Regularized Principal Component Analysis for Spatial Data

In many atmospheric and earth sciences, it is of interest to identify dominant spatial patterns of variation based on data observed at $p$ locations and $n$ time points with the possibility that $p>n$. While principal component analysis (PCA) is commonly applied to find the dominant patterns, the eigenimages produced from PCA may exhibit patterns that are too noisy to be physically meaningful when $p$ is large relative to $n$. To obtain more precise estimates of eigenimages, we propose a regularization approach incorporating smoothness and sparseness of eigenimages, while accounting for their orthogonality. Our method allows data taken at irregularly spaced or sparse locations. In addition, the resulting optimization problem can be solved using the alternating direction method of multipliers, which is easy to implement, and applicable to a large spatial dataset. Furthermore, the estimated eigenfunctions provide a natural basis for representing the underlying spatial process in a spatial random-effects model, from which spatial covariance function estimation and spatial prediction can be efficiently performed using a regularized fixed-rank kriging method. Finally, the effectiveness of the proposed method is demonstrated by several numerical example

Argument as design: a multimodal approach to academic argument in a digital age

Includes bibliographical referencesThis study posits that using a range of modes and genres to construct argument can engender different ways of thinking about argument in the academic context. It investigates the potentials and constraints of adopting a multimodal approach to constructing academic argument. The research is situated within a seminar, in a second year Media course. Within this context, the study identifies the semiotic resources that students draw on and examines how they are employed to construct academic argument in three digital domains, namely video, comics and PowerPoint. Grounded in a theory of multimodal social semiotics, this study posits that argument is a product of design, motivated by the rhetor's interest in communicating a particular message, in a particular environment, and shaped by the available resources in the given environment. It proposes that argument is a cultural text form for bringing about difference (Kress 1989). This view of argument recognises that argument occurs in relation to mode, genre, discourse and medium. The study illustrates how each of these social categories shapes argument through textual analysis. A framework based on Halliday's metafunctional principle is proposed to analyse argument in multimodal texts. The framework combines theories from rhetoric and social semiotics. It offers analysis of ideational content, the ways social relations are established, and how organising principles assist in establishing coherence in argument. The analysis of the data (video, comics and PowerPoint presentations) demonstrates that the framework can be applied across genres and media. The significance of the study is threefold. Theoretically, it contributes towards theorising a theory of argument from a multimodal perspective. Methodologically, it puts forward a framework for analysing multimodal arguments. Pedagogically, it contributes towards developing and interrogating a pedagogy of academic argument that is relevant to contemporary communication practices

A multimodal social semiotic approach to the analysis of manga : a metalanguage for sequential visual narratives

Includes abstract.Includes bibliographical references (p. 139-143).This study contributes towards an understanding of the nature of sequential visual narratives, how different semiotic resources may be employed to construct a visual narrative and how sequence of images may be developed. Over the years, extensive research has been undertaken in the area of still images. However, the particularities of meanings made in sequential images remain relatively unexplored. The significance of the study is that it contributes towards an understanding of sequential narratives by proposing a metalanguage for manga. The term ‘manga’ refers to comics that originate from Japan and it is currently a trend in popular culture worldwide

Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks

Prediction of popularity has profound impact for social media, since it offers opportunities to reveal individual preference and public attention from evolutionary social systems. Previous research, although achieves promising results, neglects one distinctive characteristic of social data, i.e., sequentiality. For example, the popularity of online content is generated over time with sequential post streams of social media. To investigate the sequential prediction of popularity, we propose a novel prediction framework called Deep Temporal Context Networks (DTCN) by incorporating both temporal context and temporal attention into account. Our DTCN contains three main components, from embedding, learning to predicting. With a joint embedding network, we obtain a unified deep representation of multi-modal user-post data in a common embedding space. Then, based on the embedded data sequence over time, temporal context learning attempts to recurrently learn two adaptive temporal contexts for sequential popularity. Finally, a novel temporal attention is designed to predict new popularity (the popularity of a new user-post pair) with temporal coherence across multiple time-scales. Experiments on our released image dataset with about 600K Flickr photos demonstrate that DTCN outperforms state-of-the-art deep prediction algorithms, with an average of 21.51% relative performance improvement in the popularity prediction (Spearman Ranking Correlation).Comment: accepted in IJCAI-1