The NP-hard maximum-entropy sampling problem (MESP) seeks a maximum (log-) determinant principal submatrix, of given order, from an input covariance matrix C. We give efficient dynamic-programming algorithm for MESP when C (or its inverse) is tridiagonal and generalize it to the situation where support graph spider with constant number legs (and beyond). class arrowhead matrices which natural g...

Frobenius is generally considered the creator of character theory finite groups. This achievement came from study group determinant, which determinant a matrix coming regular representation. We generalize several Frobenius’ results about and use them to find new formulation supercharacter in terms factorizations determinant.

The diffusion Monte Carlo technique is used to calculate and analyze the excitation spectrum of He atoms bound to a cluster of He atoms, by using a previously determined optimum filling of single-fermion orbits with well defined orbital angular momentum L, spin S and parity quantum numbers. The study concentrates on the energies and shapes of the three kinds of states for which the fermionic pa...

Assessing family- and species-level variation in physiological responses to global change across geologic time is critical for understanding factors that underlie changes in species distributions and community composition. Here, we used stable carbon isotopes, leaf nitrogen content and stomatal measurements to assess changes in leaf-level physiology in a mixed conifer community that underwent s...

Environmental turbulence is ubiquitous in natural habitats, but its effect on flying animals remains unknown because most flight studies are performed in still air or artificially smooth flow. Here we show that variability in external airflow limits maximum flight speed in wild orchid bees by causing severe instabilities. Bees flying in front of an outdoor, turbulent air jet become increasingly...

The sparse inverse covariance estimation problem is commonly solved using an l1-regularizedGaussian maximum likelihood estimator known as “graphical lasso”, but its computational cost becomes prohibitive for large data sets. A recent line of results showed–under mild assumptions–that the graphical lasso estimator can be retrieved by soft-thresholding the sample covariance matrix and solving a m...