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arXiv 2022年

作者： Chen, Changsheng Zhao, Lin Cai, Rizhao Yu, Zitong Huang, Jiwu Kot, Alex C. The Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security National Engineering Laboratory for Big Data System Computing Technology College of Electronics and Information Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China The Department of Electrical and Electrics Engineering Nanyang Technological University Singapore639798 Singapore

Recapture detection of face and document images is an important forensic task. With deep learning, the performances of face anti-spoofing (FAS) and recaptured document detection have been improved significantly. However, the performances are not yet satisfactory on samples with weak forensic cues. The amount of forensic cues can be quantified to allow a reliable forensic result. In this work, we propose a forensicability assessment network to quantify the forensicability of the questioned samples. The low-forensicability samples are rejected before the actual recapturing detection process to improve the efficiency of recapturing detection systems. We first extract forensicability features related to both image quality assessment and forensic tasks. By exploiting domain knowledge of the forensic application in image quality and forensic features, we define three task-specific forensicability classes and the initialized locations in the feature space. Based on the extracted features and the defined centers, we train the proposed forensic assessment network (FANet) with cross-entropy loss and update the centers with a momentum-based update method. We integrate the trained FANet with practical recapturing detection schemes in face anti-spoofing (FAS) and recaptured document detection tasks. Experimental results show that, for a generic CNN-based FAS scheme, FANet reduces the EERs from 33.75% to 19.23% under ROSE to IDIAP protocol by rejecting samples with the lowest 30% forensicability scores. The performance of FAS schemes is poor in the rejected samples, with EER as high as 56.48%. Similar performances in rejecting low-forensicability samples have been observed for the state-of-the-art approaches in FAS and recaptured document detection tasks. To the best of our knowledge, this is the first work that assesses the forensicability of recaptured document images and improves the system efficiency. Copyright © 2022, The Authors. All rights reserved.

关键词： Image quality

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Design of a New Pneumatic Atomizer with V-shaped Slits and Numerical Study on the Spray Flow Field 7th

Design of a New Pneumatic Atomizer with V-shaped Slits and N...

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7th International Conference on Life System Modeling and Simulation, LSMS 2021, and the 7th International Conference on intelligent computing for Sustainable Energy and Environment, ICSEE 2021

作者： Qiao, Wentong Qian, Lijuan Zhu, Chenlin Shao, Xianyue Wang, Peng College of Mechanical and Electrical Engineering China Jiliang University Hangzhou310018 China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang University Hangzhou310018 China Zhejiang Ruifeng Wufu Pneumatic Tools Co. Ltd. Taizhou317500 China Zhejiang Santian Car Air Conditioning Compressor Co. Ltd. Lishui323000 China

ISBN: (纸本)9789811672125

In order to better atomize the water-based paint, a new pneumatic atomizer with V-shaped slits is proposed in this paper. The atomizing air hole is improved by changing its configuration from the ordinary annular structure to a V-shaped groove structure. A 3D model of this atomizer is established for numerical simulation, where the turbulent transport is modeled using the realizable k- Ε model. By means of computational fluid dynamics (CFD), the velocity and pressure distributions in the interference spray flow field are numerically investigated by varying the shaping air pressure when different numbers of V-shaped slits are employed. The results show that the higher the shaping air pressure is, the more even the airflow is distributed on the target plate. With increasing the number of the slits, the local high-speed airflow near the nozzle in the spray flow field will be enhanced, which is conducive to atomizing water-based paint. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Flow fields

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Dist-PU: Positive-Unlabeled Learning from a Label Distribution Perspective

arXiv

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arXiv 2022年

作者： Zhao, Yunrui Xu, Qianqian Jiang, Yangbangyan Wen, Peisong Huang, Qingming School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS China State Key Laboratory of Information Security Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences China

Positive-Unlabeled (PU) learning tries to learn binary classifiers from a few labeled positive examples with many unlabeled ones. Compared with ordinary semi-supervised learning, this task is much more challenging due to the absence of any known negative labels. While existing cost-sensitive-based methods have achieved state-of-the-art performances, they explicitly minimize the risk of classifying unlabeled data as negative samples, which might result in a negative-prediction preference of the classifier. To alleviate this issue, we resort to a label distribution perspective for PU learning in this paper. Noticing that the label distribution of unlabeled data is fixed when the class prior is known, it can be naturally used as learning supervision for the model. Motivated by this, we propose to pursue the label distribution consistency between predicted and ground-truth label distributions, which is formulated by aligning their expectations. Moreover, we further adopt the entropy minimization and Mixup regularization to avoid the trivial solution of the label distribution consistency on unlabeled data and mitigate the consequent confirmation bias. Experiments on three benchmark datasets validate the effectiveness of the proposed method. Code available at: https://***/Ray-rui/Dist-PU-Positive-UnlabeledLearning-from-a-Label-Distribution-Perspective. Copyright © 2022, The Authors. All rights reserved.

关键词： Entropy

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6D Movable Antenna Enhanced Interference Mitigation for Cellular-Connected UAV Communications

arXiv

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arXiv 2024年

作者： Ren, Tianshi Zhang, Xianchao Zhu, Lipeng Ma, Wenyan Gao, Xiaozheng Zhang, Rui School of Information and Electronics Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Jiaxing University Jiaxing314001 China Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing314001 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

Cellular-connected unmanned aerial vehicle (UAV) communications is an enabling technology to transmit control signaling or payload data for UAVs through cellular networks. Due to the line-of-sight (LoS) dominant air-to-ground channels, efficient interference mitigation is crucial to UAV communications, while the conventional fixed-position antenna (FPA) arrays have limited degrees of freedom (DoFs) to suppress the interference between the UAV and its non-associated co-channel base stations (BSs). To address this challenge, we propose in this letter a new approach by utilizing the six-dimensional movable antenna (6DMA) arrays to enhance the interference mitigation for the UAV. Specifically, we propose an efficient block coordinate descent (BCD) algorithm to iteratively optimize the antenna position vector (APV), array rotation vector (ARV), receive beamforming vector, and associated BS of the UAV to maximize its signal-to-interference-plus-noise ratio (SINR). Numerical results show that the proposed 6DMA enhanced cellular-connected UAV communication can significantly outperform that with the traditional FPA arrays and other benchmark schemes in terms of interference mitigation. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal to noise ratio

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Randomized Latent Factor Model for High-dimensional and Sparse Matrices from Industrial Applications

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 131-141页

作者： Mingsheng Shang Xin Luo Zhigang Liu Jia Chen Ye Yuan MengChu Zhou the Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of Sciences the School of Computer Science and Engineering Beihang University the Department of Electrical and Computer Engineering New Jersey Institute of Technology

Latent factor(LF) models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS) matrices which are commonly seen in various industrial applications. An LF model usually adopts iterative optimizers,which may consume many iterations to achieve a local optima,resulting in considerable time cost. Hence, determining how to accelerate the training process for LF models has become a significant issue. To address this, this work proposes a randomized latent factor(RLF) model. It incorporates the principle of randomized learning techniques from neural networks into the LF analysis of HiDS matrices, thereby greatly alleviating computational burden. It also extends a standard learning process for randomized neural networks in context of LF analysis to make the resulting model represent an HiDS matrix *** results on three HiDS matrices from industrial applications demonstrate that compared with state-of-the-art LF models, RLF is able to achieve significantly higher computational efficiency and comparable prediction accuracy for missing data.I provides an important alternative approach to LF analysis of HiDS matrices, which is especially desired for industrial applications demanding highly efficient models.

关键词： big data high-dimensional and sparse matrix latent factor analysis latent factor model randomized learning

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning

arXiv

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arXiv 2024年

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3DAD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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Origin of minimal conductivity in Dirac materials: Momentum-dependent self-energy function from long-ranged disorder scattering

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Physical Review B 2023年第17期108卷 174205-174205页

作者： Weiwei Chen Yedi Shen Bo Fu Qinwei Shi W. Zhu Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province College of Science China Jiliang University Hangzhou 310018 China Hefei National Laboratory for Physical Sciences at the Microscale and Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of China Hefei Anhui 230026 China School of Sciences Great Bay University Dongguan 523000 Guangdong Province China Department of Physics The University of Hong Kong Pokfulam Road Hong Kong China School of Science Westlake University Hangzhou 310024 China

We present a unified understanding of the experimentally observed minimal dc conductivity in Dirac materials. First, based on the linear response theory, we reveal that the momentum-dependent self-energy function induces a tunable minimal conductivity in Dirac electrons, the magnitude of which is directly related to the coefficient of the momentum-dependent part in the self-energy function. Taking the long-ranged Gaussian and Coulomb potentials as examples, the momentum-dependent self-energy function is perturbatively derived using the Born approximation and supplemented by the self-consistent Born approximation and renormalization group analysis. Moreover, we further validate our theory via numerical simulations using the large-scale Lanczos algorithm. The explicit momentum dependence of the self-energy on the intensity, concentration, and range of potential are critically addressed. Therefore, our theory provides a reasonable interpretation of the sample-dependent minimal conductivity observed in experiments.

关键词： Dirac fermions Quantum transport Disordered systems Diagrammatic methods

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data-driven Industrial Robot Arm Calibration: A Machine Learning Perspective 18

Data-driven Industrial Robot Arm Calibration: A Machine Lear...

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18th IEEE International Conference on Networking, Sensing and Control, ICNSC 2021

作者： Li, Zhibin Li, Shuai Luo, Xin University of Chinese Academy of Sciences School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing School Chongqing China Swansea University Department of Electronics and Electrical Engineering Swansea United Kingdom University of Chinese Academy of Sciences Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing School Chongqing China

ISBN: (纸本)9781665440486

Robot arms have been widely used in industry. The absolute positioning error of robots without calibration can reach several millimeters, which cannot meet the application requirements of accurate operation. Therefore, it is almost a mandatory procedure for industrial robots to take on-site calibration before being used. Generally, most researchers on robot calibration have mechanical and instrumentation background as the collection of calibration data is tedious and it is usually difficult to access to industrial robots for researchers in other fields. This research explores the calibration problem from a machine learning perspective and provides the first open-access dataset called "RobotCali"in this area so that machine learning scientists can step into this field and verify their algorithms on this problem. In the meanwhile, a new calibration method based on the Levenberg-Marquardt (LM) algorithm and extended Kalman filter (EKF) algorithm is proposed, which can significantly improve the absolute positioning accuracy of the robot after calibration. Firstly, the error model of robot is established, and kinematic parameters are initially identified by LM algorithm. Then the EKF algorithm is used to further calibrate these parameters, which has been verified the effectiveness of the proposed method by experimental results. Lastly, the future research work is discussed. © 2021 IEEE.

关键词： Calibration

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Z16b,a natural compound from Ganoderma cochlear is a novel RyR2 stabilizer preventing catecholaminergic polymorphic ventricular tachycardia

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中国药理学报（英文版） 2022年第9期43卷 2340-2350页

作者： Jiang-fan Wan Gang Wang Fu-ying Qin Dan-ling Huang Yan Wang Ai-ling Su Hai-ping Zhang Yang Liu Shao-yin Zeng Chao-liang Wei Yong-xian Cheng Jie Liu Guangdong Key Laboratory of Genome Stability and Human Disease Prevention Department of PathophysiologySchool of MedicineShenzhen UniversityShenzhen 518000China College of Physics and Optoelectronic Engineering Shenzhen UniversityShenzhen 518060China Guangdong Key Laboratory of Genome Stability and Human Disease Prevention Department of PathophysiologySchool of MedicineShenzhen UniversityShenzhen 518000China School of Pharmaceutical Sciences Shenzhen University Health Science CenterShenzhen 518000China Center for Translation Medicine Research and Development Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518000China Center for High Performance Computing Joint Engineering Research Center for Health Big Data Intelligent Analysis TechnologyShenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen 518000China Guangdong Cardiovascular Institute Guangdong Provincial People's HospitalGuangdong Academy of Medical SciencesGuangzhou 510000China Guangdong Provincial key laboratory of South China Structure Heart Disease Department of Pediatric CardiologyGuangdong Cardiovascular InstituteGuangdong Provincial People's HospitalGuangdong Academy of Medical SciencesGuangzhou 510000China

Catecholaminergic polymorphic ventricular tachycardia(CPVT)is an inherited,lethal ventricular arrhythmia triggered by *** in genes that encode cardiac ryanodine receptor(RyR2)and proteins that regulate RyR2 activity cause enhanced diastolic Ca2+release(leak)through the RyR2 channels,resulting in *** therapies for CPVT are *** found that Z16b,a meroterpenoid isolated from Ganoderma cochlear,inhibited Ca2+spark frequency(CaSF)in R2474S/+cardiomyocytes in a dose-dependent manner,with an IC50 of 3.2 μM.Z16b also dose-dependently suppressed abnormal post-pacing Ca2+release *** injection(i.p.)of epinephrine and caffeine stimulated sustained ventricular tachycardia in all R2474S/+mice,while pretreatment with Z16b(0.5 mg/kg,i.p.)prevented ventricular arrhythmia in 9 of 10 mice,and Z16b administration immediately after the onset of VT abolished sVT in 9 of 12 *** translational significance,Z16b significantly inhibited CaSF and abnormal Ca2+release events in human CPVT ***,Z16b interacts with RyR2,enhancing the"zipping"state of the N-terminal and central domains of RyR2.A molecular docking simulation and point mutation and pulldown assays identified Z16b forms hydrogen bonds with Arg626,His1670,and Gln2126 in RyR2 as a triangle shape that anchors the NTD and CD interaction and thus stabilizes RyR2 in a tight"zipping"*** findings support that Z16b is a novel RyR2 stabilizer that can prevent *** may also serve as a lead compound with a new scaffold for the design of safer and more efficient drugs for treating CPVT.

关键词： arrhythmia catecholaminergic polymorphic ventricular tachycardia natural product ryanodine receptor induced pluripotent stem cell-derived cardiomyocytes

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LungX-Net: Lung Cancer Diagnosis from CT and Histopathological Images via Attention Based Multi-Level Feature Fusion Network

LungX-Net: Lung Cancer Diagnosis from CT and Histopathologic...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Sohaib Asif Vicky Yang Wang Dong Xu Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital) Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence Taizhou China Center of Intelligent Diagnosis and Therapy (Taizhou) Hangzhou Institute of Medicine (HIM) Chinese Academy of Sciences Taizhou China Wenling Institute of Big Data and Artificial Intelligence in Medicine Taizhou China Department of Diagnostic Ultrasound Imaging & Interventional Therapy Zhejiang Cancer Hospital Hangzhou Institute of Medicine (HIM) Chinese Academy of Sciences Hangzhou China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Lung cancer ranks among the top causes of cancer mortality worldwide. However, early detection can significantly boost survival rates, potentially increasing them by as much as 70%. While deep learning algorithms have shown promise in detecting lung cancer from CT scans, current methods are often inefficient, and single-model approaches struggle with complex medical images. Leveraging diverse model features can enhance accuracy and simplify detection. This paper introduces LungX-Net, an innovative and cost-effective deep learning model aimed at improving lung cancer diagnostic accuracy using CT and histopathological images. The proposed model unifies two networks into a unified, trainable feature extraction architecture, employing model compression to preserve essential core blocks. This approach enhances the model’s ability to propagate strong and robust features while significantly reducing computational resource requirements. The model also integrates a Convolutional Block Attention Module (CBAM) to enhance feature extraction by emphasizing the most important regions, which further improves overall accuracy. The integration of these techniques optimizes diagnostic performance while strategically minimizing complexity, making LungX-Net a powerful tool for efficient and precise lung cancer diagnosis. The proposed LungX-Net was evaluated on both CT and histopathological datasets, achieving a recognition accuracy of 99.09% on the CT dataset and 99.30% on the histopathological dataset. LungX-Net demonstrated superior performance compared to previous approaches, achieving greater accuracy with fewer parameters and faster testing times, highlighting its efficiency and cost-effectiveness.

关键词： Deep learning Accuracy Computed tomography Computational modeling Lungs Lung cancer Mortality Feature extraction Medical diagnostic imaging Testing

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