arXiv subjects

Hiroshi Watanabe

Publications and source records attributed to Hiroshi Watanabe.

At least 19 recordsLinked to original sources

arXiv2026

Optical investigation of the electronic structure of a ferromagnetic Weyl semimetal CeAlSi

To investigate electronic states during the ferromagnetic transition in a magnetic Weyl semimetal CeAlSi, we measured temperature-dependent optical conductivity [$σ_1(ω)$] spectra and compared them with DFT+DMFT band calculations. The $σ_1(ω)$ spectrum did not change significantly across the ferromagnetic ordering temperature ($T_C$), suggesting that the Ce 4f states are almost localized. DFT+DMFT calculations with almost localized Ce 4f states successfully reproduced the spectral shape and the unchanged $σ_1(ω)$ spectra across $T_C$. The dynamic effective mass evaluated from the extended Drude model is very small, which DFT+DMFT calculations also reproduce, but the scattering probability at even lower temperatures suggests ferromagnetic fluctuations. These results suggest that the interaction intensity between the Weyl fermions and Ce 4f states is very weak, as reproduced by DFT+DMFT calculations.

cond-mat.str-el↗

arXiv2026

Control of band structure in the altermagnetic candidate MnTe by temperature and strain

The temperature and strain dependences of the optical conductivity spectrum of hexagonal manganese telluride (MnTe) were measured, revealing absorption in the terahertz (THz) region from spin-split bands to acceptor levels. The temperature dependence of the THz absorption peak is consistent with that of a ferromagnetic phase transition, even though MnTe exhibits no net magnetism. The temperature dependence was attributed to a change in the altermagnetic electronic structure. Under negative uniaxial strain, the THz peak shifts to the high-energy side, suggesting spin-splitting bands at energies away from EF, consistent with the theoretical prediction that the spin-splitting angle decreases. The observed behavior of the THz peak clearly shows that MnTe has the altermagnetic electronic structure. Additionally, a Fano-like asymmetric line shape in the optical phonon absorption was observed, possibly originating from interactions with the alternative spin-split bands.

cond-mat.str-el↗

arXiv2026

Encoding of musical structures in hidden units of restricted Boltzmann machines

Restricted Boltzmann machines (RBMs) are energy-based models originating from statistical physics, in which hidden units mediate the probability distribution of high-dimensional visible configurations. In this study, we use symbolic music as a structured non-physical dataset and investigate how musical regularities are encoded in the hidden layer of a Bernoulli-Bernoulli RBM. Musical scores by J.~S.~Bach are converted into binary piano-roll representations and used to train the model in an unsupervised manner. We then analyze the visible-layer patterns induced by individual hidden units by activating hidden units separately and computing the corresponding expected visible configurations. The trained RBM reconstructs piano-roll-like inputs and assigns lower energies to piano-roll configurations than to most non-musical binary images, indicating that the learned energy function captures statistical features of the piano-roll dataset. The hidden units mainly encode local temporal and pitch-statistical structures, such as sparse piano-roll-like textures, rather than directly separable musical concepts such as melodies, chords, or keys. We also analyze hidden-layer representations using t-SNE and find that transposed versions of the same musical pieces are not necessarily mapped to nearby regions in the hidden space. This behavior indicates that the trained RBM does not robustly capture transposition equivalence, which is naturally explained by the lack of translational invariance in standard RBM architectures. Samples from the trained RBM show local pitch organization, whereas iterative continuation reveals limited long-range coherence. These results provide a statistical-physics case study of how a simple spin model represents structured creative data and clarify both the usefulness and limitations of standard RBMs as interpretable models of musical structure.

cs.SD↗

arXiv2026

GVCCTurbo: Rate-Compute Quality Scheduling for Codebook Driven Generative Compression

Codebook-driven generative compression uses a pretrained image or video generator as a zero-shot visual prior and transmits compact codebook indices to guide reconstruction at ultra-low bitrate. Current codecs tie each finite-rate correction to a fresh prior evaluation, so shortening the sampler also removes correction slots that carry target-dependent information. We propose GVCCTurbo, a BPP-driven scheduler that separates expensive prior refreshes from codebook corrections: after calibrating an atom-count operating point and skip-gap ratio once per protocol, it maps a target codebook-payload bitrate to a trajectory length and refresh period, making BPP a schedule input instead of a fixed consequence of sampler length. The same endpoint-prediction and finite-rate steering interface covers GVCC-style rectified-flow video and DDCM-style diffusion image compression, preserving zero-training deployment and compatibility with future distilled priors. Native 1080p curves position the complete zero-shot codec in the ultra-low-bitrate regime. In a controlled 720p Wan-GVCC study, the scheduler cuts prior evaluations from 20 to 9 for a $\sim\!44\%$ measured decoding-time reduction shared across the whole schedule family, at a small shared LPIPS cost on high-motion content; within that family, uniform refresh thinning (pure-skip) is a boundary point, and the BPP-aware interior point trades $2.9\%$ fewer codebook-payload bits for consistently higher PSNR at comparable LPIPS. These results support BPP-to-compute scheduling as a controllable extension of sampler-length tuning, without requiring the allocated point to dominate every boundary point.

cs.CV↗

arXiv2026

Training-Free Continuous Bitrate Control for Scalable Image Coding for Humans and Machines

Continuous variable-rate compression is highly demanded in real-world applications, but remains underexplored in scalable image coding for humans and machines. In this paper, we propose a training-free variable-rate scalable image coding framework. By adaptively adjusting quantization step sizes based on predicted scale values, the proposed method enables independent and continuous bitrate control for the machine and enhancement layers while preserving important latent information in each layer. Experimental results demonstrate the effectiveness of the proposed method and highlight the importance of bitrate allocation between the two layers.

eess.IV↗

arXiv2026

Learned Radius Estimation for UDF-Based Point Cloud Reconstruction

Surface reconstruction from point clouds is important for consumer-grade 3D capture, including AR/VR and indoor scanning. Local-patch Unsigned Distance Field (UDF) methods are lightweight and generalizable, but their accuracy depends on the support radius, traditionally fixed or selected by a one-dimensional curvature heuristic that cannot capture heterogeneous local geometry. We propose a learned per-query radius selector that predicts a continuous support radius and plugs into a frozen LoSF-UDF backbone. The selector is trained using off-grid target radii obtained by parabolic interpolation of cached UDF error curves. Experiments show improved fine-scale reconstruction accuracy.

cs.CV↗

arXiv2026

Experimental and theoretical studies of hyperfine structures in $^{21}$Na

We measured the hyperfine structure constants, $A(3s^2S_{1/2})$ and $A(3p^2P_{1/2})$, of the neutron-deficient isotope $^{21}\text{Na}$ using CLaSsy, a setup dedicated to collinear laser spectroscopy at RAON. The hyperfine structure constants of $^{21}\text{Na}$ were measured to be $103.6(10)_{\mathrm{stat}}(9)_{\mathrm{syst}}$ MHz for $A(3p^2P_{1/2})$ and $954.9(11)_{\mathrm{stat}}(25)_{\mathrm{syst}}$ MHz for $A(3s^2S_{1/2})$. A systematic comparison with the state-of-the-art ab-initio relativistic coupled cluster calculations shows the role of higher-order correlation effects such as triple excitations in $^{21}$Na. Furthermore, the measurement demonstrates a capability of the CLaSsy setup to conduct collinear laser spectroscopy experiments with a radioactive beam.

physics.atom-ph↗

arXiv2026

GVCC: Zero-Shot Video Compression via Codebook-Driven Stochastic Rectified Flow

At ultra-low bitrates, high-fidelity reconstruction requires sampling plausible videos from the posterior rather than regressing to oversmoothed conditional means. We propose Generative Video Codebook Codec (GVCC), a zero-shot framework in which a pretrained video generative model serves directly as the decoder, and the transmitted bitstream specifies its generation trajectory. Modern rectified-flow video models are typically sampled with deterministic ODE solvers, which leave no per-step stochastic channel for transmitting compressed information. GVCC addresses this by converting the deterministic flow sampler into an equivalent marginal-preserving stochastic process, so that information can be transmitted by encoding the per-step stochastic innovations. Unlike images, videos introduce longer temporal dependencies and more diverse conditioning modes. We instantiate GVCC in three practical modes: Text-to-Video (T2V) without a reference frame, autoregressive Image-to-Video (I2V) with tail latent correction, and First-Last-Frame-to-Video (FLF2V) with boundary-sharing Group of Pictures (GOP) chaining. On UVG, GVCC achieves the lowest LPIPS among evaluated baselines across three representative bitrate regimes (down to ${\sim}$0.003\,bpp), with 65\% LPIPS reduction over DCVC-RT at matched bitrate.

cs.CV↗

arXiv2026

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss

Image coding for machines (ICM) aims to compress images for machine analysis using recognition models rather than human vision. Hence, in ICM, it is important for the encoder to recognize and compress the information necessary for the machine recognition task. There are two main approaches in learned ICM; optimization of the compression model based on task loss, and Region of Interest (ROI) based bit allocation. These approaches provide the encoder with the recognition capability. However, optimization with task loss becomes difficult when the recognition model is deep, and ROI-based methods often involve extra overhead during evaluation. In this study, we propose a novel training method for learned ICM models that applies auxiliary loss to the encoder to improve its recognition capability and rate-distortion performance. Our method achieves Bjontegaard Delta rate improvements of 27.7% and 20.3% in object detection and semantic segmentation tasks, compared to the conventional training method.

cs.CV↗

arXiv2026

Hierarchical structure of primary and hybridization-induced superconducting correlations in bilayer nickelates

High-pressure superconductivity in the bilayer nickelate La$_3$Ni$_2$O$_7$, with a transition temperature approaching 80 K, has stimulated intense debate regarding its microscopic origin. Although an $s_{\pm}$ gap symmetry has been widely proposed, the electronic degrees of freedom responsible for pairing remain unsettled. Here we investigate a bilayer two-orbital Hubbard model using the variational Monte Carlo method and reveal a hierarchical pairing structure in bilayer nickelates. The primary pairing interaction originates from the bonding--antibonding splitting of the Ni $3d_{z^2}$ orbitals, while orbital hybridization redistributes superconducting correlations to the $d_{x^2-y^2}$ channel despite its weak intrinsic pairing interaction. This distinction between the origin of pairing and resulting superconducting correlations explains why the two orbital channels exhibit comparable long-range correlations. The resulting $s_{\pm}$ state is robust against changes in Fermi-surface topology. These results reconcile apparently competing theoretical scenarios and provide a comprehensive understanding, highlighting the distinctive role of orbital hybridization in multilayer correlated superconductors.

cond-mat.supr-con↗

arXiv2026

Training-Free Adaptive Quantization for Variable Rate Image Coding for Machines

Image Coding for Machines (ICM) has become increasingly important with the rapid integration of computer vision technology into real-world applications. However, most neural network-based ICM frameworks operate at a fixed rate, thus requiring individual training for each target bitrate. This limitation may restrict their practical usage. Existing variable rate image compression approaches mitigate this issue but often rely on additional training, which increases computational costs and complicates deployment. Moreover, variable rate control has not been thoroughly explored for ICM. To address these challenges, we propose a training-free framework for quantization strength control which enables flexible bitrate adjustment. By exploiting the scale parameter predicted by the hyperprior network, the proposed method adaptively modulates quantization step sizes across both channel and spatial dimensions. This allows the model to preserve semantically important regions while coarsely quantizing less critical areas. Our architectural design further enables continuous bitrate control through a single parameter. Experimental results demonstrate the effectiveness of our proposed method, achieving up to 11.07% BD-rate savings over the non-adaptive variable rate baseline. The code is available at https://github.com/qwert-top/AQVR-ICM.

eess.IV↗

arXiv2025

Contour Information Aware 2D Gaussian Splatting for Image Representation

Image representation is a fundamental task in computer vision. Recently, Gaussian Splatting has emerged as an efficient representation framework, and its extension to 2D image representation enables lightweight, yet expressive modeling of visual content. While recent 2D Gaussian Splatting (2DGS) approaches provide compact storage and real-time decoding, they often produce blurry or indistinct boundaries when the number of Gaussians is small due to the lack of contour awareness. In this work, we propose a Contour Information-Aware 2D Gaussian Splatting framework that incorporates object segmentation priors into Gaussian-based image representation. By constraining each Gaussian to a specific segmentation region during rasterization, our method prevents cross-boundary blending and preserves edge structures under high compression. We also introduce a warm-up scheme to stabilize training and improve convergence. Experiments on synthetic color charts and the DAVIS dataset demonstrate that our approach achieves higher reconstruction quality around object edges compared to existing 2DGS methods. The improvement is particularly evident in scenarios with very few Gaussians, while our method still maintains fast rendering and low memory usage.

cs.CV↗

arXiv2025

Molecular Dynamics Study of Rayleigh-Plateau Instability at Liquid-Liquid Interfaces

We investigated the Rayleigh-Plateau instability at the interface between two immiscible liquids of equal viscosity using molecular dynamics simulations. Two types of initial conditions were considered, one with an imposed single-mode perturbation at the interface and the other without any imposed perturbation. Under the single-mode perturbation, the growth rate deviated from the theoretical prediction for small cylinder radii, but progressively approached and agreed with classical macroscopic theory as the radius increased. In contrast, for the unperturbed initial condition, we found a systematic relationship between the breakup time and the minimum radius, in which the power-law exponent increased with increasing radius. These results demonstrate that, even in extremely microscopic systems with cylinder radii on the order of only about fifteen atomic diameters, the growth of the instability can follow macroscopic theoretical predictions when appropriate conditions are imposed, and that the influence of thermal fluctuations on the breakup dynamics becomes increasingly significant as the system radius decreases.

cond-mat.soft↗

arXiv2025

Accurate and Efficient Surface Reconstruction from Point Clouds via Geometry-Aware Local Adaptation

Point cloud surface reconstruction has improved in accuracy with advances in deep learning, enabling applications such as infrastructure inspection. Recent approaches that reconstruct from small local regions rather than entire point clouds have attracted attention for their strong generalization capability. However, prior work typically places local regions uniformly and keeps their size fixed, limiting adaptability to variations in geometric complexity. In this study, we propose a method that improves reconstruction accuracy and efficiency by adaptively modulating the spacing and size of local regions based on the curvature of the input point cloud.

cs.CV↗

arXiv2025

Explicit Residual-Based Scalable Image Coding for Humans and Machines

Scalable image compression is a technique that progressively reconstructs multiple versions of an image for different requirements. In recent years, images have increasingly been consumed not only by humans but also by image recognition models. This shift has drawn growing attention to scalable image compression methods that serve both machine and human vision (ICMH). Many existing models employ neural network-based codecs, known as learned image compression, and have made significant strides in this field by carefully designing the loss functions. In some cases, however, models are overly reliant on their learning capacity, and their architectural design is not sufficiently considered. In this paper, we enhance the coding efficiency and interpretability of ICMH framework by integrating an explicit residual compression mechanism, which is commonly employed in resolution scalable coding methods such as JPEG2000. Specifically, we propose two complementary methods: Feature Residual-based Scalable Coding (FR-ICMH) and Pixel Residual-based Scalable Coding (PR-ICMH). These proposed methods are applicable to various machine vision tasks. Moreover, they provide flexibility to choose between encoder complexity and compression performance, making it adaptable to diverse application requirements. Experimental results demonstrate the effectiveness of our proposed methods, with PR-ICMH achieving up to 29.57% BD-rate savings over the previous work.

eess.IV↗

arXiv2025

InterpIoU: Rethinking Bounding Box Regression with Interpolation-Based IoU Optimization

Bounding box regression (BBR) is fundamental to object detection, where the regression loss is crucial for accurate localization. Existing IoU-based losses often incorporate handcrafted geometric penalties to address IoU's non-differentiability in non-overlapping cases and enhance BBR performance. However, these penalties are sensitive to box shape, size, and distribution, often leading to suboptimal optimization for small objects and undesired behaviors such as bounding box enlargement due to misalignment with the IoU objective. To address these limitations, we propose InterpIoU, a novel loss function that replaces handcrafted geometric penalties with a term based on the IoU between interpolated boxes and the target. By using interpolated boxes to bridge the gap between predictions and ground truth, InterpIoU provides meaningful gradients in non-overlapping cases and inherently avoids the box enlargement issue caused by misaligned penalties. Simulation results further show that IoU itself serves as an ideal regression target, while existing geometric penalties are both unnecessary and suboptimal. Building on InterpIoU, we introduce Dynamic InterpIoU, which dynamically adjusts interpolation coefficients based on IoU values, enhancing adaptability to scenarios with diverse object distributions. Experiments on COCO, VisDrone, and PASCAL VOC show that our methods consistently outperform state-of-the-art IoU-based losses across various detection frameworks, with particularly notable improvements in small object detection, confirming their effectiveness.

cs.CV↗

arXiv2025

Anisotropic Non-Fermi Liquid and Dynamical Planckian Scaling of a Quasi-Kagome Kondo Lattice System

At the quantum critical point of correlated materials, a non-Fermi liquid state appears where electron correlations continuously develop to very low temperatures. The relaxation time of the interacted electrons, namely quasiparticles, is scaled with the Planckian time, $\hbar/k_{\rm B}T$. However, there is a debate over whether heavy-fermion systems can obey the Planckian time. In the optical conductivity spectra, the Drude response will appear as the scaling of $\hbarω/k_{\rm B}T$ as the dynamical Planckian scaling (DPS). Here, we report the non-Fermi liquid behavior in the Drude response of a candidate for such materials, the quasi-kagome Kondo lattice CeRhSn. Even though the material shows a strong valence fluctuation, renormalized Drude responses observed at the photon energy below 100 meV are characterized by non-Fermi-liquid-like scattering rate $1/τ$. The heavy carriers' Drude response only for the Ce quasi-kagome plane obeyed DPS below 80 K, suggesting the anisotropic quantum criticality with the strong $c \textrm{-} f$ hybridization.

cond-mat.str-el↗

arXiv2025

Seed Selection for Human-Oriented Image Reconstruction via Guided Diffusion

Conventional methods for scalable image coding for humans and machines require the transmission of additional information to achieve scalability. A recent diffusion-based approach avoids this by generating human-oriented images from machine-oriented images without extra bitrate. However, it utilizes a single random seed, which may lead to suboptimal image quality. In this paper, we propose a seed selection method that identifies the optimal seed from multiple candidates to improve image quality without increasing the bitrate. To reduce the computational cost, selection is performed based on intermediate outputs obtained from early steps of the reverse diffusion process. Experimental results demonstrate that our proposed method outperforms the baseline, which uses a single random seed without selection, across multiple evaluation metrics.

cs.CV↗