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arXiv 2020年

作者： Meng, Lubin Huang, Jian Zeng, Zhigang Jiang, Xue Yu, Shan Jung, Tzyy-Ping Lin, Chin-Teng Chavarriaga, Ricardo Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Brainnetome Center National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China La Jolla CA United States Center for Advanced Neurological Engineering Institute of Engineering in Medicine Ucsd La Jolla CA United States Centre of Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney Australia Zhaw DataLab Zürich University of Applied Sciences Winterthur8401 Switzerland

Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to deeper understanding of the brain and wide adoption of sophisticated machine learning approaches for decoding the EEG signals. However, recent studies have shown that machine learning algorithms are vulnerable to adversarial attacks. This article proposes to use narrow period pulse for poisoning attack of EEG-based BCIs, which is implementable in practice and has never been considered before. One can create dangerous backdoors in the machine learning model by injecting poisoning samples into the training set. Test samples with the backdoor key will then be classified into the target class specified by the attacker. What most distinguishes our approach from previous ones is that the backdoor key does not need to be synchronized with the EEG trials, making it very easy to implement. The effectiveness and robustness of the backdoor attack approach is demonstrated, highlighting a critical security concern for EEG-based BCIs and calling for urgent attention to address it. Copyright © 2020, The Authors. All rights reserved.

关键词： Electroencephalography

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An Evolutionary Real-Time 3D Route Planner for Aircraft

引用

Chinese Journal of Systems Engineering and Electronics 2003年第1期000卷

A novel evolutionary route planner for aircraft is proposed in this paper. In the new planner, individual candidates are evaluated with respect to the workspace, thus the computation of the configuration space is not required. By using problem-specific chromosome structure and genetic operators, the routes are generated in real time,with different mission constraints such as minimum route leg length and flying altitude, maximum turning angle, maximum climbing/diving angle and route distance constraint taken into account.

关键词： Evolutionary computation Route planning Route constraints Real time Aircraft

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Prebiotic Properties of Extruded Maize Starch-Caffeic Acid Complexes: A Study from the Small Intestine to Colon in Vitro

SSRN

引用

SSRN 2024年

作者： Zheng, Yuxue Ye, Xingqian Hu, Yanyu Wang, Shaoyun Tian, Jinhu College of Biological Science and Engineering Institute of Food and Marine Bio-Resources Fuzhou University Fuzhou350108 China College of Biosystems Engineering and Food Science National-Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment Zhejiang Key Laboratory for Agro-Food Processing Integrated Research Base of Southern Fruit and Vegetable Preservation Technology Zhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality Control Fuli Institute of Food Science Zhejiang University Hangzhou310058 China Zhejiang University Zhongyuan Institute Zhengzhou450000 China

Our study aimed to investigate the probiotic effect of maize starch–caffeic complexes in the small intestine and colon in vitro. First, maize starch was extruded with caffeic acid, and subjected to in vitro digestion. The physicochemical properties of the complexes were explored. The undigested parts were fermented in vitro, and the structural characteristics, short chain fatty acids (SCFAs) and microbiota communities were investigated. The results showed that caffeic acid could reduce the long/short-range order of maize starch after extrusion, and the level of resistant starch was significantly increased to 30.35±2.36%. In vitro fermentation, the amorphous area of the digestive residues is utilized and then the crystalline area by microbiota, and promoted the production of SCFAs. Furthermore, the digestive residue promoted the growth of Bifidobacterium and Lactococcus genus. In general, the probiotic properties of extruded maize starch with caffeic acid might be a potential functional food substance for body health. © 2024, The Authors. All rights reserved.

关键词： Medicinal chemistry

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EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks

Research Square

引用

Research Square 2021年

作者： Meng, Lubin Huang, Jian Zeng, Zhigang Jiang, Xue Yu, Shan Jung, Tzyy-Ping Lin, Chin-Teng Chavarriaga, Ricardo Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Brainnetome Center and National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China La Jolla CA United States Center for Advanced Neurological Engineering Institute of Engineering in Medicine UCSD La Jolla CA United States Centre of Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney Australia ZHAW DataLab Zürich University of Applied Sciences Winterthur8401 Switzerland

Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to the wide adoption of sophisticated machine learning approaches for decoding the EEG signals. However, recent studies have shown that machine learning algorithms are vulnerable to adversarial attacks, e.g., the attacker can add tiny adversarial perturbations to a test sample to fool the model, or poison the training data to insert a secret backdoor. Previous research has shown that adversarial attacks are also possible for EEG-based BCIs. However, only adversarial perturbations have been considered, and the approaches are theoretically sound but very difficult to implement in practice. This article proposes to use narrow period pulse for poisoning attack of EEG-based BCIs, which is more feasible in practice and has never been considered before. One can create dangerous backdoors in the machine learning model by injecting poisoning samples into the training set. Test samples with the backdoor key will then be classified into the target class specified by the attacker. What most distinguishes our approach from previous ones is that the backdoor key does not need to be synchronized with the EEG trials, making it very easy to implement. The effectiveness and robustness of the backdoor attack approach is demonstrated, highlighting a critical security concern for EEG-based BCIs. © 2021, CC BY.

关键词： Electroencephalography

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Robotic Electrospinning Actuated by Non-Circular Joint Continuum Manipulator for Endoluminal Therapy

arXiv

引用

arXiv 2021年

作者： Wu, Zicong Lou, Chuqian Jin, Zhu Huang, Shaoping Liu, Ning Zou, Yun Kovac, Mirko Gao, Anzhu Yang, Guang-Zhong Institute of Medical Robotics Department of Bioengineering Shanghai Jiao Tong University Shanghai200240 China Imperial College London Empa Swiss Federal Laboratories for Materials Science and Technology Limited Hong Kong999077 Hong Kong Aerial Robotics Laboratory Imperial College London LondonSW7 2AZ United Kingdom Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China

Electrospinning has exhibited excellent benefits to treat the trauma for tissue engineering due to its produced micro/nano fibrous structure. It can effectively adhere to the tissue surface for long-term continuous therapy. This paper develops a robotic electrospinning platform for endoluminal therapy. The platform consists of a continuum manipulator, the electrospinning device, and the actuation unit. The continuum manipulator has two bending sections to facilitate the steering of the tip needle for a controllable spinning direction. Non-circular joint profile is carefully designed to enable a constant length of the centreline of a continuum manipulator for stable fluid transmission inside it. Experiments are performed on a bronchus phantom, and the steering ability and bending limitation in each direction are also investigated. The endoluminal electrospinning is also fulfilled by a trajectory following and points targeting experiments. The effective adhesive area of the produced fibre is also illustrated. The proposed robotic electrospinning shows its feasibility to precisely spread more therapeutic drug to construct fibrous structure for potential endoluminal treatments. © 2021, CC BY-NC-SA.

关键词： Electrospinning

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An Optimize Adaptable Method for Determining the Monosaccharide Composition of Pectic Polysaccharides

SSRN

引用

SSRN 2023年

作者： Wu, Jinghua Zhu, Kai Li, Junhui Hu, Xinxin Ye, Xingqian Chen, Shiguo College of Agriculture and Biotechnology Zhejiang University Zhejiang Hangzhou310058 China College of Biosystems Engineering and Food Science National-Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment Zhejiang Key Laboratory for Agro-Food Processing Integrated Research Base of Southern Fruit and Vegetable Preservation Technology Zhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality Control Zhejiang University Hangzhou310058 China Fuli Institute of Food Science Zhejiang University Hangzhou310058 China Ningbo Research Institute Zhejiang University Hangzhou315100 China

Monosaccharide composition analysis is an essential to characterize the structure and biological activities of complex polysaccharides. However, due to the acid-induced precipitates under the acidic condition, the incomplete hydrolysis is common in monosaccharide analysis of acidic polysaccharides (especially for pectin). The main aim of this paper is to provide an improved method for pectin’s monosaccharide analysis using optimized acid hydrolysis combined with high-performance anion exchange chromatography. Firstly, the acid-induced precipitates of pectin was remitted by reducing the concentration of samples to 0.5mg/mL. Then, the optimized hydrolysis condition was performed at 110 ℃ for 6 h with 2 mol/L TFA. Finally, five acidic polysaccharides from fruits were used to optimize and validate the method and showed the method’s reproducibility and ability to analyze complicated pectic polysaccharides. It suggests that this hydrolysis method could be applied to obtain accurate quantitation of monosaccharide composition analysis for acidic polysaccharides. © 2023, The Authors. All rights reserved.

关键词： Polysaccharides

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The Digestive Residues of Maize Starch-Caffeic Acid Complexes Significantly Change the Metabolites and Microbiota Composition During in Vitro Colon Fermentation

SSRN

引用

SSRN 2024年

To investigate the effect of digestive residues of maize starch–caffeic complexes on the composition of human metabolites and gut microbiota, maize starch was extruded with caffeic acid, and subjected to in vitro gastrointestinal digestion. Then, the undigested parts were fermented with human faecal microbiota in vitro, and the morphology, short-chain fatty acids composition, microbiota communities and structural characteristics of the fermented residues were investigated. The results indicated that the amorphous area of the residues is preferentially utilized by gut microbiota and then the crystalline area. The acetate concentration in extruded maize residues with or without caffeic acid residues increased to 19.19 ± 0.40 and 23.10 ± 0.92 mmoL/L, respectively, after 24 h of fermentation. Microbiota analysis indicated that the digestive residue of maize starch-caffeic acid complexes promoted the growth of potentially beneficial microbiota, especially Bifidobacterium and Lactococcus genus. In general, extruded maize starch with caffeic acid might be a potential functional food substance for gut health. © 2024, The Authors. All rights reserved.

关键词： Fatty acids

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Preface

Lecture Notes in Control and Information Sciences

引用

Lecture Notes in control and Information Sciences 2023年 492卷 v-vi页

作者： Luan, Xiaoli He, Shuping Liu, Fei Key Laboratory of Advanced Process Control for Light Industry [Ministry of Education Institute of Automation Jiangnan University Jiangsu Wuxi China Key Laboratory of Intelligent Computing and Signal Processing [Ministry of Education School of Electrical Engineering and Automation Anhui University Anhui Hefei China

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Constructing multi-modality and multi-classifier radiomics predictive models through reliable classifier fusion

arXiv

引用

arXiv 2017年

作者： Zhou, Zhiguo Zhou, Zhi-Jie Hao, Hongxia Li, Shulong Chen, Xi Zhang, You Folkert, Michael Wang, Jing Department of Radiation Oncology UT Southwestern Medical Center DallasTX United States High-Tech Institute of Xi'an Xi'an China Department of Information and Control Engineering Xi'an University of Technology Xi'an China School of Computer Science and Technology Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an China School of Biomedical Engineering Southern Medical Unversity Guangzhou China Institute of Image Processing and Pattern Recognition Xi'an Jiaotong Unversity Xi'an China

Radiomics aims to extract and analyze large numbers of quantitative features from medical images and is highly promising in staging, diagnosing, and predicting outcomes of cancer treatments. Nevertheless, several challenges need to be addressed to construct an optimal radiomics predictive model. First, the predictive performance of the model may be reduced when features extracted from an individual imaging modality (e.g. PET, CT, MRI) are blindly combined into a single predictive model. Second, because many different types of classifiers are available to construct a predictive model, selecting an "optimal" classifier for a particular application is still challenging. In this work, we developed multi-modality and multi-classifier radiomics predictive models that address the aforementioned issues in currently available models. Specifically, a new reliable classifier fusion strategy was proposed to optimally combine output from different modalities and classifiers. In this strategy, modality-specific classifiers were first trained, and an analytic evidential reasoning (ER) rule was developed to fuse the output score from each modality to construct an optimal predictive model. One public datasets and two clinical case studies were performed to validate model performance. The experimental results indicated that the proposed ER rule based radiomics models outperformed the traditional models that rely on a single classifier or simply use combined features from different modalities. Copyright © 2017, The Authors. All rights reserved.

关键词： Diagnosis

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Protecting privacy of users in brain-computer interface applications

arXiv

引用

arXiv 2019年

作者： Agarwal, Anisha Dowsley, Rafael McKinney, Nicholas D. Wu, Dongrui Lin, Chin-Teng De Cock, Martine Nascimento, Anderson C.A. School of Engineering and Technology University of Washington TacomaWA98402 United States Department of Computer Science Bar-Ilan University Israel School of Engineering and Technology University of Washington TacomaWA98402 United States Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Automation Huazhong University of Science and Technology Wuhan China School of Software University of Technology Sydney Australia Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent9000 Belgium

Machine learning (ML) is revolutionizing research and industry. Many ML applications rely on the use of large amounts of personal data for training and inference. Among the most intimate exploited data sources is electroencephalogram (EEG) data, a kind of data that is so rich with information that application developers can easily gain knowledge beyond the professed scope from unprotected EEG signals, including passwords, ATM PINs, and other intimate data. The challenge we address is how to engage in meaningful ML with EEG data while protecting the privacy of users. Hence, we propose cryptographic protocols based on Secure Multiparty Computation (SMC) to perform linear regression over EEG signals from many users in a fully privacy-preserving (PP) fashion, i.e. such that each individual's EEG signals are not revealed to anyone else. To illustrate the potential of our secure framework, we show how it allows estimating the drowsiness of drivers from their EEG signals as would be possible in the unencrypted case, and at a very reasonable computational cost. Our solution is the first application of commodity-based SMC to EEG data, as well as the largest documented experiment of secret sharing based SMC in general, namely with 15 players involved in all the computations. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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