Nanoclusters offer a fascinating possibility of studying the evolution of properties of a physical system by varying the number, size and inter-cluster separation of a given cluster to go from one limit to another. By systematically varying the inter-cluster separation in a nanocluster assembly of NiPd alloy, that is known to be a metal in bulk, we observe an unusual and hitherto unreported, spatial dimension change as well as a change in the transport mechanism. In the nanocluster form, the temperature dependent resistance shows an activated behavior for virtually all inter-cluster separations, contrary to, the bulk metallic behaviour. At large average inter-cluster separation, the transport happens via three dimensional Efros-Shklovskii hopping, due to the opening of a Coulomb gap at the Fermi surface. With a reduction in the inter-cluster separation, the transport mechanism changes from three dimensional Efros-Shklovskii hopping to that of a three dimensional Mott variable range hopping (VRH) due to the closing up of the gap. With a further reduction in average inter-cluster separation, the three dimensional Mott VRH changes to that of a two dimensional Mott VRH with additional signatures of an insulator to a weak metal-like transition in this particular assembly. So, nanoclusters offer a paradigm for studying the important problem of evolution of charge transport in physical systems with the possibility of directly tuning the average inter-cluster separation enabling the system to go from insulating to metallic limit via intermediate changes in the charge transport mechanism.
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http://dx.doi.org/10.1038/s41598-019-43581-0 | DOI Listing |
Sensors (Basel)
November 2024
School of Computing and Information Technology, University of Wollongong, Wollongong, NSW 2522, Australia.
For solving the facial expression recognition (FER) problem, we introduce a novel feature extractor called the coordinate-based neighborhood attention mechanism (CNAM), which uses the coordinate attention (CA) method to capture the directional relationships in separate horizontal and vertical directions, the input features from a preprocessing unit, and then passes this to two residual blocks, one consisting of the neighborhood attention (NA) mechanism, which captures the local interaction of features within the neighborhood of a feature vector, while the other one contains a channel attention implemented by a multilayer perceptron (MLP). We apply the feature extractor, the CNAM module, to four FER benchmark datasets, namely, RAF-DB, AffectNet(7cls), AffectNet(8cls), and CK+, and through qualitative and quantitative analysis techniques, we conclude that the insertion of the CNAM module could decrease the intra-cluster distances and increase the inter-cluster distances among the high-dimensional feature vectors. The CNAM compares well with other state-of-the-art (SOTA) methods, being the best-performing method for the AffectNet(7cls) and CK+ datasets, while for the RAF-DB and AffectNet(8cls) datasets, its performance is among the top-performing SOTA methods.
View Article and Find Full Text PDFConsensus representation learning is one of the most popular approaches in the field of multi-view clustering. However, most of the existing methods cannot learn discriminative representations with a clustering-friendly structure since these methods ignore the separation among clusters and the compactness within each cluster. To tackle this issue, we propose a new deep multi-view clustering network with a dual contrastive mechanism to learn clustering-friendly representations.
View Article and Find Full Text PDFSci Rep
July 2024
College of Big Data & Information Engineering, Guizhou University, Guiyang, 550025, China.
In recent years, the widespread adoption of wireless sensor networks (WSN) has resulted in the growing integration of the internet of things (IoT). However, WSN encounters limitations related to energy and sensor node lifespan, making the development of an efficient routing protocol a critical concern. Cluster technology offers a promising solution to this challenge.
View Article and Find Full Text PDFBrief Bioinform
March 2024
School of Mathematical Sciences, Peking University, Beijing, China.
Motivation: Over the past decade, single-cell transcriptomic technologies have experienced remarkable advancements, enabling the simultaneous profiling of gene expressions across thousands of individual cells. Cell type identification plays an essential role in exploring tissue heterogeneity and characterizing cell state differences. With more and more well-annotated reference data becoming available, massive automatic identification methods have sprung up to simplify the annotation process on unlabeled target data by transferring the cell type knowledge.
View Article and Find Full Text PDFCommun Biol
April 2024
Department of Biomedical and Clinical Sciences, Pediatric Clinical Research Center "Romeo and Enrica Invernizzi", Università Di Milano, 20157, Milan, Italy.
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