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1,823 results on '"Liu, Yuying"'

10. A novel peptide PDHK1-241aa encoded by circPDHK1 promotes ccRCC progression via interacting with PPP1CA to inhibit AKT dephosphorylation and activate the AKT-mTOR signaling pathway

Author

Huang, Bo, Ren, Junwu, Ma, Qiang, Yang, Feifei, Pan, Xiaojuan, Zhang, Yuying, Liu, Yuying, Wang, Cong, Zhang, Dawei, Wei, Ling, Ran, Lingyu, Zhao, Hongwen, Liang, Ce, Wang, Xiaolin, Wang, Shiming, Li, Haiping, Ning, Hao, Ran, Ai, Li, Wei, Wang, Yongquan, and Xiao, Bin

Published
2024
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11. Physics-Informed Koopman Network

Author

Liu, Yuying, Sholokhov, Aleksei, Mansour, Hassan, and Nabi, Saleh

Subjects
Computer Science - Machine Learning, Mathematics - Analysis of PDEs, Mathematics - Dynamical Systems, Mathematics - Operator Algebras
Abstract

Koopman operator theory is receiving increased attention due to its promise to linearize nonlinear dynamics. Neural networks that are developed to represent Koopman operators have shown great success thanks to their ability to approximate arbitrarily complex functions. However, despite their great potential, they typically require large training data-sets either from measurements of a real system or from high-fidelity simulations. In this work, we propose a novel architecture inspired by physics-informed neural networks, which leverage automatic differentiation to impose the underlying physical laws via soft penalty constraints during model training. We demonstrate that it not only reduces the need of large training data-sets, but also maintains high effectiveness in approximating Koopman eigenfunctions., Comment: 26 pages, 5 figures

Published
2022

39. Cerebral hemodynamics and stroke risks in symptomatic intracranial atherosclerotic stenosis with internal versus cortical borderzone infarcts: A computational fluid dynamics study

Author

Li, Shuang, Tian, Xuan, Ip, Bonaventure, Feng, Xueyan, Ip, Hing Lung, Abrigo, Jill, Lan, Linfang, Liu, Haipeng, Zheng, Lina, Liu, Yuying, Liu, Yu, Ma, Karen KY, Fan, Florence SY, Ma, Sze Ho, Fang, Hui, Xu, Yuming, Lau, Alexander Y, Leung, Howan, Soo, Yannie OY, Mok, Vincent CT, Wong, Ka Sing, Leng, Xinyi, and Leung, Thomas W

Published
2024
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40. A relaxation accelerated two-sweep modulus-based matrix splitting iteration method for solving linear complementarity problems

Author

Li, Dongkai, Wang, Li, and Liu, Yuying

Subjects
Mathematics - Optimization and Control, Mathematics - Numerical Analysis
Abstract

For a linear complementarity problem, we present a relaxaiton accelerated two-sweep matrix splitting iteration method. The convergence analysis illustrates that the proposed method converges to the exact solution of the linear complementarity problem when the system matrix is an $H_+$-matrix and the convergence conditions are given. Numerical experiments show that the proposed method is more efficient than the existing ones.

Published
2020

41. Interpretable Driver Fatigue Estimation Based on Hierarchical Symptom Representations

Author

Lin, Jiaqin, Du, Shaoyi, Liu, Yuying, Tian, Zhiqiang, Qu, Ting, Zheng, Nanning, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Dang-Nguyen, Duc-Tien, editor, Gurrin, Cathal, editor, Larson, Martha, editor, Smeaton, Alan F., editor, Rudinac, Stevan, editor, Dao, Minh-Son, editor, Trattner, Christoph, editor, and Chen, Phoebe, editor

Published
2023
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42. CMFG: Cross-Model Fine-Grained Feature Interaction for Text-Video Retrieval

Author

Zhao, Shengwei, Liu, Yuying, Du, Shaoyi, Tian, Zhiqiang, Qu, Ting, Xu, Linhai, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Dang-Nguyen, Duc-Tien, editor, Gurrin, Cathal, editor, Larson, Martha, editor, Smeaton, Alan F., editor, Rudinac, Stevan, editor, Dao, Minh-Son, editor, Trattner, Christoph, editor, and Chen, Phoebe, editor

Published
2023
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45. Hierarchical Deep Learning of Multiscale Differential Equation Time-Steppers

Author

Liu, Yuying, Kutz, J. Nathan, and Brunton, Steven L.

Subjects
Computer Science - Machine Learning, Mathematics - Numerical Analysis, Physics - Computational Physics
Abstract

Nonlinear differential equations rarely admit closed-form solutions, thus requiring numerical time-stepping algorithms to approximate solutions. Further, many systems characterized by multiscale physics exhibit dynamics over a vast range of timescales, making numerical integration computationally expensive due to numerical stiffness. In this work, we develop a hierarchy of deep neural network time-steppers to approximate the flow map of the dynamical system over a disparate range of time-scales. The resulting model is purely data-driven and leverages features of the multiscale dynamics, enabling numerical integration and forecasting that is both accurate and highly efficient. Moreover, similar ideas can be used to couple neural network-based models with classical numerical time-steppers. Our multiscale hierarchical time-stepping scheme provides important advantages over current time-stepping algorithms, including (i) circumventing numerical stiffness due to disparate time-scales, (ii) improved accuracy in comparison with leading neural-network architectures, (iii) efficiency in long-time simulation/forecasting due to explicit training of slow time-scale dynamics, and (iv) a flexible framework that is parallelizable and may be integrated with standard numerical time-stepping algorithms. The method is demonstrated on a wide range of nonlinear dynamical systems, including the Van der Pol oscillator, the Lorenz system, the Kuramoto-Sivashinsky equation, and fluid flow pass a cylinder; audio and video signals are also explored. On the sequence generation examples, we benchmark our algorithm against state-of-the-art methods, such as LSTM, reservoir computing, and clockwork RNN. Despite the structural simplicity of our method, it outperforms competing methods on numerical integration., Comment: 22 pages, 10 figures

Published
2020

46. Multiresolution Convolutional Autoencoders

Author

Liu, Yuying, Ponce, Colin, Brunton, Steven L., and Kutz, J. Nathan

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Mathematics - Numerical Analysis, Statistics - Machine Learning
Abstract

We propose a multi-resolution convolutional autoencoder (MrCAE) architecture that integrates and leverages three highly successful mathematical architectures: (i) multigrid methods, (ii) convolutional autoencoders and (iii) transfer learning. The method provides an adaptive, hierarchical architecture that capitalizes on a progressive training approach for multiscale spatio-temporal data. This framework allows for inputs across multiple scales: starting from a compact (small number of weights) network architecture and low-resolution data, our network progressively deepens and widens itself in a principled manner to encode new information in the higher resolution data based on its current performance of reconstruction. Basic transfer learning techniques are applied to ensure information learned from previous training steps can be rapidly transferred to the larger network. As a result, the network can dynamically capture different scaled features at different depths of the network. The performance gains of this adaptive multiscale architecture are illustrated through a sequence of numerical experiments on synthetic examples and real-world spatial-temporal data., Comment: 20 pages, 11 figures

Published
2020

47. Study of photovoltaic integrated prefabricated components for assembled buildings based on sensing technology supported by solar energy

Author

Liu Yuying, Zhang Hai, and Chen Peng

Subjects
sensing technology, installed building, photovoltaic integration, prefabricated components, Technology, Chemical technology, TP1-1185, Chemicals: Manufacture, use, etc., TP200-248
Abstract

In recent years, domestic and international policies to support energy-efficient buildings have been intensively introduced, and a consensus has been reached in the direction of green buildings. Building photovoltaic integration is a key technology to solve the demand for electricity in energy-efficient buildings. Meanwhile, prefabricated assembly house construction, as a common construction technology in the current building field, has the advantages of a short construction period, low project cost, a wide range of applications, and high structural stability. Green building and building industrialization are the direction of future construction industry development, combining building photovoltaic (PV) integration with assembly building, breaking down the whole structure into simple components in the factory standardization, batch production, and then on-site assembly, can reduce the mold cost, speed up the construction speed, and improve economic efficiency. The PV solar integrated assembled facade is in line with the concept of energy saving, low carbon, and environmental protection, with significant social benefits, which helps to promote rapidly in the market. In this article, by analyzing the performance and characteristics of PV modules, we propose the design method of PV-integrated prefabricated components for assembled buildings based on sensing technology, extract relevant design parameters from the database of building PV-integrated components, and test the feasibility of the modeling scheme in four stages of engineering design. The use of the results shows that the method of this article can target to improve the accuracy of PV-integrated prefabricated components, ensure the efficiency of component production and processing, and play an important role in supporting the widespread application of modern construction technology for building PV-integrated prefabricated assemblies.

Published
2023
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