By leveraging the 5G enabled vehicular ad hoc network(5G-VANET), it is widely recognized that connected vehicles have the potentials to improve road safety, transportation intelligence and provide in-vehicle entertain...By leveraging the 5G enabled vehicular ad hoc network(5G-VANET), it is widely recognized that connected vehicles have the potentials to improve road safety, transportation intelligence and provide in-vehicle entertainment experience. However, many enabling applications in 5G-VANET rely on the efficient content sharing among mobile vehicles, which is a very challenging issue due to the extremely large data volume, rapid topology change, and unbalanced traffic. In this paper, we investigate content prefetching and distribution in 5G-VANET. We first introduce an edge computing based hierarchical architecture for efficient distribution of large-volume vehicular data. We then propose a multi-place multi-factor prefetching scheme to meet the rapid topology change and unbalanced traffic. The content requests of vehicles can be served by neighbors, which can improve the sharing efficiency and alleviate the burden of networks. Furthermore, we use a graph theory based approach to solve the content distribution by transforming it into a maximum weighted independent set problem. Finally, the proposed scheme is evaluated with a greedy transmission strategy to demonstrate its efficiency.展开更多
The term ＂twin cities＂ refers to a program in which cities from different places in the world form a ＂twinning＂ alliance that serves as a setting for educational, cultural, political, and social collaborations （G...The term ＂twin cities＂ refers to a program in which cities from different places in the world form a ＂twinning＂ alliance that serves as a setting for educational, cultural, political, and social collaborations （Grosspietsch 2009）. The purpose of the program is to promote the twin cities in all aspects of life （Jayne, Hubbard, and Bell 2013） and facilitate a feeling of belonging and identity among their residents （Ogawa 2012）. In the current study, the cities of Beer Sheva and Nahariya were taken as case studies for examining the contribution of the program to promoting residents＇ feeling of belonging to their Jewish identity. Specifically, the current study attempted to examine the effect of town of residence and age group on feeling of belonging, and whether familiarity with the Twin Cities program affected the feeling of belonging to Jewish identity, in the assumption that residents familiar with the program would report a stronger feeling of belonging than residents not familiar with it. The study included 147 participants aged 17-64, of them 80 residents of Beer Sheva and 67 of Nahariya. All the participants were recruited to the study voluntarily and were requested to complete an online self-report questionnaire examining feeling of belonging to Jewish identity. Moreover, an interview was conducted with the representative of the delegations at the Amal school in Nahariya, to reaffirm the findings. The research findings refuted the main research assumption that the Twin Cities program would influence the feeling of belonging. In fact, the current study indicates that no correlation was found between feeling of belonging and any of the research measures, aside from religiosity. Furthermore, and in contrast to the hypothesis, the research findings indicate that participants who were not familiar with the program reported a stronger feeling of belonging than participants who were familiar with it. Due to the surprising findings, the current study raises the possibility that the Twin Cities program is undergoing a process of change and thus promotes individual values more than collective values. This contention changes the essential purpose of the program and this is the significance of the current study.展开更多
Artificial intelligence(AI) is intrinsically data-driven.It calls for the application of statistical concepts through human-machine collaboration during the generation of data,the development of algorithms,and the eva...Artificial intelligence(AI) is intrinsically data-driven.It calls for the application of statistical concepts through human-machine collaboration during the generation of data,the development of algorithms,and the evaluation of results.This paper discusses how such human-machine collaboration can be approached through the statistical concepts of population,question of interest,representativeness of training data,and scrutiny of results(PQRS).The PQRS workflow provides a conceptual framework for integrating statistical ideas with human input into AI products and researches.These ideas include experimental design principles of randomization and local control as well as the principle of stability to gain reproducibility and interpretability of algorithms and data results.We discuss the use of these principles in the contexts of self-driving cars,automated medical diagnoses,and examples from the authors' collaborative research.展开更多
We have performed two kinds of non-linear fitting procedures to the single-particle energies in the sdgh major shell to obtain better shell model results. The low-lying energy eigenvaiues of the light Sn isotopes with...We have performed two kinds of non-linear fitting procedures to the single-particle energies in the sdgh major shell to obtain better shell model results. The low-lying energy eigenvaiues of the light Sn isotopes with A = 103 - 110 in the sdgh-shell are calculated in the framework of the nuclear shell model by using CD-Bonn two-body effective nucleon- nucleon interaction. The obtained energy eigenvalues are fitted to the corresponding experimental values by using two different non-linear fitting procedures, i.e., downhill simplex method and clonai selection method. The unknown single-particle energies of the states 2s1/2, ld3/2, and Oh11/2 are used in the fitting methods to obtain better spectra of the 104,106,108,110Sn isotopes, We compare the energy spectra of the 104,106,108,110Sn and 103,105,107,109Sn isotopes with/without a nonlinear fit to the experimental results.展开更多
基金the support of National Science and Technology Major Project of the Ministry of Science and Technology of China under Grant No.2016ZX03001025003the Natural Science Foundation of Beijing under Grant No.4181002+2 种基金the Natural Science Foundation of China under Grant No.91638204BUPT Excellent Ph.D. Students Foundation under Grant No.CX2018210Natural Sciences and Engineering Research Council (NSERC),Canada
文摘By leveraging the 5G enabled vehicular ad hoc network(5G-VANET), it is widely recognized that connected vehicles have the potentials to improve road safety, transportation intelligence and provide in-vehicle entertainment experience. However, many enabling applications in 5G-VANET rely on the efficient content sharing among mobile vehicles, which is a very challenging issue due to the extremely large data volume, rapid topology change, and unbalanced traffic. In this paper, we investigate content prefetching and distribution in 5G-VANET. We first introduce an edge computing based hierarchical architecture for efficient distribution of large-volume vehicular data. We then propose a multi-place multi-factor prefetching scheme to meet the rapid topology change and unbalanced traffic. The content requests of vehicles can be served by neighbors, which can improve the sharing efficiency and alleviate the burden of networks. Furthermore, we use a graph theory based approach to solve the content distribution by transforming it into a maximum weighted independent set problem. Finally, the proposed scheme is evaluated with a greedy transmission strategy to demonstrate its efficiency.
文摘The term ＂twin cities＂ refers to a program in which cities from different places in the world form a ＂twinning＂ alliance that serves as a setting for educational, cultural, political, and social collaborations （Grosspietsch 2009）. The purpose of the program is to promote the twin cities in all aspects of life （Jayne, Hubbard, and Bell 2013） and facilitate a feeling of belonging and identity among their residents （Ogawa 2012）. In the current study, the cities of Beer Sheva and Nahariya were taken as case studies for examining the contribution of the program to promoting residents＇ feeling of belonging to their Jewish identity. Specifically, the current study attempted to examine the effect of town of residence and age group on feeling of belonging, and whether familiarity with the Twin Cities program affected the feeling of belonging to Jewish identity, in the assumption that residents familiar with the program would report a stronger feeling of belonging than residents not familiar with it. The study included 147 participants aged 17-64, of them 80 residents of Beer Sheva and 67 of Nahariya. All the participants were recruited to the study voluntarily and were requested to complete an online self-report questionnaire examining feeling of belonging to Jewish identity. Moreover, an interview was conducted with the representative of the delegations at the Amal school in Nahariya, to reaffirm the findings. The research findings refuted the main research assumption that the Twin Cities program would influence the feeling of belonging. In fact, the current study indicates that no correlation was found between feeling of belonging and any of the research measures, aside from religiosity. Furthermore, and in contrast to the hypothesis, the research findings indicate that participants who were not familiar with the program reported a stronger feeling of belonging than participants who were familiar with it. Due to the surprising findings, the current study raises the possibility that the Twin Cities program is undergoing a process of change and thus promotes individual values more than collective values. This contention changes the essential purpose of the program and this is the significance of the current study.
基金supported by the Army Research Office(No.W911NF1710005)the National Science Foundation(Nos.DMS-1613002 and IIS 1741340)+1 种基金the Center for Science of Information,a US National Science Foundation Science and Technology Center(No.CCF-0939370)the National Library of Medicine of the NIH(No.T32LM012417)
文摘Artificial intelligence(AI) is intrinsically data-driven.It calls for the application of statistical concepts through human-machine collaboration during the generation of data,the development of algorithms,and the evaluation of results.This paper discusses how such human-machine collaboration can be approached through the statistical concepts of population,question of interest,representativeness of training data,and scrutiny of results(PQRS).The PQRS workflow provides a conceptual framework for integrating statistical ideas with human input into AI products and researches.These ideas include experimental design principles of randomization and local control as well as the principle of stability to gain reproducibility and interpretability of algorithms and data results.We discuss the use of these principles in the contexts of self-driving cars,automated medical diagnoses,and examples from the authors' collaborative research.
基金Supported in part by Sleyman Demirel University under Grant No.SDUBAP 1822-YL-09the Scientific and Technological Council of Turkey under Grant No.TUBITAK 105T092
文摘We have performed two kinds of non-linear fitting procedures to the single-particle energies in the sdgh major shell to obtain better shell model results. The low-lying energy eigenvaiues of the light Sn isotopes with A = 103 - 110 in the sdgh-shell are calculated in the framework of the nuclear shell model by using CD-Bonn two-body effective nucleon- nucleon interaction. The obtained energy eigenvalues are fitted to the corresponding experimental values by using two different non-linear fitting procedures, i.e., downhill simplex method and clonai selection method. The unknown single-particle energies of the states 2s1/2, ld3/2, and Oh11/2 are used in the fitting methods to obtain better spectra of the 104,106,108,110Sn isotopes, We compare the energy spectra of the 104,106,108,110Sn and 103,105,107,109Sn isotopes with/without a nonlinear fit to the experimental results.
