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arXiv 2021年

作者： Liu, Lingbo Zhu, Yuying Li, Guanbin Wu, Ziyi Bai, Lei Lin, Liang School of Computer Science and Engineering Sun Yat-Sen University 510000 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Engineering Research Center for Advanced Computing Engineering Software of Ministry of Education China GuangDong Province Key Laboratory of Information Security Technology China School of Electrical and Information Engineering The University of Sydney 2000 Australia

Metro origin-destination prediction is a crucial yet challenging time-series analysis task in intelligent transportation systems, which aims to accurately forecast two specific types of cross-station ridership, i.e., Origin-Destination (OD) one and Destination-Origin (DO) one. However, complete OD matrices of previous time intervals can not be obtained immediately in online metro systems, and conventional methods only used limited information to forecast the future OD and DO ridership separately. In this work, we proposed a novel neural network module termed Heterogeneous Information Aggregation Machine (HIAM), which fully exploits heterogeneous information of historical data (e.g., incomplete OD matrices, unfinished order vectors, and DO matrices) to jointly learn the evolutionary patterns of OD and DO ridership. Specifically, an OD modeling branch estimates the potential destinations of unfinished orders explicitly to complement the information of incomplete OD matrices, while a DO modeling branch takes DO matrices as input to capture the spatial-temporal distribution of DO ridership. Moreover, a Dual Information Transformer is introduced to propagate the mutual information among OD features and DO features for modeling the OD-DO causality and correlation. Based on the proposed HIAM, we develop a unified Seq2Seq network to forecast the future OD and DO ridership simultaneously. Extensive experiments conducted on two large-scale benchmarks demonstrate the effectiveness of our method for online metro origin-destination prediction. Our code is resealed at https://***/HCPLab-SYSU/HIAM. Copyright © 2021, The Authors. All rights reserved.

关键词： Forecasting

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CM-MLP: Cascade Multi-scale MLP with Axial Context Relation Encoder for Edge Segmentation of Medical Image

arXiv

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arXiv 2022年

作者： Lv, Jinkai Hu, Yuyong Fu, Quanshui Zhang, Zhiwang Hu, Yuqiang Lv, Lin Yang, Guoqing Li, Jinpeng Zhao, Yi Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China The Research Center for Ubiquitous Computing Systems Institute of Computing Technology Chinese Academy of Sciences Beijing China Suining Central Hospital Suining China Hwa Mei Hospital University of Chinese Academy of Sciences Ningbo China School of Electrical and Information Engineering The University of Sydney Sydney Australia School of Mathematics and Computer Sciences NanChang University Nanchang China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

The convolutional-based methods provide good segmentation performance in the medical image segmentation task. However, those methods have the following challenges when dealing with the edges of the medical images: (1) Previous convolutional-based methods do not focus on the boundary relationship between foreground and background around the segmentation edge, which leads to the degradation of segmentation performance when the edge changes complexly. (2) The inductive bias of the convolutional layer cannot be adapted to complex edge changes and the aggregation of multiple-segmented areas, resulting in its performance improvement mostly limited to segmenting the body of segmented areas instead of the edge. To address these challenges, we propose the CM-MLP framework on MFI (Multi-scale Feature Interaction) block and ACRE (axial context relation encoder) block for accurate segmentation of the edge of medical image. In the MFI block, we propose the cascade multi-scale MLP (Cascade MLP) to process all local information from the deeper layers of the network simultaneously and utilize a cascade multiscale mechanism to fuse discrete local information gradually. Then, the ACRE block is used to make the deep supervision focus on exploring the boundary relationship between foreground and background to modify the edge of the medical image. The segmentation accuracy (Dice) of our proposed CM-MLP framework reaches 96.96%, 96.76%, and 82.54% on three benchmark datasets: CVC-ClinicDB dataset, sub-Kvasir dataset, and our in-house dataset, respectively, which significantly outperform the state-of-the-art method. The source code and trained models will be available at https://***/ProgrammerHyy/CM-MLP. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Quantum Linear System Solvers: A Survey of Algorithms and applications

arXiv

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arXiv 2024年

作者： Morales, Mauro E.S. Pira, Lirandë Schleich, Philipp Koor, Kelvin Costa, Pedro C.S. An, Dong Aspuru-Guzik, Alán Lin, Lin Rebentrost, Patrick Berry, Dominic W. Joint Center for Quantum Information and Computer Science University of Maryland United States Centre for Quantum Software and Information University of Technology Sydney Australia Centre for Quantum Technologies National University of Singapore Singapore Department of Computer Science University of Toronto Canada Vector Institute for Artificial Intelligence Toronto Canada ContinoQuantum Sydney Australia Beijing International Center for Mathematical Research Peking University Beijing China Department of Chemistry University of Toronto Canada Canadian Institute for Advanced Research Toronto Canada Acceleration Consortium Toronto Canada Department of Chemical Engineering & Applied Chemistry University of Toronto Toronto Canada Department of Materials Science & Engineering University of Toronto Toronto Canada Lebovic Fellow Canadian Institute for Advanced Research Toronto Canada Department of Mathematics University of California Berkeley United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory Berkeley United States Department of Computer Science National University of Singapore Singapore School of Mathematical and Physical Sciences Macquarie University Sydney Australia

Solving linear systems of equations plays a fundamental role in numerous computational problems from different fields of science. The widespread use of numerical methods to solve these systems motivates investigating the feasibility of solving linear systems problems using quantum computers. In this work, we provide a survey of the main advances in quantum linear systems algorithms, together with some applications. We summarize and analyze the main ideas behind some of the algorithms for the quantum linear systems problem in the literature. The analysis begins by examining the Harrow-Hassidim-Lloyd (HHL) solver. We note its limitations and reliance on computationally expensive quantum methods, then highlight subsequent research efforts which aimed to address these limitations and optimize runtime efficiency and precision via various paradigms. We focus in particular on the post-HHL enhancements which have paved the way towards optimal lower bounds with respect to error tolerance and condition number. By doing so, we propose a taxonomy that categorizes these studies. Furthermore, by contextualizing these developments within the broader landscape of quantum computing, we explore the foundational work that have inspired and informed their development, as well as subsequent refinements. Finally, we discuss the potential applications of these algorithms in differential equations, quantum machine learning, and many-body physics. © 2024, CC BY.

关键词： Quantum computers

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CM-MLP: Cascade Multi-scale MLP with Axial Context Relation Encoder for Edge Segmentation of Medical Image

CM-MLP: Cascade Multi-scale MLP with Axial Context Relation ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jinkai Lv Yuyong Hu Quanshui Fu Zhiwang Zhang Yuqiang Hu Lin Lv Guoqing Yang Jinpeng Li Yi Zhao Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China Suining Central Hospital Suining China School of Electrical and Information Engineering The University of Sydney Sydney Australia School of Mathematics and Computer Sciences NanChang University Nanchang China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Hwa Mei Hospital University of Chinese Academy of Sciences Ningbo China The Research Center for Ubiquitous Computing Systems Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

The convolutional-based methods provide good segmentation performance in the medical image segmentation task. However, those methods have the following challenges when dealing with the edges of the medical images: (1) Previous convolutional-based methods do not focus on the boundary relationship between foreground and background around the segmentation edge, which leads to the degradation of segmentation performance when the edge changes complexly. (2) The inductive bias of the convolutional layer cannot be adapted to complex edge changes and the aggregation of multiple-segmented areas, resulting in its performance improvement mostly limited to segmenting the body of segmented areas instead of the edge. To address these challenges, we propose the CM-MLP framework on MFI (Multiscale Feature Interaction) block and ACRE (axial context relation encoder) block for accurate segmentation of the edge of medical image. In the MFI block, we propose the cascade multi-scale MLP (Cascade MLP) to process all local information from the deeper layers of the network simultaneously and utilize a cascade multiscale mechanism to fuse discrete local information gradually. Then, the ACRE block is used to make the deep supervision focus on exploring the boundary relationship between foreground and background to modify the edge of the medical image. The segmentation accuracy (Dice) of our proposed CM-MLP framework reaches 96.96%, 96.76%, and 82.54% on three benchmark datasets: CVC-ClinicDB dataset, sub-Kvasir dataset, and our inhouse dataset, respectively, which significantly outperform the state-of-the-art method. The source code and trained models will be available at https://***/ProgrammerHyy/CM-MLP.

关键词： Convolutional codes Degradation Image segmentation Fuses Convolution Image edge detection Source coding

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Topological Modes Observed in Si Photonics SSH Integrated Circuit

Topological Modes Observed in Si Photonics SSH Integrated Ci...

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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： R. Nakamura T. Nakama A. Balčytis T. Ozawa Y. Ota S. Iwamoto H. Ito T. Baba Dept of Electrical &Computer Eng. Yokohama National Univ. Hodogava-ku Yokohama Japan Integrated Photonics and Applications Centre (InPAC) School of Engineering RMIT University Melbourne VIC Australia Advanced Institute for Materials Research Tohoku Univ. Sendai Japan Dept. of Applied Physics and Physico-Informatics Keio Univ. Kohoku-ku Yokohama Japan Research Center for Advanced Science and Technology The University of Tokyo Meguro-ku Tokyo Japan

We fabricated a sophisticated Si photonic integrated circuit for investigating topological photonics SSH model. In the selective excitation and observation of SSH coupled microrings, we observed the wavefunctions of e...

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Channel Augmented Joint Learning for Visible-Infrared Recognition

Channel Augmented Joint Learning for Visible-Infrared Recogn...

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International Conference on computer Vision (ICCV)

作者： Mang Ye Weijian Ruan Bo Du Mike Zheng Shou National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Wuhan University China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China National University of Singapore Singapore

ISBN: (纸本)9781665428132

This paper introduces a powerful channel augmented joint learning strategy for the visible-infrared recognition problem. For data augmentation, most existing methods directly adopt the standard operations designed for single-modality visible images, and thus do not fully consider the imagery properties in visible to infrared matching. Our basic idea is to homogenously generate color-irrelevant images by randomly exchanging the color channels. It can be seamlessly integrated into existing augmentation operations without modifying the network, consistently improving the robustness against color variations. Incorporated with a random erasing strategy, it further greatly enriches the diversity by simulating random occlusions. For cross-modality metric learning, we design an enhanced channel-mixed learning strategy to simultaneously handle the intra-and cross-modality variations with squared difference for stronger discriminability. Besides, a channel-augmented joint learning strategy is further developed to explicitly optimize the outputs of augmented images. Extensive experiments with insightful analysis on two visible-infrared recognition tasks show that the proposed strategies consistently improve the accuracy. Without auxiliary information, it improves the state-of-the-art Rank-1/mAP by 14.59%/13.00% on the large-scale SYSU-MM01 dataset.

关键词： Measurement computer vision Image color analysis Robustness Task analysis Standards

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Experimental verification of group non-membership in optical circuits

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Photonics Research 2021年第9期9卷 1745-1751页

作者： KAI SUN ZI-JIAN ZHANG FEI MENG BIN CHENG ZHU CAO JIN-SHI XU MAN-HONG YUNG CHUAN-FENG LI GUANG-CAN GUO CAS Key Laboratory of Quantum Information University of Science and Technology of ChinaHefei 230026China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of ChinaHefei 230026China Department of Physics Southern University of Science and TechnologyShenzhen 518055China Department of Computer Science The University of Hong KongPokfulamHong Kong SARChina Centre for Quantum Software and Information Faculty of Engineering and Information TechnologyUniversity of Technology SydneySydneyNSW 2007Australia Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and TechnologyShanghai 200237China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Shenzhen Key Laboratory of Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China

The class quantum Merlin–Arthur(QMA),as the quantum analog of nondeterministic polynomial time,contains the decision problems whose YES instance can be verified efficiently with a quantum *** problem of deciding the group non-membership(GNM)of a group element is conjectured to be a member of *** works on the verification of GNM,which still lacks experimental demonstration,required a quantum circuit with O(n~5)group oracle ***,we provide an efficient way to verify GNM problems,in which each quantum circuit only contains O(1)group of oracle calls,and the number of qubits in each circuit is reduced by *** on this protocol,we then experimentally demonstrate the new verification process with a four-element group in an all-optical *** new protocol is validated experimentally by observing a significant completeness-soundness gap between the probabilities of accepting elements in and outside the *** work efficiently simplifies the verification of GNM and is helpful in constructing more quantum protocols based on the near-term quantum devices.

关键词： quantum element polynomial

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Conditional Generative Modeling for Amorphous Multi-Element Materials

arXiv

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arXiv 2025年

作者： Li, Honglin Liu, Chuhao Guo, Yongfeng Luo, Xiaoshan Chen, Yijie Liu, Guangsheng Li, Yu Wang, Ruoyu Wang, Zhenyu Wu, Jianzhuo Ma, Cheng Xie, Zhuohang Lv, Jian Ding, Yufei Zhang, Huabin Luo, Jian Zhong, Zhicheng Li, Mufan Wang, Yanchao Li, Wan-Lu Key Laboratory of Material Simulation Methods and Software Ministry of Education College of Physics Jilin University Changchun China Aiiso Yufeng Li Family Department of Chemical and Nano Engineering University of California La Jolla San DiegoCA United States Institute of Molecular Engineering Plus College of Chemistry Fuzhou University Fuzhou China College of Chemistry and Molecular Engineering Peking University Beijing China School of Physics Nankai University Tianjin China Institute of Modern Physics Fudan University Shanghai China Program in Materials Science and Engineering University of California La Jolla San DiegoCA United States School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China International Center of Future Science Jilin University Changchun China Department of Computer Science and Engineering University of California La Jolla San DiegoCA United States Center for Renewable Energy and Storage Technologies Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Suzhou Lab Suzhou215123 China

Amorphous multi-element materials offer unprecedented tunability in composition and properties, yet their rational design remains challenging due to the lack of predictive structure-property relationships and the vast configurational space. Traditional modeling struggles to capture the intricate short-range order that dictates their stability and functionality. We here introduce ApolloX, a pioneering predictive framework for amorphous multi-element materials, establishing a new paradigm by integrating physics-informed generative modeling with particle swarm optimization, using chemical short-range order as an explicit constraint. By systematically navigating the disordered energy landscape, ApolloX enables the targeted design of thermodynamically stable amorphous configurations. It accurately predicts atomic-scale arrangements, including composition-driven metal clustering and amorphization trends, which are well-validated by experiments, while also guiding synthesis by leveraging sluggish diffusion to control elemental distribution and disorder. The resulting structural evolution, governed by composition, directly impacts catalytic performance, leading to improved activity and stability with increasing amorphization. This predictive-experimental synergy transforms the discovery of amorphous materials, unlocking new frontiers in catalysis, energy storage, and functional disordered systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Amorphization

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Privacy-Preserving Federated Foundation Model for Generalist Ultrasound Artificial Intelligence

arXiv

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arXiv 2024年

作者： Jiang, Yuncheng Feng, Chun-Mei Ren, Jinke Wei, Jun Zhang, Zixun Hu, Yiwen Liu, Yunbi Sun, Rui Tang, Xuemei Du, Juan Wan, Xiang Xu, Yong Du, Bo Gao, Xin Wang, Guangyu Zhou, Shaohua Cui, Shuguang Goh, Rick Siow Mong Liu, Yong Li, Zhen Shenzhen518172 China Shenzhen518172 China Institute of High Performance Computing Agency for Science Technology and Research Singapore138632 Singapore College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Research Institute of Big Data Shenzhen518172 China South China Hospital Health Science Center Shenzhen University Shenzhen518111 China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Affiliated Hospital of North Sichuan Medical College Sichuan637000 China North Sichuan Medical College Sichuan637000 China Shenzhen518055 China School of Computer Science Wuhan University Wuhan430072 China Thuwal23955-6900 Saudi Arabia Thuwal23955-6900 Saudi Arabia Beijing University of Posts and Telecommunications Beijing100876 China School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

Ultrasound imaging is widely used in clinical diagnosis due to its non-invasive nature and real-time capabilities. However, conventional ultrasound diagnostics face several limitations, including high dependence on physician expertise and suboptimal image quality, which complicates interpretation and increases the likelihood of diagnostic errors. Artificial intelligence (AI) has emerged as a promising solution to enhance clinical diagnosis, particularly in detecting abnormalities across various biomedical imaging modalities. Nonetheless, current AI models for ultrasound imaging face critical challenges. First, these models often require large volumes of labeled medical data, raising concerns over patient privacy breaches. Second, most existing models are task-specific, which restricts their broader clinical utility. To overcome these challenges, we present UltraFedFM, an innovative privacy-preserving ultrasound foundation model. UltraFedFM is collaboratively pre-trained using federated learning across 16 distributed medical institutions in 9 countries, leveraging a dataset of over 1 million ultrasound images covering 19 organs and 10 ultrasound modalities. This extensive and diverse data, combined with a secure training framework, enables UltraFedFM to exhibit strong generalization and diagnostic capabilities. It achieves an average area under the receiver operating characteristic curve (AUROC) of 0.927 for disease diagnosis and a dice similarity coefficient (DSC) of 0.878 for lesion segmentation. Notably, UltraFedFM surpasses the diagnostic accuracy of mid-level ultrasonographers (4–8 years of experience) and matches the performance of expert-level sonographers (10+ years of experience) in the joint diagnosis of 8 common systemic diseases. These findings indicate that UltraFedFM can significantly enhance clinical diagnostics while safeguarding patient privacy, marking a significant advancement in AI-driven ultrasound imaging for future clinical applications. Copyri

关键词： Differential privacy

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Suppressing quantum errors by scaling a surface code logical qubit

arXiv

引用

arXiv 2022年

作者： Acharya, Rajeev Aleiner, Igor Allen, Richard Andersen, Trond I. Ansmann, Markus Arute, Frank Arya, Kunal Asfaw, Abraham Atalaya, Juan Babbush, Ryan Bacon, Dave Bardin, Joseph C. Basso, Joao Bengtsson, Andreas Boixo, Sergio Bortoli, Gina Bourassa, Alexandre Bovaird, Jenna Brill, Leon Broughton, Michael Buckley, Bob B. Buell, David A. Burger, Tim Burkett, Brian Bushnell, Nicholas Chen, Yu Chen, Zijun Chiaro, Ben Cogan, Josh Collins, Roberto Conner, Paul Courtney, William Crook, Alexander L. Curtin, Ben Debroy, Dripto M. Del Toro Barba, Alexander Demura, Sean Dunsworth, Andrew Eppens, Daniel Erickson, Catherine Faoro, Lara Farhi, Edward Fatemi, Reza Burgos, Leslie Flores Forati, Ebrahim Fowler, Austin G. Foxen, Brooks Giang, William Gidney, Craig Gilboa, Dar Giustina, Marissa Grajales Dau, Alejandro Gross, Jonathan A. Habegger, Steve Hamilton, Michael C. Harrigan, Matthew P. Harrington, Sean D. Higgott, Oscar Hilton, Jeremy Hoffmann, Markus Hong, Sabrina Huang, Trent Huff, Ashley Huggins, William J. Ioffe, Lev B. Isakov, Sergei V. Iveland, Justin Jeffrey, Evan Jiang, Zhang Jones, Cody Juhas, Pavol Kafri, Dvir Kechedzhi, Kostyantyn Kelly, Julian Khattar, Tanuj Khezri, Mostafa Kieferová, Mária Kim, Seon Kitaev, Alexei Klimov, Paul V. Klots, Andrey R. Korotkov, Alexander N. Kostritsa, Fedor Kreikebaum, John Mark Landhuis, David Laptev, Pavel Lau, Kim-Ming Laws, Lily Lee, Joonho Lee, Kenny Lester, Brian J. Lill, Alexander Liu, Wayne Locharla, Aditya Lucero, Erik Malone, Fionn D. Marshall, Jeffrey Martin, Orion McClean, Jarrod R. McCourt, Trevor McEwen, Matt Megrant, Anthony Meurer Costa, Bernardo Mi, Xiao Miao, Kevin C. Mohseni, Masoud Montazeri, Shirin Morvan, Alexis Mount, Emily Mruczkiewicz, Wojciech Naaman, Ofer Neeley, Matthew Neill, Charles Nersisyan, Ani Neven, Hartmut Newman, Michael Ng, Jiun How Nguyen, Anthony Nguyen, Murray Niu, Murphy Yuezhen O’Brien, Thomas E. Opremcak, Alex Platt, John Petukhov, Andre Potter, Rebecca Pryadko, Leonid Quintana, Chris Roushan, Pedram Rubin, Nicholas C. Saei, Negar Sank, Danie Google Research United States Department of Physics Columbia University New YorkNY United States Department of Electrical and Computer Engineering University of Massachusetts AmherstMA United States Department of Electrical and Computer Engineering Auburn University AuburnAL United States Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia Department of Physics Institute for Quantum Information and Matter Walter Burke Institute for Theoretical Physics California Institute of Technology PasadenaCA United States Department of Electrical and Computer Engineering University of California RiversideCA United States USRA Research Institute for Advanced Computer Science Mountain ViewCA United States QuAIL NASA Ames Research Center Moffett Field CA United States Department of Physics University of California Santa BarbaraCA United States

Practical quantum computing will require error rates that are well below what is achievable with physical qubits. Quantum error correction [1, 2] offers a path to algorithmically-relevant error rates by encoding logical qubits within many physical qubits, where increasing the number of physical qubits enhances protection against physical errors. However, introducing more qubits also increases the number of error sources, so the density of errors must be sufficiently low in order for logical performance to improve with increasing code size. Here, we report the measurement of logical qubit performance scaling across multiple code sizes, and demonstrate that our system of superconducting qubits has sufficient performance to overcome the additional errors from increasing qubit number. We find our distance-5 surface code logical qubit modestly outperforms an ensemble of distance-3 logical qubits on average, both in terms of logical error probability over 25 cycles and logical error per cycle (2.914%±0.016% compared to 3.028%±0.023%). To investigate damaging, low-probability error sources, we run a distance-25 repetition code and observe a 1.7 × 10−6 logical error per round floor set by a single high-energy event (1.6 × 10−7 when excluding this event). We are able to accurately model our experiment, and from this model we can extract error budgets that highlight the biggest challenges for future systems. These results mark the first experimental demonstration where quantum error correction begins to improve performance with increasing qubit number, illuminating the path to reaching the logical error rates required for computation. © 2022, CC BY.

关键词： Qubits

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