arXiv subjects

Hiroshi Sawada

Publications and source records attributed to Hiroshi Sawada.

At least 19 recordsLinked to original sources

arXiv2026

Adaptive Gradient-Based Methods for a Broader Class of Optimization Problems under Performative Prediction

We study optimization under performative prediction, where deploying a model affects the future data distribution. For this setting, several gradient-based approaches have been proposed. However, they typically assume specific data distributions or loss functions, which limit their practical applicability. To overcome these limitations, we propose a gradient-based optimization method with convergence guarantees under substantially weaker assumptions. Our method explicitly estimates the induced distribution shift through finite differences. It enables higher-dimensional optimization across broader classes of loss functions and data distributions. We also propose a practical variant that reduces the number of samples required. Numerical experiments demonstrate that our proposed algorithms converge faster and more consistently than existing ones.

math.OC↗

arXiv2025

FedPM: Federated Learning Using Second-order Optimization with Preconditioned Mixing of Local Parameters

We propose Federated Preconditioned Mixing (FedPM), a novel Federated Learning (FL) method that leverages second-order optimization. Prior methods--such as LocalNewton, LTDA, and FedSophia--have incorporated second-order optimization in FL by performing iterative local updates on clients and applying simple mixing of local parameters on the server. However, these methods often suffer from drift in local preconditioners, which significantly disrupts the convergence of parameter training, particularly in heterogeneous data settings. To overcome this issue, we refine the update rules by decomposing the ideal second-order update--computed using globally preconditioned global gradients--into parameter mixing on the server and local parameter updates on clients. As a result, our FedPM introduces preconditioned mixing of local parameters on the server, effectively mitigating drift in local preconditioners. We provide a theoretical convergence analysis demonstrating a superlinear rate for strongly convex objectives in scenarios involving a single local update. To demonstrate the practical benefits of FedPM, we conducted extensive experiments. The results showed significant improvements with FedPM in the test accuracy compared to conventional methods incorporating simple mixing, fully leveraging the potential of second-order optimization.

cs.LG↗

arXiv2025

Enhanced plasma heating via interaction with high-contrast laser and cone-shaped target

We investigated plasma heating enhancement using a high-intensity, high-contrast laser and a cone-attached target. Fast electron spectra and X-ray emission were measured with an electron spectrometer and a Bragg crystal spectrometer. The results were analyzed using PrismSPECT simulations with a two-component electron distribution model and empirical scaling laws. X-ray pinhole images showed that the cone effectively focused multi-spot laser light near its tip, enhancing local emission. While high-contrast laser irradiation reduced the fast electron slope temperature for flat targets, the use of a cone increased it by over threefold, corresponding to a fourfold rise in laser intensity. X-ray spectral analysis indicated an electron temperature of ~9~keV for the cone case, 17.5 times higher than that with a low-contrast laser. These findings demonstrate that combining high-contrast laser irradiation with cone-target geometry significantly improves laser energy coupling and plasma heating efficiency.

physics.plasm-ph↗

arXiv2024

Optoelectronic recurrent neural network using optical-electrical-optical converters with RC delay

Optical neural network (ONN) has been attracting intense attention owing to their low latency and low-power consumption. Among the ONNs, optical recurrent neural network (RNN) enables low-power and high-speed time-series data processing using a compact loop structure. The loop losses need to be efficiently compensated so that the time-series information is maintained in the RNN operation. For this purpose, we focus on the optoelectronic RNN (OE-RNN) with optical-electrical-optical (OEO) converters to compensate for the loop losses. However, the effect of resistive-capacitive (RC) delay of OEO converters on the RNN performance is unclear. Here, we study in simulation an OE-RNN equipped with OEO converters with RC delay. We confirm that our modeled OE-RNN achieves the high training accuracy of time-series data classification even when RC delay is comparably large to the time interval of time-series data. Our analyses reveal that the accumulation of time-series data by RC delay does not degrade the RNN performance but rather can compensate for the degraded RNN performance due to loop losses. From the theoretical analysis referring to the gradient explosion and vanishing problems, we find the region related to loss and RC delay where the high training accuracy can be achieved. In simulation, we confirm this compensation effect in the large OE-RNN circuit up to 32$\times$32 scale. Our proposed scheme opens a new way of time-series data processing by utilizing RC delay for the optical computing and optical communication.

physics.optics↗

arXiv2023

Stoichiometric growth of SrTiO3 films via Bayesian optimization with adaptive prior mean

Perovskite insulator SrTiO3 is expected to be applied to the next generation of electronic and photonic devices as high-k capacitors and photocatalysts. However, reproducible growth of highly insulating stoichiometric SrTiO3 films remains challenging due to the difficulty of the precise stoichiometry control in perovskite oxide films. Here, to grow stoichiometric SrTiO3 thin films by fine-tuning multiple growth conditions, we developed a new Bayesian optimization (BO)-based machine learning method that encourages the exploration of the search space by varying the prior mean to get out of suboptimal growth condition parameters. Using simulated data, we demonstrate the efficacy of the new BO method, which reproducibly reaches the global best conditions. With the BO method implemented in machine-learning-assisted molecular beam epitaxy (ML-MBE), highly insulating stoichiometric SrTiO3 film with no absorption in the band gap was developed in only 44 MBE growth runs. The proposed algorithm provides an efficient experimental design platform that is not as dependent on the experience of individual researchers and will accelerate not only oxide electronics but also various material syntheses.

cond-mat.mtrl-sci↗

arXiv2022

Phase convergence and crest enhancement of modulated wave trains

The Akhmediev breather (AB) solution of the nonlinear Schr$ö$dinger equation (NLSE) shows that the maximum crest height of modulated wave trains reaches triple the initial amplitude as a consequence of nonlinear long-term evolution. Several fully nonlinear numerical studies have indicated that the amplification can exceed 3, but its physical mechanism has not been clarified. This study shows that spectral broadening, bound-wave production, and phase convergence are essential to crest enhancement beyond the AB solution. The free-wave spectrum of modulated wave trains broadens owing to nonlinear quasi-resonant interaction. This enhances bound-wave production at high wavenumbers. The phases of all the wave components nearly coincide at peak modulation and enhance amplification. We find that the phase convergence observed in linear-focusing waves can also occur due to nonlinear wave evolution. These findings are obtained by numerically investigating the modulated wave trains using the higher-order spectral method (HOSM) up to the fifth order, which allows investigations of nonlinearity and spectral bandwidth beyond the NLSE framework. Moreover, we confirm the crest enhancement through a tank experiment wherein waves are generated in the transition region from non-breaking to breaking. Owing to strong nonlinearity, the maximum crest height observed in the tank begins to exceed the HOSM prediction at an initial wave steepness of 0.10.

physics.flu-dyn↗

arXiv2022

Bayesian optimization with experimental failure for high-throughput materials growth

A crucial problem in achieving innovative high-throughput materials growth with machine learning and automation techniques, such as Bayesian optimization (BO) and robotic experimentation, has been a lack of an appropriate way to handle missing data due to experimental failures. Here, we propose a new BO algorithm that complements the missing data in the optimization of materials growth parameters. The proposed method provides a flexible optimization algorithm capable of searching a wide multi-dimensional parameter space. We demonstrate the effectiveness of the method with simulated data as well as in its implementation for actual materials growth, namely machine-learning-assisted molecular beam epitaxy (ML-MBE) of SrRuO3, which is widely used as a metallic electrode in oxide electronics. Through the exploitation and exploration in a wide three-dimensional parameter space, while complementing the missing data, we attained tensile-strained SrRuO3 film with a high residual resistivity ratio of 80.1, the highest among tensile-strained SrRuO3 films ever reported, in only 35 MBE growth runs.

cond-mat.mtrl-sci↗

arXiv2022

Switching Independent Vector Analysis and Its Extension to Blind and Spatially Guided Convolutional Beamforming Algorithms

This paper develops a framework that can perform denoising, dereverberation, and source separation accurately by using a relatively small number of microphones. It has been empirically confirmed that Independent Vector Analysis (IVA) can blindly separate N sources from their sound mixture even with diffuse noise when a sufficiently large number (=M) of microphones are available (i.e., M>>N). However, the estimation accuracy seriously degrades as the number of microphones, or more specifically M-N (>=0), decreases. To overcome this limitation of IVA, we propose switching IVA (swIVA) in this paper. With swIVA, time frames of an observed signal with time-varying characteristics are clustered into several groups, each of which can be well handled by IVA using a small number of microphones, and thus accurate estimation can be achieved by applying IVA individually to each of the groups. Conventionally, a switching mechanism was introduced into a beamformer; however, no blind source separation algorithms with a switching mechanism have been successfully developed until this paper. In order to incorporate dereverberation capability, this paper further extends swIVA to blind Convolutional beamforming algorithm (swCIVA). It integrates swIVA and switching Weighted Prediction Error-based dereverberation (swWPE) in a jointly optimal way. We show that both swIVA and swCIVA can be optimized effectively based on blind signal processing, and that their performance can be further improved using a spatial guide for the initialization. Experiments show that both proposed methods largely outperform conventional IVA and its Convolutional beamforming extension (CIVA) in terms of objective signal quality and automatic speech recognition scores when using a relatively small number of microphones.

eess.AS↗

arXiv2021

Evacuation Shelter Scheduling Problem

Evacuation shelters, which are urgently required during natural disasters, are designed to minimize the burden of evacuation on human survivors. However, the larger the scale of the disaster, the more costly it becomes to operate shelters. When the number of evacuees decreases, the operation costs can be reduced by moving the remaining evacuees to other shelters and closing shelters as quickly as possible. On the other hand, relocation between shelters imposes a huge emotional burden on evacuees. In this study, we formulate the "Evacuation Shelter Scheduling Problem," which allocates evacuees to shelters in such a way to minimize the movement costs of the evacuees and the operation costs of the shelters. Since it is difficult to solve this quadratic programming problem directly, we show its transformation into a 0-1 integer programming problem. In addition, such a formulation struggles to calculate the burden of relocating them from historical data because no payments are actually made. To solve this issue, we propose a method that estimates movement costs based on the numbers of evacuees and shelters during an actual disaster. Simulation experiments with records from the Kobe earthquake (Great Hanshin-Awaji Earthquake) showed that our proposed method reduced operation costs by 33.7 million dollars: 32%.

cs.AI↗

arXiv2021

Acceleration Method for Learning Fine-Layered Optical Neural Networks

An optical neural network (ONN) is a promising system due to its high-speed and low-power operation. Its linear unit performs a multiplication of an input vector and a weight matrix in optical analog circuits. Among them, a circuit with a multiple-layered structure of programmable Mach-Zehnder interferometers (MZIs) can realize a specific class of unitary matrices with a limited number of MZIs as its weight matrix. The circuit is effective for balancing the number of programmable MZIs and ONN performance. However, it takes a lot of time to learn MZI parameters of the circuit with a conventional automatic differentiation (AD), which machine learning platforms are equipped with. To solve the time-consuming problem, we propose an acceleration method for learning MZI parameters. We create customized complex-valued derivatives for an MZI, exploiting Wirtinger derivatives and a chain rule. They are incorporated into our newly developed function module implemented in C++ to collectively calculate their values in a multi-layered structure. Our method is simple, fast, and versatile as well as compatible with the conventional AD. We demonstrate that our method works 20 times faster than the conventional AD when a pixel-by-pixel MNIST task is performed in a complex-valued recurrent neural network with an MZI-based hidden unit.

cs.LG↗

arXiv2021

Blind and neural network-guided convolutional beamformer for joint denoising, dereverberation, and source separation

This paper proposes an approach for optimizing a Convolutional BeamFormer (CBF) that can jointly perform denoising (DN), dereverberation (DR), and source separation (SS). First, we develop a blind CBF optimization algorithm that requires no prior information on the sources or the room acoustics, by extending a conventional joint DR and SS method. For making the optimization computationally tractable, we incorporate two techniques into the approach: the Source-Wise Factorization (SW-Fact) of a CBF and the Independent Vector Extraction (IVE). To further improve the performance, we develop a method that integrates a neural network(NN) based source power spectra estimation with CBF optimization by an inverse-Gamma prior. Experiments using noisy reverberant mixtures reveal that our proposed method with both blind and NN-guided scenarios greatly outperforms the conventional state-of-the-art NN-supported mask-based CBF in terms of the improvement in automatic speech recognition and signal distortion reduction performance.

eess.AS↗

arXiv2021

A Joint Diagonalization Based Efficient Approach to Underdetermined Blind Audio Source Separation Using the Multichannel Wiener Filter

This paper presents a computationally efficient approach to blind source separation (BSS) of audio signals, applicable even when there are more sources than microphones (i.e., the underdetermined case). When there are as many sources as microphones (i.e., the determined case), BSS can be performed computationally efficiently by independent component analysis (ICA). Unfortunately, however, ICA is basically inapplicable to the underdetermined case. Another BSS approach using the multichannel Wiener filter (MWF) is applicable even to this case, and encompasses full-rank spatial covariance analysis (FCA) and multichannel non-negative matrix factorization (MNMF). However, these methods require massive numbers of matrix inversions to design the MWF, and are thus computationally inefficient. To overcome this drawback, we exploit the well-known property of diagonal matrices that matrix inversion amounts to mere inversion of the diagonal elements and can thus be performed computationally efficiently. This makes it possible to drastically reduce the computational cost of the above matrix inversions based on a joint diagonalization (JD) idea, leading to computationally efficient BSS. Specifically, we restrict the N spatial covariance matrices (SCMs) of all N sources to a class of (exactly) jointly diagonalizable matrices. Based on this approach, we present FastFCA, a computationally efficient extension of FCA. We also present a unified framework for underdetermined and determined audio BSS, which highlights a theoretical connection between FastFCA and other methods. Moreover, we reveal that FastFCA can be regarded as a regularized version of approximate joint diagonalization (AJD).

cs.SD↗

arXiv2020

Quantum transport evidence of Weyl fermions in an epitaxial ferromagnetic oxide

Magnetic Weyl fermions, which occur in magnets, have novel transport phenomena related to pairs of Weyl nodes, and they are, of both, scientific and technological interest, with the potential for use in high-performance electronics, spintronics and quantum computing. Although magnetic Weyl fermions have been predicted to exist in various oxides, evidence for their existence in oxide materials remains elusive. SrRuO3, a 4d ferromagnetic metal often used as an epitaxial conducting layer in oxide heterostructures, provides a promising opportunity to seek for the existence of magnetic Weyl fermions. Advanced oxide thin film preparation techniques, driven by machine learning technologies, may allow access to such topological matter. Here we show direct quantum transport evidence of magnetic Weyl fermions in an epitaxial ferromagnetic oxide SrRuO3: unsaturated linear positive magnetoresistance (MR), chiral-anomaly-induced negative MR, Pi Berry phase accumulated along cyclotron orbits, light cyclotron masses and high quantum mobility of about 10000 cm2/Vs. We employed machine-learning-assisted molecular beam epitaxy (MBE) to synthesize SrRuO3 films whose quality is sufficiently high to probe their intrinsic quantum transport properties. We also clarified the disorder dependence of the transport of the magnetic Weyl fermions, and provided a brand-new diagram for the Weyl transport, which gives a clear guideline for accessing the topologically nontrivial transport phenomena. Our results establish SrRuO3 as a magnetic Weyl semimetal and topological oxide electronics as a new research field.

cond-mat.mtrl-sci↗

arXiv2019

Machine-learning-assisted thin-film growth: Bayesian optimization in molecular beam epitaxy of SrRuO3 thin films

Materials informatics exploiting machine learning techniques, e.g., Bayesian optimization (BO), has the potential to offer high-throughput optimization of thin-film growth conditions through incremental updates of machine learning models in accordance with newly measured data. Here, we demonstrated BO-based molecular beam epitaxy (MBE) of SrRuO3, one of the most-intensively studied materials in the research field of oxide electronics, mainly owing to its unique nature as a ferromagnetic metal. To simplify the intricate search space of entangled growth conditions, we ran the BO for a single condition while keeping the other conditions fixed. As a result, high-crystalline-quality SrRuO3 film exhibiting a high residual resistivity ratio (RRR) of over 50 as well as strong perpendicular magnetic anisotropy was developed in only 24 MBE growth runs in which the Ru flux rate, growth temperature, and O3-nozzle-to-substrate distance were optimized. Our BO-based search method provides an efficient experimental design that is not as dependent on the experience and skills of individual researchers, and it reduces experimental time and cost, which will accelerate materials research.

cond-mat.mtrl-sci↗

arXiv2019

Peta-Pascal Pressure Driven by Fast Isochoric Heating with Multi-Picosecond Intense Laser Pulse

Fast isochoric laser heating is a scheme to heat a matter with relativistic-intensity ($>$ 10$^{18}$ W/cm$^2$) laser pulse or X-ray free electron laser pulse. The fast isochoric laser heating has been studied for creating efficiently ultra-high-energy-density (UHED) state. We demonstrate an fast isochoric heating of an imploded dense plasma using a multi-picosecond kJ-class petawatt laser with an assistance of externally applied kilo-tesla magnetic fields for guiding fast electrons to the dense plasma.The UHED state with 2.2 Peta-Pascal is achieved experimentally with 4.6 kJ of total laser energy that is one order of magnitude lower than the energy used in the conventional implosion scheme. A two-dimensional particle-in-cell simulation reveals that diffusive heating from a laser-plasma interaction zone to the dense plasma plays an essential role to the efficient creation of the UHED state.

physics.plasm-ph↗

arXiv2018

Finding Appropriate Traffic Regulations via Graph Convolutional Networks

Appropriate traffic regulations, e.g. planned road closure, are important in congested events. Crowd simulators have been used to find appropriate regulations by simulating multiple scenarios with different regulations. However, this approach requires multiple simulation runs, which are time-consuming. In this paper, we propose a method to learn a function that outputs regulation effects given the current traffic situation as inputs. If the function is learned using the training data of many simulation runs in advance, we can obtain an appropriate regulation efficiently by bypassing simulations for the current situation. We use the graph convolutional networks for modeling the function, which enable us to find regulations even for unseen areas. With the proposed method, we construct a graph for each area, where a node represents a road, and an edge represents the road connection. By running crowd simulations with various regulations on various areas, we generate traffic situations and regulation effects. The graph convolutional networks are trained to output the regulation effects given the graph with the traffic situation information as inputs. With experiments using real-world road networks and a crowd simulator, we demonstrate that the proposed method can find a road to close that reduces the average time needed to reach the destination.

physics.soc-ph↗

arXiv2018

Blind Source Separation with Optimal Transport Non-negative Matrix Factorization

Optimal transport as a loss for machine learning optimization problems has recently gained a lot of attention. Building upon recent advances in computational optimal transport, we develop an optimal transport non-negative matrix factorization (NMF) algorithm for supervised speech blind source separation (BSS). Optimal transport allows us to design and leverage a cost between short-time Fourier transform (STFT) spectrogram frequencies, which takes into account how humans perceive sound. We give empirical evidence that using our proposed optimal transport NMF leads to perceptually better results than Euclidean NMF, for both isolated voice reconstruction and BSS tasks. Finally, we demonstrate how to use optimal transport for cross domain sound processing tasks, where frequencies represented in the input spectrograms may be different from one spectrogram to another.

cs.SD↗

arXiv2017

Magnetized Fast Isochoric Laser Heating for Efficient Creation of Ultra-High-Energy-Density States

The quest for the inertial confinement fusion (ICF) ignition is a grand challenge, as exemplified by extraordinary large laser facilities. Fast isochoric heating of a pre-compressed plasma core with a high-intensity short-pulse laser is an attractive and alternative approach to create ultra-high-energy-density states like those found in ICF ignition sparks. This avoids the ignition quench caused by the hot spark mixing with the surrounding cold fuel, which is the crucial problem of the currently pursued ignition scheme. High-intensity lasers efficiently produce relativistic electron beams (REB). A part of the REB kinetic energy is deposited in the core, and then the heated region becomes the hot spark to trigger the ignition. However, only a small portion of the REB collides with the core because of its large divergence. Here we have demonstrated enhanced laser-to-core energy coupling with the magnetized fast isochoric heating. The method employs a kilo-tesla-level magnetic field that is applied to the transport region from the REB generation point to the core which results in guiding the REB along the magnetic field lines to the core. 7.7 $\pm$ 1.3 % of the maximum coupling was achieved even with a relatively small radial area density core ($ρR$ $\sim$ 0.1 g/cm$^2$). The guided REB transport was clearly visualized in a pre-compressed core by using Cu-$K_α$ imaging technique. A simplified model coupled with the comprehensive diagnostics yields 6.2\% of the coupling that agrees fairly with the measured coupling. This model also reveals that an ignition-scale areal density core ($ρR$ $\sim$ 0.4 g/cm$^2$) leads to much higher laser-to-core coupling ($>$ 15%), this is much higher than that achieved by the current scheme.

physics.plasm-ph↗