1,720,968 research outputs found
Graphical Models for Multivariate Time-Series
No full textGaussian graphical models have received much attention in the last years, due
to their flexibility and expression power. In particular, lots of interests have
been devoted to graphical models for temporal data, or dynamical graphical
models, to understand the relation of variables evolving in time. While powerful
in modelling complex systems, such models suffer from computational
issues both in terms of convergence rates and memory requirements, and may
fail to detect temporal patterns in case the information on the system is partial.
This thesis comprises two main contributions in the context of dynamical
graphical models, tackling these two aspects: the need of reliable and fast
optimisation methods and an increasing modelling power, which are able to
retrieve the model in practical applications. The first contribution consists in a
forward-backward splitting (FBS) procedure for Gaussian graphical modelling
of multivariate time-series which relies on recent theoretical studies ensuring
global convergence under mild assumptions. Indeed, such FBS-based implementation
achieves, with fast convergence rates, optimal results with respect
to ground truth and standard methods for dynamical network inference. The
second main contribution focuses on the problem of latent factors, that influence
the system while hidden or unobservable. This thesis proposes the novel
latent variable time-varying graphical lasso method, which is able to take into
account both temporal dynamics in the data and latent factors influencing
the system. This is fundamental for the practical use of graphical models,
where the information on the data is partial. Indeed, extensive validation of
the method on both synthetic and real applications shows the effectiveness of
considering latent factors to deal with incomplete information
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
PALLADIO: A Parallel Framework for Robust Variable Selection in High-Dimensional Data
No full textThe main goal of supervised data analytics is to model a target phenomenon given a limited amount of samples, each represented by an arbitrarily large number of variables. Especially when the number of variables is much larger than the number of available samples, variable selection is a key step as it allows to identify a possibly reduced subset of relevant variables describing the observed phenomenon. Obtaining interpretable and reliable results, in this highly indeterminate scenario, is often a non-trivial task. In this work we present PALLADIO, a framework designed for HPC cluster architectures, that is able to provide robust variable selection in high-dimensional problems. PALLADIO is developed in Python and it integrates CUDA kernels to decrease the computational time needed for several independent element-wise operations. The scalability of the proposed framework is assessed on synthetic data of different sizes, which represent realistic scenarios
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Latent Variable Time-varying Network Inference
No full textIn many applications of finance, biology and sociology, complex systems involve entities interacting with each other. These processes have the peculiarity of evolving over time and of comprising latent factors, which influence the system without being explicitly measured. In this work we present latent variable time-varying graphical lasso (LTGL), a method for multivariate time-series graphical modelling that considers the influence of hidden or unmeasurable factors. The estimation of the contribution of the latent factors is embedded in the model which produces both sparse and low-rank components for each time point. In particular, the first component represents the connectivity structure of observable variables of the system, while the second represents the influence of hidden factors, assumed to be few with respect to the observed variables. Our model includes temporal consistency on both components, providing an accurate evolutionary pattern of the system. We derive a tractable optimisation algorithm based on alternating direction method of multipliers, and develop a scalable and efficient implementation which exploits proximity operators in closed form. LTGL is extensively validated on synthetic data, achieving optimal performance in terms of accuracy, structure learning and scalability with respect to ground truth and state-of-the-art methods for graphical inference. We conclude with the application of LTGL to real case studies, from biology and finance, to illustrate how our method can be successfully employed to gain insights on multivariate time-series data
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