Ferroelectricity in the Magnetic E-Phase of Orthorhombic Perovskites

We show that the symmetry of the spin zigzag chain E phase of the orthorhombic perovskite manganites and nickelates allows for the existence of a finite ferroelectric polarization. The proposed microscopic mechanism is independent of spin-orbit coupling. We predict that the polarization induced by the E-type magnetic order can potentially be enhanced by up to two orders of magnitude with respect to that in the spiral magnetic phases of TbMnO3 and similar multiferroic compounds.Comment: 4 pages, 2 figures, somewhat changed emphases, accepted to PR

Replica method for eigenvalues of real Wishart product matrices

We show how the replica method can be used to compute the asymptotic eigenvalue spectrum of a real Wishart product matrix. For unstructured factors, this provides a compact, elementary derivation of a polynomial condition on the Stieltjes transform first proved by M{\"u}ller [IEEE Trans. Inf. Theory. 48, 2086-2091 (2002)]. We then show how this computation can be extended to ensembles where the factors have correlated rows. Finally, we derive polynomial conditions on the average values of the minimum and maximum eigenvalues, which match the results obtained by Akemann, Ipsen, and Kieburg [Phys. Rev. E 88, 052118 (2013)] for the complex Wishart product ensemble.Comment: 35 pages, 4 figure

How much data do I need? A case study on medical data

The collection of data to train a Deep Learning network is costly in terms of effort and resources. In many cases, especially in a medical context, it may have detrimental impacts. Such as requiring invasive medical procedures or processes which could in themselves cause medical harm. However, Deep Learning is seen as a data hungry method. Here, we look at two commonly held adages i) more data gives better results and ii) transfer learning will aid you when you don't have enough data. These are widely assumed to be true and used as evidence for choosing how to solve a problem when Deep Learning is involved. We evaluate six medical datasets and six general datasets. Training a ResNet18 network on varying subsets of these datasets to evaluate `more data gives better results'. We take eleven of these datasets as the sources for Transfer Learning on subsets of the twelfth dataset -- Chest -- in order to determine whether Transfer Learning is universally beneficial. We go further to see whether multi-stage Transfer Learning provides a consistent benefit. Our analysis shows that the real situation is more complex than these simple adages -- more data could lead to a case of diminishing returns and an incorrect choice of dataset for transfer learning can lead to worse performance, with datasets which we would consider highly similar to the Chest dataset giving worse results than datasets which are more dissimilar. Multi-stage transfer learning likewise reveals complex relationships between datasets.Comment: 10 pages, 7 figure

Age and growth of four-spotted megrim (Lepidorhombus boscii Risso, 1810) from Saros Bay (Northern Aegean Sea, Turkey)

In this study, the growth parameters of the four-spotted megrim, (Lepidorhombus boscii Risso, 1810), were studied in Saros Bay, which had been closed to bottom trawl fishery since 2000. The sex ratio of females to males was 1:0.42. Length-weight relationships were W=0.0032L3.31 and W=0.0069L3.04 for females and males, respectively. Growth parameters of the populations were L∞=49.8 cm, k=0.09 year-1, t0=-2.15 year for females; L∞=39.1 cm, k=0.11 year-1, t0=-2.59 year for males. The growth performance index (Φ’) was found to be 2.35 and 2.23 for females and males, respectively

Persuasive technology for overcoming food cravings and improving snack choices

This research is partially supported by EPSRC Grant EP/G004560