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Masahiro Suzuki

Masahiro Suzuki appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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Machine LearningArtificial Intelligencecond-mat.mtrl-scicond-mat.str-elmath.APComputation and LanguageRobotics

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Published work

14 published item(s)

preprint2026arXiv

DAD4TS: Data-Augmentation-Oriented Diffusion Model for Time-Series Forecasting with Small-Scale Data

Small-scale data is a critical problem in time-series forecasting tasks. Data augmentation is an effective strategy for this task, but it has a limitation in generating meaningful data. To address this limitation, we propose DAD4TS, a diffusion-model-based data augmentation method with reinforcement learning, designed for time-series forecasting with small-scale data. In DAD4TS, a data generator is simultaneously trained with a time-series model and controlled by a reinforcement learning model to efficiently generate samples that improve the forecast accuracy of the time-series model. To support small-scale data, we use mathematical methods instead of conventional VAE methods to train the diffusion model by projecting the time-series data into the geometric space. We validated the effectiveness of DAD4TS with seven comparative methods through qualitative and quantitative experiments on six real-world datasets and eight time-series models. As a result, DAD4TS was validated on five datasets.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges

Creating autonomous robots that can actively explore the environment, acquire knowledge and learn skills continuously is the ultimate achievement envisioned in cognitive and developmental robotics. Their learning processes should be based on interactions with their physical and social world in the manner of human learning and cognitive development. Based on this context, in this paper, we focus on the two concepts of world models and predictive coding. Recently, world models have attracted renewed attention as a topic of considerable interest in artificial intelligence. Cognitive systems learn world models to better predict future sensory observations and optimize their policies, i.e., controllers. Alternatively, in neuroscience, predictive coding proposes that the brain continuously predicts its inputs and adapts to model its own dynamics and control behavior in its environment. Both ideas may be considered as underpinning the cognitive development of robots and humans capable of continual or lifelong learning. Although many studies have been conducted on predictive coding in cognitive robotics and neurorobotics, the relationship between world model-based approaches in AI and predictive coding in robotics has rarely been discussed. Therefore, in this paper, we clarify the definitions, relationships, and status of current research on these topics, as well as missing pieces of world models and predictive coding in conjunction with crucially related concepts such as the free-energy principle and active inference in the context of cognitive and developmental robotics. Furthermore, we outline the frontiers and challenges involved in world models and predictive coding toward the further integration of AI and robotics, as well as the creation of robots with real cognitive and developmental capabilities in the future.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A survey of multimodal deep generative models

Multimodal learning is a framework for building models that make predictions based on different types of modalities. Important challenges in multimodal learning are the inference of shared representations from arbitrary modalities and cross-modal generation via these representations; however, achieving this requires taking the heterogeneous nature of multimodal data into account. In recent years, deep generative models, i.e., generative models in which distributions are parameterized by deep neural networks, have attracted much attention, especially variational autoencoders, which are suitable for accomplishing the above challenges because they can consider heterogeneity and infer good representations of data. Therefore, various multimodal generative models based on variational autoencoders, called multimodal deep generative models, have been proposed in recent years. In this paper, we provide a categorized survey of studies on multimodal deep generative models.

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preprint2022arXiv

A Whole Brain Probabilistic Generative Model: Toward Realizing Cognitive Architectures for Developmental Robots

Building a humanlike integrative artificial cognitive system, that is, an artificial general intelligence (AGI), is the holy grail of the artificial intelligence (AI) field. Furthermore, a computational model that enables an artificial system to achieve cognitive development will be an excellent reference for brain and cognitive science. This paper describes an approach to develop a cognitive architecture by integrating elemental cognitive modules to enable the training of the modules as a whole. This approach is based on two ideas: (1) brain-inspired AI, learning human brain architecture to build human-level intelligence, and (2) a probabilistic generative model(PGM)-based cognitive system to develop a cognitive system for developmental robots by integrating PGMs. The development framework is called a whole brain PGM (WB-PGM), which differs fundamentally from existing cognitive architectures in that it can learn continuously through a system based on sensory-motor information. In this study, we describe the rationale of WB-PGM, the current status of PGM-based elemental cognitive modules, their relationship with the human brain, the approach to the integration of the cognitive modules, and future challenges. Our findings can serve as a reference for brain studies. As PGMs describe explicit informational relationships between variables, this description provides interpretable guidance from computational sciences to brain science. By providing such information, researchers in neuroscience can provide feedback to researchers in AI and robotics on what the current models lack with reference to the brain. Further, it can facilitate collaboration among researchers in neuro-cognitive sciences as well as AI and robotics.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Selective observation of surface and bulk bands in polar WTe2 by laser-based spin- and angle-resolved photoemission spectroscopy

The electronic state of WTe2, a candidate of type-II Weyl semimetal, is investigated by using laser-based spin- and angle-resolved photoemission spectroscopy (SARPES). We prepare the pair of WTe2 samples, one with (001) surface and the other with (00-1) surface, by "sandwich method", and measure the band structures of each surface separately. The Fermi arcs are observed on both surfaces. We identify that the Fermi arcs on the two surfaces are both originating from surface states. We further find a surface resonance band, which connects with the Fermi-arc band, forming a Dirac-cone-like band dispersion. Our results indicate that the bulk electron and hole bands are much closer in momentum space than band calculations.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Anisotropic spin distribution and perpendicular magnetic anisotropy in the layered ferromagnetic semiconductor (Ba,K)(Zn,Mn)$_{2}$As$_{2}$

Perpendicular magnetic anisotropy of the new ferromagnetic semiconductor (Ba,K)(Zn,Mn)$_{2}$As$_{2}$ is studied by angle-dependent x-ray magnetic circular dichroism measurements. The large magnetic anisotropy with the anisotropy field of 0.85 T is deduced by fitting the Stoner-Wohlfarth model to the magnetic-field-angle dependence of the projected magnetic moment. Transverse XMCD spectra highlights the anisotropic distribution of Mn 3$d$ electrons, where the $d_{xz}$ and $d_{yz}$ orbitals are less populated than the $d_{xy}$ state because of the $D_{2d}$ splitting arising from the elongated MnAs$_{4}$ tetrahedra. It is suggested that the magnetic anisotropy originates from the degeneracy lifting of $p$-$d_{xz}$, $d_{yz}$ hybridized states at the Fermi level and resulting energy gain due to spin-orbit coupling when spins are aligned along the $z$ direction.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Boundary layers of the Boltzmann equation in a three-dimensional half-space

We consider the nonlinear boundary layers of the Boltzmann equation in a three-dimensional half-space by perturbing around a Maxwellian, under the assumption that the Mach number of the Maxwellian satisfies ${\cal M}_{\infty} < -1$. In preceding works, nonlinear boundary layers of the Boltzmann equation in a half-line are considered, with stationary solutions obtained and nonlinear stability confirmed. In this paper, we establish the unique existence of stationary solutions for the three-dimensional half-space model, and show that the stationary solution is asymptotic stable.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Hybridization between the ligand $p$ band and Fe-3$d$ orbitals in the p-type ferromagnetic semiconductor (Ga,Fe)Sb

(Ga,Fe)Sb is a promising ferromagnetic semiconductor for practical spintronic device applications because its Curie temperature ($T_{\rm C}$) is above room temperature. However, the origin of ferromagnetism with high $T_{\rm C}$ remains to be elucidated. Here, we use soft x-ray angle-resolved photoemission spectroscopy (SX-ARPES) to investigate the valence-band (VB) structure of (Ga$_{0.95}$,Fe$_{0.05}$)Sb including the Fe-3$d$ impurity band (IB), to unveil the mechanism of ferromagnetism in (Ga,Fe)Sb. We find that the VB dispersion in (Ga$_{0.95}$,Fe$_{0.05}$)Sb observed by SX-ARPES is similar to that of GaSb, indicating that the doped Fe atoms hardly affect the band dispersion. The Fe-3$d$ resonant ARPES spectra demonstrate that the Fe-3$d$ IB crosses the Fermi level ($E_{\rm F}$) and hybridizes with the VB of GaSb. These observations indicate that the VB structure of (Ga$_{0.95}$,Fe$_{0.05}$)Sb is consistent with that of the IB model which is based on double-exchange interaction between the localized 3$d$ electrons of the magnetic impurities. The results indicate that the ferromagnetism in (Ga,Fe)Sb is formed by the hybridization of the Fe-3$d$ IB with the ligand $p$ band of GaSb.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Neuro-SERKET: Development of Integrative Cognitive System through the Composition of Deep Probabilistic Generative Models

This paper describes a framework for the development of an integrative cognitive system based on probabilistic generative models (PGMs) called Neuro-SERKET. Neuro-SERKET is an extension of SERKET, which can compose elemental PGMs developed in a distributed manner and provide a scheme that allows the composed PGMs to learn throughout the system in an unsupervised way. In addition to the head-to-tail connection supported by SERKET, Neuro-SERKET supports tail-to-tail and head-to-head connections, as well as neural network-based modules, i.e., deep generative models. As an example of a Neuro-SERKET application, an integrative model was developed by composing a variational autoencoder (VAE), a Gaussian mixture model (GMM), latent Dirichlet allocation (LDA), and automatic speech recognition (ASR). The model is called VAE+GMM+LDA+ASR. The performance of VAE+GMM+LDA+ASR and the validity of Neuro-SERKET were demonstrated through a multimodal categorization task using image data and a speech signal of numerical digits.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Steady States of Gas Ionization with Secondary Emission

We consider the steady states of a gas between two parallel plates that is ionized by a strong electric field so as to create a plasma. There can be a cascade of electrons due both to the electrons colliding with the gas molecules and to the ions colliding with the cathode (secondary emission). We use global bifurcation theory to prove that there is a one-parameter family $\mathscr{K}$ of such steady states with the following property. The curve $\mathscr{K}$ begins at the sparking voltage and either the particle density becomes unbounded or $\mathscr{K}$ ends at an anti-sparking voltage. These critical voltages are characterized explicitly.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Magnetization process of the insulating ferromagnetic semiconductor (Al,Fe)Sb

We have studied the magnetization process of the new insulating ferromagnetic semiconductor (Al,Fe)Sb by means of x-ray magnetic circular dichroism. For an optimally doped sample with 10% Fe, a magnetization was found to rapidly increase at low magnetic fields and to saturate at high magnetic fields at room temperature, well above the Curie temperature of 40 K. We attribute this behavior to the existence of nanoscale Fe-rich ferromagnetic domains acting as superparamagnets. By fitting the magnetization curves using the Langevin function representing superparamagnetism plus the paramagnetic linear function, we estimated the average magnetic moment of the nanoscale ferromagnetic domain to be 300-400 $μ_{B}$, and the fraction of Fe atoms participating in the nano-scale ferromagnetism to be $\sim$50%. Such behavior was also reported for (In,Fe)As:Be and Ge:Fe, and seems to be a universal characteristic of the Fe-doped ferromagnetic semiconductors. Further Fe doping up to 14% led to the weakening of the ferromagnetism probably because antiferromagnetic superexchange interaction between nearest-neighbor Fe-Fe pairs becomes dominant.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Generative Adversarial Nets from a Density Ratio Estimation Perspective

Generative adversarial networks (GANs) are successful deep generative models. GANs are based on a two-player minimax game. However, the objective function derived in the original motivation is changed to obtain stronger gradients when learning the generator. We propose a novel algorithm that repeats the density ratio estimation and f-divergence minimization. Our algorithm offers a new perspective toward the understanding of GANs and is able to make use of multiple viewpoints obtained in the research of density ratio estimation, e.g. what divergence is stable and relative density ratio is useful.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Joint Multimodal Learning with Deep Generative Models

We investigate deep generative models that can exchange multiple modalities bi-directionally, e.g., generating images from corresponding texts and vice versa. Recently, some studies handle multiple modalities on deep generative models, such as variational autoencoders (VAEs). However, these models typically assume that modalities are forced to have a conditioned relation, i.e., we can only generate modalities in one direction. To achieve our objective, we should extract a joint representation that captures high-level concepts among all modalities and through which we can exchange them bi-directionally. As described herein, we propose a joint multimodal variational autoencoder (JMVAE), in which all modalities are independently conditioned on joint representation. In other words, it models a joint distribution of modalities. Furthermore, to be able to generate missing modalities from the remaining modalities properly, we develop an additional method, JMVAE-kl, that is trained by reducing the divergence between JMVAE's encoder and prepared networks of respective modalities. Our experiments show that our proposed method can obtain appropriate joint representation from multiple modalities and that it can generate and reconstruct them more properly than conventional VAEs. We further demonstrate that JMVAE can generate multiple modalities bi-directionally.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Neural Machine Translation with Latent Semantic of Image and Text

Although attention-based Neural Machine Translation have achieved great success, attention-mechanism cannot capture the entire meaning of the source sentence because the attention mechanism generates a target word depending heavily on the relevant parts of the source sentence. The report of earlier studies has introduced a latent variable to capture the entire meaning of sentence and achieved improvement on attention-based Neural Machine Translation. We follow this approach and we believe that the capturing meaning of sentence benefits from image information because human beings understand the meaning of language not only from textual information but also from perceptual information such as that gained from vision. As described herein, we propose a neural machine translation model that introduces a continuous latent variable containing an underlying semantic extracted from texts and images. Our model, which can be trained end-to-end, requires image information only when training. Experiments conducted with an English--German translation task show that our model outperforms over the baseline.

0 citations0 reviewsNo code linkedOpen access