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724 results on '"Kang, Jiawen"'

221. Optimized Workload Allocation in Vehicular Edge Computing: A Sequential Game Approach

Author

Ye, Dongdong, Wu, Maoqiang, Kang, Jiawen, Yu, Rong, Akan, Ozgur, Series Editor, Bellavista, Paolo, Series Editor, Cao, Jiannong, Series Editor, Coulson, Geoffrey, Series Editor, Dressler, Falko, Series Editor, Ferrari, Domenico, Series Editor, Gerla, Mario, Series Editor, Kobayashi, Hisashi, Series Editor, Palazzo, Sergio, Series Editor, Sahni, Sartaj, Series Editor, Shen, Xuemin (Sherman), Series Editor, Stan, Mircea, Series Editor, Xiaohua, Jia, Series Editor, Zomaya, Albert Y., Series Editor, Li, Bo, editor, Shu, Lei, editor, and Zeng, Deze, editor

Published
2018
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224. DFMG decreases angiogenesis to uphold plaque stability by inhibiting the TLR4/VEGF pathway in mice.

Author

Bai, Pingjuan, Xiang, Xueping, Kang, Jiawen, Xiang, Xiaoqing, Jiang, Jingwen, Fu, Xiaohua, Zhang, Yong, and Li, Lesai

Subjects
NEOVASCULARIZATION, CHORIOALLANTOIS, KNOCKOUT mice, THORACIC aorta, ATHEROSCLEROTIC plaque, CHICKEN embryos
Abstract

The aim of this study was to elucidate the specific mechanism through which 7-difluoromethoxy-5,4'-dimethoxygenistein (DFMG) inhibits angiogenesis in atherosclerosis (AS) plaques, given its previously observed but poorly understood inhibitory effects. In vitro, a model using Human Umbilical Vein Endothelial (HUVEC-12) cells simulated the initial lesion in the atherosclerotic pathological process, specifically oxidative stress injury, by exposing cells to 30 μmol/L LPC. Additionally, an AS mouse model was developed in ApoE knockout mice through a 16-week period of high-fat feeding. DFMG demonstrated a reduction in tubule quantities in the tube formation assay and neovascularization induced by oxidative stress-damaged endothelial cells in the chicken embryo chorioallantoic membrane assay. Furthermore, DFMG decreased lipid levels in the blood of ApoE knockout mice with AS, along with a decrease in atherosclerotic plaques and neovascularizations in the aortic arch and descending aorta of AS animal models. DFMG treatment upregulated microRNA140 (miR-140) expression and suppressed VEGF secretion in HUVEC-12 cells. These effects were counteracted by Toll-like receptor 4 (TLR4) overexpression in HUVEC-12 cells subjected to oxidative injury or in a mouse model of AS. Dual-luciferase reporter assays demonstrated that miR-140 directly targeted TLR4. Immunohistochemical assay findings indicated a significant inverse relationship between miR-140 expression and TLR4 expression in ApoE knockout mice subjected to a high-fat diet. The study observed a close association between DFMG inhibitory effects on angiogenesis and plaque stability in AS, and the inhibition of the TLR4/NF-κB/VEGF signaling pathway, negatively regulated by miR-140. [ABSTRACT FROM AUTHOR]

Published
2024
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230. Integrated and Enhanced Pipeline System to Support Spoken Language Analytics for Screening Neurocognitive Disorders

Author

Meng, Helen, primary, Mak, Brian, additional, Mak, Man-Wai, additional, Fung, Helene, additional, Gong, Xianmin, additional, Kwok, Timothy, additional, Liu, Xunying, additional, Mok, Vincent, additional, Wong, Patrick, additional, Woo, Jean, additional, Wu, Xixin, additional, Wong, Ka Ho, additional, Xu, Shensheng, additional, Zheng, Naijun, additional, Huang, Ranzo, additional, Kang, Jiawen, additional, Ke, Xiaoquan, additional, Li, Junan, additional, Li, Jinchao, additional, and Wang, Yi, additional

Published
2023
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239. Privacy-Preserving Intelligent Resource Allocation for Federated Edge Learning in Quantum Internet

Author

Xu, Minrui, Niyato, Dusit, Yang, Zhaohui, Xiong, Zehui, Kang, Jiawen, Kim, Dong In, Xuemin, and Shen

Subjects
Computer Science - Networking and Internet Architecture, Networking and Internet Architecture (cs.NI), Signal Processing (eess.SP), FOS: Computer and information sciences, Signal Processing, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Signal Processing, Electrical and Electronic Engineering
Abstract

Federated edge learning (FEL) is a promising paradigm of distributed machine learning that can preserve data privacy while training the global model collaboratively. However, FEL is still facing model confidentiality issues due to eavesdropping risks of exchanging cryptographic keys through traditional encryption schemes. Therefore, in this paper, we propose a hierarchical architecture for quantum-secured FEL systems with ideal security based on the quantum key distribution (QKD) to facilitate public key and model encryption against eavesdropping attacks. Specifically, we propose a stochastic resource allocation model for efficient QKD to encrypt FEL keys and models. In FEL systems, remote FEL workers are connected to cluster heads via quantum-secured channels to train an aggregated global model collaboratively. However, due to the unpredictable number of workers at each location, the demand for secret-key rates to support secure model transmission to the server is unpredictable. The proposed systems need to efficiently allocate limited QKD resources (i.e., wavelengths) such that the total cost is minimized in the presence of stochastic demand by formulating the optimization problem for the proposed architecture as a stochastic programming model. To this end, we propose a federated reinforcement learning-based resource allocation scheme to solve the proposed model without complete state information. The proposed scheme enables QKD managers and controllers to train a global QKD resource allocation policy while keeping their private experiences local. Numerical results demonstrate that the proposed schemes can successfully achieve the cost-minimizing objective under uncertain demand while improving the training efficiency by about 50\% compared to state-of-the-art schemes.

Published
2023

240. A Unified Blockchain-Semantic Framework for Wireless Edge Intelligence Enabled Web 3.0

Author

Lin, Yijing, Gao, Zhipeng, Du, Hongyang, Niyato, Dusit, Kang, Jiawen, Deng, Ruilong, and Shen, Xuemin Sherman

Subjects
Computer Science - Networking and Internet Architecture, Networking and Internet Architecture (cs.NI), FOS: Computer and information sciences, Computer Science - Cryptography and Security, Electrical and Electronic Engineering, Cryptography and Security (cs.CR), Computer Science Applications
Abstract

Web 3.0 enables user-generated contents and user-selected authorities. With decentralized wireless edge computing architectures, Web 3.0 allows users to read, write, and own contents. A core technology that enables Web 3.0 goals is blockchain, which provides security services by recording content in a decentralized and transparent manner. However, the explosion of on-chain recorded contents and the fast-growing number of users cause increasingly unaffordable computing and storage resource consumption. A promising paradigm is to analyze the semantic information of contents that can convey precisely the desired meanings without consuming many resources. In this article, we propose a unified blockchain-semantic ecosystems framework for wireless edge intelligence-enabled Web 3.0. Our framework consists of six key components to exchange semantic demands. We then introduce an Oracle-based proof of semantic mechanism to implement on-chain and off-chain interactions of Web 3.0 ecosystems on semantic verification algorithms while maintaining service security. An adaptive Deep Reinforcement Learning-based sharding mechanism on Oracle is designed to improve interaction efficiency, which can facilitate Web 3.0 ecosystems to deal with varied semantic demands. Finally, a case study is presented to show that the proposed framework can dynamically adjust Oracle settings according to varied semantic demands., Comment: 8 pages, 5 figures, 1 table

Published
2023

248. Sparks of Generative Pretrained Transformers in Edge Intelligence for the Metaverse: Caching and Inference for Mobile Artificial Intelligence-Generated Content Services.

Author

Xu, Minrui, Niyato, Dusit, Zhang, Hongliang, Kang, Jiawen, Xiong, Zehui, Mao, Shiwen, and Han, Zhu

Abstract

Aiming at achieving artificial general intelligence (AGI) for the metaverse, pretrained foundation models (PFMs), e.g., generative pretrained transformers (GPTs), can effectively provide various artificial intelligence (AI) services, such as autonomous driving, digital twins (DTs), and AI-generated content (AIGC) for extended reality (XR). With the advantages of low latency and privacy-preserving, serving PFMs of mobile AI services in edge intelligence is a viable solution for caching and executing PFMs on edge servers with limited computing resources and GPU memory. However, PFMs typically consist of billions of parameters that are computation- and memory-intensive for edge servers during loading and execution. In this article, we investigate edge PFM serving problems for mobile AIGC services of the metaverse. First, we introduce the fundamentals of PFMs and discuss their characteristic fine-tuning and inference methods in edge intelligence. Then, we propose a novel framework of joint model caching and inference for managing models and allocating resources to satisfy users’ requests efficiently. Furthermore, considering the in-context learning ability of PFMs, we propose a new metric to evaluate the freshness and relevance between examples in demonstrations and executing tasks, namely the Age of Context (AoC). Finally, we propose a least-context (LC) algorithm for managing cached models at edge servers by balancing the tradeoff among latency, energy consumption, and accuracy. [ABSTRACT FROM AUTHOR]

Published
2023
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