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1,909 results on '"Su Zhou"'

113. Arbitrage Attack: Miners of the World, Unite!

Author

Wang, Yuheng, Li, Jiliang, Su, Zhou, Wang, Yuyi, 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, Eyal, Ittay, editor, and Garay, Juan, editor

Published
2022
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117. Conclusions and Future Directions

Author

Su, Zhou, Hui, Yilong, Luan, Tom H., Liu, Qiaorong, Xing, Rui, Shen, Xuemin Sherman, Series Editor, Su, Zhou, Hui, Yilong, Luan, Tom H., Liu, Qiaorong, and Xing, Rui

Published
2021
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122. Introduction

Author

Su, Zhou, Hui, Yilong, Luan, Tom H., Liu, Qiaorong, Xing, Rui, Shen, Xuemin Sherman, Series Editor, Su, Zhou, Hui, Yilong, Luan, Tom H., Liu, Qiaorong, and Xing, Rui

Published
2021
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123. Learning Visual Knowledge Memory Networks for Visual Question Answering

Author

Su, Zhou, Zhu, Chen, Dong, Yinpeng, Cai, Dongqi, Chen, Yurong, and Li, Jianguo

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Computation and Language
Abstract

Visual question answering (VQA) requires joint comprehension of images and natural language questions, where many questions can't be directly or clearly answered from visual content but require reasoning from structured human knowledge with confirmation from visual content. This paper proposes visual knowledge memory network (VKMN) to address this issue, which seamlessly incorporates structured human knowledge and deep visual features into memory networks in an end-to-end learning framework. Comparing to existing methods for leveraging external knowledge for supporting VQA, this paper stresses more on two missing mechanisms. First is the mechanism for integrating visual contents with knowledge facts. VKMN handles this issue by embedding knowledge triples (subject, relation, target) and deep visual features jointly into the visual knowledge features. Second is the mechanism for handling multiple knowledge facts expanding from question and answer pairs. VKMN stores joint embedding using key-value pair structure in the memory networks so that it is easy to handle multiple facts. Experiments show that the proposed method achieves promising results on both VQA v1.0 and v2.0 benchmarks, while outperforms state-of-the-art methods on the knowledge-reasoning related questions., Comment: Supplementary to CVPR 2018 version

Published
2018

133. Weakly Supervised Dense Video Captioning

Author

Shen, Zhiqiang, Li, Jianguo, Su, Zhou, Li, Minjun, Chen, Yurong, Jiang, Yu-Gang, and Xue, Xiangyang

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper focuses on a novel and challenging vision task, dense video captioning, which aims to automatically describe a video clip with multiple informative and diverse caption sentences. The proposed method is trained without explicit annotation of fine-grained sentence to video region-sequence correspondence, but is only based on weak video-level sentence annotations. It differs from existing video captioning systems in three technical aspects. First, we propose lexical fully convolutional neural networks (Lexical-FCN) with weakly supervised multi-instance multi-label learning to weakly link video regions with lexical labels. Second, we introduce a novel submodular maximization scheme to generate multiple informative and diverse region-sequences based on the Lexical-FCN outputs. A winner-takes-all scheme is adopted to weakly associate sentences to region-sequences in the training phase. Third, a sequence-to-sequence learning based language model is trained with the weakly supervised information obtained through the association process. We show that the proposed method can not only produce informative and diverse dense captions, but also outperform state-of-the-art single video captioning methods by a large margin., Comment: To appear in CVPR 2017

Published
2017

134. Deletion of exon 4 of the PITX2 in a child with Axenfeld–Rieger syndrome.

Author

Tian, Yu, Zhou, Xiao-Xia, Zhao, Su-Zhou, Peng, Mei, and Jia, Jia

Abstract

Background: Axenfeld-Rieger syndrome (ARS, OMIM:602482) is a genetic disease characterized by ocular and systemic features. Clinical features of ARS are highly variable among patients and associated with mutations of human PITX2 and FOXC1 genes. Herein, we present an ARS in two cases (proband and his mother) with a novel variant in the PITX2. Methods: A 3-month-old boy was admitted with an abnormal eye development at birth. Physical examination and ophthalmologic examination findings revealed an abnormal development of the anterior segments, ectropion of redundant skin in the umbilicus, single-sided deafness, teeth eruption failure, patent foramen ovale, and a mid-facial flattening. The proband's mother has been blind since the age of 12. We conducted genetic tests for the family via whole exome sequencing (WES) and quantitative PCR (qPCR) to identify the genetic etiology in the family. We also conducted a retrospective review of the ARS type I phenotype caused by the PITX2 mutations. Results: WES and qPCR results of the proband and his parents suggested that both the child and his mother carry a 1.31kbp deletion (chr4: g.111538559_111539864del [GRCh37]) spanned the exon 4 of PITX2, resulting in the typical and rare phenotype of ARS type I. It can conclude that truncating variants in the exon 3-4 of PITX2 are the more common mechanism to cause the malfunction of the gene with a broader phenotypic spectrum. Conclusion: The study has filled in a new clinical manifestation of the PITX2 and enriched the phenotype of ARS. The retrospective analysis of phenotype of PITX2 mutations provided a comprehensive understanding of the disease. [ABSTRACT FROM AUTHOR]

Published
2024
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135. Optimization of Provincial Power Source Structure Planning in Northwestern China Based on Time-Series Production Simulation.

Author

Su, Zhou, Yang, Guoqing, Yao, Lixiao, Zhou, Qingqing, and Zhang, Yuhan

Subjects
*ENERGY consumption, *RENEWABLE energy sources, *POWER resources, *ENERGY storage, *ELECTRIC power consumption
Abstract

With the increasing of renewable energy penetration rate in power source structure, the impact of its uncertainty on the stability of the power system is becoming increasingly apparent. The power system faces dual pressures of ensuring power supply and renewable energy accommodation. The way in which to accurately plan the power structure under situations with a high renewable energy penetration rate has become an urgent issue for future power infrastructure upgrading. In this paper, a rational power structure model was proposed for a provincial power grid by iteratively optimizing variables. The synergistic relationship between renewable energy, traditional power sources, and the energy storage system was considered comprehensively and calculated through the time-series production simulation method. The as-proposed model was studied using a scenario of a provincial grid plan in the year 2035. The power balance, electricity consumption, and renewable energy utilization were systematically analyzed, providing an important theoretical basis for the optimization and upgrading of provincial power systems under the rapid growth of renewable energy. [ABSTRACT FROM AUTHOR]

Published
2024
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140. A Secure Crowdsourcing-Based Indoor Navigation System

Author

Xie, Liang, Su, Zhou, Xu, Qichao, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin (Sherman), Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Loke, Seng W., editor, Liu, Zhi, editor, Nguyen, Kien, editor, Tang, Guoming, editor, and Ling, Zhen, editor

Published
2020
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143. Anomaly Detection of Hard Disk Drives Based on Multi-scale Feature

Author

Ran, Xiandong, Su, Zhou, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, He, Cheng, editor, Feng, Mengling, editor, Lee, Patrick P. C., editor, Wang, Pinghui, editor, Han, Shujie, editor, and Liu, Yi, editor

Published
2020
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144. Cross-Domain Virtual Network Resource Allocation Mechanism for IoT in Smart Grid

Author

Shi, Zhan, Su, Zhou, Lin, Peng, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Liu, Qi, editor, Mısır, Mustafa, editor, Wang, Xin, editor, and Liu, Weiping, editor

Published
2020
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150. CORA: Channel Occupancy-Aware Resource Allocation in LoRa Wireless Networks

Author

Lin, Ziqi, Huang, Zihao, Gu, Bo, Gong, Shimin, Su, Zhou, and Guizani, Mohsen

Abstract

Long Range (LoRa) technology, characterized by extended transmission range and low power consumption, is considered a crucial technology for the Industrial Internet of Things (IIoT). However, with increasing network scale, existing LoRa networks have begun to exhibit low energy efficiency (EE) due to severe collisions. Existing methodologies predominantly assume perfect SF orthogonality, neglecting the detrimental effects of interference between signals using different spreading factors (SFs) in the same channel (CH). This study investigates a joint SF, CH, and transmit power (TP) allocation problem with the objective of optimizing the system's uplink EE while fully considering quasiorthogonal SFs, which is an NP-hard optimization problem. To enhance tractability, we decompose the original problem into two subproblems: CH/SF assignment and TP allocation. First, an attention-based deep neural network is designed to generate a near-optimal CH/SF assignment, leveraging both channel state information and channel activity detection-based historical channel occupancy information. Second, given the CH/SF assignment, the optimal TP allocation is determined through the resolution of a convex optimization problem. Experimental results on a real-world LoRa network demonstrate that our proposed method significantly improves the system's uplink EE.

Published
2024
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