发布求助
文献互助 智能选刊 最新文献

Neurocomputing最新文献

筛选
英文 中文
ML-PINN: A memory-efficient physics-informed Mamba-LSTM network for fast and accurate PDE solving ML-PINN：一个内存高效的物理信息Mamba-LSTM网络，用于快速准确的PDE求解
IF 6.5 2区 计算机科学
Neurocomputing Pub Date : 2025-09-01 DOI: 10.1016/j.neucom.2025.131446
YiMing Gao, Bing Wang, Jingyi Lu, Zhou Tian
{"title":"ML-PINN: A memory-efficient physics-informed Mamba-LSTM network for fast and accurate PDE solving","authors":"YiMing Gao,&nbsp;Bing Wang,&nbsp;Jingyi Lu,&nbsp;Zhou Tian","doi":"10.1016/j.neucom.2025.131446","DOIUrl":"10.1016/j.neucom.2025.131446","url":null,"abstract":"<div><div>Physics-informed neural networks (PINNs) have emerged as a novel PDE solver and demonstrated significant potential. However, most existing models struggle to achieve satisfactory performance in both solution accuracy and computational efficiency, particularly in time-dependent modeling scenarios where extended input sequences are typically required to maintain precision. This inevitably leads to excessive GPU memory allocation and prolonged training durations. In this work, we develop a novel architecture integrating Mamba with Long Short-Term Memory networks (LSTM), hereby referred to as the Physics-informed Mamba-LSTM Neural Network (ML-PINN). By using shorter input sequences, ML-PINN is able to maintain high accuracy while achieving up to a 36 % reduction in GPU memory consumption and a 48 % decrease in training time compared to methods such as PINNMamba which employs state-space models and PINNsFormer (a Transformer-based PINN framework).</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"655 ","pages":"Article 131446"},"PeriodicalIF":6.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144997263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
All-in-one weather image restoration: Multi-modal attention and Bi-Directional Mamba Fusion 一体化天气图像恢复：多模态关注和双向曼巴融合
IF 6.5 2区 计算机科学
Neurocomputing Pub Date : 2025-08-31 DOI: 10.1016/j.neucom.2025.131431
Mingzhang Guo, Yongping Xie
{"title":"All-in-one weather image restoration: Multi-modal attention and Bi-Directional Mamba Fusion","authors":"Mingzhang Guo,&nbsp;Yongping Xie","doi":"10.1016/j.neucom.2025.131431","DOIUrl":"10.1016/j.neucom.2025.131431","url":null,"abstract":"<div><div>To address the coexistence of scattering noise and occlusion-induced structural degradation in images captured under adverse weather conditions such as rain and snow, we propose a novel Mamba-Attention Fusion Block (MAF-Block). This module is the first to integrate frequency-domain and edge-guided attention, driven by multi-modal priors, with bidirectional Mamba-based efficient long-range dependency modeling within a single Transformer block. This design enables a unified framework for dynamic denoising and structural reconstruction. In practice, the MAF-Block is incorporated into a multi-scale encoder-decoder architecture to enhance the model’s feature representation and restoration capabilities. Experimental results demonstrate that our method achieves notable performance improvements on the Outdoor Rain and Snow100K-S (L) datasets, with PSNR gains of 0.84 dB and 0.66 dB (0.14 dB) respectively, surpassing the current state-of-the-art methods for rain and snow image restoration.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"655 ","pages":"Article 131431"},"PeriodicalIF":6.5,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
SMANet: Sequence-enhanced multi-head attention network for robust neural semantic learning in noisy computational environments SMANet：序列增强多头注意网络，用于嘈杂计算环境下的鲁棒神经语义学习
IF 6.5 2区 计算机科学
Neurocomputing Pub Date : 2025-08-30 DOI: 10.1016/j.neucom.2025.131347
Jia Guo , Xinyu Jia , Jinqi Zhu , Xiang Li , Yang Liu , Weijia Feng , Wanli Xue
{"title":"SMANet: Sequence-enhanced multi-head attention network for robust neural semantic learning in noisy computational environments","authors":"Jia Guo ,&nbsp;Xinyu Jia ,&nbsp;Jinqi Zhu ,&nbsp;Xiang Li ,&nbsp;Yang Liu ,&nbsp;Weijia Feng ,&nbsp;Wanli Xue","doi":"10.1016/j.neucom.2025.131347","DOIUrl":"10.1016/j.neucom.2025.131347","url":null,"abstract":"<div><div>Traditional communication systems often fail to efficiently transmit meaningful information in noisy and dynamic environments, prompting the adoption of neural network architectures in semantic communication to prioritize semantic content over raw data. Existing neural models face persistent challenges in mitigating high noise interference, capturing long-range dependencies in sequences, and preserving semantic fidelity under varying conditions. This paper proposes SMANet, sequence-enhanced multi-head attention network for robust neural semantic learning in noisy computational environments. SMANet integrates multi-head attention mechanisms with a Dilated Normalization Block (DNB)—a specialized neural module for extracting local temporal features and global semantic representations—to enhance sequence processing capabilities, alleviate gradient vanishing/explosion issues during training, and improve network stability. At the transmitter, a neural semantic encoder employs dilated convolutions and normalization for robust feature extraction, paired with a channel encoder to achieve noise resilience; at the receiver, neural decoders precisely reconstruct semantics, facilitating applications in machine learning-driven cognitive systems. Experimental evaluations on AWGN and Rayleigh fading channels demonstrate SMANet’s superior performance, outperforming DeepSC by 23 % in BLEU scores, achieving a sentence similarity of 0.91 at SNR=18 dB, and maintaining 85 % semantic fidelity at SNR <span><math><mo>&lt;</mo></math></span> 6 dB, highlighting its potential for neurocomputing in resource-constrained networks.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"655 ","pages":"Article 131347"},"PeriodicalIF":6.5,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
IMSDO: Deep metric learning with incremental margin and standard deviation optimization IMSDO：具有增量边际和标准差优化的深度度量学习
IF 6.5 2区 计算机科学
Neurocomputing Pub Date : 2025-08-30 DOI: 10.1016/j.neucom.2025.131376
Jeremy Winston , Dae-Ki Kang
{"title":"IMSDO: Deep metric learning with incremental margin and standard deviation optimization","authors":"Jeremy Winston ,&nbsp;Dae-Ki Kang","doi":"10.1016/j.neucom.2025.131376","DOIUrl":"10.1016/j.neucom.2025.131376","url":null,"abstract":"<div><div>This study investigates two research questions in deep metric learning, each motivated by practical intuition and theoretical grounding. First, we hypothesize that systematically increasing the margin during training can guide the model to learn from easier to harder tasks, leading to better convergence and generalization. This approach is similar to warm-up learning rate and curriculum learning strategies. Second, we propose that controlling the standard deviation of feature maps through a new loss function can suppress noise and produce more consistent and discriminative representations. To validate these hypotheses, we introduce IMSDO, a method that: (1) gradually increases the margin using an increment function during training to implement a structured learning process; and (2) minimizes the standard deviation of feature maps to enhance their quality. Despite its simplicity, IMSDO significantly improves the performance of the triplet loss and outperforms several strong baselines across standard benchmarks. These results highlight IMSDO’s practical value and open new directions for research in metric learning.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"655 ","pages":"Article 131376"},"PeriodicalIF":6.5,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144988937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Vertical partitioned high-dimensional data publishing with differential privacy principal component analysis 基于差分隐私主成分分析的垂直分区高维数据发布
IF 6.5 2区 计算机科学
Neurocomputing Pub Date : 2025-08-29 DOI: 10.1016/j.neucom.2025.131415
Junming Zhang , Jingru Wang , Shigong Long , Guangyuan Liu , Lun Wang , Di Yang
{"title":"Vertical partitioned high-dimensional data publishing with differential privacy principal component analysis","authors":"Junming Zhang ,&nbsp;Jingru Wang ,&nbsp;Shigong Long ,&nbsp;Guangyuan Liu ,&nbsp;Lun Wang ,&nbsp;Di Yang","doi":"10.1016/j.neucom.2025.131415","DOIUrl":"10.1016/j.neucom.2025.131415","url":null,"abstract":"<div><div>In the era of information explosion, the massive and high-dimensional nature of data pose severe challenges to data analysis and privacy protection, particularly when multiple institutions hold different attributes of the same object. To address this challenge, this study proposes the integration of principal component analysis (PCA) and differential privacy (DP) to achieve data dimensionality reduction and enhanced privacy protection. Building on this, we have designed two differential privacy-based multi-party vertically partitioned data release methods: principal component analysis differential privacy algorithm (PCA-DP) and covariance matrix principal component analysis differential privacy algorithm (CMPCA-DP). To address the characteristics of high-dimensional data, the PCA-DP algorithm ensures privacy by introducing noise into the low-dimensional approximation of the dataset. In contrast, the CMPCA-DP algorithm employs a novel approach that involves the addition of noise to the diagonal entries of the covariance matrix. This strategy significantly enhances the efficiency of data processing and the utility of the dataset, while concurrently maintaining data privacy. Through theoretical and experimental analyses, we find that the CMPCA-DP algorithm ensures data privacy protection while also offering higher efficiency and greater data utility.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"655 ","pages":"Article 131415"},"PeriodicalIF":6.5,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144988941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
USTNet: Ultrafast style transfer between infrared and visible images USTNet：红外和可见光图像之间的超快速风格转换
IF 6.5 2区 计算机科学
Neurocomputing Pub Date : 2025-08-29 DOI: 10.1016/j.neucom.2025.131419
Haoxiang Shi, Puchun Wang, Yanqi Wu, Bo Yang, Zhaozi Zu, Zhongjun Qu
{"title":"USTNet: Ultrafast style transfer between infrared and visible images","authors":"Haoxiang Shi,&nbsp;Puchun Wang,&nbsp;Yanqi Wu,&nbsp;Bo Yang,&nbsp;Zhaozi Zu,&nbsp;Zhongjun Qu","doi":"10.1016/j.neucom.2025.131419","DOIUrl":"10.1016/j.neucom.2025.131419","url":null,"abstract":"<div><div>Visible light and infrared images belong to different, modalities, which is not conductive to the image registration. To achieve better image registration and fusion, it is necessary to convert the modality of visible light images into infrared modality before image registration. The paper proposes a lightweight real-time framework named USTNet for generating infrared-like visible-light images, which can bridge the modality gap in visible and infrared domains. To accelerate the feature representation at early stage, a Channel Acceleration block is adopted; Besides, the Faster Transfer Module (FTM) and the Detail Connection Module (DCM) ensure the speed of semantic embedding and the high-quality reconstructed image, respectively. Moreover, by introducing Matting Laplacian constraint and dWCT transform, the regionally semantically consistent features as well as coherent regional styles across source and target domain are enforced in this work. The proposed USTNet is evaluated on both near-infrared and far-infrared datasets and compared against six state-of-the-art style transfer methods. Extensive experiments show that the proposed USTNet achieves superior qualitative and quantitative results with a 2x speedup over the state-of-the-art methods, making it more compatible for real-time cross-modal vision applications.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"655 ","pages":"Article 131419"},"PeriodicalIF":6.5,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144997264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Bi-directional generative retrieval-augmented diffusion models for document-level informative argument extraction 双向生成检索增强扩散模型用于文档级信息参数提取
IF 6.5 2区 计算机科学
Neurocomputing Pub Date : 2025-08-29 DOI: 10.1016/j.neucom.2025.131360
Lei Luo, Xuanzhi Chen, Liming Mao, Xinjie Yang, Yajing Xu, Jun Guo
{"title":"Bi-directional generative retrieval-augmented diffusion models for document-level informative argument extraction","authors":"Lei Luo,&nbsp;Xuanzhi Chen,&nbsp;Liming Mao,&nbsp;Xinjie Yang,&nbsp;Yajing Xu,&nbsp;Jun Guo","doi":"10.1016/j.neucom.2025.131360","DOIUrl":"10.1016/j.neucom.2025.131360","url":null,"abstract":"<div><div>Document-level Informative Argument Extraction (IAE) presents a significant challenge in the field of information extraction. This challenge stems from the necessity for implicit coreference reasoning and the linking of long-range dependencies between events within a document. Despite recent efforts to leverage generation-based document-level extraction to enhance cross-sentence inference capabilities and capture more interactions between different events, these methods often fall short in their generation quality due to difficulties in understanding the global context. Motivated by these observations and the high-quality generation results of recent diffusion models, we propose an effective model known as <strong>BGRD</strong> (<strong>B</strong>i-directional <strong>G</strong>enerative <strong>R</strong>etrieval-augmented <strong>D</strong>iffusion models) for document-level IAE. In BGRD, a text diffusion model is designed to generate high-quality target event sequences that mutually benefit the retrieval stage, leveraging previously generated events as a retrieval source. Firstly, a bi-directional retrieval mechanism is investigated to refine the denoising process, effectively exploring the knowledge from retrieved samples. This enhances the text diffusion model’s ability to capture the global context interconnecting the events. Secondly, retrieval-augmented cross-attention is employed between the retrieved samples and the target event sequences (random Gaussian noise during the inference phase) within the text diffusion model. Through this interaction, the quality of the retrieval source is improved by generating highly informative event sequences, which benefits the bi-directional retrieval stage. Extensive experiments on the publicly available argument extraction datasets demonstrate the superiority of our proposed BGRD model over existing approaches.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"655 ","pages":"Article 131360"},"PeriodicalIF":6.5,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144933320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Hermite polynomials facilitating on-line learning analysis of layered neural networks with arbitrary activation function 埃尔米特多项式促进了具有任意激活函数的分层神经网络的在线学习分析
IF 6.5 2区 计算机科学
Neurocomputing Pub Date : 2025-08-29 DOI: 10.1016/j.neucom.2025.131328
Otavio Citton , Frederieke Richert , Michael Biehl , Michiel Straat
{"title":"Hermite polynomials facilitating on-line learning analysis of layered neural networks with arbitrary activation function","authors":"Otavio Citton ,&nbsp;Frederieke Richert ,&nbsp;Michael Biehl ,&nbsp;Michiel Straat","doi":"10.1016/j.neucom.2025.131328","DOIUrl":"10.1016/j.neucom.2025.131328","url":null,"abstract":"<div><div>Following the standard statistical mechanics methods we analyze the training by online stochastic gradient descent of two-layer neural networks in a student–teacher scenario. We focus on understanding the role that different activations play, in particular mismatches between the student and the teacher, in these learning scenarios. By expanding the activation functions in the Hermite polynomial basis, we are able to effectively approximate the relevant integrals with much less computational effort than naive numerical integration. Moreover, we also extend the framework to study scenarios of concept drift and weight decay also with arbitrary activation functions. All these extensions comprise relevant advances in the field, allowing us to obtain analytical results for more realistic scenarios.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"655 ","pages":"Article 131328"},"PeriodicalIF":6.5,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Radial search-based graph clustering method 基于径向搜索的图聚类方法
IF 6.5 2区 计算机科学
Neurocomputing Pub Date : 2025-08-28 DOI: 10.1016/j.neucom.2025.131421
Yangyang Zhao , Feng Zhu , Junyi Guan , Xiongxiong He , Sheng Li
{"title":"Radial search-based graph clustering method","authors":"Yangyang Zhao ,&nbsp;Feng Zhu ,&nbsp;Junyi Guan ,&nbsp;Xiongxiong He ,&nbsp;Sheng Li","doi":"10.1016/j.neucom.2025.131421","DOIUrl":"10.1016/j.neucom.2025.131421","url":null,"abstract":"<div><div>Graph-based clustering methods represent data samples as nodes and their relationships as edges, which effectively capture the complex structures within similarity graphs. However, many graph-based clustering methods rely on traditional spectral clustering to partition the graph, which may overlook crucial local structural information and affect clustering accuracy. To address this, we propose a Radial Search-Based Graph Clustering (RSGC) method, which can detect clusters with arbitrary shapes and densities, even in complex scenarios such as high-dimensional or multi-peak distributions. We propose a Radial Search Allocation (RSA) method for initial partitioning of the similarity graph, which constructs well-structured single-peak sub-graphs by fully considering the local structure. Additionally, we propose a method for calculating sub-graph similarity based on the importance of cross-cluster edges in the similarity graph, and obtain the final clustering result by merging highly similar sub-graphs. Experimental validations on synthetic and real datasets demonstrate the effectiveness of the RSGC method.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"655 ","pages":"Article 131421"},"PeriodicalIF":6.5,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144933313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
MP-DRA: Multi-scale memory and adaptive pseudo-anomaly enhanced open-set anomaly detection MP-DRA：多尺度记忆和自适应伪异常增强开集异常检测
IF 6.5 2区 计算机科学
Neurocomputing Pub Date : 2025-08-28 DOI: 10.1016/j.neucom.2025.131427
Yunxue Shao , Fangdi Xu , Lingfeng Wang
{"title":"MP-DRA: Multi-scale memory and adaptive pseudo-anomaly enhanced open-set anomaly detection","authors":"Yunxue Shao ,&nbsp;Fangdi Xu ,&nbsp;Lingfeng Wang","doi":"10.1016/j.neucom.2025.131427","DOIUrl":"10.1016/j.neucom.2025.131427","url":null,"abstract":"<div><div>The efficacy of open-set anomaly detection is severely constrained by the scarcity of real anomalies. To tackle this, we argue for the critical importance of effectively learning from available anomalies and generating valuable pseudo-anomalies. We introduce MP-DRA, a novel framework featuring a multi-scale feature memory bank that is dynamically updated with representative samples during training. For anomaly scoring, our model adaptively queries the memory bank to retrieve robust and discriminative features. Critically, MP-DRA dynamically calibrates the difficulty and strategy of pseudo-anomaly synthesis after each epoch based on performance metrics, leading to a more efficient training paradigm. Extensive experiments on several real-world datasets validate that MP-DRA delivers state-of-the-art and stable performance on diverse anomaly detection tasks. Our code can be found at <span><span>https://github.com/Goolubo/MLR/tree/v4.0</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"655 ","pages":"Article 131427"},"PeriodicalIF":6.5,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144922233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信