Spectroscopic and structural characterization of pascoite

Pascoite mineral having yellow-orange colour of Colorado, USA origin has been characterized by EPR, optical and NIR spectroscopy. The colour dark red-orange to yellow-orange colour of the pascoite indicates that the mineral contain mixed valency of vanadium. The optical spectrum exhibits a number of electronic bands due to presence of VO(II) ions in the mineral. From EPR studies, the parameters of g, A are evaluated and the data confirm that the ion is in distorted octahedron. Optical absorption studies reveal that two sets of VO(II) is in distorted octahedron. The bands in NIR spectra are due to the overtones and combinations of water molecules.

Are the ‘cave’ minerals archerite (K,NH4)H2PO4 and biphosphammite (K,NH4)H2PO4 identical? A molecular structural study

The molecular structure of the mineral archerite ((K,NH4)H2PO4) has been determined and compared with that of biphosphammite ((NH4,K)H2PO4). Raman spectroscopy and infrared spectroscopy has been used to characterise these ‘cave’ minerals. Both minerals originated from the Murra-el-elevyn Cave, Eucla, Western Australia. The mineral is formed by the reaction of the chemicals in bat guano with calcite substrates. Raman and infrared bands are assigned to H2PO4-, OH and NH stretching vibrations. The Raman band at 981 cm-1 is assigned to the HOP stretching vibration. Bands in the 1200 to 1800 cm-1 region are associated with NH4+ bending modes. The molecular structure of the two minerals appear to be very similar, and it is therefore concluded that the two minerals are identical.

Rademacher and Gaussian complexities: Risk bounds and structural results

We investigate the use of certain data-dependent estimates of the complexity of a function class, called Rademacher and Gaussian complexities. In a decision theoretic setting, we prove general risk bounds in terms of these complexities. We consider function classes that can be expressed as combinations of functions from basis classes and show how the Rademacher and Gaussian complexities of such a function class can be bounded in terms of the complexity of the basis classes. We give examples of the application of these techniques in finding data-dependent risk bounds for decision trees, neural networks and support vector machines.