Symmetric Functions and Generating Functions for Descents and Major Indices in Compositions

In [18], Mendes and Remmel showed how Gessel’s generating function for the distributions of the number of descents, the major index, and the number of inversions of permutations in the symmetric group could be derived by applying a ring homomorphism defined on the ring of symmetric functions to a simple symmetric function identity. We show how similar methods may be used to prove analogues of that generating function for compositions.     

Soft x‐ray techniques to study mesoscale magnetism

Femtosecond pulses from linac-based free-electron lasers are unique tools for future time-resolved experiments. In March 2004, BESSY published the Technical Design Report (TDR) for a free-electron-laser user facility that covers the VUV to soft X-ray range (BESSY FEL) [1 Krämer, D., Jaeschke, E. and Eberhardt, W., eds. 2004. Technical Design Report, Berlin, , Germany: BESSY.  [Google Scholar]]. This second-generation FEL facility is seeded and uses the High-Gain Harmonic-Generation (HGHG) [2 Doyuran, A. 2001. Phys. Rev. Lett., 86: 5902 [Google Scholar]] scheme to produce coherent radiation down to the 1 nm range.     

Common learning with intertemporal dependence

Consider two agents who learn the value of an unknown parameter by observing a sequence of private signals. Will the agents commonly learn the value of the parameter, i.e., will the true value of the parameter become approximate common-knowledge? If the signals are independent and identically distributed across time (but not necessarily across agents), the answer is yes (Cripps et al., Econometrica, 76(4):909–933, 2008). This paper explores the implications of allowing the signals to be dependent over time. We present a counterexample showing that even extremely simple time dependence can preclude common learning, and present sufficient conditions for common learning.     

Lie superalgebras whose enveloping algebras satisfy a non-matrix polynomial identity

Let L be a Lie superalgebra with its enveloping algebra U(L) over a field F. A polynomial identity is called non-matrix if it is not satisfied by the algebra of 2×2 matrices over F. We characterize L when U(L) satisfies a non-matrix polynomial identity. We also characterize L when U(L) is Lie solvable, Lie nilpotent, or Lie super-nilpotent.     

A Deterministic Method for Robust Estimation of Multivariate Location and Shape

Abstract

The existence of outliers in a data set and how to deal with them is an important problem in statistics. The minimum volume ellipsoid (MVE) estimator is a robust estimator of location and covariate structure; however its use has been limited because there are few computationally attractive methods. Determining the MVE consists of two parts—finding the subset of points to be used in the estimate and finding the ellipsoid that covers this set. This article addresses the first problem. Our method will also allow us to compute the minimum covariance determinant (MCD) estimator. The proposed method of subset selection is called the effective independence distribution (EID) method, which chooses the subset by minimizing determinants of matrices containing the data. This method is deterministic, yielding reproducible estimates of location and scatter for a given data set. The EID method of finding the MVE is applied to several regression data sets where the true estimate is known. Results show that the EID method, when applied to these data sets, produces the subset of data more quickly than conventional procedures and that there is less than 6% relative error in the estimates. We also give timing results illustrating the feasibility of our method for larger data sets. For the case of 10,000 points in 10 dimensions, the compute time is under 25 minutes.     

Euler–Mahonian statistics via polyhedral geometry

A variety of descent and major-index statistics have been defined for symmetric groups, hyperoctahedral groups, and their generalizations. Typically associated to a pair of such statistics is an Euler–Mahonian distribution, a bivariate polynomial encoding the statistics; such distributions often appear in rational bivariate generating-function identities. We use techniques from polyhedral geometry to establish new multivariate identities generalizing those giving rise to many of the known Euler–Mahonian distributions. The original bivariate identities are then specializations of these multivariate identities. As a consequence of these new techniques we obtain bijective proofs of the equivalence of the bivariate distributions for various pairs of statistics.