arXiv subjects

Masahiro Nomura

Publications and source records attributed to Masahiro Nomura.

At least 19 recordsLinked to original sources

arXiv2026

Enhancement of far-field thermal emission via polaritonic cavity modes

Controlling thermal emission is crucial for applications involving thermophotovoltaics, thermal sensing, imaging, and camouflage. While prior studies focused on the emission of thermally excited guided modes (TEGMs) inside cavities, their contribution to the far-field radiation outside cavities has remained unexplored. Here, we demonstrate a tunable far-field thermal channel enabled by TEGMs arising from the coupling of surface phonon-polaritons and cavity resonances. By combining infrared emissivity experiments with fluctuational electrodynamics simulations, we identify distinct spectral features marking the conversion of two-dimensionally confined polaritonic modes into three-dimensional radiative channels. We find that silicon cavities covered with SiO2 enhance the emissivity by up to 200% near the polaritonic spectral resonance, whereas bare silicon cavities yield only broadband enhancement. These findings provide experimental evidence of TEGMs and establish a simple cavity architecture as an effective and scalable platform for tailoring thermal radiation without complex nanofabrication.

cond-mat.mes-hall↗

arXiv2026

On the Generalization Bounds of Symbolic Regression with Genetic Programming

Symbolic regression (SR) with genetic programming (GP) aims to discover interpretable mathematical expressions directly from data. Despite its strong empirical success, the theoretical understanding of why GP-based SR generalizes beyond the training data remains limited. In this work, we provide a learning-theoretic analysis of SR models represented as expression trees. We derive a generalization bound for GP-style SR under constraints on tree size, depth, and learnable constants. Our result decomposes the generalization gap into two interpretable components: a structure-selection term, reflecting the combinatorial complexity of choosing an expression-tree structure, and a constant-fitting term, capturing the complexity of optimizing numerical constants within a fixed structure. This decomposition provides a theoretical perspective on several widely used practices in GP, including parsimony pressure, depth limits, numerically stable operators, and interval arithmetic. In particular, our analysis shows how structural restrictions reduce hypothesis-class growth while stability mechanisms control the sensitivity of predictions to parameter perturbations. By linking these practical design choices to explicit complexity terms in the generalization bound, our work offers a principled explanation for commonly observed empirical behaviors in GP-based SR and contributes towards a more rigorous understanding of its generalization properties.

cs.LG↗

arXiv2026

Beyond Match Maximization and Fairness: Retention-Optimized Two-Sided Matching

On two-sided matching platforms such as online dating and recruiting, recommendation algorithms often aim to maximize the total number of matches. However, this objective creates an imbalance, where some users receive far too many matches while many others receive very few and eventually abandon the platform. Retaining users is crucial for many platforms, such as those that depend heavily on subscriptions. Some may use fairness objectives to solve the problem of match maximization. However, fairness in itself is not the ultimate objective for many platforms, as users do not suddenly reward the platform simply because exposure is equalized. In practice, where user retention is often the ultimate goal, casually relying on fairness will leave the optimization of retention up to luck. In this work, instead of maximizing matches or axiomatically defining fairness, we formally define the new problem setting of maximizing user retention in two-sided matching platforms. To this end, we introduce a dynamic learning-to-rank (LTR) algorithm called Matching for Retention (MRet). Unlike conventional algorithms for two-sided matching, our approach models user retention by learning personalized retention curves from each user's profile and interaction history. Based on these curves, MRet dynamically adapts recommendations by jointly considering the retention gains of both the user receiving recommendations and those who are being recommended, so that limited matching opportunities can be allocated where they most improve overall retention. Naturally but importantly, empirical evaluations on synthetic and real-world datasets from a major online dating platform show that MRet achieves higher user retention, since conventional methods optimize matches or fairness rather than retention.

cs.LG↗

arXiv2026

Adaptive Stochastic Natural Gradient Method for Safe Optimization on Binary Space

Optimization problems in real-world applications across the medical and engineering domains often involve potential risks when evaluating candidate solutions. Safe optimization aims to perform optimization while suppressing unsafe solution evaluations in such situations. For continuous search spaces, there exist safe optimization methods based on evolutionary computation. However, the algorithm development of safe optimization methods for binary search spaces has not been adequately addressed. In this study, we incorporate additional mechanisms for safe optimization into a binary optimization method, the adaptive stochastic natural gradient method (ASNG) with a family of Bernoulli distributions. For safety functions that must be kept non-negative during optimization, the proposed method, safe ASNG, estimates the Lipschitz constants with respect to the Hamming distance by constructing surrogate models of safety functions based on discrete Walsh functions. Then, safe ASNG computes a safe region that consists of safe solutions around the previously evaluated safe solutions. By projecting newly generated solutions to their nearest neighbors within the safe region, safe ASNG suppresses unsafe solution evaluations. Experimental results on benchmark problems on binary domains confirm that, while the comparative methods fail to suppress unsafe solution evaluations, safe ASNG achieves efficient optimization while effectively suppressing unsafe solution evaluations.

cs.NE↗

arXiv2026

Diversified Residual Symbolic Regression

Symbolic regression (SR) aims to discover explicit mathematical expressions that explain observed data and is widely used in domains where interpretability is essential. Because interpretability requires expressions to reflect meaningful regularities, SR is sensitive to observations that deviate from the dominant relationship. Such irregular observations, or outliers, are common in real-world data and can hinder SR from identifying underlying regularities. Robust regression mitigates this by downweighting observations with large residuals. However, deciding which observations should be treated as outliers is often ambiguous and depends on user interpretation and domain knowledge, a perspective largely overlooked in existing SR studies. This motivates approaches that present multiple candidate expressions, allowing users to examine different residual patterns and choose expressions consistent with their expertise. We propose diversified residual symbolic regression (DRSR), which achieves high predictive accuracy while promoting diversity with respect to residual patterns based on the Quality-Diversity paradigm. DRSR collects multiple expressions that fit the data well but differ in how residuals are distributed, enabling post-search selection aligned with domain knowledge. On a synthetic mixture dataset, DRSR produces more diverse expressions than conventional SR while capturing multiple underlying relationships. On a real-world astronomical dataset, DRSR discovers multiple expressions consistent with known physical relationships.

cs.NE↗

arXiv2026

Enhanced Near-Field Thermal Radiation Driven by Multiple Corner and Edge Modes in Subwavelength Square Nanowires

We demonstrate that the near-field thermal radiation between subwavelength SiC nanowires with square cross sections is dominated by multiple corner and edge resonances rather than the single surface-phonon-polariton channel of planar surfaces. Fluctuational electrodynamics simulations reveal that these resonances lie within the SiC Reststrahlen band, redshift for thinner nanowires, and yield a four-fold enhancement of thermal conductance. This maximum enhancement occurs when the separation gap nearly matches the nanowire thickness, balancing dimensional confinement and interwire coupling. These findings establish square nanowires as a versatile platform for geometrycontrolled near-field heat transfer in nanoscale heat management and energy conversion.

cond-mat.mes-hall↗

arXiv2026

Intrinsic Temporal Coherence Governs Heat Transport of Zone-Folded Phonons

While spatial phonon coherence manifested through band folding is believed to be a key factor governing the anomalous thermal conductivity of periodic structures, we investigate phonon transport from the perspective of temporal coherence. Using mode-resolved analyses, we quantify temporal coherent contributions and elucidate the interplay between phonon coherence time and lifetime in heat conduction of graphene/hexagonal boron nitride superlattices. We find that intrinsic coherence of folded phonon modes dominates the enhancement in ultrashort-period superlattices. In contrast, Wigner transport equation yields only a minor effect of band folding on thermal conductivity. The predictions in temperature dependence of models with and without temporal coherence provide a falsifiable experimental signature of this effect. Temporal coherence therefore constitutes a previously overlooked but fundamental channel for heat conduction, extending the conventional picture of spatially coherent transport and deepening the understanding of phonon dynamics in superlattices.

cond-mat.mes-hall↗

arXiv2026

Takeuchi's Information Criteria as Generalization Measures for DNNs Close to NTK Regime

Generalization measures have been studied extensively in the machine learning community to better characterize generalization gaps. However, establishing a reliable generalization measure for statistically singular models such as deep neural networks (DNNs) is difficult due to their complex nature. This study focuses on Takeuchi's information criterion (TIC) to investigate the conditions under which this classical measure can effectively explain the generalization gaps of DNNs. Importantly, the developed theory indicates the applicability of TIC near the neural tangent kernel (NTK) regime. In a series of experiments, we trained more than 5,000 DNN models with 12 architectures, including large models (e.g., VGG-16), on four datasets, and estimated the corresponding TIC values to examine the relationship between the generalization gap and the TIC estimates. We applied several TIC approximation methods with feasible computational costs and assessed the accuracy trade-off. Our experimental results indicate that the estimated TIC values correlate well with the generalization gap under conditions close to the NTK regime. However, we show both theoretically and empirically that outside the NTK regime such correlation disappears. Finally, we demonstrate that TIC provides better trial pruning ability than existing methods for hyperparameter optimization.

cs.LG↗

arXiv2026

cmaes: A Simple yet Practical Python Library for CMA-ES

The covariance matrix adaptation evolution strategy (CMA-ES) has been highly effective in black-box continuous optimization, as demonstrated by its success in both benchmark problems and various real-world applications. To address the need for an accessible and powerful tool in this domain, we developed cmaes, a simple and practical Python library for CMA-ES. cmaes is characterized by its simplicity, offering intuitive use and high code readability. This makes it suitable for quick use of CMA-ES, as well as for educational purposes and seamless integration into other libraries. Despite its simple design, cmaes maintains advanced functionality. It incorporates recent advancements in CMA-ES, such as learning rate adaptation for challenging scenarios, transfer learning, mixed-variable optimization, and multi-objective optimization capabilities. These advanced features are accessible through a user-friendly API, ensuring that cmaes can be easily adopted in practical applications. We present cmaes as a strong candidate for a practical Python CMA-ES library aimed at practitioners. The software is available under the MIT license at https://github.com/CyberAgentAILab/cmaes.

cs.NE↗

arXiv2026

Gigahertz-frequency Lamb wave resonator cavities on suspended lithium niobate for quantum acoustics

Phononic nanodevices offer a promising route toward quantum technologies, as phonons combine strong confinement within matter with broad coupling capabilities to various quantum systems. In particular, the piezoelectric response of materials such as lithium niobate enables coupling between superconducting qubits and gigahertz-frequency phonons. However, bulk lithium niobate phononic devices typically rely on surface acoustic waves and are therefore inherently subject to leakage from the surface into the bulk substrate. Here, we explore the acoustic behavior of resonator cavities supporting GHz-frequency Lamb waves in a 200 nm-thick suspended lithium niobate layer. We characterize the acoustic response at both room and millikelvin temperatures. We find that our resonator cavities with strong confinement reach intrinsic quality factors of approximately 6000 at the single phonon level. We use the measured parameters of the resonators to model their coupling to a superconducting transmon qubit, allowing us to evaluate their potential as quantum acoustic devices.

cond-mat.mes-hall↗

arXiv2026

CatCMA with Margin for Single- and Multi-Objective Mixed-Variable Black-Box Optimization

This study focuses on mixed-variable black-box optimization (MV-BBO), addressing continuous, integer, and categorical variables. Many real-world MV-BBO problems involve dependencies among these different types of variables, requiring efficient methods to optimize them simultaneously. Recently, stochastic optimization methods leveraging the mechanism of the covariance matrix adaptation evolution strategy have shown promising results in mixed-integer or mixed-category optimization. However, such methods cannot handle the three types of variables simultaneously. In this study, we propose CatCMA with Margin (CatCMAwM), a stochastic optimization method for MV-BBO that jointly optimizes continuous, integer, and categorical variables. CatCMAwM is developed by incorporating novel integer handling into CatCMA, a mixed-category black-box optimization method employing a joint distribution of multivariate Gaussian and categorical distributions. The proposed integer handling is carefully designed by reviewing existing integer handling and following the design principles of CatCMA. Furthermore, we extend CatCMAwM to multi-objective MV-BBO by instantiating it within the Sofomore framework, obtaining a multi-objective optimizer termed COMO-CatCMA with Margin (COMO-CatCMAwM). Numerical experiments on single-objective MV-BBO problems show that CatCMAwM effectively handles the three types of variables, outperforming state-of-the-art Bayesian optimization methods and baselines that simply incorporate existing integer handlings into CatCMA. Moreover, on bi-objective MV-BBO benchmarks, COMO-CatCMAwM achieves competitive or superior hypervolume compared to representative baselines.

cs.NE↗

arXiv2025

Ultra-High-Temperature Vacuum Prober for Electrical and Thermal Measurements

We develop an ultra-high-temperature vacuum probe station (UHT-VPS) featuring a sample holder heated by thermal radiation from a silicon carbide heater. This contactless configuration electrically isolates the sample from the high-power heating source through a vacuum gap, ensuring reliable measurements under extreme conditions. The capability of this UHT-VPS to measure electrical signals from 30 nV upward on bulk sapphire is demonstrated using the 3w/2w method. Measurements are continuously operated from 300 to 1150 K, under high vacuum, for a total of about 66 hours without readjusting the contact. They yield the linear and quadratic temperature coefficients of resistance of chromium/platinum micro-resistances, as well as the sapphire's thermal conductivity and thermal diffusivity. By recording the heater and sensor temperature signals up to 30 kHz and fitting them with theoretical models that account for the quadratic TCR of Cr/Pt microwires, we obtain values in agreement with literature data obtained by optical methods. In this temperature range, we also measure thermal conductivity, which cannot be directly accessed by optical techniques. Our system thus provides an effective solution for simultaneously retrieving the electrical and thermal properties of materials using a single set of 3w/2w data up to unprecedented temperature levels.

physics.ins-det↗

arXiv2025

CatCMA : Stochastic Optimization for Mixed-Category Problems

Black-box optimization problems often require simultaneously optimizing different types of variables, such as continuous, integer, and categorical variables. Unlike integer variables, categorical variables do not necessarily have a meaningful order, and the discretization approach of continuous variables does not work well. Although several Bayesian optimization methods can deal with mixed-category black-box optimization (MC-BBO), they suffer from a lack of scalability to high-dimensional problems and internal computational cost. This paper proposes CatCMA, a stochastic optimization method for MC-BBO problems, which employs the joint probability distribution of multivariate Gaussian and categorical distributions as the search distribution. CatCMA updates the parameters of the joint probability distribution in the natural gradient direction. CatCMA also incorporates the acceleration techniques used in the covariance matrix adaptation evolution strategy (CMA-ES) and the stochastic natural gradient method, such as step-size adaptation and learning rate adaptation. In addition, we restrict the ranges of the categorical distribution parameters by margin to prevent premature convergence and analytically derive a promising margin setting. Numerical experiments show that the performance of CatCMA is superior and more robust to problem dimensions compared to state-of-the-art Bayesian optimization algorithms.

cs.NE↗

arXiv2025

Multi-start Optimization Method via Scalarization based on Target Point-based Tchebycheff Distance for Multi-objective Optimization

Multi-objective optimization is crucial in scientific and industrial applications where solutions must balance trade-offs among conflicting objectives. State-of-the-art methods, such as NSGA-III and MOEA/D, can handle many objectives but struggle with coverage issues, particularly in cases involving inverted triangular Pareto fronts or strong nonlinearity. Moreover, NSGA-III often relies on simulated binary crossover, which deteriorates in problems with variable dependencies. In this study, we propose a novel multi-start optimization method that addresses these challenges. Our approach introduces a newly introduced scalarization technique, the Target Point-based Tchebycheff Distance (TPTD) method, which significantly improves coverage on problems with inverted triangular Pareto fronts. For efficient multi-start optimization, TPTD leverages a target point defined in the objective space, which plays a critical role in shaping the scalarized function. The position of the target point is adaptively determined according to the shape of the Pareto front, ensuring improvement in coverage. Furthermore, the flexibility of this scalarization allows seamless integration with powerful single-objective optimization methods, such as natural evolution strategies, to efficiently handle variable dependencies. Experimental results on benchmark problems, including those with inverted triangular Pareto fronts, demonstrate that our method outperforms NSGA-II, NSGA-III, and MOEA/D-DE in terms of the Hypervolume indicator. Notably, our approach achieves computational efficiency improvements of up to 474 times over these baselines.

cs.NE↗

arXiv2025

A Memetic Algorithm based on Variational Autoencoder for Black-Box Discrete Optimization with Epistasis among Parameters

Black-box discrete optimization (BB-DO) problems arise in many real-world applications, such as neural architecture search and mathematical model estimation. A key challenge in BB-DO is epistasis among parameters where multiple variables must be modified simultaneously to effectively improve the objective function. Estimation of Distribution Algorithms (EDAs) provide a powerful framework for tackling BB-DO problems. In particular, an EDA leveraging a Variational Autoencoder (VAE) has demonstrated strong performance on relatively low-dimensional problems with epistasis while reducing computational cost. Meanwhile, evolutionary algorithms such as DSMGA-II and P3, which integrate bit-flip-based local search with linkage learning, have shown excellent performance on high-dimensional problems. In this study, we propose a new memetic algorithm that combines VAE-based sampling with local search. The proposed method inherits the strengths of both VAE-based EDAs and local search-based approaches: it effectively handles high-dimensional problems with epistasis among parameters without incurring excessive computational overhead. Experiments on NK landscapes -- a challenging benchmark for BB-DO involving epistasis among parameters -- demonstrate that our method outperforms state-of-the-art VAE-based EDA methods, as well as leading approaches such as P3 and DSMGA-II.

cs.NE↗

arXiv2025

Instance-wise Supervision-level Optimization in Active Learning

Active learning (AL) is a label-efficient machine learning paradigm that focuses on selectively annotating high-value instances to maximize learning efficiency. Its effectiveness can be further enhanced by incorporating weak supervision, which uses rough yet cost-effective annotations instead of exact (i.e., full) but expensive annotations. We introduce a novel AL framework, Instance-wise Supervision-Level Optimization (ISO), which not only selects the instances to annotate but also determines their optimal annotation level within a fixed annotation budget. Its optimization criterion leverages the value-to-cost ratio (VCR) of each instance while ensuring diversity among the selected instances. In classification experiments, ISO consistently outperforms traditional AL methods and surpasses a state-of-the-art AL approach that combines full and weak supervision, achieving higher accuracy at a lower overall cost. This code is available at https://github.com/matsuo-shinnosuke/ISOAL.

cs.CV↗

arXiv2025

Hypersonic acoustic wave control via hyperuniform phononic nanostructures

Controlling hypersonic surface acoustic waves is crucial for advanced phononic devices such as high-frequency filters, sensors, and quantum computing components. While periodic phononic crystals enable precise bandgap engineering, their ability to suppress acoustic waves is limited to specific frequency ranges. Here, we experimentally demonstrate the control of surface acoustic waves using a hyperuniform arrangement of gold nanopillars on a lithium niobate layer. The hyperuniform structure exhibits characteristics of both random and ordered systems, leading to an overall reduction in acoustic transmission and the formation of bandgap-like regions where phonon propagation is strongly suppressed. We further demonstrate effective waveguiding by incorporating linear and S-shaped waveguides into the hyperuniform pattern. Both simulations and experiments confirm high transmission through the waveguides at frequencies within the bandgaps, demonstrating the flexibility of hyperuniform structures to support waveguides of complex shapes. These findings provide a novel approach to overcoming the limitations of traditional phononic crystals and advancing acoustic technologies in applications such as mechanical quantum computing and smartphone filters.

cond-mat.mes-hall↗

arXiv2024

Harnessing the Latent Diffusion Model for Training-Free Image Style Transfer

Diffusion models have recently shown the ability to generate high-quality images. However, controlling its generation process still poses challenges. The image style transfer task is one of those challenges that transfers the visual attributes of a style image to another content image. Typical obstacle of this task is the requirement of additional training of a pre-trained model. We propose a training-free style transfer algorithm, Style Tracking Reverse Diffusion Process (STRDP) for a pretrained Latent Diffusion Model (LDM). Our algorithm employs Adaptive Instance Normalization (AdaIN) function in a distinct manner during the reverse diffusion process of an LDM while tracking the encoding history of the style image. This algorithm enables style transfer in the latent space of LDM for reduced computational cost, and provides compatibility for various LDM models. Through a series of experiments and a user study, we show that our method can quickly transfer the style of an image without additional training. The speed, compatibility, and training-free aspect of our algorithm facilitates agile experiments with combinations of styles and LDMs for extensive application.

cs.CV↗