Many important problems in science and engineering require solving the so-called parametric partial differential equations(PDEs),i.e.,PDEs with different physical parameters,boundary conditions,shapes of computational...Many important problems in science and engineering require solving the so-called parametric partial differential equations(PDEs),i.e.,PDEs with different physical parameters,boundary conditions,shapes of computational domains,etc.Typical reduced order modeling techniques accelerate the solution of the parametric PDEs by projecting them onto a linear trial manifold constructed in the ofline stage.These methods often need a predefined mesh as well as a series of precomputed solution snapshots,and may struggle to balance between the efficiency and accuracy due to the limitation of the linear ansatz.Utilizing the nonlinear representation of neural networks(NNs),we propose the Meta-Auto-Decoder(MAD)to construct a nonlinear trial manifold,whose best possible performance is measured theoretically by the decoder width.Based on the meta-learning concept,the trial manifold can be learned in a mesh-free and unsupervised way during the pre-training stage.Fast adaptation to new(possibly heterogeneous)PDE parameters is enabled by searching on this trial manifold,and optionally fine-tuning the trial manifold at the same time.Extensive numerical experiments show that the MAD method exhibits a faster convergence speed without losing the accuracy than other deep learning-based methods.展开更多
The influence of ischemia-reperfusion(I/R)action on pancreatic blood flow(PBF)and the development of acute pancreatitis(AP)in laboratory rats is evaluated in vivo by using the laser speckle contrast imaging(LSCI).Addi...The influence of ischemia-reperfusion(I/R)action on pancreatic blood flow(PBF)and the development of acute pancreatitis(AP)in laboratory rats is evaluated in vivo by using the laser speckle contrast imaging(LSCI).Additionally,the optical properties in norm and under condition of AP in rats were assessed using a modied integrating sphere spectrometer and inverse Monte Carlo(IMC)software.The results of the experimental study of microcirculation of the pancreas in 82 rats in the ischemic model are presented.The data obtained conrm the fact that local ischemia and changes in the blood°ow velocity of the main vessels cause and provoke acute pancreatitis.展开更多
Machine learning has been widely used for solving partial differential equations(PDEs)in recent years,among which the random feature method(RFM)exhibits spectral accuracy and can compete with traditional solvers in te...Machine learning has been widely used for solving partial differential equations(PDEs)in recent years,among which the random feature method(RFM)exhibits spectral accuracy and can compete with traditional solvers in terms of both accuracy and efficiency.Potentially,the optimization problem in the RFM is more difficult to solve than those that arise in traditional methods.Unlike the broader machine-learning research,which frequently targets tasks within the low-precision regime,our study focuses on the high-precision regime crucial for solving PDEs.In this work,we study this problem from the following aspects:(i)we analyze the coeffcient matrix that arises in the RFM by studying the distribution of singular values;(ii)we investigate whether the continuous training causes the overfitting issue;(ii)we test direct and iterative methods as well as randomized methods for solving the optimization problem.Based on these results,we find that direct methods are superior to other methods if memory is not an issue,while iterative methods typically have low accuracy and can be improved by preconditioning to some extent.展开更多
As one of the main governing equations in kinetic theory,the Boltzmann equation is widely utilized in aerospace,microscopic flow,etc.Its high-resolution simulation is crucial in these related areas.However,due to the ...As one of the main governing equations in kinetic theory,the Boltzmann equation is widely utilized in aerospace,microscopic flow,etc.Its high-resolution simulation is crucial in these related areas.However,due to the high dimensionality of the Boltzmann equation,high-resolution simulations are often difficult to achieve numerically.The moment method which was first proposed in Grad(Commun Pure Appl Math 2(4):331-407,1949)is among the popular numerical methods to achieve efficient high-resolution simulations.We can derive the governing equations in the moment method by taking moments on both sides of the Boltzmann equation,which effectively reduces the dimensionality of the problem.However,one of themain challenges is that it leads to an unclosed moment system,and closure is needed to obtain a closedmoment system.It is truly an art in designing closures for moment systems and has been a significant research field in kinetic theory.Other than the traditional human designs of closures,the machine learning-based approach has attracted much attention lately in Han et al.(Proc Natl Acad Sci USA 116(44):21983-21991,2019)and Huang et al.(J Non-Equilib Thermodyn 46(4):355-370,2021).In this work,we propose a machine learning-based method to derive a moment closure model for the Boltzmann-BGK equation.In particular,the closure relation is approximated by a carefully designed deep neural network that possesses desirable physical invariances,i.e.,the Galilean invariance,reflecting invariance,and scaling invariance,inherited from the original Boltzmann-BGK equation and playing an important role in the correct simulation of the Boltzmann equation.Numerical simulations on the 1D-1D examples including the smooth and discontinuous initial condition problems,Sod shock tube problem,the shock structure problems,and the 1D-3D examples including the smooth and discontinuous problems demonstrate satisfactory numerical performances of the proposed invariance preserving neural closure method.展开更多
Denoising diffusion models have demonstrated tremendous success in modeling data distributions and synthesizing high-quality samples.In the 2D image domain,they have become the state-of-the-art and are capable of gene...Denoising diffusion models have demonstrated tremendous success in modeling data distributions and synthesizing high-quality samples.In the 2D image domain,they have become the state-of-the-art and are capable of generating photo-realistic images with high controllability.More recently,researchers have begun to explore how to utilize diffusion models to generate 3D data,as doing so has more potential in real-world applications.This requires careful design choices in two key ways:identifying a suitable 3D representation and determining how to apply the diffusion process.In this survey,we provide the first comprehensive review of diffusion models for manipulating 3D content,including 3D generation,reconstruction,and 3D-aware image synthesis.We classify existing methods into three major categories:2D space diffusion with pretrained models,2D space diffusion without pretrained models,and 3D space diffusion.We also summarize popular datasets used for 3D generation with diffusion models.Along with this survey,we maintain a repository https://github.com/cwchenwang/awesome-3d-diffusion to track the latest relevant papers and codebases.Finally,we pose current challenges for diffusion models for 3D generation,and suggest future research directions.展开更多
The healthcare industry faces core challenges,including increasingly complex operational processes and entities,the rapid development of medical knowledge,and the rising demand for interdisciplinary expertise.The comp...The healthcare industry faces core challenges,including increasingly complex operational processes and entities,the rapid development of medical knowledge,and the rising demand for interdisciplinary expertise.The complexity of medical processes is evident in every aspect,from patient appointments,diagnoses,and treatments to follow-up tasks,all of which involve intricate data processing and decision-making procedures.展开更多
The sole use of single modality data often fails to capture the complex heterogeneity among patients,including the variability in resistance to anti-HER2 therapy and outcomes of combined treatment regimens,for the tre...The sole use of single modality data often fails to capture the complex heterogeneity among patients,including the variability in resistance to anti-HER2 therapy and outcomes of combined treatment regimens,for the treatment of HER2-positive gastric cancer(GC).This modality deficit has not been fully considered in many studies.Furthermore,the application of artificial intelligence in predicting the treatment response,particularly in complex diseases such as GC,is still in its infancy.Therefore,this study aimed to use a comprehensive analytic approach to accurately predict treatment responses to anti-HER2 therapy or anti-HER2 combined immunotherapy in patients with HER2-positive GC.We collected multi-modal data,comprising radiology,pathology,and clinical information from a cohort of 429 patients:310 treated with anti-HER2 therapy and 119 treated with a combination of anti-HER2 and anti-PD-1/PD-L1 inhibitors immunotherapy.We introduced a deep learning model,called the Multi-Modal model(MuMo),that integrates these data to make precise treatment response predictions.MuMo achieved an area under the curve score of 0.821 for anti-HER2 therapy and 0.914 for combined immunotherapy.Moreover,patients classified as low-risk by MuMo exhibited significantly prolonged progression-free survival and overall survival(log-rank test,P<0.05).These findings not only highlight the significance of multi-modal data analysis in enhancing treatment evaluation and personalized medicine for HER2-positive gastric cancer,but also the potential and clinical value of our model.展开更多
We develop and analyze numerical discretization to the constrained high-index saddle dynamics,the dynamics searching for the high-index saddle points confined on the high-dimensional unit sphere.Compared with the sadd...We develop and analyze numerical discretization to the constrained high-index saddle dynamics,the dynamics searching for the high-index saddle points confined on the high-dimensional unit sphere.Compared with the saddle dynamics without constraints,the constrained high-index saddle dynamics has more complex dynamical forms,and additional operations such as the retraction and vector transport are required due to the constraints,which significantly complicate the numerical scheme and the corresponding numerical analysis.Furthermore,as the existing numerical analysis results usually depend on the index of the saddle points implicitly,the proved numerical accuracy may be reduced if the index is high in many applications,which indicates the lack of robustness with respect to the index.To address these issues,we derive the error estimates for numerical discretization of the constrained high-index saddle dynamics on the high-dimensional sphere and then improve it by providing index-robust error analysis in an averaged norm by adjusting the relaxation parameters.The developed results provide mathematical support for the accuracy of numerical computations.展开更多
The authors prove error estimates for the semi-implicit numerical scheme of sphere-constrained high-index saddle dynamics,which serves as a powerful instrument in finding saddle points and constructing the solution la...The authors prove error estimates for the semi-implicit numerical scheme of sphere-constrained high-index saddle dynamics,which serves as a powerful instrument in finding saddle points and constructing the solution landscapes of constrained systems on the high-dimensional sphere.Due to the semi-implicit treatment and the novel computational procedure,the orthonormality of numerical solutions at each time step could not be fully employed to simplify the derivations,and the computations of the state variable and directional vectors are coupled with the retraction,the vector transport and the orthonormalization procedure,which significantly complicates the analysis.They address these issues to prove error estimates for the proposed semi-implicit scheme and then carry out numerical experiments to substantiate the theoretical findings.展开更多
Rotational Bose-Einstein condensates can exhibit quantized vortices as topological excitations.In this study,the ground and excited states of the rotational Bose-Einstein condensates are systematically studied by calc...Rotational Bose-Einstein condensates can exhibit quantized vortices as topological excitations.In this study,the ground and excited states of the rotational Bose-Einstein condensates are systematically studied by calculating the stationary points of the Gross-Pitaevskii energy functional.Various excited states and their connections at different rotational frequencies are revealed in solution landscapes constructed with the constrained high-index saddle dynamics method.Four excitation mechanisms are identified:vortex addition,rearrangement,merging,and splitting.We demonstrate changes in the ground state with increasing rotational frequencies and decipher the evolution of the stability of ground states.展开更多
With the rapid development of artificial intelligence,large language models(LLMs)have shown promising capabilities in mimicking human-level language comprehen-sion and reasoning.This has sparked significant interest i...With the rapid development of artificial intelligence,large language models(LLMs)have shown promising capabilities in mimicking human-level language comprehen-sion and reasoning.This has sparked significant interest in applying LLMs to enhance various aspects of healthcare,ranging from medical education to clinical decision support.However,medicine involves multifaceted data modalities and nuanced reasoning skills,presenting challenges for integrating LLMs.This review introduces the fundamental applications of general-purpose and specialized LLMs,demon-strating their utilities in knowledge retrieval,research support,clinical workflow automation,and diagnostic assistance.Recognizing the inherent multimodality of medicine,the review emphasizes the multimodal LLMs and discusses their ability to process diverse data types like medical imaging and electronic health records to augment diagnostic accuracy.To address LLMs'limitations regarding personalization and complex clinical reasoning,the review further explores the emerging develop-ment of LLM-powered autonomous agents for healthcare.Moreover,it summarizes the evaluation methodologies for assessing LLMs'reliability and safety in medical contexts.LLMs have transformative potential in medicine;however,there is a pivotal need for continuous optimizations and ethical oversight before these models can be effectively integrated into clinical practice.展开更多
Molecular dynamics(MD)is an indispensable atomistic-scale computational tool widely-used in various disciplines.In the past decades,nearly all ab initio MD and machine-learning MD have been based on the general-purpos...Molecular dynamics(MD)is an indispensable atomistic-scale computational tool widely-used in various disciplines.In the past decades,nearly all ab initio MD and machine-learning MD have been based on the general-purpose central/graphics processing units(CPU/GPU),which are well-known to suffer from their intrinsic“memory wall”and“power wall”bottlenecks.Consequently,nowadays MD calculations with ab initio accuracy are extremely time-consuming and power-consuming,imposing serious restrictions on the MD simulation size and duration.To solve this problem,here we propose a special-purpose MD processing unit(MDPU),which could reduce MD time and power consumption by about 103 times(109 times)compared to state-of-the-art machine-learningMD(ab initio MD)based on CPU/GPU,while keeping ab initio accuracy.With significantly-enhanced performance,the proposed MDPU may pave a way for the accurate atomistic-scale analysis of large-size and/or longduration problems which were impossible/impractical to compute before.展开更多
Exploratory data analysis plays a major role in obtaining insights from data.Over the last two decades,researchers have proposed several visual data exploration tools that can assist with each step of the analysis pro...Exploratory data analysis plays a major role in obtaining insights from data.Over the last two decades,researchers have proposed several visual data exploration tools that can assist with each step of the analysis process.Nevertheless,in recent years,data analysis requirements have changed significantly.With constantly increasing size and types of data to be analyzed,scalability and analysis duration are now among the primary concerns of researchers.Moreover,in order to minimize the analysis cost,businesses are in need of data analysis tools that can be used with limited analytical knowledge.To address these challenges,traditional data exploration tools have evolved within the last few years.In this paper,with an in-depth analysis of an industrial tabular dataset,we identify a set of additional exploratory requirements for large datasets.Later,we present a comprehensive survey of the recent advancements in the emerging field of exploratory data analysis.We investigate 50 academic and non-academic visual data exploration tools with respect to their utility in the six fundamental steps of the exploratory data analysis process.We also examine the extent to which these modern data exploration tools fulfill the additional requirements for analyzing large datasets.Finally,we identify and present a set of research opportunities in the field of visual exploratory data analysis.展开更多
We prove probabilistic error estimates for high-index saddle dynamics with or without constraints to account for the inaccurate values of the model,which could be encountered in various scenarios such as model uncerta...We prove probabilistic error estimates for high-index saddle dynamics with or without constraints to account for the inaccurate values of the model,which could be encountered in various scenarios such as model uncertainties or surrogate model algorithms via machine learning methods. The main contribution lies in incorporating the probabilistic error bound of the model values with the conventional error estimate methods for high-index saddle dynamics. The derived results generalize the error analysis of deterministic saddle dynamics and characterize the affect of the inaccuracy of the model on the convergence rate.展开更多
基金supported by the National Key R&D Program of China under Grant No.2021ZD0110400.
文摘Many important problems in science and engineering require solving the so-called parametric partial differential equations(PDEs),i.e.,PDEs with different physical parameters,boundary conditions,shapes of computational domains,etc.Typical reduced order modeling techniques accelerate the solution of the parametric PDEs by projecting them onto a linear trial manifold constructed in the ofline stage.These methods often need a predefined mesh as well as a series of precomputed solution snapshots,and may struggle to balance between the efficiency and accuracy due to the limitation of the linear ansatz.Utilizing the nonlinear representation of neural networks(NNs),we propose the Meta-Auto-Decoder(MAD)to construct a nonlinear trial manifold,whose best possible performance is measured theoretically by the decoder width.Based on the meta-learning concept,the trial manifold can be learned in a mesh-free and unsupervised way during the pre-training stage.Fast adaptation to new(possibly heterogeneous)PDE parameters is enabled by searching on this trial manifold,and optionally fine-tuning the trial manifold at the same time.Extensive numerical experiments show that the MAD method exhibits a faster convergence speed without losing the accuracy than other deep learning-based methods.
基金the nancial sup-port of the Project No.13.2251.21.0009 of the Ministry of Science and Higher Education of the Russian Federation.
文摘The influence of ischemia-reperfusion(I/R)action on pancreatic blood flow(PBF)and the development of acute pancreatitis(AP)in laboratory rats is evaluated in vivo by using the laser speckle contrast imaging(LSCI).Additionally,the optical properties in norm and under condition of AP in rats were assessed using a modied integrating sphere spectrometer and inverse Monte Carlo(IMC)software.The results of the experimental study of microcirculation of the pancreas in 82 rats in the ischemic model are presented.The data obtained conrm the fact that local ischemia and changes in the blood°ow velocity of the main vessels cause and provoke acute pancreatitis.
基金supported by the NSFC Major Research Plan--Interpretable and Generalpurpose Next-generation Artificial Intelligence(No.92370205).
文摘Machine learning has been widely used for solving partial differential equations(PDEs)in recent years,among which the random feature method(RFM)exhibits spectral accuracy and can compete with traditional solvers in terms of both accuracy and efficiency.Potentially,the optimization problem in the RFM is more difficult to solve than those that arise in traditional methods.Unlike the broader machine-learning research,which frequently targets tasks within the low-precision regime,our study focuses on the high-precision regime crucial for solving PDEs.In this work,we study this problem from the following aspects:(i)we analyze the coeffcient matrix that arises in the RFM by studying the distribution of singular values;(ii)we investigate whether the continuous training causes the overfitting issue;(ii)we test direct and iterative methods as well as randomized methods for solving the optimization problem.Based on these results,we find that direct methods are superior to other methods if memory is not an issue,while iterative methods typically have low accuracy and can be improved by preconditioning to some extent.
基金carried out with the financial support of the Project No.13.2251.21.0009 of the Ministry of Science and Higher Education of the Russian Federation(agreement No.075-15-2021-942).
基金supported in part by Natural Science Foundation of BeijingMunicipality(No.180001)National Natural Science Foundation of China(Grant No.12090022)supported by the National Natural Science Foundation of China(Grant No.12171026,U1930402 and 12031013).
文摘As one of the main governing equations in kinetic theory,the Boltzmann equation is widely utilized in aerospace,microscopic flow,etc.Its high-resolution simulation is crucial in these related areas.However,due to the high dimensionality of the Boltzmann equation,high-resolution simulations are often difficult to achieve numerically.The moment method which was first proposed in Grad(Commun Pure Appl Math 2(4):331-407,1949)is among the popular numerical methods to achieve efficient high-resolution simulations.We can derive the governing equations in the moment method by taking moments on both sides of the Boltzmann equation,which effectively reduces the dimensionality of the problem.However,one of themain challenges is that it leads to an unclosed moment system,and closure is needed to obtain a closedmoment system.It is truly an art in designing closures for moment systems and has been a significant research field in kinetic theory.Other than the traditional human designs of closures,the machine learning-based approach has attracted much attention lately in Han et al.(Proc Natl Acad Sci USA 116(44):21983-21991,2019)and Huang et al.(J Non-Equilib Thermodyn 46(4):355-370,2021).In this work,we propose a machine learning-based method to derive a moment closure model for the Boltzmann-BGK equation.In particular,the closure relation is approximated by a carefully designed deep neural network that possesses desirable physical invariances,i.e.,the Galilean invariance,reflecting invariance,and scaling invariance,inherited from the original Boltzmann-BGK equation and playing an important role in the correct simulation of the Boltzmann equation.Numerical simulations on the 1D-1D examples including the smooth and discontinuous initial condition problems,Sod shock tube problem,the shock structure problems,and the 1D-3D examples including the smooth and discontinuous problems demonstrate satisfactory numerical performances of the proposed invariance preserving neural closure method.
文摘Denoising diffusion models have demonstrated tremendous success in modeling data distributions and synthesizing high-quality samples.In the 2D image domain,they have become the state-of-the-art and are capable of generating photo-realistic images with high controllability.More recently,researchers have begun to explore how to utilize diffusion models to generate 3D data,as doing so has more potential in real-world applications.This requires careful design choices in two key ways:identifying a suitable 3D representation and determining how to apply the diffusion process.In this survey,we provide the first comprehensive review of diffusion models for manipulating 3D content,including 3D generation,reconstruction,and 3D-aware image synthesis.We classify existing methods into three major categories:2D space diffusion with pretrained models,2D space diffusion without pretrained models,and 3D space diffusion.We also summarize popular datasets used for 3D generation with diffusion models.Along with this survey,we maintain a repository https://github.com/cwchenwang/awesome-3d-diffusion to track the latest relevant papers and codebases.Finally,we pose current challenges for diffusion models for 3D generation,and suggest future research directions.
基金supported by the National Natural Science Foundation of China(U22A20327,12090022,11831002,81801778,and 82203881)Beijing Natural Science Foundation(7222021)+1 种基金Beijing Hospitals Authority Youth Programme(QML20231115)Clinical Medicine Plus X-Young Scholars Project of Peking University(PKU2023LCXQ041).
文摘The healthcare industry faces core challenges,including increasingly complex operational processes and entities,the rapid development of medical knowledge,and the rising demand for interdisciplinary expertise.The complexity of medical processes is evident in every aspect,from patient appointments,diagnoses,and treatments to follow-up tasks,all of which involve intricate data processing and decision-making procedures.
基金supported by the National Natural Science Foundation of China(91959205 to L.S.,U22A20327 to L.S.,82203881 to Y.C.,82272627 to XT.Z.,7232018 to Y.S.,12090022 to B.D.,11831002 to B.D.,81801778 to L.Z.)Beijing Natural Science Foundation(7222021 to Y.C.,Z200015 to XT.Z.)+1 种基金Beijing Hospitals Authority Youth Programme(QML20231115 to Y.C.)Clinical Medicine Plus X-Young Scholars Project of Peking University(PKU2023LCXQ041 to Y.C.and L.Z.).
文摘The sole use of single modality data often fails to capture the complex heterogeneity among patients,including the variability in resistance to anti-HER2 therapy and outcomes of combined treatment regimens,for the treatment of HER2-positive gastric cancer(GC).This modality deficit has not been fully considered in many studies.Furthermore,the application of artificial intelligence in predicting the treatment response,particularly in complex diseases such as GC,is still in its infancy.Therefore,this study aimed to use a comprehensive analytic approach to accurately predict treatment responses to anti-HER2 therapy or anti-HER2 combined immunotherapy in patients with HER2-positive GC.We collected multi-modal data,comprising radiology,pathology,and clinical information from a cohort of 429 patients:310 treated with anti-HER2 therapy and 119 treated with a combination of anti-HER2 and anti-PD-1/PD-L1 inhibitors immunotherapy.We introduced a deep learning model,called the Multi-Modal model(MuMo),that integrates these data to make precise treatment response predictions.MuMo achieved an area under the curve score of 0.821 for anti-HER2 therapy and 0.914 for combined immunotherapy.Moreover,patients classified as low-risk by MuMo exhibited significantly prolonged progression-free survival and overall survival(log-rank test,P<0.05).These findings not only highlight the significance of multi-modal data analysis in enhancing treatment evaluation and personalized medicine for HER2-positive gastric cancer,but also the potential and clinical value of our model.
基金supported by National Natural Science Foundation of China(Grant Nos.12225102,12050002 and 12288101)the National Key Research and Development Program of China(Grant No.2021YFF1200500).
文摘We develop and analyze numerical discretization to the constrained high-index saddle dynamics,the dynamics searching for the high-index saddle points confined on the high-dimensional unit sphere.Compared with the saddle dynamics without constraints,the constrained high-index saddle dynamics has more complex dynamical forms,and additional operations such as the retraction and vector transport are required due to the constraints,which significantly complicate the numerical scheme and the corresponding numerical analysis.Furthermore,as the existing numerical analysis results usually depend on the index of the saddle points implicitly,the proved numerical accuracy may be reduced if the index is high in many applications,which indicates the lack of robustness with respect to the index.To address these issues,we derive the error estimates for numerical discretization of the constrained high-index saddle dynamics on the high-dimensional sphere and then improve it by providing index-robust error analysis in an averaged norm by adjusting the relaxation parameters.The developed results provide mathematical support for the accuracy of numerical computations.
基金supported by the National Natural Science Foundation of China(Nos.12225102,12050002,12288101,12301555)the National Key R&D Program of China(No.2021YFF1200500)the Taishan Scholars Program of Shandong Province。
文摘The authors prove error estimates for the semi-implicit numerical scheme of sphere-constrained high-index saddle dynamics,which serves as a powerful instrument in finding saddle points and constructing the solution landscapes of constrained systems on the high-dimensional sphere.Due to the semi-implicit treatment and the novel computational procedure,the orthonormality of numerical solutions at each time step could not be fully employed to simplify the derivations,and the computations of the state variable and directional vectors are coupled with the retraction,the vector transport and the orthonormalization procedure,which significantly complicates the analysis.They address these issues to prove error estimates for the proposed semi-implicit scheme and then carry out numerical experiments to substantiate the theoretical findings.
基金L.Z.is supported by the National Key Research and Development Program of China 2021YFF1200500 and the National Natural Science Foundation of China(No.12225102,T2321001,12050002,and 12288101)J.Y.is supported by the National Research Foundation,Singapore(Project No.NRF-NRFF13-2021-0005)+1 种基金Q.D.is supported by the National Science Foundation(DMS-2012562 and DMS-1937254)Y.C.is supported by the National Natural Science Foundation of China(No.12171041)。
文摘Rotational Bose-Einstein condensates can exhibit quantized vortices as topological excitations.In this study,the ground and excited states of the rotational Bose-Einstein condensates are systematically studied by calculating the stationary points of the Gross-Pitaevskii energy functional.Various excited states and their connections at different rotational frequencies are revealed in solution landscapes constructed with the constrained high-index saddle dynamics method.Four excitation mechanisms are identified:vortex addition,rearrangement,merging,and splitting.We demonstrate changes in the ground state with increasing rotational frequencies and decipher the evolution of the stability of ground states.
基金supported by the National Natural Science Foundation of China(91959205,U22A20327,82203881,12090022,11831002,and 81801778)Beijing Natural Science Foundation(7222021)+2 种基金Beijing Hospitals Authority Youth Programme(QML20231115)Clinical Medicine Plus X-Young Scholars Project of Peking University(PKU2023LCXQ041)Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Cancer(2020B121201004).
文摘With the rapid development of artificial intelligence,large language models(LLMs)have shown promising capabilities in mimicking human-level language comprehen-sion and reasoning.This has sparked significant interest in applying LLMs to enhance various aspects of healthcare,ranging from medical education to clinical decision support.However,medicine involves multifaceted data modalities and nuanced reasoning skills,presenting challenges for integrating LLMs.This review introduces the fundamental applications of general-purpose and specialized LLMs,demon-strating their utilities in knowledge retrieval,research support,clinical workflow automation,and diagnostic assistance.Recognizing the inherent multimodality of medicine,the review emphasizes the multimodal LLMs and discusses their ability to process diverse data types like medical imaging and electronic health records to augment diagnostic accuracy.To address LLMs'limitations regarding personalization and complex clinical reasoning,the review further explores the emerging develop-ment of LLM-powered autonomous agents for healthcare.Moreover,it summarizes the evaluation methodologies for assessing LLMs'reliability and safety in medical contexts.LLMs have transformative potential in medicine;however,there is a pivotal need for continuous optimizations and ethical oversight before these models can be effectively integrated into clinical practice.
基金supported by the National Natural Science Foundation of China(62474058 and 61804049)the Yuelushan Center for Industrial Innovation(2023YCII0104)+2 种基金the Huxiang High Level Talent Gathering Project(2019RS1023)the Technology Innovation and Entrepreneurship Funds of Hunan Province,P.R.China(2019GK5029)the Fund for Distinguished Young Scholars of Changsha(kq1905012).
文摘Molecular dynamics(MD)is an indispensable atomistic-scale computational tool widely-used in various disciplines.In the past decades,nearly all ab initio MD and machine-learning MD have been based on the general-purpose central/graphics processing units(CPU/GPU),which are well-known to suffer from their intrinsic“memory wall”and“power wall”bottlenecks.Consequently,nowadays MD calculations with ab initio accuracy are extremely time-consuming and power-consuming,imposing serious restrictions on the MD simulation size and duration.To solve this problem,here we propose a special-purpose MD processing unit(MDPU),which could reduce MD time and power consumption by about 103 times(109 times)compared to state-of-the-art machine-learningMD(ab initio MD)based on CPU/GPU,while keeping ab initio accuracy.With significantly-enhanced performance,the proposed MDPU may pave a way for the accurate atomistic-scale analysis of large-size and/or longduration problems which were impossible/impractical to compute before.
文摘Exploratory data analysis plays a major role in obtaining insights from data.Over the last two decades,researchers have proposed several visual data exploration tools that can assist with each step of the analysis process.Nevertheless,in recent years,data analysis requirements have changed significantly.With constantly increasing size and types of data to be analyzed,scalability and analysis duration are now among the primary concerns of researchers.Moreover,in order to minimize the analysis cost,businesses are in need of data analysis tools that can be used with limited analytical knowledge.To address these challenges,traditional data exploration tools have evolved within the last few years.In this paper,with an in-depth analysis of an industrial tabular dataset,we identify a set of additional exploratory requirements for large datasets.Later,we present a comprehensive survey of the recent advancements in the emerging field of exploratory data analysis.We investigate 50 academic and non-academic visual data exploration tools with respect to their utility in the six fundamental steps of the exploratory data analysis process.We also examine the extent to which these modern data exploration tools fulfill the additional requirements for analyzing large datasets.Finally,we identify and present a set of research opportunities in the field of visual exploratory data analysis.
基金supported by the National Natural Science Foundation of China(Nos.12225102,T2321001,12288101 and 12301555)the National Key R&D Program of China(Nos.2021YFF1200500 and 2023YFA1008903)the Taishan Scholars Program of Shandong Province(No.tsqn202306083).
文摘We prove probabilistic error estimates for high-index saddle dynamics with or without constraints to account for the inaccurate values of the model,which could be encountered in various scenarios such as model uncertainties or surrogate model algorithms via machine learning methods. The main contribution lies in incorporating the probabilistic error bound of the model values with the conventional error estimate methods for high-index saddle dynamics. The derived results generalize the error analysis of deterministic saddle dynamics and characterize the affect of the inaccuracy of the model on the convergence rate.
