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Evidential Deep Learning for Uncertainty Quantification and Out-of-Distribution Detection in Jet Identification using Deep Neural Networks

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arXiv

arXiv 2025年

作者： Khot, Ayush Wang, Xiwei Roy, Avik Kindratenko, Volodymyr Neubauer, Mark S. Department of Physics University of Illinois Urbana-Champaign UrbanaIL61801 United States The Grainger College of Engineering Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign UrbanaIL61801 United States Center for Artificial Intelligence Innovation National Center for Supercomputing Applications University of Illinois Urbana-Champaign UrbanaIL61801 United States The Grainger College of Engineering Siebel School of Computing and Data Science University of Illinois Urbana-Champaign UrbanaIL61801 United States

Current methods commonly used for uncertainty quantification (UQ) in deep learning (DL) models utilize Bayesian methods which are computationally expensive and time-consuming. In this paper, we provide a detailed study of UQ based on evidential deep learning (EDL) for deep neural network models designed to identify jets in high energy proton-proton collisions at the Large Hadron Collider and explore its utility in anomaly detection. EDL is a DL approach that treats learning as an evidence acquisition process designed to provide confidence (or epistemic uncertainty) about test data. Using publicly available datasets for jet classification benchmarking, we explore hyperparameter optimizations for EDL applied to the challenge of UQ for jet identification. We also investigate how the uncertainty is distributed for each jet class, how this method can be implemented for the detection of anomalies, how the uncertainty compares with Bayesian ensemble methods, and how the uncertainty maps onto latent spaces for the models. Our studies uncover some pitfalls of EDL applied to anomaly detection and a more effective way to quantify uncertainty from EDL as compared with the foundational EDL setup. These studies illustrate a methodological approach to interpreting EDL in jet classification models, providing new insights on how EDL quantifies uncertainty and detects out-of-distribution data which may lead to improved EDL methods for DL models applied to classification tasks. © 2025, CC BY.

关键词： Adversarial machine learning

Simulation of 6-DOF External Fixator of Lower Limb Based on Opensim

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Simulation of 6-DOF External Fixator of Lower Limb Based on ...

Chinese Control Conference (CCC)

作者： Ming-Huan Yin Hao Sun Jian-Wen Chen Chao Zhuang Bai-Chuan An Cheng Chang Zhi-Feng Gu School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin Control Engineering Technology Innovation Center of Hebei Province Hebei University of Technology Tianjin P. R. Engineering Research Center of Intelligent Rehabilitation Device and Detection Technology Ministry of Education Tianjin Limb Deformity Correction and Function Reconstruction Department Southern University of Science and Technology Hospital Shenzhen School of Electrical and Electronic Engineering Shijiazhuang Tiedao University Hebei

When patients with limb deformities wear a 6-DOF external fixator after surgery, it is impossible to accurately analyze the muscle strength of the relevant muscles to guide patients to carry out rehabilitation training. In view of the above problems, the 3D motion capture system and force measurement platform were used to collect the marker point data set and ground reaction force data of the experimental object wearing the Taylor frame, and the biomechanical simulation software Opensim was used to establish the coupling model of the human lower limb femur with the 6-DOF external fixation support, and the muscle force of the human lower limb femur with and without the 6-DOF external fixation support was analyzed and compared through Opensim. The simulation results show that: compared with the patients without the 6-DOF external fixator, wearing the 6-DOF external fixator will increase the muscle strength of the lower limb related muscles when the patients walk after surgery, and the increase is within the patient's tolerance.

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An Association Rule Mining-Based Modeling Framework for Characterizing and Predicting Urban Road Traffic Accidents

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SSRN 2024年

作者： Du, Lijing Huang, Fa-Sheng Chen, Sijng Xiong, Yanbing Cai, Qi Guo, Qianwen School of Management Research Institute of Digital Governance and Management Decision Innovation Wuhan University of Technology Hubei China School of Safety Science and Emergency Management Wuhan University of Technology Hubei China National Engineering Research Center for Educational Big Data Central China Normal University Hubei China Department of Civil and Environmental Engineering Florida State University FL United States

The World Health Organization has identified road traffic accidents as a global crisis, particularly in urban areas. Despite numerous research efforts, there are still limitations in understanding how multiple factors interact to impact urban road traffic accidents. In this study, data from 4285 urban road traffic accidents in Hubei Province was analysed using a two-step clustering algorithm to categorize accidents into distinct groups based on specific conditions. Association rule mining was then used to identify relationships between accident characteristics within each cluster. Additionally, a Classification-based on association rule algorithm was employed to develop a prediction model for analysing factors contributing to casualties. The data was divided into clusters based on weather and road conditions, and separate discussions were conducted for each condition. The findings revealed that urban congestion is the most significant factor contributing to accidents. Surprisingly, rather than severe weather, accidents were more common during cloudy light rain weather conditions. Electric vehicles and motorcycles were identified as the most vulnerable groups. Furthermore, there was a clear interaction observed between the day of the week, time of day, and weather. The prediction model achieved an average accuracy of 86.9%. This approach enables the identification of contributing factors and mechanisms behind urban road traffic accidents in China, and can potentially be used to establish accident analysis models in similar contexts. The interactive visualization of association rules enhances the practicality of the findings. © 2024, The Authors. All rights reserved.

关键词： Association rules

Time-Aware Missing Traffic Flow Prediction for Sensors with Privacy-Preservation 11th

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Time-Aware Missing Traffic Flow Prediction for Sensors with ...

11th International Conference on Computer engineering and Networks, CENet2021

作者： Qi, Lianyong Wang, Fan Xu, Xiaolong Dou, Wanchun Zhang, Xuyun Khosravi, Mohammad R. Zhou, Xiaokang School of Computer Science Qufu Normal University Qufu China School of Computer and Software Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China Department of Computing Macquarie University SydneyNSW2109 Australia Department of Computer Engineering Persian Gulf University Bushehr Iran Department of Electrical and Electronic Engineering Shiraz University of Technology Shiraz Iran Faculty of Data Science Shiga University Hikone Japan

ISBN: (纸本)9789811665530

With the continuous development of IoT, a number of sensors establish on the roadside to monitor traffic conditions in real time. The continuously traffic data generated by these sensors makes traffic management feasible. However, loss of data may occur due to inevitable sensor failure, impeding traffic managers to understand traffic dynamics clearly. In this situation, it is becoming a necessity to predict missing traffic flow accurately for effective traffic management. Furthermore, the traffic sensor data are often distributed and stored by different agencies, which inhibits the multi-party sensor data sharing significantly due to privacy concerns. Therefore, it has become a major obstacle to balance the tradeoff between data sharing and vehicle privacy. In light of these challenges, we propose a privacy-aware and accurate missing traffic flow prediction approach based on time-aware Locality-Sensitive Hashing technique. At last, we deploy a set of experiments based on a real traffic dataset. Experimental reports demonstrate the feasibility of our proposal in terms of traffic flow prediction accuracy and efficiency while guaranteeing sensor data privacy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data privacy

Enhanced Adsorption of Copper Citrate Complex by Ferromanganese Oxides-Biochar from Water: Performance and Mechanism

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SSRN 2024年

作者： Zhu, Ying Lei, Xiang Liu, Jianguo Li, Yiting Fan, Wenhong Key Laboratory of Environmental Pollution Control and Remediation Universities of Inner Mongolia Autonomous Region College of Resources and Environmental Engineering Inner Mongolia University of Technology Inner Mongolia Hohhot010051 China School of Materials Science and Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing100191 China

Due to the stable structure of complex metals, their removal is more challenging compared to metal ions. Additionally, the removal mechanism of metal complexes is closely related to their structure. This study developed a low-cost and economically efficient ferromanganese oxide-modified biochar and explored the preparation conditions in detail. The surface of ferromanganese oxide-modified biochar was uniformly loaded with 100 nm nanoparticles, mainly comprising Mn3O4 and (FeO)0.099(MnO)0.901, and exhibited excellent copper citrate (CuCA) adsorption capacity. The removal rate of CuCA at an initial concentration of 10 mg/L by 1 g/L modified biochar reached 99.5% for Cu and 87.7% for TOC, with a copper adsorption capacity of 3.79 mg/L. The mechanism of using modified biochar to remove CuCA includes chemical adsorption at the bimetallic oxide active sites and oxygen-containing functional groups on the surface of the biochar, as well as physical adsorption due to biochar’s porous structure and other effects. The modified biochar shows good performance and stability in wide pH range and certain coexisting ions. Ferromanganese oxide-modified biochar own the advantage of efficient, economical and easy to preparation, which has significant potential for development in the removal of metal complexes in water. © 2024, The Authors. All rights reserved.

关键词： Adsorption

Spintronic Terahertz Polarization Programmable System for Information Encoding

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SSRN 2023年

作者： Li, Hongqing Jiang, Yunqing Zhang, Xiaoqiang Zhang, Fan Xiao, Yongguang Tang, Minghua Zhao, Zhenyang Xu, Yong Liu, Fengguang Zhao, Weisheng School of Materials Science and Engineering Xiangtan University Hunan Xiangtan411105 China School of Integrated Circuit Science and Engineering Hefei Innovation Research Insititute Beihang University Beijing100191 China Anhui High Reliability Chips Engineering Laboratory Hefei230013 China Shandong Dongyi Photoelectric Instruments Co. Ltd Yantai264670 China Shandong Dongyi Institute of Optoelectronic Technology for Industry Yantai264670 China Beijing Advanced Innovation Center for Big Data and Brain Computing China

Terahertz (THz) wave as an important electromagnetic wave has been increasingly applied in wireless communication and imaging sensing technology. It has been an increasingly practical application. Ultra-high data rates in communication technology push toward a broadband, controllable polarization, and multiple transmission channels signal sources. Here, we propose a programmable polarization spintronics terahertz source (PP-STS), in which contains a controllable polarization spintronic THz emitter and a microcontroller unit. The PP-STS not only can generate THz waves with wide frequency bandwidth, but also has the function of switching the orthogonal linear polarization state of the generated THz waves flexibly. By coding the microcontroller unit, the designed PP-SPS can directly program the orthogonal linear polarization states as a series of information to transmit. To achieve the interconversions between digital codes and the THz polarization information, an optimum matching THz polarization beam splitter (THz-PBS) is also designed via simulations and experiments. The THz-PBS, as a decoder, can accurately split the transverse electric (TE) and transverse magnetic (TM) modes into two THz detectors. At last, a coding and decoding prototype system of spintronic THz polarization is built by integrating the PP-STS, the THz-PBS and two THz detectors. The send and receive information with high speed and accuracy are verified in this prototype system as well. We believe that our work takes THz applications for multiplexing communications one step further and sheds light on commercializing the PP-STS with exciting manufacturing lines for efficient and economical THz polarization technology. © 2023, The Authors. All rights reserved.

关键词： Microcontrollers

Spintronic Terahertz Polarization Programmable System for Information Encoding

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SSRN 2023年

关键词： Microcontrollers

Spintronic Terahertz Polarization Programmable System for Information Encoding

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SSRN 2023年

作者： Li, Hongqing Jiang, Yunqing Zhang, Xiaoqiang Zhang, Fan Xiao, Yongguang Tang, Minghua Zhao, Zhenyang Xu, Yong Liu, Fengguang Zhao, Weisheng Beijing Advanced Innovation Center for Big Data and Brain Computing China School of Materials Science and Engineering Xiangtan University Hunan Xiangtan411105 China School of Integrated Circuit Science and Engineering Hefei Innovation Research Insititute Beihang University Beijing100191 China Anhui High Reliability Chips Engineering Laboratory Hefei230013 China Shandong Dongyi Photoelectric Instruments Co. Ltd Yantai264670 China Shandong Dongyi Institute of Optoelectronic Technology for Industry Yantai264670 China

关键词： Microcontrollers

Spintronic Terahertz Polarization Programmable System for Information Encoding

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SSRN 2023年

关键词： Microcontrollers