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Discrete-time quantum walk approach to high-dimensional quantum state transfer and quantum routing

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arXiv 2021年

作者： Li, Heng-Ji Li, Jian Chen, Xiu-Bo Information Security Center State Key Laboratory Networking and Switching Technology Beijing University of Posts Telecommunications Beijing100876 China School of Computer Science Beijing University of Posts Telecommunications Beijing100876 China GuiZhou University Guizhou Provincial Key Laboratory of Public Big Data Guizhou Guiyang550025 China

High-dimensional quantum systems can offer extended possibilities and multiple advantages while developing advanced quantum technologies. In this paper, we propose a class of quantum-walk ar-chitecture networks that admit the efficient routing of high-dimensional quantum states. Perfect state transfer of an arbitrary unknown qudit state can be achieved between two arbitrary nodes via a one-dimensional lackadaisical discrete-time quantum walk. In addition, this method can be gener-alized to the high-dimensional lattices, where it allows distillable entanglement to be shared between arbitrary input and output ports. Implementation of our scheme is more feasible through exploiting the coin degrees of freedom and the settings of the coin ipping operators are simple. These results provide a direct application in a high-dimensional computational architecture to process much more information. Copyright © 2021, The Authors. All rights reserved.

关键词： Quantum optics

SNR-based teachers-student technique for speech enhancement

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arXiv 2020年

作者： Hao, Xiang Su, Xiangdong Wang, Zhiyu Zhang, Qiang Xu, Huali Gao, Guanglai Inner Mongolia Key Laboratory of Mongolian Information Processing Technology College of Computer Science Inner Mongolia Univeristy Hohhot China

It is very challenging for speech enhancement methods to achieves robust performance under both high signal-to-noise ratio (SNR) and low SNR simultaneously. In this paper, we propose a method that integrates an SNR-based teachers-student technique and time-domain U-Net to deal with this problem. Specifically, this method consists of multiple teacher models and a student model. We first train the teacher models under multiple small-range SNRs that do not coincide with each other so that they can perform speech enhancement well within the specific SNR range. Then, we choose different teacher models to supervise the training of the student model according to the SNR of the training data. Eventually, the student model can perform speech enhancement under both high SNR and low SNR. To evaluate the proposed method, we constructed a dataset with an SNR ranging from -20dB to 20dB based on the public dataset. We experimentally analyzed the effectiveness of the SNR-based teachers-student technique and compared the proposed method with several state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Signal to noise ratio

Dataless Text Classification with Pseudo Topic Representation

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Dataless Text Classification with Pseudo Topic Representatio...

International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Rong Yan Qi Chen Guanglai Gao Inner Mongolia Key Laboratory of Mongolian Information Processing Technology College of Computer Science Inner Mongolia University Hohhot China

ISBN: (数字)9781728192284

ISBN: (纸本)9781728185361

As for an automatic text classification approach, a large body of research on latent-topic based Dataless Text Classification (DTC) has been emerged in recent years. Perusing the candidate seed words or guaranteeing the quality of the category-topics is the core mission of this approach. However, few previous studies consider the quality of specific category-topics at the collection level instead at the document level, because not all topics are equally coherent or category sparsity. In this paper, we focus on alleviating the dilemma for the seed words selection problem in DTC by using pseudo text understanding. Differently from the existing latent-topic based DTC approach, we propose an unsupervised method named Pseudo Document Labeled Classification (PDLC). It extracts the most representative word list to capture the best latent semantic category-topic description. Experimental results indicate that our PDLC scheme achieves better classification accuracy without any labeled data or external resource.

关键词： Conferences Text categorization Semantics Tools Artificial intelligence

AN EDGE information AND MASK SHRINKING BASED IMAGE INPAINTING APPROACH

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arXiv 2020年

作者： Xu, Huali Su, Xiangdong Wang, Meng Hao, Xiang Gao, Guanglai Inner Mongolia Key Laboratory of Mongolian Information Processing Technology College of Computer Science Inner Mongolia University Hohhot China

In the image inpainting task, the ability to repair both high-frequency and low-frequency information in the missing regions has a substantial influence on the quality of the restored image. However, existing inpainting methods usually fail to consider both high-frequency and low-frequency information simultaneously. To solve this problem, this paper proposes edge information and mask shrinking based image inpainting approach, which consists of two models. The first model is an edge generation model used to generate complete edge information from the damaged image, and the second model is an image completion model used to fix the missing regions with the generated edge information and the valid contents of the damaged image. The mask shrinking strategy is employed in the image completion model to track the areas to be repaired. The proposed approach is evaluated qualitatively and quantitatively on the dataset Places2. The result shows our approach outperforms state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Image reconstruction

Scene text image super-resolution with semantic-aware interaction

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Engineering Applications of Artificial Intelligence 2025年 158卷

作者： Mingle Zhou Wenlong Liu Jin Wan Delong Han Min Li Gang Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China SHANDONG SCICOM Information and Economy Research Institute Co. Ltd. China

Scene text image super-resolution aims to enhance the resolution of images containing text in various scenes, which amplifies the prominence of the text and improves its recognizability. Existing methods struggle to accurately localize text regions in high-noise environments, which hampers their ability to effectively implement targeted super-resolution. To address these issues, we propose a Text image Super-resolution Semantic-aware Interaction Network (TSSIN) by embedding a text region segmentation network. First, we propose to use a pre-trained text region segmentation network (TRSN) to extract text region information. This approach semantically guides our model to address the challenge of the model not performing targeted super-resolution processing of text in high-noise environments. Secondly, we propose a multi-modal semantic information interaction module (MSIIM) to mitigate the issue of insufficient global information exchange. Comprehensive experiments conducted on the TextZoom dataset demonstrate that our TSSIN significantly enhances image quality. Furthermore, it shows a clear superiority over state-of-the-art methods on TextZoom, achieving an average text recognition accuracy improvement of +1.0% over Transformer-Based Super-Resolution Network (TBSRN) (49.6%, 56.2%, 60.1% vs. 48.1%, 55.9%, 58.9%), +0.83% over Parallelly Contextual Attention Network (PCAN), and +1.46% over Text Prior Guided Super-Resolution (TPGSR), as evaluated by three pre-trained text recognition models. Code is available at https://***/ads2d/TSSIN .

关键词： Multi-modal semantic information interaction Scene text image super-resolution Text region semantic guidance

Scale-aware network with regional and semantic attentions for crowd counting under cluttered background

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arXiv 2021年

作者： Yi, Qiaosi Liu, Yunxing Jiang, Aiwen Li, Juncheng Mei, Kangfu Wang, Mingwen Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China School of Computer Science and Technology East China Normal University Shanghai China School of Computer and Information Engineering Jiangxi Normal University Nanchang China Department of Mathematics The Chinese University of Hong Kong [ShengZheng ShengZheng China

Crowd counting is an important task that shown great application value in public safety-related fields, which has attracted increasing attention in recent years. In the current research, the accuracy of counting numbers and crowd density estimation are the main concerns. Although the emergence of deep learning has greatly promoted the development of this field, crowd counting under cluttered background is still a serious challenge. In order to solve this problem, we propose a Scale-Aware Crowd Counting Network (SACCN) with regional and semantic attentions. The proposed SACCN distinguishes crowd and background by applying regional and semantic self-attention mechanisms on the shallow layers and deep layers, respectively. Moreover, the asymmetric multi-scale module (AMM) is proposed to deal with the problem of scale diversity, and regional attention based dense connections and skip connections are designed to alleviate the variations on crowd scales. Extensive experimental results on multiple public benchmarks demonstrate that our proposed SACCN achieves satisfied superior performances and outperform most state-of-the-art methods. All codes and pre-trained models will be released soon. © 2021, CC BY.

关键词： Semantics

Default mode network static-dynamic functional signatures in first-episode drug-naive major depressive disorder

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Comprehensive Psychoneuroendocrinology

Comprehensive Psychoneuroendocrinology 2025年 23卷

作者： Xiaoyun Liu Kaimo Ding Fan Li Xiaotong Zhang Zhenghua Hou Yingying Yin Youyong Kong Yonggui Yuan Department of Psychosomatics and Psychiatry Zhongda Hospital School of Medicine Jiangsu Provincial Key Laboratory of Brain Science and Medicine Southeast University Nanjing China Zhenjiang Mental Health Center Jiangsu Province Zhenjiang China Lab of Image Science and Technology School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University Nanjing China

Computational study the effects of venting pressure and ignition location on the fuel-air mixture explosion load in vented chamber 2nd

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Computational study the effects of venting pressure and igni...

2nd International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2018

作者： Liang, Xingxing Wang, Zhongqi Li, Pengyi Zhang, Fuquan State Key Laboratory of Explosion Science and Technology Beijing Institute of Technology Beijing100081 China Digital Performance and Simulation Technology Laboratory School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang Univeristy Fuzhou350121 China

ISBN: (纸本)9783030045845

Numerical analysis were performed to study venting explosion load of methane-air in vented chamber. The effects of venting pressure and ignition location on indoor gas explosion overpressure are discussed. The simulation results showed the expected trends are agreed with the experiments in some extent. Several peaks in overpressure were identified along the pressure–time curves. There are two typical peak overpressures in our study. The first peak is due to the vent failure. The second is caused by the external explosion. It’s not a monotonic relationship between the peak overpressure and venting explosion pressure. The influence of the venting pressure on the explosion overpressure is irregular when the venting pressure is less than 10 kPa. Then the explosion overpressure increases with the increase of the venting pressure. The results show that, the maximum peak value of overpressure is shear ignition. But the local overpressure on the pressure relief wall is stronger due to turbulence in central ignition case. Explosion venting process couples the turbulent flow and chemical reaction of combustible medium which is influenced by several parameters. © Springer Nature Switzerland AG 2019.

关键词： Computational fluid dynamics

Efficient and secure image compression and encryption based on compressive sensing and four-dimensional hyperchaotic system

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information Sciences 2025年 719卷

作者： Ming Yao Hongwei Deng Zhong Chen Pan Zhang College of Computer Science and Technology Hengyang Normal University Hengyang 421002 Hunan China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang 421002 Hunan China Hunan Engineering Research Center of Cyberspace Security Technology and Applications Hengyang 421002 Hunan China National Enterprise Research and Development Center Sunward Intelligent Equipment Co Ltd Changsha 410100 Hunan China

With the growing awareness of privacy protection, the security of image data during network transmission has become a focal point of concern. Reducing computational complexity and improving transmission efficiency while meeting high-security requirements has become a primary focus of current research. To address this, we propose a novel image privacy protection scheme that combines compressive sensing with chaotic encryption, aiming to ensure image privacy and security while enhancing transmission and storage efficiency. We employ compressive sensing technology to achieve efficient compression of image data. Unlike traditional compression and encryption schemes, the proposed method does not require explicit sparsification preprocessing, thereby avoiding the complex operations introduced by signal transformations and simplifying the signal recovery process. To enhance encryption security, a four-dimensional hyperchaotic system with stronger chaotic properties is designed to generate highly random and unpredictable key streams, ensuring the security of the encrypted data. Furthermore, this paper combines disjoint Latin squares with fractal generation strategies to design a new fractal index matrix, based on which a novel image permutation scheme is proposed. This scheme effectively eliminates the linear relationships and correlations between adjacent pixels, achieving global pixel permutation. Coupled with the proposed dual-channel bidirectional diffusion structure, the algorithm effectively diffuses pixel information across the entire image, increasing the complexity and unpredictability of the image encryption process. Experimental results indicate that the proposed algorithm exhibits excellent performance in terms of compression efficiency, encryption effectiveness, and resistance to attacks, providing a highly efficient and reliable solution in the field of image compression and encryption.

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