Electronic properties of doped magnesium thioindate ternary spinel in the normal and in the inverse structure

We present a theoretical study of the structural and electronic properties of the M-doped MgIn2S4 ternary spinel semiconductor with M = V, Cr, and Mn. All substitutions, in the normal and in the inverse structure, are analyzed. Some of these possible substitutions present intermediate-band states in the band gap with a different occupation for a spin component. It increases the possibilities of inter-band transitions and could be interesting for applications in optoelectronic devices. The contribution to, and the electronic configuration of, these intermediate bands for the octahedral and tetrahedral sites is analyzed and discussed. The study of the substitutional energies indicates that these substitutions are favorable. Comparison between the pure and doped hosts absorption coefficients shows that this deeper band opens up more photon absorption channels and could therefore increase the solar-light absorption with respect to the host.

On the Mahalanobis Distance Classification Criterion for Multidimensional Normal Distributions

Many existing engineering works model the statistical characteristics of the entities under study as normal distributions. These models are eventually used for decision making, requiring in practice the definition of the classification region corresponding to the desired confidence level. Surprisingly enough, however, a great amount of computer vision works using multidimensional normal models leave unspecified or fail to establish correct confidence regions due to misconceptions on the features of Gaussian functions or to wrong analogies with the unidimensional case. The resulting regions incur in deviations that can be unacceptable in high-dimensional models. Here we provide a comprehensive derivation of the optimal confidence regions for multivariate normal distributions of arbitrary dimensionality. To this end, firstly we derive the condition for region optimality of general continuous multidimensional distributions, and then we apply it to the widespread case of the normal probability density function. The obtained results are used to analyze the confidence error incurred by previous works related to vision research, showing that deviations caused by wrong regions may turn into unacceptable as dimensionality increases. To support the theoretical analysis, a quantitative example in the context of moving object detection by means of background modeling is given.

Deriving the full-reducing Krivine machine from the small-step operational semantics of normal order

We derive by program transformation Pierre Crégut s full-reducing Krivine machine KN from the structural operational semantics of the normal order reduction strategy in a closure-converted pure lambda calculus. We thus establish the correspondence between the strategy and the machine, and showcase our technique for deriving full-reducing abstract machines. Actually, the machine we obtain is a slightly optimised version that can work with open terms and may be used in implementations of proof assistants.