arXiv subjects

Ryota Maeda

Publications and source records attributed to Ryota Maeda.

13 recordsLinked to original sources

arXiv2026

Holographic Dual of PT Symmetric BCFT

We present a holographic dual of a two dimensional conformal field theory with non-hermitian but Parity-Time (PT) symmetric boundary conditions, by applying the AdS/BCFT duality and by introducing an imaginary valued scalar field localized on an end-of-the-world brane. We find that as we increase the strength of the non-hermitian PT symmetric interactions, the system experiences a spontaneous PT symmetry breaking. We also consider its Wick rotated setup as a new quantum quenched state and show that its growth of entanglement entropy can be larger than the standard results obtained from standard Cardy states.

hep-th↗

arXiv2026

Broadband Hyperspectral 3D Imaging using Dispersed Structured Light

Hyperspectral 3D imaging enables the capture of dense spectral information and scene geometry but has traditionally been confined to narrow spectral windows, typically the visible range. In this work, we introduce a broadband hyperspectral 3D imaging (BH3D) method to extend this capability across the full visible-near-infrared and short-wavelength infrared (SWIR) spectrum (450-1500 nm). This broad coverage is critical as it captures complementary physical cues: visible wavelengths reveal surface appearance, while SWIR bands provide insight into subsurface properties and material composition. However, realizing BH3D is challenging due to fundamental sensor constraints between visible-spectrum silicon and SWIR-spectrum InGaAs sensors, which necessitate complex multi-spectrograph designs. Here we propose a single-spectrograph BH3D system, using a stereo setup comprising visible and SWIR cameras, that reconstructs dense broadband hyperspectral reflectance together with accurate 3D geometry. Our key idea is to extend dispersed structured light to the broadband regime using a single spectrograph. We model the image formation of broadband dispersed structured light, and estimate hyperspectral reflectance and depth. We validate our approach on diverse real-world scenes, demonstrating accurate reconstruction with a mean spectral angle mapper of 0.13 rad, root mean square error of 0.03, and mean depth error of 4.5 mm. We further demonstrate identifying metameric materials, performing imaging through opaque layers, uncovering hidden features on banknotes, and revealing blood vessels.

cs.CV↗

arXiv2026

End-to-End Optimization of Polarimetric Measurement and Material Classifier

Material classification is a fundamental problem in computer vision and plays a crucial role in scene understanding. Previous studies have explored various material recognition methods based on reflection properties such as color, texture, specularity, and scattering. Among these cues, polarization is particularly valuable because it provides rich material information and enables recognition even at distances where capturing high-resolution texture is impractical. However, measuring polarimetric reflectance properties typically requires multiple modulations of the polarization state of the incident light, making the process time-consuming and often unnecessary for certain recognition tasks. While material classification can be achieved using only a subset of polarimetric measurements, the optimal configuration of measurement angles remains unclear. In this study, we propose an end-to-end optimization framework that jointly learns a material classifier and determines the optimal combinations of rotation angles for polarization elements that control both the incident and reflected light states. Using our Mueller-matrix material dataset, we demonstrate that our method achieves high-accuracy material classification even with a limited number of measurements.

cs.CV↗

arXiv2026

Dynamical Formation of Self-Similar Wormholes

We study spherically symmetric, self-similar wormhole solutions supported by colliding streams of negative-energy null dust, and their dynamical formation. Under the assumption of self-similarity, the Einstein equations reduce to a system of ordinary differential equations, which we solve numerically under boundary conditions enforcing the existence of a minimal areal radius (the throat) on constant-time hypersurfaces. For a sufficiently large throat radius, the resulting geometries remain regular at both spatial and future null infinity, while a singularity is retained in the past direction. We then construct a dynamical formation scenario by patching together three regions: a Schwarzschild black hole, negative-energy Vaidya spacetimes, and the self-similar wormhole geometry. These regions are joined across null shells using the Barrabes--Israel formalism, which provides explicit relations among the throat radius, the black hole's mass and the energy injection by the shell, demonstrating that an initial black hole can evolve into a wormhole. Our analysis generalizes the formation model for static wormhole solutions proposed by Hayward and Koyama in 2004 to non-static wormhole solutions, offering a novel perspective on the formation of regular traversable wormholes.

gr-qc↗

arXiv2025

Non-hermitian Density Matrices from Time-like Entanglement and Wormholes

We extensively explore the connections between time-like entanglement and non-hermitian density matrices in quantum many-body systems. We classify setups where we encounter non-hermitian density matrices into two types: one is due to causal influences under unitary evolutions, and the other is due to non-unitary evolutions in non-hermitian systems. We provide various examples of these setups including interacting harmonic oscillators, two dimensional conformal field theories and holographic dualities. In them, we compute the time-like entanglement entropy and imagitivity, which measures how much density matrices are non-hermitian. In both two classes, typical holographic examples are given by traversable AdS wormholes. We explain how causal influences in a wormhole dual to a pair of non-hermitian quantum systems is possible even without interactions between them. We argue that to realize a traversable wormhole we need not only ordinary quantum entanglement but also time-like entanglement.

hep-th↗

arXiv2025

Hyperspectral Polarimetric BRDFs of Real-world Materials

Acquiring bidirectional reflectance distribution functions (BRDFs) is essential for simulating light transport and analytically modeling material properties. Over the past two decades, numerous intensity-only BRDF datasets in the visible spectrum have been introduced, primarily for RGB image rendering applications. However, in scientific and engineering domains, there remains an unmet need to model light transport with polarization--a fundamental wave property of light--across hyperspectral bands. To address this gap, we present the first hyperspectral-polarimetric BRDF (hpBRDF) dataset of real-world materials, spanning wavelengths from 414 to 950\,nm and densely sampled at 68 spectral bands. This dataset covers both the visible and near-infrared (NIR) spectra, enabling detailed material analysis and light reflection simulations that incorporate polarization at each narrow spectral band. We develop an efficient hpBRDF acquisition system that captures high-dimensional hpBRDFs within a feasible acquisition time. Using this system, we demonstrate hyperspectral-polarimetric rendering using the acquired hpBRDFs. To provide insights on hpBRDF, we analyze the hpBRDFs with respect to their dependencies on wavelength, polarization state, material type, and illumination/viewing geometry. Also, we propose compact representations through principal component analysis and implicit neural hpBRDF modeling.

cs.GR↗

arXiv2025

Dynamical Formation of Charged Wormholes

We construct static, spherically symmetric, charged traversable wormhole solutions to the Einstein--Maxwell equations, supported by bidirectional (ingoing and outgoing) null dust with negative energy, and discuss a scenario for their dynamical formation from a black hole. Our solution contains a traversable throat, where the areal radius takes a minimum, although the spacetime is not asymptotically flat. In our formation scenario, the spacetime evolves sequentially from a black hole to Vaidya regions and finally to a wormhole, with each transition mediated by an impulsive null shell. We find that the radius of the wormhole throat is determined by the mass and charge of the initial black hole as well as those of the injected shell.

gr-qc↗

arXiv2025

Traversable AdS Wormhole via Non-local Double Trace or Janus Deformation

We study (i) Janus deformations and (ii) non-local double trace deformations of a pair of CFTs, as two different ways to construct CFT duals of traversable AdS wormholes. First, we construct a simple model of traversable wormholes by gluing two Poincar\'e AdS geometries and BTZ black holes and compute holographic two point functions and (pseudo) entanglement entropy. We point out that a Janus gravity solution describes a traversable wormhole when the deformation parameter takes imaginary values. On the other hand, we show that double trace deformations between two decoupled CFTs can reproduce two point functions of traversable AdS wormholes. By considering the case where the double trace deformation is given by a non-local $T\overline{T}$ deformation, we analyze the dual gravity which implies emergence of wormholes. We present toy model of these deformed CFTs by using free scalars and obtain qualitative behaviors expected for them. We argue that the crucial difference between the two constructions is that a global time slice of wormhole is described by a pure state for Janus deformations, while it is a mixed state for the double trace deformations.

hep-th↗

arXiv2024

Dense Dispersed Structured Light for Hyperspectral 3D Imaging of Dynamic Scenes

Hyperspectral 3D imaging captures both depth maps and hyperspectral images, enabling comprehensive geometric and material analysis. Recent methods achieve high spectral and depth accuracy; however, they require long acquisition times often over several minutes or rely on large, expensive systems, restricting their use to static scenes. We present Dense Dispersed Structured Light (DDSL), an accurate hyperspectral 3D imaging method for dynamic scenes that utilizes stereo RGB cameras and an RGB projector equipped with an affordable diffraction grating film. We design spectrally multiplexed DDSL patterns that significantly reduce the number of required projector patterns, thereby accelerating acquisition speed. Additionally, we formulate an image formation model and a reconstruction method to estimate a hyperspectral image and depth map from captured stereo images. As the first practical and accurate hyperspectral 3D imaging method for dynamic scenes, we experimentally demonstrate that DDSL achieves a spectral resolution of 15.5 nm full width at half maximum (FWHM), a depth error of 4 mm, and a frame rate of 6.6 fps.

cs.CV↗

arXiv2024

Event Ellipsometer: Event-based Mueller-Matrix Video Imaging

Light-matter interactions modify both the intensity and polarization state of light. Changes in polarization, represented by a Mueller matrix, encode detailed scene information. Existing optical ellipsometers capture Mueller-matrix images; however, they are often limited to capturing static scenes due to long acquisition times. Here, we introduce Event Ellipsometer, a method for acquiring a Mueller-matrix video for dynamic scenes. Our imaging system employs fast-rotating quarter-wave plates (QWPs) in front of a light source and an event camera that asynchronously captures intensity changes induced by the rotating QWPs. We develop an ellipsometric-event image formation model, a calibration method, and an ellipsometric-event reconstruction method. We experimentally demonstrate that Event Ellipsometer enables Mueller-matrix video imaging at 30fps, extending ellipsometry to dynamic scenes.

cs.CV↗

arXiv2024

A SARS-CoV-2 Interaction Dataset and VHH Sequence Corpus for Antibody Language Models

Antibodies are crucial proteins produced by the immune system to eliminate harmful foreign substances and have become pivotal therapeutic agents for treating human diseases. To accelerate the discovery of antibody therapeutics, there is growing interest in constructing language models using antibody sequences. However, the applicability of pre-trained language models for antibody discovery has not been thoroughly evaluated due to the scarcity of labeled datasets. To overcome these limitations, we introduce AVIDa-SARS-CoV-2, a dataset featuring the antigen-variable domain of heavy chain of heavy chain antibody (VHH) interactions obtained from two alpacas immunized with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike proteins. AVIDa-SARS-CoV-2 includes binary labels indicating the binding or non-binding of diverse VHH sequences to 12 SARS-CoV-2 mutants, such as the Delta and Omicron variants. Furthermore, we release VHHCorpus-2M, a pre-training dataset for antibody language models, containing over two million VHH sequences. We report benchmark results for predicting SARS-CoV-2-VHH binding using VHHBERT pre-trained on VHHCorpus-2M and existing general protein and antibody-specific pre-trained language models. These results confirm that AVIDa-SARS-CoV-2 provides valuable benchmarks for evaluating the representation capabilities of antibody language models for binding prediction, thereby facilitating the development of AI-driven antibody discovery. The datasets are available at https://datasets.cognanous.com.

cs.LG↗

arXiv2023

Polarimetric Light Transport Analysis for Specular Inter-reflection

Polarization is well known for its ability to decompose diffuse and specular reflections. However, the existing decomposition methods only focus on direct reflection and overlook multiple reflections, especially specular inter-reflection. In this paper, we propose a novel decomposition method for handling specular inter-reflection of metal objects by using a unique polarimetric feature: the rotation direction of linear polarization. This rotation direction serves as a discriminative factor between direct and inter-reflection on specular surfaces. To decompose the reflectance components, we actively rotate the linear polarization of incident light and analyze the rotation direction of the reflected light. We evaluate our method using both synthetic and real data, demonstrating its effectiveness in decomposing specular inter-reflections of metal objects. Furthermore, we demonstrate that our method can be combined with other decomposition methods for a detailed analysis of light transport. As a practical application, we show its effectiveness in improving the accuracy of 3D measurement against strong specular inter-reflection.

cs.CV↗

arXiv2023

AVIDa-hIL6: A Large-Scale VHH Dataset Produced from an Immunized Alpaca for Predicting Antigen-Antibody Interactions

Antibodies have become an important class of therapeutic agents to treat human diseases. To accelerate therapeutic antibody discovery, computational methods, especially machine learning, have attracted considerable interest for predicting specific interactions between antibody candidates and target antigens such as viruses and bacteria. However, the publicly available datasets in existing works have notable limitations, such as small sizes and the lack of non-binding samples and exact amino acid sequences. To overcome these limitations, we have developed AVIDa-hIL6, a large-scale dataset for predicting antigen-antibody interactions in the variable domain of heavy chain of heavy chain antibodies (VHHs), produced from an alpaca immunized with the human interleukin-6 (IL-6) protein, as antigens. By leveraging the simple structure of VHHs, which facilitates identification of full-length amino acid sequences by DNA sequencing technology, AVIDa-hIL6 contains 573,891 antigen-VHH pairs with amino acid sequences. All the antigen-VHH pairs have reliable labels for binding or non-binding, as generated by a novel labeling method. Furthermore, via introduction of artificial mutations, AVIDa-hIL6 contains 30 different mutants in addition to wild-type IL-6 protein. This characteristic provides opportunities to develop machine learning models for predicting changes in antibody binding by antigen mutations. We report experimental benchmark results on AVIDa-hIL6 by using machine learning models. The results indicate that the existing models have potential, but further research is needed to generalize them to predict effective antibodies against unknown mutants. The dataset is available at https://avida-hil6.cognanous.com.

cs.LG↗