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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024

作者： Ma, Jiquan Teng, Yujun Chen, Geng Jiang, Haotian Zhang, Kai Liu, Feihong Rekik, Islem Shen, Dinggang School of Computer Science and Technology Heilongjiang University Harbin China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China School of Information Science and Technology Northwest University Xi’an China BASIRA Lab Imperial-X and Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9783031869198

Diffusion-Weighted Imaging (DWI) is a significant technique for studying white matter. However, it suffers from low-resolution obstacles in clinical settings. Post-acquisition Super-Resolution (SR) can enhance the resolution of DWIs and has gained increasing research interest in recent years. An advanced generative model, the Diffusion Model (DM), exhibits particularly promising performance in image SR. However, effective conditions are required to bootstrap the DM for DWI SR. To this end, we proposed the first DM-based DWI SR model with two effective conditions based on low-solution DWIs and Track Density Imaging (TDI) maps, which possess rich high-resolution prior knowledge Additionally, we consider another condition based on features from low-resolution DWIs. These two conditions are integrated into our model, which comprises three components: DWI Resolution Enhancer (DRE), DWI Feature Extractor (DFE), and TDI Feature Extractor (TFE). DRE combines low-resolution DWI features from DFE with TDI features from TFE to progressively generate high-resolution DWIs. We performed extensive experiments on DWIs of normal subjects from human connectome projects and patients with Parkinson’s disease. The results demonstrate that our model outperforms existing DWI SR models, both qualitatively and quantitatively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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Further results on permutation pentanomials over Fq3 in characteristic two

arXiv

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arXiv 2025年

作者： Zhang, Tongliang Zheng, Lijing Wang, Hengtai Peng, Jie Li, Yanjun College of Science North China University of Science and Technology Hebei Tangshan063000 China Hebei Key Laboratory of Data Science and Application North China University of Science and Technology Hebei Tangshan063000 China School of Mathematics and Physics University of South China Hunan Hengyang421001 China Mathematics and Science College Shanghai Normal University Shanghai200234 China Institute of Statistics and Applied Mathematics Anhui University of Finance and Economics Anhui Bengbu233030 China

Let q = 2m. In a recent paper [31], Zhang and Zheng investigated several classes of permutation pentanomials of the form ϵ0xd0+L(ϵ1xd1+ϵ2xd2) over Fq3 (d0 = 1, 2, 4) from some certain linearized polynomial L(x) by using multivariate method and some techniques to determine the number of the solutions of some equations. They proposed an open problem that there are still some permutation pentanomials of that form have not been proven. In this paper, inspired by the idea of [12], we further characterize the permutation property of the pentanomials with the above form over Fq3 (d0 = 1, 2, 4). The techniques in this paper would be useful to investigate more new classes of permutation pentanomials. Copyright © 2025, The Authors. All rights reserved.

关键词： Polynomials

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Global 3D-gradient-structured steel sheets with superior strength-ductility combination

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Materials science and Engineering: A 2025年 935卷

作者： Shi, Yindong Zhang, Aojie Wang, Lina Liu, Tao Zhang, Xiliang Yang, Xinrui Xing, Zhenguo Zhu, Yuntian Hebei Key Laboratory of Wear-resistant Metallic Materials with High Strength and Toughness/Whole Processes of High Quality Fastener Application Technology Research Center of Universities in Hebei Province Hebei University of Engineering Handan056038 China Department of Materials Science and Engineering City University of Hong Kong Hong Kong 999077 China

Global gradient structures (GGS) from core to surface processed by twisting have been reported to possess exceptional strength-ductility combinations. However, it is hard to apply this technique to sheet (plate) samples because of their lower axial symmetry. Here, we report a novel 3D-GGS produced in a 304 stainless steel (304ss) sheet using cyclic twisting. The 3D-GGS 304ss sheets exhibited much better strength-ductility combinations than GGS 304ss rods due to their more pronounced heterostructural effects. During the tensile testing, a unique transformation-induced plasticity (TRIP) effect was activated in which reverse martensitic transformation (α′ → γ) was first activated, then transited to forward martensitic transformation (γ → α′) after some strain. Both the reverse and forward TRIP effects contributed to higher ductility. In addition, the 3D-structures also produced hetero-deformation induced (HDI) work hardening to retain ductility. This work developed a practical approach to constructing 3D-GGS to enhance integrated performances of metal sheets and components without changing their geometries. © 2025 Elsevier B.V.

关键词： Steel sheet

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Blockwise perturbative corrections in multiconfiguration calculations based on configuration-state-function generators: A revised analysis of the W xxxviii spectrum

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Physical Review A 2025年第4期111卷 042805-042805页

作者： Yanting Li Chaofan Shi Ran Si Kai Wang Per Jönsson Gediminas Gaigalas Michel Godefroid Chongyang Chen Shanghai EBIT Laboratory Key Laboratory of Nuclear Physics and Ion-beam Application Institute of Modern Physics Department of Nuclear Science and Technology Fudan University Shanghai 200433 China Department of Physics and Anhui Key Laboratory of Optoelectric Materials Science and Technology Key Laboratory of Functional Molecular Solids Ministry of Education Anhui Normal University Wuhu 241000 China Hebei Key Laboratory of Optic-Electronic Information and Materials The College of Physics Science and Technology Hebei University Baoding 071002 China Department of Materials Science and Applied Mathematics Malmö University SE-20506 Malmö Sweden Institute of Theoretical Physics and Astronomy Vilnius University 010222 Vilnius Lithuania Spectroscopy Quantum Chemistry and Atmospheric Remote Sensing Université Libre de Bruxelles Bruxelles Belgium

A methodology based on configuration-state-function generators (CSFGs) has recently been implemented in the General-Purpose Relativistic Atomic Structure Package (grasp) to reduce the computational load of configuration-interaction (CI) calculations Y. T. Li et al., Comput. Phys. Commun. 283, 108562 (2023). Large CI calculations can be performed if part of the interaction is treated perturbatively. Here, we present enhancements to the perturbative estimates by implementing blockwise methods, where the blocks are given by configuration-state-functions spanned by CSFGs. We compute excitation energies for 4s24p64d, 4s24p64f, and 4s24p54d2 states in Rb-like W37+ and corresponding transition wavelengths and rates to evaluate the methods. The final relative difference between calculated and experimental wavelengths is 0.01%. Thus, the calculations provide highly accurate values for W37+ properties useful for a variety of applications, including fusion applications.

关键词： Excitation energy

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Multi-View-Based High-Fidelity 3d Reconstruction of Peach Orchard

SSRN

引用

SSRN 2025年

作者： Chen, Yanan Xiao, Ke Gao, Guandong Zhang, Fan College of Information Science & Technology Hebei Agricultural University Hebei Province Baoding China Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural University Hebei Province Baoding China Department of Information Management The National Police University for Criminal Justice Hebei Province Baoding China North China University of Technology China

Accurate reconstruction of 3D orchards plays a key role in phenotyping within the field of digital agriculture. However, the model aliasing caused by occlusion presents significant challenges to high-precision 3D reconstruction during the orchard modeling process. In this paper, an improved model based on 3D Gaussian is proposed to achieve high-quality reconstruction of 3D orchard scenes, taking peach orchards as an example. datasets for three different scales of peach orchards, including multiple peach trees, a single peach tree and fruit-bearing peach trees, are created using multi-view images. In the model of adaptive density control, a strategy of dynamic opacity reset is proposed by constructing an opacity reset function, which reduces erroneous shear during densification, achieving effective capture of scene features at different scales. In the reconstruction of 3D orchard scenes, a distance-weighted filtering module is introduced to limit the representation frequency of Gaussian primitives while integrating with the super-sampling technique to increase the sampling density of pixels. Experimental results demonstrate that the improved 3D Gaussian model surpasses the 3DGS and the latest 2DGS concerning the evaluation metrics of PSNR, SSIM, and LPIPS. Specifically, it achieves improvement of 9.56% and 12.80% in PSNR, 13.67% and 12.20% in SSIM, and reduction of 21.14% and 10.75% in LPIPS, respectively. In summary, the improved 3D Gaussian model proposed can exhibit higher precision in reconstructing peach orchards across multiple scales, providing valuable reference and support for advancing 3D digitization in agricultural scenes. © 2025, The Authors. All rights reserved.

关键词： Fruits

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FrDiff: Framelet-based Conditional Diffusion Model for Multispectral and Panchromatic Image Fusion

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IEEE Transactions on Multimedia 2025年

作者： Zhang, Junkang Fang, Faming Wang, Tingting Zhang, Guixu Song, Haichuan East China Norm University School of Computer Science and Technology Shanghai200062 China Ministry of Education Key Laboratory of Advanced Theory and Application in Statistics and Data Science China

The process of fusing low-resolution multispectral (LRMS) and high-resolution panchromatic (PAN) imagery, commonly referred to as pansharpening, is intended to generate high-resolution multispectral (HRMS) imagery. Typically, most pre-existing pansharpening frameworks mainly emphasize the straightforward learning of the mapping relationship among PAN and LRMS images to HRMS images. However, a key limitation of these frameworks is their potential overemphasis on spatial information, particularly the enhancement of low-frequency components. As a result, such an oversight potentially hinders the model's ability to simultaneously restore both spectral and spatial details. To address this issue, we propose a novel pansharpening model based on the denoising diffusion probabilistic model (DDPM), dubbed FrDiff. Specifically, we build a framelet-based conditional diffusion model that leverages the generative power of diffusion models to produce more refine results. Different from conventional methods directly inferring HRMS images, our strategy is designed to project their framelet coefficients, utilizing the available PAN and LRMS images as resources. This approach enables the separation of high-frequency and low-frequency components through framelet transformation, which are subsequently recombined to create a novel set of conditional embeddings that feed into the diffusion process. At the same time, the powerful predictive power of the diffusion model is exploited to simultaneously recover the high-frequency and low-frequency components of the HRMS. Moreover, we introduce a framelet-oriented cross-attention module dedicated to honing spectral fidelity. This module is crucial for improving the spectral precision of the HRMS images, ensuring a balanced emphasis on both spatial and spectral enhancements. Quantitative and qualitative experiments on multiple benchmark datasets demonstrate that the proposed method achieves more robustness and high-quality results than other sta

关键词： Convolutional neural networks

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NSPG-Miner: Mining Repetitive Negative Sequential Patterns

arXiv

引用

arXiv 2025年

作者： Li, Yan Wang, Zhulin Liu, Jing Guo, Lei Fournier-Viger, Philippe Wu, Youxi Wu, Xindong School of Economics and Management Hebei University of Technology Tianjin300401 China School of Artificial Intelligence Hebei University of Technology Tianjin300401 China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300401 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China Hebei Key Laboratory of Big Data Computing 300401 China Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei University of Technology Hefei230009 China

Sequential pattern mining (SPM) with gap constraints (or repetitive SPM or tandem repeat discovery in bioinformatics) can find frequent repetitive subsequences satisfying gap constraints, which are called positive sequential patterns with gap constraints (PSPGs). However, classical SPM with gap constraints cannot find the frequent missing items in the PSPGs. To tackle this issue, this paper explores negative sequential patterns with gap constraints (NSPGs). We propose an efficient NSPG-Miner algorithm that can mine both frequent PSPGs and NSPGs simultaneously. To effectively reduce candidate patterns, we propose a pattern join strategy with negative patterns which can generate both positive and negative candidate patterns at the same time. To calculate the support (frequency of occurrence) of a pattern in each sequence, we explore a NegPair algorithm that employs a key-value pair array structure to deal with the gap constraints and the negative items simultaneously and can avoid redundant rescanning of the original sequence, thus improving the efficiency of the algorithm. To report the performance of NSPG-Miner, 11 competitive algorithms and 11 datasets are employed. The experimental results not only validate the effectiveness of the strategies adopted by NSPG-Miner, but also verify that NSPG-Miner can discover more valuable information than the state-ofthe-art algorithms. Algorithms and datasets can be downloaded from https://***/wuc567/PatternMining/tree/master/NSPG-Miner. Copyright © 2025, The Authors. All rights reserved.

关键词： Miners

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Optimal Scheduling for Integrated Energy System via Model-Based Reinforcement Learning 24

Optimal Scheduling for Integrated Energy System via Model-Ba...

引用

2024 International Conference on Artificial Intelligence of Things and Computing, AITC 2024

作者： Cheng, Kexin Wan, Jianxiong Liu, Chuyi Sun, Pengfei Li, Leixiao Zhang, Huaqin School of Data Science and Application Inner Mongolia University of Technology Inner Mongolia Autonomous Region Hohhot China Inner Mongolia Key Laboratory of Beijiang Cyberspace Security Inner Mongolia Autonomous Region Hohhot China Research Center of Large-Scale Energy Storage Technologies Ministry of Education of the People's Republic of China Inner Mongolia Autonomous Region Hohhot China

ISBN: (纸本)9798400709869

The Integrated Energy System (IES) integrates energy consumption, conversion, and storage to increase the utilization of renewable energy sources and fossil fuels. When using heuristic algorithms and mathematical optimization methods for optimal scheduling of IES, these methods often face challenges of high computational cost and insufficient model accuracy, for which Reinforcement Learning (RL) offers a promising solution. However, most studies have used Model-Free Reinforcement Learning (MFRL) for optimal scheduling, which is limited by problems of low sampling efficiency and long training time. Model-Based Reinforcement Learning (MBRL) is an effective alternative to accelerate learning and facilitate RL applications. Therefore, this paper compares the performance gap between MFRL and MBRL in the optimal scheduling problem of IES to determine the superiority of MBRL. Experimental results show that the MBPO algorithm significantly improves sampling efficiency and learning speed at high prediction accuracy, outperforming the Soft Actor-Critic (SAC) algorithm. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Adversarial machine learning

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The Research on V2V Cooperative Caching Method Based on Vehicle Attributes 24

The Research on V2V Cooperative Caching Method Based on Vehi...

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2024 International Conference on Artificial Intelligence of Things and Computing, AITC 2024

作者： Wang, Zirui Wang, Hui Wan, Jianxiong Liu, Chuyi Sun, Pengfei Li, Leixiao School of Data Science and Application Inner Mongolia University of Technology Inner Mongolia Autonomous Region Hohhot China Inner Mongolia Key Laboratory of Beijiang Cyberspace Security Inner Mongolia Autonomous Region Hohhot China Research Center of Large-Scale Energy Storage Technologies Ministry of Education of the People's Republic of China Inner Mongolia Autonomous Region Hohhot China

ISBN: (纸本)9798400709869

To address the increasingly severe issue of insufficient vehicular caching in the Internet of Vehicles (IoV), Vehicular edge computing uses base stations (BS) and vehicles with computing and storage capabilities as edge servers to cache content. However, most existing studies focus on reducing task costs, assuming vehicles can simultaneously act as requesters and providers, without considering whether a vehicle is suitable to serve as a provider, willing to share resources or ensure secure data transmission, thus lowering matching efficiency. Deep reinforcement learning and blockchain technology were integrated into the Internet of Vehicles to address distributed vehicle-to-vehicle collaborative caching. An optimal collaborative caching strategy was designed, considering dynamic vehicle attributes, with the system's objective being to maximize vehicle utility to encourage resource sharing. Simulation results confirm that the proposed blockchain-based vehicular edge computing collaborative caching scheme with deep reinforcement learning significantly outperforms other strategies. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Blockchain

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Bs0 → µ+µ− in a flavor violating extension of MSSM

arXiv

引用

arXiv 2025年

作者： Meng, Kai-Kai Zhang, Hai-Bin Yang, Jin-Lei Department of Physics Hebei University Baoding071002 China Hebei Key Laboratory of High-precision Computation and Application of Quantum Field Theory Baoding071002 China Hebei Research Center of the Basic Discipline for Computational Physics Baoding071002 China Institute of Life Science and Green Development Hebei University Baoding071002 China

B meson rare decays play a crucial role in exploring new physics beyond the standard model. In this study, we explore the rare decay process Bs0 → µ+µ− in a flavor violating extension of the Minimal Supersymmetric Standard Model (MSSM), namely the µ-from-ν SSM (µνSSM). Combined with the decay B¯ → Xsγ, the numerical results indicate that the µνSSM can successfully accommodate the experimental data for Bs0 → µ+µ− and additionally narrow down the parameter space. © 2025, CC BY.

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