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8,576 results on '"Wu Rui"'

101. Characteristics of Various Freight Categories Transported on Expressway in China

Author

Song, Yuanyuan, Lan, Ziye, Wu, Rui, Xu, Honglei, Zhu, Pengpeng, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, and Easa, Said, editor

Published
2024
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103. A Biometric-Based Data Protection Scheme for RSDs

Author

Wu, Rui, Jiang, Yubing, Shen, Peisong, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, You, Ilsun, editor, Choraś, Michał, editor, Shin, Seonghan, editor, Kim, Hwankuk, editor, and Astillo, Philip Virgil, editor

Published
2024
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104. A User Study of Two Downstream Single-Cell Data Analysis Methods: Clustering and Trajectory Inference

Author

Zhang, Yifan, Dascalu, Sergiu, Harris, Frederick C., Jr., Wu, Rui, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, and Latifi, Shahram, editor

Published
2024
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105. Heterogeneous Information Crossing on Graphs for Session-based Recommender Systems

Author

Zheng, Xiaolin, Wu, Rui, Han, Zhongxuan, Chen, Chaochao, Chen, Linxun, and Han, Bing

Subjects
Computer Science - Information Retrieval, Computer Science - Machine Learning
Abstract

Recommender systems are fundamental information filtering techniques to recommend content or items that meet users' personalities and potential needs. As a crucial solution to address the difficulty of user identification and unavailability of historical information, session-based recommender systems provide recommendation services that only rely on users' behaviors in the current session. However, most existing studies are not well-designed for modeling heterogeneous user behaviors and capturing the relationships between them in practical scenarios. To fill this gap, in this paper, we propose a novel graph-based method, namely Heterogeneous Information Crossing on Graphs (HICG). HICG utilizes multiple types of user behaviors in the sessions to construct heterogeneous graphs, and captures users' current interests with their long-term preferences by effectively crossing the heterogeneous information on the graphs. In addition, we also propose an enhanced version, named HICG-CL, which incorporates contrastive learning (CL) technique to enhance item representation ability. By utilizing the item co-occurrence relationships across different sessions, HICG-CL improves the recommendation performance of HICG. We conduct extensive experiments on three real-world recommendation datasets, and the results verify that (i) HICG achieves the state-of-the-art performance by utilizing multiple types of behaviors on the heterogeneous graph. (ii) HICG-CL further significantly improves the recommendation performance of HICG by the proposed contrastive learning module., Comment: Accepted by ACM Transactions on the Web (TWEB)

Published
2022

106. Time-resolved polarizations of gamma-ray burst prompt emission with observed energy spectra

Author

Wu, Rui-Rui, Tang, Qing-Wen, and Lan, Mi-Xiang

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

Time-resolved polarizations carry more physical information about the source of gamma-ray bursts (GRBs) than the time-integrated ones. Therefore, they give more strict constrains on the models of GRB prompt phase. Both time-resolved and time-integrated polarizations are considered here. The model we use is the synchrotron emission in a large-scale ordered aligned magnetic field. Time-resolved polarizations of GRB prompt phase are derived with the corresponding time-resolved energy spectra. We found the time-integrated PDs calculated with two methods are similar. So it is convenient to estimate the time-integrated PD by the time-integrated energy spectrum. Most of the time-resolved PDs calculated in this paper will increase with time. The trend could match the observed time-resolved PD curve of GRB 170114A, but contrary to the predictions of a decaying PD of both the magnetized internal shock and magnetic reconnection models. PAs calculated in this paper, in general, are roughly constants with time. The predicted PAs here can not match with the violent PA changes observed in GRB 100826A and GRB 170114A. Therefore, more accurate time-resolved polarization observations are needed to test models and to diagnose the true physical process of GRB prompt phase., Comment: The calculation method is not suitable for a time-resolved polarization

Published
2022

123. Commentary: analyzing binary data using MCPMod when zero counts are expected

Author

Liu, Yi, Bossert, Sebastian, Wu, Rui, Roy, Dooti, Fleischer, Frank, and Deng, Qiqi

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

Bretz et al (2005) proposed multiple Comparison Procedure and Modeling (MCPMod) method to design and analyze dose-finding study. Pinheiro (2014) then generalized it to various types of endpoint, including but not limited to binary endpoint, survival endpoint, count data, and longitudinal data. Pinheiro (2013) recommended to use the estimated covariance matrix from the observed data to recalculate the optimal contrast and the critical value of the test For many phase II studies it is common to have small sample sizes per arm with low placebo response rates jointly. Under such circumstances, it cannot be excluded to have a zero count observed. For example, when the placebo response rate is 10%, there is about 4% chance to observe zero responders in the placebo group, or other dose group(s), which has a similar response rate as placebo. In this manuscript, we would like to illustrate the potential problem of Pinheiro (2013) using a case study and simulations. An alternative method using Firth's logistic regression was evaluated to get a stable estimate of response for each dose group. In addition, we evaluated two options to address the issue with problematic contrast coefficients.

Published
2022

124. Conditional Generation Net for Medication Recommendation

Author

Wu, Rui, Qiu, Zhaopeng, Jiang, Jiacheng, Qi, Guilin, and Wu, Xian

Subjects
Computer Science - Machine Learning
Abstract

Medication recommendation targets to provide a proper set of medicines according to patients' diagnoses, which is a critical task in clinics. Currently, the recommendation is manually conducted by doctors. However, for complicated cases, like patients with multiple diseases at the same time, it's difficult to propose a considerate recommendation even for experienced doctors. This urges the emergence of automatic medication recommendation which can help treat the diagnosed diseases without causing harmful drug-drug interactions.Due to the clinical value, medication recommendation has attracted growing research interests.Existing works mainly formulate medication recommendation as a multi-label classification task to predict the set of medicines. In this paper, we propose the Conditional Generation Net (COGNet) which introduces a novel copy-or-predict mechanism to generate the set of medicines. Given a patient, the proposed model first retrieves his or her historical diagnoses and medication recommendations and mines their relationship with current diagnoses. Then in predicting each medicine, the proposed model decides whether to copy a medicine from previous recommendations or to predict a new one. This process is quite similar to the decision process of human doctors. We validate the proposed model on the public MIMIC data set, and the experimental results show that the proposed model can outperform state-of-the-art approaches., Comment: 11 pages. To be published at The Web Conference 2022 (WWW 2022)

Published
2022
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130. Complications, Visual Acuity, and Refractive Error 3 Years after Secondary Intraocular Lens Implantation for Pediatric Aphakia

Author

Freedman, Sharon F., Wallace, David K., Enyedi, Laura B., Prakalapakorn, Sasapin, Jones, Sarah K., Hug, Denise, Stahl, Erin D., Dent, Rebecca J., Kong, Lingkun, Wang, Serena, Gallerson, Bryan K., Hutchinson, Amy K., Lenhart, Phoebe, Brower, Judy, Morrison, David G., Ruark, Scott T., Mets-Halgrimson, Rebecca, Yoon, Hawke, Ralay-Ranaivo, Hantamalala, Hamidullah, Aaliyah, Areaux, Raymond, Anderson, Jill S., Holleschau, Ann M., Superstein, Rosanne, Belanger, Caroline, Fallaha, Nicole, Hamel, Patrick, Thibeault, Maryse, Tamkins, Susanna M., Chang, Ta, Park, Hee-Jung S., Trumler, Anya A., Liu, Xiaonong, Astle, William F., Sanders, Emi N., Traboulsi, Elias, Ghasia, Fatema, McOwen, Diana C., Gray, Michael E., Yang, Michael B., Bowman, Corey S., Galvin, Jennifer, Therriault, Margaret, Smith, Heather, Whitaker, Michele E., Orge, Faruk, Grigorian, Adriana P., Baird, Alicia M., Strominger, Mitchell B., Chen, Vicki, Klein, Shelley, Kemmer, Jacquelyn D., Neiman, Alexandra E., Mendoza, Myra N., Frohwein, Jill J., Bremer, Don, Cassady, Cybil, Golden, Richard, Jordan, Catherine, Rogers, David, Oravec, Sara A., Yanovitch, Tammy L., Lunsford, Keven, Nye, Christina, Shea, Caroline, Stillman, SueAnn M., LaRoche, G. Robert, Van Iderstine, Stephen C., Robertson, Elisa, Cruz, Oscar A., Ghadban, Rafif, Govreau, Dawn, Larson, Scott A., Longmuir, Susannah, Shan, Xiaoyan, Clarke, Michael P., Taylor, Kate, Powell, Christine, Hammond, Benjamin P., Gearinger, Matthew D., Czubinski, Andrea, Hendricks, Dorothy H., Jin, Jing, Salvin, Jonathan H., Fisher, Alicia, Lee, Katherine A., Brooks, Daniel, Schweinler, Bonita R., Sala, Nicholas A., Sala, Allyson M., Summers, Allison I., Karr, Daniel J., Wilson, Lorri B., Rauch, Paula K., O'Hara, Mary, Gandhi, Nandini, Hashmi, Tania, Colburn, Jeffrey, Dittman, Eileen, Whitfill, Charles R., Wheeler, Amy M., McCourt, Emily A., Singh, Jasleen, Welnick, Nanastasia, Azar, Nathalie F., Baker, Joseph, Droste, Patrick J., Peters, Robert J., Hilbrands, Jan, Pineles, Stacy L., Bernardo, Marianne J., Peterson, Edward, Peterson, Charla H., Kumar, Kartik, Melese, Ephrem, Lingua, Robert, Grijalva, Jeff, Crouch, Earl R., jr., Crouch, Earl R., III, Ventura, Gaylord, Anninger, William, Benson, Shawn L., Karp, Karen A., Smith, Jordana M., Brickman-Kelleher, Jill, Ticho, Benjamin H., Khammar, Alexander J., Clausius, Deborah A., Guo, Suquin, Suh, Donny, Chamberlain, Carolyn, Schloff, Susan, Madigan, William P., Burkman, Donna, Christiansen, Stephen P., Ramsey, Jean E., McConnell, Kate H., Friedman, Ilana, Rosado, Jose, Sauberan, Donald P., Hemberger, Jody C., Davis, Patricia L., Rudaitis, Indre, Lowery, Robert S., Cupit, Shawn, Bothun, Erick D., Mohney, Brian G., Wernimont, Suzanne M., Neilsen, Rebecca A., Herlihy, Erin P., Baran, Francine, Gladstone, Amy, Smith, Justin, Mellott, Mei, Kieser, Troy, Erzurum, S. Ayse, Colon, Beth, Shah, Birva, Quebbemann, Micaela, Beck, Roy W., Austin, Darrell S., Boyle, Nicole M., Conner, Courtney L., Chandler, Danielle L., Donahue, Quayleen, Fimbel, Brooke P., Robinson, Julianne L., Hercinovic, Amra, Hoepner, James E., Kaplon, Joseph D., Henderson, Robert J., Melia, B. Michele, Ortiz, Gillaine, Woodard, Victoria C., Stutz, Kathleen M., Sutherland, Desirae R., Wu, Rui, Everett, Donald F., Diener-West, Marie, Baker, John D., Davis, Barry, Phelps, Dale L., Poff, Stephen W., Saunders, Richard A., Tychsen, Lawrence, Bradfield, Yasmin S., Foster, Nicole C., Plager, David A., Salchow, Daniel J., Birch, Eileen E., Manny, Ruth E., Silver, Jayne L., Weise, Katherine K., Verderber, Lisa C., Repka, Michael X., Dean, Trevano W., Kraker, Raymond T., Li, Zhuokai, Yen, Kimberly G., de Alba Campomanes, Alejandra G., Young, Marielle P., Rahmani, Bahram, Haider, Kathryn M., Whitehead, George F., Lambert, Scott R., Kurup, Sudhi P., Kraus, Courtney L., Cotter, Susan A., Holmes, Jonathan M., Hatt, Sarah R., and Traboulsi, Elias I.

Published
2024
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