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arXiv 2025年

作者： Ren, Wencai Bøggild, Peter Redwing, Joan Marie Novoselov, Kostya Sun, Luzhao Qi, Yue Jia, Kaicheng Liu, Zhongfan Burton, Oliver Alexander-Webber, Jack Hofmann, Stephan Cao, Yang Long, Yu Yang, Quan-Hong Li, Dan Choi, Soo Ho Kim, Ki Kang Lee, Young Hee Li, Mian Huang, Qing Gogotsi, Yury Clark, Nicholas Carl, Amy Gorbachev, Roman Olsen, Thomas Rosen, Johanna Thygesen, Kristian Sommer Efetov, Dmitri K. Jessen, Bjarke S. Yankowitz, Matthew Barrier, Julien Kumar, Roshan Krishna Koppens, Frank H.L. Deng, Hui Li, Xiaoqin Dai, Siyuan Basov, D.N. Wang, Xinran Das, Saptarshi Duan, Xiangfeng Yu, Zhihao Borsch, Markus Ferrari, Andrea C. Huber, Rupert Kira, Mackillo Xia, Fengnian Wang, Xiao Wu, Zhong-Shuai Feng, Xinliang Simon, Patrice Cheng, Hui-Ming Liu, Bilu Xie, Yi Jin, Wanqin Nair, Rahul Raveendran Xu, Yan Zhang, Qing Katiyar, Ajit K. Ahn, Jong-Hyun Aharonovich, Igor Hersam, Mark C. Roche, Stephan Hua, Qilin Shen, Guozhen Ren, Tianling Zhang, Hao-Bin Koo, Chong Min Koratkar, Nikhil Pellegrini, Vittorio Young, Robert J. Qu, Bill Lemme, Max Pollard, Andrew J. Shenyang National Laboratory for Materials Science Institute of Metal Research Chinese Academy of Sciences 72 Wenhua Road Shenyang110016 China Technical University of Denmark Denmark The Pennsylvania State University United States University of Manchester United Kingdom Institute for Functional Intelligent Materials National University of Singapore Singapore Beijing Graphene Institute China University of Cambridge United Kingdom Department of Chemical Engineering The University of Melbourne Victoria Australia Nanoyang Group Tianjin Key Laboratory of Advanced Carbon and Electrochemical Energy Storage School of Chemical Engineering and Technology Tianjin University Tianjin300072 China The Hong Kong University of Science and Technology Hong Kong Center for Integrated Nanostructure Physics Institute for Basic Science Suwon16419 Korea Republic of Sungkyunkwan University Suwon16419 Korea Republic of Zhejiang Key Laboratory of Data-Driven High-Safety Energy Materials and Applications Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences China Drexel University United States Linköping University Sweden Ludwig-Maximilians-Universität München Germany University of Washington United States ICFO The Institute of Photonic Sciences Castelldefels Barcelona08860 Spain University of Michigan United States University of Texas Austin United States Auburn University United States Columbia University United States State Key Laboratory of Catalysis Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian China University of California Los Angeles United States Suzhou Laboratory Suzhou China School of Integrated Circuit Science and Engineering Nanjing University of Posts and Telecommunications Nanjing210023 China Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States University of Regensburg Germany Department of Electrical and Computer Engineering Yale University New Have

Over the past two decades, 2D materials have rapidly evolved into a diverse and expanding family of material platforms. Many members of this materials class have demonstrated their potential to deliver transformative impact on fundamental research and technological applications across different fields. In this roadmap, we provide an overview of the key aspects of 2D material research and development, spanning synthesis, properties and commercial applications. We specifically present roadmaps for high impact 2D materials, including graphene and its derivatives, transition metal dichalcogenides, MXenes as well as their heterostructures and moiré systems. The discussions are organized into thematic sections covering emerging research areas (e.g., twisted electronics, moiré nano-optoelectronics, polaritronics, quantum photonics, and neuromorphic computing), breakthrough applications in key technologies (e.g., 2D transistors, energy storage, electrocatalysis, filtration and separation, thermal management, flexible electronics, sensing, electromagnetic interference shielding, and composites) and other important topics (computational discovery of novel materials, commercialization and standardization). This roadmap focuses on the current research landscape, future challenges and scientific and technological advances required to address, with the intent to provide useful references for promoting the development of 2D materials. © 2025, CC BY.

关键词： Integrated optoelectronics

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First machine learning gravitational-wave search mock data challenge

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Physical Review D 2023年第2期107卷 023021-023021页

作者： Marlin B. Schäfer Ondřej Zelenka Alexander H. Nitz He Wang Shichao Wu Zong-Kuan Guo Zhoujian Cao Zhixiang Ren Paraskevi Nousi Nikolaos Stergioulas Panagiotis Iosif Alexandra E. Koloniari Anastasios Tefas Nikolaos Passalis Francesco Salemi Gabriele Vedovato Sergey Klimenko Tanmaya Mishra Bernd Brügmann Elena Cuoco E. A. Huerta Chris Messenger Frank Ohme Max-Planck-Institut für Gravitationsphysik Albert-Einstein-Institut D-30167 Hannover Germany Leibniz Universität Hannover D-30167 Hannover Germany Friedrich-Schiller-Universität Jena D-07743 Jena Germany Michael Stifel Center Jena D-07743 Jena Germany CAS Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing 100190 China Department of Astronomy Beijing Normal University Beijing 100875 China Peng Cheng Laboratory Shenzhen 518055 China Department of Informatics Aristotle University of Thessaloniki GR-54124 Thessaloniki Greece Department of Physics Aristotle University of Thessaloniki GR-54124 Thessaloniki Greece GSI Helmholtz Center for Heavy Ion Research Planckstraße 1 64291 Darmstadt Germany Università di Trento Dipartimento di Fisica I-38123 Povo Trento Italy INFN Trento Institute for Fundamental Physics and Applications I-38123 Povo Trento Italy INFN Sezione di Padova I-35131 Padova Italy Department of Physics University of Florida PO Box 118440 Gainesville Florida 32611-8440 USA European Gravitational Observatory (EGO) I-56021 Cascina Pisa Italy Scuola Normale Superiore Piazza dei Cavalieri 7 I-56126 Pisa Italy INFN Sezione di Pisa Largo Bruno Pontecorvo 3 I-56127 Pisa Italy Data Science and Learning Division Argonne National Laboratory Lemont Illinois 60439 USA Department of Computer Science University of Chicago Chicago Illinois 60637 USA SUPA School of Physics and Astronomy University of Glasgow Glasgow G12 8QQ United Kingdom

We present the results of the first Machine Learning Gravitational-Wave Search Mock data Challenge. For this challenge, participating groups had to identify gravitational-wave signals from binary black hole mergers of increasing complexity and duration embedded in progressively more realistic noise. The final of the 4 provided datasets contained real noise from the O3a observing run and signals up to a duration of 20 s with the inclusion of precession effects and higher order modes. We present the average sensitivity distance and run-time for the 6 entered algorithms derived from 1 month of test data unknown to the participants prior to submission. Of these, 4 are machine learning algorithms. We find that the best machine learning based algorithms are able to achieve up to 95% of the sensitive distance of matched-filtering based production analyses for simulated Gaussian noise at a false-alarm rate (FAR) of one per month. In contrast, for real noise, the leading machine learning search achieved 70%. For higher FARs the differences in sensitive distance shrink to the point where select machine learning submissions outperform traditional search algorithms at FARs ≥200 per month on some datasets. Our results show that current machine learning search algorithms may already be sensitive enough in limited parameter regions to be useful for some production settings. To improve the state-of-the-art, machine learning algorithms need to reduce the false-alarm rates at which they are capable of detecting signals and extend their validity to regions of parameter space where modeled searches are computationally expensive to run. Based on our findings we compile a list of research areas that we believe are the most important to elevate machine learning searches to an invaluable tool in gravitational-wave signal detection.

关键词： Gravitational wave detection Gravitational waves Machine learning

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A novel hybrid GWO-FPA algorithm for optimization applications 1st

A novel hybrid GWO-FPA algorithm for optimization applicatio...

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1st International Conference on Smart Vehicular Technology, Transportation, Communication and applications, VTCA 2017

作者： Pan, Jeng-Shyang Dao, Thi-Kien Chu, Shu-Chuan Nguyen, Trong-The Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Information Science and Engineering Fujian University of Technology Fuzhou China School of Computer Science Engineering and Mathematics Flinders University Adelaide Australia Department of Information Technology Haiphong Private University Haiphong Viet Nam

ISBN: (纸本)9783319707297

The recent trend of research is to hybridize two or several numbers of variants to find out the better quality of solution in practical optimization applications. In this paper, a new approach hybrid Grey Wolf Optimizer (GWO)-Flower Pollination Algorithm (FPA) is proposed based on the combination of exploitation phase in GWO and exploration stage in FPA. The hybrid proposed GWOFPA improves movement directions and speed of the grey wolves in updating positions of FPA. The simulation uses six benchmark tests for evaluating the performance of the proposed method. Compared other metaheuristics such as Particle Swarm Optimization (PSO), FPA, and GWO, the simulation results demonstrate that the proposed approach offers the better performance in solving optimization problems with or without unknown search areas. © Springer International Publishing AG 2018.

关键词： Particle swarm optimization (PSO)

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An automatic biomedical ontology meta-matching technique

Journal of Network Intelligence

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Journal of Network Intelligence 2019年第3期4卷 109-113页

作者： Xue, Xingsi Yang, Haiyan Zhang, Jie Zhang, Jing Chen, Dongxu College of Information Science and Engineering Intelligent Information Processing Research Center Fujian Key Lab for Automotive Electronics and Electric Drive Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No.3 Xueyuan Road University Town Minhou Fuzhou CityFujian Province350118 China College of Information Science and Engineering Fujian University of Technology No.33 Xuefu South Road University Town Minhou Fuzhou CityFujian Province350118 China School of Computer Science and Engineering Yulin Normal University No.299 Education Middle Road Yulin CityGuanxi Province537000 China School of Computing Ulster University Jordanstown BT370QB United Kingdom Fujian Medical University Union Hospital No.29 Xinquan Road Gulou Fuzhou CityFujian Province350001 China

Biomedical ontology matching aims at determining the heterogeneous biomed-ical concepts, and bridging the semantic gap between heterogeneous biomedical ontologies. The foundation of a biomedical ontology matching technique is the Biomedical Concept Similarity Measure (BCSM), which calculates the similarity value between two biomed-ical concepts. Since various BCSMs have different advantages, usually several BCSMs are aggregated together to improve the result’s confidence. How to tune the aggregating weights to ensure the quality of the alignment is called biomedical ontology meta-matching problem, which is a challenge in the ontology matching domain. Currently, researchers mainly focus on how to tune the aggregating weights for various similarity measures to improve the quality of the ontology alignments. However, the ignorance of the effects brought about by different biomedical concept mapping’s preference on different similarity measures significantly reduces the alignment’s quality. To overcome this drawback, in this work, we formally define the biomedical ontology meta-matching problem, and then present an Ordered Weighted Average (OWA) based approach to automatically aggregate various biomedical concept similarity measures. In our method, the aggregating weights determined for each concept mapping is associated with the ordered position of the similarity value instead of a particular concept similarity measure. The experiment utilizes the large biomed track provided by Ontology Alignment Evaluation Initiative (OAEI) to test our proposal’s performance, and the experimental results show the effectiveness of our proposal. © 2019, Taiwan Ubiquitous Information. All rights reserved.

关键词： Semantics

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Relay Node Selection Strategy for Wireless Sensor Network 4th

Relay Node Selection Strategy for Wireless Sensor Network

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4th Euro-China Conference on Intelligent data Analysis and applications, ECC 2017

作者： Kong, Lingping Pan, Jeng-Shyang Chu, Shu-Chuan Roddick, John F. Innovative Information Industry Research Center Harbin Institute of Technology Shenzhen Graduate School Shenzhen China Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Information Science and Engineering Fujian University of Technology Fujian China School of Computer Science Engineering and Mathematics Flinders University Adelaide Australia

ISBN: (纸本)9783319685267

In the past few years, people have experienced advanced interest in the potential use of wireless sensor network in applications such as environment surveillance, military field protection and medical treatment. Usually hundreds even thousands of sensors are scattered randomly in remote environment. In generally, for the scalability of sensor network, cluster techniques are often used to group nodes into several sets. And in this paper, we proposed a new method called GF_CENTER to select the positions of centers, which is also a kind of k-center problem. And a new fitness function is presented for optimize the resolution. We compare our method with two other algorithms. GF_CENTER method minimizes the number of centers in the network. From the experiments, GF_CENTER find smaller number of centers than the other methods, which lower the construction fee of network. © 2018, Springer International Publishing AG.

关键词： Genetic algorithms

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The QUasi-Affine TRansformation Evolution (QUATRE) Algorithm: An Overview 4th

The QUasi-Affine TRansformation Evolution (QUATRE) Algorithm...

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4th Euro-China Conference on Intelligent data Analysis and applications, ECC 2017

作者： Meng, Zhenyu Pan, Jeng-Shyang Li, Xiaoqing Department of Computer Science and Technology Harbin Institute of Technology Shenzhen Graduate School Shenzhen China Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9783319685267

QUasi-Affine TRansformation Evolution (QUATRE) algorithm is a new simple but powerful stochastic optimization algorithm proposed recently. The QUATRE algorithm aims to tackle the representational/positional bias inborn with DE algorithm and secures an overall better performance on commonly used Conference of Evolutionary Computation (CEC) benchmark functions. Recently, several QUATRE variants have been already proposed since its inception in 2016 and performed very well on many benchmark functions. In this paper, we mainly have a brief overview of all these proposed QUATRE variants first and then make simple contrasts between these QUATRE variants and several state-of-the-art DE variants under CEC2013 test suites for real-parameter single objective optimization benchmark functions. Experiment results show that the movement trajectory of individuals in the QUATRE structure is much more efficient than DE structure on most of the tested benchmark functions. © 2018, Springer International Publishing AG.

关键词： Global optimization

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Directional shuffled frog leaping algorithm 1st

Directional shuffled frog leaping algorithm

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1st International Conference on Smart Vehicular Technology, Transportation, Communication and applications, VTCA 2017

作者： Kong, Lingping Pan, Jeng-Shyang Chu, Shu-Chuan Roddick, John F. Innovative Information Industry Research Center Harbin Institute of Technology Shenzhen Graduate School Shenzhen China Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Information Science and Engineering Fujian University of Technology FuzhouFujian China School of Computer Science Engineering and Mathematics Flinders University Adelaide Australia

ISBN: (纸本)9783319707297

Shuffled frog leaping algorithm is one of the popular used optimization algorithms. This algorithm includes the local search and global search two solving modes, but in this method only the worst frog from divided group is considered for improving location. In this paper, we propose a directional shuffled frog leaping algorithm (DSFLA) by introducing the directional updating and real-time interacting concepts. A direction flag is set for a frog before moving, if the frog goes better in a certain direction, it will get better in a big probability by moving a little further along that direction. The movement counter is set for preventing the frog move forward infinite. Real-time interacting works by sharing the currently optimal positions from the other groups. There should have some similarities among the best ones, and the worst individual could be improved by using those similarities. The experimental results show that the proposed approach is a very effective method for solving test functions. © Springer International Publishing AG 2018.

关键词： Swarm intelligence

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Exploiting Box Expansion and Grid Partitioning for Parallel FPGA Routing

Exploiting Box Expansion and Grid Partitioning for Parallel ...

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Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM)

作者： Minghua Shen Guojie Luo Nong Xiao School of Data and Computer Science Sun Yat-sen University China Center for Energy-efficient Computing and Applications Peking University China

FPGA is reconfigurable architecture able to implement a good trade-off in terms of energy, performance, and flexibility. However, the main drawback in using FPGAs is their very long routing time. Multi-core processors are now ubiquitous, making parallelism an increasingly attractive direction to accelerate the routing time. In this paper, we propose a task-level distributed parallel router that exploits conflict-aware box expansion and equal-sized grid partitioning for parallel FPGA routing. In box expansion, we adopt net bounding box to limit the search space of routing and based on current congestion state among nets, we design conflict-aware box expansion to optimize the serial routing time and provide more non-conflicting nets to obtain significant parallelism. In grid partitioning, we employ equal-sized grid to partition the overall routing region, where the nets in same grid are routed in serial and the grids are processed in parallel. Specifically, the nets distributed in multiple grids can be used to balance the workloads among processor cores. Experiments show that the parallel implementation scales to an average speedup of about 21 × using 32 processor cores compared to state-of-the-art VPR router.

关键词： Routing Field programmable gate arrays Parallel processing Task analysis Servers computer science Electronic mail

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Why Are There Six Degrees of Separation in a Social Network?

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Physical Review X 2023年第2期13卷 021032-021032页

作者： I. Samoylenko D. Aleja E. Primo K. Alfaro-Bittner E. Vasilyeva K. Kovalenko D. Musatov A. M. Raigorodskii R. Criado M. Romance D. Papo M. Perc B. Barzel S. Boccaletti Moscow Institute of Physics and Technology 9 Institutskiy Pereulok Dolgoprudny Moscow Region 141701 Russia National Research University Higher School of Economics 6 Ulitsa Usacheva Moscow 119048 Russia Universidad Rey Juan Carlos Calle Tulipán s/n 28933 Móstoles Madrid Spain Department of Internal Medicine University of Michigan Ann Arbor Michigan 48109 USA Laboratory of Mathematical Computation on Complex Networks and their Applications Universidad Rey Juan Carlos Calle Tulipán s/n Móstoles 28933 Madrid Spain P.N. Lebedev Physical Institute of the Russian Academy of Sciences 53 Leninsky Prospekt Moscow 119991 Russia Scuola Superiore Meridionale Largo S. Marcellino 10 80138 Napoli NA Italy Russian Academy of National Economy and Public Administration 84 Prospekt Vernadskogo Moscow 119606 Russia Caucasus Mathematical Center at Adyghe State University 208 Pervomayskaya Ulitsa Maykop Adygea 385000 Russia Moscow State University 1 Leninskie Gory Moscow 119991 Russia Institute of Mathematics and Computer Science Buryat State University 5 Ulitsa Ranzhurova Ulan-Ude Buryatia 670000 Russia Data Complex Networks and Cybersecurity Research Institute Universidad Rey Juan Carlos Plaza Manuel Becerra 14 28028 Madrid Spain Department of Neuroscience and Rehabilitation University of Ferrara Ferrara Italy Center for Translational Neurophysiology of Speech and Communication Fondazione Istituto Italiano di Tecnologia Ferrara Italy Faculty of Natural Sciences and Mathematics University of Maribor Koroška cesta 160 2000 Maribor Slovenia Department of Medical Research China Medical University Hospital China Medical University Taichung 404332 Taiwan Complexity Science Hub Vienna Josefstädterstraße 39 1080 Vienna Austria Alma Mater Europaea Slovenska ulica 17 2000 Maribor Slovenia Department of Physics Kyung Hee University 26 Kyungheedae-ro Dongdaemun-gu Seoul Republic of Korea Department of Mathematics Bar-Ilan University Ramat-Gan 52900 Israel The Gonda Multi

A wealth of evidence shows that real-world networks are endowed with the small-world property, i.e., that the maximal distance between any two of their nodes scales logarithmically rather than linearly with their size. In addition, most social networks are organized so that no individual is more than six connections apart from any other, an empirical regularity known as the six degrees of separation. Why social networks have this ultrasmall-world organization, whereby the graph’s diameter is independent of the network size over several orders of magnitude, is still unknown. We show that the “six degrees of separation” is the property featured by the equilibrium state of any network where individuals weigh between their aspiration to improve their centrality and the costs incurred in forming and maintaining connections. We show, moreover, that the emergence of such a regularity is compatible with all other features, such as clustering and scale-freeness, that normally characterize the structure of social networks. Thus, our results show how simple evolutionary rules of the kind traditionally associated with human cooperation and altruism can also account for the emergence of one of the most intriguing attributes of social networks.

关键词： Degree distributions Network evolution Real world networks Small-world networks

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Mean-field theory for the Nagel-Schreckenberg model with overtaking strategy 5

Mean-field theory for the Nagel-Schreckenberg model with ove...

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5th International Workshop on Statistical Physics and Mathematics for Complex Systems, SPMCS 2017

作者： Su, Zhu Deng, Weibing Han, Jihui Li, Wei Cai, Xu National Engineering Laboratory for Technology of Big Data Applications in Education Central China Normal University Wuhan China Complexity Science Center Institute of Particle Physics Central China Normal University Wuhan China School of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou China

Based on the Nagel-Schreckenberg (NS) model with periodic boundary conditions, a modified model considered overtaking strategy (NSOS) has been proposed [1, 2]. In this paper, we focus on the theoretical analysis of traffic flow for NSOS model by using mean-field method. In the special case of vmax = 1 where vehicles can not overtake preceding ones, the features of stationary state can be obtained exactly. However, in the case of vmax > 1 where overtaking happens, some approximative methods have to be took into account. The main results are that we find the reason why traffic flow is increased in the regime where densities exceed the maximum flow density, and the influence of traffic flow on the transition density is dominated by the braking probability p. © Published under licence by IOP Publishing Ltd.

关键词： Mean field theory

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