Calogero-Sutherland Model and Singular Vectors of W Algebras

1997 ◽  
pp. 211-217
Author(s):  
Y. Matsuo
Keyword(s):  
Singular Vectors ◽  
Sutherland Model

scholarly journals Recursions in Calogero—Sutherland Model Based on Virasoro Singular Vectors

2012 ◽  
Vol 57 (5) ◽  
pp. 743-758 ◽  
Author(s):  
Jian-Feng Wu ◽  
Ying-Ying Xu ◽  
Ming Yu
Keyword(s):  
Singular Vectors ◽  
Model Based ◽  
Sutherland Model

scholarly journals Excited states of the Calogero-Sutherland model and singular vectors of the WN algebra

1995 ◽  
Vol 449 (1-2) ◽  
pp. 347-374 ◽  
Author(s):  
Hidetoshi Awata ◽  
Yutaka Matsuo ◽  
Satoru Odake ◽  
Jun'ichi Shiraishi
Keyword(s):  
Excited States ◽  
Singular Vectors ◽  
Sutherland Model

Using NOGAPS Singular Vectors to Diagnose Large-scale Influences on Tropical Cyclogenesis

2011 ◽  
Author(s):  
Sharanya J. Majumdar ◽  
Melinda S. Peng ◽  
Carolyn A. Reynolds ◽  
James D. Doyle ◽  
Chun-Chieh Wu ◽  
...  
Keyword(s):  
Large Scale ◽  
Tropical Cyclogenesis ◽  
Singular Vectors

scholarly journals Thermodynamic properties of the Shastry-Sutherland model throughout the dimer-product phase

2019 ◽  
Vol 1 (3) ◽  
Author(s):  
Alexander Wietek ◽  
Philippe Corboz ◽  
Stefan Wessel ◽  
B. Normand ◽  
Frédéric Mila ◽  
...  
Keyword(s):  
Thermodynamic Properties ◽  
Product Phase ◽  
Sutherland Model

scholarly journals Classification of Degenerate Verma Modules for E(5, 10)

2021 ◽  
Author(s):  
Nicoletta Cantarini ◽  
Fabrizio Caselli ◽  
Victor Kac
Keyword(s):  
Infinite Number ◽  
Verma Module ◽  
Lie Superalgebra ◽  
Verma Modules ◽  
Singular Vectors ◽  
Finite Dimensional ◽  
De Rham ◽  
Via Classification

AbstractGiven a Lie superalgebra $${\mathfrak {g}}$$ g with a subalgebra $${\mathfrak {g}}_{\ge 0}$$ g ≥ 0 , and a finite-dimensional irreducible $${\mathfrak {g}}_{\ge 0}$$ g ≥ 0 -module F, the induced $${\mathfrak {g}}$$ g -module $$M(F)={\mathcal {U}}({\mathfrak {g}})\otimes _{{\mathcal {U}}({\mathfrak {g}}_{\ge 0})}F$$ M ( F ) = U ( g ) ⊗ U ( g ≥ 0 ) F is called a finite Verma module. In the present paper we classify the non-irreducible finite Verma modules over the largest exceptional linearly compact Lie superalgebra $${\mathfrak {g}}=E(5,10)$$ g = E ( 5 , 10 ) with the subalgebra $${\mathfrak {g}}_{\ge 0}$$ g ≥ 0 of minimal codimension. This is done via classification of all singular vectors in the modules M(F). Besides known singular vectors of degree 1,2,3,4 and 5, we discover two new singular vectors, of degrees 7 and 11. We show that the corresponding morphisms of finite Verma modules of degree 1,4,7, and 11 can be arranged in an infinite number of bilateral infinite complexes, which may be viewed as “exceptional” de Rham complexes for E(5, 10).

scholarly journals A Machine Learning-Based Seismic Data Compression and Interpretation Using a Novel Shifted-Matrix Decomposition Algorithm

2021 ◽  
Vol 11 (11) ◽  
pp. 4874
Author(s):  
Milan Brankovic ◽  
Eduardo Gildin ◽  
Richard L. Gibson ◽  
Mark E. Everett
Keyword(s):  
Real Time ◽  
Seismic Data ◽  
Matrix Decomposition ◽  
Original Data ◽  
Velocity Estimation ◽  
Singular Vectors ◽  
Well Stimulation ◽  
Marine Seismic ◽  
Value Decomposition ◽  
Seismic Data Compression

Seismic data provides integral information in geophysical exploration, for locating hydrocarbon rich areas as well as for fracture monitoring during well stimulation. Because of its high frequency acquisition rate and dense spatial sampling, distributed acoustic sensing (DAS) has seen increasing application in microseimic monitoring. Given large volumes of data to be analyzed in real-time and impractical memory and storage requirements, fast compression and accurate interpretation methods are necessary for real-time monitoring campaigns using DAS. In response to the developments in data acquisition, we have created shifted-matrix decomposition (SMD) to compress seismic data by storing it into pairs of singular vectors coupled with shift vectors. This is achieved by shifting the columns of a matrix of seismic data before applying singular value decomposition (SVD) to it to extract a pair of singular vectors. The purpose of SMD is data denoising as well as compression, as reconstructing seismic data from its compressed form creates a denoised version of the original data. By analyzing the data in its compressed form, we can also run signal detection and velocity estimation analysis. Therefore, the developed algorithm can simultaneously compress and denoise seismic data while also analyzing compressed data to estimate signal presence and wave velocities. To show its efficiency, we compare SMD to local SVD and structure-oriented SVD, which are similar SVD-based methods used only for denoising seismic data. While the development of SMD is motivated by the increasing use of DAS, SMD can be applied to any seismic data obtained from a large number of receivers. For example, here we present initial applications of SMD to readily available marine seismic data.

scholarly journals Singular vectors and conservation laws of quantum KdV type equations

1992 ◽  
Vol 278 (1-2) ◽  
pp. 79-84 ◽  
Author(s):  
P. Di Francesco ◽  
P. Mathieu
Keyword(s):  
Conservation Laws ◽  
Singular Vectors ◽  
Kdv Type Equations
Sign in / Sign up

Export Citation Format

Share Document