1,608 research outputs found
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
Impacts of Improper Land Uses in Cities on the Natural Environment and Ecological Landscape Planning
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
Age and growth of four-spotted megrim (Lepidorhombus boscii Risso, 1810) from Saros Bay (Northern Aegean Sea, Turkey)
Persuasive technology for overcoming food cravings and improving snack choices
This research is partially supported by EPSRC Grant EP/G004560
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