Spin injection and detection using perpendicularly magnetized Mn/Co bilayers grown on GaAs via all electrical methods

The electrical spin injection and detection in perpendicularly magnetized Mn/Co/n-GaAs junction was investigated using a non-local method. Clear non-local spin-valve signals and Hanle effect signals were observed at 77 K, providing direct evidence of the injection and detection of perpendicularly polarized spins through all electrical methods. The magnitude of the spin-valve signal was one order of magnitude smaller than that observed in a reference sample with an in-plane magnetized CoFe due to the low spin polarization of the ultrathin Mn/Co electrodes. It was found that the spin polarization at the interface between Mn/Co electrode and n(+)-GaAs had a relatively weak bias-current dependence in contrast to that at CoFe/n(+)-GaAs interface. The estimated spin lifetime of perpendicular spins injected from the Mn/Co bilayer into n-GaAs was approximately 1.9 ns at 77 K. This value is similar to that of in-plane spins injected from CoFe, indicating that the spin lifetime was not strongly dependent on the spin orientation in the bulk GaAs channel

On Ainu Possessive Constructions : From the Perspective of Subjectivity

本稿では，アイヌ語における所有表現とそれに伴う主観性の関係を考察した。先行研究においては，主に英語と日本語の対照研究が盛んに行われてきたが，それ以外の言語を対象とした研究が少ない。本研究はアイヌ語を対象とし，その特異な所有表現の特徴を明らかにすることを目的とした。池上（1985，2011 など）による主観性の理論を基に，英語と日本語の主観性の相違を明確にし，アイヌ語における所有の指標を比較した。アイヌ語は，日本語と類似したSOV語順があるものの，異なる言語であり，独特な文法構造を持つ。アイヌ語の叙述所有表現と限定所有表現にはいくつかの構文が見られる。叙述所有は主に「kor」と「an」の動詞によって表され，限定所有は主要部標示型，従属部標示型，無標示型，二重標示型に分類できる。アイヌ語における所有表現の構造を詳細に記述し，主観性との関連性を考察することで，所有表現の類型の再考を提案した。所有表現の分析で，アイヌ語で明確に言語化される譲渡不可能性を主観性の指標の一つとして指摘した。また，譲渡不可能名詞の所有者に対しては，前景化（英語またはアイヌ語）と背景化（日本語）という二つの取扱いが存在し，所有表現に影響を与えることを示した

投射動作における自己および他者の誤差に基づく試行間学習 [全文の要約]

この博士論文全文の閲覧方法については、以下のサイトをご参照ください。https://www.lib.hokudai.ac.jp/dissertations/copy-guides

Carotenoid analysis for photosynthetic organisms in green lineage

近年，様々な研究手法の発展により，緑色系統(Green lineage)の光合成生物内，さらには光合成色素蛋白質複合体内におけるカロテノイドの局在や組成，その化学構造の分析の必要性が高まっている．本稿では，光合成色素の各複合体への蓄積パターンや生合成経路等から，緑色系統の光合成生物のカロテノイドの戦略的な分析方法について記載する．Recent advancements in various research techniques have highlighted the increasing need to analyze the localization, composition, and chemical structures of carotenoids within the pigment-protein complexes of photosynthetic organisms in the green lineage. This paper outlines strategic approaches for analyzing carotenoids in these organisms, focusing on accumulation patterns within pigment complexes, biosynthetic pathways, and related aspects

深層学習手法を用いた冠動脈解析のための画像前処理から3D モデリングに関する研究

Coronary artery disease (CAD) remains a leading cause of mortality worldwide, necessitating advancements in diagnostic and treatment methods. This thesis explores novel deep learning approaches to improve the analysis of coronary artery imaging, addressing challenges in stenosis detection, and 3D reconstruction. Three key contributions are presented: (1) a Hessian-based image preprocessing method integrated with image fusion, enhancing visualization of coronary angiography; (2) an attention-enhanced YOLO-based framework for stenosis localization, improving detection accuracy in complex angiographic scenarios; and (3) an automated deep learning pipeline leveraging GhostNet and transformers for the extraction and 3D reconstruction of the right coronary artery (RCA) from CT images. Comprehensive experiments validate the efficacy of the proposed methods across multiple tasks in coronary artery analysis. The Hessian-based image preprocessing combined with image fusion significantly enhanced vessel visualization, leading to a 5% increase in detection accuracy (AP50 = 87.1%) using the YOLOv10-X model compared to raw dataset. The proposed attention-enhanced YOLO-based network achieved outstanding performance in stenosis localization, with an AP50 of 90.5% at the zero-degree left coronary artery (LCA) imaging angle, the highest accuracy across all tested angles. Additionally, the automated deep learning pipeline for the extraction and 3D reconstruction of the right coronary artery achieved a segmentation F1 score of 0.866 and a mean IoU of 0.835. The 3D reconstruction process provided superior visual clarity, making it suitable for clinical applications. These contributions collectively advance the accuracy, efficiency, and practicality of coronary artery disease analysis, offering transformative potential for real-time medical diagnostics and treatment planning. This study highlights the transformative potential of advanced deep learning architectures in CAD diagnosis, offering pathways for real-time, precise, and robust medical imaging solutions. Future research will focus on extending these techniques to broader datasets and refining latency for real-time clinical applications