摘要:
Social media data are used to enhance crisis management, as people widely adopt social media to share and acquire information to cope with uncertainties in crises. Identification and extraction of informative communications out of large volumes of data is critical for accurate situational awareness and timely response. Existing studies use conditions of geolocations, keywords, and topics separately or jointly to retrieve data that can be crisis related, but are not enough to filter subsets of data for different crisis management tasks. We propose that the crisis communication purposes of users can be detected to enhance data selection and prioritization for different crisis management tasks. A classification framework was built to identify three facets of a message: content type, audience type, and information source. The definitions of these categories are not dependent on a specific type of crises. So the classification framework can be potentially applied to different crisis scenarios. Machine learning models were created for the automatic classification of messages. Results showed the CNN-based model achieved the best accuracy (88.5%) for the classification of content type. The proposed Naive Bayes and logistic repression with predetermined features can best differentiate audience types and information source with an accuracy of 72.7% and 72.2%, respectively.
作者机构:
[Zhao, Wenbin; Liu, Yu-Xi; Song, Huichao] Peking Univ, Dept Phys, Beijing 100871, Peoples R China.;[Zhao, Wenbin; Liu, Yu-Xi; Song, Huichao] Peking Univ, State Key Lab Nucl Phys & Technol, Beijing 100871, Peoples R China.;[Zhao, Wenbin; Liu, Yu-Xi; Song, Huichao] Collaborat Innovat Ctr Quantum Matter, Beijing 100871, Peoples R China.;[Ko, Che Ming] Texas A&M Univ, Cyclotron Inst, College Stn, TX 77843 USA.;[Ko, Che Ming] Texas A&M Univ, Dept Phys & Astron, College Stn, TX 77843 USA.
通讯机构:
[Zhao, Wenbin] P;[Zhao, Wenbin] C;Peking Univ, Dept Phys, Beijing 100871, Peoples R China.;Peking Univ, State Key Lab Nucl Phys & Technol, Beijing 100871, Peoples R China.;Collaborat Innovat Ctr Quantum Matter, Beijing 100871, Peoples R China.
会议名称:
28th International Conference on Ultrarelativistic Nucleus-Nucleus Collisions (Quark Matter)
会议时间:
NOV 04-09, 2019
会议地点:
Wuhan, PEOPLES R CHINA
会议主办单位:
[Zhao, Wenbin;Liu, Yu-Xi;Song, Huichao] Peking Univ, Dept Phys, Beijing 100871, Peoples R China.^[Zhao, Wenbin;Liu, Yu-Xi;Song, Huichao] Peking Univ, State Key Lab Nucl Phys & Technol, Beijing 100871, Peoples R China.^[Zhao, Wenbin;Liu, Yu-Xi;Song, Huichao] Collaborat Innovat Ctr Quantum Matter, Beijing 100871, Peoples R China.^[Ko, Che Ming] Texas A&M Univ, Cyclotron Inst, College Stn, TX 77843 USA.^[Ko, Che Ming] Texas A&M Univ, Dept Phys & Astron, College Stn, TX 77843 USA.^[Qin, Guang-You] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Hubei, Peoples R China.^[Qin, Guang-You] Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Hubei, Peoples R China.^[Qin, Guang-You] Lawrence Berkeley Natl Lab, Nucl Sci Div, Berkeley, CA 94270 USA.^[Song, Huichao] Peking Univ, Ctr High Energy Phys, Beijing 100871, Peoples R China.
关键词:
small collision systems;partonic degrees of freedom;the number of constituent quark scaling;coalescence
摘要:
We briefly summarize our recent study on the number of constituent quark (NCQ) scaling of hadron elliptic flows in high multiplicity p-Pb collisions at root s(NN) = 5.02 TeV. With the inclusion of hadron production via the quark coalescence model at intermediate p(T), the viscous hydrodynamics at low p(T), and jet fragmentation at high p(T), our Hydro - Coal - Frag model provides a nice description of the pT-spectra and differential elliptic flow nu(2)(p(T)) of pions, kaons and protons over the p(T) range from 0 to 6 GeV. Our results demonstrate that including the quark coalescence is essential for reproducing the observed approximate NCQ scaling of hadron nu(2) at intermediate p(T) in experiments, indicating strongly the existence of partonic degrees of freedom and the formation of quark-gluon plasma in high multiplicityp-Pb collisions at the LHC.
作者机构:
[Wang, X-N; Luo, T.; He, Y.; Chen, W.] Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.;[Wang, X-N; Luo, T.; He, Y.; Chen, W.] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.;[Cao, S.] Wayne State Univ, Dept Phys & Astron, Detroit, MI 48201 USA.;[Wang, X-N; Pang, L-G] Univ Calif Berkeley, Phys Dept, Berkeley, CA 94720 USA.;[Wang, X-N; Pang, L-G] Lawrence Berkeley Natl Lab, Nucl Sci Div, Mailstop 70R0319, Berkeley, CA 94740 USA.
通讯机构:
[He, Y.] C;Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.
会议名称:
28th International Conference on Ultra-Relativistic Nucleus-Nucleus Collisions (Quark Matter)
会议时间:
NOV 04-09, 2019
会议地点:
Wuhan, PEOPLES R CHINA
会议主办单位:
[He, Y.;Chen, W.;Luo, T.;Wang, X-N] Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.^[He, Y.;Chen, W.;Luo, T.;Wang, X-N] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.^[Cao, S.] Wayne State Univ, Dept Phys & Astron, Detroit, MI 48201 USA.^[Pang, L-G;Wang, X-N] Univ Calif Berkeley, Phys Dept, Berkeley, CA 94720 USA.^[Pang, L-G;Wang, X-N] Lawrence Berkeley Natl Lab, Nucl Sci Div, Mailstop 70R0319, Berkeley, CA 94740 USA.
关键词:
quark-gluon plasma;jet quenching;jet R-AA;jet energy loss distributions;Bayesian analysis
摘要:
The observed inclusive jet suppression in heavy-ion collisions at LHC has a very weak PT dependence over a large range of P-T = 50-1000 GeV and is almost independent of the colliding energy, though the initial energy density of the bulk medium has increased from root s = 2.76 to 5.02 TeV by about 20%. This interesting phenomenon is investigated in the linear Boltzmann transport (LBT) model for jet propagation in an event-by-event 3+1D hydro background. We show that the PT dependence of jet RAA is determined by the initial spectrum in p + p collisions and P-T dependence of jet energy loss. Furthermore, jet energy loss distributions for inclusive jet and y-jet at both LHC energies are extracted directly from experimental data through the state-of-art Bayesian analysis. The averaged jet energy loss has a weak PT dependence and the scaled jet energy loss distributions have a large width, both of which are consistent with the LBT simulations and indicate that jet quenching is caused by only a few out-of-cone jet medium scatterings.
作者机构:
[Prado, Caio A. G.; Qin, Guang-You; Wang, Xin-Nian; Xing, Wen-Jing] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Hubei, Peoples R China.;[Prado, Caio A. G.; Qin, Guang-You; Wang, Xin-Nian; Xing, Wen-Jing] Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Hubei, Peoples R China.;[Cao, Shanshan] Wayne State Univ, Dept Phys & Astron, Detroit, MI 48201 USA.;[Cao, Shanshan] Texas A&M Univ, Cyclotron Inst, College Stn, TX 77843 USA.;[Qin, Guang-You; Wang, Xin-Nian] Lawrence Berkeley Natl Lab, Nucl Sci Div, Berkeley, CA 94720 USA.
通讯机构:
[Prado, Caio A. G.] C;Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Hubei, Peoples R China.;Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Hubei, Peoples R China.
会议名称:
28th International Conference on Ultrarelativistic Nucleus-Nucleus Collisions (Quark Matter)
会议时间:
NOV 04-09, 2019
会议地点:
Wuhan, PEOPLES R CHINA
会议主办单位:
[Prado, Caio A. G.;Xing, Wen-Jing;Qin, Guang-You;Wang, Xin-Nian] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Hubei, Peoples R China.^[Prado, Caio A. G.;Xing, Wen-Jing;Qin, Guang-You;Wang, Xin-Nian] Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Hubei, Peoples R China.^[Cao, Shanshan] Wayne State Univ, Dept Phys & Astron, Detroit, MI 48201 USA.^[Cao, Shanshan] Texas A&M Univ, Cyclotron Inst, College Stn, TX 77843 USA.^[Qin, Guang-You;Wang, Xin-Nian] Lawrence Berkeley Natl Lab, Nucl Sci Div, Berkeley, CA 94720 USA.
关键词:
heavy ion collisions;quark-gluon plasma;open heavy flavor;parton suppression
摘要:
It is widely acknowledged that heavy flavor probes are sensitive to the properties of the quark-gluon plasma and are often considered an important tool for the plasma tomography studies. Forward rapidity observables can provide further insight on the dynamics of the medium due to the interplay between the medium size and the differences in the production spectra of heavy quark probes. In this proceedings we present the nuclear modification factor R-AA's for B and D mesons, as well as heavy flavor leptons, in the rapidity range -4.0 < y < 4.0 obtained from relativistic Langevin equation with gluon radiation coupled with a (3+1)-dimensional viscous hydrodynamics medium background. We present comparison with experimental data at mid-rapidity as well as predictions for different rapidity ranges.
作者机构:
[Chen, Meng; Xu, Jian; Wu, Chen; Ma, Jiman; Wu, Di] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;[Ma, Binbin] South Cent Univ Nationalities, Sch Management, Wuhan, Peoples R China.
会议名称:
7th International Conference of the Immersive-Learning-Research-Network (iLRN)
会议时间:
MAY 17-JUN 10, 2021
会议地点:
ELECTR NETWORK
会议主办单位:
[Wu, Chen;Chen, Meng;Wu, Di;Ma, Jiman;Xu, Jian] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.^[Ma, Binbin] South Cent Univ Nationalities, Sch Management, Wuhan, Peoples R China.
关键词:
ICT evaluation process in education;real-time monitoring;3D visualization;CesiumJS
摘要:
ICT (information and communications technology) evaluation in education is a key step in measuring the development level of education informatization. At present, the statistical data of evaluation process mainly be transmitted in the form of static chart files. How to grasp the overall evaluation status of each evaluation area in real time and understand the latest progress and bottlenecks of evaluation work is an urgent problem. This work-in-progress paper proposes a real-time monitoring system for ICT evaluation process in education based on CesiumJS 3D visualization, which can provide multi-modal 3D visualization for dynamic evaluation process data and further support knowledge mining.
作者机构:
[Zhang, Dingwei] Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.;[Zhang, Dingwei] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.
通讯机构:
[Zhang, Dingwei] C;Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.
会议名称:
28th International Conference on Ultrarelativistic Nucleus-Nucleus Collisions (Quark Matter)
会议时间:
NOV 04-09, 2019
会议地点:
Wuhan, PEOPLES R CHINA
会议主办单位:
[Zhang, Dingwei] Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.^[Zhang, Dingwei] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.
关键词:
Triton;Coalescence parameters;Neutron density fluctuation
摘要:
In high-energy nuclear collisions, light nuclei can be regarded as a cluster of baryons and their yields are sensitive to the baryon density fluctuations. Thus, the production of light nuclei can be used to study the QCD phase transition, at which the baryon density fluctuation will be enhanced. A yield ratio of light nuclei, defined as N(t)xN(p)/N-2(d), is predicted to be a sensitive observable to search for the 1st-order phase transition and/or QCD critical point in heavy-ion collisions. In this paper, we present the energy and centrality dependence of (anti)deuteron and triton production in Au+Au collisions at root s(NN) = 7.7, 11.5, 14.5, 19.6, 27, 39, 54.4, 62.4, and 200 GeV measured by the STAR experiment at RHIC. We show beam-energy dependence for the coalescence parameter, B-2(d) and B-3(t), particle ratios, d/p, t/p, and t/d, and the yield ratio of N(t)xN(p)/N-2(d). More importantly, non-monotonic energy dependence is observed for the yield ratio, N(t)xN(p)/N-2(d), in 0-10% central Au+Au collisions with a peak around 20-30 GeV. Their physics implications on QCD critical point search and change of the equation of state will be discussed.
摘要:
In this paper, we propose a new model that combines reinforcement learning and adversarial training to exploit the data generated by distant supervision for named entity recognition. Our model can not only reduce the influence of noise in generated data, but also find more informative instances for training. In the pre-training stage of the model, in order to make full use of the data generated by distant supervision, we use reinforcement learning to select reliable instances to pre-train a classifier. In the training stage of the model, we introduce the adversarial training mechanism, which can not only find more reliable instances to enhance the ability of the classifier, but also use noise data to improve the ability of the model to resist noise. To evaluate the performance of the model, we conduct experiments on two public datasets, Species800 dataset in biology and EC dataset in e-commerce domain. The experimental results show that in Species800 dataset, the F1 score of our model is 1.68% higher than that of baseline, and in EC dataset, the F1 score of our model is 6.32% higher than that of baseline. Compared to the state of art models, our model can achieve comparable performance just using word2vec embedding.
作者机构:
[Xiong Kewei] Cent China Normal Univ, Wuhan, Peoples R China.;[Peng, Binhui] Univ Warwick, Coventry, W Midlands, England.;[Jiang, Yang] Sun Yat Sen Univ Guangzhou, Guangzhou, Peoples R China.;[Lu, Tiying] Univ Calif Irvine, Irvine, CA USA.
会议名称:
IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE)
会议时间:
JAN 15-17, 2021
会议地点:
Guangzhou, PEOPLES R CHINA
会议主办单位:
[Xiong Kewei] Cent China Normal Univ, Wuhan, Peoples R China.^[Peng, Binhui] Univ Warwick, Coventry, W Midlands, England.^[Jiang, Yang] Sun Yat Sen Univ Guangzhou, Guangzhou, Peoples R China.^[Lu, Tiying] Univ Calif Irvine, Irvine, CA USA.
摘要:
Nowadays, credit cards are becoming more and more widely used for both online and offline transactions. But along with this trend comes more credit card fraud. According to the Nilson report the global loss to credit card fraud is expected to reach $35 billion this year, so there is a desperate need for accurate and efficient fraud detection systems. In this paper, we propose a deep-learning-based method to tackle this problem. We employed multiple techniques, including feature engineering, memory compression, mixed precision, and ensemble loss to boost the performance of our model. The model is trained and evaluated on the IEEE-CIS fraud dataset provided by Vesta Corporation consisting of over 1 million records. Experiments show that our model outperforms traditional machine-learning-based methods like Bayes and SVM.