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119 results on '"Ding, Tianben"'

2. Online regularization of complex-valued neural networks for structure optimization in wireless-communication channel prediction

Author

Ding, Tianben and Hirose, Akira

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

This paper proposes online-learning complex-valued neural networks (CVNNs) to predict future channel states in fast-fading multipath mobile communications. CVNN is suitable for dealing with a fading communication channel as a single complex-valued entity. This framework makes it possible to realize accurate channel prediction by utilizing its high generalization ability in the complex domain. However, actual communication environments are marked by rapid and irregular changes, thus causing fluctuation of communication channel states. Hence, an empirically selected stationary network gives only limited prediction accuracy. In this paper, we introduce regularization in the update of the CVNN weights to develop online dynamics that can self-optimize its effective network size by responding to such channel-state changes. It realizes online adaptive, highly accurate and robust channel prediction with dynamical adjustment of the network size. We demonstrate its online adaptability in simulations and real wireless-propagation experiments., Comment: 40 pages, 9 figures

Published
2019
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3. High-Q silicon microring resonator with ultrathin sub-wavelength thicknesses for sensitive gas sensing

Author

Guo, Rongxiang, primary, He, Qi, additional, Zhang, Zunyue, additional, Xu, Yingqi, additional, Zhang, Shujiao, additional, Lang, Qiyue, additional, Xiao, Shuqi, additional, Han, Peize, additional, Wang, Jiaqi, additional, Ding, Tianben, additional, Liu, Tiegen, additional, Tsang, Hon Ki, additional, Goda, Keisuke, additional, and Cheng, Zhenzhou, additional

Published
2024
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7. Proposal of Online Regularization for Dynamical Structure Optimization in Complex-Valued Neural Networks

Author

Ding, Tianben, Hirose, Akira, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Gedeon, Tom, editor, Wong, Kok Wai, editor, and Lee, Minho, editor

Published
2019
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9. Single-Molecule Orientation Imaging Reveals the Nano-Architecture of Amyloid Fibrils Undergoing Growth and Decay

Author

Sun, Brian, Ding, Tianben, Zhou, Weiyan, Porter, Tara S., and Lew, Matthew D.

Abstract

Amyloid-beta (Aβ42) aggregates are characteristic Alzheimer’s disease signatures, but probing how their nanoscale architectures influence their growth and decay remains challenging using current technologies. Here, we apply time-lapse single-molecule orientation-localization microscopy (SMOLM) to measure the orientations and rotational “wobble” of Nile blue (NB) molecules transiently binding to Aβ42 fibrils. We correlate fibril architectures measured by SMOLM with their growth and decay over the course of 5 to 20 min visualized by single-molecule localization microscopy (SMLM). We discover that stable Aβ42 fibrils tend to be well-ordered and signified by well-aligned NB orientations and small wobble. SMOLM also shows that increasing order and disorder are signatures of growing and decaying fibrils, respectively. We also observe SMLM-invisible fibril remodeling, including steady growth and decay patterns that conserve β-sheet organization. SMOLM reveals that increased fibril architectural heterogeneity is correlated with dynamic remodeling and that large-scale fibril remodeling tends to originate from strongly heterogeneous local regions.

Published
2024
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12. Is AI essential? Examining the need for deep learning in image-activated sorting of Saccharomyces cerevisiae.

Author

Hayashi, Mika, Ohnuki, Shinsuke, Tsai, Yating, Kondo, Naoko, Zhou, Yuqi, Zhang, Hongqian, Ishii, Natsumi Tiffany, Ding, Tianben, Herbig, Maik, Isozaki, Akihiro, Ohya, Yoshikazu, and Goda, Keisuke

Subjects
DEEP learning, SACCHAROMYCES cerevisiae, ARTIFICIAL intelligence, IMAGE recognition (Computer vision), CONVOLUTIONAL neural networks, MACHINE learning
Abstract

Artificial intelligence (AI) has become a focal point across a multitude of societal sectors, with science not being an exception. Particularly in the life sciences, imaging flow cytometry has increasingly integrated AI for automated management and categorization of extensive cell image data. However, the necessity of AI over traditional classification methods when extending imaging flow cytometry to include cell sorting remains uncertain, primarily due to the time constraints between image acquisition and sorting actuation. AI-enabled image-activated cell sorting (IACS) methods remain substantially limited, even as recent advancements in IACS have found success while largely relying on traditional feature gating strategies. Here we assess the necessity of AI for image classification in IACS by contrasting the performance of feature gating, classical machine learning (ML), and deep learning (DL) with convolutional neural networks (CNNs) in the differentiation of Saccharomyces cerevisiae mutant images. We show that classical ML could only yield a 2.8-fold enhancement in target enrichment capability, albeit at the cost of a 13.7-fold increase in processing time. Conversely, a CNN could offer an 11.0-fold improvement in enrichment capability at an 11.5-fold increase in processing time. We further executed IACS on mixed mutant populations and quantified target strain enrichment via downstream DNA sequencing to substantiate the applicability of DL for the proposed study. Our findings validate the feasibility and value of employing DL in IACS for morphology-based genetic screening of S. cerevisiae, encouraging its incorporation in future advancements of similar technologies. [ABSTRACT FROM AUTHOR]

Published
2023
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14. Proposal of Channel Prediction by Complex-Valued Neural Networks that Deals with Polarization as a Transverse Wave Entity

Author

Murata, Tetsuya, Ding, Tianben, Hirose, Akira, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Arik, Sabri, editor, Huang, Tingwen, editor, Lai, Weng Kin, editor, and Liu, Qingshan, editor

Published
2015
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16. Fading Channel Prediction Based on Self-optimizing Neural Networks

Author

Ding, Tianben, Hirose, Akira, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Loo, Chu Kiong, editor, Yap, Keem Siah, editor, Wong, Kok Wai, editor, Teoh, Andrew, editor, and Huang, Kaizhu, editor

Published
2014
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17. Single-molecule orientation localization microscopy of amyloid fibrils

Author

Lew, Matthew, Ding, Tianben, and Wu, Tingting

Subjects
humanities
Abstract

Single-molecule orientation-localization data, animations, and interactive demonstrations accompanying our manuscript: "Single-molecule orientation localization microscopy for resolving structural heterogeneities within amyloid fibrils" https://doi.org/10.1364/OPTICA.388157

Published
2022
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