the dispersibility of graphene is modeled as a mathematical function of the molecular structure of solvent represented by simplified molecular input-line entry systems (smiles) together with the graph of atomic orbitals (gao). the gao is molecular graph where atomic orbitals e.g. 1s1, 2p4, 3d7 etc., are vertexes of the graph instead of the chemical elements used as the graph vertexes in the tra...

Graphical symbol recognition and spotting recently have become an important research activity. In this work we present a descriptor for symbols, especially for line drawings. The descriptor is based on the graph representation of graphical objects. We construct graphs from the vectorized information of the binarized images, where the critical points detected by the vectorization algorithm are c...

In this letter, we propose a novelbinary graph descriptor to improve loop detection for visual SLAM systems. Our contribution is twofold: i) embedding technique generating binary descriptors which conserve both spatial and histogram information extracted from images; ii) generic mean of combining multiple layers heterogeneous data into the proposed descriptor, coupled with matching geometric ch...

abstract. in this paper we study some relations between the power andquotient power graph of a finite group. these interesting relations motivateus to find some graph theoretical properties of the quotient power graphand the proper quotient power graph of a finite group g. in addition, weclassify those groups whose quotient (proper quotient) power graphs areisomorphic to trees or paths.

Combining the properties of conformal geometry and graph-based topological information for 3D object retrieval, a non-rigid 3D object descriptor is proposed, which is both robust and efficient in terms of retrieval accuracy and computation speed. In previous works, graph-based methods for non-rigid 3D object retrieval, have shown high discriminative power and robustness, while geometry-based me...

(Received 00 Month 200x; in final form 00 Month 200x) Recognizing actions from a monocular video is a very hot topic in computer vision recently. In this paper, we propose a new representation of actions, the co-occurrence matrices de-scriptor, on the intrinsic shape manifold learned by graph embedding. The co-occurrence matrices descriptor captures more temporal information than the bag of wor...

With the explosive growth of video capture capable mobile handsets and online visual data repositories, the popular query-by-capture applications call for a compact visual descriptor with minimum descriptor length. How to preserve the visual descriptor information while minimizing the bit rate for representing the descriptors, is a focus of the ongoing MPEG7 CDVS (Compact Descriptor for Visual ...

Abstract The semidirect product of a finitely generated group dual with the symmetric can be described through so-called group-theoretical categories partitions (covers only special case; due to Raum–Weber, 2015) and skew (more general; Maaßen, 2018). We generalize these results case graph categories, which allows replace by automorphisms some graph.

Predicting molecular properties with Graph Neural Networks (GNNs) has recently drawn a lot of attention, compound toxicity prediction being one the biggest challenges. In cases where there is insufficient labeled molecule data, an effective approach to pre-train GNNs on large-scale unlabeled data and then fine-tune them for downstream tasks. Among pre-training strategies, node-level involves ma...