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Molecular Biomarkers for Gastric Cancer, 2nd edition
Gastric cancer (GC) represents a serious health problem on a global scale. Despite some recent advances in the field, the prognosis in GC remains poor. A better understanding of molecular biology, which would lead to improved treatment options, is needed. Many potential biomarkers of prognostic significance have been identified. However, inhibition of only HER2 protein has led to a modest survival benefit. A deeper understanding of the pathogenesis and biological features of gastric cancer, including the identification and characterization of diagnostic, prognostic, predictive, and therapeutic biomarkers, hopefully will provide improved clinical outcomes. This Research Topic aims to provide ...
Strategies of Sustainable Development in China’s Wind Power Industry
This book reviews the status quo and visions for the future in the wind energy industry in China and around the globe, focusing on its roles in optimizing energy structure, alleviating environmental pollution, and coping with climate change. Providing a blueprint of wind power development till 2050, it suggests a series of further measures in the context of policies, regulations, laws, and marketing in order to overcome the existing bottlenecks. Moreover, it proposes a number of potential innovative technologies related to IT+ and advanced manufacturing, including integrated & distributed power and micro-grid systems, multi-energy complement, green and intelligent manufacturing, reliability design, blade design, manufacturing and maintenance, drive drain systems, and offshore wind farms. This book offers researchers and engineers insights into sustainable development in the wind power industry.
Computer Vision – ECCV 2022 Workshops
The 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online. The 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows: Part I: W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision Part II: W05 - Learning With Limited and Imperfect Data; W06 - Ad...
Natural Language Processing and Chinese Computing
This two-volume set of LNAI 13551 and 13552 constitutes the refereed proceedings of the 11th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2022, held in Guilin, China, in September 2022. The 62 full papers, 21 poster papers, and 27 workshop papers presented were carefully reviewed and selected from 327 submissions. They are organized in the following areas: Fundamentals of NLP; Machine Translation and Multilinguality; Machine Learning for NLP; Information Extraction and Knowledge Graph; Summarization and Generation; Question Answering; Dialogue Systems; Social Media and Sentiment Analysis; NLP Applications and Text Mining; and Multimodality and Explainability.
Artificial Neural Networks and Machine Learning – ICANN 2020
The proceedings set LNCS 12396 and 12397 constitute the proceedings of the 29th International Conference on Artificial Neural Networks, ICANN 2020, held in Bratislava, Slovakia, in September 2020.* The total of 139 full papers presented in these proceedings was carefully reviewed and selected from 249 submissions. They were organized in 2 volumes focusing on topics such as adversarial machine learning, bioinformatics and biosignal analysis, cognitive models, neural network theory and information theoretic learning, and robotics and neural models of perception and action. *The conference was postponed to 2021 due to the COVID-19 pandemic.
Representation Learning for Natural Language Processing
This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.
