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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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TDiffSal: Text-Guided Diffusion Saliency Prediction Model for Images 27th

TDiffSal: Text-Guided Diffusion Saliency Prediction Model fo...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhang, Nana Xiong, Min Zhu, Dandan Zhu, Kun Zhai, Guangtao School of Computer Science and Technology Donghua University No. 2999 Renmin North Road Songjiang District Shanghai201620 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210093 China Institute of AI Education Shanghai East China Normal University No. 3663 Zhongshan North Road Putuo District Shanghai200333 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University No. 4800 Cao’an Highway Jiading District Shanghai201804 China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University No. 800 Dongchuan Road Minhang District Shanghai200240 China

ISBN: (纸本)9783031781858

Existing visual saliency prediction methods mainly focus on single-modal visual saliency prediction, while ignoring the significant impact of text on visual saliency. To more comprehensively explore the influence of text on human attention in images, we propose a text-guided diffusion saliency prediction model, named TDiffSal. In specific, recent studies on stable diffusion models have shown promising performance in unifying tasks due to their inherent generalization ability. Inspired by this, a novel diffusion model for generalized visual-text saliency prediction is proposed, which formulates the prediction issue as a conditional generative task of the saliency map by employing input visual and text as the conditions. Meanwhile, we introduce a multi-head fusion module to effectively integrate text features and image features, which can efficiently guide the image denoising process and progressively refine the generated saliency map to make it semantically relevant to the text. Additionally, we employ an efficient pre-training strategy to enhance the robustness and generalization of the proposed model. We conduct extensive experiments on benchmark datasets to demonstrate its superior performance compared to other state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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MixSA: Training-free Reference-based Sketch Extraction via Mixture-of-Self-Attention

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arXiv 2025年

作者： Yang, Rui Wu, Xiaojun He, Shengfeng The School of Computer Sciences Shaanxi Normal University Xi'An China The School of Computer Science The Key Laboratory of Intelligent Computing and Service Technology for Folk Song of the Ministry of Culture and Tourism Shaanxi Normal University Xi'An710119 China The School of Computing and Information Systems Singapore Management University Singapore

Current sketch extraction methods either require extensive training or fail to capture a wide range of artistic styles, limiting their practical applicability and versatility. We introduce Mixture-of-Self-Attention (MixSA), a training-free sketch extraction method that leverages strong diffusion priors for enhanced sketch perception. At its core, MixSA employs a mixture-of-self-attention technique, which manipulates self-attention layers by substituting the keys and values with those from reference sketches. This allows for the seamless integration of brushstroke elements into initial outline images, offering precise control over texture density and enabling interpolation between styles to create novel, unseen styles. By aligning brushstroke styles with the texture and contours of colored images, particularly in late decoder layers handling local textures, MixSA addresses the common issue of color averaging by adjusting initial outlines. Evaluated with various perceptual metrics, MixSA demonstrates superior performance in sketch quality, flexibility, and applicability. This approach not only overcomes the limitations of existing methods but also empowers users to generate diverse, high-fidelity sketches that more accurately reflect a wide range of artistic expressions. Copyright © 2025, The Authors. All rights reserved.

关键词： Image texture

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Dual prototypes contrastive learning based semi-supervised segmentation method for intelligent medical applications

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Engineering Applications of Artificial Intelligence 2025年 154卷

作者： Yue, Tianai Xu, Rongtao Wu, Jingqian Yang, Wenjie Du, Shide Wang, Changwei Johns Hopkins University Baltimore21218 United States Jinan250014 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan250014 China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The University of Hong Kong 999077 Hong Kong College of Computer and Big Data Fuzhou University Fuzhou350108 China

In medical intelligence applications, the labeling of medical data is crucial and expensive, so it becomes urgent to explore labeling-efficient ways to train applications. Semi-supervised techniques for medical image segmentation have demonstrated potential, effectively training models using scarce labeled data alongside a wealth of unlabeled data. Therefore, semi-supervised medical image segmentation is a key issue in engineering applications of medical intelligence. Consistency constraints based on prototype alignment provide an intuitively sensible way to discover valuable insights from unlabeled data that can motivate segmentation performance. In this work, we propose a Dual prototypes Contrastive Network to motivate semi-supervised medical segmentation accuracy by imposing image-level global prototype and pixel-level local prototype constraints. First, we introduce a Background-Separation Global Prototype Contrastive Learning technique that utilizes the natural mutual exclusivity of foreground and background to separate the inter-class distances and encourage the segmentation network to obtain segmentation results that are more complete and do not contain background regions. Second, we design a Cross-Consistent Local Prototype Contrastive Learning techniques to extend the perturbation consistency of the two networks to the prototype's localized response to the feature map, thereby shaping a more stable intra-class prototype space and producing accurate and robust pixel-level predictions. Finally, we comprehensively evaluate our method on mainstream semi-supervised medical image segmentation benchmarks and settings, and experimental results show that our proposed method outperforms current state-of-the-art methods. Specifically, our method achieves a Dice Coefficient score of 91.8 on the Automatic Cardiac Diagnosis Challenge dataset using only 10% labeled data training, 1.1% ahead of the second best method. Code is available at https://***/yuelily2024/DPC

关键词： Contrastive Learning

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Remote State Estimation for Complex Networks Under Probabilistic Bit Flips: A Transmission Power Allocation Scheme

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IEEE Transactions on Automatic Control 2025年

作者： Song, Jiahao Wang, Zidong Liu, Qinyuan He, Xiao Tsinghua University Department of Automation Beijing100084 China Brunel University London Department of Computer Science UxbridgeUB8 3PH United Kingdom Tongji University Department of Computer Science and Technology Shanghai201804 China Ministry of Education Tongji University Key Laboratory of Embedded System and Service Computing Shanghai200092 China Shanghai Artificial Intelligence Laboratory Shanghai China

In this paper, the problem of remote state estimation is investigated for a class of complex networks with noisy wireless communication channels. The employment of the binary encoding scheme allows for the description of the influence of channel noises as probabilistic bit flips, where the probability of these bit flips is influenced by several factors with the signal-to-noise ratio (SNR) being a crucial one. In engineering practice, the method of adjusting the transmission power for each node in a complex network is commonly applied to modify the SNR so as to reduce the data distortion caused by bit flips. Furthermore, due to restricted communication resources, the total available transmission power is often limited particularly in large-scale systems such as complex networks. Consequently, the allocation of transmission power for all nodes under a total transmission power constraint becomes a concern. Utilizing the ultimately bounded filtering method, we devise a transmission-power-dependent state estimator, by which the combined effect of probabilistic bit flips and transmission power allocation on estimation performance is analyzed. Moreover, the task of co-designing the transmission power allocation scheme and estimator gains is modeled as an optimization problem, which is then addressed through a two-step optimization strategy. The existence of a unique optimal transmission power allocation scheme is also proven. Finally, numerical simulation examples are provided to demonstrate the effectiveness of the proposed co-design approach. © 1963-2012 IEEE.

关键词： Large scale systems

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E2DAS: An Efficient Equivariant Dynamic Aggregation Saliency Model for Omnidirectional Images 27th

E2DAS: An Efficient Equivariant Dynamic Aggregation Saliency...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhang, Nana Liu, Qian Zhu, Dandan Zhu, Kun Zhai, Guangtao Yang, Xiaokang School of Computer Science and Technology Donghua University No.2999 Renmin North Road Songjiang District Shanghai201620 China Institute of AI Education Shanghai East China Normal University No.3663 Zhongshan North Road Putuo District Shanghai200333 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University No.4800 Cao’an Highway Jiading District Shanghai201804 China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University No. 800 Dongchuan Road Minhang District Shanghai200240 China

ISBN: (纸本)9783031781216

Recent years have witnessed rapid progress of convolutional neural networks (CNNs) and their successful application in the task of saliency prediction for omnidirectional images (ODIs). Albeit achieving tremendous performance improvements, these CNNs-based saliency models are plagued by two major shortcomings: spatial content-agnostic and computationally intensive. Inspired by the effectiveness of equivariant network in the majority of computer vision tasks, we propose a novel efficient equivariant dynamic aggregation saliency (E2DAS) model to efficiently tackle the issue of human fixation prediction in ODIs. To be specific, our proposed model consists of an efficient equivariant module, a dynamic convolutional aggregation module, and an optimization computation module. Different from existing saliency models for ODIs, we are the first attempt to introduce an efficient equivariant dynamic convolutional aggregation operation into the saliency prediction task, which can fundamentally alleviate the projection distortion problem and can effectively learn spatial content-adaptive features. Moreover, we clearly observe a considerable decrease in the number of parameters resulting from the replacement of standard convolution with dynamic convolution aggregation. Extensive experiments on several benchmark datasets show the proposed model’s superiority over other state-of-the-art methods in terms of performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Equivariant dynamic aggregation light-weight model omnidirectional images saliency prediction spatial content-adaptive

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Adaptive frequency-domain enhanced deep model driven by heterogeneous networks for medical image segmentation

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Knowledge-Based systems 2025年 319卷

作者： Liu, Dong Kuang, Jin Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems Xiangnan University Chenzhou423300 China School of Computer and Artificial Intelligence Xiangnan University Chenzhou423300 China Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province Xiangnan University Chenzhou423300 China School of Geosciences Yangtze University Wuhan430100 China

Accurate medical image segmentation necessitates precise localization of global structures and local boundaries due to the high variability in lesion shapes and sizes. However, existing models are limited by conventional spatiotemporal features and single-network architectures, which restrict the simultaneous captures of semantic information and boundary details, thereby challenging generalizable medical image segmentation. To overcome these limitations, we propose a heterogeneous network-driven adaptive frequency-domain enhanced deep model(AFDSeg). First, we introduce the Frequency Domain Adaptive High-Frequency Feature Selection(FAHS) module, which adaptively extracts high-frequency features to enhance contour and detail representation while integrating spatiotemporal and frequency-domain features for improved consistency. Additionally, Prototype-Guided Low-Frequency Feature Aware(PFLA) and Local High-Frequency Salient-Feature Denoising (LHSD) modules are developed, which extract discriminative low-frequency features while suppressing local noise in high-frequency components, thereby facilitating efficient multi-scale feature fusion. Furthermore, the Multi-Level Prototype Feature Refinement(MPFR) Module is introduced to align low- and high-dimensional features during decoding and enhance semantic consistency. Finally, a heterogeneous network framework capable of accommodating multiple network architecture for medical image segmentation is proposed. Our method achieves mDice scores of 93.91%, 88.64%, 91.27%, 90.74%, and 81.38% on the Kvasir-SEG, BUSI, ISIC-2017, ACDC, and Synapse datasets, respectively, and attains 92.09%, 93.50%, and 83.92% in cross-domain experiments on three unseen datasets (Kvasir Capsule-SEG, BUS42, and M&Ms). Our approach consistently outperforms state-of-the-art methods on both benchmark and cross-domain datasets. Extensive quantitative and qualitative experiments demonstrated that AFDSeg accurately segments global structures and local detai

关键词： Semantic Segmentation

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Impact of the Next-Nearest-Neighbor Interaction on Traffic Flow of Highway with Slopes

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Communications in Theoretical Physics 2012年第10期58卷 590-598页

作者：李志鹏周盈 The Key Laboratory of Embedded System and Service Computing supported by Ministry of Education Tongji University

In this paper,we study the motion course of traffic flow on the slopes of a highway by applying a microscopic traffic model,which takes into account the next-nearest-neighbor interaction in an intelligent transportation system *** common gradients of the highway,which are sag terrain,uphill terrain,and downhill terrain on a single-lane roadway,are selected to clarify the impact on the traffic flow by the next-nearest-neighbor interaction in relative *** obtain the current-density relation for traffic flow on the sag,the uphill and the downhill under the next-nearest-neighbor interaction *** is observed that the current saturates when the density is greater than a critical value and the current decreases when the density is greater than another critical *** the density falls into the intermediate range between the two critical densities it is also found that the oscillatory jam,easily leads to traffic accidents,often appears in the downhill stage,and the next-nearest-neighbor interaction in relative velocity has a strong suppressing effect on this kind of dangerous congestion.A theoretical analysis is also presented to explain this important conclusion.

关键词： traffic flow traffic jam next-nearest-neighbor interaction

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Unified output feedback based prescribed performance consensus tracking control of heterogeneous multi-agent systems

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IEEE/CAA Journal of Automatica Sinica 2025年

作者： Luo, Dahui Wang, Yujuan Lewis, Frank L. Song, Yongduan Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University The School of Automation Chongqing University Chongqing400044 China Star Institute for Intelligent Systems Chongqing400044 China Uta Research Institute University of Texas at Arlington Fort WorthTX76118 United States

This paper proposes an output-feedback based prescribed performance consensus tracking control methodology for a class of heterogeneous multi-agent systems (HMASs) with inconsistent system structure, where the performance behavior is allowed to be different from that of each other. Both the heterogeneous system structures and the nonidentical performance requirements make the control problem much more challenging than that of MASs with identical structure and performance requirement. This is mainly due to the coupling effect of the system dynamics and performance restriction of each agent in the cooperative control action. The key to solve this problem is to introduce a dual-phase performance-guaranteed method, in which the consensus tracking error is decomposed into auxiliary tracking error and filter tracking error and then the whole performance control is decomposed into two phases. By confining the two errors respectively, the practical tracking error can be proved to be explicitly confined within an arbitrarily given performance envelope by merely adjusting the design parameters rather than modifying control structure. Moreover, the prescribed performance control (PPC) result is not only uniform with any initial conditions and design parameters, allowing it to be global, but also unifying both the global and semi-global result into one frame, distinguishing itself from most existing PPC works where either only global or only semi-global result is guaranteed. Finally, the effectiveness of the proposed control scheme is confirmed by the simulation conducted on a group of tunnel-diode circuits (TDC). © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Levees

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DFMC:Feature-Driven Data-Free Knowledge Distillation

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Zhang, Zherui Xu, Rongtao Wang, Changwei Xu, Wenhao Chen, Shunpeng Xu, Shibiao Xu, Guangyuan Guo, Li Beijing University of Posts and Telecommunications School of Artificial Intelligence Beijing 100876 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing 100190 China Qilu University of Technology Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan 250316 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan 250014 China

Data-Free Knowledge Distillation (DFKD) enables knowledge transfer from teacher networks without access to the real dataset. However, generator-based DFKD methods often suffer from insufficient diversity or low-confidence in synthetic images, negatively impacting student network performance. This paper introduces DFMC, a generative feature-driven framework to mitigate the inherent limitations of DFKD. We propose exploiting semantic description between generative feature domains to guide augmentation strategies, avoiding random abstract inputs caused by inconsistent semantic quality. Then, by applying noise to the generative features, we produce contrastive learning pairs indirectly, limiting the sampling range of the feature domain to encourage the student network to learn domain-invariant features. Finally, we guide the student network to deeply mimic the teacher’s layer-wise implicit classification behavior for the augmented synthetic images. Extensive experiments across various datasets and downstream tasks demonstrate the effectiveness of DFMC, achieving significant improvements while preventing student networks from overfitting to semantic ambiguous images. © 1991-2012 IEEE.

关键词： Contrastive Learning Data-Free Knowledge Distillation Feature Augmentation

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