The existing multi-view subspace clustering algorithms based on tensor singular value decomposition(t-SVD)predominantly utilize tensor nuclear norm to explore the intra view correlation between views of the same sampl...The existing multi-view subspace clustering algorithms based on tensor singular value decomposition(t-SVD)predominantly utilize tensor nuclear norm to explore the intra view correlation between views of the same samples,while neglecting the correlation among the samples within different views.Moreover,the tensor nuclear norm is not fully considered as a convex approximation of the tensor rank function.Treating different singular values equally may result in suboptimal tensor representation.A hypergraph regularized multi-view subspace clustering algorithm with dual tensor log-determinant(HRMSC-DTL)was proposed.The algorithm used subspace learning in each view to learn a specific set of affinity matrices,and introduced a non-convex tensor log-determinant function to replace the tensor nuclear norm to better improve global low-rankness.It also introduced hyper-Laplacian regularization to preserve the local geometric structure embedded in the high-dimensional space.Furthermore,it rotated the original tensor and incorporated a dual tensor mechanism to fully exploit the intra view correlation of the original tensor and the inter view correlation of the rotated tensor.At the same time,an alternating direction of multipliers method(ADMM)was also designed to solve non-convex optimization model.Experimental evaluations on seven widely used datasets,along with comparisons to several state-of-the-art algorithms,demonstrated the superiority and effectiveness of the HRMSC-DTL algorithm in terms of clustering performance.展开更多
Background While Nordic hamstring exercise(NHE)training has been shown to reduce hamstring strains,the muscle-specific adaptations to NHE across the 4 hamstrings remain unclear.This study investigates architectural an...Background While Nordic hamstring exercise(NHE)training has been shown to reduce hamstring strains,the muscle-specific adaptations to NHE across the 4 hamstrings remain unclear.This study investigates architectural and microstructural adaptations of the biceps femoris short head(BFsh),biceps femoris long head(BFlh),semitendinosus(ST),and semimembranosus(SM)in response to an NHE intervention.Methods Eleven subjects completed 9 weeks of supervised NHE training followed by 3 weeks of detraining.Magnetic resonance imaging was performed at pre-training,post-training,and detraining to assess architectural(volume,fiber tract length,and fiber tract angle)and microstructural(axial(AD),mean(MD),radial(RD)diffusivities,and fractional anisotropy(FA))parameters of the 4 hamstrings.Results NHE training induced significant but non-uniform hamstring muscle hypertrophy(BFsh:22%,BFlh:9%,ST:26%,SM:6%)and fiber tract length increase(BFsh:11%,BFlh:7%,ST:18%,SM:10%).AD(5%),MD(4%),and RD(5%)showed significant increases,but fiber tract angle and FA remained unchanged.After detraining,only ST showed a significant reduction(8%)in volume,which remained higher than the pre-training value.While fiber tract lengths returned to baseline,AD,MD,and RD remained higher than pre-training levels for all hamstrings.Conclusion The 9-week NHE training substantially increased hamstring muscle volume with greater hypertrophy in ST and BFsh.Hypertrophy was accompanied by increases in fiber tract lengths and cross-sections(increased RD).After 3 weeks of detraining,fiber tract length gains across all hamstrings declined,emphasizing the importance of sustained training to maintain all the protective adaptations.展开更多
The problem of low accuracy of POI(Points of Interest)recommendation in LBSN(Location-Based Social Networks)has not been effectively solved.In this paper,a POI recommendation algorithm based on non-convex regularized ...The problem of low accuracy of POI(Points of Interest)recommendation in LBSN(Location-Based Social Networks)has not been effectively solved.In this paper,a POI recommendation algorithm based on non-convex regularized tensor completion is proposed.The fourth-order tensor is constructed by using the current location category,the next location category,time and season,the regularizer is added to the objective function of tensor completion to prevent over-fitting and reduce the error of the model.The proximal algorithm is used to solve the objective function,and the adaptive momentum is introduced to improve the efficiency of the solution.The experimental results show that the algorithm can improve recommendation accuracy while reducing the time cost.展开更多
基金supported by National Natural Science Foundation of China(No.61806006)Priority Academic Program Development of Jiangsu Higher Education Institutions。
文摘The existing multi-view subspace clustering algorithms based on tensor singular value decomposition(t-SVD)predominantly utilize tensor nuclear norm to explore the intra view correlation between views of the same samples,while neglecting the correlation among the samples within different views.Moreover,the tensor nuclear norm is not fully considered as a convex approximation of the tensor rank function.Treating different singular values equally may result in suboptimal tensor representation.A hypergraph regularized multi-view subspace clustering algorithm with dual tensor log-determinant(HRMSC-DTL)was proposed.The algorithm used subspace learning in each view to learn a specific set of affinity matrices,and introduced a non-convex tensor log-determinant function to replace the tensor nuclear norm to better improve global low-rankness.It also introduced hyper-Laplacian regularization to preserve the local geometric structure embedded in the high-dimensional space.Furthermore,it rotated the original tensor and incorporated a dual tensor mechanism to fully exploit the intra view correlation of the original tensor and the inter view correlation of the rotated tensor.At the same time,an alternating direction of multipliers method(ADMM)was also designed to solve non-convex optimization model.Experimental evaluations on seven widely used datasets,along with comparisons to several state-of-the-art algorithms,demonstrated the superiority and effectiveness of the HRMSC-DTL algorithm in terms of clustering performance.
基金financial support from the general electric (GE) healthcareAustralian Research Council Discovery Project (DP200101476)+5 种基金in parts by National Institutes of Health (NIH) grants (R01 AR077604, R01 EB002524, R01 AR079431, P41 EB02706)Stanford Graduate FellowshipThe University of Queensland Graduate ScholarshipNational Health and Medical Research Council of Australia Fellowship (#1194937)Wu Tsai Human Performance Alliance at Stanford Universitythe Joe and Clara Tsai Foundation
文摘Background While Nordic hamstring exercise(NHE)training has been shown to reduce hamstring strains,the muscle-specific adaptations to NHE across the 4 hamstrings remain unclear.This study investigates architectural and microstructural adaptations of the biceps femoris short head(BFsh),biceps femoris long head(BFlh),semitendinosus(ST),and semimembranosus(SM)in response to an NHE intervention.Methods Eleven subjects completed 9 weeks of supervised NHE training followed by 3 weeks of detraining.Magnetic resonance imaging was performed at pre-training,post-training,and detraining to assess architectural(volume,fiber tract length,and fiber tract angle)and microstructural(axial(AD),mean(MD),radial(RD)diffusivities,and fractional anisotropy(FA))parameters of the 4 hamstrings.Results NHE training induced significant but non-uniform hamstring muscle hypertrophy(BFsh:22%,BFlh:9%,ST:26%,SM:6%)and fiber tract length increase(BFsh:11%,BFlh:7%,ST:18%,SM:10%).AD(5%),MD(4%),and RD(5%)showed significant increases,but fiber tract angle and FA remained unchanged.After detraining,only ST showed a significant reduction(8%)in volume,which remained higher than the pre-training value.While fiber tract lengths returned to baseline,AD,MD,and RD remained higher than pre-training levels for all hamstrings.Conclusion The 9-week NHE training substantially increased hamstring muscle volume with greater hypertrophy in ST and BFsh.Hypertrophy was accompanied by increases in fiber tract lengths and cross-sections(increased RD).After 3 weeks of detraining,fiber tract length gains across all hamstrings declined,emphasizing the importance of sustained training to maintain all the protective adaptations.
文摘The problem of low accuracy of POI(Points of Interest)recommendation in LBSN(Location-Based Social Networks)has not been effectively solved.In this paper,a POI recommendation algorithm based on non-convex regularized tensor completion is proposed.The fourth-order tensor is constructed by using the current location category,the next location category,time and season,the regularizer is added to the objective function of tensor completion to prevent over-fitting and reduce the error of the model.The proximal algorithm is used to solve the objective function,and the adaptive momentum is introduced to improve the efficiency of the solution.The experimental results show that the algorithm can improve recommendation accuracy while reducing the time cost.
