The time-delay reconstruction of the state space of a system from observed scalar data requires a time lag and an integer embedding dimension. We demonstrate a reliable method to estimate the minimum necessary embedding dimension that improves upon previous methods by correcting for systematic effects due to temporal oversampling, autocorrelation, and changing time lag. The method gives a sharp and reliable indication of the proper dimension. With little computational cost, the method also distinguish easily between infinite-dimensional colored noise-including noisy periodicity-and low-dimensional dynamics.
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http://dx.doi.org/10.1103/PhysRevE.66.026209 | DOI Listing |
Neural Netw
January 2025
School of Cyber Science and Engineering, Xi'an Jiaotong University, China. Electronic address:
Detecting anomalies in attributed networks has become a subject of interest in both academia and industry due to its wide spectrum of applications. Although most existing methods achieve desirable performance by the merit of various graph neural networks, the way they bundle node-affiliated multidimensional attributes into a whole for embedding calculation hinders their ability to model and analyze anomalies at the fine-grained feature level. To characterize anomalies from each feature dimension, we propose Eagle, a deep framework based on bipartitE grAph learninG for anomaLy dEtection.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100811, China.
While deep learning techniques have been extensively employed in malware detection, there is a notable challenge in effectively embedding malware features. Current neural network methods primarily capture superficial characteristics, lacking in-depth semantic exploration of functions and failing to preserve structural information at the file level. Motivated by the aforementioned challenges, this paper introduces MalHAPGNN, a novel framework for malware detection that leverages a hierarchical attention pooling graph neural network based on enhanced call graphs.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Guangdong Institute of Intelligence Science and Technology, 519031 Hengqin, Zhuhai, Guangdong, China.
Manifold learning techniques have emerged as crucial tools for uncovering latent patterns in high-dimensional single-cell data. However, most existing dimensionality reduction methods primarily rely on 2D visualization, which can distort true data relationships and fail to extract reliable biological information. Here, we present DTNE (diffusive topology neighbor embedding), a dimensionality reduction framework that faithfully approximates manifold distance to enhance cellular relationships and dynamics.
View Article and Find Full Text PDFBioengineering (Basel)
January 2025
Department of Pathology, College of Veterinary Medicine, The University of Georgia, Athens, GA 30602, USA.
Three-dimensional printing was introduced in the 1980s, though bioprinting started developing a few years later. Today, 3D bioprinting is making inroads in medical fields, including the production of biomedical supplies intended for internal use, such as biodegradable staples. Medical bioprinting enables versatility and flexibility on demand and is able to modify and individualize production using several established printing methods.
View Article and Find Full Text PDFEval Rev
January 2025
Department of Basic Psychology, University of Valencia. Valencia, Spain.
The foremost index of caregiving quality is child attachment, as supported by attachment theory. Research supports the relevance of early parenting interventions in improving child outcomes in attachment quality to promote public health because of their long-term effects on mental health and functioning. This study aimed at evaluating the impact on both parenting and child outcomes of the Parent-Child Psychological Support Programme® (PCPS), a community-based program individually tailored to parents and their infants during periodic center-based visits to promote attachment security.
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