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3. Tight Regret Bounds for Noisy Optimization of a Brownian Motion.

Author

Wang, Zexin, Tan, Vincent Y. F., and Scarlett, Jonathan

Subjects
*RANDOM noise theory, *NONSMOOTH optimization, *NOISE measurement, *WIENER processes
Abstract

We consider the problem of Bayesian optimization of a one-dimensional Brownian motion in which the $T$ adaptively chosen observations are corrupted by Gaussian noise. We show that the smallest possible expected cumulative regret and the smallest possible expected simple regret scale as $\Omega (\sigma \sqrt {T / \log (T)}) \cap \mathcal {O}(\sigma \sqrt {T} \cdot \log T)$ and $\Omega (\sigma / \sqrt {T \log (T)}) \cap \mathcal {O}(\sigma \log T / \sqrt {T})$ respectively, where $\sigma ^2$ is the noise variance. Thus, our upper and lower bounds are tight up to a factor of $\mathcal {O} ((\log T)^{1.5})$. The upper bound uses an algorithm based on confidence bounds and the Markov property of Brownian motion (among other useful properties), and the lower bound is based on a reduction to binary hypothesis testing. [ABSTRACT FROM AUTHOR]

Published
2022
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5. VisDrone-DET2019: The Vision Meets Drone Object Detection in Image Challenge Results

Author

Du, Dawei, primary, Zhu, Pengfei, additional, Wen, Longyin, additional, Bian, Xiao, additional, Lin, Haibin, additional, Hu, Qinghua, additional, Peng, Tao, additional, Zheng, Jiayu, additional, Wang, Xinyao, additional, Zhang, Yue, additional, Bo, Liefeng, additional, Shi, Hailin, additional, Zhu, Rui, additional, Kumar, Aashish, additional, Li, Aijin, additional, Zinollayev, Almaz, additional, Askergaliyev, Anuar, additional, Schumann, Arne, additional, Mao, Binjie, additional, Lee, Byeongwon, additional, Liu, Chang, additional, Chen, Changrui, additional, Pan, Chunhong, additional, Huo, Chunlei, additional, Yu, Da, additional, Cong, DeChun, additional, Zeng, Dening, additional, Pailla, Dheeraj Reddy, additional, Li, Di, additional, Wang, Dong, additional, Cho, Donghyeon, additional, Zhang, Dongyu, additional, Bai, Furui, additional, Jose, George, additional, Gao, Guangyu, additional, Liu, Guizhong, additional, Xiong, Haitao, additional, Qi, Hao, additional, Wang, Haoran, additional, Qiu, Heqian, additional, Li, HongLiang, additional, Lu, Huchuan, additional, Kim, Ildoo, additional, Kim, Jaekyum, additional, Shen, Jane, additional, Lee, Jihoon, additional, Ge, Jing, additional, Xu, Jingjing, additional, Zhou, Jingkai, additional, Meier, Jonas, additional, Choi, Jun Won, additional, Hu, Junhao, additional, Zhang, Junyi, additional, Huang, Junying, additional, Huang, Kaiqi, additional, Wang, Keyang, additional, Sommer, Lars, additional, Jin, Lei, additional, Zhang, Lei, additional, Huang, Lianghua, additional, Sun, Lin, additional, Steinmann, Lucas, additional, Jia, Meixia, additional, Xu, Nuo, additional, Zhang, Pengyi, additional, Chen, Qiang, additional, Lv, Qingxuan, additional, Liu, Qiong, additional, Cheng, Qishang, additional, Chennamsetty, Sai Saketh, additional, Chen, Shuhao, additional, Wei, Shuo, additional, Kruthiventi, Srinivas S S, additional, Hong, Sungeun, additional, Kang, Sungil, additional, Wu, Tong, additional, Feng, Tuo, additional, Kollerathu, Varghese Alex, additional, Li, Wanqi, additional, Dai, Wei, additional, Qin, Weida, additional, Wang, Weiyang, additional, Wang, Xiaorui, additional, Chen, Xiaoyu, additional, Chen, Xin, additional, Sun, Xin, additional, Zhang, Xin, additional, Zhao, Xin, additional, Zhang, Xindi, additional, Zhang, Xinyu, additional, Chen, Xuankun, additional, Wei, Xudong, additional, Zhang, Xuzhang, additional, Li, Yanchao, additional, Chen, Yifu, additional, Toh, Yu Heng, additional, Zhang, Yu, additional, Zhu, Yu, additional, Zhong, Yunxin, additional, Wang, Zexin, additional, Wang, Zhikang, additional, Song, Zichen, additional, and Liu, Ziming, additional

Published
2019
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8. Simple and Effective: Spatial Rescaling for Person Reidentification.

Author

Wang, Haoran, Jiao, Licheng, Yang, Shuyuan, Li, Lingling, and Wang, Zexin

Subjects
CONVOLUTIONAL neural networks
Abstract

Global average pooling (GAP) allows convolutional neural networks (CNNs) to localize discriminative information for recognition using only image-level labels. While GAP helps CNNs to attend to the most discriminative features of an object, e.g., head of a bird or one man’s bag, it may suffer if that information is missing due to camera viewpoint changes and intraclass variations in some tasks. To circumvent this issue, we propose one new module to help CNNs to see more, namely, Spatial Rescaling (SpaRs) layer. It introduces spatial relations among the feature map activations back to the model, guiding the model to focus on a broad area in the feature map. With simple implementation, it can be inserted into CNNs of various architectures directly. SpaRs layer consistently improves the performance over the reidentification (re-ID) models. Besides, the new module based on different normalization methods also demonstrates the superiority of fine-grained and general image classification benchmarks. The visualization method shows the changes in activated regions when equipped with the SpaRs layer for better understanding. Our code is publicly available at https://github.com/HRanWang/Spatial-Re-Scaling. [ABSTRACT FROM AUTHOR]

Published
2022
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10. Approach civil Integrated Modular Avionics Airworthiness Certification by Iterative Incremental Certification Process

Author

Ou Xupo, Wang Zexin, Cai Yong, and Zhu Liang

Subjects
ComputingMilieux_THECOMPUTINGPROFESSION, Aerospace electronics, Process (engineering), Airworthiness, Computer science, Systems engineering, Resource management, Certification, Integrated modular avionics
Abstract

This paper discussed the main difficulties of the Integrated Modular Avionics Airworthiness Certification, analyzing the disadvantages of the traditional airworthiness certification. On the basis of incremental acceptance of IMA, Iterative Incremental Certification Process (IICP) are introduced to resolve the gaps between certification methods and development practice under the situation that Open IMA are now wildly used in civil aircraft.

Published
2010

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