Article
Multidisciplinary Sciences
Henrik Seckler, Ralf Metzler
Summary: Machine-learning techniques are used to decode anomalous-diffusion data and provide both predicted output and uncertainty estimates.
NATURE COMMUNICATIONS
(2022)
Article
Computer Science, Information Systems
Kamal Berahmand, Elahe Nasiri, Saman Forouzandeh, Yuefeng Li
Summary: This article proposes an improved method for local random walk by encouraging the movement towards nodes with stronger influence, resulting in higher prediction accuracy. A comparison with other similarity-based methods was conducted on 11 real-world networks, and the results demonstrated its superior performance in link prediction.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Physics, Fluids & Plasmas
Zhenqi Lu, Johan Wahlstrom, Arye Nehorai
Summary: The study focuses on spreading phenomena in networks, especially disease transmission, and proposes a method to effectively contain and suppress epidemic outbreaks through a combination of antidote distribution and partial quarantine. By improving existing antidote distribution schemes based on personalized PageRank, the study shows that the probability of infection spreading to the whole network is bounded, and the infection inside the subnetwork will disappear after a period proportional to the logarithm of the initially infected nodes. The strategy is dependent only on infection rate, recovery rate, and the topology around initially infected nodes, regardless of the rest of the network.
Article
Mathematics, Applied
Jiri Cerny
Summary: This study considers the zero-average Gaussian free field on finite d-regular graphs with fixed d (greater than or equal to 3). This class includes d-regular expanders of large girth and typical realisations of random d-regular graphs. The study shows that the level set of the zero-average Gaussian free field above level h has a giant component in the whole supercritical phase.
COMMUNICATIONS ON PURE AND APPLIED MATHEMATICS
(2023)
Article
Chemistry, Physical
Takashi Tsuchimochi, Yoohee Ryo, Seiichiro L. Ten-no, Kazuki Sasasako
Summary: Quantum imaginary time evolution (QITE) is a hybrid algorithm that can guarantee reaching the lowest state of a system. This study improves upon QITE, specifically for molecular applications. The derivation of the QITE equation is analyzed step-by-step, and a theoretically well-founded modification is proposed. The results demonstrate the effectiveness of the derived equation, providing a better approximation for imaginary time propagation. Additionally, accurate estimation of the norm of an imaginary-time-evolved state is discussed and applied in excited state calculations using the quantum Lanczos algorithm. The folded-spectrum QITE scheme is also introduced as an extension for general excited-state simulations.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2023)
Article
Chemistry, Physical
Takashi Tsuchimochi, Yoohee Ryo, Seiichiro L. Ten-no, Kazuki Sasasako
Summary: In this study, several improvements are made to the Quantum Imaginary Time Evolution (QITE) algorithm, with a focus on molecular applications. By analyzing the derivation of the QITE equation and suggesting a theoretically grounded modification, our results demonstrate the soundness of the derived equation and its ability to better approximate imaginary time propagation. We also discuss accurately estimating the norm of an imaginary-time-evolved state and its application to excited state calculations. Additionally, the folded-spectrum QITE scheme is proposed as a straightforward extension for general excited-state simulations.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2023)
Article
Multidisciplinary Sciences
M. Smerlak
Summary: This paper explores the link between selection for mutational robustness and the navigability of neutral networks, showing that sequences of neutral mutations follow a maximal entropy random walk. The study also revisits a word-game model of evolution, finding that the likelihood of certain substitution sequences can decrease with population size. These counterintuitive results highlight the intersection of evolutionary dynamics, information theory, and physics.
Article
Cell Biology
Feng Liang, Xin Fu, ShiJian Ding, Lin Li
Summary: Hearing loss has a significant impact on children, and the discovery of new genes helps to unravel the genetic mechanisms involved.
FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY
(2021)
Article
Physics, Multidisciplinary
Anthony Baptista, Aitor Gonzalez, Anais Baudot
Summary: The amount and variety of data have been increasing, requiring new methods to deal with the diversity and complexity of multilayer networks. The authors propose MultiXrank, a framework that uses random walks with restart to study multilayer networks, highlighting the important influence of bipartite networks.
COMMUNICATIONS PHYSICS
(2022)
Article
Chemistry, Physical
Anirban Ghosh, Sudipta Mandal, Dipanjan Chakraborty
Summary: This paper investigates the persistence probability of an active Brownian particle with shape asymmetry, extending the study to an active anisotropic particle. The analytical expression is validated against numerical simulations and the method proposed is tested to distinguish between active and passive anisotropic particles.
JOURNAL OF CHEMICAL PHYSICS
(2022)
Article
Chemistry, Physical
Chen Qu, Paul L. Houston, Riccardo Conte, Apurba Nandi, Joel M. Bowman
Summary: The study represents a significant advancement in applying Delta machine learning method to the challenging case of acetylacetone, successfully deriving a new potential energy surface which shows a barrier height close to the benchmark value.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
(2021)
Article
Quantum Science & Technology
Yusuke Yoshie, Kiyoto Yoshino
Summary: This paper presents a discrete-time quantum walk model for finding one of the edges of a given subgraph in a complete graph. By constructing a perturbed quantum walk, we can quickly locate one of the edges, achieving quantum searching.
QUANTUM INFORMATION PROCESSING
(2022)
Article
Quantum Science & Technology
Takashi Komatsu, Norio Konno, Iwao Sato, Shunya Tamura
Summary: This study introduces the concept of Mahler measure and its applications in number theory and physics. It explores a new relation between Mahler measure and the zeta function for quantum walks through the investigation of a new class of zeta functions. This research provides insights into the connection between Mahler measure and the zeta function through quantum walks.
QUANTUM INFORMATION PROCESSING
(2022)
Article
Mathematics, Applied
Lihua Feng, Weijun Liu, Lu Lu, Wei Wang, Guihai Yu
Summary: This paper investigates the maximum and minimum access times in the set of trees T(s,t), and obtains sharp upper and lower bounds, as well as corresponding extremal graphs. Additionally, it is proven that the path Pn and the star K-1,K-n-1 have the maximum and minimum average access times, respectively, among all trees.
APPLIED MATHEMATICS AND COMPUTATION
(2022)
Article
Biochemical Research Methods
Zihao Gao, Huifang Ma, Xiaohui Zhang, Yike Wang, Zheyu Wu
Summary: In this study, a new model called Similarity Measures-based Graph Co-contrastive Learning (SMGCL) is proposed for predicting drug-disease associations. The model learns the similarities between drugs and diseases through three associated views, and introduces a graph co-contrastive learning method to improve prediction accuracy. The effectiveness of SMGCL is validated through cross-validations and a case study of Alzheimer's disease.