High resolution numerical-relativity simulations for the merger of binary magnetized neutron stars

We perform high-resolution magnetohydrodynamics simulations of binary neutron star mergers in numerical relativity on the Japanese supercomputer K. The neutron stars and merger remnants are covered by a grid spacing of 70\,m, which yields the highest-resolution results among those derived so far. By an in-depth resolution study, we clarify several amplification mechanisms of magnetic fields during the binary neutron star merger for the first time. First, the Kelvin-Helmholtz instability developed in the shear layer at the onset of the merger significantly amplifies the magnetic fields. A hypermassive neutron star (HMNS) formed after the merger is then subject to the nonaxisymmetric magnetorotational instability, which amplifies the magnetic field in the HMNS. These two amplification mechanisms cannot be found with insufficient-resolution runs. We also show that the HMNS eventually collapses to a black hole surrounded by an accretion torus which is strongly magnetized at birth.Comment: 5 pages, 4 figures, to be appeared in PRD rapid communicatio

High-resolution magnetohydrodynamics simulation of black hole-neutron star merger: Mass ejection and short gamma-ray burst

We report results of a high-resolution numerical-relativity simulation for the merger of black hole-magnetized neutron star binaries on Japanese supercomputer "K". We focus on a binary that is subject to tidal disruption and subsequent formation of a massive accretion torus. We find the launch of thermally driven torus wind, subsequent formation of a funnel wall above the torus and a magnetosphere with collimated poloidal magnetic field, and high Blandford-Znajek luminosity. We show for the first time this picture in a self-consistent simulation. The turbulence-like motion induced by the non-axisymmetric magnetorotational instability as well as the Kelvin-Helmholtz instability inside the accretion torus works as an agent to drive the mass accretion and converts the accretion energy to thermal energy, which results in the generation of a strong wind. By an in-depth resolution study, we reveal that high resolution is essential to draw such a picture. We also discuss the implication for the r-process nucleosynthesis, the radioactively-powered transient emission, and short gamma-ray bursts.Comment: 8 pages, 8 figures, to be appeared in PR

Effect of Suplatast Tosilate on Antileukotriene Non-Responders with Mild-to-Moderate Persistent Asthma

ABSTRACTBackgroundImmunomodulatory therapy has been recently introduced for the management of asthma. Suplatast tosilate (ST), a new immune-modifying drug, is known to improve the airway function by inhibiting the release of Th-2 cytokines. However, its efficacy as a controller listed in the guideline, Global Initiative for Asthma 2005 has not been established. In this study we investigated the role of ST in leukotriene receptor antagonist (LTRA) non-responders with mild-to-moderate persistent asthma before initiating corticosteroids inhalation therapy.MethodsThis was a prospective open-level clinical trial. LTRAs was given to 41 patients with asthma for 4 weeks and clinical efficacy was assessed using daily symptom scores. The 10 patients, aged 2.5-8.5 years, who failed to show clinical improvement, were defined as LTRA non-responders. After a 1-week washout period, the efficacy of ST was investigated and compared with LTRA non-responders for the following 4 weeks.ResultsLTRA non-responders showed a significant improvement in the average symptom score, peak expiratory flow, use of rescue medication and the proportion of symptom-free days with ST therapy.ConclusionsST is a good choice for patients who have failed to respond to LTRAs. ST should therefore be added to the list of treatment options for such patients

Towards Understanding the Mechanism of Contrastive Learning via Similarity Structure: A Theoretical Analysis

Contrastive learning is an efficient approach to self-supervised representation learning. Although recent studies have made progress in the theoretical understanding of contrastive learning, the investigation of how to characterize the clusters of the learned representations is still limited. In this paper, we aim to elucidate the characterization from theoretical perspectives. To this end, we consider a kernel-based contrastive learning framework termed Kernel Contrastive Learning (KCL), where kernel functions play an important role when applying our theoretical results to other frameworks. We introduce a formulation of the similarity structure of learned representations by utilizing a statistical dependency viewpoint. We investigate the theoretical properties of the kernel-based contrastive loss via this formulation. We first prove that the formulation characterizes the structure of representations learned with the kernel-based contrastive learning framework. We show a new upper bound of the classification error of a downstream task, which explains that our theory is consistent with the empirical success of contrastive learning. We also establish a generalization error bound of KCL. Finally, we show a guarantee for the generalization ability of KCL to the downstream classification task via a surrogate bound

Growing Neural Gas with Different Topologies for 3D Space Perception

Three-dimensional space perception is one of the most important capabilities for an autonomous mobile robot in order to operate a task in an unknown environment adaptively since the autonomous robot needs to detect the target object and estimate the 3D pose of the target object for performing given tasks efficiently. After the 3D point cloud is measured by an RGB-D camera, the autonomous robot needs to reconstruct a structure from the 3D point cloud with color information according to the given tasks since the point cloud is unstructured data. For reconstructing the unstructured point cloud, growing neural gas (GNG) based methods have been utilized in many research studies since GNG can learn the data distribution of the point cloud appropriately. However, the conventional GNG based methods have unsolved problems about the scalability and multi-viewpoint clustering. In this paper, therefore, we propose growing neural gas with different topologies (GNG-DT) as a new topological structure learning method for solving the problems. GNG-DT has multiple topologies of each property, while the conventional GNG method has a single topology of the input vector. In addition, the distance measurement in the winner node selection uses only the position information for preserving the environmental space of the point cloud. Next, we show several experimental results of the proposed method using simulation and RGB-D datasets measured by Kinect. In these experiments, we verified that our proposed method almost outperforms the other methods from the viewpoint of the quantization and clustering errors. Finally, we summarize our proposed method and discuss the future direction on this research

Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization

Domain shifts in the training data are common in practical applications of machine learning, they occur for instance when the data is coming from different sources. Ideally, a ML model should work well independently of these shifts, for example, by learning a domain-invariant representation. Moreover, privacy concerns regarding the source also require a domain-invariant representation. In this work, we provide theoretical results that link domain invariant representations -- measured by the Wasserstein distance on the joint distributions -- to a practical semi-supervised learning objective based on a cross-entropy classifier and a novel domain critic. Quantitative experiments demonstrate that the proposed approach is indeed able to practically learn such an invariant representation (between two domains), and the latter also supports models with higher predictive accuracy on both domains, comparing favorably to existing techniques.Comment: 20 pages including appendix. Under Revie

マウスガードの自律神経活動への影響 : 瞳孔フラッシュ応答による定量的評価

Background:Recently, it has been reported that mouth guards (MGs), which reduce the incidence and severity of traumatic oral injuries in contact sports, may actually affect sports performance. We have observed that a majority of subjects showed improved dynamic visual acuity during head rotation when using a MG, but subjects who were unwilling to use a MG showed the opposite effect. Thus, we hypothesized that unpleasant sensations due to MGs may decrease sports performance.Methods:In this study, we measured autonomic nervous system activity to evaluate unpleasant sensations objectively and quantitatively by measuring the pupillary flash response (PFR) and heart rate variability (HRV), before, during, and after wearing 3- and 5-mm-thick custom-made MGs in 10 healthy subjects.Results:It was found that the 5-mm MG had a higher incidence of unpleasant sensations (50% of subjects) than did the 3-mm MG (10%). PFR (not HRV) analysis showed that both sympathetic and parasympathetic nervous system activities increased in subjects with unpleasant sensations.Conclusions:We suggest that the unpleasant sensation induced this unusual autonomic nervous system response, which could not be detected by traditional methods such as HRV analysis. By using PFR analysis, it is possible to make MGs without unpleasant sensations for better sports performance.博士（医学）・乙第1306号・平成24年11月27日Copyright © 2012 Japanese Stomatological Society. Published by Elsevier Japan K