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Stable Geodesic Surface Signatures

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Tsinghua Science and Technology 2012年第4期17卷 471-480页

作者： Min Zhang Wei Zeng Shiqing Xin Ying He xianfeng gu Department of Mathematics Zhejiang University Hangzhou 310058 China School of Computing and Information Sciences Florida International University Miami FL 33199 USA School of Computer Engineering Nanyang Technological University Singapore 639798 Department of Computer Science Stony Brook University Stony Brook NY 11794 USA

Shape analysis plays a fundamental role in computer graphics. We present a novel global and intrinsic shape representation for shape analysis, called stable geodesic signature. It is based on the theory of stable closed geodesics and surface Ricci flow. We examine the surface by dynamically deforming it by metric design through Ricci flow, then we observe the behavior of the stable closed geodesics under the evolving Riemannian metrics. When a metric is deforming some stable geodesic loops will become unstable and shrink to points, or some geodesic loops may merge. The number of stable geodesics forms the signature, which is general for arbitrary surfaces. Experiments on a large amount of surfaces demonstrate the efficiency and efficacy of the stable geodesic signature for shape analysis

关键词： Ricci flow stable geodesic shape analysis

Concurrent optimization of structural topology and infill properties with a CBF-based level set method

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Frontiers of Mechanical Engineering 2019年第2期14卷 171-189页

作者： Long JIANG Yang guO Shikui CHEN Peng WEI Na LEI xianfeng David gu Department of Mechanical Engineering State University of New York at Stony BrookStony BrookNY 11794USA Department of Computer Science State University of New York at Stony BrookStony BrookNY 11794USA State Key Laboratory of Subtropical Building Science School of Civil Engineering and TransportationSouth China University of TechnologyGuangzhou 510641China DUT-RU International School of Information Science&Engineering Dalian University of TechnologyDalian 116620China

In this paper,a parametric level-set-based topology optimization framework is proposed to concurrently optimize the structural topology at the macroscale and the effective infill properties at the micro/meso *** concurrent optimization is achieved by a computational framework combining a new parametric level set approach with mathematical *** the proposed framework,both the structural boundary evolution and the effective infill property optimization can be driven by mathematical programming,which is more advantageous compared with the conventional partial differential equatiodriven level set ***,the proposed approach will be more efficient in handling nonlinear problems with multiple *** of using radial basis functions(RBF),in this paper,we propose to construct a new type of cardinal basis functions(CBF)for the level set function *** proposed CBF parameterization ensures an explicit impose of the lower and upper bounds of the design *** overcomes the intrinsic disadvantage of the conventional RBF-based parametric level set method,where the lower and upper bounds of the design variables oftentimes have to be set by trial and error;A variational distance regularization method is utilized in this research to regularize the level set function to be a desired distanceregularized *** the distance information embedded in the level set model,the wrapping boundary layer and the interior infill region can be naturally *** isotropic infill achieved via the mesoscale topology optimization is conformally fit into the wrapping boundary layer using the shape-preserving conformal mapping method,which leads to a hierarchical physical structure with optimized overall topology and effective infill *** proposed method is expected to provide a timely solution to the increasing demand for multiscale and multifunctional structure design.

关键词： concurrent topology optimization parametric level set method cardinal basis function shell-infill structure design confonnal mapping

Autoencoder-based conditional optimal transport generative adversarial network for medical image generation

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Visual Informatics 2024年第1期8卷 15-25页

作者： Jun Wang Bohan Lei Liya Ding Xiaoyin Xu xianfeng gu Min Zhang School of Software Technology Zhejiang UniversityNingboZhejiangChina College of Computer Science&Technology Zhejiang UniversityHangzhouZhejiangChina Department of Pathology Sir Run Run Shaw HospitalZhejiang University School of MedicineHangzhouZhejiangChina College of Biomedical Engineering&Instrument Science Zhejiang UniversityHangzhouZhejiangChina Department of Computer Science State University of New York at Stony BrookStony BrookNYUSA

Medical image generation has recently garnered significant interest among ***,the primary generative models,such as Generative Adversarial Networks(GANs),often encounter challenges during training,including mode *** address these issues,we proposed the AECOT-GAN model(Autoencoder-based Conditional Optimal Transport Generative Adversarial Network)for the generation of medical images belonging to specific *** training process of our model comprises three fundamental *** training process of our model encompasses three fundamental ***,we employ an autoencoder model to obtain a low-dimensional manifold representation of real ***,we apply extended semi-discrete optimal transport to map Gaussian noise distribution to the latent space distribution and obtain corresponding labels *** procedure leads to the generation of new latent codes with known ***,we integrate a GAN to train the decoder further to generate medical *** evaluate the performance of the AE-COT-GAN model,we conducted experiments on two medical image datasets,namely DermaMNIST and *** model’s performance was compared with state-of-the-art generative *** show that the AE-COT-GAN model had excellent performance in generating medical ***,it effectively addressed the common issues associated with traditional GANs.

关键词： Medical image generation Mode collapse Mode mixing Optimal transport Generative adversarial networks

A Geometric Understanding of Deep Learning

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Engineering 2020年第3期6卷 361-374页

作者： Na Lei Dongsheng An Yang guo Kehua Su Shixia Liu Zhongxuan Luo Shing-Tung Yau xianfeng gu DUT-RU Co-Research Center of Advanced ICT for Active Life Dalian University of TechnologyDalian 116620China Department of Computer Science Stony Brook UniversityStony BrookNY 11794-2424USA School of Computer Science Wuhan UniversityWuhan 430072China School of Software Tsinghua UniversityBeijing 100084China Center of Mathematical Sciences and Applications Harvard UniversityCambridgeMA 02138USA

This work introduces an optimal transportation(OT)view of generative adversarial networks(GANs).Natural datasets have intrinsic patterns,which can be summarized as the manifold distribution principle:the distribution of a class of data is close to a low-dimensional *** mainly accomplish two tasks:manifold learning and probability distribution *** latter can be carried out using the classical OT *** the OT perspective,the generator computes the OT map,while the discriminator computes the Wasserstein distance between the generated data distribution and the real data distribution;both can be reduced to a convex geometric optimization ***,OT theory discovers the intrinsic collaborative-instead of competitive-relation between the generator and the discriminator,and the fundamental reason for mode *** also propose a novel generative model,which uses an autoencoder(AE)for manifold learning and OT map for probability distribution *** AE–OT model improves the theoretical rigor and transparency,as well as the computational stability and efficiency;in particular,it eliminates the mode *** experimental results validate our hypothesis,and demonstrate the advantages of our proposed model.

关键词： Generative Adversarial Deep learning Optimal transportation Mode collapse

A dual-modality hydrogen sulfide-specific probe integrating chemiluminescence with NIR fluorescence for targeted cancer imaging

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Science China Chemistry 2023年第6期66卷 1869-1876页

作者： Xuemei Dong Lixin Sun Ziwen Zhang Tianli Zhu Jie Sun Jinzhu Gao Chengjun Dong Rongchen Wang xianfeng gu Chunchang Zhao Key Laboratory for Advanced Materials and Feringa Nobel Prize Scientist Joint Research Center Institute of Fine ChemicalsSchool of Chemistry and Molecular EngineeringEast China University of Science and TechnologyShanghai 200237China Department of Medicinal Chemistry School of PharmacyFudan UniversityShanghai 201203China

Taking apart in numerous physiological and pathological activities,hydrogen sulfide(H_(2)S)has been selected as an excellent target spot for the early diagnosis of *** far,there are many mature probes that apply single optical imaging to monitor endogenous H_(2)***,a single modality is not an ideal method to afford accurate diagnostic information in comprehensive biological ***,we developed a dual-modal imaging probe *** designed probe showed a specific response to H_(2)S with both chemiluminescence and NIR fluorescence *** concurrence of fluorescence and chemiluminescence signal provided“double insurances”for highly accurate monitoring of H_(2)***,this dual-modal imaging probe performed precise visualization of endogenous H_(2)S in living cells and in *** envisaged that this chemiluminescence/fluorescence real-time dual-modality strategy for H_(2)S detection will expand and optimize the multimodal imaging methods for efficient diagnosis and treatment of cancer.

关键词： chemiluminescence fluorescence BODIPY dual-modal imaging H_(2)S

A multi-parameter-induced activation of gas therapeutic platform to remarkably amplify photodynamic therapy efficacy

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Science China Chemistry 2024年第7期67卷 2403-2411页

作者： Jie Sun Ziwen Zhang Hongyu Wu Xuemei Dong Chengjun Dong Lixin Sun Zhiqian guo Yingchao Liu xianfeng gu Chunchang Zhao Key Laboratory for Advanced Materials and Feringa Nobel Prize Scientist Joint Research Center Institute of Fine ChemicalsSchool of Chemistry and Molecular EngineeringEast China University of Science and TechnologyShanghai 200237China Department of Medicinal Chemistry School of PharmacyFudan UniversityShanghai 201203China Shandong Provincial Hospital Affiliated to Shandong First Medical University Shandong First Medical University&Shandong Academy of Medical ScienceJinan 250021China

Gas therapy(GT)combined with photodynamic therapy(PDT)is an effective strategy to compensate for the PDT limitation caused by the hypoxic tumor microenvironment,which can greatly improve PDT *** uncontrolled leakage of gas molecules during delivery seriously hinders its practical biological ***,we report a multifunction nanomedicine that enables precise gas therapy(including carbon monoxide(CO)release and H_(2)S depletion)using a multi-parameter-induced activation gas release strategy,enlarging the PDT *** nanomedicine uses a disulfide bond to covalently link a photosensitizer with the CO donor 3-hydroxyflavone(3-HF).The disulfide bond can be specifically consumed in H_(2)S-rich tumor areas,releasing the CO donor(3-HF),and also depleting H_(2)*** importantly,the photo-controlled production of^(1)O_(2)can induce 3-HF precise release of CO in the tumor *** H_(2)S,light,and^(1)O_(2)multi-parameter-induced activation of gas release strategy ensures the accuracy of GT to amplify PDT *** expected,in vitro and in vivo investigations show that GT makes up for the PDT limitation,exhibiting the highest tumor therapeutic *** multi-parameter-activated design strategy provides a new way to improve the precision and efficacy of multimodal synergistic therapy of tumors.

关键词： gas therapy precision gas release hydrogen sulfide depletion photodynamic therapy multimodal synergistic therapy

Hydrogen Sulfide-Specific and NIR-Light-Controllable Synergistic Activation of Fluorescent Theranostic Prodrugs for Imaging-guided Chemo- Photothermal Cancer Therapy

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CCS Chemistry 2020年第4期2卷 527-538页

作者： Ge Xu Wei guo xianfeng gu Zhijun Wang Rongchen Wang Tianli Zhu He Tian Chunchang Zhao Key Laboratory for Advanced Materials and Feringa Nobel Prize Scientist Joint Research Center Institute of Fine ChemicalsSchool of Chemistry and Molecular EngineeringEast China University of Science and TechnologyShanghai 200237 Department of Medicinal Chemistry School of PharmacyFudan UniversityShanghai 201203.

Theranostic prodrugs are promising for cancer medicine;however,the inability to activate these systems exclusively at the desired tumor location compromises the specificity and efficacy of cancer ***,we developed a fluorescent theranostic nanoprodrug with synergistic hydrogen-sulfidespecific and near-infrared(NIR)-light-controllable activation for imaging-guided chemo-photothermal cancer *** nanoprodrug system was fabricated by the inclusion of hydrogen sulfide(H2S)-activatable small molecule to the theranostic prodrug and a photothermal transducer in the interior of a NIR-light-responsive container.

关键词： theranostic prodrug hydrogen sulfide NIR fluorescence light-controllable cancer therapy

Robust and accurate optimal transportation map by self-adaptive

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Frontiers of Information Technology & Electronic Engineering 2021年第9期22卷 1207-1220页

作者： Yingshi WANG Xiaopeng ZHENG Wei CHEN Xin QI Yuxue REN Na LEI xianfeng gu Department of Computer Science Inner Mongolia University of Finance andEconomicsHohhot 010010China School of Software Dalian University ofTechnologyDalian 116620China Beijing Advanced Innovation Center forImaging Technology Capital Normal UniversityBeijing 100048China Department of Computer Science Stony Brook UniversityStony BrookNY11794USA

Optimal transportation plays a fundamental role in many fi elds in engineering and medicine,including surface parameterization in graphics,registration in computer vision,and generative models in deep *** quadratic distance cost,optimal transportation map is the gradient of the Brenier potential,which can be obtained by solving the Monge-Ampère ***,it is induced to a geometric convex optimization *** Monge-Ampère equation is highly non-linear,and during the solving process,the intermediate solutions have to be strictly *** cally,the accuracy of the discrete solution heavily depends on the sampling pattern of the target *** this work,we propose a self-adaptive sampling algorithm which greatly reduces the sampling bias and improves the accuracy and robustness of the discrete *** results demonstrate the efficiency and efficacy of our method.

关键词： Optimal transportation Monge-Ampère equation Self-adaptive sampling

Alkaline Phosphatase-Initiated Sensitive Responsiveness of Activatable Probes to Hydrogen Sulfide for Accurate Cancer Imaging and Differentiation

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CCS Chemistry 2022年第12期4卷 3715-3723页

作者： Rongchen Wang Kai Yin Muye Ma Tianli Zhu Jinzhu Gao Jie Sun Xuemei Dong Chengjun Dong xianfeng gu He Tian Chunchang Zhao Key Laboratory for Advanced Materials and Feringa Nobel Prize Scientist Joint Research Center Institute of Fine ChemicalsSchool of Chemistry and Molecular EngineeringFrontiers Science Center for Materiobiology and Dynamic ChemistryEast China University of Science and TechnologyShanghai 200237 Department of Medicinal Chemistry School of PharmacyFudan UniversityShanghai 201203

Optical imaging with molecular probes is becoming an essential tool for advancing biological research and clinical ***,most currently available molecular probes show limited sensitivity,specificity,and accuracy due to their typical responsiveness to a single stimulation for biomarker-based *** this study,we develop a novel molecular probe that shows alkaline phosphatase(ALP)-instructed sensitive responsiveness to hydrogen sulfide for accurate cancer imaging and *** designed probe in an aggregated state under physiological conditions bears negatively charged surfaces,giving poor optical response to H_(2)*** ALP-mediated dephosphorylation reaction yields an assembled product with a positively charged surface,affording significantly aggregation-enhanced responsiveness to H_(2)S with light-up NIR fluorescence at 755 *** charge reversal of assembled probe from negative to positive plays a vital role in allowing precise visualization and differentiation of cancers based on differences in ALP upregulation and H_(2)S *** envisage that our charge-reversal strategy for multiple-parameter-activated molecule probes will facilitate boosting the specificity and precision of cancer imaging.

关键词： near-infrared fluorescence activatable probe alkaline phosphatase H_(2)S cancer imaging