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2. Site-selective doublon-holon dynamics in a pumped one-dimensional Hubbard superlattice with staggered Coulomb interactions

Author

Cheng, Zhenyu, Li, Ying, Lu, Hantao, Hu, Xiang, Huang, Zhongbing, Fiete, Gregory A., and Du, Liang

Subjects
Condensed Matter - Strongly Correlated Electrons
Abstract

Doublon-holon dynamics is investigated in a pumped one-dimensional Hubbard model with a staggered on?site Coulomb interaction at half-filling. When the system parameters are set to be in the Mott insulating regime the equilibrium sublattice density of states exhibits several characteristic peaks, corresponding to the lower and upper Hubbard bands as well as hybridization bands. We study the linear absorption spectrum and find two main peaks characterizing the photon frequencies which excite the ground state to an excited state. For a system driven by a laser pulse with general intensity and frequency, both the energy absorption and the doublon-holon dynamics exhibit distinct behaviors as a function of laser amplitude and frequency. Single-photon processes are observed at low laser intensity where the energy is absorbed for resonance laser frequencies. For strong laser intensity multi-photon induced dynamics are observed in the system, which are confirmed by an evaluation of the Loschmidt amplitude. The contribution of multi-photon processes to site-specific double occupancy is also characterized by the generalized Loschmidt amplitude. The site-selective doublon-holon dynamics are observed in both the one and multi-photon processes and the site-selective behavior is explained within a quasiparticle picture. Our study suggests strategies to optically engineer the doublon-holon dynamics in one dimensional strongly correlated many-body systems., Comment: 10 pages, 7 figures

Published
2023
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6. ProxyKiller: An Anonymous Proxy Traffic Attack Model Based on Traffic Behavior Graphs

Author

Xu, Hongbo, Cheng, Zhenyu, Li, Shuhao, Wang, Chenxu, Sun, Peishuai, Xie, Jiang, Liu, Qingyun, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Garcia-Alfaro, Joaquin, editor, Kozik, Rafał, editor, Choraś, Michał, editor, and Katsikas, Sokratis, editor

Published
2024
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27. VT-GAT: A Novel VPN Encrypted Traffic Classification Model Based on Graph Attention Neural Network

Author

Xu, Hongbo, Li, Shuhao, Cheng, Zhenyu, Qin, Rui, Xie, Jiang, Sun, Peishuai, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin, Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Gao, Honghao, editor, Wang, Xinheng, editor, Wei, Wei, editor, and Dagiuklas, Tasos, editor

Published
2022
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28. Effective Malicious URL Detection by Using Generative Adversarial Networks

Author

Geng, Jinbu, Li, Shuhao, Liu, Zhicheng, Cheng, Zhenyu, Fan, Li, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Di Noia, Tommaso, editor, Ko, In-Young, editor, Schedl, Markus, editor, and Ardito, Carmelo, editor

Published
2022
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29. HNOP: Attack Traffic Detection Based on Hierarchical Node Hopping Features of Packets

Author

Geng, Jinbu, Cheng, Zhenyu, Liu, Zhicheng, Li, Shuhao, Qin, Rui, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Groen, Derek, editor, de Mulatier, Clélia, editor, Paszynski, Maciej, editor, Krzhizhanovskaya, Valeria V., editor, Dongarra, Jack J., editor, and Sloot, Peter M. A., editor

Published
2022
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30. DITA-NCG: Detecting Information Theft Attack Based on Node Communication Graph

Author

Cheng, Zhenyu, Yun, Xiaochun, Li, Shuhao, Geng, Jinbu, Qin, Rui, Fan, Li, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Groen, Derek, editor, de Mulatier, Clélia, editor, Paszynski, Maciej, editor, Krzhizhanovskaya, Valeria V., editor, Dongarra, Jack J., editor, and Sloot, Peter M. A., editor

Published
2022
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31. RAAM: A Restricted Adversarial Attack Model with Adding Perturbations to Traffic Features

Author

Sun, Peishuai, Li, Shuhao, Xie, Jiang, Xu, Hongbo, Cheng, Zhenyu, Qin, Rui, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Tröltzsch, Fredi, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Reis, Ricardo, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Meng, Weizhi, editor, Fischer-Hübner, Simone, editor, and Jensen, Christian D., editor

Published
2022
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40. Preparation of Composites Derived from Modified Loess/Chitosan and Its Adsorption Performance for Methyl Orange.

Author

Hu, Haobin, Song, Haiyan, Cheng, Zhenyu, Wang, Yufeng, Zhang, Qi, Hu, Huaisheng, and Zhang, Lala

Abstract

A modified loess/chitosan composite (ML@CS) was prepared via solution. The microstructure and physicochemical properties of ML@CS were characterised via scanning electron microscope (SEM), Zeta potential, X-ray diffraction (XRD), Fourier transform infrared spectrum (FT-IR), and thermogravimetric analysis (TGA). An aqueous solution of methyl orange (MO) was used as simulated wastewater from which the influence of the initial concentration and pH of MO, the dosage amount and regeneration performance of ML@CS, adsorption temperature, and time on the adsorption effect of MO were systematically investigated. The adsorption kinetics, isothermal adsorption, and adsorption mechanism were also analysed. The results indicate that ML@CS had a good adsorption effect on MO. When the initial concentration of MO was 200 mg/L, pH was 5.0, and ML@CS dosage was 1.0 g/L, the adsorption equilibrium could be reached within 180 min at room temperature, and the equilibrium adsorption capacity and removal rate reached 199.52 mg/g and 99.75%, respectively. After five adsorption–desorption cycles, the MO removal rate remained above 82%. The adsorption behaviour of ML@CS for MO conforms to the pseudo–second–order kinetic model and the Langmuir isotherm adsorption model. The spontaneous exothermic process was mainly controlled by monolayer chemical adsorption and the physical adsorption only played an auxiliary role. ML@CS efficiently adsorbed MO in water and can be used as a high-efficiency, low-cost adsorbent for printing and dyeing wastewater treatment. [ABSTRACT FROM AUTHOR]

Published
2024
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41. Advancing cerebral small vessel disease diagnosis: Integrating quantitative susceptibility mapping with MRI‐based radiomics.

Author

Cheng, Zhenyu, Yang, Linfeng, Liang, Changhu, Li, Meng, Li, Xianglin, Chen, Yiwen, Liang, Pengcheng, Wang, Yuanyuan, Zhang, Xinyue, Wang, Na, Gao, Yian, Sui, Chaofan, and Guo, Lingfei

Subjects
*CEREBRAL small vessel diseases, *TRAIL Making Test, *PROCESS capability, *COGNITIVE testing, *RADIOMICS
Abstract

Cerebral small vessel disease (CSVD) is a neurodegenerative disease with hidden symptoms and difficult to diagnose. The diagnosis mainly depends on clinical symptoms and neuroimaging. Therefore, we explored the potential of combining clinical detection with MRI‐based radiomics features for the diagnosis of CSVD in a large cohort. A total of 118 CSVD patients and 127 healthy controls underwent quantitative susceptibility mapping and 3D‐T1 scans, and all completed multiple cognitive tests. Lasso regression was used to select features, and the radiomics model was constructed based on the regression coefficients of these features. Clinical cognitive and motor tests were added to the model to construct a hybrid model. All models were cross‐validated to analyze the generalization ability of the models. The AUCs of the radiomics and hybrid models in the internal test set were 0.80 and 0.87, respectively. In the validation set, the AUCs were 0.77 and 0.79, respectively. The hybrid model demonstrated higher decision efficiency. The Trail Making Test, which enhances the diagnostic performance of the model, is associated with multiple brain regions, particularly the right cortical nuclei and the right fimbria. The hybrid model based on radiomics features and cognitive tests can achieve quantitative diagnosis of CSVD and improve the diagnostic efficiency. Furthermore, the reduced processing capacity due to atrophy of the right cortical nucleus and right fimbria suggests the importance of these regions in improving the diagnostic accuracy of the model. [ABSTRACT FROM AUTHOR]

Published
2024
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