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Irony
\"Irony is an intriguing topic, central to the study of meaning in language. This book provides an introduction to the pragmatics of irony. It surveys key work carried out on irony in a range of disciplines such as semantics, pragmatics, philosophy and literary studies, and from a variety of theoretical perspectives including Grice's approach, Sperber and Wilson's echoic account, and Clark and Gerrig's pretense theory. It looks at a number of uses of irony and explores how irony can be misunderstood cross-culturally, before delving into the key debates on the pragmatics of irony: is irony always negative? Why do speakers communicate via irony, and which strategies do they usually employ? How are irony and sarcasm different? Is irony always funny? To answer these questions, basic pragmatic notions are introduced and explained. It includes multiple examples and activities to enable the reader to apply the theoretical frameworks to actual everyday instances of irony\"-- Provided by publisher.
Book
The Pragmatics of Irony and Banter
This is a book-length study analysing irony and banter together. This approach, inherited from Geoffrey Leech's research, implies that the two notions are intrinsically related. The various contributors (linguists, stylisticians, discourse analysts, and literary scholars), while not necessarily agreeing on every aspect of this theoretical premise, discuss and develop the idea. In turn, they consider the workings of these two discursive practices in various corpora (face-to-face or digitally-mediated interactions, novels, comedy shows, etc.) thus providing a wealth of examples and case studies.
eBook
Using corpora to analyze gender
\"Corpus linguistics uses specialist software to identify linguistic patterns in large computerised collections of text - patterns which then must be interpreted and explained by human researchers. This book critically explores how corpus linguistics techniques can help analysis of language and gender by conducting a number of case studies on topics which include: directives in spoken conversations, changes in sexist and non-sexist language use over time, personal adverts, press representation of gay men, and the ways that boys and girls are constructed through language. The book thus covers both gendered usage (e.g. how do males and females use language differently, or not, from each other), and gendered representations (e.g. in what ways are males and females written or spoken about). Additionally, the book shows ways that readers can either explore their own hypotheses, or approach the corpus from a \"nai;ve\" position, letting the data drive their analysis from the outset. The book covers a range of techniques and measures including frequencies, keywords, collocations, dispersion, word sketches, downsizing and triangulation, all in an accessible style\"-- Provided by publisher.
Book
Strategy for an Army Center for Network Science, Technology, and Experimentation
The U.S. military has committed to a strategy of network-centric warfare. As a result, the Army has become increasingly interested in the critical role of network science. To a significant extent, this interest was stimulated by an earlier NRC report, Network Science. To build on that book, the Army asked the NRC to conduct a study to define advanced operating models and architectures for future Army laboratories and centers focused on network science, technologies, and experimentation (NSTE). The challenges resulting from base realignment and closure (BRAC) relocations of Army research, development, and engineering resources-as they affected the NSTE program-were also to be a focus of the study. This book provides a discussion of what NSTE is needed by the Army; an examination of the NSTE currently carried out by the Army; an assessment of needed infrastructure resources for Army NSTE; and an analysis of goals, models, and alternatives for an NSTE center.
eBook
Advancing Cancer Systems Biology: Introducing the Center for the Development of a Virtual Tumor, CViT
Integrative cancer biology research relies on a variety of data-driven computational modeling and simulation methods and techniques geared towards gaining new insights into the complexity of biological processes that are of critical importance for cancer research. These include the dynamics of gene-protein interaction networks, the percolation of sub-cellular perturbations across scales and the impact they may have on tumorigenesis in both experiments and clinics. Such innovative ‘systems’ research will greatly benefit from enabling Information Technology that is currently under development, including an online collaborative environment, a Semantic Web based computing platform that hosts data and model repositories as well as high-performance computing access. Here, we present one of the National Cancer Institute's recently established Integrative Cancer Biology Programs, i.e. the Center for the Development of a Virtual Tumor, CViT, which is charged with building a cancer modeling community, developing the aforementioned enabling technologies and fostering multi-scale cancer modeling and simulation.
Journal Article
Recent progress in semantic image segmentation
Semantic image segmentation, which becomes one of the key applications in image processing and computer vision domain, has been used in multiple domains such as medical area and intelligent transportation. Lots of benchmark datasets are released for researchers to verify their algorithms. Semantic segmentation has been studied for many years. Since the emergence of Deep Neural Network (DNN), segmentation has made a tremendous progress. In this paper, we divide semantic image segmentation methods into two categories: traditional and recent DNN method. Firstly, we briefly summarize the traditional method as well as datasets released for segmentation, then we comprehensively investigate recent methods based on DNN which are described in the eight aspects: fully convolutional network, up-sample ways, FCN joint with CRF methods, dilated convolution approaches, progresses in backbone network, pyramid methods, Multi-level feature and multi-stage method, supervised, weakly-supervised and unsupervised methods. Finally, a conclusion in this area is drawn.
Journal Article
Semantic expressivism for epistemic modals
Expressivists about epistemic modals deny that ‘Jane might be late’ canonically serves to express the speaker’s acceptance of a certain propositional content. Instead, they hold that it expresses a lack of acceptance (that Jane isn’t late). Prominent expressivists embrace pragmatic expressivism: the doxastic property expressed by a declarative is not helpfully identified with (any part of) that sentence’s compositional semantic value. Against this, we defend semantic expressivism about epistemic modals: the semantic value of a declarative from this domain is (partly) the property of doxastic attitudes it canonically serves to express. In support, we synthesize data from the critical literature on expressivism—largely reflecting interactions between modals and disjunctions—and present a semantic expressivism that readily predicts the data. This contrasts with salient competitors, including: pragmatic expressivism based on domain semantics or dynamic semantics; semantic expressivism à la Moss (Semant Pragmat 8(5):1–81, 2015. https://doi.org/10.3765/sp. 8.5); and the bounded relational semantics of Mandelkern (Philos Rev 128(1):1–61, 2019. https://doi.org/10.1215/00318108-7213001).
Journal Article
A review of convolutional neural networks in computer vision
In computer vision, a series of exemplary advances have been made in several areas involving image classification, semantic segmentation, object detection, and image super-resolution reconstruction with the rapid development of deep convolutional neural network (CNN). The CNN has superior features for autonomous learning and expression, and feature extraction from original input data can be realized by means of training CNN models that match practical applications. Due to the rapid progress in deep learning technology, the structure of CNN is becoming more and more complex and diverse. Consequently, it gradually replaces the traditional machine learning methods. This paper presents an elementary understanding of CNN components and their functions, including input layers, convolution layers, pooling layers, activation functions, batch normalization, dropout, fully connected layers, and output layers. On this basis, this paper gives a comprehensive overview of the past and current research status of the applications of CNN models in computer vision fields, e.g., image classification, object detection, and video prediction. In addition, we summarize the challenges and solutions of the deep CNN, and future research directions are also discussed.
Journal Article
Negation and modality in unilateral truthmaker semantics
Fine (J Philos Logic 46(6):625–674, 2017) develops a unilateral and a bilateral truthmaker semantics for propositional logic. The unilateral approach trades off the primitive exact falsification relation of the bilateral approach for a primitive exclusion relation between states, thereby raising the question if exclusion serves any purpose other than to avoid exact falsification. We argue that exclusion is motivated independently of its use in avoiding exact falsification, namely as a foundation for the reconstruction of modal notions such as possibility and necessity. This reconstruction in turn motivates what we call emergent exclusion: an atomic state can exclude a sum of atomic states collectively without excluding any of these atomic states individually. Emergent exclusion is banned in Fine (2017a) in order to maintain exact equivalence in de Morgan’s law ¬(P∧Q)⇔¬P∨¬Q; we argue that the two sides of this law are not exactly equivalent and discuss a variety of state spaces that feature emergent exclusion. This paper aims to be accessible to linguists without prior exposure to truthmaker semantics. We highlight points of contact with natural language semantics, such as event semantics and algebraic semantics of plurals and conjunction.
Journal Article