Focusing on two case studies from East Asia and Europe, Yinan He argues that the key to interstate reconciliation is the harmonization of national memories.
Why have some former enemy countries established durable peace while others remain mired in animosity? When and how does historical memory matter in post-conflict interstate relations? Focusing on two case studies, Yinan He argues that the key to interstate reconciliation is the harmonization of national memories. Conversely, memory divergence resulting from national mythmaking harms long-term prospects for reconciliation. After WWII, Sino-Japanese and West German-Polish relations were both antagonized by the Cold War structure, and pernicious myths prevailed in national collective memory. In the 1970s, China and Japan brushed aside historical legacy for immediate diplomatic normalization. But the progress of reconciliation was soon impeded from the 1980s by elite mythmaking practices that stressed historical animosities. Conversely, from the 1970s West Germany and Poland began to de-mythify war history and narrowed their memory gap through restitution measures and textbook cooperation, paving the way for significant progress toward reconciliation after the Cold War.
剧场这一术语,被中国戏剧教授李亦男在特定语境下加以介绍和定义。这本独特的选集收录了其与九位1980年代后期在不同方面贡献卓著的中国戏剧人的访谈。这些与拥有不同的成长背景、年龄、艺术观点的剧场人的对话彰显了剧场这一概念开放和包容的特质。受访者们都曾在不同时期,不同时代精神下活跃在中国戏剧界,彼此之间拥有深刻的联结并相互影响。该选集是一部剧场人立足于创作实践,从崭新的视角看待当代中国戏剧不断变化形态的开创之作。 The term juchang is introduced, contextualized and defined by leading professor of theatre in China, Li Yinan, and forms a unique and individual selection of interviews with nine juchang theatre-makers from different periods after 1980, who have contributed to different and interesting developments. All have different backgrounds, ages and perspectives on theatre that reflect the open and inclusive nature of the concept of juchang. The interviewees were all active in Chinese theatre when a different creative spirit reigned, and yet they seem to have had extensive cohesion between, and influence on, each other. A ground breaking look at the changing shape of contemporary Chinese theatre from the perspectives of those who are in the process of creating it.
Formal methods is a field of computer science that emphasizes the use of rigorous mathematical techniques for verification and design of hardware and software systems. Analysis and design of nonlinear control design plays an important role across many disciplines of engineering and applied sciences, ranging from the control of an aircraft engine to the design of genetic circuits in synthetic biology. While linear control is a well-established subject, analysis and design of nonlinear control systems remains a challenging topic due to some of the fundamental difficulties caused by nonlinearity. Formal Methods for Control of Nonlinear Systems provides a unified computational approach to analysis and design of nonlinear systems. Features Constructive approach to nonlinear control. Rigorous specifications and validated computation. Suitable for graduate students and researchers who are interested in learning how formal methods and validated computation can be combined together to tackle nonlinear control problems with complex specifications from an algorithmic perspective. Combines mathematical rigor with practical applications.
This thesis transports you to a wonderful and fascinating small-scale world and tells you the origin of several new phenomena. The investigative tool is the improved discrete dislocation-based multi-scale approaches, bridging the continuum modeling and atomistic simulation. Mechanism-based theoretical models are put forward to conveniently predict the mechanical responses and defect evolution. The findings presented in this thesis yield valuable new guidelines for microdevice design, reliability analysis and defect tuning.
Dislocation Based Crystal Plasticity: Theory and Computation at Micron and Submicron Scale provides a comprehensive introduction to the continuum and discreteness dislocation mechanism-based theories and computational methods of crystal plasticity at the micron and submicron scale. Sections cover the fundamental concept of conventional crystal plasticity theory at the macro-scale without size effect, strain gradient crystal plasticity theory based on Taylar law dislocation, mechanism at the mesoscale, phase-field theory of crystal plasticity, computation at the submicron scale, including single crystal plasticity theory, and the discrete-continuous model of crystal plasticity with three-dimensional discrete dislocation dynamics coupling finite element method (DDD-FEM). Three kinds of plastic deformation mechanisms for submicron pillars are systematically presented. Further sections discuss dislocation nucleation and starvation at high strain rate and temperature effect for dislocation annihilation mechanism. - Covers dislocation mechanism-based crystal plasticity theory and computation at the micron and submicron scale - Presents crystal plasticity theory without size effect - Deals with the 3D discrete-continuous (3D DCM) theoretic and computational model of crystal plasticity with 3D discrete dislocation dynamics (3D DDD) coupling finite element method (FEM) - Includes discrete dislocation mechanism-based theory and computation at the submicron scale with single arm source, coating micropillar, lower cyclic loading pillars, and dislocation starvation at the submicron scale
Machine Learning for Low-Latency Communications presents the principles and practice of various deep learning methodologies for mitigating three critical latency components: access latency, transmission latency, and processing latency. In particular, the book develops learning to estimate methods via algorithm unrolling and multiarmed bandit for reducing access latency by enlarging the number of concurrent transmissions with the same pilot length. Task-oriented learning to compress methods based on information bottleneck are given to reduce the transmission latency via avoiding unnecessary data transmission. Lastly, three learning to optimize methods for processing latency reduction are given which leverage graph neural networks, multi-agent reinforcement learning, and domain knowledge. Low-latency communications attracts considerable attention from both academia and industry, given its potential to support various emerging applications such as industry automation, autonomous vehicles, augmented reality and telesurgery. Despite the great promise, achieving low-latency communications is critically challenging. Supporting massive connectivity incurs long access latency, while transmitting high-volume data leads to substantial transmission latency. - Presents the challenges and opportunities of leveraging data and model-driven machine learning methodologies for achieving low-latency communications - Explains the principles and practices of modern machine learning algorithms (e.g., algorithm unrolling, multiarmed bandit, graph neural network, and multi-agent reinforcement learning) for achieving low-latency communications - Gives design, modeling, and optimization methods for low-latency communications that apply appropriate learning methods to solve longstanding problems - Provides full details of the simulation setup and benchmarking algorithms, with downloadable code - Outlines future research challenges and directions
Machine Learning for Low-Latency Communications presents the principles and practice of various deep learning methodologies for mitigating three critical latency components: access latency, transmission latency, and processing latency. In particular, the book develops learning to estimate methods via algorithm unrolling and multiarmed bandit for reducing access latency by enlarging the number of concurrent transmissions with the same pilot length. Task-oriented learning to compress methods based on information bottleneck are given to reduce the transmission latency via avoiding unnecessary data transmission. Lastly, three learning to optimize methods for processing latency reduction are given which leverage graph neural networks, multi-agent reinforcement learning, and domain knowledge. Low-latency communications attracts considerable attention from both academia and industry, given its potential to support various emerging applications such as industry automation, autonomous vehicles, augmented reality and telesurgery. Despite the great promise, achieving low-latency communications is critically challenging. Supporting massive connectivity incurs long access latency, while transmitting high-volume data leads to substantial transmission latency. - Presents the challenges and opportunities of leveraging data and model-driven machine learning methodologies for achieving low-latency communications - Explains the principles and practices of modern machine learning algorithms (e.g., algorithm unrolling, multiarmed bandit, graph neural network, and multi-agent reinforcement learning) for achieving low-latency communications - Gives design, modeling, and optimization methods for low-latency communications that apply appropriate learning methods to solve longstanding problems - Provides full details of the simulation setup and benchmarking algorithms, with downloadable code - Outlines future research challenges and directions
Dislocation Based Crystal Plasticity: Theory and Computation at Micron and Submicron Scale provides a comprehensive introduction to the continuum and discreteness dislocation mechanism-based theories and computational methods of crystal plasticity at the micron and submicron scale. Sections cover the fundamental concept of conventional crystal plasticity theory at the macro-scale without size effect, strain gradient crystal plasticity theory based on Taylar law dislocation, mechanism at the mesoscale, phase-field theory of crystal plasticity, computation at the submicron scale, including single crystal plasticity theory, and the discrete-continuous model of crystal plasticity with three-dimensional discrete dislocation dynamics coupling finite element method (DDD-FEM). Three kinds of plastic deformation mechanisms for submicron pillars are systematically presented. Further sections discuss dislocation nucleation and starvation at high strain rate and temperature effect for dislocation annihilation mechanism. - Covers dislocation mechanism-based crystal plasticity theory and computation at the micron and submicron scale - Presents crystal plasticity theory without size effect - Deals with the 3D discrete-continuous (3D DCM) theoretic and computational model of crystal plasticity with 3D discrete dislocation dynamics (3D DDD) coupling finite element method (FEM) - Includes discrete dislocation mechanism-based theory and computation at the submicron scale with single arm source, coating micropillar, lower cyclic loading pillars, and dislocation starvation at the submicron scale
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