24,095 research outputs found
Interlayer tunnelling in Bi2Sr2CaCu2O8+d single crystals
We present measurements of the intrinsic quasi-particle conductivity along the c-axis of 2212-BSCCO single-crystal mesa structures in the superconducting and normal states. Direct measurement of the mesa temperature enables corrections to be made for self-heating and permits the acquisition of reliable I-V characteristics over a wide range of temperatures and voltages. Unlike a conventional superconductor, there is no evidence for any change in the quasiparticle conductivity at Tc, consistent with precursor pairing of electrons in the normal state. At low temperatures the initial low-voltage linear conductivity exhibits a T2 dependence, approaching a limiting value at zero temperature
Understanding the newly observed Y(4008) by Belle
Very recently a new enhancement around 4.05 GeV was observed by Belle
experiment. In this short note, we discuss some possible assignments for this
enhancement, i.e. and molecular state. In these two
assignments, Y(4008) can decay into with comparable
branching ratio with that of . Thus one suggests
high energy experimentalists to look for Y(4008) in channel.
Furthermore one proposes further experiments to search missing channel
, and especially and
, which will be helpful to distinguish and
molecular state assignments for this new enhancement.Comment: 4 pages, 5 figures. Typos correcte
Adversarial Deep Structured Nets for Mass Segmentation from Mammograms
Mass segmentation provides effective morphological features which are
important for mass diagnosis. In this work, we propose a novel end-to-end
network for mammographic mass segmentation which employs a fully convolutional
network (FCN) to model a potential function, followed by a CRF to perform
structured learning. Because the mass distribution varies greatly with pixel
position, the FCN is combined with a position priori. Further, we employ
adversarial training to eliminate over-fitting due to the small sizes of
mammogram datasets. Multi-scale FCN is employed to improve the segmentation
performance. Experimental results on two public datasets, INbreast and
DDSM-BCRP, demonstrate that our end-to-end network achieves better performance
than state-of-the-art approaches.
\footnote{https://github.com/wentaozhu/adversarial-deep-structural-networks.git}Comment: Accepted by ISBI2018. arXiv admin note: substantial text overlap with
arXiv:1612.0597
Many-body effects in tracer particle diffusion with applications for single-protein dynamics on DNA
30% of the DNA in E. coli bacteria is covered by proteins. Such high degree
of crowding affect the dynamics of generic biological processes (e.g. gene
regulation, DNA repair, protein diffusion etc.) in ways that are not yet fully
understood. In this paper, we theoretically address the diffusion constant of a
tracer particle in a one dimensional system surrounded by impenetrable crowder
particles. While the tracer particle always stays on the lattice, crowder
particles may unbind to a surrounding bulk and rebind at another or the same
location. In this scenario we determine how the long time diffusion constant
(after many unbinding events) depends on (i) the unbinding rate of
crowder particles , and (ii) crowder particle line density ,
from simulations (Gillespie algorithm) and analytical calculations. For small
, we find when crowder particles
are immobile on the line, and when
they are diffusing; is the free particle diffusion constant. For large
, we find agreement with mean-field results which do not depend on
. From literature values of and , we show that
the small -limit is relevant for in vivo protein diffusion on a
crowded DNA. Our results applies to single-molecule tracking experiments.Comment: 10 pages, 8 figure
Mostly Music: Sounds of China, Sep. 14, 2001
Betty Xiang, Wei Yanghttps://neiudc.neiu.edu/mostlymusic/1008/thumbnail.jp
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