Incidence Matrix Diagonal Forms and Integral Hecke Algebras

2018 ◽  
Author(s):  
Robert A. Liebler
Keyword(s):  
Incidence Matrix ◽  
Hecke Algebras ◽  
Diagonal Forms

Integral Hecke Algebras for Finite Generalized Polygons

2011 ◽  
Vol 18 (02) ◽  
pp. 259-272
Author(s):  
İlhan Hacıoglu
Keyword(s):  
Normal Form ◽  
Incidence Matrix ◽  
Hecke Algebras ◽  
Hecke Algebra ◽  
Generalized Polygons ◽  
Completely Reducible ◽  
Standard Module ◽  
Rank 2

Suppose that (P,B,F) is a triple consisting of the points, blocks and flags of a generalized m-gon, and H(F) the associated rank-2 Iwahori–Hecke algebra. H(F) acts naturally on the integral standard module ZF based on F. This work gives arithmetic conditions on a subring R, where R contains the integers and is contained in the rationals, that insure the associated R-ary Iwahori–Hecke algebra to be completely reducible on RF. The constituent multiplicities are related to the R-normal form of the incidence matrix of (P,B,F).

scholarly journals Identifying cell types from single-cell data based on similarities and dissimilarities between cells

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Yuanyuan Li ◽  
Ping Luo ◽  
Yi Lu ◽  
Fang-Xiang Wu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Spectral Clustering ◽  
Incidence Matrix ◽  
Expression Patterns ◽  
Cell Types ◽  
Clustering Method ◽  
Different Types ◽  
Cell Data ◽  
Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

Measure-wise disjoint Rauzy fractals with the same incidence matrix

2021 ◽  
Author(s):  
Klaus Scheicher ◽  
Víctor F. Sirvent ◽  
Paul Surer
Keyword(s):  
Incidence Matrix

scholarly journals Twisted cubic and point-line incidence matrix in $$\mathrm {PG}(3,q)$$

2021 ◽  
Vol 89 (10) ◽  
pp. 2211-2233 ◽  
Author(s):  
Alexander A. Davydov ◽  
Stefano Marcugini ◽  
Fernanda Pambianco
Keyword(s):  
Incidence Matrix ◽  
Twisted Cubic ◽  
Point Line

scholarly journals Dominant and global dimension of blocks of quantised Schur algebras

2021 ◽  
Author(s):  
Ming Fang ◽  
Wei Hu ◽  
Steffen Koenig
Keyword(s):  
Hecke Algebras ◽  
Symmetric Groups ◽  
Global Dimension ◽  
Group Algebras ◽  
Dominant Dimension ◽  
Schur Algebras ◽  
Homological Dimensions ◽  
Weyl Duality

AbstractGroup algebras of symmetric groups and their Hecke algebras are in Schur-Weyl duality with classical and quantised Schur algebras, respectively. Two homological dimensions, the dominant dimension and the global dimension, of the indecomposable summands (blocks) of these Schur algebras S(n, r) and $$S_q(n,r)$$ S q ( n , r ) with $$n \geqslant r$$ n ⩾ r are determined explicitly, using a result on derived invariance in Fang, Hu and Koenig (J Reine Angew Math 770:59–85, 2021).

Sign in / Sign up

Export Citation Format

Share Document