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4. Defining Global Benchmarks for Laparoscopic Right Posterior Sectionectomy/H67: An International Multicenter Study

Author

Zhao, Jianping, Lu, Yuanxiang, Zhang, Wanguang, Chua, Darren W., Liu, Qu, Liu, Rong, Pratschke, Johann, Ratti, Francesca, Zimmitti, Giuseppe, Aghayan, Davit L., Edwin, Bjørn, Siow, Tiing-Foong, Scatton, Olivier, Herman, Paulo, Marino, Marco V., Mazzaferro, Vincenzo, Chiow, Adrian K. H., Sucandy, Iswanto, Ivanecz, Arpad, Choi, Sung Hoon, Lee, Jae Hoon, Gastaca, Mikel, Vivarelli, Marco, Giuliante, Felice, Ruzzenente, Andrea, Yong, Chee-Chien, Yin, Mengqiu, Fondevila, Constantino, Efanov, Mikhail, Morise, Zenichi, Di Benedetto, Fabrizio, Brustia, Raffaele, Dalla Valle, Raffaele, Boggi, Ugo, Geller, David, Belli, Andrea, Memeo, Riccardo, Mejia, Alejandro, Park, James O., Rotellar, Fernando, Choi, Gi-Hong, Robles-Campos, Ricardo, Wang, Xiaoying, Sutcliffe, Robert P., Lai, Eric C. H., Chong, Charing C. N., D’Hondt, Mathieu, Monden, Kazuteru, Lopez-Ben, Santiago, Kingham, T. Peter, Ferrero, Alessandro, Ettorre, Giuseppe Maria, Cherqui, Daniel, Liang, Xiao, Soubrane, Olivier, Fuks, David, Wakabayashi, Go, Troisi, Roberto I., Cheung, Tan-To, Sugioka, Atsushi, Long, Tran Cong duy, Abu Hilal, Mohammad, Aldrighetti, Luca, Chen, Kuo-Hsin, Han, Ho-Seong, Goh, Brian K. P., Prieto, Mikel, De Meyere, Celine, Meurs, Juul, Ng, Kelvin K., Lee, Kit-Fai, Tarakanov, Pavel, Alikhanov, Ruslan, Thiruchelvam, Nita, Jang, Jae Young, Kojima, Masayuki, Kato, Yutaro, Kruger, Jaime Arthur Pirola, Coelho, Fabricio Ferreira, Lopez-Lopez, Victor, Casellas I Robert, Margarida, Montalti, Roberto, Giglio, Mariano, Lee, Boram, Wang, Hao-Ping, Salleh, Mansour, Pascual, Franco, Yu, Shian, Chen, Zewei, Vani, Simone, Ardito, Francesco, Giustizieri, Ugo, Citterio, Davide, Mocchegiani, Federico, Colasanti, Marco, Berardi, Giammauro, Guzmán, Yoelimar, Labadie, Kevin P., Conticchio, Maria, Dogeas, Epameinondas, Kauffmann, Emanuele F., Giuffrida, Mario, Sommacale, Daniele, Amory, Boris, Magistri, Paolo, Mishima, Kohei, Kadam, Prashant, Tang, Chung-Ngai, Ghotbi, Jacob, Fretland, Åsmund Avdem, Forchino, Fabio, Mazzotta, Alessandro, Barbara, Marco, Pagano, Duilio, Gruttadauria, Salvatore, Krenzien, Felix, Schmelzle, Moritz, Cipriani, Federica, Lim, Chetana, Zheng, Junhao, Nghia, Phan Phuoc, Valle, Bernardo Dalla, and Cipriani, Federica

Published
2024
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8. Impact of Tumor Size on the Difficulty of Laparoscopic Major Hepatectomies: An International Multicenter Study

Author

Kato, Yutaro, Sugioka, Atsushi, Kojima, Masayuki, Syn, Nicholas L., Zhongkai, Wang, Liu, Rong, Cipriani, Federica, Armstrong, Thomas, Aghayan, Davit L., Siow, Tiing-Foong, Lim, Chetana, Scatton, Olivier, Herman, Paulo, Coelho, Fabricio Ferreira, Marino, Marco V., Mazzaferro, Vincenzo, Chiow, Adrian K. H., Sucandy, Iswanto, Ivanecz, Arpad, Choi, Sung Hoon, Lee, Jae Hoon, Gastaca, Mikel, Vivarelli, Marco, Giuliante, Felice, Dalla Valle, Bernardo, Ruzzenente, Andrea, Yong, Chee-Chien, Fondevila, Constantino, Efanov, Mikhail, Di Benedetto, Fabrizio, Belli, Andrea, Park, James O., Rotellar, Fernando, Choi, Gi-Hong, Robles-Campos, Ricardo, Wang, Xiaoying, Sutcliffe, Robert P., Schmelzle, Moritz, Pratschke, Johann, Lai, Eric C. H., Chong, Charing C. N., D’Hondt, Mathieu, Monden, Kazuteru, Lopez-Ben, Santiago, Kingham, T. Peter, Forchino, Fabio, Ferrero, Alessandro, Ettorre, Giuseppe Maria, Levi Sandri, Giovanni Battista, Pascual, Franco, Cherqui, Daniel, Soubrane, Olivier, Wakabayashi, Go, Troisi, Roberto I., Cheung, Tan-To, Chen, Zewei, Yin, Mengqiu, D’Silva, Mizelle, Han, Ho-Seong, Nghia, Phan Phuoc, Long, Tran Cong duy, Edwin, Bjørn, Fuks, David, Chen, Kuo-Hsin, Abu Hilal, Mohammad, Aldrighetti, Luca, and Goh, Brian K. P.

Published
2023
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9. Layer-Parallel Training of Residual Networks with Auxiliary-Variable Networks

Author

Sun, Qi, Dong, Hexin, Chen, Zewei, Sun, Jiacheng, Li, Zhenguo, and Dong, Bin

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Mathematics - Dynamical Systems
Abstract

Gradient-based methods for the distributed training of residual networks (ResNets) typically require a forward pass of the input data, followed by back-propagating the error gradient to update model parameters, which becomes time-consuming as the network goes deeper. To break the algorithmic locking and exploit synchronous module parallelism in both the forward and backward modes, auxiliary-variable methods have attracted much interest lately but suffer from significant communication overhead and lack of data augmentation. In this work, a novel joint learning framework for training realistic ResNets across multiple compute devices is established by trading off the storage and recomputation of external auxiliary variables. More specifically, the input data of each independent processor is generated from its low-capacity auxiliary network (AuxNet), which permits the use of data augmentation and realizes forward unlocking. The backward passes are then executed in parallel, each with a local loss function that originates from the penalty or augmented Lagrangian (AL) methods. Finally, the proposed AuxNet is employed to reproduce the updated auxiliary variables through an end-to-end training process. We demonstrate the effectiveness of our methods on ResNets and WideResNets across CIFAR-10, CIFAR-100, and ImageNet datasets, achieving speedup over the traditional layer-serial training method while maintaining comparable testing accuracy.

Published
2021

11. Impact of neoadjuvant chemotherapy on short-term outcomes after simple and complex minimally invasive minor hepatectomy for colorectal liver metastases: A propensity-score matched and coarsened exact matched study

Author

Syn, Nicholas L., Prieto, Mikel, Meurs, Juul, De Meyere, Celine, Lee, Kit-Fai, Ng, Kelvin K., Salimgereeva, Diana, Alikhanov, Ruslan, Thiruchelvam, Nita, Jang, Jae Young, Kato, Yutaro, Kojima, Masayuki, Coelho, Fabricio Ferreira, Pirola Kruger, Jaime Arthur, Lopez-Lopez, Victor, Casellas I Robert, Margarida, Montalti, Roberto, Giglio, Mariano, Lee, Boram, D'Silva, Mizelle, Wang, Hao-Ping, Saleh, Mansour, Pascual, Franco, Chen, Zewei, Yu, Shian, Vani, Simone, Ardito, Francesco, Giustizieri, Ugo, Citterio, Davide, Mocchegiani, Federico, Colasanti, Marco, Berardi, Giammauro, Guzmán, Yoelimar, Labadie, Kevin P., Conticchio, Maria, Dogeas, Epameinondas, Kauffmann, Emanuele F., Giuffrida, Mario, Sommacale, Daniele, Laurent, Alexis, Magistri, Paolo, Nghia, Phan Phuoc, Mishima, Kohei, Valle, Bernardo Dalla, Krenzien, Felix, Schmelzle, Moritz, Kadam, Prashant, Liu, Qu, Lai, Eric C.H., Zheng, Junhao, Siow, Tiing Foong, Forchino, Fabio, Ghotbi, Jacob, Fretland, Åsmund A., Lim, Chetana, Mazzotta, Alessandro, Cauchy, Francois, Karam, Elias, Barbara, Marco, Pagano, Duilio, Ratti, Francesca, Hoogteijling, Tijs J., Abu Hilal, Mohammad, Zimmitti, Giuseppe, Aghayan, Davit L., Wu, Andrew G.R., Cipriani, Federica, Gruttadauria, Salvatore, Scatton, Olivier, Long, Tran Cong duy, Herman, Paulo, Marino, Marco V., Mazzaferro, Vincenzo, Chiow, Adrian K.H., Sucandy, Iswanto, Ivanecz, Arpad, Choi, Sung Hoon, Lee, Jae Hoon, Gastaca, Mikel, Vivarelli, Marco, Giuliante, Felice, Ruzzenente, Andrea, Yong, Chee Chien, Yin, Mengqiu, Fondevila, Constantino, Efanov, Mikhail, Morise, Zenichi, Di Benedetto, Fabrizio, Brustia, Raffaele, Dalla Valle, Raffaele, Boggi, Ugo, Geller, David, Belli, Andrea, Memeo, Riccardo, Mejia, Alejandro, Park, James O., Rotellar, Fernando, Choi, Gi Hong, Robles-Campos, Ricardo, Wang, Xiaoying, Sutcliffe, Robert P., Pratschke, Johann, Tang, Chung-Ngai, Chong, Charing C.N., D'Hondt, Mathieu, Monden, Kazuteru, Lopez-Ben, Santiago, Kingham, T. Peter, Ferrero, Alessandro, Ettorre, Giuseppe Maria, Cherqui, Daniel, Liang, Xiao, Soubrane, Olivier, Wakabayashi, Go, Troisi, Roberto I., Han, Ho Seong, Cheung, Tan To, Sugioka, Atsushi, Dokmak, Safi, Chen, Kuo Hsin, Liu, Rong, Fuks, David, Zhang, Wanguang, Aldrighetti, Luca, Edwin, Bjørn, and Goh, Brian K.P.

Published
2024
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13. Towards Understanding the Generative Capability of Adversarially Robust Classifiers

Author

Zhu, Yao, Ma, Jiacheng, Sun, Jiacheng, Chen, Zewei, Jiang, Rongxin, and Li, Zhenguo

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition
Abstract

Recently, some works found an interesting phenomenon that adversarially robust classifiers can generate good images comparable to generative models. We investigate this phenomenon from an energy perspective and provide a novel explanation. We reformulate adversarial example generation, adversarial training, and image generation in terms of an energy function. We find that adversarial training contributes to obtaining an energy function that is flat and has low energy around the real data, which is the key for generative capability. Based on our new understanding, we further propose a better adversarial training method, Joint Energy Adversarial Training (JEAT), which can generate high-quality images and achieve new state-of-the-art robustness under a wide range of attacks. The Inception Score of the images (CIFAR-10) generated by JEAT is 8.80, much better than original robust classifiers (7.50)., Comment: Accepted by ICCV 2021, Oral

Published
2021

18. TransNAS-Bench-101: Improving Transferability and Generalizability of Cross-Task Neural Architecture Search

Author

Duan, Yawen, Chen, Xin, Xu, Hang, Chen, Zewei, Liang, Xiaodan, Zhang, Tong, and Li, Zhenguo

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Recent breakthroughs of Neural Architecture Search (NAS) extend the field's research scope towards a broader range of vision tasks and more diversified search spaces. While existing NAS methods mostly design architectures on a single task, algorithms that look beyond single-task search are surging to pursue a more efficient and universal solution across various tasks. Many of them leverage transfer learning and seek to preserve, reuse, and refine network design knowledge to achieve higher efficiency in future tasks. However, the enormous computational cost and experiment complexity of cross-task NAS are imposing barriers for valuable research in this direction. Existing NAS benchmarks all focus on one type of vision task, i.e., classification. In this work, we propose TransNAS-Bench-101, a benchmark dataset containing network performance across seven tasks, covering classification, regression, pixel-level prediction, and self-supervised tasks. This diversity provides opportunities to transfer NAS methods among tasks and allows for more complex transfer schemes to evolve. We explore two fundamentally different types of search space: cell-level search space and macro-level search space. With 7,352 backbones evaluated on seven tasks, 51,464 trained models with detailed training information are provided. With TransNAS-Bench-101, we hope to encourage the advent of exceptional NAS algorithms that raise cross-task search efficiency and generalizability to the next level. Our dataset file will be available at Mindspore, VEGA., Comment: Published at CVPR 2021. 8 pages main paper, 13 pages in total

Published
2021

19. Impact of liver cirrhosis, severity of cirrhosis and portal hypertension on the difficulty of laparoscopic and robotic minor liver resections for primary liver malignancies in the anterolateral segments

Author

Syn, Nicholas, Gastaca, Mikel, De Meyere, Celine, Meurs, Juul, Ng, Kelvin K., Lee, Kit-Fai, Tarakanov, Pavel, Alikhanov, Ruslan, Thiruchelvam, Nita, Jang, Jae Young, Kojima, Masayuki, Pirola Kruger, Jaime Arthur, Coelho, Fabricio Ferreira, Lopez-Lopez, Victor, Casellas I Robert, Margarida, Montalti, Roberto, Giglio, Mariano, Lee, Boram, D'Silva, Mizelle, Wang, Hao-Ping, Pascual, Franco, Saleh, Mansour, Yu, Shian, Vani, Simone, Ardito, Francesco, Giustizieri, Ugo, Citterio, Davide, Mocchegiani, Federico, Colasanti, Marco, Berardi, Giammauro, Guzmán, Yoelimar, Labadie, Kevin P., Conticchio, Maria, Dogeas, Epameinondas, Kauffmann, Emanuele F., Giuffrida, Mario, Sommacale, Daniele, Laurent, Alexis, Magistri, Paolo, Mishima, Kohei, Schmelzle, Moritz, Krenzien, Felix, Kadam, Prashant, Tang, Chung-Ngai, Ghotbi, Jacob, Fretland, Åsmund Avdem, Forchino, Fabio, Valle, Bernardo Dalla, Mazzotta, Alessandro, Nghia, Phan Phuoc, Cauchy, Francois, Liu, Qu, Kawaguchi, Yoshikuni, Zheng, Junhao, Liang, Xiao, Wu, Andrew G.R., Kabir, Tousif, Scatton, Olivier, Lim, Chetana, Hasegawa, Kiyoshi, Sijberden, Jasper P., Aghayan, Davit L., Siow, Tiing Foong, Dokmak, Safi, Herman, Paulo, Marino, Marco V., Mazzaferro, Vincenzo, Chiow, Adrian K.H., Sucandy, Iswanto, Ivanecz, Arpad, Choi, Sung Hoon, Lee, Jae Hoon, Prieto, Mikel, Vivarelli, Marco, Giuliante, Felice, Ruzzenente, Andrea, Yong, Chee-Chien, Yin, Mengqiu, Chen, Zewei, Fondevila, Constantino, Efanov, Mikhail, Morise, Zenichi, Di Benedetto, Fabrizio, Brustia, Raffaele, Dalla Valle, Raffaele, Boggi, Ugo, Geller, David, Belli, Andrea, Memeo, Riccardo, Gruttadauria, Salvatore, Mejia, Alejandro, Park, James O., Rotellar, Fernando, Choi, Gi-Hong, Robles-Campos, Ricardo, Wang, Xiaoying, Sutcliffe, Robert P., Pratschke, Johann, Lai, Eric C.H., Chong, Charing C.N., D'Hondt, Mathieu, Monden, Kazuteru, Lopez-Ben, Santiago, Kingham, T. Peter, Ferrero, Alessandro, Ettorre, Giuseppe Maria, Cherqui, Daniel, Cipriani, Federica, Soubrane, Olivier, Zhang, Wanguang, Wakabayashi, Go, Troisi, Roberto I., Cheung, Tan-To, Kato, Yutaro, Sugioka, Atsushi, Long, Tran Cong duy, Liu, Rong, Edwin, Bjørn, Fuks, David, Abu Hilal, Mohammad, Chen, Kuo-Hsin, Aldrighetti, Luca, Han, Ho-Seong, and Goh, Brian K.P.

Published
2024
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21. A Practical Layer-Parallel Training Algorithm for Residual Networks

Author

Sun, Qi, Dong, Hexin, Chen, Zewei, Dian, Weizhen, Sun, Jiacheng, Sun, Yitong, Li, Zhenguo, and Dong, Bin

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

Gradient-based algorithms for training ResNets typically require a forward pass of the input data, followed by back-propagating the objective gradient to update parameters, which are time-consuming for deep ResNets. To break the dependencies between modules in both the forward and backward modes, auxiliary-variable methods such as the penalty and augmented Lagrangian (AL) approaches have attracted much interest lately due to their ability to exploit layer-wise parallelism. However, we observe that large communication overhead and lacking data augmentation are two key challenges of these methods, which may lead to low speedup ratio and accuracy drop across multiple compute devices. Inspired by the optimal control formulation of ResNets, we propose a novel serial-parallel hybrid training strategy to enable the use of data augmentation, together with downsampling filters to reduce the communication cost. The proposed strategy first trains the network parameters by solving a succession of independent sub-problems in parallel and then corrects the network parameters through a full serial forward-backward propagation of data. Such a strategy can be applied to most of the existing layer-parallel training methods using auxiliary variables. As an example, we validate the proposed strategy using penalty and AL methods on ResNet and WideResNet across MNIST, CIFAR-10 and CIFAR-100 datasets, achieving significant speedup over the traditional layer-serial training methods while maintaining comparable accuracy.

Published
2020

22. CATCH: Context-based Meta Reinforcement Learning for Transferrable Architecture Search

Author

Chen, Xin, Duan, Yawen, Chen, Zewei, Xu, Hang, Chen, Zihao, Liang, Xiaodan, Zhang, Tong, and Li, Zhenguo

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Neural Architecture Search (NAS) achieved many breakthroughs in recent years. In spite of its remarkable progress, many algorithms are restricted to particular search spaces. They also lack efficient mechanisms to reuse knowledge when confronting multiple tasks. These challenges preclude their applicability, and motivate our proposal of CATCH, a novel Context-bAsed meTa reinforcement learning (RL) algorithm for transferrable arChitecture searcH. The combination of meta-learning and RL allows CATCH to efficiently adapt to new tasks while being agnostic to search spaces. CATCH utilizes a probabilistic encoder to encode task properties into latent context variables, which then guide CATCH's controller to quickly "catch" top-performing networks. The contexts also assist a network evaluator in filtering inferior candidates and speed up learning. Extensive experiments demonstrate CATCH's universality and search efficiency over many other widely-recognized algorithms. It is also capable of handling cross-domain architecture search as competitive networks on ImageNet, COCO, and Cityscapes are identified. This is the first work to our knowledge that proposes an efficient transferrable NAS solution while maintaining robustness across various settings., Comment: Published at ECCV2020

Published
2020

23. DPCP-p: A Distributed Locking Protocol for Parallel Real-Time Tasks

Author

Yang, Maolin, Chen, Zewei, Jiang, Xu, Guan, Nan, and Lei, Hang

Subjects
Computer Science - Operating Systems
Abstract

Real-time scheduling and locking protocols are fundamental facilities to construct time-critical systems. For parallel real-time tasks, predictable locking protocols are required when concurrent sub-jobs mutually exclusive access to shared resources. This paper for the first time studies the distributed synchronization framework of parallel real-time tasks, where both tasks and global resources are partitioned to designated processors, and requests to each global resource are conducted on the processor on which the resource is partitioned. We extend the Distributed Priority Ceiling Protocol (DPCP) for parallel tasks under federated scheduling, with which we proved that a request can be blocked by at most one lower-priority request. We develop task and resource partitioning heuristics and propose analysis techniques to safely bound the task response times. Numerical evaluation (with heavy tasks on 8-, 16-, and 32-core processors) indicates that the proposed methods improve the schedulability significantly compared to the state-of-the-art locking protocols under federated scheduling.

Published
2020

24. New Interpretations of Normalization Methods in Deep Learning

Author

Sun, Jiacheng, Cao, Xiangyong, Liang, Hanwen, Huang, Weiran, Chen, Zewei, and Li, Zhenguo

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

In recent years, a variety of normalization methods have been proposed to help train neural networks, such as batch normalization (BN), layer normalization (LN), weight normalization (WN), group normalization (GN), etc. However, mathematical tools to analyze all these normalization methods are lacking. In this paper, we first propose a lemma to define some necessary tools. Then, we use these tools to make a deep analysis on popular normalization methods and obtain the following conclusions: 1) Most of the normalization methods can be interpreted in a unified framework, namely normalizing pre-activations or weights onto a sphere; 2) Since most of the existing normalization methods are scaling invariant, we can conduct optimization on a sphere with scaling symmetry removed, which can help stabilize the training of network; 3) We prove that training with these normalization methods can make the norm of weights increase, which could cause adversarial vulnerability as it amplifies the attack. Finally, a series of experiments are conducted to verify these claims., Comment: Accepted by AAAI 2020

Published
2020

25. Multi-objective Neural Architecture Search via Non-stationary Policy Gradient

Author

Chen, Zewei, Zhou, Fengwei, Trimponias, George, and Li, Zhenguo

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

Multi-objective Neural Architecture Search (NAS) aims to discover novel architectures in the presence of multiple conflicting objectives. Despite recent progress, the problem of approximating the full Pareto front accurately and efficiently remains challenging. In this work, we explore the novel reinforcement learning (RL) based paradigm of non-stationary policy gradient (NPG). NPG utilizes a non-stationary reward function, and encourages a continuous adaptation of the policy to capture the entire Pareto front efficiently. We introduce two novel reward functions with elements from the dominant paradigms of scalarization and evolution. To handle non-stationarity, we propose a new exploration scheme using cosine temperature decay with warm restarts. For fast and accurate architecture evaluation, we introduce a novel pre-trained shared model that we continuously fine-tune throughout training. Our extensive experimental study with various datasets shows that our framework can approximate the full Pareto front well at fast speeds. Moreover, our discovered cells can achieve supreme predictive performance compared to other multi-objective NAS methods, and other single-objective NAS methods at similar network sizes. Our work demonstrates the potential of NPG as a simple, efficient, and effective paradigm for multi-objective NAS.

Published
2020

26. Propensity Score-Matching Analysis Comparing Robotic Versus Laparoscopic Limited Liver Resections of the Posterosuperior Segments: An International Multicenter Study

Author

Krenzien, Felix, Schmelzle, Moritz, Pratschke, Johann, Feldbrügge, Linda, Liu, Rong, Liu, Qu, Zhang, Wanguang, Zhao, Joseph J., Tan, Hwee-Leong, Cipriani, Federica, Hoogteijling, Tijs J., Aghayan, Davit L., Fretland, Åsmund A., Siow, Tiing Foong, Lim, Chetana, Scatton, Olivier, Herman, Paulo, Coelho, Fabricio F., Marino, Marco V., Mazzaferro, Vincenzo, Chiow, Adrian K.H., Sucandy, Iswanto, Ivanecz, Arpad, Choi, Sung Hoon, Lee, Jae Hoon, Gastaca, Mikel, Vivarelli, Marco, Giuliante, Felice, Dalla Valle, Bernardo, Ruzzenente, Andrea, Yong, Chee-Chien, Chen, Zewei, Yin, Mengqiu, Fondevila, Constantino, Efanov, Mikhail, Morise, Zenichi, Di Benedetto, Fabrizio, Brustia, Raffaele, Dalla Valle, Raffaele, Boggi, Ugo, Geller, David, Belli, Andrea, Memeo, Riccardo, Gruttadauria, Salvatore, Mejia, Alejandro, Park, James O., Rotellar, Fernando, Choi, Gi-Hong, Robles-Campos, Ricardo, Wang, Xiaoying, Sutcliffe, Robert P., Hasegawa, Kiyoshi, Tang, Chung-Ngai, Chong, Charing C. N., Lee, Kit-Fai, Meurs, Juul, D’Hondt, Mathieu, Monden, Kazuteru, Lopez-Ben, Santiago, Kingham, T. Peter, Ferrero, Alessandro, Ettorre, Giuseppe M., Pascual, Franco, Cherqui, Daniel, Zheng, Junhao, Liang, Xiao, Soubrane, Olivier, Wakabayashi, Go, Troisi, Roberto I., Cheung, Tan-To, Kato, Yutaro, Sugioka, Atsushi, Dokmak, Safi, D’Silva, Mizelle, Han, Ho-Seong, Nghia, Phan Phuoc, Long, Tran Cong duy, Hilal, Mohammad Abu, Chen, Kuo-Hsin, Fuks, David, Aldrighetti, Luca, Edwin, Bjørn, Goh, Brian K. P., Syn, Nicholas L., Prieto, Mikel, De Meyere, Celine, Ng, Kelvin K., Salimgereeva, Diana, Alikhanov, Ruslan, Thiruchelvam, Nita, Jang, Jae Young, Kojima, Masayuki, Kruger, Jaime Arthur Pirola, Lopez-Lopez, Victor, Robert, Margarida Casellas I, Montalti, Roberto, Giglio, Mariano, Lee, Boram, Wang, Hao-Ping, Salleh, Mansour, Yu, Shian, Vani, Simone, Ardito, Francesco, Giustizieri, Ugo, Citterio, Davide, Mocchegiani, Federico, Colasanti, Marco, Berardi, Giammauro, Guzmán, Yoelimar, Labadie, Kevin P., Conticchio, Maria, Dogeas, Epameinondas, Kauffmann, Emanuele F., Giuffrida, Mario, Sommacale, Daniele, Amory, Boris, Magistri, Paolo, Mishima, Kohei, Kadam, Prashant, Lai, Eric C. H., Ghotbi, Jacob, Forchino, Fabio, Mazzotta, Alessandro, Kawaguchi, Yoshikuni, Cauchy, Francois, Karam, Elias, Barbara, Marco, and Pagano, Duilio

Published
2024
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28. Impact of liver cirrhosis and portal hypertension on minimally invasive limited liver resection for primary liver malignancies in the posterosuperior segments: An international multicenter study

Author

Syn, Nicholas, Gastaca, Mikel, De Meyere, Celine, Meurs, Juul, Ng, Kelvin K., Lee, Kit-Fai, Salimgereeva, Diana, Alikhanov, Ruslan, Thiruchelvam, Nita, Jang, Jae Young, Kato, Yutaro, Kojima, Masayuki, Coelho, Fabricio Ferreira, Pirola Kruger, Jaime Arthur, Lopez-Lopez, Victor, Casellas I Robert, Margarida, Montalti, Roberto, Giglio, Mariano, Lee, Boram, D'Silva, Mizelle, Wang, Hao-Ping, Pascual, Franco, Saleh, Mansour, Yu, Shian, Chen, Zewei, Vani, Simone, Ardito, Francesco, Giustizieri, Ugo, Citterio, Davide, Mocchegiani, Federico, Colasanti, Marco, Berardi, Giammauro, Guzmán, Yoelimar, Labadie, Kevin P., Conticchio, Maria, Dogeas, Epameinondas, Kauffmann, Emanuele F., Giuffrida, Mario, Sommacale, Daniele, Laurent, Alexis, Magistri, Paolo, Mishima, Kohei, Schmelzle, Moritz, Krenzien, Felix, Kadam, Prashant, Tang, Chung-Ngai, Ghotbi, Jacob, Fretland, Åsmund Avdem, Forchino, Fabio, Valle, Bernardo Dalla, Zheng, Junhao, Mazzotta, Alessandro, Nghia, Phan Phuoc, Kawaguchi, Yoshikuni, Liu, Qu, Cauchy, Francois, Lim, Chetana, Scatton, Olivier, Wu, Andrew G.R., Zhang, Wanguang, Hasegawa, Kiyoshi, Cipriani, Federica, Sijberden, Jasper, Aghayan, Davit L., Siow, Tiing-Foong, Dokmak, Safi, Herman, Paulo, Marino, Marco V., Mazzaferro, Vincenzo, Chiow, Adrian K.H., Sucandy, Iswanto, Ivanecz, Arpad, Choi, Sung-Hoon, Lee, Jae Hoon, Prieto, Mikel, Vivarelli, Marco, Giuliante, Felice, Ruzzenente, Andrea, Yong, Chee-Chien, Yin, Mengqiu, Fondevila, Constantino, Efanov, Mikhail, Morise, Zenichi, Di Benedetto, Fabrizio, Brustia, Raffaele, Dalla Valle, Raffaele, Boggi, Ugo, Geller, David, Belli, Andrea, Memeo, Riccardo, Gruttadauria, Salvatore, Mejia, Alejandro, Park, James O., Rotellar, Fernando, Choi, Gi-Hong, Robles-Campos, Ricardo, Wang, Xiaoying, Sutcliffe, Robert P., Pratschke, Johann, Lai, Eric C.H., Chong, Charing C.N., D'Hondt, Mathieu, Monden, Kazuteru, Lopez-Ben, Santiago, Kingham, T. Peter, Ferrero, Alessandro, Ettorre, Giuseppe Maria, Cherqui, Daniel, Liang, Xiao, Soubrane, Olivier, Wakabayashi, Go, Troisi, Roberto I., Cheung, Tan-To, Sugioka, Atsushi, Han, Ho-Seong, Long, Tran Cong duy, Liu, Rong, Edwin, Bjørn, Fuks, David, Chen, Kuo-Hsin, Abu Hilal, Mohammad, Aldrighetti, Luca, and Goh, Brian K.P.

Published
2023
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31. Impact of liver cirrhosis, the severity of cirrhosis, and portal hypertension on the outcomes of minimally invasive left lateral sectionectomies for primary liver malignancies

Author

Syn, Nicholas L., Prieto, Mikel, Meurs, Juul, De Meyere, Celine, Lee, Kit-Fai, Salimgereeva, Diana, Alikhanov, Ruslan, Kato, Yutaro, Kojima, Masayuki, Robert, Margarida Casellas I., Lee, Boram, D’Silva, Mizelle, Saleh, Mansour, Pascual, Franco, Ardito, Francesco, Citterio, Davide, Mocchegiani, Federico, Berardi, Giammauro, Colasanti, Marco, Guzmán, Yoelimar, Labadie, Kevin P., Conticchio, Maria, Dogeas, Epameinondas, Kauffmann, Emanuele F., Giuffrida, Mario, Laurent, Alexis, Magistri, Paolo, Mishima, Kohei, Schmelzle, Moritz, Krenzien, Felix, Kadam, Prashant, Lai, Eric C., Ghotbi, Jacob, Fretland, Åsmund Avdem, Forchino, Fabio, Mazzotta, Alessandro, Cauchy, Francois, Kawaguchi, Yoshikuni, Lim, Chetana, Valle, Bernardo Dalla, Liu, Qu, Zheng, Junhao, Nghia, Phan Phuoc, Chen, Zewei, Yu, Shian, Coelho, Fabricio Ferreira, Herman, Paulo, Kruger, Jaime A.P., Wu, Andrew G.R., Chin, Ken-Min, Hasegawa, Kiyoshi, Zhang, Wanguang, Alzoubi, Mohammad, Aghayan, Davit L., Siow, Tiing-Foong, Scatton, Olivier, Kingham, T. Peter, Marino, Marco V., Mazzaferro, Vincenzo, Chiow, Adrian K.H., Sucandy, Iswanto, Ivanecz, Arpad, Choi, Sung Hoon, Lee, Jae Hoon, Gastaca, Mikel, Vivarelli, Marco, Giuliante, Felice, Ruzzenente, Andrea, Yong, Chee-Chien, Dokmak, Safi, Fondevila, Constantino, Efanov, Mikhail, Morise, Zenichi, Di Benedetto, Fabrizio, Brustia, Raffaele, Valle, Raffaele Dalla, Boggi, Ugo, Geller, David, Belli, Andrea, Memeo, Riccardo, Gruttadauria, Salvatore, Mejia, Alejandro, Park, James O., Rotellar, Fernando, Choi, Gi Hong, Robles-Campos, Ricardo, Wang, Xiaoying, Sutcliffe, Robert P., Pratschke, Johann, Lai, Eric C.H., Chong, Charing C.N., D’Hondt, Mathieu, Monden, Kazuteru, Lopez-Ben, Santiago, Liu, Rong, Ferrero, Alessandro, Ettorre, Giuseppe Maria, Cipriani, Federica, Cherqui, Daniel, Liang, Xiao, Soubrane, Olivier, Wakabayashi, Go, Troisi, Roberto I., Yin, Mengqiu, Cheung, Tan-To, Sugioka, Atsushi, Han, Ho-Seong, Long, Tran Cong Duy, Fuks, David, Abu Hilal, Mohammad, Chen, Kuo-Hsin, Aldrighetti, Luca, Edwin, Bjørn, and Goh, Brian K.P.

Published
2023
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32. Propensity-score Matched and Coarsened-exact Matched Analysis Comparing Robotic and Laparoscopic Major Hepatectomies: An International Multicenter Study of 4822 Cases

Author

Liu, Qu, Zhang, Wanguang, Zhao, Joseph J., Syn, Nicholas L., Cipriani, Federica, Alzoubi, Mohammad, Aghayan, Davit L., Siow, Tiing-Foong, Lim, Chetana, Scatton, Olivier, Herman, Paulo, Coelho, Fabricio Ferreira, Marino, Marco V., Mazzaferro, Vincenzo, Chiow, Adrian K. H., Sucandy, Iswanto, Ivanecz, Arpad, Choi, Sung-Hoon, Lee, Jae Hoon, Prieto, Mikel, Vivarelli, Marco, Giuliante, Felice, Dalla Valle, Bernardo, Ruzzenente, Andrea, Yong, Chee-Chien, Chen, Zewei, Yin, Mengqiu, Fondevila, Constantino, Efanov, Mikhail, Morise, Zenichi, Di Benedetto, Fabrizio, Brustia, Raffaele, Dalla Valle, Raffaele, Boggi, Ugo, Geller, David, Belli, Andrea, Memeo, Riccardo, Gruttadauria, Salvatore, Mejia, Alejandro, Park, James O., Rotellar, Fernando, Choi, Gi-Hong, Robles-Campos, Ricardo, Wang, Xiaoying, Sutcliffe, Robert P., Schmelzle, Moritz, Pratschke, Johann, Tang, Chung-Ngai, Chong, Charing C. N., Lee, Kit-Fai, Meurs, Juul, D’Hondt, Mathieu, Monden, Kazuteru, Lopez-Ben, Santiago, Kingham, Thomas Peter, Ferrero, Alessandro, Ettorre, Giuseppe Maria, Levi Sandri, Giovanni Battista, Saleh, Mansour, Cherqui, Daniel, Zheng, Junhao, Liang, Xiao, Mazzotta, Alessandro, Soubrane, Olivier, Wakabayashi, Go, Troisi, Roberto I., Cheung, Tan-To, Kato, Yutaro, Sugioka, Atsushi, D’Silva, Mizelle, Han, Ho-Seong, Nghia, Phan Phuoc, Long, Tran Cong duy, Edwin, Bjørn, Fuks, David, Chen, Kuo-Hsin, Abu Hilal, Mohammad, Aldrighetti, Luca, Liu, Rong, Goh, Brian K. P., Gastaca, Mikel, De Meyere, Celine, Ng, Kelvin K., Salimgereeva, Diana, Alikhanov, Ruslan, Lee, Lip-Seng, Jang, Jae Young, Kojima, Masayuki, Kruger, Jaime Arthur Pirola, Lopez-Lopez, Victor, Casellas I Robert, Margarida, Roberto, Montalti, Giglio, Mariano, Lee, Boram, Wang, Hao-Ping, Pascual, Franco, Yu, Shian, Vani, Simone, Ardito, Francesco, Giustizieri, Ugo, Citterio, Davide, Mocchegiani, Federico, Colasanti, Marco, Guzmán, Yoelimar, Labadie, Kevin P., Conticchio, Maria, Dogeas, Epameinondas, Kauffmann, Emanuele F., Giuffrida, Mario, Sommacale, Daniele, Laurent, Alexis, Magistri, Paolo, Mishima, Kohei, Krenzien, Felix, Kadam, Prashant, Lai, Eric C. H., Ghotbi, Jacob, Fretland, Åsmund Avdem, and Forchino, Fabio

Published
2023
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34. Impact of body mass index on the difficulty and outcomes of laparoscopic left lateral sectionectomy

Author

Gastaca, Mikel, Meurs, Juul, De Meyere, Celine, Lee, Kit-Fai, Ng, Kelvin K., Salimgereeva, Diana, Alikhanov, Ruslan, Lee, Lip-Seng, Jang, Jae-Young, Kojima, Masayuki, Pirola Kruger, Jaime Arthur, Coelho, Fabricio Ferreira, Lopez-Lopez, Victor, Casellas I Robert, Margarida, Montalti, Roberto, Giglio, Mariano, D'Silva, Mizelle, Lee, Boram, Wang, Hao-Ping, Pascual, Franco, Saleh, Mansour, Vani, Simone, Ardito, Francesco, Giustizieri, Ugo, Citterio, Davide, Mocchegiani, Federico, Berardi, Giammauro, Colasanti, Marco, Guzmán, Yoelimar, Labadie, Kevin P., Conticchio, Maria, Dogeas, Epameinondas, Kauffmann, Emanuele F., Giuffrida, Mario, Sommacale, Daniele, Laurent, Alexis, Magistri, Paolo, Mishima, Kohei, Schmelzle, Moritz, Krenzien, Felix, Kadam, Prashant, Lai, Eric C.H., Ghotbi, Jacob, Fretland, Åsmund Avdem, Forchino, Fabio, Mazzotta, Alessandro, Cauchy, Francois, Lim, Chetana, Valle, Bernardo Dalla, Zheng, Junhao, Nghia, Phan Phuoc, Chen, Zewei, Yin, Mengqiu, Fu, Junhao, Yu, Shian, Syn, Nicholas L., Chua, Darren W., Kingham, T. Peter, Zhang, Wanguang, Hoogteijling, Tijs J., Aghayan, Davit L., Siow, Tiing Foong, Scatton, Olivier, Herman, Paulo, Marino, Marco V., Mazzaferro, Vincenzo, Chiow, Adrian K.H., Sucandy, Iswanto, Ivanecz, Arpad, Choi, Sung Hoon, Lee, Jae Hoon, Prieto, Mikel, Vivarelli, Marco, Giuliante, Felice, Ruzzenente, Andrea, Yong, Chee-Chien, Dokmak, Safi, Fondevila, Constantino, Efanov, Mikhail, Morise, Zenichi, Di Benedetto, Fabrizio, Brustia, Raffaele, Dalla Valle, Raffaele, Boggi, Ugo, Geller, David, Belli, Andrea, Memeo, Riccardo, Gruttadauria, Salvatore, Mejia, Alejandro, Park, James O., Rotellar, Fernando, Choi, Gi-Hong, Robles-Campos, Ricardo, Wang, Xiaoying, Sutcliffe, Robert P., Pratschke, Johann, Chong, Charing C.N., D'Hondt, Mathieu, Monden, Kazuteru, Lopez-Ben, Santiago, Liu, Qu, Liu, Rong, Ferrero, Alessandro, Ettorre, Giuseppe Maria, Cipriani, Federica, Cherqui, Daniel, Liang, Xiao, Soubrane, Olivier, Wakabayashi, Go, Troisi, Roberto I., Cheung, Tan-To, Kato, Yutaro, Sugioka, Atsushi, Han, Ho-Seong, Long, Tran Cong duy, Fuks, David, Abu Hilal, Mohammad, Aldrighetti, Luca, Chen, Kuo-Hsin, Edwin, Bjørn, and Goh, Brian K.P.

Published
2023
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35. Impact of body mass index on perioperative outcomes of laparoscopic major hepatectomies

Author

Prieto, Mikel, De Meyere, Celine, Lee, Kit-Fai, Ng, Kelvin K., Salimgereeva, Diana, Alikhanov, Ruslan, Lee, Lip-Seng, Jang, Jae Young, Kojima, Masayuki, Kruger, Jaime Arthur Pirola, Lopez-Lopez, Victor, Robert, Margarida Casellas I., Montalti, Roberto, Giglio, Mariano, Lee, Boram, Wang, Hao-Ping, Saleh, Mansour, Yu, Shian, Vani, Simone, Ardito, Francesco, Giustizieri, Ugo, Citterio, Davide, Mocchegiani, Federico, Colasanti, Marco, Guzmán, Yoelimar, Labadie, Kevin P., Conticchio, Maria, Dogeas, Epameinondas, Kauffmann, Emanuele F., Giuffrida, Mario, Sommacale, Daniele, Laurent, Alexis, Magistri, Paolo, Mishima, Kohei, Krenzien, Felix, Kadam, Prashant, Lai, Eric C.H., Ghotbi, Jacob, Fretland, Åsmund Avdem, Forchino, Fabio, Mazzotta, Alessandro, Berardi, Giammauro, Kingham, T. Peter, Zhang, Wanguang, Syn, Nicholas L., Koh, Ye-Xin, Jaber, Bashar, Aghayan, Davit L., Siow, Tiing Foong, Lim, Chetana, Scatton, Olivier, Herman, Paulo, Coelho, Fabricio Ferreira, Marino, Marco V., Mazzaferro, Vincenzo, Chiow, Adrian K.H., Sucandy, Iswanto, Ivanecz, Arpad, Choi, Sung Hoon, Lee, Jae Hoon, Gastaca, Mikel, Vivarelli, Marco, Giuliante, Felice, Dalla Valle, Bernardo, Ruzzenente, Andrea, Yong, Chee-Chien, Chen, Zewei, Yin, Mengqiu, Fondevila, Constantino, Efanov, Mikhail, Morise, Zenichi, Di Benedetto, Fabrizio, Brustia, Raffaele, Dalla Valle, Raffaele, Boggi, Ugo, Geller, David, Belli, Andrea, Memeo, Riccardo, Gruttadauria, Salvatore, Mejia, Alejandro, Park, James O., Rotellar, Fernando, Choi, Gi-Hong, Robles-Campos, Ricardo, Wang, Xiaoying, Sutcliffe, Robert P., Schmelzle, Moritz, Pratschke, Johann, Chong, Charing C.N., Meurs, Juul, D’Hondt, Mathieu, Monden, Kazuteru, Lopez-Ben, Santiago, Liu, Qu, Liu, Rong, Ferrero, Alessandro, Ettorre, Giuseppe Maria, Cipriani, Federica, Pascual, Franco, Cherqui, Daniel, Zheng, Junhao, Liang, Xiao, Soubrane, Olivier, Wakabayashi, Go, Troisi, Roberto I., Cheung, Tan-To, Kato, Yutaro, Sugioka, Atsushi, D’Silva, Mizelle, Han, Ho-Seong, Nghia, Phan Phuoc, Long, Tran Cong duy, Edwin, Bjørn, Fuks, David, Abu Hilal, Mohammad, Aldrighetti, Luca, Chen, Kuo-Hsin, and Goh, Brian K.P.

Published
2023
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36. MANAS: Multi-Agent Neural Architecture Search

Author

Lopes, Vasco, Carlucci, Fabio Maria, Esperança, Pedro M, Singh, Marco, Gabillon, Victor, Yang, Antoine, Xu, Hang, Chen, Zewei, and Wang, Jun

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Multiagent Systems
Abstract

The Neural Architecture Search (NAS) problem is typically formulated as a graph search problem where the goal is to learn the optimal operations over edges in order to maximise a graph-level global objective. Due to the large architecture parameter space, efficiency is a key bottleneck preventing NAS from its practical use. In this paper, we address the issue by framing NAS as a multi-agent problem where agents control a subset of the network and coordinate to reach optimal architectures. We provide two distinct lightweight implementations, with reduced memory requirements (1/8th of state-of-the-art), and performances above those of much more computationally expensive methods. Theoretically, we demonstrate vanishing regrets of the form O(sqrt(T)), with T being the total number of rounds. Finally, aware that random search is an, often ignored, effective baseline we perform additional experiments on 3 alternative datasets and 2 network configurations, and achieve favourable results in comparison.

Published
2019

37. Impact of neoadjuvant chemotherapy on the difficulty and outcomes of laparoscopic and robotic major liver resections for colorectal liver metastases: A propensity-score and coarsened exact-matched controlled study

Author

Gastaca, Mikel, Meurs, Juul, De Meyere, Celine, Lee, Kit-Fai, Ng, Kelvin K., Salimgereeva, Diana, Alikhanov, Ruslan, Lee, Lip-Seng, Jang, Jae Young, Kato, Yutaro, Kojima, Masayuki, Pirola Kruger, Jaime Arthur, Lopez-Lopez, Victor, Casellas I Robert, Margarida, Montalti, Roberto, Lee, Boram, D'Silva, Mizelle, Wang, Hao-Ping, Saleh, Mansour, Chen, Zewei, Yu, Shian, Vani, Simone, Ardito, Francesco, Giustizieri, Ugo, Citterio, Davide, Mocchegiani, Federico, Colasanti, Marco, Guzmán, Yoelimar, Labadie, Kevin P., Conticchio, Maria, Dogeas, Epameinondas, Kauffmann, Emanuele F., Giuffrida, Mario, Sommacale, Daniele, Laurent, Alexis, Magistri, Paolo, Nghia, Phan Phuoc, Mishima, Kohei, Valle, Bernardo Dalla, Krenzien, Felix, Schmelzle, Moritz, Kadam, Prashant, Liu, Qu, Lai, Eric C.H., Zheng, Junhao, Siow, Tiing Foong, Forchino, Fabio, Ghotbi, Jacob, Aghayan, Davit, Fretland, Åsmund, Edwin, Bjørn, Syn, Nicholas L., Cipriani, Federica, Alzoubi, Mohammed, Lim, Chetana, Scatton, Olivier, Long, Tran Cong duy, Herman, Paulo, Coelho, Fabricio Ferreira, Marino, Marco V., Mazzaferro, Vincenzo, Chiow, Adrian K.H., Sucandy, Iswanto, Ivanecz, Arpad, Choi, Sung-Hoon, Lee, Jae Hoon, Prieto, Mikel, Vivarelli, Marco, Giuliante, Felice, Ruzzenente, Andrea, Yong, Chee-Chien, Yin, Mengqiu, Fondevila, Constantino, Efanov, Mikhail, Morise, Zenichi, Di Benedetto, Fabrizio, Brustia, Raffaele, Dalla Valle, Raffaele, Boggi, Ugo, Geller, David, Belli, Andrea, Memeo, Riccardo, Mejia, Alejandro, Park, James O., Rotellar, Fernando, Choi, Gi-Hong, Robles-Campos, Ricardo, Wang, Xiaoying, Sutcliffe, Robert P., Pratschke, Johann, Tang, Chung-Ngai, Chong, Charing C.N., D'Hondt, Mathieu, Monden, Kazuteru, Lopez-Ben, Santiago, Kingham, T. Peter, Ferrero, Alessandro, Ettorre, Giuseppe Maria, Levi Sandri, Giovanni Battista, Pascual, Franco, Cherqui, Daniel, Liang, Xiao, Mazzotta, Alessandro, Wakabayashi, Go, Giglio, Mariano, Troisi, Roberto I., Han, Ho-Seong, Cheung, Tan-To, Sugioka, Atsushi, Chen, Kuo-Hsin, Liu, Rong, Soubrane, Olivier, Fuks, David, Aldrighetti, Luca, Abu Hilal, Mohammad, and Goh, Brian K.P.

Published
2023
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38. Generalized Falicov-Kimball Models

Author

Li, Xiao-Hui, Chen, Zewei, and Ng, Tai-Kai

Subjects
Condensed Matter - Strongly Correlated Electrons
Abstract

In this paper we extend the Falicov-Kimball model (FKM) to the case where the "quasi-particles" entering the FKM are not "ordinary" fermions. As an example we first discuss how the FKM can be generalized to the case with spin-dependent hopping. Afterwards we discuss several cases where the "quasi-particles" entering the FKM are Majorana fermions (extended Majorana-Falicov-Kimball Model (MFKM). Two examples of extended MFKM are discussed in detail: (i) a $p$-wave BCS superconductor on a bipartite lattice and (ii) a BCS-Anderson model. We also discuss the most general forms of extended MFKM, including a brief discussion on the case where the Majorana fermions represent spins, but not real fermion particles., Comment: 9 pages, 6 figures

Published
2019
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42. Interaction induced edge states in HgTe/CdTe Quantum Well under magnetic field

Author

Chen, Zewei and Ng, Tai Kai

Subjects
Condensed Matter - Strongly Correlated Electrons
Abstract

In this paper, we study doped HgTe/CdTe quantum well with Hubbard-type interaction under perpendicular magnetic field using a lattice Bernevig-Hughes-Zhang (BHZ) model with a bulk inversion asymmetry (BIA) term. We show that the BIA term is strongly enhanced by interaction around the region when the band inversion of the topological insulator is destroyed by a magnetic field. The enhanced BIA term creates edge-like electronic states which can explain the experimentally discovered edge conductance in doped HgTe/CdTe quantum well at similar magnetic field regime., Comment: 8 pages and 7 figures

Published
2018
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48. An exactly solvable BCS-Hubbard Model in arbitrary dimensions

Author

Chen, Zewei, Li, Xiaohui, and Ng, Tai Kai

Subjects
Condensed Matter - Strongly Correlated Electrons
Abstract

We introduce in this paper an exact solvable BCS-Hubbard model in arbitrary dimensions. The model describes a p-wave BCS superconductor with equal spin pairing moving on a bipartite (cubic, square etc.) lattice with on site Hubbard interaction $U$. We show that the model becomes exactly solvable for arbitrary $U$ when the BCS pairing amplitude $\Delta$ equals the hopping amplitude $t$. The nature of the solution is described in detail in this paper. The construction of the exact solution is parallel to the exactly solvable Kitaev honeycomb model for $S=1/2$ quantum spins and can be viewed as a generalization of Kitaev's construction to $S=1/2$ interacting lattice fermions. The BCS-Hubbard model discussed in this paper is just an example of a large class of exactly solvable lattice fermion models that can be constructed similarly., Comment: 5 pages, 3 figures

Published
2017
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49. Magnetic properties of 2D topological insulators

Author

Chen, Zewei and Ng, Tai Kai

Subjects
Condensed Matter - Strongly Correlated Electrons
Abstract

The effects of Hubbard-type on-site interactions on the BHZ model is studied in this paper for model parameters appropriate for the HgTe/CdTe quantum well. Within a simple mean field theory we search for plausible magnetic instabilities in the model and find that the ground state becomes {\em ferromagnetic} when the interaction strength between electrons in hole orbital is strong enough. The result can be understood by an approximate mapping of the Hubbard-BHZ model to the one band Hubbard model. The same mapping suggests that the magnetic and/or other ordered phases are more likely to occur in large gap topological insulators whose occupations are close to 1/2 for both electron and hole orbital., Comment: 5 pages, 5 figures

Published
2017

50. Layer‐parallel training of residual networks with auxiliary variable networks.

Author

Sun, Qi, Dong, Hexin, Chen, Zewei, Sun, Jiacheng, Li, Zhenguo, and Dong, Bin

Subjects
DATA augmentation, ORDINARY differential equations
Abstract

Gradient‐based methods for training residual networks (ResNets) typically require a forward pass of input data, followed by back‐propagating the error gradient to update model parameters, which becomes time‐consuming as the network structure goes deeper. To break the algorithmic locking and exploit synchronous module parallelism in both forward and backward modes, auxiliary‐variable methods have emerged but suffer from communication overhead and a lack of data augmentation. By trading off the recomputation and storage of auxiliary variables, a joint learning framework is proposed in this work for training realistic ResNets across multiple compute devices. Specifically, the input data of each processor is generated from its low‐capacity auxiliary network (AuxNet), which permits the use of data augmentation and realizes forward unlocking. Backward passes are then executed in parallel, each with a local loss function derived from the penalty or augmented Lagrangian (AL) method. Finally, the AuxNet is adjusted to reproduce updated auxiliary variables through an end‐to‐end training process. We demonstrate the effectiveness of our method on ResNets and WideResNets across CIFAR‐10, CIFAR‐100, and ImageNet datasets, achieving speedup over the traditional layer‐serial training approach while maintaining comparable testing accuracy. [ABSTRACT FROM AUTHOR]

Published
2024
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