15,952 research outputs found
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 locations
and time points with the possibility that . 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 is large relative to . 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 āmangaā 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
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