The need to determine the structure of a graph arises in many applications. This paper studies directed graphs and defines the notions of -chained and -chained directed graphs. These notions reveal structural properties of directed graphs that shed light on how the nodes of the graph are connected. Applications include city planning, information transmission, and disease propagation. We also discuss the notion of in-center and out-center vertices of a directed graph, which are vertices at the center of the graph. Computed examples provide illustrations, among which is the investigation of a bus network for a city.
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http://dx.doi.org/10.1007/s41109-022-00502-x | DOI Listing |
Mol Inform
January 2025
Department of Applied Chemistry, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa 214-8571, Japan.
Recent advances in machine learning have significantly impacted molecular design, notably the molecular generation method combining the chemical variational autoencoder (VAE) with Gaussian mixture regression (GMR). In this method, a mathematical model is constructed with X as the latent variable of the molecule and Y as the target properties and activities. Through direct inverse analysis of this model, it is possible to generate molecules with the desired target properties.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Computer Science, South China Normal University, Guangzhou 510555, China.
Multivariate time series anomaly detection (MTSAD) can effectively identify and analyze anomalous behavior in complex systems, which is particularly important in fields such as financial monitoring, industrial equipment fault detection, and cybersecurity. MTSAD requires simultaneously analyze temporal dependencies and inter-variable relationships have prompted researchers to develop specialized deep learning models to detect anomalous patterns. In this paper, we conducted a structured and comprehensive overview of the latest techniques in deep learning for multivariate time series anomaly detection methods.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
To cope with the challenges posed by high-concurrency training tasks involving large models and big data, Directed Acyclic Graph (DAG) and shard were proposed as alternatives to blockchain-based federated learning, aiming to enhance training concurrency. However, there is insufficient research on the specific consensus designs and the effects of varying shard sizes on federated learning. In this paper, we combine DAG and shard by designing three tip selection consensus algorithms and propose an adaptive algorithm to improve training performance.
View Article and Find Full Text PDFJ Mol Graph Model
December 2024
Faculty of Chemistry and Center for Computational Science, Hanoi National University of Education, Hanoi, Viet Nam; Institute of Natural Sciences, Hanoi National University of Education, Hanoi, Viet Nam.
The Hepatitis C virus (HCV) causes a variety of liver diseases, making it a global health issue that affects millions of people in the world. The NS3/4A protease has been considered a common target for anti-HCV treatments using direct-acting antiviral agents and their derivatives. Of the natural products that have been proposed for novel therapeutic product alternatives, the soft coral compounds are found to contain steroids with various bioactive properties for effective HCV treatments.
View Article and Find Full Text PDFMed Image Anal
January 2025
Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands; Amsterdam University Medical Center, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.
Histopathological analysis of whole slide images (WSIs) has seen a surge in the utilization of deep learning methods, particularly Convolutional Neural Networks (CNNs). However, CNNs often fail to capture the intricate spatial dependencies inherent in WSIs. Graph Neural Networks (GNNs) present a promising alternative, adept at directly modeling pairwise interactions and effectively discerning the topological tissue and cellular structures within WSIs.
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