Convolutional neural networks (CNNs) and visual Transformers have become the dominant approaches for hyperspectral image (HSI) classification due to their powerful feature representation capabilities. However, CNNs are limited in capturing long-range dependencies, while Transformers suffer from quadratic computational complexity when modeling spectral relationships in high-dimensional data. The selective state space model has linear complexity but still faces challenges of long-range decay and insufficient spatial-spectral modeling. To address these limitations, we propose a spatial-spectral mamba-convolution fusion network (SSMFMamba) for HSI classification. The proposed network effectively combines the advantages of both Mamba and CNN paradigms in global and local representation, respectively, enabling efficient spatial-spectral feature learning. A spatial-guided neighborhood scanning module (SGNSM) is further designed to reconstruct the spatial domain based on Euclidean distance to alleviate long-range decay. Then, SGNSM performs unidirectional scanning to effectively capture global dependencies and reduce redundancy. Meanwhile, a spatial-spectral convolutional module (SSCM) is designed to extract local structural information by combining 2-D and 3-D convolutions. To adaptively balance global-local information, a multiattention fusion module subsequently fuses the outputs from SGNSM and SSCM. Finally, a spatially enhanced spectral scanning module enhances spectral representations by performing spatial attention-guided fusion followed by bidirectional spectral scanning, enabling more effective spectral interaction modeling. To validate the effectiveness of SSMFMamba, experiments are conducted on three benchmark datasets, including Houston2013 (HS2013), WHU-Hi-LongKou (LK), and Pavia University (PU). The results indicate that SSMFMamba shows superiority over the state-of-the-art hyperspectral classification networks, which include CNNs, Transformers, and hybrid CNN-Transformer models.

Enhancing the detection capabilities of rotated objects in aerial imagery is a vital aspect of the burgeoning field of remote sensing technology. The objective is to identify and localize objects oriented in arbitrary directions within the image. In recent years, the capacity for rotated object detection has seen continuous improvement. However, existing methods largely employ traditional backbone networks, where static convolutions excel at extracting features from objects oriented at a specific angle. In contrast, most objects in aerial imagery are oriented in various directions. This poses a challenge for backbone networks to extract high-quality features from objects of different orientations. In response to the challenge above, we propose the Dynamic Rotational Convolution (DRC) module. By integrating it into the ResNet backbone network, we form the backbone network presented in this paper, DRC-ResNet. Within the proposed DRC module, rotation parameters are predicted by the Adaptive Routing Unit (ARU), employing a data-driven approach to adaptively rotate convolutional kernels to extract features from objects oriented in various directions within different images. Building upon this foundation, we introduce a conditional computation mechanism that enables convolutional kernels to more flexibly and efficiently adapt to the dramatic angular changes of objects within images. To better integrate key information within images after obtaining features rich in angular details, we propose the Multi-Order Spatial-Channel Aggregation Block (MOSCAB) module, which is aimed at enhancing the integration capacity of key information in images through selective focusing and global information aggregation. Meanwhile, considering the significant semantic gap between features at different levels during the feature pyramid fusion process, we propose a new multi-scale fusion network named AugFPN+. This network reduces the semantic gap between different levels before feature fusion, achieves more effective feature integration, and minimizes the spatial information loss of small objects to the greatest extent possible. Experiments conducted on popular benchmark datasets DOTA-V1.0 and HRSC2016 demonstrate that our proposed model has achieved mAP scores of 77.56% and 90.4%, respectively, significantly outperforming current rotated detection models.

Spatial-regularized spectral unmixing has achieved great progress and attracted widespread attention for addressing homogeneous regions with identical spectral characteristics. In this paper, a new hyperspectral unmixing algorithm with graph Laplacian regularization and local collaborative sparse regression is proposed, based on superpixel segmentation, namely GLCGSU. Considering mixed pixels in homogeneous areas have similar endmembers and abundances, we utilize superpixel image clustering (SLIC) to cluster similar pixels, leveraging boundary information for uniform area extraction. Spatial similarity is investigated via graph Laplacian regularization. Meanwhile, we apply local collaborative weighted sparse regression to achieve abundance matrix sparsity. Experimental results demonstrates the effectiveness of the proposed method both on simulated and real data, proving its superiority for hyperspectral unmixing.

Urban road extraction is important for the applications of urban planning and transportation. High-resolution image (HRI) has been one of the most popular data sources for extracting roads with high efficiency and low cost. However, roads in HRI are easily obscured by buildings, trees, and other landscapes, resulting in discontinuity of the extracted roads. While current road extraction techniques by multimodal data fusion have shown improved results compared to single-modal methods by incorporating additional information, most existing fusion methods fail to fully exploit the features from different modalities and consider prior knowledge of roads. To address the above problems, a dual encoder-based cross-modal complementary fusion network (DECCFNet) is proposed in this article. The proposed network takes full advantage of the rich feature information contained in HRI and the immunity of LiDAR data to the influence of shadows. By effectively fusing the complementary information from HRI and LiDAR data, DECCFNet, respectively, achieved an improvement by at least 2.94% and 2.8% in IOU compared to those only using a single data modality on the two datasets. The proposed DECCFNet mainly contains two modules: 1) cross-modal feature fusion (CMFF) module: in the dual encoder part, CMFF is employed to fuse the deep features of different modalities from the channel and spatial dimension, while a multiscale fusion strategy is utilized to extract the contextual information; 2) multi-direction strip convolution (MDSC) module: since roads have the characteristics of narrowness and continuity, adopting classical convolution kernels directly on road features may introduce irrelevant pixels into the computation, blurring the extraction results. To mitigate this issue, MDSC is applied to strip the convolution of road features from multiple directions based on square convolution and make the network focus more on the specific road features. By comparing several deep-learning multimodal data fusion networks in the two road datasets, the proposed network exhibits the best road extraction results.

The use of multisource remote sensing data to obtain urban impervious surface has become a popular research topic. Multisource remote sensing data fusion techniques can provide object interpretation with a higher accuracy. However, most decision-level fusion methods make insufficient use of the complementary information and degree of association between similar object data. To fill this gap, in this article, we propose a dual-view learning fusion classification method (DvLF) based on multiview learning. First, DvLF uses cotraining algorithm to combine multiple data sources for accurate classification, extracting easy-to-classify area while separating difficult-to-classify regions for further analysis. Second, a canonical correlation analysis method is adopted to mine the degree of association of similar object data for constructing a subspace projection field of each object sample. The data in the difficult-to-classify regions are classified in the projection field of each object, and then the results of each classification are fused by voting. Finally, the classification results of the two regions are combined into the classification results of the whole image to achieve impervious surface mapping. The proposed method is applied to the dual-sensor (high-resolution image and LiDAR) Buffalo dataset and the dual-sensor (RGB and multispectral LiDAR) Houston dataset. The experimental results show that our method achieved a significant improvement in classification accuracy compared to other methods. The overall classification accuracy of this new DvLF fusion method on the Buffalo and Houston datasets is 83.35% and 88.84%, respectively, leading to accurate high-resolution impervious surface mapping.

Most transfer learning methods have the problem of insufficient distance constraint that plays a very important role in improving image classification performance. Therefore, this letter proposes a new method called joint distance transfer metric learning (JDTML) for remote-sensing image classification. First, the JDTML method establishes the constraints of marginal distribution, intraclass distance, interclass distance, and intraclass divergence based on the maximum mean discrepancy. Second, the objective function is to combine these constraints. So, JDTML can not only reduce the differences between the two domains on the whole and in each class, but also gather the samples of the same class and expand the distance from each class to the rest classes. By solving the objective function, the transfer metric matrix is obtained. Finally, the source and target domains are transferred to a common subspace for dimension reduction. The data after dimension reduction is used for classification, and the accuracy of classification is improved by iteration. The experimental results show that JDTML is more accurate than other methods compared.

Deep learning has achieved impressive results on hyperspectral images (HSIs) classification. Among them, both convolutional neural networks (CNNs) and graph neural networks (GNNs) have great potential for hyperspectral image classification. Supervised CNNs can efficiently extract hierarchical spatial-spectral features of hyperspectral images, but these methods face the problem of high time complexity as the number of network layers increases. Semi-supervised GNNs can rapidly capture the structural information of HSIs, while they cannot be well extended to hyperspectral image applications because of the process of adjacency matrix consuming large amount of memory resources. In this paper, we propose a fast dynamic graph convolutional network (dynamic GCN) and CNN parallel network (FDGC) for HSI classification. We first obtain two classification features by flattening and pooling operations on the results of the convolution layers, which fully exploits the spatial-spectral information contained in the hyperspectral data cube. Then a dynamic graph convolution module is applied to extract the intrinsic structural information of each patch. Finally, we can obtain the HSI classification results based on these spatial, spectral and structural features. By using three branches, FDGC can parallel process multiple features of HSI in a supervised learning manner. In addition, regularization techniques such as DropBlock and label smoothing are applied to further improve the generalization capability of the model. Experimental results on three datasets show that our proposed algorithm is comparable with the state-of-the-art supervised learning models in terms of accuracy while also significantly outperforming in terms of training and inference time.

Self-Rating Depression Scale (SDS) questionnaire has frequently been used for efficient depression preliminary screening. However, the uncontrollable self-administered measure can be easily affected by insouciantly or deceptively answering, and producing the different results with the clinician-administered Hamilton Depression Rating Scale (HDRS) and the final diagnosis. Clinically, facial expression (FE) and actions play a vital role in clinician-administered evaluation, while FE and action are underexplored for self-administered evaluations. In this work, we collect a novel dataset of 200 subjects to evidence the validity of self-rating questionnaires with their corresponding question-wise video recording. To automatically interpret depression from the SDS evaluation and the paired video, we propose an end-to-end hierarchical framework for the long-term variable-length video, which is also conditioned on the questionnaire results and the answering time. Specifically, we resort to a hierarchical model which utilizes a 3D CNN for local temporal pattern exploration and a redundancy-aware self-attention (RAS) scheme for question-wise global feature aggregation. Targeting for the redundant long-term FE video processing, our RAS is able to effectively exploit the correlations of each video clip within a question set to emphasize the discriminative information and eliminate the redundancy based on feature pair-wise affinity. Then, the question-wise video feature is concatenated with the questionnaire scores for final depression detection. Our thorough evaluations also show the validity of fusing SDS evaluation and its video recording, and the superiority of our framework to the conventional state-of-the-art temporal modeling methods.

本文基于"科学家在线"平台数据,运用复杂网络分析方法,按照年份分别构建2010-2017年间全国34个省份之间科研人员流动网络模型,测度并分析8年间科研人员流动的复杂性网络整体特征和随时间变化的趋势.研究发现:(1)8年间科研人员流动呈现整体较为明显的"无标度"特性,即北京、上海、江苏等经济科技发达地区不仅流入科研人员的规模较大,流出科研人员的规模也不小,但是全国大部分省份流动科研人员规模较为有限;(2)除了小部分省份之外其余省份的人员流入流出相对较为均衡,一些经济科技发达省份在一定程度上也是全国或本区域科研人员流动的中心节点;(3)从发展态势来看,8年间全国省级科技人员流动态势相对平稳,但是省份之间科研人员流动规模差距越来越明显,经济科技发达省份与其他省份科研人员流动的规模相比差距越来越大;(4)最为突出的变化是流动人员规模显著增加,人员流动更加便利.

Background: In recent years, more and more people suffer from depression and anxiety. These symptoms are hard to be spotted and can be very dangerous. Currently, the Self-Reported Anxiety Scale (SAS) and Self-Reported Depression Scale (SDS) are commonly used for initial screening for depression and anxiety disorders. Howev-er, the information contained in these two scales is limited, while the symptoms of subjects are various and complex, which results in the inconsistency between the questionnaire evaluation results and the clinician's diagnosis results. To fully mine the scale data, we propose a method to extract the features from the facial expression and movements, which are generated from the video recorded simultaneously when subjects fill in the scale. Then we collect the facial expression, movements and scale information to establish a multimodal framework for improving the accuracy and robustness of the diagnosis of depression and anxiety.Methods: We collect the scale results of the subjects and the videos when filling in the scales. Given the two scales, SAS and SDS, we construct a model with two branches, where each branch processes the multimodal data of SAS and SDS, respectively. In the branch, we first build a convolutional neural network (CNN) to extracts the facial expression features in each frame of images. Secondly, we establish a long short-term memory (LSTM) network to further embedding the facial expression feature and build the connections between various frames, so that the movement feature in the video can be generated. Thirdly, we transform the scale scores into one-hot format, and feed them into the corresponding branch of the network to further mining the information of the multimodal data. Finally, we fuse the embeddings of these two branches to generate inference results of depression and anxiety.Results and conclusions: Based on the score results of SAS and SDS, our multimodal model further mines the video information, and can reach the accuracy of 0.946 in diagnosing depression and anxiety. This study demonstrates the feasibility of using our CNN-LSTM-based multimodal model for initial screening and diagnosis of depression and anxiety disorders with high diagnostic performance.

从区域协同发展的角度出发探究连片特困区的致贫原因对于巩固拓展脱贫攻坚成果、接续推动脱贫地区发展和乡村全面振兴意义重大.以往对集中连片特困区区域协同发展的研究多为质性研究,本文用经济地理学框架,利用方差分析、脉冲响应函数和多元回归等多种方法,以罗霄山区为例探究连片特困区成因与行政边界的关系.研究发现,在大部分连片特困区内,来自不同省份县区的经济发展水平存在显著差异;周边增长极发挥扩散效应时会受到行政边界阻隔带来的负向影响.这一探索有助于理解连片特困区的致贫原因、理解不同行政单元因缺乏协同对经济扩散造成的阻碍,对于增强脱贫稳定性、推动脱贫地区乡村全面振兴具有重大意义.

中国特色社会主义进入了新时代,中国科技外交工作立足于新的历史方位,以构建人类命运共同体重要思想引领实践,正在加快实现战略层面的优化调整.基于实践视角,分析了百年未有之大变局下中国科技外交面临的前所未有的机遇和挑战,凝练了人类命运共同体重要思想对科技外交的指导意义,总结了中国科技界践行人类命运共同体重要思想的实践探索,提出了加快调整中国科技外交战略的建议.

Jointly organized by the Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,and the World Federation of Engineering Organizations,the Forum on An Innovative Solution on Geographical Indications for Environment&Sustainability(GIES)was convened at the 2021 China International Fair for Trade in Services(CIFTIS).More than 80 experts from 30 organizations participated the forum on site and more than a thousand of people joining virtually.The participates focused on 6 key issues to emphasize the roles of scientific communities,governments,private sectors,technology innovation,education,scientific communications and civil societies.Representatives from 11 cases demonstrated the progress and experiences.A common understanding and agreement was made to launch the Decade Initiative on Geographical Indications for Environment&Sustainability 2021-2030(GIES).The partners agreed to establish a best practices and cases network during the following 10 years.All agreed that it was a critical issue in using big data and internet of things technology to encourage cooperation and collaboration among the multi-stakeholders,which could archive the United Nations 2030 Sustainable Development Goals,especially Goal 1,Goal 2,Goal 14,Goal 15 and Goal 17.

Once, the fast-growing economy has dependence on resources and environment, especially in Central Plains Urban Agglomeration (CPUA). Assessing the relationship between economic growth and resources and environment can be helpful in planning future region development. As there were fewer researches on the decoupling analysis in CPUA, therefore, according to the decoupling index designed by Tapio, this paper connected the resources and the environment to describe the comprehensive decoupling state of economic growth and resources environment as a whole with the latest available data in 2004–2015. The results showed that: (1) The change of environmental decoupling index had a greater impact on the comprehensive decoupling index. Economic growth has been less dependent on resources consumption and environment pollution since 2011, and the relationship between economic growth and resources environment reached strong decoupling in 2015. (2) The decoupling state was towards the direction of strong decoupling in Luoyang, Pingdingshan, Jiaozuo, Xuchang, Nanyang, and Xinyang. The economic growth was less dependent on resources consumption and the environment pollution. (3) Economic growth depended strongly on resources consumption and environment pollution in Changzhi, Jincheng, Heze, and Anyang. They had not yet achieved the strong decoupling state among economic growth, resources and the environment. Thus, the policy implementations were put forward to realize strong decoupling in CPUA.

科研价值观作为对科学活动必然产生影响的因素,是科学活动主体承载的价值理念.虽然科研价值观是全世界科学共同体共同秉持的信念,近年来美国等发达国家却把自身倡导的科研价值观当作压制中国在内的广大发展中国家和新兴经济体学术界的手段,不断挑起争端.本文通过文献和案例研究,研判美欧国家在科技外交中强化本国本民族特色的科研价值观的策略,重点分析欧盟在推进共同科研价值观领域的做法,并以此为鉴提出全世界在面临动荡变革期之时,应该大力倡导世界科学共同体守护和遵守国际科学界共同秉持之科研价值观.

Spectral unmixing methods with medium-resolution remote sensing images have become the main approach to mapping urban impervious-surface information. However, as more tall buildings appear, numerous visible shadows exist in medium-resolution images; these have usually been ignored in previous research, but they seriously affect accuracy. To solve this problem, we propose a combined unmixing framework to extract impervious surface in nonshadow and shadow areas, using linear and nonlinear unmixing models, respectively. First shadow is separated from nonshadow. Then a nonlinear unmixing method is selected to map impervious surface in shadow, which is more suitable to the complex imaging environment in shadow, and a classic linear unmixing model in nonshadow. Through experimental tests, the proposed combined unmixing framework is shown to effectively reduce error in two study areas compared with classical unmixing methods.

以5G、大数据、人工智能、云计算、AR/VR、区块链等为代表的新一代信息技术正在日益成为全球经济发展的重要驱动力.在数字经济发展中,中小企业是不可忽视的重要主体.本文基于文献和案例研究,认为数字经济时代广泛的数字化转型正在重构产业生态,中小企业面临实现规模经济、拓展应用场景的新机遇,并提出加快中小企业数字化转型的措施建议,以期为相关实践和理论研究提供参考.

新一轮科技革命和产业变革为全球科技创新中心发展带来新的机遇,我国近年来的科技创新中心建设已取得较大进展.本文通过总结部分全球知名科技创新中心的基本特征,分析相关文献资料,提炼全球知名科技创新中心建设的发展规律,并研究影响科技创新中心发展的基本因素,对当前出现的新现象、新趋势进行分析,提出建设科技创新中心的对策建议,为相关理论研究和政策制定提供启示与参考.

后疫情时代,全球气候变化影响和应对呈现新趋势,突发性、全球性和关联性成为鲜明的特征.当前,要着力解决应对气候变化的重大科学问题,就必须继续务实加强基础研究,妥善处理应对全球变化的挑战和可持续发展之间的关系;根据中国国情选择自己的气候变化应对之路;进一步处理好减排与增汇、减缓与适应的关系;设立国家碳中和重大专项;制定和实施更大规模植树造林计划;要立足未来,密切关注与气候变化高度关联的人类长期发展议程,实现人类可持续发展.

外交是主权国家的政治活动,随着中国外交全面进入世界舞台的中央,涉及不同行为主体和不同领域的各种形式的外交应运而生,其中,科技外交备受关注,特别是全球科技治理中的矛盾和问题日益凸显,有关科技外交的战略、政策、规则、方式、风险防范等逐渐成为热点,亟待跨学科融合研究科技外交问题.本文基于现有文献和现实资料,对科技外交的内涵、制度规范、方式方法等进行了初步探讨,以期为相关实践和理论研究提供参考.