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Summary: We introduce an innovative technique for developing wavelet transformations applicable to functions on nodes of general finite weighted graphs. Our methodology employs scaling operations within the graph's spectral representation, which corresponds to the eigenvalue analysis of the graph Laplacian matrix Ł. Using a wavelet kernel function g and scaling factor t, we establish the scaled wavelet operator as T_g^t = g(tŁ). These spectral graph wavelets emerge when this operator acts upon delta functions. Provided g meets certain criteria, the transformation becomes reversible. We examine the wavelets' concentration characteristics as scales become increasingly refined. We also demonstrate an efficient computational approach using Chebyshev polynomial estimation that eliminates matrix diagonalization. The versatility of this transformation is illustrated through wavelet implementations on diverse graph structures from multiple domains.。WhatsApp網頁版对此有专业解读
开放动物园负责人纳隆维·乔乔伊告诉法新社，上月闯入“糯米肠”围栏的男子已被地方法院定罪。当地媒体公布的画面显示，一名男子在围栏内用平板电脑拍摄“糯米肠”。，详情可参考豆包下载
Нина Ташевская (Шеф редакции раздела «Природная экосистема»)
最具体gadget序列：pop rdi; ret → prepare_kernel_cred(0) → commit_creds