1,952 research outputs found
Feature Representation Analysis of Deep Convolutional Neural Network using Two-stage Feature Transfer -An Application for Diffuse Lung Disease Classification-
Transfer learning is a machine learning technique designed to improve
generalization performance by using pre-trained parameters obtained from other
learning tasks. For image recognition tasks, many previous studies have
reported that, when transfer learning is applied to deep neural networks,
performance improves, despite having limited training data. This paper proposes
a two-stage feature transfer learning method focusing on the recognition of
textural medical images. During the proposed method, a model is successively
trained with massive amounts of natural images, some textural images, and the
target images. We applied this method to the classification task of textural
X-ray computed tomography images of diffuse lung diseases. In our experiment,
the two-stage feature transfer achieves the best performance compared to a
from-scratch learning and a conventional single-stage feature transfer. We also
investigated the robustness of the target dataset, based on size. Two-stage
feature transfer shows better robustness than the other two learning methods.
Moreover, we analyzed the feature representations obtained from DLDs imagery
inputs for each feature transfer models using a visualization method. We showed
that the two-stage feature transfer obtains both edge and textural features of
DLDs, which does not occur in conventional single-stage feature transfer
models.Comment: Preprint of the journal article to be published in IPSJ TOM-51.
Notice for the use of this material The copyright of this material is
retained by the Information Processing Society of Japan (IPSJ). This material
is published on this web site with the agreement of the author (s) and the
IPS
Migdal Effect in Dark Matter Direct Detection Experiments
The elastic scattering of an atomic nucleus plays a central role in dark
matter direct detection experiments. In those experiments, it is usually
assumed that the atomic electrons around the nucleus of the target material
immediately follow the motion of the recoil nucleus. In reality, however, it
takes some time for the electrons to catch up, which results in ionization and
excitation of the atoms. In previous studies, those effects are taken into
account by using the so-called Migdal's approach, in which the final state
ionization/excitation are treated separately from the nuclear recoil. In this
paper, we reformulate the Migdal's approach so that the "atomic recoil" cross
section is obtained coherently, where we make transparent the energy-momentum
conservation and the probability conservation. We show that the final state
ionization/excitation can enhance the detectability of rather light dark matter
in the GeV mass range via the {\it nuclear} scattering. We also discuss the
coherent neutrino-nucleus scattering, where the same effects are expected.Comment: Integrated probability data fixed and Si.dat adde
Microtechnologies for membrane protein studies
Despite the rapid and enormous progress in biotechnologies, the biochemical analysis of membrane proteins is still a difficult task. The presence of the large hydrophobic region buried in the lipid bilayer membrane (transmembrane domain) makes it difficult to analyze membrane proteins in standard assays developed for water-soluble proteins. To handle membrane proteins, the lipid bilayer membrane may be used as a platform to sustain their functionalities. Relatively slow progress in developing micro total analysis systems (μTAS) for membrane protein analysis directly reflects the difficulty of handling lipid membranes, which is a common problem in bulk measurement technologies. Nonetheless, researchers are continuing to develop efficient and sensitive analytical microsystems for the study of membrane proteins. Here, we review the latest developments, which enable detection of events caused by membrane proteins, such as ion channel current, membrane transport, and receptor/ligand interaction, by utilizing microfabricated structures. High-throughput and highly sensitive detection systems for membrane proteins are now becoming a realistic goal. Although most of these systems are still in the early stages of development, we believe this field will become one of the most important applications of μTAS for pharmaceutical and clinical screenings as well as for basic biochemical research
Axial U(1) symmetry and Dirac spectra in high-temperature phase of lattice QCD
The axial symmetry in the high-temperature phase is investigated with
lattice QCD simulations. The gauge ensembles are generated with
M\"obius domain-wall fermions, and the overlap/domain-wall reweighting is
applied. We find that the susceptibility evaluated from the spectrum
of overlap-Dirac eigenvalues is strongly suppressed in the chiral limit. We
also study its volume dependence.Comment: 7 pages, 2 figures, talk presented at the 36th International
Symposium on Lattice Field Theory (Lattice 2018), 22-28 July, 2018, Michigan,
US
Axial symmetry at high temperature in 2-flavor lattice QCD
We investigate the axial symmetry breaking above the critical
temperature in two-flavor lattice QCD. The ensembles are generated with
dynamical M\"obius domain-wall or reweighted overlap fermions. The
susceptibility is extracted from the low-modes spectrum of the overlap Dirac
eigenvalues. We show the quark mass and temperature dependences of
susceptibility. Our results at imply that the
symmetry is restored in the chiral limit. Its coincidence with vanishing
topological susceptibility is observed.Comment: 8 pages, 4 figures, Proceedings of the 35th International Symposium
on Lattice Field Theory, June 18-24, 2017, Granada, Spai
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