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

Note on massless bosonic states in two-dimensional field theories

In a wide class of $G_L\times G_R$ invariant two-dimensional super-renormalizable field theories, the parity-odd part of the two-point function of global currents is completely determined by a fermion one-loop diagram. For any non-trivial fermion content, the two-point function possesses a massless pole which corresponds to massless bosonic physical states. As an application, we show that two-dimensional $\mathcal{N}=(2,2)$ supersymmetric gauge theory without a superpotential possesses $U(1)_L\times U(1)_R$ symmetry and contains one massless bosonic state per fixed spatial momentum. The $\mathcal{N}=(4,4)$ supersymmetric pure Yang-Mills theory possesses $SU(2)_L\times SU(2)_R$ symmetry, and there exist at least three massless bosonic states.Comment: 17pages, 4 figures, uses PTPTeX.cls and feynMF, added an appendi

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

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

Behavioral destabilization induced by the selective serotonin reuptake inhibitor fluoxetine

<p>Abstract</p> <p>Background</p> <p>Selective serotonin reuptake inhibitors (SSRIs) are widely used to treat mood and anxiety disorders. However, neuronal bases for both beneficial and adverse effects of SSRIs remain poorly understood. We have recently shown that the SSRI fluoxetine can reverse the state of maturation of hippocampal granule cells in adult mice. The granule cell "dematuration" is induced in a large population of granule cells, and greatly changes functional and physiological properties of these cells. Here we show that this unique form of neuronal plasticity is correlated with a distinct change in behavior of mice.</p> <p>Results</p> <p>We chronically treated adult male mice with fluoxetine, and examined its effect on several forms of behavior of mice. During fluoxetine treatments, mice showed a marked increase in day-to-day fluctuations of home cage activity levels that was characterized by occasional switching between hypoactivity and hyperactivity within a few days. This destabilized cage activity was accompanied by increased anxiety-related behaviors and could be observed up to 4 weeks after withdrawal from fluoxetine. As reported previously, the granule cell dematuration by fluoxetine includes a reduction of synaptic facilitation at the granule cell output, mossy fiber, synapse to the juvenile level. Mossy fiber synaptic facilitation examined electrophysiologically in acute hippocampal slices also remained suppressed after fluoxetine withdrawal and significantly correlated with the fluctuation of cage activity levels in individual mice. Furthermore, in mice lacking the 5-HT₄receptor, in which the granule cell dematuration has been shown to be attenuated, fluoxetine had no significant effect on the fluctuation of cage activity levels.</p> <p>Conclusions</p> <p>Our results demonstrate that the SSRI fluoxetine can induce marked day-to-day changes in activity levels of mice in the familiar environment, and that the dematuration of the hippocampal granule cells is closely associated with the expression of this destabilized behavior. Based on these results, we propose that the granule cell dematuration can be a potential cellular basis underlying switching-like changes in the behavioral state associated with SSRI treatments.</p