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2,211 results on '"Zhu, Jiaqi"'

55. Abdominal aortic calcification is independently associated with increased atrophy and fatty infiltration of the lumbar paraspinal muscles: a retrospective cross-sectional study

Author

Schönnagel, Lukas, Muellner, Maximilian, Caffard, Thomas, Tani, Soji, Camino-Willhuber, Gaston, Zhu, Jiaqi, Haffer, Henryk, Suwalski, Phillip, Arzani, Artine, Chiapparelli, Erika, Amoroso, Krizia, Moser, Manuel, Shue, Jennifer, Tan, Ek Tsoon, Carrino, John A., Sama, Andrew A., Cammisa, Frank P., Girardi, Federico P., and Hughes, Alexander P.

Published
2023
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56. High-sensitivity computational miniaturized terahertz spectrometer using a plasmonic filter array and a modified multilayer residual CNN

Author

Liu Mengjuan, Yang Meichen, Zhu Jiaqi, Zhu He, Wang Yao, Ren Ziyang, Zhai Yihui, Zhu Haiming, Shan Yufeng, Qi Hongxing, Duan Junli, Wu Huizhen, and Dai Ning

Subjects
computational miniaturized terahertz spectrometer, plasmonic filters, adaptive deep-learning algorithm, Physics, QC1-999
Abstract

Spectrometer miniaturization is desired for handheld and portable applications, yet nearly no miniaturized spectrometer is reported operating within terahertz (THz) waveband. Computational strategy, which can acquire incident spectral information through encoding and decoding it using optical devices and reconstruction algorithms, respectively, is widely employed in spectrometer miniaturization as artificial intelligence emerges. We demonstrate a computational miniaturized THz spectrometer, where a plasmonic filter array tailors the spectral response of a blocked-impurity-band detector. Besides, an adaptive deep-learning algorithm is proposed for spectral reconstructions with curbing the negative impact from the optical property of the filter array. Our spectrometer achieves modest spectral resolution (2.3 cm−1) compared with visible and infrared miniaturized spectrometers, outstanding sensitivity (e.g., signal-to-noise ratio, 6.4E6: 1) superior to common benchtop THz spectrometers. The combination of THz optical devices and reconstruction algorithms provides a route toward THz spectrometer miniaturization, and further extends the applicable sphere of the THz spectroscopy technique.

Published
2023
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57. Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?

Author

Luan, Sitao, Hua, Chenqing, Lu, Qincheng, Zhu, Jiaqi, Zhao, Mingde, Zhang, Shuyuan, Chang, Xiao-Wen, and Precup, Doina

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

Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by using the graph structures based on the relational inductive bias (homophily assumption). Though GNNs are believed to outperform NNs in real-world tasks, performance advantages of GNNs over graph-agnostic NNs seem not generally satisfactory. Heterophily has been considered as a main cause and numerous works have been put forward to address it. In this paper, we first show that not all cases of heterophily are harmful for GNNs with aggregation operation. Then, we propose new metrics based on a similarity matrix which considers the influence of both graph structure and input features on GNNs. The metrics demonstrate advantages over the commonly used homophily metrics by tests on synthetic graphs. From the metrics and the observations, we find some cases of harmful heterophily can be addressed by diversification operation. With this fact and knowledge of filterbanks, we propose the Adaptive Channel Mixing (ACM) framework to adaptively exploit aggregation, diversification and identity channels in each GNN layer to address harmful heterophily. We validate the ACM-augmented baselines with 10 real-world node classification tasks. They consistently achieve significant performance gain and exceed the state-of-the-art GNNs on most of the tasks without incurring significant computational burden.

Published
2021

70. Product Marketing Diffusion Under the Background of Digital Economy Based on Improved Bass Model

Author

Zhu Jiaqi

Subjects
marketing diffusion, forwarding motive, interest attenuation effect, improved bass model, 60g25, Mathematics, QA1-939
Abstract

Based on the improved Bass model, this paper studies online social platforms’ product information dissemination mode. This paper studies the Bass model based on the attenuation effect of user benefits. The dissemination mode of product information is established. It is the effect of product information quality on product information dissemination. Finally, an empirical analysis is made of the communication materials of the films released from 2013 to 2022. It is found that the user switching engine and the user’s consumption have obvious effects on the information transmission. This model has better prediction accuracy and fitting performance than the Bass model. This model can be applied to the calculation of other damped transport rates.

Published
2023
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78. Relationship between lumbar spinal stenosis and axial muscle wasting

Author

Schönnagel, Lukas, Zhu, Jiaqi, Camino-Willhuber, Gaston, Guven, Ali E., Tani, Soji, Caffard, Thomas, Haffer, Henryk, Muellner, Maximilian, Chiapparelli, Erika, Arzani, Artine, Amoroso, Krizia, Moser, Manuel, Shue, Jennifer, Tan, Ek T., Carrino, John A., Sama, Andrew A., Cammisa, Frank P., Girardi, Federico P., and Hughes, Alexander P.

Published
2024
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79. Predicting postoperative outcomes in lumbar spinal fusion: development of a machine learning model

Author

Schönnagel, Lukas, Caffard, Thomas, Vu-Han, Tu-Lan, Zhu, Jiaqi, Nathoo, Isaac, Finos, Kyle, Camino-Willhuber, Gaston, Tani, Soji, Guven, Ali. E., Haffer, Henryk, Muellner, Maximilian, Arzani, Artine, Chiapparelli, Erika, Amoroso, Krizia, Shue, Jennifer, Duculan, Roland, Pumberger, Matthias, Zippelius, Timo, Sama, Andrew A., Cammisa, Frank P., Girardi, Federico P., Mancuso, Carol A., and Hughes, Alexander P.

Published
2024
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85. Decision-making Algorithm for the Surgical Treatment of Degenerative Lumbar Spondylolisthesis of L4/L5

Author

Schönnagel, Lukas, Caffard, Thomas, Zhu, Jiaqi, Tani, Soji, Camino-Willhuber, Gaston, Amini, Dominik A., Haffer, Henryk, Muellner, Maximilian, Guven, Ali E., Chiapparelli, Erika, Arzani, Artine, Amoroso, Krizia, Shue, Jennifer, Duculan, Roland, Zippelius, Timo, Sama, Andrew A., Cammisa, Frank P., Girardi, Federico P., Mancuso, Carol A., and Hughes, Alexander P.

Published
2024
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86. Severe Intervertebral Vacuum Phenomenon is Associated With Higher Preoperative Low Back Pain, ODI, and Indication for Fusion in Patients With Degenerative Lumbar Spondylolisthesis

Author

Camino-Willhuber, Gaston, Schönnagel, Lukas, Caffard, Thomas, Zhu, Jiaqi, Tani, Soji, Chiapparelli, Erika, Arzani, Artine, Shue, Jennifer, Duculan, Roland, Bendersky, Mariana, Zelenty, William D., Sokunbi, Gbolabo, Lebl, Darren R., Cammisa, Frank P., Girardi, Federico P., Mancuso, Carol A., Hughes, Alexander P., and Sama, Andrew A.

Published
2024
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87. Understanding the Interplay Between Paraspinal Muscle Atrophy and Lumbar Endplate Degeneration: A 3-Year Longitudinal Study

Author

Schönnagel, Lukas, Zhu, Jiaqi, Guven, Ali E., Camino-Willhuber, Gaston, Tani, Soji, Caffard, Thomas, Haffer, Henryk, Muellner, Maximilian, Chiapparelli, Erika, Amoroso, Krizia, Arzani, Artine, Moser, Manuel, Shue, Jennifer, Tan, Ek T., Carrino, John A., Jöns, Thomas, Sama, Andrew A., Girardi, Federico P., Cammisa, Frank P., and Hughes, Alexander P.

Published
2023
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92. Inference for Network Structure and Dynamics from Time Series Data via Graph Neural Network

Author

Chen, Mengyuan, Zhang, Jiang, Zhang, Zhang, Du, Lun, Hu, Qiao, Wang, Shuo, and Zhu, Jiaqi

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

Network structures in various backgrounds play important roles in social, technological, and biological systems. However, the observable network structures in real cases are often incomplete or unavailable due to measurement errors or private protection issues. Therefore, inferring the complete network structure is useful for understanding complex systems. The existing studies have not fully solved the problem of inferring network structure with partial or no information about connections or nodes. In this paper, we tackle the problem by utilizing time series data generated by network dynamics. We regard the network inference problem based on dynamical time series data as a problem of minimizing errors for predicting future states and proposed a novel data-driven deep learning model called Gumbel Graph Network (GGN) to solve the two kinds of network inference problems: Network Reconstruction and Network Completion. For the network reconstruction problem, the GGN framework includes two modules: the dynamics learner and the network generator. For the network completion problem, GGN adds a new module called the States Learner to infer missing parts of the network. We carried out experiments on discrete and continuous time series data. The experiments show that our method can reconstruct up to 100% network structure on the network reconstruction task. While the model can also infer the unknown parts of the structure with up to 90% accuracy when some nodes are missing. And the accuracy decays with the increase of the fractions of missing nodes. Our framework may have wide application areas where the network structure is hard to obtained and the time series data is rich.

Published
2020

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