arXiv subjects

Masahiro Nomura

Publications and source records attributed to Masahiro Nomura.

At least 55 records · Page 3Linked to original sources

arXiv2023

Polaritonic Waveguide Emits Super-Planckian Thermal Radiation

Classical Planck's theory of thermal radiation predicts an upper limit of the heat transfer between two bodies separated by a distance longer than the dominant radiation wavelength (far-field regime). This limit can be overcome when the dimensions of the absorbent bodies are smaller than the dominant wavelength due to hybrid electromagnetic waves, known as surface phonon-polaritons (SPhPs). Here, we experimentally demonstrate that the far-field radiative heat transfer between two non-absorbent bodies can also overcome Planck's limit, by coating them with an absorbent material to form a polaritonic waveguide. This super-Planckian far-field thermal radiation is confirmed by measuring the radiative thermal conductance between two silicon plates coated with silicon dioxide nanolayers. The observed conductance is twice higher than Planck's limit and agrees with the predictions of our model for the SPhP waveguide modes. Our findings could be applied to thermal management in microelectronics and silicon photonics.

physics.optics↗

arXiv2022

Optimal Best Arm Identification in Two-Armed Bandits with a Fixed Budget under a Small Gap

We consider fixed-budget best-arm identification in two-armed Gaussian bandit problems. One of the longstanding open questions is the existence of an optimal strategy under which the probability of misidentification matches a lower bound. We show that a strategy following the Neyman allocation rule (Neyman, 1934) is asymptotically optimal when the gap between the expected rewards is small. First, we review a lower bound derived by Kaufmann et al. (2016). Then, we propose the "Neyman Allocation (NA)-Augmented Inverse Probability weighting (AIPW)" strategy, which consists of the sampling rule using the Neyman allocation with an estimated standard deviation and the recommendation rule using an AIPW estimator. Our proposed strategy is optimal because the upper bound matches the lower bound when the budget goes to infinity and the gap goes to zero.

stat.ML↗

arXiv2022

Asymmetry of tensile vs. compressive elasticity and permeability contributes to the regulation of exchanges in collagen gels

The Starling principle describes exchanges in tissues based on the balance of hydrostatic and osmotic flows. This balance neglects the coupling between mechanics and hydrodynamics, a questionable assumption in strained elastic tissues due to intravascular pressure. Here, we measure the elasticity and permeability of collagen gels under tensile and compressive stress via the comparison of the temporal evolution of pressure in an air cavity sealed at the outlet of a collagen slab with an analytical kinetic model. We observe a drop in the permeability and enhanced strain-stiffening of native collagen gels under compression, both effects being essentially lost after chemical cross-linking. Further, we prove that this asymmetric response accounts for the accumulation of compressive stress upon sinusoidal fluid injection, which modulates the material's permeability. Our results thus show that the properties of collagen gels regulate molecular exchanges and could help understand drug transport in tissues.

physics.bio-ph↗

arXiv2022

Observation of phonon Poiseuille flow in isotopically-purified graphite ribbons

In recent times, the unique collective transport physics of phonon hydrodynamics motivates theoreticians and experimentalists to explore it in micro- and nanoscale and at elevated temperatures. Graphitic materials have been predicted to facilitate hydrodynamic heat transport with their intrinsically strong normal scattering. However, owing to the experimental difficulties and vague theoretical understanding, the observation of phonon Poiseuille flow in graphitic systems remains challenging. In this study, based on a microscale experimental platform and the pertinent occurrence criterion in anisotropic solids, we demonstrate the phonon Poiseuille flow in a 5 μm-wide suspended graphite ribbon with purified 13C isotope concentration. Our observation is well supported by our theoretical model based on a kinetic theory with fully first-principles inputs. Thus, this study paves the way for deeper insight into phonon hydrodynamics and cutting-edge heat manipulating applications.

cond-mat.mes-hall↗

arXiv2022

Assessing Phonon Coherence Using Spectroscopy

As a fundamental physical quantity of thermal phonons, temporal coherence participates in a broad range of thermal and phononic processes, while a clear methodology for the measurement of phonon coherence is still lacking. In this Lettter, we derive a theoretical model for the experimental exploration of phonon coherence based on spectroscopy, which is then validated by comparison with Brillouin light scattering data and direct molecular dynamic simulations of confined modes in nanostructures. The proposed model highlights that confined modes exhibit a pronounced wavelike behavior characterized by a higher ratio of coherence time to lifetime. The dependence of phonon coherence on system size is also demonstrated from spectroscopy data. The proposed theory allows for reassessing data of conventional spectroscopy to yield coherence times, which are essential for the understanding and the estimation of phonon characteristics and heat transport in solids in general.

physics.app-ph↗

arXiv2022

Fast Moving Natural Evolution Strategy for High-Dimensional Problems

In this work, we propose a new variant of natural evolution strategies (NES) for high-dimensional black-box optimization problems. The proposed method, CR-FM-NES, extends a recently proposed state-of-the-art NES, Fast Moving Natural Evolution Strategy (FM-NES), in order to be applicable in high-dimensional problems. CR-FM-NES builds on an idea using a restricted representation of a covariance matrix instead of using a full covariance matrix, while inheriting an efficiency of FM-NES. The restricted representation of the covariance matrix enables CR-FM-NES to update parameters of a multivariate normal distribution in linear time and space complexity, which can be applied to high-dimensional problems. Our experimental results reveal that CR-FM-NES does not lose the efficiency of FM-NES, and on the contrary, CR-FM-NES has achieved significant speedup compared to FM-NES on some benchmark problems. Furthermore, our numerical experiments using 200, 600, and 1000-dimensional benchmark problems demonstrate that CR-FM-NES is effective over scalable baseline methods, VD-CMA and Sep-CMA.

cs.NE↗

arXiv2022

Towards Resolving Propensity Contradiction in Offline Recommender Learning

We study offline recommender learning from explicit rating feedback in the presence of selection bias. A current promising solution for the bias is the inverse propensity score (IPS) estimation. However, the performance of existing propensity-based methods can suffer significantly from the propensity estimation bias. In fact, most of the previous IPS-based methods require some amount of missing-completely-at-random (MCAR) data to accurately estimate the propensity. This leads to a critical self-contradiction; IPS is ineffective without MCAR data, even though it originally aims to learn recommenders from only missing-not-at-random feedback. To resolve this propensity contradiction, we derive a propensity-independent generalization error bound and propose a novel algorithm to minimize the theoretical bound via adversarial learning. Our theory and algorithm do not require a propensity estimation procedure, thereby leading to a well-performing rating predictor without the true propensity information. Extensive experiments demonstrate that the proposed approach is superior to a range of existing methods both in rating prediction and ranking metrics in practical settings without MCAR data.

stat.ML↗

arXiv2022

Towards a Principled Learning Rate Adaptation for Natural Evolution Strategies

Natural Evolution Strategies (NES) is a promising framework for black-box continuous optimization problems. NES optimizes the parameters of a probability distribution based on the estimated natural gradient, and one of the key parameters affecting the performance is the learning rate. We argue that from the viewpoint of the natural gradient method, the learning rate should be determined according to the estimation accuracy of the natural gradient. To do so, we propose a new learning rate adaptation mechanism for NES. The proposed mechanism makes it possible to set a high learning rate for problems that are relatively easy to optimize, which results in speeding up the search. On the other hand, in problems that are difficult to optimize (e.g., multimodal functions), the proposed mechanism makes it possible to set a conservative learning rate when the estimation accuracy of the natural gradient seems to be low, which results in the robust and stable search. The experimental evaluations on unimodal and multimodal functions demonstrate that the proposed mechanism works properly depending on a search situation and is effective over the existing method, i.e., using the fixed learning rate.

cs.NE↗

arXiv2021

Strong phase correlation between diffusons governs heat conduction in amorphous silicon

Understanding the thermal vibrations and thermal transport in amorphous materials is an important but long-standing issue in several theoretical and practical fields. Using direct molecular dynamic simulations, we demonstrate that the strong phase correlation between local and non-propagating modes, commonly named diffusons in the terminology of amorphous systems, triggers conduction of heat. By considering the predominance of collective excitations in amorphous silicon, the predominant contribution of diffusons, due to phase correlation, is predicted, which further reveals the unique temperature and length dependences of thermal conductivity in amorphous silicon. The explored wavelike behavior of diffusons uncovers the unsolved physical picture of mode correlation in prevailing models and further provides an interpretation of their ability to transport heat. This work introduces a framework for understanding thermal vibrations and thermal transport in amorphous materials, as well as perspectives on the wave nature of thermal vibrations.

physics.comp-ph↗

arXiv2021

Heat conduction theory including phonon coherence

Understanding and quantifying the fundamental physical property of coherence of thermal excitations is a long-standing and general problem in physics. The conventional theory, i.e. the phonon gas model, fails to describe coherence and its impact on thermal transport. In this letter, we propose a general heat conduction formalism supported by theoretical arguments and direct atomic simulations, which takes into account both the conventional phonon gas model and the wave nature of thermal phonons. By naturally introducing wavepackets in the heat flux from fundamental concepts, we derive an original thermal conductivity expression including coherence times and lifetimes. Our theory and simulations reveal two distinct types of coherence, i.e., intrinsic and mutual, appearing in two different temperature ranges. This contribution establishes a fundamental frame for understanding and quantifying the coherence of thermal phonons, which should have a general impact on the estimation of the thermal properties of solids.

physics.comp-ph↗

arXiv2021

Natural Evolution Strategy for Unconstrained and Implicitly Constrained Problems with Ridge Structure

In this paper, we propose a new natural evolution strategy for unconstrained black-box function optimization (BBFO) problems and implicitly constrained BBFO problems. BBFO problems are known to be difficult because explicit representations of objective functions are not available. Implicit constraints make the problems more difficult because whether or not a solution is feasible is revealed when the solution is evaluated with the objective function. DX-NES-IC is one of the promising methods for implicitly constrained BBFO problems. DX-NES-IC has shown better performance than conventional methods on implicitly constrained benchmark problems. However, DX-NES-IC has a problem in that the moving speed of the probability distribution is slow on ridge structure. To address the problem, we propose the Fast Moving Natural Evolution Strategy (FM-NES) that accelerates the movement of the probability distribution on ridge structure by introducing the rank-one update into DX-NES-IC. The rank-one update is utilized in CMA-ES. Since naively introducing the rank-one update makes the search performance deteriorate on implicitly constrained problems, we propose a condition of performing the rank-one update. We also propose to reset the shape of the probability distribution when an infeasible solution is sampled at the first time. In numerical experiments using unconstrained and implicitly constrained benchmark problems, FM-NES showed better performance than DX-NES-IC on problems with ridge structure and almost the same performance as DX-NES-IC on the others. Furthermore, FM-NES outperformed xNES, CMA-ES, xNES with the resampling technique, and CMA-ES with the resampling technique.

cs.NE↗

arXiv2021

Efficient Hyperparameter Optimization under Multi-Source Covariate Shift

A typical assumption in supervised machine learning is that the train (source) and test (target) datasets follow completely the same distribution. This assumption is, however, often violated in uncertain real-world applications, which motivates the study of learning under covariate shift. In this setting, the naive use of adaptive hyperparameter optimization methods such as Bayesian optimization does not work as desired since it does not address the distributional shift among different datasets. In this work, we consider a novel hyperparameter optimization problem under the multi-source covariate shift whose goal is to find the optimal hyperparameters for a target task of interest using only unlabeled data in a target task and labeled data in multiple source tasks. To conduct efficient hyperparameter optimization for the target task, it is essential to estimate the target objective using only the available information. To this end, we construct the variance reduced estimator that unbiasedly approximates the target objective with a desirable variance property. Building on the proposed estimator, we provide a general and tractable hyperparameter optimization procedure, which works preferably in our setting with a no-regret guarantee. The experiments demonstrate that the proposed framework broadens the applications of automated hyperparameter optimization.

cs.LG↗

arXiv2021

Observation of quasi-ballistic thermal transport of surface phonon-polaritons over hundreds of micrometres

Long-distance propagation of heat carriers is essential for efficient heat dissipation in microelectronics. However, in dielectric nanomaterials, the primary heat carriers - phonons - can propagate ballistically only for hundreds of nanometres, which limits their heat conduction efficiency. Theory predicts that surface phonon-polaritons (SPhPs) can overcome this limitation and conduct heat without dissipation for hundreds of micrometres. In this work, we experimentally demonstrate such long-distance heat transport by SPhPs. Using the 3$ω$ technique, we measure the in-plane thermal conductivity of SiN nanomembranes for different heater-sensor distances (100 and 200 $μ$m), membrane thicknesses (30 - 200 nm), and temperatures (300 - 400 K). We find that in contrast with thick membranes, thin nanomembranes support heat conduction by SPhPs, as evidenced by an increase in the thermal conductivity with temperature. Remarkably, the thermal conductivity measured 200 $μ$m away from the heater are consistently higher than that measured 100 $μ$m closer. This result suggests that heat conduction by SPhPs is quasi-ballistic over at least hundreds of micrometres. Thus, our findings show that SPhPs can enhance heat dissipation in polar nanomembranes and find applications in thermal management, near-field radiation, and polaritonics.

cond-mat.mes-hall↗

arXiv2021

Thermal conductivity minimum of graded superlattices due to phonon localization

The Anderson localization of thermal phonons has been shown only in few nano-structures with strong random disorder by the exponential decay of transmission to zero and a thermal conductivity maximum when increasing system length. In this work, we present a path to demonstrate the phonon localization with distinctive features in graded superlattices with short-range order and long-range disorder. A thermal conductivity minimum with system length appears due to the exponential decay of transmission to a non-zero constant, which is a feature of partial phonon localization caused by the moderate disorder. We provide clear evidence of localization through the combined analysis of the participation ratio, transmission, and real-space phonon number density distribution based on our quantum transport simulation. The present work would promote heat conduction engineering by localization via the wave nature of phonons.

cond-mat.mes-hall↗

arXiv2021

Anharmonic phonon-phonon scattering at interface by non-equilibrium Green's function formalism

The understanding and modeling of inelastic scattering of thermal phonons at a solid/solid interface remain an open question. We present a fully quantum theoretical scheme to quantify the effect of anharmonic phonon-phonon scattering at an interface via non-equilibrium Green's function (NEGF) formalism. Based on the real-space scattering rate matrix, a decomposition of the interfacial spectral energy exchange is made into contributions from local and non-local anharmonic interactions, of which the former is shown to be predominant for high-frequency phonons whereas both are important for low-frequency phonons. The anharmonic decay of interfacial phonon modes is revealed to play a crucial role in bridging the bulk modes across the interface. The overall quantitative contribution of anharmonicity to thermal boundary conductance is found to be moderate. The present work promotes a deeper understanding of heat transport at the interface and an intuitive interpretation of anharmonic phonon NEGF formalism.

cond-mat.mes-hall↗

arXiv2020

Warm Starting CMA-ES for Hyperparameter Optimization

Hyperparameter optimization (HPO), formulated as black-box optimization (BBO), is recognized as essential for automation and high performance of machine learning approaches. The CMA-ES is a promising BBO approach with a high degree of parallelism, and has been applied to HPO tasks, often under parallel implementation, and shown superior performance to other approaches including Bayesian optimization (BO). However, if the budget of hyperparameter evaluations is severely limited, which is often the case for end users who do not deserve parallel computing, the CMA-ES exhausts the budget without improving the performance due to its long adaptation phase, resulting in being outperformed by BO approaches. To address this issue, we propose to transfer prior knowledge on similar HPO tasks through the initialization of the CMA-ES, leading to significantly shortening the adaptation time. The knowledge transfer is designed based on the novel definition of task similarity, with which the correlation of the performance of the proposed approach is confirmed on synthetic problems. The proposed warm starting CMA-ES, called WS-CMA-ES, is applied to different HPO tasks where some prior knowledge is available, showing its superior performance over the original CMA-ES as well as BO approaches with or without using the prior knowledge.

cs.LG↗

arXiv2020

Quantum mechanical modeling of anharmonic phonon-phonon scattering in nanostructures

The coherent quantum effect becomes increasingly important in the heat dissipation bottleneck of semiconductor nanoelectronics with the characteristic size shrinking down to few nano-meters scale nowadays. However, the quantum mechanical model remains elusive for anharmonic phonon-phonon scattering in extremely small nanostructures with broken translational symmetry. It is a long-term challenging task to correctly simulate quantum heat transport including anharmonic scattering at a scale relevant to practical applications. In this article, we present a clarified theoretical formulation of anharmonic phonon non-equilibrium Green function (NEGF) formalism for both 1D and 3D nanostructures, through a diagrammatic perturbation expansion and an introduction of Fourier representation to both harmonic and anharmonic terms. A parallelized computational framework with first-principle force constants input is developed for large-scale quantum heat transport simulation. Some crucial approximations in numerical implementation are investigated to ensure the balance between numerical accuracy and efficiency. A quantitative validation is demonstrated for the anharmonic phonon NEGF formalism and computational framework by modeling cross-plane heat transport through silicon thin film. The phonon-phonon scattering is shown to be appreciable and to introduce about 20% reduction of thermal conductivity at room temperature even for a film thickness around 10 nm. The present methodology provides a robust platform for the device quantum thermal modeling, as well as the study on the transition from coherent to incoherent heat transport in nano-phononic crystals. This work thus paves the way to understand and to manipulate heat conduction via the wave nature of phonons.

cond-mat.mes-hall↗

arXiv2020

Simple and Scalable Parallelized Bayesian Optimization

In recent years, leveraging parallel and distributed computational resources has become essential to solve problems of high computational cost. Bayesian optimization (BO) has shown attractive results in those expensive-to-evaluate problems such as hyperparameter optimization of machine learning algorithms. While many parallel BO methods have been developed to search efficiently utilizing these computational resources, these methods assumed synchronous settings or were not scalable. In this paper, we propose a simple and scalable BO method for asynchronous parallel settings. Experiments are carried out with a benchmark function and hyperparameter optimization of multi-layer perceptrons, which demonstrate the promising performance of the proposed method.

stat.ML↗