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It is shown that the deformed Seiberg-Witten curve equation after Fourier transform is mapped into a differential equation for the AGT dual 2d CFT cnformal block containing an extra completely degenerate field. We carefully match parameters…

High Energy Physics - Theory · Physics 2016-05-11 Rubik Poghossian

We revisit a graph width parameter that we dub bipartite treewidth (btw). Bipartite treewidth can be seen as a common generalization of treewidth and the odd cycle transversal number, and is closely related to odd-minors. Intuitively, a…

Data Structures and Algorithms · Computer Science 2025-12-01 Lars Jaffke , Laure Morelle , Ignasi Sau , Dimitrios M. Thilikos

At the heart of recent progress in AdS/CFT is the question of subregion duality, or entanglement wedge reconstruction: which part(s) of the boundary CFT are dual to a given subregion of the bulk? This question can be answered by appealing…

High Energy Physics - Theory · Physics 2020-02-19 Chi-Fang Chen , Geoffrey Penington , Grant Salton

The explicit computation of higher-point conformal blocks in any dimension is usually a challenging task. For two-dimensional conformal field theories in Euclidean signature, the oscillator formalism proves to be very efficient. We…

High Energy Physics - Theory · Physics 2025-05-15 Martin Ammon , Jakob Hollweck , Tobias Hössel , Katharina Wölfl

We consider correlation functions in symmetric product ($S_N$) orbifold CFTs at large $N$ with arbitrary seed CFT, expanding on our earlier work arXiv:2211.04633 . Using covering space techniques, we calculate descent relations using…

High Energy Physics - Theory · Physics 2023-03-22 Benjamin A. Burrington , A. W. Peet

In this paper, we develop and explore recursive methods to investigate the 2d CFT 5-point conformal block with a level 2 degenerate insertion, as well as its AGT dual, by solving the BPZ differential equation. First, we represent the…

High Energy Physics - Theory · Physics 2025-08-29 Hasmik Poghosyan , Rubik Poghossian

Convolutional neural networks (CNNs) are very popular nowadays for image processing. CNNs allow one to learn optimal filters in a (mostly) supervised machine learning context. However this typically requires abundant labelled training data…

Computer Vision and Pattern Recognition · Computer Science 2019-07-04 Matej Ulicny , Vladimir A. Krylov , Rozenn Dahyot

This paper presents a phenomenon which sometimes occurs in tetravalent bipartite locally dart-transitive graphs, called a Base Graph -- Connection Graph dissection. In this dissection, each white vertex is split into two vertices of valence…

Combinatorics · Mathematics 2020-04-07 Gabriel Verret , Primož Potočnik , Steve Wilson

We propose a graph-theoretic description to determine and characterize 5d superconformal field theories (SCFTs) that arise as circle reductions of 6d $\mathcal{N} = (1,0)$ SCFTs. Each 5d SCFT is captured by a graph, called a Combined Fiber…

High Energy Physics - Theory · Physics 2020-01-08 Fabio Apruzzi , Craig Lawrie , Ling Lin , Sakura Schafer-Nameki , Yi-Nan Wang

We propose a novel approach to study conformal field theories (CFTs) in general dimensions. In the conformal bootstrap program, one usually searches for consistent CFT data that satisfy crossing symmetry. In the new approach, we reverse the…

High Energy Physics - Theory · Physics 2018-01-19 Wenliang Li

In this letter we provide a complete holographic reconstruction of the cubic couplings in the minimal bosonic higher-spin theory in AdS$_{d+1}$. For this purpose we also determine the OPE coefficients of all single-trace conserved currents…

High Energy Physics - Theory · Physics 2016-05-11 Charlotte Sleight , Massimo Taronna

Cutwidth is a widely studied parameter that quantifies how well a graph can be decomposed along small edge-cuts. It complements pathwidth, which captures decomposition by small vertex separators, and it is well-known that cutwidth…

Data Structures and Algorithms · Computer Science 2025-02-25 Narek Bojikian , Vera Chekan , Stefan Kratsch

In computer vision, convolutional networks (CNNs) often adopts pooling to enlarge receptive field which has the advantage of low computational complexity. However, pooling can cause information loss and thus is detrimental to further…

Computer Vision and Pattern Recognition · Computer Science 2019-07-09 Pengju Liu , Hongzhi Zhang , Wei Lian , Wangmeng Zuo

The correlators of free four dimensional conformal field theories (CFT4) have been shown to be given by amplitudes in two-dimensional $so(4,2)$ equivariant topological field theories (TFT2), by using a vertex operator formalism for the…

High Energy Physics - Theory · Physics 2020-08-13 Robert de Mello Koch , Sanjaye Ramgoolam

Conformal blocks are the building blocks for correlation functions in conformal field theories. The four-point function is the most well-studied case. We consider conformal blocks for $n$-point correlation functions. For conformal field…

High Energy Physics - Theory · Physics 2019-03-27 Vladimir Rosenhaus

In order to perform network analysis tasks, representations that capture the most relevant information in the graph structure are needed. However, existing methods do not learn representations that can be interpreted in a straightforward…

Machine Learning · Computer Science 2021-01-05 Effrosyni Simou , Dorina Thanou , Pascal Frossard

We propose a systematic approach to constructing microscopic models with fractional excitations in three-dimensional (3D) space. Building blocks are quantum wires described by the (1+1)-dimensional conformal field theory (CFT) associated…

Strongly Correlated Electrons · Physics 2019-06-19 Yohei Fuji , Akira Furusaki

The Wilson Coefficients for all 4-parton operators which arise in matching QCD to Soft-Collinear Effective Theory (SCET) are computed at 1-loop. Any dijet observable calculated in SCET beyond leading order will require these results. The…

High Energy Physics - Phenomenology · Physics 2012-04-18 Randall Kelley , Matthew D. Schwartz

The aim of this work is to develop a fully-distributed algorithmic framework for training graph convolutional networks (GCNs). The proposed method is able to exploit the meaningful relational structure of the input data, which are collected…

Machine Learning · Computer Science 2022-12-21 Simone Scardapane , Indro Spinelli , Paolo Di Lorenzo

This short note modifies a reconstruction method by the author (Comm. PDE, 45(9):1118-1133, 2020), for reconstructing piecewise constant conductivities in the Calder\'on problem (electrical impedance tomography). In the former paper, a…

Analysis of PDEs · Mathematics 2025-12-08 Henrik Garde
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