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| 1 | SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer显示文摘This study proposes a novel general image fusion framework based on cross-domain long-range learning and Swin Transformer,termed as SwinFusion.On the one hand,an attention-guided cross-domain module is devised to achieve sufficient integration of complementary information and global interaction.More specifically,the proposed method involves an intra-domain fusion unit based on self-attention and an interdomain fusion unit based on cross-attention,which mine and integrate long dependencies within the same domain and across domains.Through long-range dependency modeling,the network is able to fully implement domain-specific information extraction and cross-domain complementary information integration as well as maintaining the appropriate apparent intensity from a global perspective.In particular,we introduce the shifted windows mechanism into the self-attention and cross-attention,which allows our model to receive images with arbitrary sizes.On the other hand,the multi-scene image fusion problems are generalized to a unified framework with structure maintenance,detail preservation,and proper intensity control.Moreover,an elaborate loss function,consisting of SSIM loss,texture loss,and intensity loss,drives the network to preserve abundant texture details and structural information,as well as presenting optimal apparent intensity.Extensive experiments on both multi-modal image fusion and digital photography image fusion demonstrate the superiority of our SwinFusion compared to the state-of-theart unified image fusion algorithms and task-specific alternatives.Implementation code and pre-trained weights can be accessed at http://gffzz188fe103f8f1460asx50xnk65wwqk6vo0.ffgz.tsg.suse.edu.cn/Linfeng-Tang/SwinFusion. | Jiayi Ma Linfeng Tang Fan Fan Jun Huang Xiaoguang Mei Yong Ma | 2022 | IEEE/CAA Journal of Automatica Sinica2022，9，7： | 8 |
| 2 | Embeddings of weighted Sobolev spaces and degenerate elliptic problems显示文摘New embeddings of some weighted Sobolev spaces with weights a(x)and b(x)are established.The weights a(x)and b(x)can be singular.Some applications of these embeddings to a class of degenerate elliptic problems of the form-div(a(x)?u)=b(x)f(x,u)in?,u=0 on??,where?is a bounded or unbounded domain in RN,N 2,are presented.The main results of this paper also give some generalizations of the well-known Caffarelli-Kohn-Nirenberg inequality. | GUO ZongMing MEI LinFeng WAN FangShu GUAN XiaoHong | 2017 | Science China Mathematics2017，60，8： | 3 |
| 3 | Intercalated 2D nanoclay for emerging drug delivery in cancer therapy显示文摘自然二维(2D ) kaolinite nanoclay 被合并了到一个新兴的药交货系统。kaolinite nanoclay 的基础间距通过不同的链长度的各种各样的器官的客人种类的置闰从 0.72 ~ 4.16 nm 被扩展,它能在药交货增加效率并且减少 doxorubicin (纪录影片) 的毒性。原来的 kaolinite (白陶土) 和白陶土置闰混合物向胰腺的癌症,胃的癌症,前列腺癌症,乳癌, colorectal 癌症,食道的癌症,和区分的甲状腺的房间展出了 biocompatibility 和很低的毒性的高水平癌症。然而,肺癌症和 hepatocellular 癌症房间为药交货搬运人需要更严格的组合、结构、词法的调整。纪录影片白陶土和纪录影片白陶土置闰混合物比处于中立状况在中等酸的答案显示出戏剧性地更快的药版本,并且以一种剂量依赖者方式对十种模型癌症房间文化展出了提高的治疗学的效果。为一个新奇的药交货系统的 2D nanoclay 材料的使用能 feasibly 向高效的 nanotherapeutics 铺平一条道路,与优异 antitumor 功效并且显著地减少副作用。 | Yi Zhang Mei Long Peng Huang Huaming Yang Shi Chang Yuehua Hu Aidong Tang Linfeng Mao | 2017 | Nano Research2017，10，8： | 2 |
| 4 | Nonlinear optical materials based on MBe2 BO3 F2 ( M = Na, K)显示文摘 | MEI Linfeng WANG Yebin CHEN Chuangtian | 1993 | J Appl Phys1993，74，： | 1 |
| 5 | Multiple solutions for an indefinite superlinear elliptic problem on R^N显示文摘 | DONG Wei MEI Linfeng | 2010 | Nonlinear Analysis: Theory Methods & Applications2010，73，7： | 1 |
| 6 | A Top-Down Approach towards Cu(I)Alkynyl Clusters with Unusual Geometry显示文摘Main observation and conclusion To search for novel Cu(I)complexes,traditional approaches focus on designing and screening ligands,metal precursors and reaction parameters.In this report,we propose a top-down approach,in which Cu precursor is first reduced to nanoparticles in the presence of alkynyl and phosphine ligands. | Huan Li Ting Li Shuimiao Liu Mei Qu Linfeng Liang Fengwei Zhang Xian-Ming Zhang | 2021 | Chinese Journal of Chemistry2021，39，4： | 1 |
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