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9th International Conference on Computer and Communications, ICCC 2023

作者： Zhu, Xiaoke Hu, Qi Chen, Xiaopan Yuan, Caihong Li, Minghao Henan University School of Computer and Information Engineering Kaifeng China Henan University Henan Engineering Research Center of Intelligent Technology and Application China Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China

ISBN: (纸本)9798350317251

In recent years, oriented object detection technology has made significant progress. Most oriented object detectors represent oriented bounding boxes by adding additional angle information to the horizontal bounding box representation, which leads to serious angle periodicity issues. To address these issues, this paper proposes double midpoint vectors (DMvectors) to represent oriented objects. For any oriented bounding box, we align the center point with the origin in the Cartesian coordinate system, and evenly distribute the midpoints of the box edges across the four quadrants. We use the midpoint of the first and second quadrants, as well as the width and height of the box, to represent the oriented bounding box. DMvectors solve the periodic problem of angles by implicitly including angle information in vectors. To demonstrate the effectiveness of our proposed method, we conducted extensive experiments on two common remote sensing datasets (DOTA and HRSC2016), and our method achieved competitive results. © 2023 IEEE.

关键词： Object detection

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COMPREHENSIVE ANALYSIS OF OVER-SMOOTHING IN GRAPH NEURAL NETWORKS FROM MARKOV CHAINS PERSPECTIVE

arXiv

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arXiv 2022年

作者： Zhao, Weichen Wang, Chenguang Han, Congying Guo, Tiande Key Laboratory of Big Data Mining and Knowledge Management CAS Beijing China

The over-smoothing problem is an obstacle of developing deep graph neural network (GNN). Although many approaches to improve the over-smoothing problem have been proposed, there is still a lack of comprehensive understanding and conclusion of this problem. In this work, we analyze the over-smoothing problem from the Markov chain perspective. We focus on message passing of GNN and first establish a connection between GNNs and Markov chains on the graph. GNNs are divided into two classes of operator-consistent and operator-inconsistent based on whether the corresponding Markov chains are time-homogeneous. Next we attribute the over-smoothing problem to the convergence of an arbitrary initial distribution to a stationary distribution. Based on this, we prove that although the previously proposed methods can alleviate over-smoothing, but these methods cannot avoid the over-smoothing problem. In addition, we give the conclusion of the over-smoothing problem in two types of GNNs in the Markovian sense. On the one hand, operator-consistent GNN cannot avoid over-smoothing at an exponential rate. On the other hand, operator-inconsistent GNN is not always over-smoothing. Further, we investigate the existence of the limiting distribution of the time-inhomogeneous Markov chain, from which we derive a sufficient condition for operator-inconsistent GNN to avoid over-smoothing. Finally, we design experiments to verify our findings. Results show that our proposed sufficient condition can effectively improve over-smoothing problem in operator-inconsistent GNN and enhance the performance of the model. © 2022, CC BY-NC-SA.

关键词： Message passing

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On-Line System of Garbage Image-Orientated Intelligent Classification, Submission and Examination

On-Line System of Garbage Image-Orientated Intelligent Class...

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IEEE International Conference on e-Business engineering (ICEBE)

作者： Jiayin Tian Yaozhi Wang Jiaxin Liu Yan Chen School of Computer Science and Technology Xi'an Jiaotong University Xi'an Shaanxi China Shaanxi Key Lab of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an Shaanxi China Shaanxi Key Lab of Big Data Knowledge Engineering School of Computer Science and Technology Xi'an Jiaotong University Xi'an Jiaotong University Xi'an Shaanxi China

ISBN: (数字)9798350365856

ISBN: (纸本)9798350365863

In a world brimming with new products continually, novel waste types are ubiquitous. This makes current image-based garbage classification systems difficult to perform well due to the long-tailed effects of distribution of garbage types, and necessitates an urgent and efficient garbage classification with abilities of detecting new and rare wastes and class-incremental learning for environmental sustainability. Therefore, we propose a framework of Online System of Garbage Image-Oriented Intelligent Classification, Submission, and Examination, facilitating the incremental garbage classification efforts. In which, to identify novel garbage effectively, we also introduced few-shot object detection method with two key algorithms: Two-Stage Object Detection Learning Algorithm and Dynamic Query-based Incremental Few-shot Learning Algorithm. Our experiment results show that Both outperform the current existing ones in dataset, MS COCO. Then, a strategy of Class-Incremental learning based Residual Network is proposed to meet the need of new waste class-incremental learning. The experimental results support our strategy. Finally, a prototype system employed the above algorithms and the strategy is described.

关键词： Image recognition Heavily-tailed distribution Heuristic algorithms Green products Prototypes Object detection Classification algorithms Object recognition Few shot learning Residual neural networks

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Multiple valence states of Fe boosting SERS activity of Fe_(3)O_(4) nanoparticles and enabling effective SERS-MRI bimodal cancer imaging

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Fundamental Research 2024年第4期4卷 858-867页

作者： Jie Lin Xuehua Ma Anran Li Ozioma Udochukwu Akakuru Chunshu Pan Meng He Chenyang Yao Wenzhi Ren Yanying Li Dinghu Zhang Yi Cao Tianxiang Chen Aiguo Wu Cixi Institute of Biomedical Engineering International Cooperation Base of Biomedical Materials Technology and ApplicationChinese Academy of Science Key Laboratory of Magnetic Materials and DevicesZhejiang Engineering Research Center for Biomedical MaterialsNingbo Institute of Materials Technology and EngineeringCASNingbo315201China School of Engineering Medicine Key Laboratory of Big Data-Based Precision Medicine(Beihang University)Ministry of Industry and Information TechnologyBeihang UniversityBeijing100191China Advanced Energy Science and Technology Guangdong Laboratory Huizhou 516000China University of Chinese Academy of Sciences Bejing 100049China

Developing novel nanoparticle-based bioprobes utilized in clinical settings with imaging resolutions ranging from cell to tissue levels is a major challenge for tumor diagnosis and ***,an optimized strategy for designing a Fe_(3)O_(4)-based bioprobe for dual-modal cancer imaging based on surface-enhanced Raman scattering(SERS)and magnetic resonance imaging(MRI)is *** SERS activity of ultrasmall Fe_(3)O_(4) nanoparticles(NPs)was discovered,and a 5×10^(-9)M limit of detection for crystal violet molecules was successfully *** high-efficiency interfacial photon-induced charge transfer in Fe_(3)O_(4) NPs was promoted by multiple electronic energy levels ascribed to the multiple valence states of Fe,which was observed using ultraviolet-visible diffuse reflectance *** functional theory calculations were utilized to reveal that the narrow band gap and high electron density of states of ultrasmall Fe_(3)O_(4) NPs significantly boosted the vibronic coupling resonances in the SERS system upon *** subtypes of cancer cells were accurately recognized via high-resolution SERS imaging in vitro using the prepared Feg Og-based bioprobe with high sensitivity and good ***,Fe_(3)O_(4)-based bioprobes simultaneously exhibited T,-weighted MRI contrast enhancement with an active targeting capability for tumors in *** the best of our knowledge,this is the first report on the use of pure semiconductor-based SERS-MRI dual-modal nanoprobes in tumor imaging in vivo and in vitro,which has been previously realized only using semiconductor-metal complex *** non-metallic materials with SERS-MRI dual-modal imaging established in this report are a promising cancer diagnostic platform,which not only showed excellent performance in early tumor diagnosis but also possesses great potential for image-guided tumor treatment.

关键词： Fe_(3)O_(4)NPs SERS-MRI dual-modal nanoprobes PICT Multiple valence states Cancer diagnosis

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Impact of Consumer Perceived Characteristics on Fruit Sales: Evidence from Community Group-buying 11

Impact of Consumer Perceived Characteristics on Fruit Sales:...

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11th International Conference on Information Technology and Quantitative Management, ITQM 2024

作者： Jiang, Hailiang Tian, Xin Tao, Ye Wang, Lulu Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China Beijing HAOLINJU CVS Co. Ltd Beijing100088 China Shanghai HEADING Information Engineering Co. Ltd. Shanghai201112 China

There is a growing trend among retailers to sell fruits through their community group-buying (CGB) channels. Based on real operational data, we employ econometric models to empirically analyze how consumer perception characteristics in CGB affect fruit sales. Our findings suggest that incorporating subjective experiences into product descriptions can positively impact fruit sales for retailers;fresh produce stores outperform regular stores in fruit sales of CGB;Dark-colored fruits sell more than light-colored fruits in the CGB channel. Our study offers valuable insights for retailers selling fruits through CGB channels. © 2024 The Authors.

关键词： community group-buying dark-colored fruits fresh produce stores subjectivity

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Quantum partial least squares regression algorithm for multiple correlation problem

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Chinese Physics B 2022年第3期31卷 177-186页

作者： Yan-Yan Hou Jian Li Xiu-Bo Chen Yuan Tian School of Artificial Intelligence Beijing University of Post and TelecommunicationsBeijing 100876China College of Information Science and Engineering Zaozhuang UniversityZaozhuang 277160China Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Post and TelecommunicationsBeijing 100876China GuiZhou University Guizhou Provincial Key Laboratory of Public Big DataGuiyang 550025China

Partial least squares(PLS) regression is an important linear regression method that efficiently addresses the multiple correlation problem by combining principal component analysis and multiple regression. In this paper, we present a quantum partial least squares(QPLS) regression algorithm. To solve the high time complexity of the PLS regression, we design a quantum eigenvector search method to speed up principal components and regression parameters construction. Meanwhile, we give a density matrix product method to avoid multiple access to quantum random access memory(QRAM)during building residual matrices. The time and space complexities of the QPLS regression are logarithmic in the independent variable dimension n, the dependent variable dimension w, and the number of variables m. This algorithm achieves exponential speed-ups over the PLS regression on n, m, and w. In addition, the QPLS regression inspires us to explore more potential quantum machine learning applications in future works.

关键词： quantum machine learning partial least squares regression eigenvalue decomposition

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Reinforcement Learning Framework for Delivery Capacity Scheduling with Vehicle Constraints 20

Reinforcement Learning Framework for Delivery Capacity Sched...

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20th IEEE International Conference on e-Business engineering, ICEBE 2024

作者： Ma, Zhongneng Qin, Zhuorui Shi, Shuxia Wu, Qingyao South China University of Technology Guangzhou China School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Guangzhou Henll Electronics Equipment Co. Ltd Guangzhou China Peng Cheng Laboratory China

ISBN: (纸本)9798350365856

Improving the risk prevention capability of grid operations is a crucial requirement for smart resilient grids, where efficient dispatch and scheduling of delivery capacity play a pivotal role in enhancing grid resilience. Therefore, it is necessary to develop targeted intelligent dispatch algorithms based on operational data. To address this issue, this paper proposes a framework with inherent constraints and utilizes reinforcement learning using recursive neural networks. A constraint module is designed to identify available vehicle combinations based on de-livery capacity, allocate goods considering vehicle number, load, and size constraints, and generate delivery routes sequentially. Additionally, a reinforcement learning model is introduced, using negative trip length as a reward signal, optimizing parameters of the recursive neural network through policy gradient methods to automatically improve strategies for selecting optimal deliv-ery routes with improved vehicle combinations. To validate the effectiveness of this algorithm, extensive experiments were conducted using both real-world application validation data and the TSPLIB public dataset. Within a limited computational budget, the proposed algorithm outperforms several mature methods in terms of effectiveness and real-time performance, achieving near-optimal results in Euclidean graphs with nearly 100 nodes. These results demonstrate that this approach effectively addresses the intelligent scheduling of delivery capacity in digital twin systems for power storage © 2024 IEEE.

关键词： Budget control

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Pulsed polarized vortex beam enabled by metafiber lasers

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PhotoniX 2024年第1期5卷 110-120页

作者： Chenxi Zhang Lei Zhang He Zhang Bo Fu Jiyong Wang Min Qiu Key Laboratory of Precision Opto-Mechatronics Technology of Education Ministry School of Instrumentation and Optoelectronic EngineeringBeihang UniversityBeijing 100191China Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province School of EngineeringWestlake University18 Shilongshan RoadHangzhou 310024Zhejiang ProvinceChina Institute of Advanced Technology Westlake Institute for Advanced Study18 Shilongshan RoadHangzhou 310024Zhejiang ProvinceChina QianYuan National Laboratory Hangzhou 310000China Key Laboratory of Big Data-Based Precision Medicine Ministry of Industry and Information Technology School of Engineering MedicineBeihang UniversityBeijing 100191China Ministry of Education Engineering Research Center of Smart Microsensors and Microsystems School of Electronics and InformationHangzhou Dianzi UniversityHangzhou 310018China Westlake Institute for Optoelectronics FuyangHangzhou 311421China

Pulsed polarized vortex beams,a special form of structured light,are generated by tailoring the light beam spatiotemporally and witness the growing application demands in nonlinear optics such as ultrafast laser processing and surface plasma ***,existing techniques for generating polarized vortex beams suffer from either low compactness due to the use of bulky components or limited controlment of pulse ***,an all-fiber technique combining plasmonic metafibers with mode conversion method is harnessed to generate high-performance pulsed polarized vortex *** metafibers are utilized as saturable absorbers to produce Q-switched pulses with micro-second duration,while the offset splicing method is employed to partially convert the fundamental transverse mode(LP_(01))to higher-order mode(LP_(11)).Eventually,a polarized vortex beams laser is achieved at the telecom band with a repetition frequency of 116.0 *** impact of geometrical parameters including period of metafibers and offset of splicing on the spatiotemporal properties of pulsed polarized vortex beams is systematically *** findings could pave the way for design,control and generation of all-fiber pulsed polarized vortex beams,and also offer insights into the development of other types of structured laser sources.

关键词： Metafiber Q-switched pulse Polarized vortex beam

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Fine-Grained Tuple Transfer for Pipelined Query Execution on CPU-GPU Coprocessor 28th

Fine-Grained Tuple Transfer for Pipelined Query Execution o...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Yang, Zhenhua Pan, Qingfeng Xu, Chen Shanghai Engineering Research Center of Big Data Management East China Normal University Shanghai200062 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9783031306365

To leverage the massively parallel capability of GPU for query execution, GPU databases have been studied for over a decade. Recently, researchers proposed to execute queries with both CPU and GPU in a pipelined approach. In the pipelined query execution, the cross-processor tuple transfer plays a crucial role for the overall query execution performance. The state-of-the-art solution achieves cross-processor tuple transfer using a queue-like data structure. However, it is coarse-grained due to the use of a single spin lock to achieve thread-safety. This design causes performance issues as it prevents the threads from accessing the queue simultaneously. In this paper, we propose a fine-grained tuple transfer mechanism. It employs decoupled enqueue/dequeue to enable two threads on different processors to access the queue at the same time. Moreover, this mechanism explores subqueue-based locking to enable the threads on the same processor to access the queue at the same time. In particular, we implement a prototype system, namely π QC, which adopts fine-grained tuple transfer. Our experiments show that π QC achieves an order of magnitude better performance than existing GPU databases such as HeavyDB. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphics processing unit

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Research and implementation of image enhancement method based on improved retinex algorithm 3

Research and implementation of image enhancement method base...

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3rd International Conference on Optics and Image Processing, ICOIP 2023

作者： Zhang, Diya Wang, Yumeng Wang, Yanrui Pu, Yile Bi, Zhenbo Rao, Wei School of Information Engineering Zhejiang Ocean University Zhoushan316022 China Department of Physics Key Laboratory of Oceanographic Big Data Mining Application of Zhejiang Province Zhejiang Ocean University Zhoushan316000 China

ISBN: (纸本)9781510667426

In view of the phenomenon of detail loss and edge blurring in color images collected underwater, noise reduction is mostly ignored in underwater image enhancement research. In this paper, an improved image enhancement method based on Retinex algorithm is proposed. First,the original underwater image is transferred from RGB channels to HSI color space, and the saturation component is adaptively linearly stretched by gamma correction, The improved Retinex decomposition network based on homomorphic filtering is used to decompose and enhance the Lightness component separately. Eventually, the fused image is transferred from HSI to RGB color space, and the processed image is output. According to the experimental results, this algorithm can not only eliminate the halo artifacts, but also improve the brightness and contrast of the image, restore the original details of the image, and effectively remove the image noise. It greatly improves the visual effect and objective evaluation results of images. © 2023 SPIE. All rights reserved.

关键词： Image enhancement

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