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On the -differential spectra of two classes of power functions over finite fields

Applicable Algebra in Engineering, Communication and Computing 2025年 1-30页

作者： Liu, Qian Huang, Zhiwei Chen, Zhixiong Jiang, Rong Zhang, Liupiao Key Laboratory of Information Security of Network Systems College of Computer and Data Science Fuzhou University Fuzhou China Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and Statistics Hubei University Wuhan China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Kunming China Fujian Key Laboratory of Financial Information Processing Putian University Putian China School of Computer Science and Technology Dongguan University of Technology Dongguan China

Recently, a new concept called multiplicative differential was introduced by Ellingsen et al. [7]. As an extension of the differential uniformity, it is theoretically appealing to determine the properties of c-differential uniformity and the corresponding c-differential spectrum. In this paper, based on certain quadratic character sums and two special elliptic curves over $$\mathbb {F}_p$$ , the $$(-1)$$ -differential spectra of the following two classes of power functions over $$\mathbb {F}_{p^n}$$ is completely determined: (1) $$f_1(x)=x^{\frac{p^n+3}{2}}$$ , where $$p>3$$ and $$p\equiv 3\pmod 4$$ ; (2) $$f_2(x)=x^{p^n-3}$$ , where $$p>3$$ . The obtained result shows that the $$(-1)$$ -differential spectra of $$f_1(x)$$ and $$f_2(x)$$ can be expressed explicitly in terms of n. Moreover, an upper bound of the c-differential uniformity of $$f_2(x)$$ is given.

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A Triangular Stable Node network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, IcasSP 2025

作者： Liu, Qing Gao, Qian Fan, Jun Zhang, Zhiqiang 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 Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd. No.1999 Shunhua road Shandong Jinan250101 China

ISBN: (纸本)9798350368741

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN-a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model's effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets. © 2025 IEEE.

关键词： graph contrastive learning graph neural networks recommender systems self-supervised learning

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Magnetic measurements under high pressure with a quantum sensor in Hexagonal Boron Nitride

arXiv

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arXiv 2025年

作者： Yu, Lun-Xuan Guo, Nai-Jie Liu, Lin Liu, Wei Yan, Gui-Zhen Cui, Jin-Ming Tang, Jian-Shun Li, Chuan-Feng Liu, Xiao-Di Key Laboratory of Materials Physics Institute of Solid State Physics HFIPS Chinese Academy of Sciences Hefei230031 China CAS Key Laboratory of Quantum Information University of Science and Technology of China Anhui Hefei230026 China Anhui Province Key Laboratory of Quantum Network University of Science and Technology of China China CAS Center For Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Anhui Hefei230026 China Hefei National Laboratory University of Science and Technology of China Hefei230088 China

Magnetic measurements under high-pressure conditions are pivotal for the study of superconductivity and magnetic materials but remain challenging due to the micrometer-sized sample in diamond anvil cells (DAC). In this study, we propose a quantum sensing approach utilizing negatively charged boron-vacancy (VB- ) spin defects in two-dimensional hexagonal boron nitride for high resolution magnetic measurements under pressure. The optical and spin properties of VB- defects were systematically studied under high-pressure conditions, revealing a significant pressure-induced shift in zero-field splitting (ZFS), approximately three times larger than that of nitrogen-vacancy (NV) center. Furthermore, we demonstrate the pressure-dependent magnetic transition and variations in the Curie temperature of van der Waals ferromagnet Fe3GeTe2 flake using VB- defects under pressures. Notably, the maximum operational pressure for VB- defects was determined to be approximately 11 GPa, attributed to a structural phase transition in hexagonal boron nitride (hBN). This work establishes the way for two-dimensional quantum sensing technologies under high-pressure environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Van der Waals forces

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TinyML-Based Approach for Dynamic Transmission Power in LoRaWAN network 18th

TinyML-Based Approach for Dynamic Transmission Power in Lo...

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18th China Conference on Wireless Sensor networks, CWSN 2024

作者： Lodhi, Muhammad Ali Wang, Lei Qureshi, Khalid Ibrahim Mahmood, Khalid Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Technology Dalian University of Technology Dalian China Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Douliu64002 Taiwan

ISBN: (纸本)9789819621880

The Blind Adaptive Rate (B-ADR) algorithm, designed by Semtech for LoRaWAN networks, is tailored for mobile applications to allocate the spreading factor before each uplink transmission. This enhances reliability even in areas with fluctuating signal quality, a common occurrence in mobile applications. However, B-ADR predominantly focuses on spreading factor (SF) adjustment and overlooks dynamic transmission power control, leading to increased energy consumption. Consequently, this article proposes a novel and efficient B-ADR methodology tailored for mobile applications, leveraging Tiny Machine Learning (TinyML). In this approach, TinyML is deployed on each End Device (ED), enabling intelligent selection of optimal transmission power alongside SF before each uplink transmission. The proposed framework entails offline model training followed by real-time prediction execution on deployed EDs. Simulation results demonstrate superior performance of the proposed approach compared to typical B-ADR implemented on EDs. Thus, the proposed methodology proves to be well-suited for dense mobile applications within LoRaWAN networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Nanoscale positioning and in-situ enhancement of single G center in silicon using a fluorescence-localization technique

arXiv

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arXiv 2025年

作者： Ma, Yu-Hang Guo, Nai-Jie Liu, Wei Zeng, Xiao-Dong Xie, Lin-Ke Liu, Jun-You Wu, Ya-Qi Wang, Yi-Tao Wang, Zhao-An Ren, Jia-Ming Ao, Chun Lu, Haifei Tang, Jian-Shun Li, Chuan-Feng Guo, Guang-Can CAS Key Laboratory of Quantum Information University of Science and Technology of China Anhui Hefei230026 China Anhui Province Key Laboratory of Quantum Network University of Science and Technology of China Anhui Hefei230026 China CAS Center For Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Hefei230026 China Hefei National Laboratory University of Science and Technology of China Hefei230088 China School of Physics and Mechanics Wuhan University of Technology Wuhan430070 China

Silicon-based semiconductor nanofabrication technology has achieved a remarkable level of sophistication and maturity, and color centers in silicon naturally inherit this advantage. Besides, their emissions appear in telecommunication bands, which makes them play a crucial role in the construction of quantum network. To address the challenge of weak spontaneous emission, different optical cavities are fabricated to enhance the emission rate. However, the relative location between cavity and emitter is random, which greatly reduce the success probability of enhancement. Here, we report on a fluorescence-localization technique (FLT) for precisely locating single G center in silicon and embedding it in the center of a circular Bragg grating cavity in situ, achieving 240-times improvement of the success probability. We observe a 30-fold enhancement in luminescence intensity, 2.5-fold acceleration of the emission from single G center, corresponding to a Purcell factor exceeding 11. Our findings pave the way for the large-scale integration of quantum light sources including those with spins. Copyright © 2025, The Authors. All rights reserved.

关键词： Fluorescence

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Differentially Private Federated Stochastic Primal-Dual Learning for Internet of Vehicles

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IEEE Internet of Things Journal 2025年

作者： Li, Yiwei Wang, Shuai Chang, Tsung-Hui Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China The Chinese University of Hong Kong Shenzhen School of Science and Engineering Shenzhen Research Institute of Big Data Shenzhen518172 China

Federated learning (FL) has the potential to empower Internet-of-Vehicles (IoV) networks by enabling smart vehicles (SVs) to participate in the learning process under the orchestration of a vehicular service provider while keeping data locally. In this paper, we propose a novel federated stochastic primal-dual algorithm with differential privacy (FedSPD-DP) to ensure robust privacy protection for FL based IoV (FL-IoV) systems. The FedSPD-DP algorithm leverages multiple steps of local stochastic gradient descent (SGD) and partial client participation (PCP) to improve communication efficiency while incorporating differential privacy (DP) to ensure privacy protection. Our theoretical analysis explores the impact of these strategies on learning performance. Specifically, we demonstrate that the data sampling strategy and PCP enhance data privacy, while a larger number of local SGD steps may increase privacy leakage, revealing a non-trivial tradeoff between communication efficiency and privacy protection. Extensive experiments on real-world data validate the effectiveness of the proposed algorithm, showing superior performance compared to state-of-the-art FL algorithms, and confirming the analytical results and properties. © 2014 IEEE.

关键词： Federated learning

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A High-Quality and Efficient Bus-Aware Global Router

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Chinese Journal of Electronics 2025年第2期34卷 444-456页

作者： Liu, Genggeng Wei, Ling Yu, Yantao Xu, Ning College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350108 China School of Information Engineering Wuhan University of Technology Wuhan430070 China

As advanced technology nodes enter the nanometer era, the complexity of integrated circuit design is increasing, and the proportion of bus in the net is also increasing. The bus routing has become a key factor affecting the performance of the chip. In addition, the existing research does not distinguish between bus and non-bus in the complete global routing process, which directly leads to the expansion of bus deviation and the degradation of chip performance. In order to solve these problems, we propose a high-quality and efficient bus-aware global router, which includes the following key strategies: By introducing the routing density graph, we propose a routing model that can simultaneously consider the routability of non-bus and the deviation value of bus;A dynamic routing resource adjustment algorithm is proposed to optimize the bus deviation and wirelength simultaneously, which can effectively reduce the bus deviation;We propose a layer assignment algorithm consider deviation to significantly reduce the bus deviation of the 3D routing solution;And a depth-first search (DFS)-based algorithm is proposed to obtain multiple routing solutions, from which the routing result with the lowest deviation is selected. Experimental results show that the proposed algorithms can effectively reduce bus deviation compared with the existing algorithms, so as to obtain high-quality 2D and 3D routing solutions considering bus deviation. © 2015 Chinese Institute of Electronics.

关键词： Integrated circuit layout

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Towards precision diagnosis: a novel hybrid DC-CAD model for lung disease detection leveraging multi-scale capsule networks and temporal dynamics

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Complex and Intelligent Systems 2025年第6期11卷 1-24页

作者： Aggrey, Esther Stacy E. B. Zhen, Qin Kodjiku, Seth Larweh Fiasam, Linda Delali Sey, Collins Ukwuoma, Chiagoziem C. Aidoo, Evans Osei-Mensah, Emmanuel School of Information and Software Engineering Chengdu 610054 China The Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu 610054 China School of Computer Science and Technology Hangzhou 310018 China College of Nuclear Technology and Automation Engineering Chengdu China School of Computer Science and Informatics Huddersfield United Kingdom

The early detection of lung diseases, including cancer, is essential for improving patient outcomes. However, traditional diagnostic approaches and standard deep learning models often face challenges in effectively analyzing the complex spatial and temporal variations in medical imaging data, particularly in CT scans. To address these challenges, we propose DC-CAD, a novel hybrid framework that integrates Dilated Capsule networks, Channel-wise Attention Mechanisms, and Distanced Long Short-Term Memory for precise and early diagnosis of lung diseases. DC-CAD is innovative in its ability to combine multi-scale feature extraction and temporal dynamic analysis, enabling the model to capture intricate spatial relationships and sequential changes in lung tissue. The model consists of three main contributions: (1) Dilated Capsule networks for improved multi-scale context aggregation, which captures subtle textural variations, (2) a Channel-wise Attention Mechanism to focus on the most relevant regions of interest, minimizing the impact of irrelevant features, and (3) Distanced LSTM layers to model temporal dependencies across sequential CT scans, providing insights into disease progression. Through comprehensive experiments on the LC25000 dataset, DC-CAD achieves 99.52% accuracy, significantly outperforming baseline models such as standard Capsule networks and Convolutional Neural networks. The model also reduces the error rate to 0.48%, demonstrating substantial improvements in diagnostic performance, including increased accuracy, sensitivity, and specificity. These results establish DC-CAD as a powerful and reliable tool for automated lung disease diagnosis, with significant potential to enhance clinical workflow by reducing radiologists’ workload through its interpretability and efficiency. Moving forward, we plan to extend the model to handle multi-modal data and investigate advanced attention mechanisms to further improve diagnostic accuracy and generalizability. © Th

关键词： Channel-wise attention mechanisms CT scans Dilated capsule networks Lung diseases Medical imaging

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Symmetry-induced fragmentation and dissipative time crystal

arXiv

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arXiv 2025年

作者： Li, Haowei Yi, Wei CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei230026 China Anhui Province Key Laboratory of Quantum Network University of Science and Technology of China Hefei230026 China CAS Center For Excellence in Quantum Information and Quantum Physics Hefei230026 China Hefei National Laboratory University of Science and Technology of China Hefei230088 China 5Anhui Center for Fundamental Sciences in Theoretical Physics University of Science and Technology of China Hefei230026 China

We propose a general protocol for engineering dissipative time crystals in quantum open systems through symmetry-induced fragmentation and ergodicity breaking. Building upon a U(1)-symmetry-induced Liouville-space fragmentation, we devise a generic Liouvillian possessing a series of non-dissipative eigenmodes with purely imaginary eigenvalues, which give rise to long-time oscillations breaking both ergodicity and time-translation symmetry. Illustrating our proposal using a dissipative lattice model, we show that, even when the U(1) symmetry is broken, a prethermal time-crystal behavior survives, with distinct oscillation frequencies at different times of the steady-state approaching dynamics. The richly structured prethermal time-crystal dynamics derive from Fermi statistics and the Liouvillian skin effect of our model, under which excitations above the boundary-localized dark states can be mapped to the irreducible representations of the permutation group. The branching rules of the permutation group then dictate that the Liouvillian gaps acquired by the non-dissipative eigenmodes should be higher-order powers of the symmetry-breaking perturbation, leading to the prethermal time-crystal dynamics. Copyright © 2025, The Authors. All rights reserved.

关键词： Crystal symmetry

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Ultrasound-Guided Registration Pseudo-Labels for Semi-Supervised Brachial Plexus Segmentation

Ultrasound-Guided Registration Pseudo-Labels for Semi-Superv...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, IcasSP 2025

作者： Luo, Jia Zhang, Ze Ding, Yi Wang, Yiqian Zhang, Jian Network and Data Security Key Laboratory of Sichuan Province School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Yibin GREAT Technology Co. Ltd. Yibin644000 China Sichuan Provincial Hospital for Women and Children/Affilated Women and Children's Hospital of Chengdu Medical College Chengdu610054 China

ISBN: (纸本)9798350368741

In semi-supervised medical image segmentation, two main challenges arise. First, the quality of pseudo-labels generated by segmentation networks in data-limited scenarios is often poor, reducing segmentation accuracy. Second, many methods fail to effectively utilize the temporal context in video data. Moreover, the intricate relationships among multiple targets make the conventional teacher-student network consistency loss unsuitable for multi-target tasks, leading to inaccurate feature capture and degraded performance. To address these issues, we propose a registration-based pseudo-label generation strategy that produces pseudo-labels more closely aligned with actual labels. Additionally, we introduce multiple guidance mechanisms into the teacher-student network to ensure correct optimization. Experimental results show that our approach significantly outperforms leading semi-supervised segmentation methods. Specifically, at a 50% label rate, our method improves the Dice coefficient by 6.9% over existing methods. © 2025 IEEE.

关键词： Multi-Object Registration Semi-Supervised Medical Image Segmentation

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