細胞機能を可視化するイメージングプローブのためのゼラチンからなるキャリアのデザインと作製

京都大学新制・課程博士博士(工学)甲第23161号工博第4805号新制||工||1751(附属図書館)京都大学大学院工学研究科高分子化学専攻(主査)教授 田畑 泰彦, 教授 秋吉 一成, 教授 沼田 圭司学位規則第4条第1項該当Doctor of Philosophy (Engineering)Kyoto UniversityDFA

Evaluation of water film by reynolds' equation in deep drawing using high-pressured water jet

The authors had proposed a deep drawing method using high-pressured jet waters as lubricant. This method aimed to suppress the usage of oil or other chemical lubricants, which might require some additional processes for lubricant removal and become a nuisance in environment. The conditions had been determined through trial and error approach without knowing water behaviors as lubricant. As a result, some scars and dimples were observed on the surface of deformed cup. In the present paper, a numerical model was composed for the evaluation of the water behaviors as lubricant. Darcy-Weisbach equation was used for evaluation of pressure drop between nozzle exit and pump, while Reynolds' equation was used for the thin film of fluid between the die and blank. The data of blank deformation in FEM was considered for the determination of the thickness distribution of the fluid film. The characteristics of the water were evaluated by the composed numerical method, and the results were used for examination of lubrication characteristics in experiments

Topological defect formation in quenched ferromagnetic Bose-Einstein condensates

We study the dynamics of the quantum phase transition of a ferromagnetic spin-1 Bose-Einstein condensate from the polar phase to the broken-axisymmetry phase by changing magnetic field, and find the spontaneous formation of spinor domain walls followed by the creation of polar-core spin vortices. We also find that the spin textures depend very sensitively on the initial noise distribution, and that an anisotropic and colored initial noise is needed to reproduce the Berkeley experiment [Sadler et al., Nature 443, 312 (2006)]. The dynamics of vortex nucleation and the number of created vortices depend also on the manner in which the magnetic field is changed. We point out an analogy between the formation of spin vortices from domain walls in a spinor BEC and that of vortex-antivortex pairs from dark solitons in a scalar BEC.Comment: 10 pages, 11 figure

Evolutionary NAS with Gene Expression Programming of Cellular Encoding

The renaissance of neural architecture search (NAS) has seen classical methods such as genetic algorithms (GA) and genetic programming (GP) being exploited for convolutional neural network (CNN) architectures. While recent work have achieved promising performance on visual perception tasks, the direct encoding scheme of both GA and GP has functional complexity deficiency and does not scale well on large architectures like CNN. To address this, we present a new generative encoding scheme -- $symbolic\ linear\ generative\ encoding$ (SLGE) -- simple, yet powerful scheme which embeds local graph transformations in chromosomes of linear fixed-length string to develop CNN architectures of variant shapes and sizes via evolutionary process of gene expression programming. In experiments, the effectiveness of SLGE is shown in discovering architectures that improve the performance of the state-of-the-art handcrafted CNN architectures on CIFAR-10 and CIFAR-100 image classification tasks; and achieves a competitive classification error rate with the existing NAS methods using less GPU resources.Comment: Accepted at IEEE SSCI 2020 (7 pages, 3 figures

Variable Gain Type PID Control Using PSO for Ultrasonic Motor

Ultrasonic motor exhibits non-linearity that relates the input (Phase difference) and output (Velocity). It also causes serious characteristic changes during operation. PID control has been widely used as the design scheme for USM. However, it is difficult for the conventional PID control to compensate such characteristic changes of the plant and non-linearity. To overcome this problem, we propose a variable gain type PID control in which PID gains are optimized using a particle swarm optimization (PSO)

On the Equivalence of Consistency-Type Models: Consistency Models, Consistent Diffusion Models, and Fokker-Planck Regularization

The emergence of various notions of ``consistency'' in diffusion models has garnered considerable attention and helped achieve improved sample quality, likelihood estimation, and accelerated sampling. Although similar concepts have been proposed in the literature, the precise relationships among them remain unclear. In this study, we establish theoretical connections between three recent ``consistency'' notions designed to enhance diffusion models for distinct objectives. Our insights offer the potential for a more comprehensive and encompassing framework for consistency-type models

Unsupervised vocal dereverberation with diffusion-based generative models

Removing reverb from reverberant music is a necessary technique to clean up audio for downstream music manipulations. Reverberation of music contains two categories, natural reverb, and artificial reverb. Artificial reverb has a wider diversity than natural reverb due to its various parameter setups and reverberation types. However, recent supervised dereverberation methods may fail because they rely on sufficiently diverse and numerous pairs of reverberant observations and retrieved data for training in order to be generalizable to unseen observations during inference. To resolve these problems, we propose an unsupervised method that can remove a general kind of artificial reverb for music without requiring pairs of data for training. The proposed method is based on diffusion models, where it initializes the unknown reverberation operator with a conventional signal processing technique and simultaneously refines the estimate with the help of diffusion models. We show through objective and perceptual evaluations that our method outperforms the current leading vocal dereverberation benchmarks.Comment: 6 pages, 2 figures, submitted to ICASSP 202

GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration

Pre-trained diffusion models have been successfully used as priors in a variety of linear inverse problems, where the goal is to reconstruct a signal from noisy linear measurements. However, existing approaches require knowledge of the linear operator. In this paper, we propose GibbsDDRM, an extension of Denoising Diffusion Restoration Models (DDRM) to a blind setting in which the linear measurement operator is unknown. GibbsDDRM constructs a joint distribution of the data, measurements, and linear operator by using a pre-trained diffusion model for the data prior, and it solves the problem by posterior sampling with an efficient variant of a Gibbs sampler. The proposed method is problem-agnostic, meaning that a pre-trained diffusion model can be applied to various inverse problems without fine-tuning. In experiments, it achieved high performance on both blind image deblurring and vocal dereverberation tasks, despite the use of simple generic priors for the underlying linear operators

SAN: Inducing Metrizability of GAN with Discriminative Normalized Linear Layer

Generative adversarial networks (GANs) learn a target probability distribution by optimizing a generator and a discriminator with minimax objectives. This paper addresses the question of whether such optimization actually provides the generator with gradients that make its distribution close to the target distribution. We derive metrizable conditions, sufficient conditions for the discriminator to serve as the distance between the distributions by connecting the GAN formulation with the concept of sliced optimal transport. Furthermore, by leveraging these theoretical results, we propose a novel GAN training scheme, called slicing adversarial network (SAN). With only simple modifications, a broad class of existing GANs can be converted to SANs. Experiments on synthetic and image datasets support our theoretical results and the SAN's effectiveness as compared to usual GANs. Furthermore, we also apply SAN to StyleGAN-XL, which leads to state-of-the-art FID score amongst GANs for class conditional generation on ImageNet 256$\times$256.Comment: 24 pages with 12 figure

Thymoproteasomes produce unique peptide motifs for positive selection of CD8+ T cells

Positive selection in the thymus provides low-affinity T-cell receptor (TCR) engagement to support the development of potentially useful self-major histocompatibility complex class I (MHC-I)-restricted T cells. Optimal positive selection of CD8+ T cells requires cortical thymic epithelial cells that express β5t-containing thymoproteasomes (tCPs). However, how tCPs govern positive selection is unclear. Here we show that the tCPs produce unique cleavage motifs in digested peptides and in MHC-I-associated peptides. Interestingly, MHC-I-associated peptides carrying these tCP-dependent motifs are enriched with low-affinity TCR ligands that efficiently induce the positive selection of functionally competent CD8+ T cells in antigen-specific TCR-transgenic models. These results suggest that tCPs contribute to the positive selection of CD8+ T cells by preferentially producing low-affinity TCR ligand peptides