arXiv subjects

Hiroshi Fukui

Publications and source records attributed to Hiroshi Fukui.

13 recordsLinked to original sources

arXiv2026

Observation of coherently modulated phonon band and lifetime in superlattice

Similar to the behavior of elementary particles, such as photons and electrons, the interference of phonon waves in artificial periodic nanostructures coherently modulates phonon band structures, serving as the foundation for phonon band engineering. However, direct observation of such coherently modulated phonon band structures remains challenging despite substantial insights from existing literature. Here, utilizing high-resolution inelastic X-ray scattering, we observed coherently modulated phonon band structures with phononic band gaps in a short-period GaAs/AlAs superlattice at 300 K and 500 K. Our findings provide the first direct evidence of phonon coherence at and above room temperatures, signifying a major advancement in the artificial engineering of phonon band structures. Furthermore, our experimental observations and ab initio lattice dynamics revealed that the coherently modulated phonon band structure enhances three-phonon scattering channels, strengthening high-order anharmonic effects such as three-phonon scattering and optical phonon softening. Our observations demonstrate the robustness of phonon coherence at high temperatures, and opens new routes for engineering phonon band structure and high-order phonon-phonon scattering by employing a flexible, bottom-up nanostructuring approach, with extensive applications in phononic metamaterials, microelectronics, and thermoelectrics.

cond-mat.mtrl-sci↗

arXiv2026

Antiferromagnetic Dimers in the Parent Phase of a Correlated Kagome Superconductor

Kagome metals are prone to charge-density wave (CDW), magnetic, and superconducting phases, with their flat electronic band conducive for correlated physics. In contrast to the weakly correlated $A$V$_3$Sb$_5$ ($A$ = K, Rb, Cs) kagome metals with a $2\times2$ CDW, CsCr$_3$Sb$_5$ is a correlated metal with a flat band close to the Fermi level, and exhibits a $4\times1$ CDW intertwined with magnetic order. Under pressure, the intertwined orders are suppressed and give way to a dome of superconductivity that emerges from a non-Fermi liquid normal state. Here, we solve the crystal structure of the $4\times 1$ CDW state in CsCr$_3$Sb$_5$, and show it consists of Cr dimers separated by Cr chains. First-principles calculations show the dominant exchange interaction is antiferromagnetic within the dimers, while the intra-chain and dimer-chain couplings are much weaker. The CDW transition of CsCr$_3$Sb$_5$ is found to be more strongly first-order than those in $A$V$_3$Sb$_5$, without significant soft phonons or diffuse scattering above the CDW transition temperature. These findings suggest that fluctuating antiferromagnetic dimers may play a major role in the electron pairing of superconducting CsCr$_3$Sb$_5$.

cond-mat.str-el↗

arXiv2024

Charge Density Fluctuations with Enhanced Superconductivity at the Proposed Nematic Quantum Critical Point

A quantum critical point (QCP) represents a continuous phase transition at absolute zero. At the QCP of an unconventional superconductor, enhanced superconducting transition temperature and magnetic fluctuations strength are often observed together, indicating magnetism-mediated superconductivity. This raises the question of whether quantum fluctuations in other degrees of freedom, such as charge, could similarly boost superconductivity. However, because charge is frequently intertwined with magnetism, isolating and understanding its specific role in Cooper pair formation poses a significant challenge. Here, we report persistent charge density fluctuations (CDF) down to 15 K in the non-magnetic superconductor Sr$_{0.77}$Ba$_{0.23}$Ni$_{2}$As$_{2}$, which lies near a proposed nematic QCP associated with a six-fold enhancement of superconductivity. Our results show that the quasi-elastic CDF does not condense into resolution-limited Bragg peaks and displays non-saturated strength. The CDF completely softens at 25 K, with its critical behavior described by the same mathematical framework as the antiferromagnetic Fermi liquid model, yielding a fitted Curie-Weiss temperature of $θ\approx 0$ K. Additionally, we find that the nematic fluctuations are not lattice-driven, as evidenced by the absence of softening in nematic-coupled in-plane transverse acoustic phonons. Our discovery positions Sr$_{x}$Ba$_{1-x}$Ni$_{2}$As$_{2}$ as a promising candidate for charge-fluctuation-driven nematicity and superconductivity.

cond-mat.supr-con↗

arXiv2024

Density deficit of the Earth's core revealed by a multi-megabar primary pressure scale

An accurate pressure scale is a fundamental requirement to understand planetary interiors. Here, we establish a primary pressure scale extending to the multi-megabar pressures of the Earth's core, by combined measurement of the acoustic velocities and the density from a rhenium sample in a diamond anvil cell using inelastic x-ray scattering and x-ray diffraction. Our scale agrees well with previous primary scales and shock Hugoniots in each experimental pressure range, and reveals that previous scales have overestimated laboratory pressures by at least 20% at 230 gigapascals. It suggests that the light element content in the Earth's inner core (the density deficit relative to iron) is likely to be double what was previously estimated, or the Earth's inner core temperature is much higher than expected, or some combination thereof.

physics.geo-ph↗

arXiv2023

Multi-Object Tracking as Attention Mechanism

We propose a conceptually simple and thus fast multi-object tracking (MOT) model that does not require any attached modules, such as the Kalman filter, Hungarian algorithm, transformer blocks, or graph networks. Conventional MOT models are built upon the multi-step modules listed above, and thus the computational cost is high. Our proposed end-to-end MOT model, \textit{TicrossNet}, is composed of a base detector and a cross-attention module only. As a result, the overhead of tracking does not increase significantly even when the number of instances ($N_t$) increases. We show that TicrossNet runs \textit{in real-time}; specifically, it achieves 32.6 FPS on MOT17 and 31.0 FPS on MOT20 (Tesla V100), which includes as many as $>$100 instances per frame. We also demonstrate that TicrossNet is robust to $N_t$; thus, it does not have to change the size of the base detector, depending on $N_t$, as is often done by other models for real-time processing.

cs.CV↗

arXiv2023

Competing charge-density wave instabilities in the kagome metal ScV$_6$Sn$_6$

Owing to its unique geometry, the kagome lattice hosts various many-body quantum states including frustrated magnetism, superconductivity, and charge-density waves (CDWs), with intense efforts focused on kagome metals exhibiting $2\times2$ CDWs associated with the nesting of van Hove saddle points. Recently, a $\sqrt{3}\times\sqrt{3}$ CDW was discovered in the kagome metal ScV$_6$Sn$_6$ below $T_{\rm CDW}\approx91$~K, whose underlying mechanism and formation process remain unclear. Using inelastic X-ray scattering, we discover a short-range $\sqrt{3}\times\sqrt{3}\times2$ CDW that is dominant in ScV$_6$Sn$_6$ well above $T_{\rm CDW}$, distinct from the $\sqrt{3}\times\sqrt{3}\times3$ CDW below $T_{\rm CDW}$. The short-range CDW grows upon cooling, and is accompanied by the softening of phonons, indicative of its dynamic nature. As the $\sqrt{3}\times\sqrt{3}\times3$ CDW appears, the short-range CDW becomes suppressed, revealing a competition between these CDW instabilities. Our first-principles calculations indicate that the $\sqrt{3}\times\sqrt{3}\times2$ CDW is energetically favored, consistent with experimental observations at high temperatures. However, the $\sqrt{3}\times\sqrt{3}\times3$ CDW is selected as the ground state likely due to a large wavevector-dependent electron-phonon coupling, which also accounts for the enhanced electron scattering above $T_{\rm CDW}$. The competing CDW instabilities in ScV$_6$Sn$_6$ lead to an unusual CDW formation process, with the most pronounced phonon softening and the static CDW occurring at different wavevectors.

cond-mat.str-el↗

arXiv2022

Visual Explanation of Deep Q-Network for Robot Navigation by Fine-tuning Attention Branch

Robot navigation with deep reinforcement learning (RL) achieves higher performance and performs well under complex environment. Meanwhile, the interpretation of the decision-making of deep RL models becomes a critical problem for more safety and reliability of autonomous robots. In this paper, we propose a visual explanation method based on an attention branch for deep RL models. We connect attention branch with pre-trained deep RL model and the attention branch is trained by using the selected action by the trained deep RL model as a correct label in a supervised learning manner. Because the attention branch is trained to output the same result as the deep RL model, the obtained attention maps are corresponding to the agent action with higher interpretability. Experimental results with robot navigation task show that the proposed method can generate interpretable attention maps for a visual explanation.

cs.RO↗

arXiv2021

Nature of low-temperature dense ice up to 80 GPa observed by x-ray diffraction

We have measured the lattice volume of ice VIII in different pressure-temperature pathways and found isothermal compression at low-temperature conditions makes the volume larger. Ice VIII has become its high-pressure phase with the molar volume of 6.45 cm^3 at 10 K where the pressure can be estimated as 60.4 GPa based on the third-order Birch-Murnaghan equation with parameters determined in this study (K_0= 32.4 GPa, K'_0= 3.7, and V_0= 11.9 cm^3). The present results indicate that this high-pressure state is paraelectric with tetragonal symmetry.

cond-mat.mtrl-sci↗

arXiv2019

Embedding Human Knowledge into Deep Neural Network via Attention Map

In this work, we aim to realize a method for embedding human knowledge into deep neural networks. While the conventional method to embed human knowledge has been applied for non-deep machine learning, it is challenging to apply it for deep learning models due to the enormous number of model parameters. To tackle this problem, we focus on the attention mechanism of an attention branch network (ABN). In this paper, we propose a fine-tuning method that utilizes a single-channel attention map which is manually edited by a human expert. Our fine-tuning method can train a network so that the output attention map corresponds to the edited ones. As a result, the fine-tuned network can output an attention map that takes into account human knowledge. Experimental results with ImageNet, CUB-200-2010, and IDRiD demonstrate that it is possible to obtain a clear attention map for a visual explanation and improve the classification performance. Our findings can be a novel framework for optimizing networks through human intuitive editing via a visual interface and suggest new possibilities for human-machine cooperation in addition to the improvement of visual explanations.

cs.CV↗

arXiv2019

Auxiliary Optics For meV-IXS at SPring-8: KB, Analyzer Masks, Soller Slit & Screen, BPM

This paper discusses several optical elements now in use at BL43LXU, the RIKEN Quantum NanoDynamics Beamline, of the RIKEN SPring-8 Center. BL43LXU is dedicated to meV-resolved inelastic x-rays scattering using spherical analyzers operating between 15.8 and 25.7 keV. The work described here is relevant for setups on the high-resolution spectrometer (10m two-theta arm) with resolution between 2.8 and 0.8 meV. Specific optics discussed include a multilayer Kirkpatrick-Baez (KB) mirror pair that focuses the full (~1x3mm2) beam at 17.79 keV to a 4.4 X 4.1 μm2 spot with ~60% throughput, two different types of Soller slits that help reduce backgrounds, masks for the analyzers that allow increased solid angle to be collected while preserving momentum resolution, and a diamond quadrant beam position monitor (BPM). These elements have been used and facilitate experiments in extreme conditions with diamond anvil cells, and liquid measurements at low momentum transfers, among other work.

physics.ins-det↗

arXiv2019

Attention Branch Network: Learning of Attention Mechanism for Visual Explanation

Visual explanation enables human to understand the decision making of Deep Convolutional Neural Network (CNN), but it is insufficient to contribute the performance improvement. In this paper, we focus on the attention map for visual explanation, which represents high response value as the important region in image recognition. This region significantly improves the performance of CNN by introducing an attention mechanism that focuses on a specific region in an image. In this work, we propose Attention Branch Network (ABN), which extends the top-down visual explanation model by introducing a branch structure with an attention mechanism. ABN can be applicable to several image recognition tasks by introducing a branch for attention mechanism and is trainable for the visual explanation and image recognition in end-to-end manner. We evaluate ABN on several image recognition tasks such as image classification, fine-grained recognition, and multiple facial attributes recognition. Experimental results show that ABN can outperform the accuracy of baseline models on these image recognition tasks while generating an attention map for visual explanation. Our code is available at https://github.com/machine-perception-robotics-group/attention_branch_network.

cs.CV↗

arXiv2018

The impact of Hubbard- and van der Waals-corrections on the DFT calculation of epsilon-zeta transition pressure in solid oxygen

The aim of this study is to clarify the physics which governs the transition from epsilon phase to zeta phase of solid oxygen observed experimentally at 96 GPa using density functional theory (DFT). The transition was predicted at 40 GPa with PBE functional. Then the Hubbard correction was added to enhance the localization of p-orbital of oxygen. The epsilon-zeta transition pressure was significantly improved to 70 GPa. Finally, we included the non-local van der Waals correction. The transition pressure slightly increases to 80 GPa. These results demonstrate that the contribution from Hubbard term is superior to van der Waals term.

cond-mat.mtrl-sci↗

arXiv2017

Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adversarial Nets

In this paper, we propose a method for cloud removal from visible light RGB satellite images by extending the conditional Generative Adversarial Networks (cGANs) from RGB images to multispectral images. Satellite images have been widely utilized for various purposes, such as natural environment monitoring (pollution, forest or rivers), transportation improvement and prompt emergency response to disasters. However, the obscurity caused by clouds makes it unstable to monitor the situation on the ground with the visible light camera. Images captured by a longer wavelength are introduced to reduce the effects of clouds. Synthetic Aperture Radar (SAR) is such an example that improves visibility even the clouds exist. On the other hand, the spatial resolution decreases as the wavelength increases. Furthermore, the images captured by long wavelengths differs considerably from those captured by visible light in terms of their appearance. Therefore, we propose a network that can remove clouds and generate visible light images from the multispectral images taken as inputs. This is achieved by extending the input channels of cGANs to be compatible with multispectral images. The networks are trained to output images that are close to the ground truth using the images synthesized with clouds over the ground truth as inputs. In the available dataset, the proportion of images of the forest or the sea is very high, which will introduce bias in the training dataset if uniformly sampled from the original dataset. Thus, we utilize the t-Distributed Stochastic Neighbor Embedding (t-SNE) to improve the problem of bias in the training dataset. Finally, we confirm the feasibility of the proposed network on the dataset of four bands images, which include three visible light bands and one near-infrared (NIR) band.

cs.CV↗