Information Sciences最新文献

Adversarial attacks on large language models using regularized relaxation 使用正则化松弛的大型语言模型的对抗性攻击

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-01-12 DOI: 10.1016/j.ins.2026.123112

Samuel Jacob Chacko , Sajib Biswas , Chashi Mahiul Islam, Fatema Tabassum Liza, Xiuwen Liu

As Large Language Models (LLMs) have become integral to numerous practical applications, ensuring their robustness and safety is critical. Despite advancements in alignment techniques significantly improving overall safety, LLMs remain susceptible to adversarial inputs designed to exploit vulnerabilities. Existing adversarial attack methods have notable limitations: discrete token-based methods suffer from inefficiency, whereas continuous optimization methods typically fail to produce valid tokens from the model’s vocabulary, making them impractical for real-world applications.

In this paper, we propose Regularized Relaxation, a novel technique for adversarial attacks that overcomes these limitations by leveraging regularized gradients, computed with a constraint that encourages optimized embeddings to stay close to valid token representations. This enables continuous optimization to produce discrete tokens directly from the model’s vocabulary while preserving attack effectiveness. Our approach achieves a two-order-of-magnitude speed improvement compared to the state-of-the-art greedy coordinate gradient-based method. It significantly outperforms other recent methods in runtime and efficiency, while consistently achieving higher attack success rates across the majority of tested models and datasets. Crucially, our method produces valid tokens directly from the model’s vocabulary, overcoming a significant limitation of previous continuous optimization approaches. We demonstrate the effectiveness of our attack through extensive experiments on five state-of-the-art LLMs across four diverse datasets. Our implementation is publicly available at: https://github.com/sj21j/Regularized_Relaxation.

随着大型语言模型（llm）成为许多实际应用中不可或缺的一部分，确保它们的鲁棒性和安全性至关重要。尽管对齐技术的进步显著提高了整体安全性，llm仍然容易受到旨在利用漏洞的对抗性输入的影响。现有的对抗性攻击方法有明显的局限性：基于令牌的离散方法效率低下，而连续优化方法通常无法从模型的词汇表中生成有效的令牌，这使得它们在现实世界的应用程序中不切实际。在本文中，我们提出了正则化松弛，这是一种针对对抗性攻击的新技术，它通过利用正则化梯度来克服这些限制，并使用一个约束来计算，该约束鼓励优化的嵌入保持接近有效的令牌表示。这使得持续优化能够直接从模型的词汇表中生成离散的令牌，同时保持攻击的有效性。与最先进的基于贪婪坐标梯度的方法相比，我们的方法实现了两个数量级的速度提高。它在运行时间和效率方面明显优于其他最新方法，同时在大多数测试模型和数据集中始终实现更高的攻击成功率。至关重要的是，我们的方法直接从模型的词汇表中生成有效的标记，克服了以前连续优化方法的一个重要限制。我们通过在四个不同数据集的五个最先进的llm上进行广泛的实验来证明我们的攻击的有效性。我们的实现可以在：https://github.com/sj21j/Regularized_Relaxation上公开获得。

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引用次数: 0

Large language model assisted evolutionary neural architecture search with population knowledge base enhancement 基于种群知识库增强的大语言模型辅助进化神经结构搜索

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-01-12 DOI: 10.1016/j.ins.2026.123110

Weilin Fang , Yu Xue , Lilian Yuan , Mohammad Kamrul Hasan , Khursheed Aurangzeb

In the task of evolutionary neural architecture search (ENAS), computational efficiency and resource consumption remain significant bottlenecks. To mitigate these challenges, traditional methods typically employ neural network-based surrogate models to predict the performance of candidate architectures. However, the design and parameter tuning of these surrogate models are heavily reliant on expert knowledge and experience. This paper introduces a novel approach based on large language models (LLMs) to replace conventional machine learning surrogate models, thereby reducing the burden on experts. Although LLMs exhibit powerful zero-shot capabilities, they often lack the domain-specific expertise required for tasks such as neural network architecture evaluation. To overcome this limitation, we propose an LLM-assisted ENAS framework, enhanced by a population knowledge base (PKB). We integrate the LLM as both regression-based and classification-based surrogate models and conduct experiments on NAS-Bench-101, NAS-Bench-201, and the DARTS search space. Our experimental results demonstrate that, with PKB assistance, LLMs can effectively replace traditional surrogate models in evaluating the fitness of neural network architectures. The code is publicly available at: https://github.com/baigeixiaowang/LLM-ENAS-PKB.

在进化神经结构搜索（ENAS）任务中，计算效率和资源消耗仍然是显著的瓶颈。为了缓解这些挑战，传统方法通常使用基于神经网络的代理模型来预测候选架构的性能。然而，这些代理模型的设计和参数调优严重依赖于专家知识和经验。本文介绍了一种基于大型语言模型（llm）的新方法来取代传统的机器学习代理模型，从而减轻了专家的负担。尽管法学硕士表现出强大的零射击能力，但他们往往缺乏神经网络架构评估等任务所需的特定领域专业知识。为了克服这一限制，我们提出了一个由人口知识库（PKB）增强的llm辅助ENAS框架。我们将LLM整合为基于回归和基于分类的代理模型，并在NAS-Bench-101、NAS-Bench-201和DARTS搜索空间上进行了实验。我们的实验结果表明，在PKB的帮助下，llm可以有效地取代传统的代理模型来评估神经网络架构的适应度。该代码可在https://github.com/baigeixiaowang/LLM-ENAS-PKB公开获取。

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引用次数: 0

Integrating third-party logistics (3PL), forecast accuracy and emission management in triadic supply chains − a large language model-based approach 整合第三方物流（3PL），预测准确性和排放管理在三元供应链-一个大型语言模型为基础的方法

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-01-10 DOI: 10.1016/j.ins.2026.123084

Mariusz Kmiecik

This article presents an innovative and sustainable approach to enhancing triadic collaboration in supply chains by integrating forecast accuracy and transport emission management with the support of Large Language Models (LLMs). The study analyzed operational, forecasting, and environmental data from 22 triads managed by a single 3PL provider over a three-month period. The Gemini model was applied to detect anomalies, generate strategic recommendations, and support SQL-based data aggregation, enabling a holistic assessment of triadic structures. The results demonstrate that closed and concentred triads are associated with higher forecast accuracy, while forecast quality alone does not directly determine emission efficiency. The LLM successfully identified hidden inefficiencies and suggested structural transformations, such as shifting from derived to concentred or from open to closed triads, which were positively validated by an expert panel. The findings are interpreted through Resource-Based View, Dynamic Capabilities, and Network Governance, highlighting that LLMs function not only as analytical tools but also as integrators of resources and coordination mechanisms. The study contributes to theory by bridging forecasting, sustainability, and governance perspectives, and to practice by offering actionable guidelines for logistics managers. While limited by its single-case scope and the absence of financial data, the research provides a replicable methodological framework and opens avenues for applying LLMs in managing both operational performance and sustainability in supply chains.

本文提出了一种创新和可持续的方法，通过将预测准确性和运输排放管理与大型语言模型（llm）的支持相结合，加强供应链中的三方合作。该研究分析了一家第三方物流供应商在三个月内管理的22个三方的运营、预测和环境数据。Gemini模型用于检测异常，生成战略建议，并支持基于sql的数据聚合，从而实现对三元结构的整体评估。结果表明，封闭和集中的三联线具有较高的预报精度，而预报质量本身并不能直接决定排放效率。LLM成功地发现了隐藏的低效率，并提出了结构转型建议，例如从派生到集中或从开放到封闭三元组的转变，这些建议得到了专家小组的积极验证。研究结果通过资源基础观、动态能力和网络治理来解释，强调法学硕士不仅是分析工具，而且是资源和协调机制的整合者。该研究通过衔接预测、可持续性和治理视角来促进理论，并通过为物流管理人员提供可操作的指导方针来促进实践。虽然受到单一案例范围和缺乏财务数据的限制，但该研究提供了一个可复制的方法框架，并为应用法学硕士管理供应链的运营绩效和可持续性开辟了道路。

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引用次数: 0

HyDaST: Mortality risk prediction via EHR-hypergraph and dual-scale temporal pattern extraction HyDaST：基于ehr超图和双尺度时间模式提取的死亡率风险预测

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-01-10 DOI: 10.1016/j.ins.2026.123093

Wenxiang Li , K.L. Eddie Law

Mortality risk prediction from electronic health records (EHRs) is essential for precision medicine, enabling early identification of high-risk patients and optimized resource allocation. Existing methods often struggle to capture complex event interactions and balance short-term dynamics with long-term dependencies in temporal modeling, leading to reduced accuracy. To address these challenges, we propose HyDaST, a novel model that integrates hypergraph structures with dual-scale temporal modeling. At the code level, large language models (e.g., GPT-4o) and Sentence-BERT provide semantically enriched embeddings of medical concepts. At the visit level, a heterogeneous hypergraph captures high-order interactions across diagnoses, procedures, and medications. At the patient level, temporal convolutional networks model acute short-term changes, while a Transformer captures chronic long-term dependencies, jointly providing a comprehensive representation of disease progression. Extensive experiments on MIMIC-III and MIMIC-IV demonstrate that HyDaST significantly outperforms state-of-the-art methods in mortality risk prediction. The results highlight the effectiveness of HyDaST in capturing complex interactions and dual-scale temporal dependencies. This confirms that our novel and reliable model offers high accuracy for EHR-driven mortality risk prediction.

基于电子健康记录（EHRs）的死亡风险预测对于精准医疗至关重要，它能够早期识别高风险患者并优化资源分配。现有的方法常常难以捕获复杂的事件交互，并在时间建模中平衡短期动态与长期依赖关系，从而导致准确性降低。为了解决这些挑战，我们提出了HyDaST，一个将超图结构与双尺度时间建模相结合的新模型。在代码层，大型语言模型（例如，gpt - 40）和Sentence-BERT提供了医学概念的语义丰富的嵌入。在访问级别，异构超图捕获诊断、程序和药物之间的高阶相互作用。在患者层面，时间卷积网络模拟急性短期变化，而Transformer捕获慢性长期依赖性，共同提供疾病进展的全面表示。在MIMIC-III和MIMIC-IV上进行的大量实验表明，HyDaST在死亡率风险预测方面明显优于最先进的方法。结果突出了HyDaST在捕获复杂相互作用和双尺度时间依赖性方面的有效性。这证实了我们新颖可靠的模型为ehr驱动的死亡率风险预测提供了很高的准确性。

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引用次数: 0

Short complexity of ordinal pattern positioned slope measure for short-length data analysis 排序模式定位坡度测量短长度数据分析的短复杂度

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-01-10 DOI: 10.1016/j.ins.2026.123095

Jean Sire Armand Eyebe Fouda

The complexity of ordinal pattern positioned slopes (COPPS) is efficient for measuring time series complexity. Given the short data-length constraint in some experiments, it fails to evaluate the complexity of the system under investigation, hence requiring some improvement for effective application. This paper presents the short COPPS (sCOPPS) as a new complexity measure for the analysis of short-length time series. The method takes advantage of the multi-lag approach already used in the COPPS algorithm to extend the data length. The patterns obtained from the set of time lags are combined into a single set, allowing us to consider shorter data sequences. A relationship is established between data length, maximum time lag, embedding dimension, and network depth. The modified algorithm is successfully applied to discriminate short-length stochastic and deterministic data generated from the logistic and sine circle maps. Matlab built-in audio files are also well identified as stochastic data and discriminated among them with high resolution. The above high separation power is confirmed in the classification of ECG beats to detect arrhythmia. A comparison with COPPS shows a better performance of sCOPPS for the classification of time series, offering the possibility to improve the resolution power with increasing network depth.

排序模式定位斜率（COPPS）的复杂度是测量时间序列复杂度的有效方法。由于某些实验中数据长度约束较短，无法评估所研究系统的复杂性，因此需要进行一些改进才能有效应用。本文提出了短COPPS （sCOPPS）作为分析短时间序列的一种新的复杂度度量。该方法利用了COPPS算法中已经使用的多滞后方法来扩展数据长度。从一组时间滞后中获得的模式被组合成一个单一的集合，允许我们考虑更短的数据序列。建立了数据长度、最大时延、嵌入维数和网络深度之间的关系。将改进后的算法成功地应用于判别由logistic和正弦圆映射生成的短长度随机和确定性数据。Matlab内置音频文件也可以很好地识别为随机数据，并以高分辨率进行区分。上述高分离功率在心电搏动分类检测心律失常中得到证实。与COPPS的比较表明，sCOPPS对时间序列的分类性能更好，随着网络深度的增加，可以提高分辨率。

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引用次数: 0

Heuristic-informed mixture of experts for link prediction in multilayer networks 多层网络中启发式信息混合专家的链路预测

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-01-10 DOI: 10.1016/j.ins.2026.123106

Lucio La Cava , Domenico Mandaglio , Lorenzo Zangari , Andrea Tagarelli

Link prediction algorithms for multilayer networks are in principle required to effectively account for the entire layered structure while capturing the unique contexts offered by each layer. However, many existing approaches excel at predicting specific links in certain layers but struggle with others, as they fail to effectively leverage the diverse information encoded across different network layers. In this paper, we present MoE-ML-LP, the first Mixture-of-Experts (MoE) framework specifically designed for multilayer link prediction. Building on top of multilayer heuristics for link prediction, MoE-ML-LP synthesizes the decisions taken by diverse experts, resulting in significantly enhanced predictive capabilities. Our extensive experimental evaluation on real-world and synthetic networks demonstrates that MoE-ML-LP consistently outperforms several baselines and competing methods, achieving remarkable improvements of +60% in Mean Reciprocal Rank, +82% in Hits@1, +55% in Hits@5, and +41% in Hits@10. Furthermore, MoE-ML-LP features a modular architecture that enables the seamless integration of newly developed experts without necessitating the re-training of the entire framework, fostering efficiency and scalability to new experts, and paving the way for future advancements in link prediction.

原则上，多层网络的链路预测算法需要有效地考虑整个分层结构，同时捕获每层提供的独特上下文。然而，许多现有的方法在预测某些层中的特定链接方面表现出色，但在其他层中却表现不佳，因为它们无法有效地利用跨不同网络层编码的各种信息。在本文中，我们提出了MoE- ml - lp，这是第一个专门为多层链路预测设计的专家混合（MoE）框架。在多层启发式链接预测的基础上，MoE-ML-LP综合了不同专家的决策，从而显著增强了预测能力。我们在现实世界和合成网络上的广泛实验评估表明，MoE-ML-LP始终优于几种基线和竞争方法，在平均互惠排名上实现了+60%的显着改进，在Hits@1上实现了+82%，在Hits@5上实现了+55%，在Hits@10上实现了+41%。此外，MoE-ML-LP具有模块化架构，可以无缝集成新开发的专家，而无需重新培训整个框架，促进新专家的效率和可扩展性，并为链接预测的未来发展铺平道路。

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引用次数: 0

Sparse knowledge guided multiobjective multimodal optimization for identification of personalized critical biomarkers in cancer 稀疏知识指导下的多目标多模态优化用于癌症个性化关键生物标志物的鉴定

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-01-10 DOI: 10.1016/j.ins.2026.123073

Guo Wei-Feng , Sun Zening , Zhao Mengtong , Yue Cai-Tong , Cheng Han

It is challenging to identify personalized critical biomarkers (PCBs) from high-throughput omics data of individual cancer patients. While evolutionary computation has shown promise in discovering PCBs via multi-objective (i.e., minimizing PCB count, maximizing early warning scores) and multimodal (i.e., multiple effective PCB sets) optimization, current methods fail to leverage the sparsity of PCB problems(i.e., fewer efficient PCBs)—limiting their search ability in high-dimensional data. To tackle this challenge, we introduce TSSKEA, a two-stage evolutionary algorithm guided by sparse knowledge that integrates sparse knowledge from molecular interaction networks and historical/current non-dominated solutions into multi-objective multimodal optimization. It uses a variable striped sparse population sampling (VSSPS) strategy and two-stage knowledge guidance to handle large-scale sparsity. Validated across three TCGA cancer datasets—specifically BRCA, LUSC, and LUAD—TSSKEA demonstrates superior performance compared to alternative approaches by delivering the highest early warning signal score in detecting personalized node and edge biomarkers. Compared with the existing representative method MMPDNB-RBM, on the three cancer datasets the early warning scores of PDNB were increased by 2.7 times, 1.4 times, and 11.1 times, while those of PDENB were enhanced by 1.5 times, 0.5 times, and 1.8 times, respectively. Additionally, TSSKEA exhibits considerable advantages compared to other state-of-the-art approaches with regard to algorithmic convergence, diversity and multimodal characteristics.

从个体癌症患者的高通量组学数据中识别个性化的关键生物标志物（PCBs）具有挑战性。虽然进化计算在通过多目标（即最小化PCB数量，最大化早期预警分数）和多模态（即多个有效PCB集）优化发现PCB方面显示出了希望，但目前的方法未能利用PCB问题的稀疏性(即。比如效率更低的pcb)——限制了它们在高维数据中的搜索能力。为了解决这一挑战，我们引入了一种基于稀疏知识的两阶段进化算法TSSKEA，该算法将来自分子相互作用网络的稀疏知识和历史/当前非支配解集成到多目标多模态优化中。该算法采用可变条纹稀疏总体抽样（vsps）策略和两阶段知识指导来处理大规模稀疏性。通过三个TCGA癌症数据集（特别是BRCA， LUSC和luad）的验证，tsskea通过在检测个性化节点和边缘生物标志物方面提供最高的早期预警信号得分，与其他方法相比表现出卓越的性能。与现有代表性方法MMPDNB-RBM相比，PDNB在三个癌症数据集上的预警评分分别提高了2.7倍、1.4倍和11.1倍，PDENB的预警评分分别提高了1.5倍、0.5倍和1.8倍。此外，与其他最先进的方法相比，TSSKEA在算法收敛、多样性和多模态特征方面表现出相当大的优势。

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引用次数: 0

A novel reference solution selection strategy based on auxiliary space for large-scale multi-objective optimization 一种基于辅助空间的大规模多目标优化参考解选择策略

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-01-09 DOI: 10.1016/j.ins.2026.123101

Meiji Cui , Wenping Wang , Shuwei Zhu , Wei Fang , Mengchu Tian

Problem-transformation-based methods have been acknowledged as effective approaches for addressing Large-Scale Multi-objective Optimization Problems (LSMOPs), as they enable the conversion of the original high-dimensional search space into a relatively low-dimensional one. Nevertheless, such methods often exhibit an extremely rapid convergence rate, which is concomitantly accompanied by a significant loss of diversity. Within this context, the selection of reference solutions—upon which the problem transformation is constructed—plays a pivotal role in influencing population diversity. To address these limitations, this paper proposes a novel Reference Solution Selection strategy based on Auxiliary Spaces (RSAS) designed to preserve population diversity. Specifically, orthogonal bases are constructed utilizing extreme feasible solutions positioned on the coordinate axes, with each basis spanning a distinct subspace. The ensemble of subspaces generated by multiple such orthogonal bases is defined as auxiliary spaces. These auxiliary spaces, characterized by their relatively uniform distribution throughout the original decision space, facilitate the algorithm in generating a reference solution set with substantially enhanced diversity. Notably, RSAS can be seamlessly integrated into any problem-transformation-based framework that incorporates a reference selection mechanism, thereby improving their overall performance. To demonstrate the efficacy of RSAS, comprehensive experimental investigations are conducted on benchmark suites with dimensions ranging from 500 to 5000. The experimental results conclusively validate the superiority of RSAS in comparison to other state-of-the-art algorithms on most instances.

基于问题转换的方法被认为是解决大规模多目标优化问题（LSMOPs）的有效方法，因为它们能够将原始的高维搜索空间转换为相对低维的搜索空间。然而，这种方法往往表现出极快的收敛速度，同时伴随着多样性的大量丧失。在这种背景下，参考解决方案的选择——问题转换的基础——在影响人口多样性方面起着关键作用。为了解决这些问题，本文提出了一种基于辅助空间（RSAS）的参考方案选择策略，以保护种群多样性。具体来说，正交基是利用位于坐标轴上的极值可行解构造的，每个基跨越一个不同的子空间。由多个这样的正交基生成的子空间的集合被定义为辅助空间。这些辅助空间在原始决策空间中的分布相对均匀，有助于算法生成具有显著增强多样性的参考解集。值得注意的是，RSAS可以无缝地集成到任何包含参考选择机制的基于问题转换的框架中，从而提高它们的整体性能。为了验证RSAS的有效性，我们在500 ~ 5000个维度的基准套件上进行了全面的实验研究。实验结果最终验证了RSAS在大多数情况下与其他最先进算法相比的优越性。

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引用次数: 0

MRFP-TDG: A detection transformer with hybrid encoder and position-aware decoder for photovoltaic cell defect detection MRFP-TDG：一种用于光伏电池缺陷检测的混合编码器和位置感知解码器检测变压器

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-01-09 DOI: 10.1016/j.ins.2026.123092

Wenfu Huang , Jie Zhao , Ran Wei , Jingyuan Yang , Jing Ruan , Li Fan , Xinwen Zhou

Defects arising during photovoltaic (PV) cell manufacturing critically compromise performance and operational safety. Existing computer vision-based detection methods struggle with defects characterized by small scales, dense distributions, and background confusion. To address this, we propose MRFP-TDG, a Detection Transformer-based detector featuring an optimized hybrid encoder for enhanced fine-grained feature extraction and background suppression. Specifically, our Multi-scale Receptive Field Projection (MRFP) module leverages channel-split depthwise separable convolutions with multi-scale kernels to project backbone features into tokens while preserving spatial relationships, significantly improving small-defect detection. The Token-Driven Gathering (TDG) module further integrates spatial attention to fuse multi-scale tokens, compensating for tokenization-induced spatial information loss while suppressing background noise. Furthermore, a relational position embedding mechanism in the decoder models positional relationships of bounding boxes across layers, accelerating convergence during iterative refinement. Evaluated on the public PVEL-AD dataset, MRFP-TDG achieves 95.1% mAP@50 in nine-category defect detection, outperforming state-of-the-art PV defect detectors in both accuracy and efficiency. Specifically, it surpasses the baseline model by 1.0% mAP while requiring only 59.6% of the computational cost compared with the best-performing SOTA method.

光伏（PV）电池制造过程中产生的缺陷严重影响了性能和操作安全。现有的基于计算机视觉的缺陷检测方法存在着尺度小、分布密集、背景混乱等问题。为了解决这个问题，我们提出了MRFP-TDG，这是一种基于检测变压器的检测器，具有优化的混合编码器，用于增强细粒度特征提取和背景抑制。具体来说，我们的多尺度感受野投影（MRFP）模块利用多尺度核的通道分裂深度可分离卷积，在保留空间关系的同时将骨干特征投影到token中，显著提高了小缺陷检测。Token-Driven Gathering （TDG）模块进一步集成空间注意力来融合多尺度token，补偿token化引起的空间信息丢失，同时抑制背景噪声。此外，解码器中的关系位置嵌入机制对跨层边界框的位置关系进行建模，加快了迭代细化过程中的收敛速度。在公开的PVEL-AD数据集上进行评估，MRFP-TDG在9类缺陷检测中达到95.1% mAP@50，在准确性和效率方面都优于最先进的光伏缺陷检测器。具体来说，它比基准模型高出1.0% mAP，而与性能最好的SOTA方法相比，它只需要59.6%的计算成本。

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引用次数: 0

FC-KAN: Function combinations in Kolmogorov-Arnold networks FC-KAN: Kolmogorov-Arnold网络中的函数组合

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-01-09 DOI: 10.1016/j.ins.2026.123103

Hoang-Thang Ta , Duy-Quy Thai , Abu Bakar Siddiqur Rahman , Grigori Sidorov , Alexander Gelbukh

In this paper, we introduce FC-KAN, a Kolmogorov-Arnold Network (KAN) that leverages combinations of popular mathematical functions such as B-splines, wavelets, and radial basis functions on low-dimensional data through element-wise operations. We explore several methods for combining the outputs of these functions, including sum, element-wise product, the addition of sum and element-wise product, representations of quadratic and cubic functions, concatenation, linear transformation of the concatenated output, and others. In our experiments, we compare FC-KAN with a multi-layer perceptron network (MLP) and other existing KANs, such as BSRBF-KAN, EfficientKAN, FastKAN, and FasterKAN, on the MNIST and Fashion-MNIST datasets. Two variants of FC-KAN, which use a combination of outputs from B-splines and Difference of Gaussians (DoG) and from B-splines and linear transformations in the form of a quadratic function, outperformed all other models on the average of 5 independent training runs. However, FC-KAN still has limitations, including challenges with parameter scalability and efficiency, as well as limited capability compared to CNNs when handling multi-channel datasets such as CIFAR-10 and CIFAR-100. We expect that FC-KAN can leverage function combinations to design future KANs. Our repository is publicly available at: https://github.com/hoangthangta/FC_KAN.

在本文中，我们介绍了FC-KAN，一种Kolmogorov-Arnold网络（KAN），它利用流行的数学函数（如b样条、小波和径向基函数）的组合，通过元素操作处理低维数据。我们探索了几种组合这些函数输出的方法，包括求和、元素积、求和和元素积的加法、二次函数和三次函数的表示、串联、串联输出的线性变换等。在我们的实验中，我们将FC-KAN与多层感知器网络（MLP）和其他现有的kan（如BSRBF-KAN、EfficientKAN、FastKAN和FasterKAN）在MNIST和Fashion-MNIST数据集上进行了比较。FC-KAN的两种变体使用了b样条和高斯差分（DoG）的输出组合，以及b样条和二次函数形式的线性变换的输出组合，在5次独立训练运行的平均表现上优于所有其他模型。然而，FC-KAN仍然存在局限性，包括参数可扩展性和效率方面的挑战，以及在处理多通道数据集（如CIFAR-10和CIFAR-100）时与cnn相比的有限能力。我们期望FC-KAN能够利用功能组合来设计未来的kan。我们的存储库可以在：https://github.com/hoangthangta/FC_KAN上公开获取。

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引用次数: 0