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393 results on '"Wu, Junyu"'

1. SafePtrX: Research on Mitigation of Heap-Based Memory Safety Violations for Intel x86-64

Author

Chen, LiLie, Wu, JunYu, Liu, Yuan, Goos, Gerhard, Series 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, Bai, Guangdong, editor, Ishikawa, Fuyuki, editor, Ait-Ameur, Yamine, editor, and Papadopoulos, George A., editor

Published
2025
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16. Research of Unrepeatered Transmission System based on Amplifier Optimization

Author

WU Junyu, AO Xueyuan, DENG Lei, DAI Xiaoxiao, FU Songnian, LIU Chen, and YANG Qi

Subjects
unrepeatered transmission, real-time transmission, VPI simulation, fiber optics, EDFA, Applied optics. Photonics, TA1501-1820
Abstract

【Objective】There is a problem that traditional unrepeatered transmission system need to use disturbed Raman amplifier and remote optical pump amplifier, while both of these two amplifiers need to use high power pump laser which will increase the complexity of fiber link in unrepeatered transmission system. In this paper, we propose a new structure of unrepeatered transmission system using Erbium Doped Optical Fiber Amplifier (EDFA) to replace the forward disturbed Raman amplifier.【Methods】We analyze and compare the performance of the proposed unrepeatered transmission system, and realize a real-time unrepeatered transmission experiment with the proposed structure.【Results】The results show that the gain effect of forward Raman pump in traditional unrepeatered communication system can be achieved by using high power amplifier to increase the signal launch power, and the new structure of unrepeatered transmission system is more suitable to achieve higher bitrate with the help of Wavelength Division Multiplexing (WDM) technology. We confirm the unrepeatered transmission system can achieve 500 km span distance at 10 Gbit/s bit rate in single channel communication transmission, and 500 km span distance at 4×10 Gbit/s bit rate in the WDM technology.【Conclusion】The proposed unrepeatered transmission system can simplify the traditional unrepeatered transmission system, and increase the bit rate by using multi-channel WDM system with EDFA. It has important practical significance to simplify the structure and improve the bit rate in future unrepeatered transmission system.

Published
2024
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20. Mitochondrial dysfunction of Astrocyte induces cell activation under high salt condition

Author

Yuemin Qiu, Gengxin Lu, Shifeng Zhang, Li Minping, Xu Xue, Wu Junyu, Zhihui Zheng, Weiwei Qi, Junjie Guo, Dongxiao Zhou, Haiwei Huang, and Zhezhi Deng

Subjects
Astrocyte, Mitochondrial dysfunction, High salt, TSPO ligand, Science (General), Q1-390, Social sciences (General), H1-99
Abstract

Excess dietary sodium can accumulate in brain and adversely affect human health. We have confirmed in previous studies that high salt can induce activation of astrocyte manifested by the secretion of various inflammatory factors. In order to further explore the effect of high salt on the internal cell metabolism of astrocytes, RNA sequencing was performed on astrocytes under high salt environment, which indicated the oxidative phosphorylation and glycolysis pathways of astrocytes were downregulated. Next, we found that high salt concentrations elicited astrocyte mitochondrial morphology change, as evidenced by swelling from a short rod to a round shape through a High Intelligent and Sensitive Structured Illumination Microscope (HIS-SIM). Furthermore, we found that high salt concentrations reduced astrocyte mitochondrial oxygen consumption and membrane potential. Treatment with 18-kDa translocator protein (TSPO) ligands FGIN-1-27 improved mitochondrial networks and reversed astrocyte activation under high-salt circumstances. Our study shows that high salt can directly disrupt astrocytic mitochondrial homeostasis and function. Targeting translocator protein signaling may have therapeutic potential against high-salt neurotoxicity.

Published
2024
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43. Advancing economic sustainability in E7 economies: the impact of green finance, environmental benefits, and natural resource management.

Author

Dai, Xulong, Wu, Junyu, Fan, Suqing, and Zhou, Yanqi

Subjects
SUSTAINABLE development, GREENHOUSE gas mitigation, EMISSIONS (Air pollution), NATURAL resources management, TECHNOLOGICAL innovations
Abstract

Green finance (GFN) plays a critical role in reducing greenhouse gas emissions and air pollution, serving as a key financial mechanism to promote green growth and economic sustainability (ESS). This paper explores how GFN, along with fintech (FNT), environmental benefits (ENB), urbanization (URB), and natural resource management (NRM), influences ESS in E7 countries from 2000 to 2022. While fintech's rapid technological advancements offer significant economic potential, they also introduce increased complexity and systemic risks. Using rigorous methods such as method of moments quantile regression (MMQR) and Westerlund analysis, this study validates the relationships and heterogeneity among these variables through preliminary tests, including matrix correlation, cross-sectional dependence (CSD), slope heterogeneity (SH), and CIPS unit root tests. The findings indicate that GFN, ENB, and FNT positively contribute to ESS, while URB and NRM have an inverse relationship with ESS. These insights not only deepen our understanding of how ecological factors influence economic sustainability but also provide practical policy recommendations for E7 countries. The study's conclusions offer valuable guidance for optimizing ESS and advancing toward sustainable development goals (SDGs). [ABSTRACT FROM AUTHOR]

Published
2025
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47. Automatically Building Face Datasets of New Domains from Weakly Labeled Data with Pretrained Models

Author

Ding, Shengyong, Wu, Junyu, Xu, Wei, and Chao, Hongyang

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Training data are critical in face recognition systems. However, labeling a large scale face data for a particular domain is very tedious. In this paper, we propose a method to automatically and incrementally construct datasets from massive weakly labeled data of the target domain which are readily available on the Internet under the help of a pretrained face model. More specifically, given a large scale weakly labeled dataset in which each face image is associated with a label, i.e. the name of an identity, we create a graph for each identity with edges linking matched faces verified by the existing model under a tight threshold. Then we use the maximal subgraph as the cleaned data for that identity. With the cleaned dataset, we update the existing face model and use the new model to filter the original dataset to get a larger cleaned dataset. We collect a large weakly labeled dataset containing 530,560 Asian face images of 7,962 identities from the Internet, which will be published for the study of face recognition. By running the filtering process, we obtain a cleaned datasets (99.7+% purity) of size 223,767 (recall 70.9%). On our testing dataset of Asian faces, the model trained by the cleaned dataset achieves recognition rate 93.1%, which obviously outperforms the model trained by the public dataset CASIA whose recognition rate is 85.9%.

Published
2016

48. Deep Joint Face Hallucination and Recognition

Author

Wu, Junyu, Ding, Shengyong, Xu, Wei, and Chao, Hongyang

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Deep models have achieved impressive performance for face hallucination tasks. However, we observe that directly feeding the hallucinated facial images into recog- nition models can even degrade the recognition performance despite the much better visualization quality. In this paper, we address this problem by jointly learning a deep model for two tasks, i.e. face hallucination and recognition. In particular, we design an end-to-end deep convolution network with hallucination sub-network cascaded by recognition sub-network. The recognition sub- network are responsible for producing discriminative feature representations using the hallucinated images as inputs generated by hallucination sub-network. During training, we feed LR facial images into the network and optimize the parameters by minimizing two loss items, i.e. 1) face hallucination loss measured by the pixel wise difference between the ground truth HR images and network-generated images; and 2) verification loss which is measured by the classification error and intra-class distance. We extensively evaluate our method on LFW and YTF datasets. The experimental results show that our method can achieve recognition accuracy 97.95% on 4x down-sampled LFW testing set, outperforming the accuracy 96.35% of conventional face recognition model. And on the more challenging YTF dataset, we achieve recognition accuracy 90.65%, a margin over the recognition accuracy 89.45% obtained by conventional face recognition model on the 4x down-sampled version., Comment: 10 pages, 2 figures

Published
2016

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