Asymptotic Spectral Representation of Linear Convolutional Layers

2022 ◽  
pp. 1-1
Author(s):  
Xinping Yi
Keyword(s):  
Spectral Representation

scholarly journals Loops in AdS: from the spectral representation to position space. Part II

2021 ◽  
Vol 2021 (7) ◽  
Author(s):  
Dean Carmi
Keyword(s):  
Spectral Representation ◽  
Tree Level ◽  
Point Function ◽  
Position Space ◽  
Gross Neveu Model ◽  
Loop Amplitudes

Abstract We continue the study of AdS loop amplitudes in the spectral representation and in position space. We compute the finite coupling 4-point function in position space for the large-N conformal Gross Neveu model on AdS3. The resummation of loop bubble diagrams gives a result proportional to a tree-level contact diagram. We show that certain families of fermionic Witten diagrams can be easily computed from their companion scalar diagrams. Thus, many of the results and identities of [1] are extended to the case of external fermions. We derive a spectral representation for ladder diagrams in AdS. Finally, we compute various bulk 2-point correlators, extending the results of [1].

Spectral representation in stochastic quantization

1990 ◽  
Vol 42 (4) ◽  
pp. 1166-1178 ◽  
Author(s):  
Hiromichi Nakazato
Keyword(s):  
Spectral Representation ◽  
Stochastic Quantization

The modified group delay feature: a new spectral representation of speech

2004 ◽  
Author(s):  
Hema A. Murthy ◽  
Rajesh Mahanand Hegde ◽  
Venkata Ramana Rao Gadde
Keyword(s):  
Group Delay ◽  
Spectral Representation ◽  
Modified Group Delay

An efficient Cholesky decomposition and applications for the simulation of large-scale random wind velocity fields

2018 ◽  
Vol 22 (6) ◽  
pp. 1255-1265 ◽  
Author(s):  
Yongle Li ◽  
Chuanjin Yu ◽  
Xingyu Chen ◽  
Xinyu Xu ◽  
Koffi Togbenou ◽  
...  
Keyword(s):  
Wind Velocity ◽  
Large Scale ◽  
Spectral Representation ◽  
Cholesky Decomposition ◽  
Velocity Fields ◽  
Data Driven ◽  
Long Span Bridges ◽  
Long Span ◽  
Spectral Representation Method ◽  
Representation Method

A growing number of long-span bridges are under construction across straits or through valleys, where the wind characteristics are complex and inhomogeneous. The simulation of inhomogeneous random wind velocity fields on such long-span bridges with the spectral representation method will require significant computation resources due to the time-consuming issues associated with the Cholesky decomposition of the power spectrum density matrixes. In order to improve the efficiency of the decomposition, a novel and efficient formulation of the Cholesky decomposition, called “Band-Limited Cholesky decomposition,” is proposed and corresponding simulation schemes are suggested. The key idea is to convert the coherence matrixes into band matrixes whose decomposition requires less computational cost and storage. Subsequently, each decomposed coherence matrix is also a band matrix with high sparsity. As the zero-valued elements have no contribution to the simulation calculation, the proposed method is further expedited by limiting the calculation to the non-zero elements only. The proposed methods are data-driven ones, which can be applicable broadly for simulating many complicated large-scale random wind velocity fields, especially for the inhomogeneous ones. Through the data-driven strategies presented in the study, a numerical example involving inhomogeneous random wind velocity field simulation on a long-span bridge is performed. Compared to the traditional spectral representation method, the simulation results are with high accuracy and the entire simulation procedure is about 2.5 times faster by the proposed method for the simulation of one hundred wind velocity processes.

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