Abstract: Graph has been proven to be an emerging tool for spectrum sensing (SS), with detection performance closely related to the graph characteristics. Existing graph-based SS has been mainly ...

Abstract: In this talk, I will present a new combinatorial algorithm for maximum flow that is based on running the weighted push-relabel algorithm introduced in [BBST ...

Graclus (latest: Version 1.2) is a fast graph clustering software that computes normalized cut and ratio association for a given undirected graph without any eigenvector computation. This is possible ...

High sparse Knowledge Graph is a key challenge to solve the Knowledge Graph Completion task. Due to the sparsity of the KGs, there are not enough first-order neighbors to learn the features of ...

While retrieval-augmented generation is effective for simpler queries, advanced reasoning questions require deeper connections between information that exist across documents. They require a knowledge ...

Abstract: The sparse unmixing (SU) technique is widely used in hyperspectral image (HSI) unmixing because it does not need to estimate the number of pure endmembers but directly obtains the spectra ...

A simple check could be something like symmetric = not np.any((matrix!=matrix.T).data) (here assuming that it is scipy.sparse.csr_matrix). Here is a simple example (not the most efficient way to check ...

There are large inconsistency results when running a single forward pass of a torchrec model (a dense layer, a sparse layer, a weighted sparse layer, and an over layer) under distributed and ...