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Note on a New Construction of Kantorovich Form q-Bernstein Operators Related to Shape Parameter λ

computer Modeling in Engineering & Sciences 2022年第3期130卷 1479-1493页

作者： Qingbo Cai Resat Aslan Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information ProcessingSchool of Mathematics and Computer ScienceQuanzhou Normal UniversityQuanzhou362000China Department of Mathematics Faculty of Sciences and ArtsHarran UniversitySanlıurfa63300Turkey

The main purpose of this paper is to introduce some approximation properties of a Kantorovich kind q-Bernstein operators related to B′ezier basis functions with shape parameterλ∈[−1,1].Firstly,we compute some basic results such as moments and central moments,and derive the Korovkin type approximation theorem for these ***,we estimate the order of convergence in terms of the usual modulus of continuity,for the functions belong to Lipschitz-type class and Peetre’s K-functional,***,with the aid of Maple software,we present the comparison of the convergence of these newly defined operators to the certain function with some graphical illustrations and error estimation table.

关键词： q-calculus (λ,q)-Bernstein polynomials order of convergence Lipschitz-type function Peetre’s K-functional

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Optimizing Antenna Design through AI Integration: A Concise Review

Optimizing Antenna Design through AI Integration: A Concise ...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Xiaotian Liu Kwok L. Chung Wenbiao Li Ruihong Li Shuwei Huang Yingsong Li School of Computer Science and Engineering Huizhou University Huizhou China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

The integration of artificial intelligence with antenna design and optimization represents a promising research frontier. This paper provides a concise overview of current research, emphasizing the computational cost challenges associated with implementing machine learning in antenna design and optimization. It explores the advantages and limitations of utilizing the Kriging model and the Surrogate-assisted Differential Evolution Algorithm (SADEA) series to tackle these cost constraints.

关键词： Microwave antennas Technological innovation Analytical models Reviews Computational modeling Machine learning Transforms

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PHT-CAD: Efficient CAD Parametric Primitive Analysis with Progressive Hierarchical Tuning

arXiv

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arXiv 2025年

作者： Niu, Ke Chen, Yuwen Yu, Haiyang Chen, Zhuofan Que, Xianghui Li, Bin Xue, Xiangyang Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University China

computer-Aided Design (CAD) plays a pivotal role in industrial manufacturing, yet 2D Parametric Primitive Analysis (PPA) remains underexplored due to two key challenges: structural constraint reasoning and advanced semantic understanding. To tackle these challenges, we first propose an Efficient Hybrid Parametrization (EHP) for better representing 2D engineering drawings. EHP contains four types of atomic component (i.e., point, line, circle, and arc). Additionally, we propose PHT-CAD, a novel 2D PPA framework that harnesses the modality alignment and reasoning capabilities of Vision-Language Models (VLMs) for precise engineering drawing analysis. In PHT-CAD, we introduce four dedicated regression heads to predict corresponding atomic components. To train PHT-CAD, a three-stage training paradigm Progressive Hierarchical Tuning (PHT) is proposed to progressively enhance PHT-CAD’s capability to perceive individual primitives, infer structural constraints, and align annotation layers with their corresponding geometric representations. Considering that existing datasets lack complete annotation layers and real-world engineering drawings, we introduce ParaCAD, the first large-scale benchmark that explicitly integrates both the geometric and annotation layers. ParaCAD comprises over 10 million annotated drawings for training and 3,000 real-world industrial drawings with complex topological structures and physical constraints for test. Extensive experiments demonstrate the effectiveness of PHT-CAD and highlight the practical significance of ParaCAD in advancing 2D PPA research. The code and adopted datasets are available at https://***/yuwen-chen616/ PHT-CAD. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Slew-Driven Layer Assignment for Advanced Non-default-rule Wires 20th

Slew-Driven Layer Assignment for Advanced Non-default-rule W...

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Proceedings of the 20th Web Information Systems and Applications Conference, WISA 2023

作者： Lu, Ren Zhang, Wei Jiang, Lieqiu Liu, Genggeng College of Computer and Data Science Fuzhou University Fuzhou350116 China Key Laboratory of Network Computing and Intelligent Information Processing Fujian Province Fuzhou350116 China

ISBN: (纸本)9789819962211

With the rapid increase in circuit density in Very Large Scale Integration, the proportion of interconnect delay in circuit timing is also increasing. This makes the importance of layer assignment algorithms increasingly prominent in circuit design. However, most previous layer assignment algorithms prioritize optimizing timing exclusively from the perspective of interconnect delay, thereby disregarding the impact of slew violations on circuits. Therefore, this paper proposes a slew-driven layer assignment algorithm, which considers the timing of different routing layers and introduces non-default-rule wires to design a layer assignment algorithm that can significantly optimize delay, congestion, and slew violations. This algorithm mainly includes three key technologies: 1) Introducing non-default-rule wires technology to optimize timing and adopting a negotiation based approach to ensure that the final routing scheme does not have overflow;2) Proposing a slew prioritization strategy that comprehensively considers slew and interconnect delay during the routing process;3) Proposing a timing critical awareness strategy to further optimize the slew and interconnect delay without worsening the overflow. The experimental results show that the proposed algorithm has significant effects on optimizing delay and reducing slew violations. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Wire

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Robust Video Object Segmentation with Restricted Attention 48

Robust Video Object Segmentation with Restricted Attention

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48th IEEE International Conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Zhang, Huaizheng Guo, Pinxue Le, Zhongwen Zhang, Wenqiang Shanghai Key Laboratory of Intelligent Information Processing China Fudan University School of Computer Science Shanghai China Fudan University Academy for Engineering and Technology Shanghai China

ISBN: (纸本)9781728163277

This paper focuses on the two problems of the similar objects distraction and the lack of robustness for unseen object categories in semi-supervised video object segmentation task. Existing methods have achieved great results on the benchmark dataset, but these two problems still have not been completely solved. We propose the Robust Video Object Segmentation With Restricted Attention (RVOSR), which can suppress the effects caused by similar objects and filter out noise confusion from other irrelevant regions. Meanwhile augmenting the semantic information of features, which makes the features more suitable for video object segmentation task. Extensive experiments demonstrate the effectiveness of our approach and achieve the state-of-the-art performance on the widely-used VOS benchmarks including DAVIS-2016 (92.1% J& F), DAVIS-2017 (86.8% J& F) and YouTubeVOS-2019 (84.8%). © 2023 IEEE.

关键词： Semantics

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The Analysis of Phase Synchronisation in the Uniform Scale-Free Hypernetwork 9th

The Analysis of Phase Synchronisation in the Uniform Scale-F...

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9th International Conference of Pioneering computer Scientists, Engineers and Educators, ICPCSEE 2023

作者： Du, Juan Ma, Xiujuan Ma, Fuxiang Zhou, Bin Yu, Wenqian College of Computer Qinghai Normal University Qinghai Xining810016 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Xining810008 China

ISBN: (纸本)9789819959709

Many phenomena in realistic complex systems can be explained by the synchronisation behavior of complex systems, such as cricket chirping in unison. The synchronisation behavior occurring on a hypernetwork can be used to explain the swarming behavior occurring on a multivariate interacting system, such as the synchronised forwarding of group messages. There is a lack of results related to phase synchronization of hypernetwork in the existing studies on the synchronization behavior of hypernetworks. To address this problem, this paper investigates the node-based and hyperedge-based phase synchronisation of a scale-free hypernetwork using the Kuramoto model with the order parameter r as the synchronisation degree indicator. The comparative analysis reveals that the phase synchronisation of the scale-free hypernetwork is related to the uniformity k of the hypernetwork but not to the number of nodes and hyperedges, and the phase synchronisation based on hyperedges is more likely to occur than that based on nodes as the coupling strength increases. In addition, the degree of phase synchronisation of scale-free hypernetworks is related to the number of new_nodes of newly added nodes when the hyperedge grows during the construction of the hypernetwork, which shows that the smaller the new_nodes is, the better the degree of synchronisation of the hypernetwork is. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Synchronization

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Denoising Diffusion Path: Attribution Noise Reduction with An Auxiliary Diffusion Model 38

Denoising Diffusion Path: Attribution Noise Reduction with A...

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38th Conference on Neural Information processing Systems, NeurIPS 2024

作者： Lei, Yiming Li, Zilong Zhang, Junping Shan, Hongming Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University China Institute of Science and Technology for Brain-Inspired Intelligence MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence MOE Frontiers Center for Brain Science Fudan University China

The explainability of deep neural networks (DNNs) is critical for trust and reliability in AI systems. Path-based attribution methods, such as integrated gradients (IG), aim to explain predictions by accumulating gradients along a path from a baseline to the target image. However, noise accumulated during this process can significantly distort the explanation. While existing methods primarily concentrate on finding alternative paths to circumvent noise, they overlook a critical issue: intermediate-step images frequently diverge from the distribution of training data, further intensifying the impact of noise. This work presents a novel Denoising Diffusion Path (DDPath) to tackle this challenge by harnessing the power of diffusion models for denoising. By exploiting the inherent ability of diffusion models to progressively remove noise from an image, DDPath constructs a piece-wise linear path. Each segment of this path ensures that samples drawn from a Gaussian distribution are centered around the target image. This approach facilitates a gradual reduction of noise along the path. We further demonstrate that DDPath adheres to essential axiomatic properties for attribution methods and can be seamlessly integrated with existing methods such as IG. Extensive experimental results demonstrate that DDPath can significantly reduce noise in the attributions-resulting in clearer explanations-and achieves better quantitative results than traditional path-based methods. © 2024 Neural information processing systems foundation. All rights reserved.

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Robust DOA Estimation Against Outliers Based on Bias-compensated Adaptive Filtering

Robust DOA Estimation Against Outliers Based on Bias-compens...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Wudang Xiao Canping Yu Yingsong Li Liping Li Dingcheng Zou Nikola Zlatanov Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Faculty of Computer and Engineering Sciences Innopolis University Innopolis Russia

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

The direction-of-arrival (DOA) estimation approach utilizing the adaptive nulling array framework has gained widespread attention due to its low complexity. However, in impulsive noise environments, traditional least mean squares (LMS) algorithm may experience a sharp decline in performance or even fail to work properly. To address this challenge, we propose a DOA estimation approach designed for impulsive noise situations based on a robust nonlinear function. In addition, the algorithm can mitigate bias caused by auxiliary array noise and uses the variable step-size strategy to enhance its performance. Furthermore, the mean stability of the proposed approach is also assessed. Simulation results demonstrate its superior performance in impulsive noise environments.

关键词： Resistance Direction-of-arrival estimation Simulation Noise Estimation Adaptive arrays Information filters

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An Improved Method for Approximating the Infinite-Horizon Value Function of the Discrete-Time Switched LQR Problem

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Journal of Systems Science & Complexity 2024年第1期37卷 22-39页

作者： HOU Tan LI Yuanlong LIN Zongli Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Charles L.Brown Department of Electrical and Computer Engineering University of VirginiaCharlottesvilleVA 22904-4743USA

This paper considers the problem of approximating the infinite-horizon value function of the discrete-time switched LQR *** particular,the authors propose a new value iteration method to generate a sequence of monotonically decreasing functions that converges exponentially to the value *** method facilitates us to use coarse approximations resulting from faster but less accurate algorithms for further value iteration,and thus,the proposed approach is capable of achieving a better approximation for a given computation time compared with the existing *** numerical examples are presented in this paper to illustrate the effectiveness of the proposed method.

关键词： Optimal control switched linear systems switched LQR problem.

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SpliceCannon: A novel framework for the prediction of canonical and non-canonical splice sites based on deep learning

SpliceCannon: A novel framework for the prediction of canoni...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhang, Yanju Qi, Wangjing Lin, Ruohan Fan, Jinli Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China Huaqiao University College of Computer Science and Technology Xiamen China

ISBN: (纸本)9798350337488

Accurately predicting splice sites in DNA sequences is not only of utmost importance in the study of gene expression and protein synthesis, but also crucial to precisely understanding the pathogenesis of various diseases related to splicing. In the past few decades, several tools for identifying splice sites have been developed, however, many of them ignore the contextual influence in DNA sequences in the process of encoding, consequently leading to considerable false positives in prediction. Additionally, most methods solely investigate canonical splicing whereas pay little attention to non-canonical ones. To address the issues mentioned above, we propose a comprehensive method, SpliceCannon, for accurately predicting both canonical and non-canonical splice sites based on neural networks. This method uses the combination of k-nucleotide frequencies and Doc2vec to encode the sequence, then employs a series of comprehensive neural networks such as bidirectional long short-term memory networks and residual networks to effectively extract features from the original sequence while avoiding the problems such as gradient vanishing. The results demonstrate that our method consistently achieves high accuracy when tested on multiple species. Compared to other recent methods, our model exhibits superior performance, stronger generalization ability, and wider applicability. In particular, The recognition accuracy is also remarkably high on the non-canonical dataset. We envision that this proposed method will serve as a promising tool for splice sites prediction to facilitate genomic research. © 2023 IEEE.

关键词： Bidirectional long short-term memory networks Canonical and non-canonical splice sites Deep learning Skip connection

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