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 Nf=2N_f=2 lattice QCD

The axial $U(1)$ symmetry in the high-temperature phase is investigated with $N_f = 2$ 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 $U(1)_A$ 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 U(1)U(1) symmetry at high temperature in 2-flavor lattice QCD

We investigate the axial $U(1)_A$ 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 $U(1)_A$ susceptibility is extracted from the low-modes spectrum of the overlap Dirac eigenvalues. We show the quark mass and temperature dependences of $U(1)_A$ susceptibility. Our results at $T=220 \, \mathrm{MeV}$ imply that the $U(1)_A$ 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