We give an intuitive geometric explanation for the apparent breakdown of standard finite-size scaling in systems with periodic boundaries above the upper critical dimension. The Ising model and self-avoiding walk are simulated on five-dimensional hypercubic lattices with free and periodic boundary conditions, by using geometric representations and recently introduced Markov-chain Monte Carlo algorithms. We show that previously observed anomalous behavior for correlation functions, measured on the standard Euclidean scale, can be removed by defining correlation functions on a scale which correctly accounts for windings.
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http://dx.doi.org/10.1103/PhysRevLett.118.115701 | DOI Listing |
Zoology (Jena)
January 2025
Department of Biological Science, Florida State University, Tallahassee, Florida 32306-4295, USA.
Spiny pocket mice are usually divided into two genera, Heteromys and Liomys, and more recently the latter have been subsumed into the former, leaving subfamily Heteromyinae with one genus. However, this arrangement conveys false equivalency among heteromyines, and does not represent the great morphological, molecular, and ecological diversity in this subfamily. To address this, geometric morphometric methods were used to explore interspecific cranial variation in this subfamily, which were then evaluated in the context of recent phylogenetic and taxonomic findings.
View Article and Find Full Text PDFBMC Public Health
January 2025
Xinjiang Uighur Autonomous Region Center for Disease Control and Prevention, Urumqi, 830002, China.
Background: New indicators of potential human immunodeficiency virus (HIV) transmission are being actively explored. We aim to categorical testing of the viral load (VL) of persons living with HIV (PLWH) in order to explore new indicators to measure the intensity of the epidemic and the effectiveness of the response in the community.
Methods: A dynamic cohort study was conducted in Yining to monitor the VL of all persons living with HIV from 2017 to 2019.
J Transl Med
December 2024
Department of Rheumatology and Immunology, Third Affiliated Hospital of Sun Yat-Sen University, 600 Tianhe Road, Tianhe District, Guangzhou, China.
Background: Corona virus disease 2019 (COVID-19) reinfection, particularly short-term reinfection, poses challenges to the management of rheumatic diseases and may increase adverse clinical outcomes. This study aims to develop machine learning models to predict and identify the risk of short-term COVID-19 reinfection in patients with rheumatic diseases.
Methods: We developed four prediction models using explainable machine learning to assess the risk of short-term COVID-19 reinfection in 543 patients with rheumatic diseases.
Chaos
December 2024
Department of Electrical and Computer Engineering, the Clarkson Center for Complex Systems Science, Clarkson University, Potsdam, New York 13699, USA.
Artificial Neural Networks (ANNs) have proven to be fantastic at a wide range of machine learning tasks, and they have certainly come into their own in all sorts of technologies that are widely consumed today in society as a whole. A basic task of machine learning that neural networks are well suited to is supervised learning, including when learning orbits from time samples of dynamical systems. The usual construct in ANN is to fully train all of the perhaps many millions of parameters that define the network architecture.
View Article and Find Full Text PDFPhys Rev Lett
November 2024
CNR-IOM Istituto Officina dei Materiali, Strada Costiera 11, 34149 Trieste, Italy and Donostia International Physics Center, 20018 San Sebastián, Spain.
The electric field of light induces-in a noncentrosymmetric insulator-a dc current, quadratic in the field magnitude, called the "shift current." When addressed from a many-electron viewpoint, the shift current has a simple explanation and a simple formulation as well, deeply rooted in quantum geometry. The basic formula is then specialized to the independent-electron case, first for a disordered system in a supercell formulation, and then for a crystalline system.
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