Linear codes associated to skew-symmetric determinantal varieties

In this article we consider linear codes coming from skew-symmetric determinantal varieties, which are defined by the vanishing of minors of a certain fixed size in the space of skew-symmetric matrices. In odd characteristic, the minimum distances of these codes are determined and a recursive formula for the weight of a general codeword in these codes is given.     

Carlsson's rank conjecture and a conjecture on square-zero upper triangular matrices

Let k be an algebraically closed field and A the polynomial algebra in r variables with coefficients in k. In case the characteristic of k is 2, Carlsson [9] conjectured that for any DG-A-module M of dimension N as a free A-module, if the homology of M is nontrivial and finite dimensional as a k-vector space, then $2^r\le N$. Here we state a stronger conjecture about varieties of square-zero upper triangular $N\times N$ matrices with entries in A. Using stratifications of these varieties via Borel orbits, we show that the stronger conjecture holds when $N<8$ or $r<3$ without any restriction on the characteristic of k. As a consequence, we obtain a new proof for many of the known cases of Carlsson's conjecture and give new results when $N>4$ and $r=2$.     

Norms and lower bounds of some matrix operators on Fibonacci weighted difference sequence space

Norm of an operator T:X→Y is the best possible value of U satisfying the inequality and lower bound for T is the value of L satisfying the inequality where ‖.‖_X and ‖.‖_Y are the norms on the spaces X and Y, respectively. The main goal of this paper is to compute norms and lower bounds for some matrix operators from the weighted sequence space ?_p(w) into a new space called as Fibonacci weighted difference sequence space. For this purpose, we firstly introduce the Fibonacci difference matrix and the space consisting of sequences whose ‐transforms are in .     

An optimal perturbation bound

Cai and Zhang establish separate perturbation bounds for distances with spectral and Frobenius norms (Cai T, Zhang A. Rate‐optimal perturbation bounds for singular subspaces with applications to high‐dimensional statistics. The Annals of Statistics. 2018; Vol. 46, No. 1: 60?89). We extend their theorem to each unitarily invariant norm. It turns out that our estimation is optimal as well.     

Simultaneous quantification of ondansetron and tariquidar in rat and human plasma using a high performance liquid chromatography‐ultraviolet method

Ondansetron, a widely used antiemetic agent, is a P‐glycoprotein (P‐gp) substrate and therefore expression of P‐gp at the blood–brain barrier limits its distribution to the central nervous system (CNS), which was observed to be reversed by coadministration with P‐gp inhibitors. Tariquidar is a potent and selective third‐generation P‐gp inhibitor, and coadministration with ondansetron has shown improved ondansetron distribution to the CNS. There is currently no reported bioanalytical method for simultaneously quantifying ondansetron with a third‐generation P‐gp inhibitor. Therefore, we aimed to develop and validate a method for ondansetron and tariquidar in rat and human plasma samples. A full validation was performed for both ondansetron and tariquidar, and sample stability was tested under various storage conditions. To demonstrate its utility, the method was applied to a preclinical pharmacokinetic study following coadministration of ondansetron and tariquidar in rats. The presented method will be valuable in pharmacokinetic studies of ondansetron and tariquidar in which simultaneous determination may be required. In addition, this is the first report of a bioanalytical method validated for quantification of tariquidar in plasma samples.     

Multidimensional Scaling With Very Large Datasets

Multidimensional scaling has a wide range of applications when observations are not continuous but it is possible to define a distance (or dissimilarity) among them. However, standard implementations are limited when analyzing very large datasets because they rely on eigendecomposition of the full distance matrix and require very long computing times and large quantities of memory. Here, a new approach is developed based on projection of the observations in a space defined by a subset of the full dataset. The method is easily implemented. A simulation study showed that its performance are satisfactory in different situations and can be run in a short time when the standard method takes a very long time or cannot be run because of memory requirements.