Article
Physics, Fluids & Plasmas
Fei Ma, Ping Wang
Summary: The study proposes a simple algorithmic framework for generating power-law graphs with small diameters and examines their structural properties. The results show that these graphs have unique features such as density characteristics and higher trapping efficiency compared to existing scale-free models, confirmed through extensive simulations.
Article
Quantum Science & Technology
Rebekah Herrman, Thomas G. Wong
Summary: This paper investigates the simplification methods of quantum walks on dynamic graphs, proposes six scenarios for graph simplification, and provides examples of how to simplify dynamic graphs to achieve parallel single-qubit gates.
QUANTUM INFORMATION PROCESSING
(2022)
Article
Mathematics
Shirshendu Ganguly, Nikhil Srivastava
Summary: We have improved the bounds on how localized an eigenvector of a high girth regular graph can be, and presented examples showing the sharpness of these bounds. This study was initiated based on the observation that certain suitably normalized averaging operators on high girth graphs are hyper-contractive, which can be used to approximate projectors onto the eigenspaces of such graphs.
INTERNATIONAL MATHEMATICS RESEARCH NOTICES
(2021)
Article
Physics, Multidisciplinary
Yi-Cong Yu, Xiaoming Cai
Summary: We investigate novel transport properties of chiral continuous-time quantum walks (CTQWs) on graphs. By employing a gauge transformation, we demonstrate that CTQWs on chiral chains are equivalent to those on non-chiral chains, but with additional momenta from initial wave packets. This explains the novel transport phenomenon numerically studied in (Khalique et al 2021 New J. Phys. 23 083005). Building on this, we delve deeper into the analysis of chiral CTQWs on the Y-junction graph, introducing phases to account for the chirality. The phase plays a key role in controlling both asymmetric transport and directed complete transport among the chains in the Y-junction graph.
NEW JOURNAL OF PHYSICS
(2023)
Article
Computer Science, Artificial Intelligence
Dimitris Berberidis, Georgios B. Giannakis
Summary: Node embedding is crucial for graph analytics and learning tasks like node classification, link prediction, and community detection. Despite facing challenges, an adaptive node embedding framework has been proposed to adjust the embedding process in an unsupervised manner based on the underlying graph. This approach shows superior performance in various real-world graph experiments and provides interpretable information on the graph structure.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Optics
Thomas G. Wong, Joshua Lockhart
Summary: This paper investigates the evolution of continuous-time quantum walks under different graph conditions and discovers cases where the probability distributions of quantum walks in irregular graphs are the same.
Article
Quantum Science & Technology
Gabriele Bressanini, Claudia Benedetti, Matteo G. A. Paris
Summary: This paper addresses the decoherence and classicalization of continuous-time quantum walks on graphs. Three different models of decoherence are investigated, and the quantum-classical (QC) dynamical distance is employed to assess the classicalization of the CTQW due to decoherence. The results show that intrinsic decoherence only partially preserves quantum features, while decoherence in the position basis completely destroys the quantumness of the walker. Additionally, the speed of the classicalization process is also examined.
QUANTUM INFORMATION PROCESSING
(2022)
Article
Statistics & Probability
Guillaume Conchon-Kerjan
Summary: This paper proves the cutoff phenomenon for the random walk on random n-lifts of finite weighted graphs, even in the case where the random walk on the base graph of the lift is not reversible. It is also discovered that the mixing time t(mix) = h(-1) log n is the smallest among all n-lifts of G.
ANNALS OF PROBABILITY
(2022)
Article
Multidisciplinary Sciences
Max Ehrhardt, Robert Keil, Lukas J. Maczewsky, Christoph Dittel, Matthias Heinrich, Alexander Szameit
Summary: Graph representations are powerful for solving complex problems in natural science, particularly in quantum communication and search algorithms in highly branched quantum networks. An unidentified paradigm has been introduced for the direct experimental realization of excitation dynamics in three-dimensional networks by utilizing the hybrid action of spatial and polarization degrees of freedom of photon pairs. This experimental exploration of multiparticle quantum walks on complex, highly connected graphs paves the way towards harnessing the applicative potential of fermionic dynamics in integrated quantum photonics.
Article
Quantum Science & Technology
Arnbjorg Soffia Arnadottir, Chris Godsil
Summary: This paper characterizes perfect state transfer in Cayley graphs for abelian groups that have a cyclic Sylow-2-subgroup, generalizing a similar characterization for Cayley graphs of cyclic groups provided by Basic in 2013.
QUANTUM INFORMATION PROCESSING
(2022)
Article
Physics, Multidisciplinary
Yanbing Zhang, Tingting Song, Zhihao Wu
Summary: Quantum walks, as the quantum version of classical random walks, can provide acceleration for difficult classical problems. Hitting probabilities in quantum walks have been extensively researched. Guan et al. proposed an HHL-based algorithm to calculate the hitting probabilities and achieve acceleration for the corresponding classical algorithm under specific conditions. However, we propose an improved algorithm with exponential improvement in accuracy dependence compared to the HHL-based algorithm.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Lixin Cui, Ming Li, Lu Bai, Yue Wang, Jing Li, Yanchao Wang, Zhao Li, Yunwen Chen, Edwin R. Hancock
Summary: This paper proposes a novel framework for computing Quantum-based Entropic Representations (QBER) for un-attributed graphs using Continuous-time Quantum Walk (CTQW). By transforming each original graph into a family of k-level neighborhood graphs, the framework captures multi-level topological information of the original global graph. The structure of each neighborhood graph is characterized using the Average Mixing Matrix (AMM) of CTQW, enabling the computation of Quantum Shannon Entropy and entropic signature. Experimental results demonstrate the effectiveness of the proposed approach in classification accuracies, outperforming other entropic complexity measuring methods, graph kernel methods, and graph deep learning methods.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Information Systems
Serafeim Papadias, Zoi Kaoudi, Jorge-Arnulfo Quiane-Ruiz, Volker Markl
Summary: The article introduces a system called Wharf for efficient storage and updating of random walks on streaming graphs. It achieves succinct representation through compressed binary trees and pairing functions, and efficient walk updates through pruning the walk search space. Experimental results demonstrate the superior performance of Wharf compared to other baseline methods.
PROCEEDINGS OF THE VLDB ENDOWMENT
(2022)
Article
Physics, Multidisciplinary
Ido Tishby, Ofer Biham, Eytan Katzav
Summary: This study presents analytical results for the distribution of cover times of random walks on random regular graphs, showing very good agreement with results obtained from computer simulations. The analysis includes deriving a master equation for the distribution of the number of distinct nodes visited by the random walk, calculating the cumulative distribution of cover times, and determining the distributions of partial cover and random cover times.
JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL
(2022)
Article
Multidisciplinary Sciences
Pauline Formaglio, Marina E. Wosniack, Raphael M. Tromer, Jaderson G. Polli, Yuri B. Matos, Hang Zhong, Ernesto P. Raposo, Marcos G. E. da Luz, Rogerio Amino
Summary: Plasmodium sporozoites actively migrate in the dermis and enter blood vessels to induce infection. Through intravital imaging, researchers found that sporozoites adopt a strategy of alternating global superdiffusive skin exploration and local subdiffusive blood vessel exploitation, enabling them to find intravasation hotspots associated with pericytes, enter the bloodstream and initiate malaria infection.
NATURE COMMUNICATIONS
(2023)
Article
Multidisciplinary Sciences
Elena Biselli, Elena Agliari, Adriano Barra, Francesca Romana Bertani, Annamaria Gerardino, Adele De Ninno, Arianna Mencattini, Davide Di Giuseppe, Fabrizio Mattei, Giovanna Schiavoni, Valeria Lucarini, Erika Vacchelli, Guido Kroemer, Corrado Di Natale, Eugenio Martinelli, Luca Businaro
SCIENTIFIC REPORTS
(2017)
Article
Physics, Mathematical
Elena Agliari, Adriano Barra, Raffaella Burioni, Aldo Di Biasio
JOURNAL OF MATHEMATICAL PHYSICS
(2012)
Article
Physics, Multidisciplinary
Elena Agliari, Lorenzo Asti, Adriano Barra, Raffaella Burioni, Guido Uguzzoni
JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL
(2012)
Article
Computer Science, Artificial Intelligence
Elena Agliari, Adriano Barra, Andrea De Antoni, Andrea Galluzzi
Article
Physics, Fluids & Plasmas
Elena Agliari, Adriano Barra, Silvia Bartolucci, Andrea Galluzzi, Francesco Guerra, Francesco Moauro
Article
Multidisciplinary Sciences
Elena Agliari, Lorenzo Asti, Adriano Barra, Rossana Scrivo, Guido Valesini, Robert S. Wallis
Article
Physics, Multidisciplinary
Elena Agliari, Francesco Alemanno, Adriano Barra, Martino Centonze, Alberto Fachechi
PHYSICAL REVIEW LETTERS
(2020)
Article
Multidisciplinary Sciences
Elena Agliari, Adriano Barra, Orazio Antonio Barra, Alberto Fachechi, Lorenzo Franceschi Vento, Luciano Moretti
SCIENTIFIC REPORTS
(2020)
Article
Multidisciplinary Sciences
Elena Agliari, Pablo J. Saez, Adriano Barra, Matthieu Piel, Pablo Vargas, Michele Castellana
Article
Physics, Multidisciplinary
Elena Agliari, Linda Albanese, Adriano Barra, Gabriele Ottaviani
JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL
(2020)
Article
Computer Science, Artificial Intelligence
Elena Agliari, Francesco Alemanno, Adriano Barra, Alberto Fachechi
Article
Multidisciplinary Sciences
Elena Agliari, Francesco Alemanno, Adriano Barra, Orazio Antonio Barra, Alberto Fachechi, Lorenzo Franceschi Vento, Luciano Moretti
SCIENTIFIC REPORTS
(2020)
Article
Physics, Multidisciplinary
Elena Agliari, Adriano Barra, Peter Sollich, Lenka Zdeborova
JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL
(2020)
Article
Physics, Multidisciplinary
Elena Agliari, Giordano De Marzo
EUROPEAN PHYSICAL JOURNAL PLUS
(2020)
Article
Physics, Fluids & Plasmas
Junhao Peng, Elena Agliari
Article
Physics, Multidisciplinary
Xiaoyu Shi, Jian Zhang, Xia Jiang, Juan Chen, Wei Hao, Bo Wang
Summary: This study presents a novel framework using offline reinforcement learning to improve energy consumption in road transportation. By leveraging real-world human driving trajectories, the proposed method achieves significant improvements in energy consumption. The offline learning approach demonstrates generalizability across different scenarios.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Junhyuk Woo, Soon Ho Kim, Hyeongmo Kim, Kyungreem Han
Summary: Reservoir computing (RC) is a new machine-learning framework that uses an abstract neural network model to process information from complex dynamical systems. This study investigates the neuronal and network dynamics of liquid state machines (LSMs) using numerical simulations and classification tasks. The findings suggest that the computational performance of LSMs is closely related to the dynamic range, with a larger dynamic range resulting in higher performance.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Yuwei Yang, Zhuoxuan Li, Jun Chen, Zhiyuan Liu, Jinde Cao
Summary: This paper proposes an extreme learning machine (ELM) algorithm based on residual correction and Tent chaos sequence (TRELM-DROP) for accurate prediction of traffic flow. The algorithm reduces the impact of randomness in traffic flow through the Tent chaos strategy and residual correction method, and avoids weight optimization using the iterative method. A DROP strategy is introduced to improve the algorithm's ability to predict traffic flow under varying conditions.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Chengwei Dong, Min Yang, Lian Jia, Zirun Li
Summary: This work presents a novel three-dimensional system with multiple types of coexisting attractors, and investigates its dynamics using various methods. The mechanism of chaos emergence is explored, and the periodic orbits in the system are studied using the variational method. A symbolic coding method is successfully established to classify the short cycles. The flexibility and validity of the system are demonstrated through analogous circuit implementation. Various chaos-based applications are also presented to show the system's feasibility.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Viorel Badescu
Summary: This article discusses the maximum work extraction from confined particles energy, considering both reversible and irreversible processes. The results vary for different types of particles and conditions. The concept of exergy cannot be defined for particles that undergo spontaneous creation and annihilation. It is also noted that the Carnot efficiency is not applicable to the conversion of confined thermal radiation into work.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
P. M. Centres, D. J. Perez-Morelo, R. Guzman, L. Reinaudi, M. C. Gimenez
Summary: In this study, a phenomenological investigation of epidemic spread was conducted using a model of agent diffusion over a square region based on the SIR model. Two possible contagion mechanisms were considered, and it was observed that the number of secondary infections produced by an individual during its infectious period depended on various factors.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Zuan Jin, Minghui Ma, Shidong Liang, Hongguang Yao
Summary: This study proposes a differential variable speed limit (DVSL) control strategy considering lane assignment, which sets dynamic speed limits for each lane to attract vehicle lane-changing behaviors before the bottleneck and reduce the impact of traffic capacity drop. Experimental results show that the proposed DVSL control strategy can alleviate traffic congestion and improve efficiency.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Matthew Dicks, Andrew Paskaramoorthy, Tim Gebbie
Summary: In this study, we investigate the learning dynamics of a single reinforcement learning optimal execution trading agent when it interacts with an event-driven agent-based financial market model. The results show that the agents with smaller state spaces converge faster and are able to intuitively learn to trade using spread and volume states. The introduction of the learning agent has a robust impact on the moments of the model, except for the Hurst exponent, which decreases, and it can increase the micro-price volatility as trading volumes increase.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Zhouzhou Yao, Xianyu Wu, Yang Yang, Ning Li
Summary: This paper developed a cooperative lane-changing decision system based on digital technology and indirect reciprocity. By introducing image scoring and a Q-learning based reinforcement learning algorithm, drivers can continuously evaluate gains and adjust their strategies. The study shows that this decision system can improve driver cooperation and traffic efficiency, achieving over 50% cooperation probability under any connected vehicles penetration and traffic density, and reaching 100% cooperation probability under high penetration and medium to high traffic density.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Josephine Nanyondo, Henry Kasumba
Summary: This paper presents a multi-class Aw-Rascle (AR) model with area occupancy expressed in terms of vehicle class proportions. The qualitative properties of the proposed equilibrium velocity and the stability conditions of the model are established. The numerical results show the effect of proportional densities on the flow of vehicle classes, indicating the realism of the proposed model.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Oliver Smirnov
Summary: This study proposes a new method for simultaneously estimating the parameters of the 2D Ising model. The method solves a constrained optimization problem, where the objective function is a pseudo-log-likelihood and the constraint is the Hamiltonian of the external field. Monte Carlo simulations were conducted using models of different shapes and sizes to evaluate the performance of the method with and without the Hamiltonian constraint. The results demonstrate that the proposed estimation method yields lower variance across all model shapes and sizes compared to a simple pseudo-maximum likelihood.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Przemyslaw Chelminiak
Summary: The study investigates the first-passage properties of a non-linear diffusion equation with diffusivity dependent on the concentration/probability density through a power-law relationship. The survival probability and first-passage time distribution are determined based on the power-law exponent, and both exact and approximate expressions are derived, along with their asymptotic representations. The results pertain to diffusing particles that are either freely or harmonically trapped. The mean first-passage time is finite for the harmonically trapped particle, while it is divergent for the freely diffusing particle.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Hidemaro Suwa
Summary: The choice of transition kernel is crucial for the performance of the Markov chain Monte Carlo method. A one-parameter rejection control transition kernel is proposed, and it is shown that the rejection process plays a significant role in determining the sampling efficiency.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Xudong Wang, Yao Chen
Summary: This article investigates the joint influence of expanding medium and constant force on particle diffusion. By starting from the Langevin picture and introducing the effect of external force in two different ways, two models with different force terms are obtained. Detailed analysis and derivation yield the Fokker-Planck equations and moments for the two models. The sustained force behaves as a decoupled force, while the intermittent force changes the diffusion behavior with specific effects depending on the expanding rate of the medium.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
