Let A be m by n matrix, M and N be positive definite matrices of order in and n respectively. This paper presents an efficient method for computing (M-N) singular value decomposition((M-N) SVD) of A on a cube connecte...Let A be m by n matrix, M and N be positive definite matrices of order in and n respectively. This paper presents an efficient method for computing (M-N) singular value decomposition((M-N) SVD) of A on a cube connected single instruction stream-multiple data stream(SIMD) parallel computer. This method is based on a one-sided orthogonalization algorithm due to Hestenes. On the cube connected SIMD parallel computer with o(n) processors, the (M -- N) SVD of a matrix A requires a computation time of o(m3 log m/n).展开更多
This paper lays out a hierarchical,appropriate-complexity framework for conceptualizing movement-path segments at different spatiotemporal scales in a way that facilitates comparative analyses and bridges behavior and...This paper lays out a hierarchical,appropriate-complexity framework for conceptualizing movement-path segments at different spatiotemporal scales in a way that facilitates comparative analyses and bridges behavior and mathematical concepts.It then outlines a process for generating a multimode,multiscale stochastic simulation model that can be used to test animal movement hypotheses and make predictions of movement responses to management and global change.Many methods for analyzing movement data begin by generating step-length(SL)and turning-angle(TA)distributions from relocation time-series data,some of which are linked to ecological,landscape,and environmental covariates.The frequency at which these data are collected may vary from sub-seconds to several hours.The kinds of questions that may be asked of these data,however,are very much scale dependent.The hierarchical path-segmentation(HPS)framework presented here clarifies how the scale at which SL and TA data are collected relates to other sub-and super-diel scales.Difficulties arise because the information contained in SL and TA time series are often not directly relatable to the physiological,ecological,and sociological factors that drive the structure of movement paths at longer scales.These difficulties are overcome by anchoring the classification of movement types around the concept of fixed-period(24 h)diel activity routines and providing a bridge between behavioral/ecological and stochastic-walk concepts(means,variances,correlations,individual-state and local environmental covariates).This bridge is achieved through the generation of relatively short segments conceived as characteristic sequences of fundamental movement elements.These short segments are then used to characterize longer canonical-activity-mode segments that emerge through movement at behaviorally relevant sub-diel scales.HPS thus provides a novel system for integrating sub-minute movement sequences into canonical activity modes(CAMs)that,in turn,can be strung together into various types of diel activity routines(DARs).These DARs both vary among individuals within a given day,and for any given individual across time and under the influence of landscape factors.An understanding of how DARs are influenced by environmental inputs will help us predict the response of supra-diel lifetime movement phases(LiMPs)of individuals,as well as their complete lifetime tracks(LiTs),to anthropogenically induced global change.展开更多
文摘Let A be m by n matrix, M and N be positive definite matrices of order in and n respectively. This paper presents an efficient method for computing (M-N) singular value decomposition((M-N) SVD) of A on a cube connected single instruction stream-multiple data stream(SIMD) parallel computer. This method is based on a one-sided orthogonalization algorithm due to Hestenes. On the cube connected SIMD parallel computer with o(n) processors, the (M -- N) SVD of a matrix A requires a computation time of o(m3 log m/n).
基金Funded by the A Starker Leopold Chair of Wildlife Ecology at UC Berkeley.
文摘This paper lays out a hierarchical,appropriate-complexity framework for conceptualizing movement-path segments at different spatiotemporal scales in a way that facilitates comparative analyses and bridges behavior and mathematical concepts.It then outlines a process for generating a multimode,multiscale stochastic simulation model that can be used to test animal movement hypotheses and make predictions of movement responses to management and global change.Many methods for analyzing movement data begin by generating step-length(SL)and turning-angle(TA)distributions from relocation time-series data,some of which are linked to ecological,landscape,and environmental covariates.The frequency at which these data are collected may vary from sub-seconds to several hours.The kinds of questions that may be asked of these data,however,are very much scale dependent.The hierarchical path-segmentation(HPS)framework presented here clarifies how the scale at which SL and TA data are collected relates to other sub-and super-diel scales.Difficulties arise because the information contained in SL and TA time series are often not directly relatable to the physiological,ecological,and sociological factors that drive the structure of movement paths at longer scales.These difficulties are overcome by anchoring the classification of movement types around the concept of fixed-period(24 h)diel activity routines and providing a bridge between behavioral/ecological and stochastic-walk concepts(means,variances,correlations,individual-state and local environmental covariates).This bridge is achieved through the generation of relatively short segments conceived as characteristic sequences of fundamental movement elements.These short segments are then used to characterize longer canonical-activity-mode segments that emerge through movement at behaviorally relevant sub-diel scales.HPS thus provides a novel system for integrating sub-minute movement sequences into canonical activity modes(CAMs)that,in turn,can be strung together into various types of diel activity routines(DARs).These DARs both vary among individuals within a given day,and for any given individual across time and under the influence of landscape factors.An understanding of how DARs are influenced by environmental inputs will help us predict the response of supra-diel lifetime movement phases(LiMPs)of individuals,as well as their complete lifetime tracks(LiTs),to anthropogenically induced global change.
