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1. MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering

Author

Hu, Jun, Hooi, Bryan, Qian, Shengsheng, Fang, Quan, and Xu, Changsheng

Subjects
Computer Science - Social and Information Networks, Computer Science - Machine Learning
Abstract

Graph Neural Networks (GNNs) have recently been utilized to build Collaborative Filtering (CF) models to predict user preferences based on historical user-item interactions. However, there is relatively little understanding of how GNN-based CF models relate to some traditional Network Representation Learning (NRL) approaches. In this paper, we show the equivalence between some state-of-the-art GNN-based CF models and a traditional 1-layer NRL model based on context encoding. Based on a Markov process that trades off two types of distances, we present Markov Graph Diffusion Collaborative Filtering (MGDCF) to generalize some state-of-the-art GNN-based CF models. Instead of considering the GNN as a trainable black box that propagates learnable user/item vertex embeddings, we treat GNNs as an untrainable Markov process that can construct constant context features of vertices for a traditional NRL model that encodes context features with a fully-connected layer. Such simplification can help us to better understand how GNNs benefit CF models. Especially, it helps us realize that ranking losses play crucial roles in GNN-based CF tasks. With our proposed simple yet powerful ranking loss InfoBPR, the NRL model can still perform well without the context features constructed by GNNs. We conduct experiments to perform detailed analysis on MGDCF., Comment: Accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE)

Published
2022
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3. Air Traffic Flow Prediction with Spatiotemporal Knowledge Distillation Network

Author

Shen, Zhiqi, Cai, Kaiquan, Fang, Quan, and Luo, Xiaoyan

Subjects
Traffic congestion -- Analysis, Teachers -- Analysis, Weather -- Analysis, Transportation industry
Abstract

Accurate air traffic flow prediction assists controllers formulate control strategies in advance and alleviate air traffic congestion, which is important to flight safety. While existing works have made significant efforts in exploring the high dynamics and heterogeneous interactions of historical air traffic flow, two key challenges still remain. (1) The transfer patterns of air traffic are intricate, subject to numerous constraints and limitations such as controllers, flight regulations, and other regulatory factors. Relying solely on mining historical traffic evolution patterns makes it difficult to accurately predict the constrained air traffic flow. (2) Weather conditions exert a substantial influence on air traffic, making it exceptionally difficult to simulate the impact of external factors (such as thunderstorms) on the evolution of air traffic flow patterns. To address these two challenges, we propose a Spatiotemporal Knowledge Distillation Network (ST-KDN) for air traffic flow prediction. Firstly, recognizing the inherent future insights embedded within flight plans, we develop a 'teacher-student' distillation model. This model leverages the prior knowledge of upstream-downstream migration patterns and future air traffic trends inherent in flight plans. Subsequently, to model the influence of external factors and predict air traffic flow disturbed by thunderstorm weather, we propose a student network based on the 'parallel-fusion' structure. Finally, employing a feature-based knowledge distillation approach to integrate prior knowledge from flight plans and extract meteorological features, our method can accurately capture complex and constrained spatiotemporal dependencies in air traffic and explicitly model the impact of weather on air traffic flow. Experimental results on real-world flight data demonstrate that our method can achieve better prediction performance than other state-of-the-art comparison methods, and the advantages of the proposed method are particularly prominent in modeling the complicated transfer pattern of air traffic and inferring nonrecurrent flow patterns., Author(s): Zhiqi Shen [1]; Kaiquan Cai (corresponding author) [1]; Quan Fang [2]; Xiaoyan Luo [3] 1. Introduction With the rapid development of civil aviation industry, the number of aircraft has [...]

Published
2024
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4. Contrastive Adaptive Propagation Graph Neural Networks for Efficient Graph Learning

Author

Hu, Jun, Qian, Shengsheng, Fang, Quan, and Xu, Changsheng

Subjects
Computer Science - Machine Learning, Computer Science - Social and Information Networks
Abstract

Graph Neural Networks (GNNs) have achieved great success in processing graph data by extracting and propagating structure-aware features. Existing GNN research designs various propagation schemes to guide the aggregation of neighbor information. Recently the field has advanced from local propagation schemes that focus on local neighbors towards extended propagation schemes that can directly deal with extended neighbors consisting of both local and high-order neighbors. Despite the impressive performance, existing approaches are still insufficient to build an efficient and learnable extended propagation scheme that can adaptively adjust the influence of local and high-order neighbors. This paper proposes an efficient yet effective end-to-end framework, namely Contrastive Adaptive Propagation Graph Neural Networks (CAPGNN), to address these issues by combining Personalized PageRank and attention techniques. CAPGNN models the learnable extended propagation scheme with a polynomial of a sparse local affinity matrix, where the polynomial relies on Personalized PageRank to provide superior initial coefficients. In order to adaptively adjust the influence of both local and high-order neighbors, a coefficient-attention model is introduced to learn to adjust the coefficients of the polynomial. In addition, we leverage self-supervised learning techniques and design a negative-free entropy-aware contrastive loss to explicitly take advantage of unlabeled data for training. We implement CAPGNN as two different versions named CAPGCN and CAPGAT, which use static and dynamic sparse local affinity matrices, respectively. Experiments on graph benchmark datasets suggest that CAPGNN can consistently outperform or match state-of-the-art baselines. The source code is publicly available at https://github.com/hujunxianligong/CAPGNN., Comment: 12 pages, 7 figures

Published
2021

5. GRecX: An Efficient and Unified Benchmark for GNN-based Recommendation

Author

Cai, Desheng, Hu, Jun, Zhao, Quan, Qian, Shengsheng, Fang, Quan, and Xu, Changsheng

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

In this paper, we present GRecX, an open-source TensorFlow framework for benchmarking GNN-based recommendation models in an efficient and unified way. GRecX consists of core libraries for building GNN-based recommendation benchmarks, as well as the implementations of popular GNN-based recommendation models. The core libraries provide essential components for building efficient and unified benchmarks, including FastMetrics (efficient metrics computation libraries), VectorSearch (efficient similarity search libraries for dense vectors), BatchEval (efficient mini-batch evaluation libraries), and DataManager (unified dataset management libraries). Especially, to provide a unified benchmark for the fair comparison of different complex GNN-based recommendation models, we design a new metric GRMF-X and integrate it into the FastMetrics component. Based on a TensorFlow GNN library tf_geometric, GRecX carefully implements a variety of popular GNN-based recommendation models. We carefully implement these baseline models to reproduce the performance reported in the literature, and our implementations are usually more efficient and friendly. In conclusion, GRecX enables uses to train and benchmark GNN-based recommendation baselines in an efficient and unified way. We conduct experiments with GRecX, and the experimental results show that GRecX allows us to train and benchmark GNN-based recommendation baselines in an efficient and unified way. The source code of GRecX is available at https://github.com/maenzhier/GRecX.

Published
2021

12. Efficient Graph Deep Learning in TensorFlow with tf_geometric

Author

Hu, Jun, Qian, Shengsheng, Fang, Quan, Wang, Youze, Zhao, Quan, Zhang, Huaiwen, and Xu, Changsheng

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

We introduce tf_geometric, an efficient and friendly library for graph deep learning, which is compatible with both TensorFlow 1.x and 2.x. tf_geometric provides kernel libraries for building Graph Neural Networks (GNNs) as well as implementations of popular GNNs. The kernel libraries consist of infrastructures for building efficient GNNs, including graph data structures, graph map-reduce framework, graph mini-batch strategy, etc. These infrastructures enable tf_geometric to support single-graph computation, multi-graph computation, graph mini-batch, distributed training, etc.; therefore, tf_geometric can be used for a variety of graph deep learning tasks, such as transductive node classification, inductive node classification, link prediction, and graph classification. Based on the kernel libraries, tf_geometric implements a variety of popular GNN models for different tasks. To facilitate the implementation of GNNs, tf_geometric also provides some other libraries for dataset management, graph sampling, etc. Different from existing popular GNN libraries, tf_geometric provides not only Object-Oriented Programming (OOP) APIs, but also Functional APIs, which enable tf_geometric to handle advanced graph deep learning tasks such as graph meta-learning. The APIs of tf_geometric are friendly, and they are suitable for both beginners and experts. In this paper, we first present an overview of tf_geometric's framework. Then, we conduct experiments on some benchmark datasets and report the performance of several popular GNN models implemented by tf_geometric., Comment: 7 pages, 5 figures

Published
2021

16. Patterns of oral anticoagulant use and outcomes in Asian patients with atrial fibrillation: a post-hoc analysis from the GLORIA-AF Registry

Author

Abban, Dzifa Wosornu, Abdul, Nasser, Abud, Atilio Marcelo, Adams, Fran, Addala, Srinivas, Adragão, Pedro, Ageno, Walter, Aggarwal, Rajesh, Agosti, Sergio, Agostoni, Piergiuseppe, Aguilar, Francisco, Linares, Julio Aguilar, Aguinaga, Luis, Ahmed, Jameel, Aiello, Allessandro, Ainsworth, Paul, Aiub, Jorge Roberto, Al-Dallow, Raed, Alderson, Lisa, Aldrete Velasco, Jorge Antonio, Alexopoulos, Dimitrios, Manterola, Fernando Alfonso, Aliyar, Pareed, Alonso, David, Alves da Costa, Fernando Augusto, Amado, José, Amara, Walid, Amelot, Mathieu, Amjadi, Nima, Ammirati, Fabrizio, Andrade, Marianna, Andrawis, Nabil, Annoni, Giorgio, Ansalone, Gerardo, Ariani, M.Kevin, Arias, Juan Carlos, Armero, Sébastien, Arora, Chander, Aslam, Muhammad Shakil, Asselman, M., Audouin, Philippe, Augenbraun, Charles, Aydin, S., Ayryanova, Ivaneta, Aziz, Emad, Backes, Luciano Marcelo, Badings, E., Bagni, Ermentina, Baker, Seth H., Bala, Richard, Baldi, Antonio, Bando, Shigenobu, Banerjee, Subhash, Bank, Alan, Esquivias, Gonzalo Barón, Barr, Craig, Bartlett, Maria, Kes, Vanja Basic, Baula, Giovanni, Behrens, Steffen, Bell, Alan, Benedetti, Raffaella, Mazuecos, Juan Benezet, Benhalima, Bouziane, Bergler-Klein, Jutta, Berneau, Jean-Baptiste, Bernstein, Richard A., Berrospi, Percy, Berti, Sergio, Berz, Andrea, Best, Elizabeth, Bettencourt, Paulo, Betzu, Robert, Bhagwat, Ravi, Bhatta, Luna, Biscione, Francesco, Bisignani, Giovanni, Black, Toby, Bloch, Michael J., Bloom, Stephen, Blumberg, Edwin, Bo, Mario, Bøhmer, Ellen, Bollmann, Andreas, Bongiorni, Maria Grazia, Boriani, Giuseppe, Boswijk, D.J., Bott, Jochen, Bottacchi, Edo, Kalan, Marica Bracic, Bradman, Drew, Brautigam, Donald, Breton, Nicolas, Brouwers, P.J.A.M., Browne, Kevin, Cortada, Jordi Bruguera, Bruni, A., Brunschwig, Claude, Buathier, Hervé, Buhl, Aurélie, Bullinga, John, Cabrera, Jose Walter, Caccavo, Alberto, Cai, Shanglang, Caine, Sarah, Calò, Leonardo, Calvi, Valeria, Sánchez, Mauricio Camarillo, Candeias, Rui, Capuano, Vincenzo, Capucci, Alessandro, Caputo, Ronald, Rizo, Tatiana Cárdenas, Cardona, Francisco, Carlos da Costa Darrieux, Francisco, Duarte Vera, Yan Carlos, Carolei, Antonio, Carreño, Susana, Carvalho, Paula, Cary, Susanna, Casu, Gavino, Cavallini, Claudio, Cayla, Guillaume, Celentano, Aldo, Cha, Tae-Joon, Cha, Kwang Soo, Chae, Jei Keon, Chalamidas, Kathrine, Challappa, Krishnan, Chand, Sunil Prakash, Chandrashekar, Harinath, Chartier, Ludovic, Chatterjee, Kausik, Chavez Ayala, Carlos Antero, Cheema, Aamir, Cheema, Amjad, Chen, Lin, Chen, Shih-Ann, Chen, Jyh Hong, Chiang, Fu-Tien, Chiarella, Francesco, Chih-Chan, Lin, Cho, Yong Keun, Choi, Jong-Il, Choi, Dong Ju, Chouinard, Guy, Hoi-Fan Chow, Danny, Chrysos, Dimitrios, Chumakova, Galina, José Roberto, Eduardo Julián, Valenzuela, Chuquiure, Nica, Nicoleta Cindea, Cislowski, David J., Clay, Anthony, Clifford, Piers, Cohen, Andrew, Cohen, Michael, Cohen, Serge, Colivicchi, Furio, Collins, Ronan, Colonna, Paolo, Compton, Steve, Connolly, Derek, Conti, Alberto, Buenostro, Gabriel Contreras, Coodley, Gregg, Cooper, Martin, Coronel, Julian, Corso, Giovanni, Sales, Juan Cosín, Cottin, Yves, Covalesky, John, Cracan, Aurel, Crea, Filippo, Crean, Peter, Crenshaw, James, Cullen, Tina, Darius, Harald, Dary, Patrick, Dascotte, Olivier, Dauber, Ira, Davalos, Vicente, Davies, Ruth, Davis, Gershan, Davy, Jean-Marc, Dayer, Mark, De Biasio, Marzia, De Bonis, Silvana, De Caterina, Raffaele, De Franceschi, Teresiano, de Groot, J.R., De Horta, José, De La Briolle, Axel, Topete, Gilberto de la Pena, Vicenzo de Paola, Angelo Amato, de Souza, Weimar, de Veer, A., De Wolf, Luc, Decoulx, Eric, Deepak, Sasalu, Defaye, Pascal, Del-Carpio Munoz, Freddy, Brkljacic, Diana Delic, Deumite, N. Joseph, Di Legge, Silvia, Diemberger, Igor, Dietz, Denise, Dionísio, Pedro, Dong, Qiang, Rossi dos Santos, Fabio, Dotcheva, Elena, Doukky, Rami, D'Souza, Anthony, Dubrey, Simon, Ducrocq, Xavier, Dupljakov, Dmitry, Duque, Mauricio, Dutta, Dipankar, Duvilla, Nathalie, Duygun, A., Dziewas, Rainer, Eaton, Charles B., Eaves, William, Ebels-Tuinbeek; Clifford Ehrlich, L.A., Eichinger-Hasenauer, Sabine, Eisenberg, Steven J., El Jabali, Adnan, El Shahawy, Mahfouz, Hernandes, Mauro Esteves, Izal, Ana Etxeberria, Evonich, Rudolph, III, Evseeva, Oksana, Ezhov, Andrey, Fahmy, Raed, Fang, Quan, Farsad, Ramin, Fauchier, Laurent, Favale, Stefano, Fayard, Maxime, Fedele, Jose Luis, Fedele, Francesco, Fedorishina, Olga, Fera, Steven R., Gomes Ferreira, Luis Gustavo, Ferreira, Jorge, Ferri, Claudio, Ferrier, Anna, Ferro, Hugo, Finsen, Alexandra, First, Brian, Fischer, Stuart, Fonseca, Catarina, Almeida, Luísa Fonseca, Forman, Steven, Frandsen, Brad, French, William, Friedman, Keith, Friese, Athena, Fruntelata, Ana Gabriela, Fujii, Shigeru, Fumagalli, Stefano, Fundamenski, Marta, Furukawa, Yutaka, Gabelmann, Matthias, Gabra, Nashwa, Gadsbøll, Niels, Galinier, Michel, Gammelgaard, Anders, Ganeshkumar, Priya, Gans, Christopher, Quintana, Antonio Garcia, Gartenlaub, Olivier, Gaspardone, Achille, Genz, Conrad, Georger, Frédéric, Georges, Jean-Louis, Georgeson, Steven, Giedrimas, Evaldas, Gierba, Mariusz, Ortega, Ignacio Gil, Gillespie, Eve, Giniger, Alberto, Giudici, Michael C., Gkotsis, Alexandros, Glotzer, Taya V., Gmehling, Joachim, Gniot, Jacek, Goethals, Peter, Goldbarg, Seth, Goldberg, Ronald, Goldmann, Britta, Golitsyn, Sergey, Gómez, Silvia, Mesa, Juan Gomez, Gonzalez, Vicente Bertomeu, Gonzalez Hermosillo, Jesus Antonio, González López, Víctor Manuel, Gorka, Hervé, Gornick, Charles, Gorog, Diana, Gottipaty, Venkat, Goube, Pascal, Goudevenos, Ioannis, Graham, Brett, Greer, G. Stephen, Gremmler, Uwe, Grena, Paul G., Grond, Martin, Gronda, Edoardo, Grönefeld, Gerian, Gu, Xiang, Torres Torres, Ivett Guadalupe, Guardigli, Gabriele, Guevara, Carolina, Guignier, Alexandre, Gulizia, Michele, Gumbley, Michael, Günther, Albrecht, Ha, Andrew, Hahalis, Georgios, Hakas, Joseph, Hall, Christian, Han, Bing, Han, Seongwook, Hargrove, Joe, Hargroves, David, Harris, Kenneth B., Haruna, Tetsuya, Hayek, Emil, Healey, Jeff, Hearne, Steven, Heffernan, Michael, Heggelund, Geir, Heijmeriks, J.A., Hemels, Maarten, Hendriks, I., Henein, Sam, Her, Sung-Ho, Hermany, Paul, Hernández Del Río, Jorge Eduardo, Higashino, Yorihiko, Hill, Michael, Hisadome, Tetsuo, Hishida, Eiji, Hoffer, Etienne, Hoghton, Matthew, Hong, Kui, Hong, Suk keun, Horbach, Stevie, Horiuchi, Masataka, Hou, Yinglong, Hsing, Jeff, Huang, Chi-Hung, Huckins, David, Hughes, kathy, Huizinga, A., Hulsman, E.L., Hung, Kuo-Chun, Hwang, Gyo-Seung, Ikpoh, Margaret, Imberti, Davide, Ince, Hüseyin, Indolfi, Ciro, Inoue, Shujiro, Irles, Didier, Iseki, Harukazu, Israel, C. Noah, Iteld, Bruce, Iyer, Venkat, Jackson-Voyzey, Ewart, Jaffrani, Naseem, Jäger, Frank, James, Martin, Jang, Sung-Won, Jaramillo, Nicolas, Jarmukli, Nabil, Jeanfreau, Robert J., Jenkins, Ronald D., Sánchez, Carlos Jerjes, Jimenez, Javier, Jobe, Robert, Joen-Jakobsen, Tomas, Jones, Nicholas, Moura Jorge, Jose Carlos, Jouve, Bernard, Jung, Byung Chun, Jung, Kyung Tae, Jung, Werner, Kachkovskiy, Mikhail, Kafkala, Krystallenia, Kalinina, Larisa, Kallmünzer, Bernd, Kamali, Farzan, Kamo, Takehiro, Kampus, Priit, Kashou, Hisham, Kastrup, Andreas, Katsivas, Apostolos, Kaufman, Elizabeth, Kawai, Kazuya, Kawajiri, Kenji, Kazmierski, John F., Keeling, P., Kerr Saraiva, José Francisco, Ketova, Galina, Khaira, Ajit Singh, Khripun, Aleksey, Kim, Doo-Il, Kim, Young Hoon, Kim, Nam Ho, Kim, Dae Kyeong, Kim, Jeong Su, Kim, June Soo, Kim, Ki Seok, Kim, Jin bae, Kinova, Elena, Klein, Alexander, Kmetzo, James J., Kneller, G. Larsen, Knezevic, Aleksandar, Angela Koh, Su Mei, Koide, Shunichi, Kollias, Anastasios, Kooistra, J.A., Koons, Jay, Koschutnik, Martin, Kostis, William J., Kovacic, Dragan, Kowalczyk, Jacek, Koziolova, Natalya, Kraft, Peter, Kragten, Johannes A., Krantz, Mori, Krause, Lars, Krenning, B.J., Krikke, F., Kromhout, Z., Krysiak, Waldemar, Kumar, Priya, Kümler, Thomas, Kuniss, Malte, Kuo, Jen-Yuan, Küppers, Achim, Karla Kurrelmeyer, Kwak, Choong Hwan, Laboulle, Bénédicte, Labovitz, Arthur, Lai, Wen Ter, Lam, Andy, Lam, Yat Yin, Zanetti, Fernando Lanas, Landau, Charles, Landini, Giancarlo, Figueiredo, Estêvão Lanna, Larsen, Torben, Lavandier, Karine, LeBlanc, Jessica, Lee, Moon Hyoung, Lee, Chang-Hoon, Lehman, John, Leitão, Ana, Lellouche, Nicolas, Lelonek, Malgorzata, Lenarczyk, Radoslaw, Lenderink, T., González, Salvador León, Leong-Sit, Peter, Leschke, Matthias, Ley, Nicolas, Li, Zhanquan, Li, Xiaodong, Li, Weihua, Li, Xiaoming, Lichy, Christhoh, Lieber, Ira, Limon Rodriguez, Ramon Horacio, Lin, Hailong, Lip, Gregory Y.H., Liu, Feng, Liu, Hengliang, Esperon, Guillermo Llamas, Navarro, Nassip Llerena, Lo, Eric, Lokshyn, Sergiy, López, Amador, López-Sendón, José Luís, Lorga Filho, Adalberto Menezes, Lorraine, Richard S., Luengas, Carlos Alberto, Luke, Robert, Luo, Ming, Lupovitch, Steven, Lyrer, Philippe, Ma, Changsheng, Ma, Genshan, Madariaga, Irene, Maeno, Koji, Magnin, Dominique, Maid, Gustavo, Mainigi, Sumeet K., Makaritsis, Konstantinos, Malhotra, Rohit, Manning, Rickey, Manolis, Athanasios, Manrique Hurtado, Helard Andres, Mantas, Ioannis, Jattin, Fernando Manzur, Maqueda, Vicky, Marchionni, Niccolo, Ortuno, Francisco Marin, Santana, Antonio Martín, Martinez, Jorge, Maskova, Petra, Hernandez, Norberto Matadamas, Matsuda, Katsuhiro, Maurer, Tillmann, Mauro, Ciro, May, Erik, Mayer, Nolan, McClure, John, McCormack, Terry, McGarity, William, McIntyre, Hugh, McLaurin, Brent, Medina Palomino, Feliz Alvaro, Melandri, Francesco, Meno, Hiroshi, Menzies, Dhananjai, Mercader, Marco, Meyer, Christian, Meyer, Beat J., Miarka, Jacek, Mibach, Frank, Michalski, Dominik, Michel, Patrik, Chreih, Rami Mihail, Mikdadi, Ghiath, Mikus, Milan, Milicic, Davor, Militaru, Constantin, Minaie, Sedi, Minescu, Bogdan, Mintale, Iveta, Mirault, Tristan, Mirro, Michael J., Mistry, Dinesh, Miu, Nicoleta Violeta, Miyamoto, Naomasa, Moccetti, Tiziano, Mohammed, Akber, Nor, Azlisham Mohd, Mollerus, Michael, Molon, Giulio, Mondillo, Sergio, Moniz, Patrícia, Mont, Lluis, Montagud, Vicente, Montaña, Oscar, Monti, Cristina, Moretti, Luciano, Mori, Kiyoo, Moriarty, Andrew, Morka, Jacek, Moschini, Luigi, Moschos, Nikitas, Mügge, Andreas, Mulhearn, Thomas J., Muresan, Carmen, Muriago, Michela, Musial, Wlodzimierz, Musser, Carl W., Musumeci, Francesco, Nageh, Thuraia, Nakagawa, Hidemitsu, Nakamura, Yuichiro, Nakayama, Toru, Nam, Gi-Byoung, Nanna, Michele, Natarajan, Indira, Nayak, Hemal M., Naydenov, Stefan, Nazlić, Jurica, Cristian Nechita, Alexandru, Nechvatal, Libor, Negron, Sandra Adela, Neiman, James, Neuenschwander, Fernando Carvalho, Neves, David, Neykova, Anna, Miguel, Ricardo Nicolás, Nijmeh, George, Nizov, Alexey, Campos, Rodrigo Noronha, Nossan, Janko, Novikova, Tatiana, Nowalany-Kozielska, Ewa, Nsah, Emmanuel, Nunez Fragoso, Juan Carlos, Nurgalieva, Svetlana, Nuyens, Dieter, Nyvad, Ole, Odin de Los Rios Ibarra, Manuel, O'Donnell, Philip, O'Donnell, Martin, Oh, Seil, Oh, Yong Seog, Oh, Dongjin, O'Hara, Gilles, Oikonomou, Kostas, Olivares, Claudia, Oliver, Richard, Ruiz, Rafael Olvera, Olympios, Christoforos, omaszuk-Kazberuk, Anna, Asensi, Joaquín Osca, Jose, eena Padayattil, Padilla Padilla, Francisco Gerardo, Rios, Victoria Padilla, Pajes, Giuseppe, Pandey, Shekhar, Paparella, Gaetano, Paris, F., Park, Hyung Wook, Park, Jong Sung, Parthenakis, Fragkiskos, Passamonti, Enrico, Patel, Rajesh J., Patel, Jaydutt, Patel, Mehool, Patrick, Janice, Jimenez, Ricardo Pavón, Paz, Analía, Pengo, Vittorio, Pentz, William, Pérez, Beatriz, Pérez Ríos, Alma Minerva, Pérez-Cabezas, Alejandro, Perlman, Richard, Persic, Viktor, Perticone, Francesco, Peters, Terri K., Petkar, Sanjiv, Pezo, Luis Felipe, Pflücke, Christian, Pham, David N., Phillips, Roland T., Phlaum, Stephen, Pieters, Denis, Pineau, Julien, Pinter, Arnold, Pinto, Fausto, Pisters, R., Pivac, Nediljko, Pocanic, Darko, Podoleanu, Cristian, Politano, Alessandro, Poljakovic, Zdravka, Pollock, Stewart, Garcéa, Jose Polo, Poppert, Holger, Porcu, Maurizio, Reino, Antonio Pose, Prasad, Neeraj, Précoma, Dalton Bertolim, Prelle, Alessandro, Prodafikas, John, Protasov, Konstantin, Pye, Maurice, Qiu, Zhaohui, Quedillac, Jean-Michel, Raev, Dimitar, Raffo Grado, Carlos Antonio, Rahimi, Sidiqullah, Raisaro, Arturo, Rama, Bhola, Ramos, Ricardo, Ranieri, Maria, Raposo, Nuno, Rashba, Eric, Rauch-Kroehnert, Ursula, Reddy, Ramakota, Renda, Giulia, Reza, Shabbir, Ria, Luigi, Richter, Dimitrios, Rickli, Hans, Rieker, Werner, Vera, Tomas Ripolil, Ritt, Luiz Eduardo, Roberts, Douglas, Briones, Ignacio Rodriguez, Rodriguez Escudero, Aldo Edwin, Pascual, Carlos Rodríguez, Roman, Mark, Romeo, Francesco, Ronner, E., Roux, Jean-Francois, Rozkova, Nadezda, Rubacek, Miroslav, Rubalcava, Frank, Russo, Andrea M., Rutgers, Matthieu Pierre, Rybak, Karin, Said, Samir, Sakamoto, Tamotsu, Salacata, Abraham, Salem, Adrien, Bodes, Rafael Salguero, Saltzman, Marco A., Salvioni, Alessandro, Vallejo, Gregorio Sanchez, Fernández, Marcelo Sanmartín, Saporito, Wladmir Faustino, Sarikonda, Kesari, Sasaoka, Taishi, Sati, Hamdi, Savelieva, Irina, Scala, Pierre-Jean, Schellinger, Peter, Scherr, Carlos, Schmitz, Lisa, Schmitz, Karl-Heinz, Schmitz, Bettina, Schnabel, Teresa, Schnupp, Steffen, Schoeniger, Peter, Schön, Norbert, Schwimmbeck, Peter, Seamark, Clare, Searles, Greg, Seidl, Karl-Heinz, Seidman, Barry, Sek, Jaroslaw, Sekaran, Lakshmanan, Serrati, Carlo, Shah, Neerav, Shah, Vinay, Shah, Anil, Shah, Shujahat, Sharma, Vijay Kumar, Shaw, Louise, Sheikh, Khalid H., Shimizu, Naruhito, Shimomura, Hideki, Shin, Dong-Gu, Shin, Eun-Seok, Shite, Junya, Sibilio, Gerolamo, Silver, Frank, Sime, Iveta, Simmers, Tim A., Singh, Narendra, Siostrzonek, Peter, Smadja, Didier, Smith, David W., Snitman, Marcelo, Filho, Dario Sobral, Soda, Hassan, Sofley, Carl, Sokal, Adam, Oi Yan, Yannie Soo, Sotolongo, Rodolfo, Ferreira de Souza, Olga, Sparby, Jon Arne, Spinar, Jindrich, Sprigings, David, Spyropoulos, Alex C., Stakos, Dimitrios, Steinwender, Clemens, Stergiou, Georgios, Stiell, Ian, Stoddard, Marcus, Stoikov, Anastas, Streb, Witold, Styliadis, Ioannis, Su, Guohai, Su, Xi, Sudnik, Wanda, Sukles, Kai, Sun, Xiaofei, Swart, H., Szavits-Nossan, Janko, Taggeselle, Jens, Takagi, Yuichiro, Singh Takhar, Amrit Pal, Tamm, Angelika, Tanaka, Katsumi, Tanawuttiwat, Tanyanan, Tang, Sherman, Tang, Aylmer, Tarsi, Giovanni, Tassinari, Tiziana, Tayal, Ashis, Tayebjee, Muzahir, Berg, J.M. ten, Tesloianu, Dan, The, Salem H.K., Thomas, Dierk, Timsit, Serge, Tobaru, Tetsuya, Tomasik, Andrzej R., Torosoff, Mikhail, Touze, Emmanuel, Trendafilova, Elina, Tsai, W. Kevin, Tse, Hung Fat, Tsutsui, Hiroshi, Tu, Tian Ming, Tuininga, Ype, Turakhia, Minang, Turk, Samir, Turner, Wayne, Tveit, Arnljot, Tytus, Richard, Valadão, C., van Bergen, P.F.M.M., van de Borne, Philippe, van den Berg, B.J., van der Zwaan, C., Van Eck, M., Vanacker, Peter, Vasilev, Dimo, Vasilikos, Vasileios, Vasilyev, Maxim, Veerareddy, Srikar, Miño; Asok Venkataraman, Mario Vega, Verdecchia, Paolo, Versaci, Francesco, Vester, Ernst Günter, Vial, Hubert, Victory, Jason, Villamil, Alejandro, Vincent, Marc, Vlastaris, Anthony, Dahl, Jürgen vom, Vora, Kishor, Vranian, Robert B., Wakefield, Paul, Wang, Ningfu, Wang, Mingsheng, Wang, Xinhua, Wang, Feng, Wang, Tian, Warner, Alberta L., Watanabe, Kouki, Wei, Jeanne, Weimar, Christian, Weiner, Stanislav, Weinrich, Renate, Wen, Ming-Shien, Wiemer, Marcus, Wiggers, Preben, Wilke, Andreas, Williams, David, Williams, Marcus L., Witzenbichler, Bernhard, Wong, Brian, Lawrence Wong, Ka Sing, Wozakowska-Kaplon, Beata, Wu, Shulin, Wu, Richard C., Wunderlich, Silke, Wyatt, Nell, Wylie, John (Jack), Xu, Yong, Xu, Xiangdong, Yamanoue, Hiroki, Yamashita, Takeshi, Bryan Yan, Ping Yen, Yang, Tianlun, Yao, Jing, Yeh, Kuo-Ho, Yin, Wei Hsian, Yotov, Yoto, Zahn, Ralf, Zarich, Stuart, Zenin, Sergei, Zeuthen, Elisabeth Louise, Zhang, Huanyi, Zhang, Donghui, Zhang, Xingwei, Zhang, Ping, Zhang, Jun, Zhao, Shui Ping, Zhao, Yujie, Zhao, Zhichen, Zheng, Yang, Zhou, Jing, Zimmermann, Sergio, Zini, Andrea, Zizzo, Steven, Zong, Wenxia, Zukerman, L. Steven, Romiti, Giulio Francesco, Corica, Bernadette, Proietti, Marco, Mei, Davide Antonio, Frydenlund, Juliane, Bisson, Arnaud, Olshansky, Brian, Chan, Yi-Hsin, Huisman, Menno V., and Chao, Tze-Fan

Published
2023
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23. Sequence Recommendation Based on Global Enhanced Graph Neural Network

Author

ZHOU Fang-quan, CHENG Wei-qing

Subjects
session-based recommendations, graph neural network, attention mechanism, position information, Computer software, QA76.75-76.765, Technology (General), T1-995
Abstract

Most of the existing session based recommendation systems recommend based on the correlation between the last clicked item and the user preference of the current session,and ignore that there may be item transitions related to the current session in other sessions,while these item transitions may also have a certain impact on users' current preferences Hence,it is indispensable to analyze users' preferences comprehensively from the perspective of local session and global session.Furthermore,most of these recommendation systems ignore the importance of location information,whereas items closer to the predicted location may be more relevant to the current user's interests.To solve these problems,this paper proposes a recommendation model based on global enhanced graph neural network with LSTM(GEL-GNN).GEL-GNN aims to predict the behavior of users according to all sessions,and GNN is employed to capture the global and local relationship of the current session,while LSTM is employed to capture the relationship between sessions at the global level.Firstly,users' preferences are to be translated as a combination of conversation interests based on global and local levels through the attention mechanism layer.Then,the distance between the current position and the predicted position is measured with the reverse position information,so that user behavior can be predicted more accurately.A number of experiments are conducted on three real data sets.Experimental results show that GEL-GNN is superior to the existing session-based graph neural network recommendation models.

Published
2022
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24. Dual Variational Multi-modal Attention Network for Incomplete Social Event Classification

Author

ZHOU Xu, QIAN Sheng-sheng, LI Zhang-ming, FANG Quan, XU Chang-sheng

Subjects
multi-modal, social event classification, social media, incomplete data learning, Computer software, QA76.75-76.765, Technology (General), T1-995
Abstract

The rapid development of the Internet and the continuous expansion of social media have brought a wealth of social event information,and the task of social event classification has become increasingly challenging.Making full use of image-level and text-level information is the key to social event classification.However,most of existing methods have the following limitations:1) Most of the existing multi-modal methods have an ideal assumption that the samples of each modality are sufficient and complete,but in real applications this assumption does not always hold and there will be cases where a certain modality of events is missing;2) Most methods simply concatenate image features and text features of social events to obtain multi-modal features to classify social events.To address these challenges,this paper proposes a dual variational multi-modal attention network(DVMAN) for social event classification to address the limitations of these existing methods.In the DVMAN network,this paper proposes a novel dual variational autoencoders network to generate public representations of social events and further reconstruct the missing modal information in incomplete social event learning.Through distribution alignment and cross-reconstruction alignment,image and text latent representations are doubly aligned to mitigate the gap between different modalities,and for the mis-sing modality information,a generative model is utilized to synthesize its latent representations.In addition,this paper designs a multi-modal fusion module to integrate the fine-grained information of images and texts of social events,so as to realize the complementation and enhancement of information between modalities.This paper conducts extensive experiments on two publicly available event datasets,compared with the existing advanced methods,the accuracy of DVMAN improves by more than 4%.It demonstrates the superior performance of the proposed method for social event classification.

Published
2022
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32. Energy Efficient Service Composition with Delay Guarantee in a Cloud-Edge System

Author

Fang, Quan, Xu, Menghan, Li, Hao, Yu, Jun, Li, Xin, Qian, Zhuzhong, 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 (Sherman), Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Jiang, Hongbo, editor, Wu, Hongyi, editor, and Zeng, Fanzi, editor

Published
2021
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35. Complete chloroplast genome of Lonicera crassifolia Batalin (Caprifoliaceae) and its phylogenetic implications

Author

Chao Chen, Da-Hao Qu, Fang-Quan Shan, Ze-Xin Jin, and Zhong-Shuai Sun

Subjects
lonicera crassifolia, lonicera, chloroplast genome, phylogenomics, Genetics, QH426-470
Abstract

Lonicera crassifolia is a prostrate or creeping, evergreen Lonicera species endemic to southwest China. Here, we reported the complete chloroplast (cp) genome of L. crassifolia (GenBank accession number: OK393707). The cp genome was 154,731 bp long, with a large single-copy region (LSC) of 88,619 bp and a small single-copy region (SSC) of 18,642 bp separated by a pair of inverted repeats (IRs) of 23,735 bp. It encodes 130 genes, including 85 protein-coding genes, 37 tRNA genes, and 8 ribosomal RNA genes. We also reconstructed the phylogeny of Lonicera using the maximum-likelihood (ML) method, including our data and previously reported cp genomes of related taxa. The phylogenetic analysis showed that L. crassifolia is a sister to the remaining Nintooa clade with strong bootstrap support.

Published
2022
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37. Case Report: Temporary pacing using active fixation lead and invasive electrophysiology studies for immune checkpoint inhibitor associated reversible advanced atrioventricular block

Author

Wang, Yifan, primary, Qian, Min, additional, Jin, Xiaofeng, additional, Wang, Jiaqi, additional, Chen, Taibo, additional, Gao, Peng, additional, Cheng, Zhongwei, additional, Lai, Jinzhi, additional, Liu, Yongtai, additional, Fan, Jingbo, additional, Zhang, Lihua, additional, Cheng, Kangan, additional, Deng, Hua, additional, Fang, Quan, additional, and Yang, Deyan, additional

Published
2024
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45. Rapid identification of SARS‐CoV‐2 variants using stable high‐frequency mutation sites.

Author

Fu, Yu, He, Xiaobai, Fang, Quan, Kong, Fei, Zhang, Yan, Fu, Ting, Chen, Liang, Liu, YanXin, Wang, Zhen, Lyu, Jianxin, and Chen, Linjie

Subjects
SARS-CoV-2, GENETIC mutation, FLUORIMETRY, MOLECULAR probes, RESPIRATORY infections
Abstract

Respiratory infectious viruses, including SARS‐CoV‐2, undergo rapid genetic evolution, resulting in diverse subtypes with complex mutations. Detecting and differentiating these subtypes pose significant challenges in respiratory virus surveillance. To address these challenges, we integrated ARMS‐PCR with molecular beacon probes, allowing selective amplification and discrimination of subtypes based on adjacent mutation sites. The method exhibited high specificity and sensitivity, detecting as low as 104 copies/mL via direct fluorescence analysis and ~106 copies/mL using real‐time PCR. Our robust detection approach offers a reliable and efficient solution for monitoring evolving respiratory infections, aiding early diagnosis and control measures. Further research could extend its application to other respiratory viruses and optimize its implementation in clinical settings. [ABSTRACT FROM AUTHOR]

Published
2024
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46. Progress in the treatment of diabetic cardiomyopathy, a systematic review.

Author

Shou, Yiyi, Li, Xingyu, Fang, Quan, Xie, Aqiong, Zhang, Yinghong, Fu, Xinyan, Wang, Mingwei, Gong, Wenyan, Zhang, Xingwei, and Yang, Dong

Subjects
DIABETIC cardiomyopathy, CARDIAC hypertrophy, MYOCARDIAL injury, ARRHYTHMIA, CARDIAC arrest, HEART failure
Abstract

Diabetic cardiomyopathy (DCM) is a condition characterized by myocardial dysfunction that occurs in individuals with diabetes, in the absence of coronary artery disease, valve disease, and other conventional cardiovascular risk factors such as hypertension and dyslipidemia. It is considered a significant and consequential complication of diabetes in the field of cardiovascular medicine. The primary pathological manifestations include myocardial hypertrophy, myocardial fibrosis, and impaired ventricular function, which can lead to widespread myocardial necrosis. Ultimately, this can progress to the development of heart failure, arrhythmias, and cardiogenic shock, with severe cases even resulting in sudden cardiac death. Despite several decades of both fundamental and clinical research conducted globally, there are currently no specific targeted therapies available for DCM in clinical practice, and the incidence and mortality rates of heart failure remain persistently high. Thus, this article provides an overview of the current treatment modalities and novel techniques pertaining to DCM, aiming to offer valuable insights and support to researchers dedicated to investigating this complex condition. [ABSTRACT FROM AUTHOR]

Published
2024
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