Semantic communication(SemCom)aims to achieve high-fidelity information delivery under low communication consumption by only guaranteeing semantic accuracy.Nevertheless,semantic communication still suffers from unexpe...Semantic communication(SemCom)aims to achieve high-fidelity information delivery under low communication consumption by only guaranteeing semantic accuracy.Nevertheless,semantic communication still suffers from unexpected channel volatility and thus developing a re-transmission mechanism(e.g.,hybrid automatic repeat request[HARQ])becomes indispensable.In that regard,instead of discarding previously transmitted information,the incremental knowledge-based HARQ(IK-HARQ)is deemed as a more effective mechanism that could sufficiently utilize the information semantics.However,considering the possible existence of semantic ambiguity in image transmission,a simple bit-level cyclic redundancy check(CRC)might compromise the performance of IK-HARQ.Therefore,there emerges a strong incentive to revolutionize the CRC mechanism,thus more effectively reaping the benefits of both SemCom and HARQ.In this paper,built on top of swin transformer-based joint source-channel coding(JSCC)and IK-HARQ,we propose a semantic image transmission framework SC-TDA-HARQ.In particular,different from the conventional CRC,we introduce a topological data analysis(TDA)-based error detection method,which capably digs out the inner topological and geometric information of images,to capture semantic information and determine the necessity for re-transmission.Extensive numerical results validate the effectiveness and efficiency of the proposed SC-TDA-HARQ framework,especially under the limited bandwidth condition,and manifest the superiority of TDA-based error detection method in image transmission.展开更多
In this study,a multisensor system consisting of 23 potentiometric sensors was applied for long-term online measurements in outlet flow of the water treatment plant.Within 1 month of continuous measurements,the data s...In this study,a multisensor system consisting of 23 potentiometric sensors was applied for long-term online measurements in outlet flow of the water treatment plant.Within 1 month of continuous measurements,the data set of more than 295,000 observations was acquired.The processing of this dataset with conventional chemometric tools was cumbersome and not very informative.Topological data analysis(TDA)was recently suggested in chemometric literature to deal with large spectroscopic datasets.In this research,we explore the opportunities of TDA with respect to multisensor data with only 23 variables.It is shown that TDA allows for convenient data visualization,studying the evolution of water quality during the measurements and tracking the periodical structure in the data related to the water quality depending on the time of the day and the day of the week.TDA appears to be a valuable tool for multisensor data exploration.展开更多
With the great advancement of experimental tools,a tremendous amount of biomolecular data has been generated and accumulated in various databases.The high dimensionality,structural complexity,the nonlinearity,and enta...With the great advancement of experimental tools,a tremendous amount of biomolecular data has been generated and accumulated in various databases.The high dimensionality,structural complexity,the nonlinearity,and entanglements of biomolecular data,ranging from DNA knots,RNA secondary structures,protein folding configurations,chromosomes,DNA origami,molecular assembly,to others at the macromolecular level,pose a severe challenge in their analysis and characterization.In the past few decades,mathematical concepts,models,algorithms,and tools from algebraic topology,combinatorial topology,computational topology,and topological data analysis,have demonstrated great power and begun to play an essential role in tackling the biomolecular data challenge.In this work,we introduce biomolecular topology,which concerns the topological problems and models originated from the biomolecular systems.More specifically,the biomolecular topology encompasses topological structures,properties and relations that are emerged from biomolecular structures,dynamics,interactions,and functions.We discuss the various types of biomolecular topology from structures(of proteins,DNAs,and RNAs),protein folding,and protein assembly.A brief discussion of databanks(and databases),theoretical models,and computational algorithms,is presented.Further,we systematically review related topological models,including graphs,simplicial complexes,persistent homology,persistent Laplacians,de Rham-Hodge theory,Yau-Hausdorff distance,and the topology-based machine learning models.展开更多
In recent years,the solid oxide fuel cell(SOFC)scientific community has invested continuous efforts to employ artificial intelligence methods to design and develop new energy systems.It is crucial to gain a better und...In recent years,the solid oxide fuel cell(SOFC)scientific community has invested continuous efforts to employ artificial intelligence methods to design and develop new energy systems.It is crucial to gain a better understanding of the microscale phenomena that occur in the electrodes.In this review,we present a literature review of the field,discussing the limitations of including microstructural data in existing research and possible research directions to overcome them.This review focuses on a particular research area that uses artificial neural networks(ANNs)to predict the performance of SOFCs.Herein,we show that neural networks are used not only to conform to the newest trends but also for improving the design and providing a better understanding of microscale phenomena that occur in the electrodes.The review concludes by highlighting topological data analysis as a promising area of research that can incorporate detailed microstructure characterization in ANNs for performance prediction.展开更多
基金supported in part by the National Key Research and Development Program of China under Grant 2024YFE0200600in part by the National Natural Science Foundation of China under Grant 62071425+3 种基金in part by the Zhejiang Key Research and Development Plan under Grant 2022C01093in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LR23F010005in part by the National Key Laboratory of Wireless Communications Foundation under Grant 2023KP01601in part by the Big Data and Intelligent Computing Key Lab of CQUPT under Grant BDIC-2023-B-001.
文摘Semantic communication(SemCom)aims to achieve high-fidelity information delivery under low communication consumption by only guaranteeing semantic accuracy.Nevertheless,semantic communication still suffers from unexpected channel volatility and thus developing a re-transmission mechanism(e.g.,hybrid automatic repeat request[HARQ])becomes indispensable.In that regard,instead of discarding previously transmitted information,the incremental knowledge-based HARQ(IK-HARQ)is deemed as a more effective mechanism that could sufficiently utilize the information semantics.However,considering the possible existence of semantic ambiguity in image transmission,a simple bit-level cyclic redundancy check(CRC)might compromise the performance of IK-HARQ.Therefore,there emerges a strong incentive to revolutionize the CRC mechanism,thus more effectively reaping the benefits of both SemCom and HARQ.In this paper,built on top of swin transformer-based joint source-channel coding(JSCC)and IK-HARQ,we propose a semantic image transmission framework SC-TDA-HARQ.In particular,different from the conventional CRC,we introduce a topological data analysis(TDA)-based error detection method,which capably digs out the inner topological and geometric information of images,to capture semantic information and determine the necessity for re-transmission.Extensive numerical results validate the effectiveness and efficiency of the proposed SC-TDA-HARQ framework,especially under the limited bandwidth condition,and manifest the superiority of TDA-based error detection method in image transmission.
基金The authors are grateful to O.Lominoga and Zh.Lyadova from SUE“Vodokanal of St.Petersburg”for their valuable help in organizing the experiments.DK acknowledges financial support from RFBR project#17-33-50101.EL and AL acknowledge partial financial support from the Government of Russian Federation,Grant 08-08.VB thanks the Russian Ministry of Education and Science for support of this work within the framework of the basic part of the state task on the theme:“Adaptive technologies of analytical control based on optical sensors”(Project No.4.7001.2017/BP).
文摘In this study,a multisensor system consisting of 23 potentiometric sensors was applied for long-term online measurements in outlet flow of the water treatment plant.Within 1 month of continuous measurements,the data set of more than 295,000 observations was acquired.The processing of this dataset with conventional chemometric tools was cumbersome and not very informative.Topological data analysis(TDA)was recently suggested in chemometric literature to deal with large spectroscopic datasets.In this research,we explore the opportunities of TDA with respect to multisensor data with only 23 variables.It is shown that TDA allows for convenient data visualization,studying the evolution of water quality during the measurements and tracking the periodical structure in the data related to the water quality depending on the time of the day and the day of the week.TDA appears to be a valuable tool for multisensor data exploration.
基金supported by Nanyang Technological University Startup Grant M4081842Singapore Ministry of Education Academic Research fund Tier 1 RG109/19,MOE-T2EP20120-0013 and MOE-T2EP20220-0010+10 种基金supported by NIH grant GM126189NSF grants DMS-2052983,DMS-1761320,and IIS-1900473supported by Natural Science Foundation of China(NSFC)grant(11971144)Highlevel Scientific Research Foundation of Hebei Provincethe Start-up Research Fund from Yanqi Lake Beijing Institute of Mathematical Sciences and Applicationssupported by Tianjin Natural Science Foundation(Grant No.19JCYBJC30200)supported by National Natural Science Foundation of China(NSFC)grant(12171275)Tsinghua University Spring Breeze Fund(2020Z99CFY044)Tsinghua University Start-up FundTsinghua University Education Foundation fund(042202008)National Center for Theoretical Sciences(NCTS)for providing an excellent research environment while part of this research was done。
文摘With the great advancement of experimental tools,a tremendous amount of biomolecular data has been generated and accumulated in various databases.The high dimensionality,structural complexity,the nonlinearity,and entanglements of biomolecular data,ranging from DNA knots,RNA secondary structures,protein folding configurations,chromosomes,DNA origami,molecular assembly,to others at the macromolecular level,pose a severe challenge in their analysis and characterization.In the past few decades,mathematical concepts,models,algorithms,and tools from algebraic topology,combinatorial topology,computational topology,and topological data analysis,have demonstrated great power and begun to play an essential role in tackling the biomolecular data challenge.In this work,we introduce biomolecular topology,which concerns the topological problems and models originated from the biomolecular systems.More specifically,the biomolecular topology encompasses topological structures,properties and relations that are emerged from biomolecular structures,dynamics,interactions,and functions.We discuss the various types of biomolecular topology from structures(of proteins,DNAs,and RNAs),protein folding,and protein assembly.A brief discussion of databanks(and databases),theoretical models,and computational algorithms,is presented.Further,we systematically review related topological models,including graphs,simplicial complexes,persistent homology,persistent Laplacians,de Rham-Hodge theory,Yau-Hausdorff distance,and the topology-based machine learning models.
文摘In recent years,the solid oxide fuel cell(SOFC)scientific community has invested continuous efforts to employ artificial intelligence methods to design and develop new energy systems.It is crucial to gain a better understanding of the microscale phenomena that occur in the electrodes.In this review,we present a literature review of the field,discussing the limitations of including microstructural data in existing research and possible research directions to overcome them.This review focuses on a particular research area that uses artificial neural networks(ANNs)to predict the performance of SOFCs.Herein,we show that neural networks are used not only to conform to the newest trends but also for improving the design and providing a better understanding of microscale phenomena that occur in the electrodes.The review concludes by highlighting topological data analysis as a promising area of research that can incorporate detailed microstructure characterization in ANNs for performance prediction.
