检索结果-内蒙古大学图书馆

Reconstructing Missing Modalities in Multi-Modal Endoscopic Ultrasound Via Cross-Modal Feature Replacement Representation

学校读者我要写书评

暂无评论

SSRN 2024年

作者： Zheng, Cenyang Gong, Xun Fan, Lin Li, Jiao School of Computing and Artificial Intelligence Southwest Jiaotong University Sichuan Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China Department of Gastroenterology The Third People’s Hospital of Chendu Affiliated Hospital of Southwest Jiaotong University Chengdu610031 China

Endoscopic ultrasound (EUS) is the primary diagnostic imaging technique for gastrointestinal stromal tumors. However, accurately identifying these tumors through EUS imaging alone remains challenging, even for experienced clinicians. They often rely on additional data, such as white light endoscopy (WLE) and EUS text reports, to aid in the diagnosis. Unfortunately, in practice, these supplementary modalities are sometimes overlooked during patient evaluation. To address these issues, we propose the Cross-modal Feature Reconstruction Network (CFRN), which integrates multimodal data modeling for medical diagnostic processes and performs specific learning for different modalities with missing data. The network fuses the multi-modal features based on the clinical medical process, realizes the feature-level missing modality reconstruction, and learns the refined features of different modal true cases by decoding the mode-specific missing. We trained and tested the model on two publicly available datasets, Audiovision-MNIST, MM-IMDb, and data sets collected from real clinical scenarios at three hospitals. Our results show that CFRN outperforms state-of-the-art methods, achieving higher prediction accuracy than using EUS alone. By simultaneously incorporating multimodal or reconstructed modal data for disease diagnosis, our single-modal EUS model proves to be more suitable for clinical scenarios. © 2024, The Authors. All rights reserved.

关键词： Endoscopy

Impact of Stochasticity on Platoon Stability of Car-Following Models for Emissions Estimation

学校读者我要写书评

暂无评论

SSRN 2022年

作者： Meng, Dongli Song, Guohua Lu, Hongyu Wu, Yizheng Zhai, Zhiqiang Yu, Lei Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University 3 Shangyuancun Haidian District Beijing100044 China School of Civil and Environmental Engineering Georgia Institute of Technology 790 Atlantic Drive AtlantaGA30332 United States Department of Transportation Studies Texas Southern University 3100 Cleburne Avenue HoustonTX77004 United States Xuchang University Henan Xuchang461000 China

In the application of microscopic traffic simulations for vehicle emissions modeling, the reality and platoon stability of vehicle trajectories derived from the car-following component has been questioned. This study compared the platoon stability of the Gipps car-following model, the full velocity difference model (FVDM), the intelligent driver model (IDM), and the Wiedemann model for emissions modeling, followed by a parameter sensitivity analysis and a stochasticity impact investigation. The results indicated the superior performance of the FVDM and IDM for emissions estimation compared with the Wiedemann model, and the emissions estimation errors were stable along the platoon for the above models. Gipps model generated realistic vehicle dynamics of the first following vehicle, however, the emissions estimation errors increased along the platoon. The parameter calibration failed to improve the platoon stability of the Gipps model, while incorporating the stochastic parameters was proven to reproduce representative platoon trajectories for accurate emissions estimations. © 2022, The Authors. All rights reserved.

关键词： Stochastic systems

Impact of Stochasticity on Platoon Stability of Car-Following Models for Emissions Estimation

学校读者我要写书评

暂无评论

SSRN 2022年

In the application of microscopic traffic simulations for vehicle emissions modeling, the reality and platoon stability of vehicle trajectories derived from the car-following component has been questioned. This study compared the platoon stability of the Gipps car-following model, the full velocity difference model (FVDM), the intelligent driver model (IDM), and the Wiedemann model for emissions modeling, followed by a parameter sensitivity analysis and a stochasticity impact investigation. The results indicated the superior performance of the FVDM and IDM for emissions estimation compared with the Wiedemann model, and the emissions estimation errors were stable along the platoon for the above models. Gipps model generated realistic vehicle dynamics of the first following vehicle, however, the emissions estimation errors increased along the platoon. This study found that it is difficult to improve the platoon stability of the Gipps model by parameter calibration. While taking into account the driving behavior heterogeneity and the human factors, the Gipps model was modified by incorporating the stochastic parameters to reproduce representative platoon trajectories for accurate emissions estimations. © 2022, The Authors. All rights reserved.

关键词： Stochastic systems

Predictability of real temporal networks

学校读者我要写书评

暂无评论

arXiv

arXiv 2020年

作者： Tang, Disheng Du, Wenbo Shekhtman, Louis Wang, Yijie Havlin, Shlomo Cao, Xianbin Yan, Gang School of Electronic and Information Engineering Beihang University Beijing100191 China School of Physics Science and Engineering Tongji University Shanghai200092 China National Engineering Laboratory of Big Data Application Technologies of Comprehensive Transportation Beijing100191 China Network Science Institute Northeastern University BostonMA02115 United States Department of Physics Bar Ilan University Ramat Gan5290002 Israel Shanghai Institute of Intelligence Science and Technology Tongji University Shanghai200092 China CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China

Links in most real networks often change over time. Such temporality of links encodes the ordering and causality of interactions between nodes and has a profound effect on network dynamics and function. Empirical evidence has shown that the temporal nature of links in many real-world networks is not random. Nonetheless, it is challenging to predict temporal link patterns while considering the entanglement between topological and temporal link patterns. Here, we propose an entropy-rate-based framework, based on combined topological–temporal regularities, for quantifying the predictability of any temporal network. We apply our framework on various model networks, demonstrating that it indeed captures the intrinsic topological–temporal regularities whereas previous methods considered only temporal aspects. We also apply our framework on 18 real networks of different types and determine their predictability. Interestingly, we find that, for most real temporal networks, despite the greater complexity of predictability brought by the increase in dimension, the combined topological–temporal predictability is higher than the temporal predictability. Our results demonstrate the necessity for incorporating both temporal and topological aspects of networks in order to improve predictions of dynamical processes. Copyright © 2020, The Authors. All rights reserved.

关键词： Topology

Unsupervised evaluation of airspace complexity based on kernel principal component analysis

学校读者我要写书评

暂无评论

Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica 2019年第8期40卷

作者： Zhang, Zhuxi Zhu, Xi Zhu, Shaochuan Zhang, Mingyuan Du, Wenbo National Engineering Laboratory of Big Data Application Technologies of Comprehensive Transportation Beihang University Beijing100083 China Unit 32751 Force of PLA China Research Institute of Frontier Science Beihang University Beijing100083 China School of Transportation Science and Engineering Beihang University Beijing100083 China School of Electronic and Information Engineering Beihang University Beijing100083 China

Airspace complexity evaluation is a key means to measure the airspace operational situation and the controller workload, providing the basis for the operation optimization. Its accurate evaluation is a challenging problem in the aviation domain due to numerous influencing factors, the complex correlations between factors, and the high difficulty of collecting labelled samples. This paper proposes an unsupervised evaluation method for airspace complexity. Firstly, the kernel principal component analysis is utilized to mine the nonlinear correlations in different sample dimensions, and extract several principal components in which the airspace complexity information is maximized. Furthermore, the principal component clustering which can be customized according to user requirements is designed. The proposed method achieves accurate complexity evaluation capacity under the unsupervised condition, providing effective technical support for air traffic management like airspace configuration and traffic management. © 2019, Press of Chinese Journal of Aeronautics. All right reserved.

关键词： Principal component analysis

Structure and Evolution of Urban Heavy Truck Mobility Networks

学校读者我要写书评

暂无评论

SSRN 2022年

作者： Yang, Yitao Jia, Bin Liu, Erjian Yan, Xiao-Yong de Bok, Michiel Tavasszy, Lóránt A. Gao, Ziyou Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of Transport Beijing Jiaotong University Beijing100044 China Institute of Transportation System Science and Engineering Beijing Jiaotong University Beijing100044 China Department of Transport & Planning Faculty of Civil Engineering and Geosciences Delft University of Technology Stevinweg1 Delft2628 CN Netherlands Instituto de Física Interdisciplinar y Sistemas Complejos IFISC CSIC UIB Palma de Mallorca07122 Spain

Revealing the structural properties and understanding the evolutionary mechanisms of the urban heavy truck mobility network (UHTMN) provide insights in assessment of freight policies to manage and regulate the urban freight system, and are of vital importance for improving the livability and sustainability of cities. Although massive urban heavy truck mobility data become available in recent years, in-depth studies on the structure and evolution of UHTMN are still lacking. Here we use massive urban heavy truck GPS data in China to construct the UHTMN and reveal its a wide range of structure properties. We further develop an evolving network model that simultaneously considers weight, space and system element duplication. Our model reproduces the observed structure properties of UHTMN and helps us understand its underlying evolutionary mechanisms. Our model also provides new perspectives for modeling the evolution of many other real-world networks, such as citation networks and air transportation networks. © 2022, The Authors. All rights reserved.

关键词： Complex networks

A Certification Scheme for Realizing the Value of Future Accounts Receivable Claims

学校读者我要写书评

暂无评论

SSRN 2023年

作者： He, Juan Li, Zhengbo Xianglin, Jiang Ren, Jun Xue, Qian School of Transportation and Logistics Southwest Jiaotong University Sichuan Chengdu611756 China Institute For Supply Chain Finance Studies National Engineering Laboratory of Application Technology of Integrated Transportation Big Date Southwest Jiaotong University Sichuan Chengdu611756 China Institute for Financial Studies Fudan University Shanghai200433 China China State Railway Group Company Limited Beijing100080 China

There exists a certain cycle of workload data such as volume of engineering, supply, service, etc. between the buyer and seller while the transaction is not completed. The workload data value belongs to the enterprise's future receivables, existing a huge intrinsic economic value. However, due to difficulty in activating the value of workload data, as well as the authenticity and uniqueness of transactions and goods are not guaranteed, resulting in the financing service based on future receivables is not promoted. Therefore, this paper proposes a certification scheme for realizing the value of future receivables claims. The scheme uses pseudo-anonymous identifiers to symmetrically encrypt workload data on the chain. It also embeds cryptographic promises to strengthen the privacy protection of the data, as well as digital evidence detailing the protocols and conditions for certifying the value of future receivables claims with the help of smart contracts. The study shows that the proposed authentication mechanism can ensure the measurable, observable, and authenticated value of future receivable claims, and avoid the same workload data from being authenticated twice or falsely. At the same time, the scheme also has certain security attributes, dispute resolution and auditing functions. In addition, the research will accelerate the realization of the flow of future receivables and open a brand-new channel for product innovation based on future receivables under data credit empowerment. © 2023, The Authors. All rights reserved.

关键词： Smart contract

Reconstruction of hidden representation for robust feature extraction

学校读者我要写书评

暂无评论

arXiv

arXiv 2017年

作者： Zeng, Y.U. Tianrui, L.I. Ning, Y.U. Yi, P.A.N. Chen, Hongmei Bing, L.I.U. Southwest Jiaotong University School of Information Science and Technology National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China College at Brockport State University of New York Department of Computing Sciences BrockportNY14420 United States Georgia State University Department of Computer Science AtlantaGA30302 United States University of Illinois at Chicago Department of Computer Science ChicagoIL60607 United States

This paper aims to develop a new and robust approach to feature representation. Motivated by the success of Auto-Encoders, we first theoretically analyze and summarize the general properties of all algorithms that are based on traditional Auto-Encoders: 1) The reconstruction error of the input can not be lower than a lower bound, which can be viewed as a guiding principle for reconstructing the input. Additionally, when the input is corrupted with noises, the reconstruction error of the corrupted input also can not be lower than a lower bound. 2) The reconstruction of a hidden representation achieving its ideal situation is the necessary condition for the reconstruction of the input to reach the ideal state. 3) Minimizing the Frobenius norm of the Jacobian matrix of the hidden representation has a deficiency and may result in a much worse local optimum value. We believe that minimizing the reconstruction error of the hidden representation is more robust than minimizing the Frobenius norm of the Jacobian matrix of the hidden representation. Based on the above analysis, we propose a new model termed Double Denoising Auto-Encoders (DDAEs), which uses corruption and reconstruction on both the input and the hidden representation. We demonstrate that the proposed model is highly flexible and extensible and has a potentially better capability to learn invariant and robust feature representations. We also show that our model is more robust than Denoising Auto-Encoders (DAEs) for dealing with noises or inessential features. Furthermore, we detail how to train DDAEs with two different pre-training methods by optimizing the objective function in a combined and separate manner, respectively. Comparative experiments illustrate that the proposed model is significantly better for representation learning than the state-of-the-art models. Copyright © 2017, The Authors. All rights reserved.

关键词： Jacobian matrices

Understanding and Detecting Privacy Leakage Vulnerabilities in Hyperledger Fabric Chaincodes

学校读者我要写书评

暂无评论

Understanding and Detecting Privacy Leakage Vulnerabilities ...

International Symposium on Software Reliability engineering (ISSRE)

作者： Ziming Chen Yue Li Jianbo Gao Jiashuo Zhang Ke Wang Jianbin Hu Zhi Guan Zhong Chen School of Computer Science Peking University Beijing China College of Computer Science and Technology Taiyuan University of Technology Taiyuan China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing China Peking University Chongqing Research Institute of Big Data Chongqing China National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (数字)9798350353884

ISBN: (纸本)9798350353891

The application on a blockchain cannot maintain secrecy because its data is replicated across all peers in the network. To remedy this problem, Hyperledger Fabric introduces private data collection (PDC) into its smart contract (i.e. chaincode) to facilitate applications that require privacy. However, recent studies have revealed that PDC is too complex for chaincode developers to fully understand and use correctly, leading to privacy leaks vulnerabilities. In this paper, we present an empirical study on the prevalence of PDC misuse in chaincodes by extracting privacy leakage cases from StackOverflow posts and Hyperledger Fabric repositories on GitHub. Subsequently, we systematically categorize the misuse of PDC into three categories of vulnerabilities resulting in the leakage of private data and provide formal definitions for them. Furthermore, we develop PDChecker, an automated security analysis framework for identifying the privacy and security vulnerabilities in Fabric chaincodes. We evaluated PDChecker on 956 real-world chaincodes applying PDC and found that 67.78% of them contain at least one privacy leakage vulnerability. In addition, PDChecker uncovered 10 zero-day vulnerabilities documented by the China national Vulnerability database.

关键词： Privacy data privacy Distributed ledger databases Smart contracts Fabrics Blockchains Software reliability Security Software development management