT. Our proposal can apply to general boundaries and agrees with arXiv:1105.5165 for the special case of a disk and half plane. Using…

High Energy Physics - Theory · Physics 2017-08-16 Rong-Xin Miao , Chong-Sun Chu , Wu-Zhong Guo

We formulate rational conformal field theory in terms of a symmetric special Frobenius algebra A and its representations. A is an algebra in the modular tensor category of Moore-Seiberg data of the underlying chiral CFT. The multiplication…

High Energy Physics - Theory · Physics 2008-11-26 Jürgen Fuchs , Ingo Runkel , Christoph Schweigert

The crossing equations of a conformal field theory can be systematically truncated to a finite, closed system of polynomial equations. In certain cases, solutions of the truncated equations place strict bounds on the space of all unitary…

High Energy Physics - Theory · Physics 2019-06-26 Nima Afkhami-Jeddi , Thomas Hartman , Amirhossein Tajdini

We introduce a new numerical algorithm based on semidefinite programming to efficiently compute bounds on operator dimensions, central charges, and OPE coefficients in 4D conformal and N=1 superconformal field theories. Using our algorithm,…

High Energy Physics - Theory · Physics 2014-07-31 David Poland , David Simmons-Duffin , Alessandro Vichi

In the univariate setting, using the kernel spectral representation is an appealing approach for generating stationary covariance functions. However, performing the same task for multiple-output Gaussian processes is substantially more…

Machine Learning · Statistics 2021-03-15 Fergus Simpson , Alexis Boukouvalas , Vaclav Cadek , Elvijs Sarkans , Nicolas Durrande

We consider two $d \geq 2$ conformal field theories (CFTs) glued together along a codimension one conformal interface. The conformal anomaly of such a system contains both bulk and interface contributions. In a curved-space setup, we…

High Energy Physics - Theory · Physics 2020-12-02 Christopher P. Herzog , Kuo-Wei Huang , Dmitri V. Vassilevich

We consider the problem of accurately identifying cell boundaries and labeling individual cells in confocal microscopy images, specifically, 3D image stacks of cells with tagged cell membranes. Precise identification of cell boundaries,…

Computer Vision and Pattern Recognition · Computer Science 2019-09-06 Jiaxiang Jiang , Po-Yu Kao , Samuel A. Belteton , Daniel B. Szymanski , B. S. Manjunath

Kernel methods in machine learning use a kernel function that takes two data points as input and returns their inner product after mapping them to a Hilbert space, implicitly and without actually computing the mapping. For many kernel…

Machine Learning · Computer Science 2024-10-17 Kamaledin Ghiasi-Shirazi , Mohammadreza Qaraei

State-of-the-art embeddings often capture distinct yet complementary discriminative features: For instance, one image embedding model may excel at distinguishing fine-grained textures, while another focuses on object-level structure.…

Machine Learning · Computer Science 2025-10-31 Youqi Wu , Jingwei Zhang , Farzan Farnia

Various observables in compact CFTs are required to obey positivity, discreteness, and integrality. Positivity forms the crux of the conformal bootstrap, but understanding of the abstract implications of discreteness and integrality for the…

High Energy Physics - Theory · Physics 2021-02-24 Justin Kaidi , Eric Perlmutter

In this paper, we define and study a nested family of reproducing kernel Hilbert spaces of vector fields that is indexed by a range of scales, from which we construct a reproducing kernel Hilbert space of scale-dependent vector fields. We…

Numerical Analysis · Mathematics 2025-01-09 Yechen Liu , Laurent Younes

In this contribution we present some results of potential energy surfaces of actinide and transfermium nuclei from multi-dimensional constrained relativistic mean field (MDC-RMF) models. Recently we developed multi-dimensional constrained…

Nuclear Theory · Physics 2014-10-23 Bing-Nan Lu , Jie Zhao , En-Guang Zhao , Shan-Gui Zhou
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