842 research outputs found
Electric Dipolar Susceptibility of the Anderson-Holstein Model
The temperature dependence of electric dipolar susceptibility \chi_P is
discussed on the basis of the Anderson-Holstein model with the use of a
numerical renormalization group (NRG) technique. Note that P is related with
phonon Green's function D. In order to obtain correct temperature dependence of
P at low temperatures, we propose a method to evaluate P through the Dyson
equation from charge susceptibility \chi_c calculated by the NRG, in contrast
to the direct NRG calculation of D. We find that the irreducible charge
susceptibility estimated from \chi_c agree with the perturbation calculation,
suggesting that our method works well.Comment: 4 pages, 4 figure
Electron Mass Enhancement due to Anharmonic Local Phonons
In order to understand how electron effective mass is enhanced by anharmonic
local oscillation of an atom in a cage composed of other atoms, i.e., {\it
rattling}, we analyze anharmonic Holstein model by using a Green's function
method. Due to the evaluation of an electron mass enhancement factor , we
find that becomes maximum when zero-point energy is comparable with
potential height at which the amplitude of oscillation is rapidly enlarged.
Cooperation of such quantum and rattling effects is considered to be a key
issue to explain the electron mass enhancement in electron-rattling systems.Comment: 3 pages, 3 figures, to appear in J. Phys. Soc. Jpn. Suppl.
(Proceedings for International Conference on Heavy Electrons
Four-electron shell structures and an interacting two-electron system in carbon nanotube quantum dots
Low-temperature transport measurements have been carried out on single-wall
carbon nanotube quantum dots in a weakly coupled regime in magnetic fields up
to 8 Tesla. Four-electron shell filling was observed, and the magnetic field
evolution of each Coulomb peak was investigated, in which magnetic field
induced spin flip and resulting spin polarization were observed. Excitation
spectroscopy measurements have revealed Zeeman splitting of single particle
states for one electron in the shell, and demonstrated singlet and triplet
states with direct observation of the exchange splitting at zero-magnetic field
for two electrons in the shell, the simplest example of the Hund's rule. The
latter indicates the direct analogy to an artificial He atom.Comment: 4 pages, 3 figures, submitted to Physical Review Letter
Cytosolic recognition of flagellin by mouse macrophages restricts Legionella pneumophila infection.
To restrict infection by Legionella pneumophila, mouse macrophages require Naip5, a member of the nucleotide-binding oligomerization domain leucine-rich repeat family of pattern recognition receptors, which detect cytoplasmic microbial products. We report that mouse macrophages restricted L. pneumophila replication and initiated a proinflammatory program of cell death when flagellin contaminated their cytosol. Nuclear condensation, membrane permeability, and interleukin-1beta secretion were triggered by type IV secretion-competent bacteria that encode flagellin. The macrophage response to L. pneumophila was independent of Toll-like receptor signaling but correlated with Naip5 function and required caspase 1 activity. The L. pneumophila type IV secretion system provided only pore-forming activity because listeriolysin O of Listeria monocytogenes could substitute for its contribution. Flagellin monomers appeared to trigger the macrophage response from perforated phagosomes: once heated to disassemble filaments, flagellin triggered cell death but native flagellar preparations did not. Flagellin made L. pneumophila vulnerable to innate immune mechanisms because Naip5+ macrophages restricted the growth of virulent microbes, but flagellin mutants replicated freely. Likewise, after intratracheal inoculation of Naip5+ mice, the yield of L. pneumophila in the lungs declined, whereas the burden of flagellin mutants increased. Accordingly, macrophages respond to cytosolic flagellin by a mechanism that requires Naip5 and caspase 1 to restrict bacterial replication and release proinflammatory cytokines that control L. pneumophila infection
Heavy-Electron Formation and Bipolaronic Transition in the Anharmonic Holstein Model
The emergence of the bipolaronic phase and the formation of the
heavy-electron state in the anharmonic Holstein model are investigated using
the dynamical mean-field theory in combination with the exact diagonalization
method. For a weak anharmonicity, it is confirmed that the first-order
polaron-bipolaron transition occurs from the observation of a discontinuity in
the behavior of several physical quantities. When the anharmonicity is
gradually increased, the polaron-bipolaron transition temperature is reduced as
well as the critical values of the electron-phonon coupling constant for
polaron-bipolaron transition. For a strong anharmonicity, the polaron-bipolaron
transition eventually changes to a crossover behavior. The effect of
anharmonicity on the formation of the heavy-electron state near the
polaron-bipolaron transition and the crossover region is discussed in detail.Comment: 11 pages, 13 figure
Deep domain adaptation by weighted entropy minimization for the classification of aerial images
Fully convolutional neural networks (FCN) are successfully used for the automated pixel-wise classification of aerial images and possibly additional data. However, they require many labelled training samples to perform well. One approach addressing this issue is semi-supervised domain adaptation (SSDA). Here, labelled training samples from a source domain and unlabelled samples from a target domain are used jointly to obtain a target domain classifier, without requiring any labelled samples from the target domain. In this paper, a two-step approach for SSDA is proposed. The first step corresponds to a supervised training on the source domain, making use of strong data augmentation to increase the initial performance on the target domain. Secondly, the model is adapted by entropy minimization using a novel weighting strategy. The approach is evaluated on the basis of five domains, corresponding to five cities. Several training variants and adaptation scenarios are tested, indicating that proper data augmentation can already improve the initial target domain performance significantly resulting in an average overall accuracy of 77.5%. The weighted entropy minimization improves the overall accuracy on the target domains in 19 out of 20 scenarios on average by 1.8%. In all experiments a novel FCN architecture is used that yields results comparable to those of the best-performing models on the ISPRS labelling challenge while having an order of magnitude fewer parameters than commonly used FCNs. © 2020 Copernicus GmbH. All rights reserved
Self-Supervised Adversarial Shape Completion
The goal of this paper is 3D shape completion: given an incomplete instance of a known category, hallucinate a complete version of it that is geometrically plausible. We develop an adversarial framework that makes it possible to learn shape completion in a self-supervised fashion, only from incomplete examples. This is enabled by a discriminator network that rejects incomplete shapes, via a loss function that separately assesses local sub-regions of the generated example and accepts only regions with sufficiently high point count. This inductive bias against empty regions forces the generator to output complete shapes. We demonstrate the effectiveness of this approach on synthetic data from ShapeNet and ModelNet, and on a real mobile mapping dataset with nearly 9'000 incomplete cars. Moreover, we apply it to the KITTI autonomous driving dataset without retraining, to highlight its ability to generalise to different data characteristics
Building Change Detection in Airborne Laser Scanning and Dense Image Matching Point Clouds Using a Residual Neural Network
National Mapping Agencies (NMAs) acquire nation-wide point cloud data from Airborne Laser Scanning (ALS) sensors as well as using Dense Image Matching (DIM) on aerial images. As these datasets are often captured years apart, they contain implicit information about changes in the real world. While detecting changes within point clouds is not a new topic per se, detecting changes in point clouds from different sensors, which consequently have different point densities, point distributions and characteristics, is still an on-going problem. As such, we approach this task using a residual neural network, which detects building changes using height and class information on a raster level. In the experiments, we show that this approach is capable of detecting building changes automatically and reliably independent of the given point clouds and for various building sizes achieving mean F1-Scores of 80.5% and 79.8% for ALS-ALS and ALS-DIM point clouds on an object-level and F1-Scores of 91.1% and 86.3% on a raster-level, respectively
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