The growing demand for wireless connectivity has made massive multiple-input multiple-output(MIMO)a cornerstone of modern communication systems.To optimize network performance and resource allocation,an efficient and ...The growing demand for wireless connectivity has made massive multiple-input multiple-output(MIMO)a cornerstone of modern communication systems.To optimize network performance and resource allocation,an efficient and robust approach is joint device activity detection and channel estimation.In this paper,we present an approach utilizing score-based generative models to address the underdetermined nature of channel estimation,which is data-driven and well-suited for the complex and dynamic environment of massive MIMO systems.Our experimental results,based on a comprehensive dataset generated through Monte-Carlo sampling,demonstrate the high precision of our channel estimation approach,with errors reduced to as low as-45 d B,and exceptional accuracy in detecting active devices.展开更多
Integrated sensing and communications(ISAC)is a key enabler for next-generation wireless systems,aiming to support both high-throughput communication and high-accuracy environmental sensing using shared spectrum and h...Integrated sensing and communications(ISAC)is a key enabler for next-generation wireless systems,aiming to support both high-throughput communication and high-accuracy environmental sensing using shared spectrum and hardware.Theoretical performance metrics,such as mutual information(MI),minimum mean squared error(MMSE),and Bayesian Cram´er-Rao bound(BCRB),play a key role in evaluating ISAC system performance limits.However,in practice,hardware impairments,multipath propagation,interference,and scene constraints often result in nonlinear,multimodal,and non-Gaussian distributions,making it challenging to derive these metrics analytically.Recently,there has been a growing interest in applying score-based generative models to characterize these metrics from data,although not discussed for ISAC.This paper provides a tutorial-style summary of recent advances in score-based performance evaluation,with a focus on ISAC systems.We refer to the summarized framework as scoring ISAC,which not only reflects the core methodology based on score functions but also emphasizes the goal of scoring(i.e.,evaluating)ISAC systems under realistic conditions.We present the connections between classical performance metrics and the score functions and provide the practical training techniques for learning score functions to estimate performance metrics.Proof-of-concept experiments on target detection and localization validate the accuracy of score-based performance estimators against groundtruth analytical expressions,illustrating their ability to replicate and extend traditional analyses in more complex,realistic settings.This framework demonstrates the great potential of score-based generative models in ISAC performance analysis,algorithm design,and system optimization.展开更多
Purpose-Conventional diagnostic techniques,on the other hand,may be prone to subjectivity since they depend on assessment of motions that are often subtle to individual eyes and hence hard to classify,potentially resu...Purpose-Conventional diagnostic techniques,on the other hand,may be prone to subjectivity since they depend on assessment of motions that are often subtle to individual eyes and hence hard to classify,potentially resulting in misdiagnosis.Meanwhile,early nonmotor signs of Parkinson’s disease(PD)can be mild and may be due to variety of other conditions.As a result,these signs are usually ignored,making early PD diagnosis difficult.Machine learning approaches for PD classification and healthy controls or individuals with similar medical symptoms have been introduced to solve these problems and to enhance the diagnostic and assessment processes of PD(like,movement disorders or other Parkinsonian syndromes).Design/methodology/approach-Medical observations and evaluation of medical symptoms,including characterization of a wide range of motor indications,are commonly used to diagnose PD.The quantity of the data being processed has grown in the last five years;feature selection has become a prerequisite before any classification.This study introduces a feature selection method based on the score-based artificial fish swarm algorithm(SAFSA)to overcome this issue.Findings-This study adds to the accuracy of PD identification by reducing the amount of chosen vocal features while to use the most recent and largest publicly accessible database.Feature subset selection in PD detection techniques starts by eliminating features that are not relevant or redundant.According to a few objective functions,features subset chosen should provide the best performance.Research limitations/implications-In many situations,this is an Nondeterministic Polynomial Time(NPHard)issue.This method enhances the PD detection rate by selecting the most essential features from the database.To begin,the data set’s dimensionality is reduced using Singular Value Decomposition dimensionality technique.Next,Biogeography-Based Optimization(BBO)for feature selection;the weight value is a vital parameter for finding the best features in PD classification.Originality/value-PD classification is done by using ensemble learning classification approaches such as hybrid classifier of fuzzy K-nearest neighbor,kernel support vector machines,fuzzy convolutional neural network and random forest.The suggested classifiers are trained using data from UCIMLrepository,and their results are verified using leave-one-person-out cross validation.The measures employed to assess the classifier efficiency include accuracy,F-measure,Matthews correlation coefficient.展开更多
Diffusion models, a family of generative models based on deep learning, have become increasinglyprominent in cutting-edge machine learning research. With distinguished performance in generating samples thatresemble th...Diffusion models, a family of generative models based on deep learning, have become increasinglyprominent in cutting-edge machine learning research. With distinguished performance in generating samples thatresemble the observed data, diffusion models are widely used in image, video, and text synthesis nowadays. Inrecent years, the concept of diffusion has been extended to time-series applications, and many powerful models havebeen developed. Considering the deficiency of a methodical summary and discourse on these models, we providethis survey as an elementary resource for new researchers in this area and to provide inspiration to motivate futureresearch. For better understanding, we include an introduction about the basics of diffusion models. Except forthis, we primarily focus on diffusion-based methods for time-series forecasting, imputation, and generation, andpresent them, separately, in three individual sections. We also compare different methods for the same applicationand highlight their connections if applicable. Finally, we conclude with the common limitation of diffusion-basedmethods and highlight potential future research directions.展开更多
Headache is one of the commonest complaints that doctors need to address in clinical settings.The genetic mechanisms of different types of headache are not well understood while it has been suggested that self-reporte...Headache is one of the commonest complaints that doctors need to address in clinical settings.The genetic mechanisms of different types of headache are not well understood while it has been suggested that self-reported headache and self-reported migraine were genetically correlated.In this study,we performed a meta-analysis of genome-wide association studies(GWAS)on the self-reported headache phenotype from the UK Biobank and the self-reported migraine phenotype from the 23andMe using the Unified Score-based Association Test(metaUSAT)software for genetically correlated phenotypes(N=397,385).We identified 38 loci for headaches,of which 34 loci have been reported before and four loci were newly suggested.The LDL receptor related protein 1(LRP1)-Signal Transducer and Activator of Transcription 6(STAT6)-Short chain Dehydrogenase/Reductase family 9C member 7(SDR9C7)region in chromosome 12 was the most significantly associated locus with a leading p value of 1.24×10^(-62)of rs11172113.The One Cut homeobox 2(ONECUT2)gene locus in chromosome 18 was the strongest signal among the four new loci with a p value of 1.29×10^(-9)of rs673939.Our study demonstrated that the genetically correlated phenotypes of self-reported headache and self-reported migraine can be meta-analysed together in theory and in practice to boost study power to identify more variants for headaches.This study has paved way for a large GWAS meta-analysis involving cohorts of different while genetically correlated headache phenotypes.展开更多
文摘The growing demand for wireless connectivity has made massive multiple-input multiple-output(MIMO)a cornerstone of modern communication systems.To optimize network performance and resource allocation,an efficient and robust approach is joint device activity detection and channel estimation.In this paper,we present an approach utilizing score-based generative models to address the underdetermined nature of channel estimation,which is data-driven and well-suited for the complex and dynamic environment of massive MIMO systems.Our experimental results,based on a comprehensive dataset generated through Monte-Carlo sampling,demonstrate the high precision of our channel estimation approach,with errors reduced to as low as-45 d B,and exceptional accuracy in detecting active devices.
基金supported in part by the General Research Fund from the Research Grants Council of Hong Kong,China,under Project 14202421,Project 14214122,Project 14202723,and Project 14207624in part by Area of Excellence Scheme grant from the Research Grants Council of Hong Kong,China,under Project number AoE/E-601/22-R+2 种基金in part by NSFC/RGC Collaborative Research Scheme from the Research Grants Council of Hong Kong,China,under Project CRS HKUST603/22 and Project CRS HKU702/24in part by the National Natural Science Foundation of China under Grant 62571087in part by Sichuan Science and Technology Program under Grant 2024ZYD0036.
文摘Integrated sensing and communications(ISAC)is a key enabler for next-generation wireless systems,aiming to support both high-throughput communication and high-accuracy environmental sensing using shared spectrum and hardware.Theoretical performance metrics,such as mutual information(MI),minimum mean squared error(MMSE),and Bayesian Cram´er-Rao bound(BCRB),play a key role in evaluating ISAC system performance limits.However,in practice,hardware impairments,multipath propagation,interference,and scene constraints often result in nonlinear,multimodal,and non-Gaussian distributions,making it challenging to derive these metrics analytically.Recently,there has been a growing interest in applying score-based generative models to characterize these metrics from data,although not discussed for ISAC.This paper provides a tutorial-style summary of recent advances in score-based performance evaluation,with a focus on ISAC systems.We refer to the summarized framework as scoring ISAC,which not only reflects the core methodology based on score functions but also emphasizes the goal of scoring(i.e.,evaluating)ISAC systems under realistic conditions.We present the connections between classical performance metrics and the score functions and provide the practical training techniques for learning score functions to estimate performance metrics.Proof-of-concept experiments on target detection and localization validate the accuracy of score-based performance estimators against groundtruth analytical expressions,illustrating their ability to replicate and extend traditional analyses in more complex,realistic settings.This framework demonstrates the great potential of score-based generative models in ISAC performance analysis,algorithm design,and system optimization.
文摘Purpose-Conventional diagnostic techniques,on the other hand,may be prone to subjectivity since they depend on assessment of motions that are often subtle to individual eyes and hence hard to classify,potentially resulting in misdiagnosis.Meanwhile,early nonmotor signs of Parkinson’s disease(PD)can be mild and may be due to variety of other conditions.As a result,these signs are usually ignored,making early PD diagnosis difficult.Machine learning approaches for PD classification and healthy controls or individuals with similar medical symptoms have been introduced to solve these problems and to enhance the diagnostic and assessment processes of PD(like,movement disorders or other Parkinsonian syndromes).Design/methodology/approach-Medical observations and evaluation of medical symptoms,including characterization of a wide range of motor indications,are commonly used to diagnose PD.The quantity of the data being processed has grown in the last five years;feature selection has become a prerequisite before any classification.This study introduces a feature selection method based on the score-based artificial fish swarm algorithm(SAFSA)to overcome this issue.Findings-This study adds to the accuracy of PD identification by reducing the amount of chosen vocal features while to use the most recent and largest publicly accessible database.Feature subset selection in PD detection techniques starts by eliminating features that are not relevant or redundant.According to a few objective functions,features subset chosen should provide the best performance.Research limitations/implications-In many situations,this is an Nondeterministic Polynomial Time(NPHard)issue.This method enhances the PD detection rate by selecting the most essential features from the database.To begin,the data set’s dimensionality is reduced using Singular Value Decomposition dimensionality technique.Next,Biogeography-Based Optimization(BBO)for feature selection;the weight value is a vital parameter for finding the best features in PD classification.Originality/value-PD classification is done by using ensemble learning classification approaches such as hybrid classifier of fuzzy K-nearest neighbor,kernel support vector machines,fuzzy convolutional neural network and random forest.The suggested classifiers are trained using data from UCIMLrepository,and their results are verified using leave-one-person-out cross validation.The measures employed to assess the classifier efficiency include accuracy,F-measure,Matthews correlation coefficient.
文摘Diffusion models, a family of generative models based on deep learning, have become increasinglyprominent in cutting-edge machine learning research. With distinguished performance in generating samples thatresemble the observed data, diffusion models are widely used in image, video, and text synthesis nowadays. Inrecent years, the concept of diffusion has been extended to time-series applications, and many powerful models havebeen developed. Considering the deficiency of a methodical summary and discourse on these models, we providethis survey as an elementary resource for new researchers in this area and to provide inspiration to motivate futureresearch. For better understanding, we include an introduction about the basics of diffusion models. Except forthis, we primarily focus on diffusion-based methods for time-series forecasting, imputation, and generation, andpresent them, separately, in three individual sections. We also compare different methods for the same applicationand highlight their connections if applicable. Finally, we conclude with the common limitation of diffusion-basedmethods and highlight potential future research directions.
基金Funding This study was mainly funded by the Wellcome Trust Strategic Award“Stratifying Resilience and Depression Longitudinally”(STRADL)with Reference Number 104036/Z/14/Z.
文摘Headache is one of the commonest complaints that doctors need to address in clinical settings.The genetic mechanisms of different types of headache are not well understood while it has been suggested that self-reported headache and self-reported migraine were genetically correlated.In this study,we performed a meta-analysis of genome-wide association studies(GWAS)on the self-reported headache phenotype from the UK Biobank and the self-reported migraine phenotype from the 23andMe using the Unified Score-based Association Test(metaUSAT)software for genetically correlated phenotypes(N=397,385).We identified 38 loci for headaches,of which 34 loci have been reported before and four loci were newly suggested.The LDL receptor related protein 1(LRP1)-Signal Transducer and Activator of Transcription 6(STAT6)-Short chain Dehydrogenase/Reductase family 9C member 7(SDR9C7)region in chromosome 12 was the most significantly associated locus with a leading p value of 1.24×10^(-62)of rs11172113.The One Cut homeobox 2(ONECUT2)gene locus in chromosome 18 was the strongest signal among the four new loci with a p value of 1.29×10^(-9)of rs673939.Our study demonstrated that the genetically correlated phenotypes of self-reported headache and self-reported migraine can be meta-analysed together in theory and in practice to boost study power to identify more variants for headaches.This study has paved way for a large GWAS meta-analysis involving cohorts of different while genetically correlated headache phenotypes.
