The generation of terminology related to Rhetoric in pre-modern Japan evolved in roughly three phases.The first phase(1603-1771),dominated by linguistic contact with missionaries,rendered“Rhetorica”by direct katakan...The generation of terminology related to Rhetoric in pre-modern Japan evolved in roughly three phases.The first phase(1603-1771),dominated by linguistic contact with missionaries,rendered“Rhetorica”by direct katakana transliteration as retorika(レトリカ)or retōrika(レトーリカ)during the initial introduction of Western learning.This phase is characterized by knowledge transplantation primarily through transliteration.In the second phase(1771-1789),as Western knowledge was introduced more systematically via Chinese translations of Western works—especially through the dissemination of Xi Xue Fan and Tengaku shokan taiisho,which used Lèduólǐjiāas the translation of“Rhetorica”—Japanese intellectuals gained a preliminary,systematic understanding of the content,nature,and teaching methods of Western rhetoric.The third phase began in the Kansei era(1789),when the term giron was gradually adopted as a translation of Western logic and applied in related translation practices.By 1814,the term rhetorick was clearly translated as giron in Angeria gorin taisei.Research evidence suggests that the term giron originated from the“Giron no hō”proposed in Xi Xue Fan,and that this book clearly influenced the adoption of“giron”in Dutch-Japanese dictionaries.From the first to the third phase,both Japanese missionaries and Chinese intellectuals drew on Buddhist knowledge to introduce Western rhetoric,interpreting it through terms such as dangi,rongi and giron,thereby reframing it within the Buddhist concept of debate.After the Meiji period,giron was gradually abandoned and replaced by shūji.This shift in terminology not only marked the modernization of Japan’s rhetorical terminology system but also signaled a cognitive and identity transformation:Japanese intellectuals began to reposition China from an object of cultural identification to a cultural“Other”.展开更多
To enhance the computational density and energy efficiency of on-chip neuromorphic hardware,this study introduces a novel network architecture for multi-task processing with in-memory optical computing.On-chip optical...To enhance the computational density and energy efficiency of on-chip neuromorphic hardware,this study introduces a novel network architecture for multi-task processing with in-memory optical computing.On-chip optical neural networks are celebrated for their capability to transduce a substantial volume of parameters into optical form while conducting passive computing,yet they encounter challenges in scalability and multitasking.Leveraging the principles of transfer learning,this approach involves embedding the majority of parameters into fixed optical components and a minority into adjustable electrical components.Furthermore,with deep regression algorithm in modeling physical propagation process,a compact optical neural network achieve to handle diverse tasks.In this work,two ultra-compact in-memory diffraction-based chips with integration of more than 60,000 parameters/mm^(2) were fabricated,employing deep neural network model and the hard parameter sharing algorithm,to perform multifaceted classification and regression tasks,respectively.The experimental results demonstrate that these chips achieve accuracies comparable to those of electrical networks while significantly reducing the power-intensive digital computation by 90%.Our work heralds strong potential for advancing in-memory optical computing frameworks and next generation of artificial intelligence platforms.展开更多
This study presents an efficient method for automatically identifying the crystal structure and orientation of Y-doped HfO_(2)-based thin films using deep learning.This approach enables large-scale crystallographic an...This study presents an efficient method for automatically identifying the crystal structure and orientation of Y-doped HfO_(2)-based thin films using deep learning.This approach enables large-scale crystallographic analysis with sub-nanometer spatial resolution using only scanning transmission electron microscopy(STEM)atomic images,thereby reducing the reliance on manual expert interpretation.The Xception network-based model extracts detailed crystallographic information through structure and entropy maps,effectively identifying subtle pattern changes and local structural discontinuities.Entropy maps are utilized to analyze the atomic structure disorder and detect ambiguous boundaries and strained regions.Analysis of Y-doped HfO_(2)thin films reveals that the film thickness significantly affects the ferroelectric properties,with theOphase dominant in 5 nmfilms and the M phase proportion increasing as the thickness increases.This machine-learning-based STEM atomic image analysis method provides an automated solution to accelerate ferroelectric material research and promote the development of next-generation electronic devices,offering an accurate understanding and control of microstructural characteristics.展开更多
基金Research leading to this paper was funded by the NSSFC Post-Funding Project(22FZXB088)Guizhou University Research Project in Humanities and Social Sciences(GDYB2022007).
文摘The generation of terminology related to Rhetoric in pre-modern Japan evolved in roughly three phases.The first phase(1603-1771),dominated by linguistic contact with missionaries,rendered“Rhetorica”by direct katakana transliteration as retorika(レトリカ)or retōrika(レトーリカ)during the initial introduction of Western learning.This phase is characterized by knowledge transplantation primarily through transliteration.In the second phase(1771-1789),as Western knowledge was introduced more systematically via Chinese translations of Western works—especially through the dissemination of Xi Xue Fan and Tengaku shokan taiisho,which used Lèduólǐjiāas the translation of“Rhetorica”—Japanese intellectuals gained a preliminary,systematic understanding of the content,nature,and teaching methods of Western rhetoric.The third phase began in the Kansei era(1789),when the term giron was gradually adopted as a translation of Western logic and applied in related translation practices.By 1814,the term rhetorick was clearly translated as giron in Angeria gorin taisei.Research evidence suggests that the term giron originated from the“Giron no hō”proposed in Xi Xue Fan,and that this book clearly influenced the adoption of“giron”in Dutch-Japanese dictionaries.From the first to the third phase,both Japanese missionaries and Chinese intellectuals drew on Buddhist knowledge to introduce Western rhetoric,interpreting it through terms such as dangi,rongi and giron,thereby reframing it within the Buddhist concept of debate.After the Meiji period,giron was gradually abandoned and replaced by shūji.This shift in terminology not only marked the modernization of Japan’s rhetorical terminology system but also signaled a cognitive and identity transformation:Japanese intellectuals began to reposition China from an object of cultural identification to a cultural“Other”.
基金supported by the National Key R&D Plan of China(2024YFE0203600)the National Natural Science Foundation of China(62135009).
文摘To enhance the computational density and energy efficiency of on-chip neuromorphic hardware,this study introduces a novel network architecture for multi-task processing with in-memory optical computing.On-chip optical neural networks are celebrated for their capability to transduce a substantial volume of parameters into optical form while conducting passive computing,yet they encounter challenges in scalability and multitasking.Leveraging the principles of transfer learning,this approach involves embedding the majority of parameters into fixed optical components and a minority into adjustable electrical components.Furthermore,with deep regression algorithm in modeling physical propagation process,a compact optical neural network achieve to handle diverse tasks.In this work,two ultra-compact in-memory diffraction-based chips with integration of more than 60,000 parameters/mm^(2) were fabricated,employing deep neural network model and the hard parameter sharing algorithm,to perform multifaceted classification and regression tasks,respectively.The experimental results demonstrate that these chips achieve accuracies comparable to those of electrical networks while significantly reducing the power-intensive digital computation by 90%.Our work heralds strong potential for advancing in-memory optical computing frameworks and next generation of artificial intelligence platforms.
基金supported by the Ministry of Trade,Industry,and Energy(MOTIE)of Korea(No.P0022331)supervised by the Korea Institute for Advancement of Technology(KIAT)+4 种基金the Technology Innovation Program(Alchemist Project,AI-based supercritical materials discovery)funded by MOTIE,Korea(No.20012196)supervised by the Korea Evaluation Institute of Industrial Technology(KEIT)the National R&D Program through the National Research Foundation of Korea(NRF),funded by the Ministry of Science and ICT(RS-2024-00450561)the institutional research program(2E33912)of the Korea Institute of Science and Technology(KIST)National Research Foundation of Korea(NRF)grant funded by the Ministry of Science and ICT(NRF-2020M3F3A2A01081572).
文摘This study presents an efficient method for automatically identifying the crystal structure and orientation of Y-doped HfO_(2)-based thin films using deep learning.This approach enables large-scale crystallographic analysis with sub-nanometer spatial resolution using only scanning transmission electron microscopy(STEM)atomic images,thereby reducing the reliance on manual expert interpretation.The Xception network-based model extracts detailed crystallographic information through structure and entropy maps,effectively identifying subtle pattern changes and local structural discontinuities.Entropy maps are utilized to analyze the atomic structure disorder and detect ambiguous boundaries and strained regions.Analysis of Y-doped HfO_(2)thin films reveals that the film thickness significantly affects the ferroelectric properties,with theOphase dominant in 5 nmfilms and the M phase proportion increasing as the thickness increases.This machine-learning-based STEM atomic image analysis method provides an automated solution to accelerate ferroelectric material research and promote the development of next-generation electronic devices,offering an accurate understanding and control of microstructural characteristics.
