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	1. Defect-correction variational multiscale methods for the stationary incompressible magnetohydrodynamics flow
	ZHANG Hui-Min,Zhang Tong
	Mathematics 25 March 2024
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	Abstract:In this paper, the defect correction variational multiscale method (DCVMM) based on local Gauss integrations is developed for the stationary incompressible magnetohydrodynamics (MHD) problem. Firstly, we review the variational multiscale method (VMM) for solving the stationary incompressible magnetohydrodynamics problem, and give the stability and convergence of the numerical solutions. Sencondly, the DCVMM based on local Gauss integrations is developed for the stationary incompressible MHD problem, and the stability and optimal error results of the numerical solutions are established. Finally, some numerical examples are provided to indicate with the obtained theoretical findings and show the performances of the considered numerical schemes.
	TO cite this article:ZHANG Hui-Min,Zhang Tong. Defect-correction variational multiscale methods for the stationary incompressible magnetohydrodynamics flow[OL].[25 March 2024] http://en.paper.edu.cn/en_releasepaper/content/4762956


	2. GPALzz: A state-leading fuzzing framework via basic network communication interface
	HAO Wen-Peng,GUO Yan-Hui
	Computer Science and Technology 25 March 2024
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	Abstract:Today, the rapid adoption of IoT devices has brought security risks while facilitating people's lives. Latest research focuses on information acquisition and state establishment, however exists the defects of relys on vendors and lacks universality; focuses on communication formats rather than device information; difficult to accurately guide fuzzing into specific states then in a stable state. To address the above issues, we propose an interactive leading fuzzing scheme, named GPALzz. It uses active automata learning to break free from vendor dependence, establishes device service state guidance fuzzy testing, and utilies interaction capabilities to establish an equivalent automaton that describes device service state information. Furthermore, we selected 9 devices to verify our scheme and discovered 24 crashes in 7 devices.
	TO cite this article:HAO Wen-Peng,GUO Yan-Hui. GPALzz: A state-leading fuzzing framework via basic network communication interface[OL].[25 March 2024] http://en.paper.edu.cn/en_releasepaper/content/4762868

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	3. Scaling behavior in the asymmetric quantum Rabi model
	QING Yu-Qi, LIU Mao-Xin
	Physics 21 March 2024
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	Abstract:The scaling behavior and critical exponents are examined in the asymmetric quantum Rabi model, where the parity symmetry is violated. A phase transition occurs in this model, allowing for the determination of critical exponents. A general scaling function is formulated, encompassing two scaling variables. Additionally, a quantum hyper-scaling relation between critical exponents is established. The investigation reveals that the scaling form remains applicable, employing an alternative scaling variable, even when the coupling is below the critical point. This discovery offers valuable insights into the experimental investigation of the super-radiant phase transition, particularly in the cavity platform, which has been limited by the well-known no-go theorem.
	TO cite this article:QING Yu-Qi, LIU Mao-Xin. Scaling behavior in the asymmetric quantum Rabi model[OL].[21 March 2024] http://en.paper.edu.cn/en_releasepaper/content/4763013


	4. Bright solitons for a (3+1)-dimensional hyperbolic nonlinear Schr\"{o}dinger equation
	WANG Jing-Ying,JIANG Yan,TIAN Bo
	Mathematics 21 March 2024
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	Abstract:In this paper, the (3+1)-dimensional hyperbolic nonlinear Schr\"{o}dinger equation is studied, which describes the propagation dynamics of optical solitons in mono-mode fibers. Based on the bilinear forms of the equation,the bright one- and two-solitons solutions of the equation are obtained by using the Hirota method. At the same time, the properties of soliton solutions are further analyzed by images. It can be observed that the amplitude and shape of the bright one soliton remain unchanged during propagation, and that the interaction between the bright two solitons is elastic. The influence of parameter change on soliton solutions is also explained.
	TO cite this article:WANG Jing-Ying,JIANG Yan,TIAN Bo. Bright solitons for a (3+1)-dimensional hyperbolic nonlinear Schr\"{o}dinger equation[OL].[21 March 2024] http://en.paper.edu.cn/en_releasepaper/content/4762952


	5. Multi-Object Tracking with decoupled representations and unreliable detections in complex scenes
	XIAO Yuan, ZOU Qi
	Computer Science and Technology 21 March 2024
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	Abstract:Joint-Detection-and-Embedding paradigm achieves fast tracking by simultaneously learning detection and Re-ID features. However, it still faces performance degradation in complex scenes and the misalignment between detection and Re-ID features. In this paper, we propose a decoupling module based on channel-wise attention mechanism to obtain task-aligned features served for different demands of detection and Re-ID. To improve the performance of data association, we fuse motion, location, appearance information and perform a two-round matching for high and low confidence detections respectively by the Motion-GIoU matrix and the Embedding-GIoU matrix. Additionally, we apply the camera motion compensation to get a more accurate motion estimation, resulting in a more robust tracking in the scenes of camera motion and low-frame-rate. Extensive experiments show that our proposed method outperforms a wide range of existing methods on the MOTChallenge and HiEvE datasets.
	TO cite this article:XIAO Yuan, ZOU Qi. Multi-Object Tracking with decoupled representations and unreliable detections in complex scenes[OL].[21 March 2024] http://en.paper.edu.cn/en_releasepaper/content/4763002


	6. End-to-End Virtual Shadow Generation Based on Shadow Detection
	Xue Junsheng, Huang Hai
	Computer Science and Technology 21 March 2024
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	Abstract:With the rapid development of the augmented reality field, virtual shadow generation technology has garnered widespread attention. However, traditional methods involve complex computational requirements for environmental modeling and display, and the effectiveness of using fewer 3D parameter estimation methods is suboptimal. To address these issues, this paper proposes an end-to-end virtual shadow generation method based on shadow detection. Firstly, we introduce a shadow detector designed to identify real shadows and their corresponding occlusions in the background context, while also learning the mapping relationship between occlusions and shadows in the current scene. Secondly, we devise a virtual shadow generator. Utilizing the mapping relationship obtained from the detector as guidance information, it is encoded into the generator's input. Through feature extraction, encoding, and decoding of the input information, we ultimately obtain the virtual shadow image. Experimental results demonstrate the exceptional performance of the proposed virtual shadow generation method. In comparison to existing methods for direct virtual shadow generation, our approach significantly enhances the harmony and realism of the synthesized images.
	TO cite this article:Xue Junsheng, Huang Hai. End-to-End Virtual Shadow Generation Based on Shadow Detection[OL].[21 March 2024] http://en.paper.edu.cn/en_releasepaper/content/4762999


	7. Delay-Aware Hybrid Consensus Time Synchronization
	Xiao Mei,Deng Zhongliang,Wang Gan
	Electrics, Communication and Autocontrol Technology 21 March 2024
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	Abstract:In contemporary distributed systems, ensuring precise time synchronization is critical for coordinating operations, optimizing resource allocation, enhancing data consistency, and supporting temporal analysis. Traditional synchronization methods, such as central clock sources or simple broadcasting protocols, struggle to meet the demands of increasingly complex and dynamic network environments, especially in large-scale, highly variable, and energy-constrained settings. To address these challenges, this paper introduces a hybrid consistency time synchronization algorithm(DA-HCTS) ) that combines the strengths of Maximum Consistency Time Synchronization (MTS) and Average Consistency Time Synchronization (ATS), incorporating delay considerations. This approach quickly aligns all nodes to the network\'s maximum clock value for swift synchronization while averaging clock deviations for long-term precision and stability, offering an innovative solution to enhance synchronization accuracy and robustness in dynamic network environments.
	TO cite this article:Xiao Mei,Deng Zhongliang,Wang Gan. Delay-Aware Hybrid Consensus Time Synchronization[OL].[21 March 2024] http://en.paper.edu.cn/en_releasepaper/content/4762531


	8. Causal-Proto SSRL: Learning Dynamic-Necessary State Variables for Multi-Environment Reinforcement Learning
	Zhang Meng,Zhang Chunhong,Hu Zheng,Zhuang Benhui
	Computer Science and Technology 19 March 2024
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	Abstract:The ability to learn directly from high-dimensional observations such as pixels allows reinforcement learning(RL) to achieve more widely applications. However, high-dimensional observations contain entangled task-relevant and task-irrelevant informations, as well as informations related to actions but unnecessary, which leads to non-essential dependencies and thus affects the generalization and robustness of the reinforcement learning. In order to learn abstract state representations from high-dimensional observations which generalized acorss multiple tasks and robust in environments with different task-unnecessary informations, this paper formulates the POMDP as Partially Observable Temporal Causal Dynamic Models (POTCDMs) and proposes a self-supervised RL with causal representation learning, Causal-Proto RL. This method seperates encoded observations into dynamic-necessary and dynamic-unnecessary state variables where only dynamic-necessary state variables are fed into RL by predicting the causal relationships simultaneously. This method is pretrained in absence of specific task rewards with an intrinsic rewards fo curiosity of causal relationships and are implemented in multiple difficult downstream tasks. This paper evaluate the algorithm in DeepMind Control Suit. This algorithm performs as well as other SOTA slef-supervised RL on a series of downstream tasks in environents as same as pretraining, and demonstrates generalization and robustness in downstream task environments different from pretraining.
	TO cite this article:Zhang Meng,Zhang Chunhong,Hu Zheng, et al. Causal-Proto SSRL: Learning Dynamic-Necessary State Variables for Multi-Environment Reinforcement Learning[OL].[19 March 2024] http://en.paper.edu.cn/en_releasepaper/content/4762946


	9. Second-order projection algorithm of Kelvin-Voigt model
	GONG Xiao-Juan,CHEN Chuan-Jun
	Mathematics 18 March 2024
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	Abstract:This paper considers a second-order projection scheme for solving the Kelvin-Voigt problem using the Crank-Nicolson scheme. The backward Euler scheme is employed for the time derivative term, while a semi-implicit scheme is used for the nonlinear term discretization. To enhance computational efficiency, a projection method is adopted to decouple velocity and pressure, thereby decomposing the considered model into two linear sub-problems, thereby simplifying the problem solution. The paper provides stability and convergence analysis of the numerical solution, demonstrating that the numerical scheme is unconditionally stable under certain conditions on the stabilization factor, and the errors of the velocity field in the L2 and H1 norms are second-order and first-order, respectively. Finally, the effectiveness of the numerical scheme is verified through numerical examples.
	TO cite this article:GONG Xiao-Juan,CHEN Chuan-Jun. Second-order projection algorithm of Kelvin-Voigt model[OL].[18 March 2024] http://en.paper.edu.cn/en_releasepaper/content/4762481


	10. Research on the hippocampus medical imaging segmentation method for small samples
	QI Shu-Wen,Jiang Zhu-qing1,Jiang Zhu-qing1,Jiang Zhu-qing1
	Computer Science and Technology 16 March 2024
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	Abstract:The hippocampus is located between the thalamus and the medial temporal lobe. It is mainly responsible for cognition, learning, and long and short memory. It is closely related to many diseases such as Alzheimer's disease and temporal lobe epilepsy. Therefore, the accurate segmentation of the hippocampal structure in magnetic resonance imaging is of great significance for the diagnosis of brain injury and brain disease prediction in clinical medicine. In recent years, the rapid development of deep learning technology has brought about brand-new changes to the field of hippocampal segmentation. Deep learning is data-driven, and the quantity and quality of data directly affect the accuracy of hippocampal segmentation. However, due to the difficulty of MR imaging acquisition and expensive manual annotation, hippocampus MR imaging is relatively scarce, which limits the performance improvement of deep learning models in hippocampal segmentation tasks to some extent. In order to overcome the challenges in small sample data scenarios and improve the accuracy of hippocampal segmentation, this paper proposes a data augmentation method, which aims to expand the data (brain magnetic resonance images) and label (hippocampus mask) simultaneously, so as to alleviate the problem of data scarcity and annotation scarcity. Through experiments, the proposed method can effectively improve the accuracy of hippocampal segmentation.
	TO cite this article:QI Shu-Wen,Jiang Zhu-qing1,Jiang Zhu-qing1, et al. Research on the hippocampus medical imaging segmentation method for small samples[OL].[16 March 2024] http://en.paper.edu.cn/en_releasepaper/content/4762832

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