Hong Qian – författare

Focusing on multimodality in translation studies, this edited volume presents insights into the models, trends, and practices of multimodal translation across a variety of media contexts in contemporary China.

The book is structured into five main themes, investigating audiovisual translation in digital media, multimodal translation of Chinese classics in print media, multimodal design in website translation, stance and ideology of paratexts in news translation, and the use of paralanguage and visual cues in quasi-on-site multimodal translation such as conference interpreting. Contributors draw on various theoretical models and research methods, including systemic functional multimodal discourse analysis, narrative theory, Skopos-functional theory, multimodal analysis of digital discourse, corpus-assisted multimodal analysis, questionnaire surveys, and interviews. The volume covers major topics in multimodal translation studies, ranging from emerging multimodal translation models to multimodal creativity in inter-lingual subtitling for social media, image framing in multimodal metaphor translation, and intersemiotic structure, information value, cohesion, and coherence in different textures of media translation. Through ample solid empirical studies, it aims to shed lights on the methodological development of multimodal translation across various media forms, including social media, websites, on-site interactions, and books.

The title will be of great value to scholars and students studying linguistics, translation studies, multimodal discourse analysis, and digital media.

Focusing on multimodality in translation studies, this edited volume presents insights into the models, trends, and practices of multimodal translation across a variety of media contexts in contemporary China.

The book is structured into five main themes, investigating audiovisual translation in digital media, multimodal translation of Chinese classics in print media, multimodal design in website translation, stance and ideology of paratexts in news translation, and the use of paralanguage and visual cues in quasi-on-site multimodal translation such as conference interpreting. Contributors draw on various theoretical models and research methods, including systemic functional multimodal discourse analysis, narrative theory, Skopos-functional theory, multimodal analysis of digital discourse, corpus-assisted multimodal analysis, questionnaire surveys, and interviews. The volume covers major topics in multimodal translation studies, ranging from emerging multimodal translation models to multimodal creativity in inter-lingual subtitling for social media, image framing in multimodal metaphor translation, and intersemiotic structure, information value, cohesion, and coherence in different textures of media translation. Through ample solid empirical studies, it aims to shed lights on the methodological development of multimodal translation across various media forms, including social media, websites, on-site interactions, and books.

The title will be of great value to scholars and students studying linguistics, translation studies, multimodal discourse analysis, and digital media.

This book provides an introduction to the analysis of stochastic dynamic models in biology and medicine. The main aim is to offer a coherent set of probabilistic techniques and mathematical tools which can be used for the simulation and analysis of various biological phenomena. These tools are illustrated on a number of examples. For each example, the biological background is described, and mathematical models are developed following a unified set of principles. These models are then analyzed and, finally, the biological implications of the mathematical results are interpreted. The biological topics covered include gene expression, biochemistry, cellular regulation, and cancer biology. The book will be accessible to graduate students who have a strong background in differential equations, the theory of nonlinear dynamical systems, Markovian stochastic processes, and both discrete and continuous state spaces, and who are familiar with the basic concepts of probability theory.

This book offers a pioneering exploration of classification-based derivative-free optimization (DFO), providing researchers and professionals in artificial intelligence, machine learning, AutoML, and optimization with a robust framework for addressing complex, large-scale problems where gradients are unavailable. By bridging theoretical foundations with practical implementations, it fills critical gaps in the field, making it an indispensable resource for both academic and industrial audiences.

The book introduces innovative frameworks such as sampling-and-classification (SAC) and sampling-and-learning (SAL), which underpin cutting-edge algorithms like Racos and SRacos. These methods are designed to excel in challenging optimization scenarios, including high-dimensional search spaces, noisy environments, and parallel computing. A dedicated section on the ZOOpt toolbox provides practical tools for implementing these algorithms effectively. The book’s structure moves from foundational principles and algorithmic development to advanced topics and real-world applications, such as hyperparameter tuning, neural architecture search, and algorithm selection in AutoML.

Readers will benefit from a comprehensive yet concise presentation of modern DFO methods, gaining theoretical insights and practical tools to enhance their research and problem-solving capabilities. A foundational understanding of machine learning, probability theory, and algorithms is recommended for readers to fully engage with the material.