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Quantum oscillation in Hopf-link semimetals

Physical Review B 2025年第20期111卷 L201103-L201103页

作者： Lei Shi Xiaoxiong Liu C. M. Wang Tianyu Liu Hai-Zhou Lu X. C. Xie State Key Laboratory of Quantum Functional Materials Department of Physics and Guangdong Basic Research Center of Excellence for Quantum Science Southern University of Science and Technology (SUSTech) Shenzhen 518055 China Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area (Guangdong) Shenzhen 518045 China Department of Physics Shanghai Normal University Shanghai 200234 China International Quantum Academy Shenzhen 518048 China Shenzhen Key Laboratory of Quantum Science and Engineering Shenzhen 518055 China International Center for Quantum Materials School of Physics Peking University Beijing 100871 China Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai 200433 China Hefei National Laboratory Hefei 230088 China

Since the discovery of the relation between the Chern number and quantum Hall effect, searching for observables of topological invariants has been an intriguing topic. Topological Hopf-link semimetals have attracted tremendous interest, in which the conduction and valence energy bands touch at linked nodal lines. However, it is challenging to identify this sophisticated topology. We propose to use the quantum oscillation in strong magnetic fields to probe the Hopf links. For a generic model of a Hopf-link semimetal that captures the linked-trivial phase transition, we figure out the phase shifts of oscillation for all Fermi pockets in all magnetic-field directions, by presenting self-consistent results from the Fermi-surface tomography, Landau fan diagram, and electrical resistivity. As the magnetic field is rotated, the phase shifts exhibit a unique pattern, which could help to identify Hopf links in real materials, such as those in Li2NaN.

关键词： Hall effect

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YOLOv5s-SLAM: A Monocular Visual SLAM method in Dynamic Environments 9

YOLOv5s-SLAM: A Monocular Visual SLAM method in Dynamic Envi...

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9th International Conference on Computer and Communications, ICCC 2023

作者： Yin, Zhenyu Feng, Dan Fan, Chao Wang, Xiaohui Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang110168 China University of Chinese Academy of Sciences School of Computer Science and Technology Shenyang110168 China Liaoning Key Lab. of Domestic Industrial Control Platform Technology on Basic Hardware Software Shenyang110168 China

ISBN: (纸本)9798350317251

To deal with the problem that traditional visual SLAM methods in dynamic environments are susceptible to the influence of moving targets, which leads to a decrease in the system's localization accuracy. This paper presents a dynamic visual SLAM system named YOLOv5s-SLAM. YOLOv5s is embedded in the ORB-SLAM3 system to recognize dynamic targets in the surrounding environment by deep learning methods and simultaneously extract ORB feature points with the SLAM system at the front end as a way to improve real-time performance. Combined with the constructed dynamic object detection rules, the dynamic feature points are divided and removed, which effectively reduces the mismatching in the feature extraction process and the impact on the system performance, and achieves the optimization of the visual SLAM localization performance. Tests on the TUM dataset show that the proposed method has smaller trajectory errors and higher position estimation accuracy, which can effectively enhance the robustness of localization. © 2023 IEEE.

关键词： Object detection

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SP-VSLAM:Monocular Visual-SLAM Algorithm Based on SuperPoint Network 15

SP-VSLAM:Monocular Visual-SLAM Algorithm Based on SuperPoint...

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15th International Conference on Communication Software and Networks, ICCSN 2023

作者： Yin, Zhenyu Feng, Dan Fan, Chao Ju, Chengen Zhang, Feiqing Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang110168 China School of Computer Science and Technology University of Chinese Academy of Sciences Shenyang110168 China Liaoning Key Lab. of Domestic Industrial Control Platform Technology on Basic Hardware Software Shenyang110168 China

ISBN: (纸本)9798350329360

For scenes with significant changes in light intensity or perspective, the traditional ORB feature extraction method is prone to the problems of local homogenization of feature points and errors arising from the accumulated drift. A monocular visual Simultaneous Localization and Mapping algorithm (SLAM) based on SuperPoint network (SP-VSLAM) is proposed. The SuperPoint network is used to extract feature points and calculate binary descriptors simultaneously, combined with Non-Maximal Suppression methods to achieve uniform detection and description of image feature points. Then construct robust and accurate feature association information. It is fused with the back-end local mapping, loop & map merging modules of the ORB-SLAM3 system to construct a complete monocular vision SLAM system. Using the public measurement datasets TUM, the localization accuracy is calculated for five sequences in monocular mode, and the experimental results show that the proposed method has smaller trajectory error and higher pose estimation accuracy in static, partial low dynamic and high dynamic environments, which can effectively enhance the robustness of tracking. © 2023 IEEE.

关键词： Conformal mapping

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FedMID: A Data-Free Method for Using Intermediate Outputs as a Defense Mechanism Against Poisoning Attacks in Federated Learning

arXiv

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arXiv 2024年

作者： Han, Sungwon Song, Hyeonho Park, Sungwon Cha, Meeyoung School of Computing KAIST Daejeon Korea Republic of Samsung Research Seoul Korea Republic of Data Science Group Institute for Basic Science Daejeon Korea Republic of Max Planck Institute for Security and Privacy Bochum Germany

Federated learning combines local updates from clients to produce a global model, which is susceptible to poisoning attacks. Most previous defense strategies relied on vectors derived from projections of local updates on a Euclidean space;however, these methods fail to accurately represent the functionality and structure of local models, resulting in inconsistent performance. Here, we present a new paradigm to defend against poisoning attacks in federated learning using functional mappings of local models based on intermediate outputs. Experiments show that our mechanism is robust under a broad range of computing conditions and advanced attack scenarios, enabling safer collaboration among data-sensitive participants via federated learning. © 2024, CC BY.

关键词： Vector spaces

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Deciphering the Geometric Bonferroni Mean Operator in Pythagorean Neutrosophic Sets Framework

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Neutrosophic Sets and Systems 2025年 75卷 139-161页

作者： Kamari, Mohammad Shafiq Bin Mohammad Rodzi, Zahari Bin Md. Al-Obaidi, R.H. Al-Sharqi, Faisal Al-Quran, Ashraf Shlaka, Rawan A. College of Computing Informatics and Mathematics Studies MARA University of Technology Negeri Sembilan Branch Seremban Campus Negeri Sembilan Seremban70300 Malaysia Faculty of Science and Technology National University of Malaysia Selangor Bandar Baru Bangi43600 Malaysia Fuel and Energy Techniques Engineering Department College of Engineering and Technologies AI-mustaqbal University Babylon51001 Iraq Department of Mathematics Faculty of Education for Pure Sciences University of Anbar Anbar Ramadi Iraq Department of Basic Sciences Preparatory Year King Faisal University Al-Ahsa31982 Saudi Arabia College of Pharmacy National University of Science and Technology Dhi Qar Iraq

The Geometric Bonferroni Mean (GBM), is an extension of The Bonferroni mean (BM), that combines both BM and the geometric mean, allowing for the representation of correlations among the combined factors while acknowledging the inherent uncertainty within the decision-making process. Within the framework of Pythagorean neutrosophic set (PNS) that encompasses truth, indeterminacy, and falsity-membership degrees, each criterion can be integrated into a unified PNS value, portraying the overall evaluation of that criterion by employing the Geometric Bonferroni mean. This study aims to enhance decision-making in Pythagorean neutrosophic framework by introducing an aggregation operator to PNS using the Geometric Bonferroni Mean. Additionally, it proposes a normalized approach to resolve decision-making quandaries within the realm of PNS, striving for improved solutions. The novel Pythagorean Neutrosophic Normalized Weighted Geometric Bonferroni Mean (PNNWGBM) aggregating operator has been tested in a case of multi-criteria decision-making (MCDM) problem involving the selection of Halal products suppliers with several criteria. The result shows that this aggregating operator is offering dependable and pragmatic method for intricate decision-making challenges and able to effectively tackle uncertainty and ambiguity in MCDM problem. © 2025, University of New Mexico. All rights reserved.

关键词： Mathematical operators

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DSNet: A Superior Real-Time Semantic Segmentation Model for Road Scenes 9

DSNet: A Superior Real-Time Semantic Segmentation Model for ...

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9th International Conference on Computer and Communications, ICCC 2023

作者： Yin, Zhenyu Shi, Xiaoqiang Zhang, Feiqing Xu, Guangyuan Qin, Li Li, Shurui Chinese Academy of Sciences Shenyang Institute of Computing Technology Shenyang China University of Chinese Academy of Sciences Shenyang China Liaoning Key Lab. of Domestic Industrial Control Platform Technology on Basic Hardware Software Shenyang China Shenyang Aerospace University School of Computer Science Shenyang China

ISBN: (纸本)9798350317251

A series of semantic segmentation models have achieved remarkable accuracy, but their high computational cost limits their practical applications in areas such as autonomous driving and robotics. Recently, some multi-branch real-time semantic segmentation models have been proposed to improve segmentation accuracy by introducing detail branches. However, there is a lack of auxiliary guidance in the design of detail branches, and there is a high computational cost problem in designing multi-branch information fusion modules. Therefore, to address these issues, in this paper, we propose a real-time semantic segmentation model called DSNet, which achieves a good balance between speed and accuracy. DSNet consists of two branches: the detail branch and the semantic branch. The detail branch effectively extracts detail information by introducing edge loss as auxiliary supervision, while the semantic branch extracts deeper-level semantic information by increasing network depth and channel count. Finally, the fusion of detail and semantic information serves as the feature for predicting the segmentation map. To fuse the detail and semantic information, we design a novel feature fusion module called LSAF, which efficiently integrates features from different branches with low computational costs. Our method achieves a speed of 152.3 FPS with an accuracy of 72.8% on the CityScapes test dataset. © 2023 IEEE.

关键词： Semantic Segmentation

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Matching between TMD and twist-3 factorizations in the transversely polarized hyperon production 31

Matching between TMD and twist-3 factorizations in the trans...

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31st International Workshop on Deep Inelastic Scattering, DIS 2024

作者： Ikarashi, Riku Koike, Yuji Yoshida, Shinsuke Graduate School of Science and Technology Niigata University Ikarashi Niigata950-2181 Japan Department of Physics Niigata University Ikarashi Niigata950-2181 Japan Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter Institute of Quantum Matter South China Normal University Guangzhou510006 China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Guangdong Provincial Key Laboratory of Nuclear Science Southern Nuclear Science Computing Center South China Normal University Guangzhou510006 China

We focus on the origin of the transverse polarization of Λ hyperon produced in the proton-proton collision which has been a mystery in high energy hadron reactions over the past half century. There are two different perturbative QCD frameworks for the description of the origin with respect to the size of the transverse momentum of the produced hyperon PℎT, transverse-momentum-dependent(TMD) factorization in small PℎT and the collinear twist-3 factorization in high PℎT. We can consider the intermediate region of PℎT where both frameworks are valid in semi-inclusive deep inelastic scattering and it was once shown that the TMD Boer-Mulders effect and the twist-3 distribution effect from the initial state proton give consistent cross section formulas in this region. We will here show that the consistency can be also found between the TMD polarizing fragmentation and the twist-3 fragmentation contributions generated by the polarized hyperon. This result supports the idea that the two frameworks describe a unique origin of the transverse polarization in different kinematic regions. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

关键词： Inelastic scattering

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Fusionmlp: A Mlp-Based Unified Image Fusion Framework

SSRN

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SSRN 2024年

作者： Liu, Shaolei Li, Shiman Qu, Linhao Wang, Manning Song, Zhijian Digital Medical Research Center School of Basic Medical Science Fudan University Shanghai200032 China Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention China

Due to the powerful feature representation capacity, deep learning-based image fusion methods have improved the fusion results for better information integration. However, some inherent limitations in convolutional neural networks (CNN) and Transformer networks impede further performance improvement. Specifically, CNN-based methods have difficulties in mining global context information;Transformer-based methods are unable to adapt to different image sizes and the computational complexity grows exponentially with the image size. To overcome these problems, we propose the first MLP-based multi-scene image fusion framework, denoted as FusionMLP. In addition, we propose a novel loss function combining the spatial and frequency domain information for model training. Extensive experiments show that FusionMLP not only outperform SOTA multi-scene image fusion methods, but also outperform SOTA fusion algorithms for specific scenes. © 2024, The Authors. All rights reserved.

关键词： Image fusion

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Knowledge sharing via domain adaptation in customs fraud detection

arXiv

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arXiv 2022年

作者： Park, Sungwon Kim, Sundong Cha, Meeyoung School Of Computing Korea Advanced Institute Of Science And Technology Korea Republic of Data Science Group Institute For Basic Science Korea Republic of

Knowledge of the changing traffic is critical in risk management. Customs offices worldwide have traditionally relied on local resources to accumulate knowledge and detect tax fraud. This naturally poses countries with weak infrastructure to become tax havens of potentially illicit trades. The current paper proposes DAS, a memory bank platform to facilitate knowledge sharing across multi-national customs administrations to support each other. We propose a domain adaptation method to share transferable knowledge of frauds as prototypes while safeguarding the local trade information. Data encompassing over 8 million import declarations have been used to test the feasibility of this new system, which shows that participating countries may benefit up to 2-11 times in fraud detection with the help of shared knowledge. We discuss implications for substantial tax revenue potential and strengthened policy against illicit trades. © 2022, CC BY-NC-SA.

关键词： Knowledge management

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The rise of AI language pathologists: exploring two-level prompt learning for few-shot weakly-supervised whole slide image classification 23

The rise of AI language pathologists: exploring two-level pr...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Linhao Qu Xiaoyuan Luo Kexue Fu Manning Wang Zhijian Song Digital Medical Research Center School of Basic Medical Science Fudan University and Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention

This paper introduces the novel concept of few-shot weakly supervised learning for pathology Whole Slide Image (WSI) classification, denoted as FSWC. A solution is proposed based on prompt learning and the utilization of a large language model, GPT-4. Since a WSI is too large and needs to be divided into patches for processing, WSI classification is commonly approached as a Multiple Instance Learning (MIL) problem. In this context, each WSI is considered a bag, and the obtained patches are treated as instances. The objective of FSWC is to classify both bags and instances with only a limited number of labeled bags. Unlike conventional few-shot learning problems, FSWC poses additional challenges due to its weak bag labels within the MIL framework. Drawing inspiration from the recent achievements of vision-language models (V-L models) in downstream few-shot classification tasks, we propose a two-level prompt learning MIL framework tailored for pathology, incorporating language prior knowledge. Specifically, we leverage CLIP to extract instance features for each patch, and introduce a prompt-guided pooling strategy to aggregate these instance features into a bag feature. Subsequently, we employ a small number of labeled bags to facilitate few-shot prompt learning based on the bag features. Our approach incorporates the utilization of GPT-4 in a question-and-answer mode to obtain language prior knowledge at both the instance and bag levels, which are then integrated into the instance and bag level language prompts. Additionally, a learnable component of the language prompts is trained using the available few-shot labeled data. We conduct extensive experiments on three real WSI datasets encompassing breast cancer, lung cancer, and cervical cancer, demonstrating the notable performance of the proposed method in bag and instance classification. Codes will be available at https://***/miccaiif/TOP.

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