Infrared photoluminescence - a tool for HgCdTe crystal research

Material and structural parameters of narrow-gap p-type HgCdTe are investigated by infrared photoluminescence. The method is used for analyzing the energy gap, the carrier lifetime, and heterointerfaces and to monitor defects like inclusions. The spatial distribution of the quantities mentioned has been investigated by applying the mapping technique known for III – V semiconductors. The methods is a powerful tool for both crystal growth and device manufacturing.     

Polarization optics for X-rays

An experimental arrangement for X-rays is described which is analogous to the optical bench for polarization investigations. Using this arrangement elliptically polarized X-rays should be generated and proved. Although the dynamical theory of X-ray diffraction is needed for X-ray optics the polarization phenomena can be phenomenologically interpreted in analogy to visible light optics. The experimental results showed that with a wedge-shaped crystal used as the phase shifter any polarization state of X-rays can be generated.     

The Crystal and Molecular Structure of the Smectogenic Compound 5-Hexyl-2-(4-bromophenyl)-1,3-dioxane

The title compound with the formula C₆H₁₃ C₄O₂H₆ C₆H ₄Br (HBPD) crystallizes in the monoclinic space group C 2/c with lattice parameters a = 28.48 Å, b = 5.71 Å, c = 20.19 Å, β = 97.4° and molecules per unit cell. The structure has been solved by the heavy atom method and refined to a final R value of 0.094. – The HBPD molecules adopt a fully stretched form, both the alkyl and phenyl substituent are in equatorial position with respect to the chair-shaped dioxane ring. The orientation of the phenyl group is an intermediate between the perpendicular and parallel conformation. The molecules show a nearly perfect parallel arrangement in the crystal lattice.     

A Maximal Correlation Framework for Fair Machine Learning

As machine learning algorithms grow in popularity and diversify to many industries, ethical and legal concerns regarding their fairness have become increasingly relevant. We explore the problem of algorithmic fairness, taking an information–theoretic view. The maximal correlation framework is introduced for expressing fairness constraints and is shown to be capable of being used to derive regularizers that enforce independence and separation-based fairness criteria, which admit optimization algorithms for both discrete and continuous variables that are more computationally efficient than existing algorithms. We show that these algorithms provide smooth performance–fairness tradeoff curves and perform competitively with state-of-the-art methods on both discrete datasets (COMPAS, Adult) and continuous datasets (Communities and Crimes).