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Learning the optimally coordinated routes from the statistical mechanics of polymers

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arXiv 2019年

作者： Liao, Hao Wu, Xingtong Zhou, Mingyang Yeung, Chi Ho National Engineering Laboratory for big data computing systems Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Department of Science and Environmental Studies Education University of Hong Kong Hong Kong

Many major cities suffer from severe traffic congestion. Road expansion in the cites is usually infeasible, and an alternative way to alleviate traffic congestion is to coordinate the route of vehicles. Various path selection and planning algorithms are thus proposed, but most existing methods only plan paths separately and provide un-coordinated solutions. Recently, an analogy between the coordination of vehicular routes and the interaction of polymers is drawn;the spin glass theory in statistical physics is employed to optimally coordinate transportation routes. To further examine the advantages brought by path coordination, we incorporate the link congestion function developed by the Bureau of Public Roads (BPR) into the polymer routing algorithm. We then estimate in simulations the traveling time of all users saved by the polymer-BPR algorithm in randomly generated networks and real transportation networks in major cities including London, New York and Beijing. We found that a large amount of traveling time is saved in all studied networks, suggesting that the approach inspired by polymer physics is effective in minimizing the traveling time via path coordination, which is a promising tool for alleviating traffic congestions. Copyright © 2019, The Authors. All rights reserved.

关键词： Statistical mechanics

Enhancing countries' fitness with recommender systems on the international trade network

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arXiv 2019年

作者： Liao, Hao Wu, Xing-Tong Vidmer, Alexandre Huang, Xiao-Min Liu, Ming-Kai Zhou, Mingyang Zhang, Yi-Cheng National Engineering Laboratory for Big Data System Computing Technology Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Physics University of Fribourg Fribourg1700 Switzerland

Prediction is one of the major challenges in complex systems. The prediction methods have shown to be effective predictors of the evolution of networks. These methods can help policy makers to solve practical problems successfully and make better strategy for the future. In this work, we focus on exporting countries' data of the international trading network. A recommendation system is then used to identify the products corresponding to the production capacity of each individual country, but are somehow overlook by the country. Then, we simulate the evolution of the country's fitness if it would have followed the recommendations. The result of this work is the combination combine these two methods to provide insights to countries on how to enhance the diversification of their exported products in a scientific way and improve national competitiveness significantly, especially for developing countries. Copyright © 2019, The Authors. All rights reserved.

关键词： Recommender systems

A DECONSTRUCTION OF METHODS TO DERIVE ONE-POINT LENSING STATISTICS

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arXiv 2024年

作者： Alfradique, Viviane Castro, Tiago Marra, Valerio Quartin, Miguel Giocoli, Carlo Monaco, Pierluigi Instituto de Física Universidade Federal do Rio de Janeiro RJ Rio de Janeiro21941-972 Brazil Centro Brasileiro de Pesquisas Físicas Rua Dr. Xavier Sigaud 150 RJ Rio de Janeiro22290-180 Brazil INAF - Osservatorio Astronomico di Trieste via Tiepolo 11 TriesteI-34131 Italy INFN - Sezione di Trieste TriesteI-34100 Italy IFPU - Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy ICSC - Centro Nazionale di Ricerca in High Performance Computing Big Data e Quantum Computing Via Magnanelli 2 Bologna Italy Departamento de Física Universidade Federal do Espírito Santo ES Vitória29075-910 Brazil PPGCosmo Universidade Federal do Espírito Santo ES Vitória29075-910 Brazil Observatório do Valongo Universidade Federal do Rio de Janeiro RJ Rio de Janeiro20080-090 Brazil INAF - Osservatorio Astronomico di Bologna via Piero Gobetti 93/3 BolognaI-40129 Italy INFN Sezione di Bologna viale Berti Pichat 6/2 BolognaI-40127 Italy Università di Trieste Dipartimento di Fisica via Tiepolo 11 Trieste34143 Italy

Gravitational lensing is a crucial tool for exploring cosmic phenomena, providing insights into galaxy clustering, dark matter, and dark energy. Given the substantial computational demands of N-body simulations, approximate methods like PINOCCHIO and turboGL have been proposed as viable alternatives for simulating lensing probability density functions (PDFs). This paper evaluates these methods and their effectiveness across both weak and strong lensing regimes, with a focus in the context where baryonic effects are negligible. Our comparative analysis reveals that these methods are effective for applications where lensing is mild, such as the majority of sources of electromagnetic and gravitational waves. However, both PINOCCHIO and turboGL break down for large values of convergence and magnification due to their loss of accuracy in capturing small-scale nonlinear matter fields, owing to oversimplified assumptions about internal halo structures and reliance on perturbation theory. PINOCCHIO yields second-to-fourth moments of the lensing PDFs, which are 6-10% smaller than those resulting from N-body simulations in regimes where baryonic effects are minimal. These findings aim to inform future studies on gravitational lensing of point sources, which are increasingly relevant with upcoming supernova and gravitational wave datasets. © 2024, CC BY.

关键词： Probability density function

Cosmology with Galaxy Cluster Properties using Machine Learning

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arXiv 2023年

作者： Qiu, Lanlan Napolitano, Nicola R. Borgani, Stefano Zhong, Fucheng Li, Xiaodong Radovich, Mario Lin, Weipeng Dolag, Klaus Tortora, Crescenzo Wang, Yang Remus, Rhea-Silvia Wu, Sirui Longo, Giuseppe School of Physics and Astronomy Sun Yat-sen University Zhuhai Campus 2 Daxue Road Tangjia Zhuhai519082 China CSST Science Center for Guangdong-Hong Kong-Macau Great Bay Area Zhuhai519082 China Department of Physics E. Pancini University Federico II Via Cinthia 6 Naples80126-I Italy Astronomy Unit Department of Physics University of Trieste via Tiepolo 11 TriesteI-34131 Italy INAF-Osservatorio Astronomico di Trieste via G. B. Tiepolo 11 TriesteI-34143 Italy IFPU Institute for Fundamental Physics of the Universe Via Beirut 2 Trieste34014 Italy INFN Instituto Nazionale di Fisica Nucleare Via Valerio 2 TriesteI-34127 Italy ICSC - Italian Research Center on High Performance Computing Big Data and Quantum Computing INAF - Osservatorio Astronomico di Padova via dell’Osservatorio 5 Padova35122 Italy Universitäts-Sternwarte Fakultät für Physik Ludwig-Maximilians-Universität München Scheinerstr.1 München81679 Germany Max-Planck-Institut für Astrophysik Karl-Schwarzschild-Straße 1 Garching85741 Germany INAF – Osservatorio Astronomico di Capodimonte Salita Moiariello 16 Napoli80131 Italy Peng Cheng Laboratory No.2 Xingke 1st Street Shenzhen518000 China

Context. Galaxy clusters are the largest gravitating structures in the universe and their mass assembly is sensitive to the underlying cosmology. Their mass function, baryon fraction, and mass distribution have been used to infer cosmological parameters, despite the presence of systematics. However, the complexity of the scaling relations among galaxy cluster properties has never been fully exploited, limiting their potential as a cosmological probe. Aims. We propose the first Machine Learning (ML) method using galaxy cluster properties from hydrodynamical simulations in different cosmologies to predict cosmological parameters combining a series of canonical cluster observables, like gas mass, gas bolometric luminosity, gas temperature, stellar mass, cluster radius, total mass, and velocity dispersion at different redshifts. Methods. The machine learning model is trained on mock "measurements" of these observable quantities from Magneticum multi-cosmology simulations to derive unbiased constraints on a set of cosmological parameters. These include the mass density parameter, Ωm, the power spectrum normalization, σ8, the baryonic density parameter, Ωb, and the reduced Hubble constant, h0. Results. We test the ML model on catalogs of a few hundred clusters taken, in turn, from each simulation and find that the ML model can correctly predict the cosmology they have been picked from. The cumulative accuracy depends on the cosmology, ranging from 21% to 75%. We demonstrate that this is sufficient to derive unbiased constraints on the main cosmological parameters with errors of the order of ∼ 14% for Ωm, ∼ 8% for σ8, ∼ 6% for Ωb, and ∼ 3% for h0. Conclusions. This proof-of-concept analysis, yet based on a limited variety of multi-cosmology simulations, shows that machine learning can efficiently map the correlations in the multi-dimensional space of the observed quantities to the cosmological parameter space and narrow down the probability that a given sample belongs to a

关键词： Cosmology

Maximum Principle Based Algorithms for Deep Learning

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JOURNAL OF MACHINE LEARNING RESEARCH 2018年第154期18卷 1-29页

作者： Li, Qianxiao Chen, Long Tai, Cheng Weinan, E. Inst High Performance Computing Agcy Sci Technol & Res 1Fusionopolis Way Connexis North Singapore 138632 Singapore Peking Univ Beijing 100080 Peoples R China Peking Univ Beijing Inst Big Data Res Beijing 100080 Peoples R China Princeton Univ Princeton NJ 08544 USA Peking Univ Beijing Inst Big Data Res Beijing 100080 Peoples R China

The continuous dynamical system approach to deep learning is explored in order to devise alternative frameworks for training algorithms. Training is recast as a control problem and this allows us to formulate necessary optimality conditions in continuous time using the Pontryagin's maximum principle (PMP). A modification of the method of successive approximations is then used to solve the PMP, giving rise to an alternative training algorithm for deep learning. This approach has the advantage that rigorous error estimates and convergence results can be established. We also show that it may avoid some pitfalls of gradient-based methods, such as slow convergence on flat landscapes near saddle points. Furthermore, we demonstrate that it obtains favorable initial convergence rate per iteration, provided Hamiltonian maximization can be efficiently carried out - a step which is still in need of improvement. Overall, the approach opens up new avenues to attack problems associated with deep learning, such as trapping in slow manifolds and inapplicability of gradient-based methods for discrete trainable variables.

关键词： deep learning optimal control Pontryagin's maximum principle method of successive approximations

Accumulative time-based ranking method to reputation evaluation in information networks

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arXiv 2019年

作者： Liao, Hao Liu, Qi-Xin Huang, Ze-Cheng Yeung, Chi Ho Zhang, Yi-Cheng National Engineering Laboratory on Big Data System Computing Technology Guangdong Province Engineering Center of China-made High Performance Data Computing System College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Institute of Big Data Intelligent Management and Decision Shenzhen University Shenzhen518060 China Department of Science and Environmental Studies The Education University of Hong Kong Hong Kong Hong Kong Department of Physics University of Fribourg Fribourg Switzerland

With the rapid development of modern technology, the Web has become an important platform for users to make friends and acquire information. However, since information on the Web is over-abundant, information filtering becomes a key task for online users to obtain relevant suggestions. As most Websites can be ranked according to users’ rating and preferences, relevance to queries, and recency, how to extract the most relevant item from the over-abundant information is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed accumulative time-based ranking (ATR) algorithm, we incorporate two behavioral weighting factors which are updated when users select or rate items, to reflect the evolution of user reputation and item quality over time. We showed that our algorithm outperforms state-of-the-art ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications. Copyright © 2019, The Authors. All rights reserved.

关键词： Information filtering

eROSITA clusters dynamical state and their impact on the BCG luminosity

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arXiv 2025年

作者： Zenteno, A. Kluge, M. Kharkrang, R. Hernandez-Lang, D. Damke, G. Saro, A. Monteiro-Oliveira, R. Carrasco, E.R. Salvato, M. Comparat, J. Fabricius, M. Snigula, J. Arevalo, P. Cuevas, H. Castellon, J.L. Nilo Ramirez, A. Astudillo, S. Véliz Landriau, M. Myers, A.D. Schlafly, E. Valdes, F. Weaver, B. Mohr, J.J. Grandis, S. Klein, M. Liu, A. Bulbul, E. Zhang, X. Sanders, J.S. Bahar, Y.E. Ghirardini, V. Ramos, M. Balzer, F. Cerro Tololo Inter-American Observatory NSF’s National Optical-Infrared Astronomy Research Laboratory Casilla 603 La Serena Chile Max Planck Institute for Extraterrestrial Physics Giessenbachstrasse 1 Garching85748 Germany Universitè Paris-Saclay CNRS Institut dÁstrophysique Spatiale Orsay91405 France Faculty of Physics Ludwig-Maximilians-Universität München Scheinerstr. 1 Munich81679 Germany INAF - Osservatorio Astronomico di Trieste via G. B. Tiepolo 11 Trieste34143 Italy IFPU - Institute for Fundamental Physics of the Universe Via Beirut 2 Trieste34014 Italy Astronomy Unit Department of Physics University of Trieste via Tiepolo 11 Trieste34131 Italy INFN - National Institute for Nuclear Physics Via Valerio 2 TriesteI-34127 Italy ICSC - Italian Research Center on High Performance Computing Big Data and Quantum Computing Italy Institute of Astronomy and Astrophysics Academia Sinica Taipei10617 Taiwan International Gemini Observatory NSF NOIRLab Casilla 603 La Serena Chile Instituto de Física y Astronomía Universidad de Valparaíso Gran Bretaña 1111 Valparaíso Chile Chile Departamento de Astronomía Universidad de La Serena Av. Raúl Bitrán 1305 La Serena Chile Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Department of Physics & Astronomy University of Wyoming 1000 E. University Dept. 3905 LaramieWY82071 United States Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States 950 N. Cherry Ave TucsonAZ85732 United States Universität Innsbruck Institut für Astro- und Teilchenphysik Technikerstr. 25/8 Innsbruck6020 Austria

Context. The SRG (Spectrum Roentgen Gamma)/eROSITA first public release contains 12,247 clusters and groups from the first 6 months of operation. We use the offset between the Brightest Cluster Galaxy (BCG) and the X–ray peak (DBCG−X) to classify the cluster dynamical state of 3,946 galaxy clusters and groups. Aims. We aim to investigate the evolution of the merger and relaxed cluster distributions with redshift and mass, and their impact on the BCG. Methods. We use the X–ray peak from the eROSITA survey and the BCG position from the DECaLS DR10 optical data, which includes the DECam eROSITA Survey optical data, to measure the DBCG−X offset. We model the distribution of DBCG−X, in units of r500, as the sum of two Rayleigh distributions representing the cluster’s relaxed and disturbed populations, and explore their evolution with redshift and mass. To explore the impact of the cluster’s dynamical state on the BCG luminosity, we separate the main sample according to the dynamical state. We define clusters as relaxed if DBCG−X500, disturbed if DBCG−X>0.5r500, and as ‘diverse’ if 0.25BCG−X Copyright © 2025, The Authors. All rights reserved.

关键词： Photons

Multi-wavelength properties of z 6 LISA detectable events

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arXiv 2025年

作者： Chakraborty, Srija Gallerani, Simona Di Mascia, Fabio Zana, Tommaso Valentini, Milena Carniani, Stefano Vito, Fabio Bhatt, Maulik Scuola Normale Superiore Piazza dei Cavalieri 7 PI Pisa56126 Italy Dipartimento di Fisica Sapienza Universita di Roma Piazzale Aldo Moro 5 Roma00185 Italy Dipartimento di Fisica dell’Universita di Trieste Sez. di Astronomia via Tiepolo 11 TriesteI-34131 Italy INAF Osservatorio Astronomico di Trieste via Tiepolo 11 TriesteI-34131 Italy INFN Instituto Nazionale di Fisica Nucleare Via Valerio 2 TriesteI-34127 Italy ICSC - Italian Research Center on High Performance Computing Big Data and Quantum Computing via Magnanelli 2 Casalecchio di Reno40033 Italy INAF - Osservatorio Astronomico di Bologna via Piero Gobetti 93/3 BolognaI-40129 Italy

We investigate the intrinsic and observational properties of z 6 galaxies that host the coalescence of massive (MBH ∼ 105−6 M☉) black holes (MBHs) giving rise to gravitational waves (GWs) detectable with the Laser Interferometer Space Antenna (LISA). We adopt a zoom-in cosmological hydrodynamical simulation of galaxy formation and black hole (BH) co-evolution, based on the GADGET-3 code, zoomed-in on a Mℎ ∼ 1012 M☉ dark matter halo at z = 6, which hosts a fast accreting (M¤ ∼ 35 M☉ yr−1) super-massive black hole (SMBH, MBH ∼ 109 M☉) and a star-forming galaxy (SFR ∼ 100 M☉ yr−1). Following the SMBH formation backward in time, we identify the merging events that concurred to its formation and we pick up the ones that are detectable with LISA. Among these LISA detectable events (LDEs), we select those that, based on their intrinsic properties (M¤ , SFR, gas metallicity, and dust mass), are expected to be bright in one or more electromagnetic (EM) bands, e.g. rest-frame X-ray, ultra-violet (UV) and far-infrared (FIR). We further restrict our selection to those LDEs that, after considering the effect of delay due to dynamical friction in the MBH coalescence, are still occurring at z 6. We find that ∼20-30% of the LDEs and their host galaxies are also detectable with EM telescopes. We post-process these events with dust radiative transfer calculations to make accurate predictions about their spectral energy distributions (SEDs) and continuum maps in the JWST to ALMA wavelength range. We compare the spectra arising from galaxies hosting the merging MBHs with those arising from AGN powered by single accreting BHs. We find that it will be impossible to identify an LDE from the continuum SEDs because of the absence of specific imprints from the merging MBHs. Finally, we compute the profile of the Hα line arising from LDEs, taking into account both the contribution from their star-forming regions and the accreting MBHs. We find that the presence of two accreting MBHs would be

关键词： Dark Matter

The effects of different travel modes and travel destinations on COVID-19 transmission in global cities

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Science Bulletin 2022年第6期67卷 588-592,M0003页

作者： Rui Zhu Luc Anselin Michael Batty Mei-Po Kwan Min Chen Wei Luo Tao Cheng Che Kang Lim Paolo Santi Cheng Cheng Qiushi Gu Man Sing Wong Kai Zhang Guonian Lü Carlo Ratti Department of Land Surveying and Geo-Informatics the Hong Kong Polytechnic UniversityHong KongChina Key Laboratory of Virtual Geographic Environment(Ministry of Education) Nanjing Normal UniversityNanjing 210023China Senseable City Laboratory Future Urban Mobility IRGSingapore-MIT Alliance for Research and TechnologySingapore 138602Singapore Center for Spatial Data Science Division of the Social SciencesUniversity of ChicagoChicago IL 60637USA School of Geography Science Nanjing Normal UniversityNanjing 210023China Centre for Advanced Spatial Analysis Faculty of the Built EnvironmentUniversity College LondonLondon WC1E 6BTUK Department of Geography and Resource Management the Chinese University of Hong KongHong KongChina Institute of Space and Earth Information Science the Chinese University of Hong KongHong KongChina Department of Human Geography and Spatial Planning Utrecht UniversityUtrecht 3584 CSNetherlands State Key Laboratory Cultivation Base of Geographical Environment Evolution Nanjing 210023China Department of Geography National University of SingaporeSingapore 117570Singapore SpaceTimeLab Department of CivilEnvironmental and Geomatic EngineeringUniversity College LondonLondon WC1E 6BTUK Department of Clinical Translational Research Singapore General HospitalSingapore 169856Singapore Division of Clinical Immunology and Transfusion Medicine Department of Laboratory MedicineKarolinska InstitutetStockholm 17177Sweden Senseable City Laboratory Department of Urban Studies and PlanningMassachusetts Institute of TechnologyCambridgeMA 02139USA Istituto di Informatica e Telematica del CNR Pisa 56124Italy Department of System Science Institute of High Performance Computing Agency for ScienceTechnology and ResearchSingapore 138632Singapore Department of Tourism Studies School of HumanitiesSoutheast UniversityNanjing 210096China

The international community has made significant efforts to flatten the COVID-19 curve,including predicting transmission[1,2],executing unprecedented global lockdowns and social distancing[3,4],promoting the wearing of facemasks and social distancing measures[5],and isolating confirmed cases and contacts[6].Because of the adverse consequences of these lockdown measures[7],many cities have reopened so they can rebuild their ***,as mobility has gradually returned towards normal,imported cases from unknown sources have disrupted the recovery situation,and cities are continually at high risk of new waves of infection[8,9]since airborne transmission is the dominant transmission route[10].

关键词：多变量时间序列疫情传播出行方式谷歌公司交通出行病毒传播出行行为大城市