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We discuss applications to class numbers and conjugacy class zeta functions of p-groups and unipotent group schemes, respectively. Introduction Class numbers. Let k(G) denote the class number (= number of conjugacy classes) of a finite group G. As is well-known,k(G)=#Irr(G) is the ...
Using some of tdoi:10.1007/s12220-017-9891-3IsralowitzJoshuaSpringer USJournal of Geometric AnalysisJ. Isralowitz, A matrix weighted T1 theorem for matrix kernelled CZOs and a matrix weighted John-Nirenberg theorem Preprint, arXiv:1508.02474 (2015)....
2024: "Android Binder Attack Matrix" by Utkarsh [article] [part 2] [part 3] [part 4] [part 5]2024: "Driving forward in Android drivers" by Seth Jenkins [article] [video] [CVE-2023-32837] [CVE-2023-32832]2024: "Attacking Android Binder: Analysis and Exploitation of CVE-2023-20938" ...
{// Make sure the thread does get an matrix element.intpixVal=0;// result initializationintpixels=0;// Track how many elements join the averaging op.// Get average of the surrounding (2*BLUR_SIZE+1) x (2*BLUR_SIZE+1) boxfor(intblurRow=-BLUR_SIZE;blurRow<BLUR_SIZE+1;++blurRow){...
Rockchip's media drivers, among them is the continuing support to the RK3399 ISP. Since it was upstreamed inv5.6as a staging driver we have worked intensively on solving bugs and other issues to make it more capable and stable. As a result it will finally be moved out ofstagingin v...
Let A be a positive definite l×l matrix. Then κ(x,y)=xTAy is a kernel. • If κ1(x,y):X×X⟼R, then κ(x,y)=exp(κ1(x,y)) is also a kernel, and if p(⋅) is a polynomial with nonnegative coefficients, κ(x,y)=p(κ1(x,y)) is also a kernel. The in...
Let\({\mathcal{S}}=({{\mathbf{X}}_1}, \ldots ,{{\mathbf{X}}_N})\)be a finite set of matrices in a matrix Hilbert space\({\mathcal{H}}\). Define\({\mathcal{H}} \circ {\mathcal{S}}=\{ (\langle {\langle {\mathbf{W}},{{\mathbf{X}}_1}\rangle _{\mathcal{H}...
exp() # Symbolic (1e6,2e6,1) Gaussian kernel matrix # We come back to vanilla PyTorch Tensors or NumPy arrays using # reduction operations such as .sum(), .logsumexp() or .argmin() # on one of the two "symbolic" dimensions 0 and 1. # Here, the kernel density estimation a_i...
The core of this theory is the eigenvalue decomposition of the Laplacian matrix of the weighted graph obtained from data. In fact, there is a close relationship between the second smallest eigenvalue of the Laplacian and the graph cut [7], [8]. The aim of this paper is to present a ...