1,721,272 research outputs found
Does a rising tide lift all boats? An empirical analysis of the relationship between country digitalization and low-tech SMEs performance
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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
DyCAST: learning dynamic causal structure from time series
No full textUnderstanding the dynamics of causal structures is crucial for uncovering the underlying processes in time series data. Previous approaches rely on static assumptions, where contemporaneous and time-lagged dependencies are assumed to have invariant topological structures. However, these models fail to capture the evolving causal relationship between variables when the underlying process exhibits such dynamics. To address this limitation, we propose DyCAST, a novel framework designed to learn dynamic causal structures in time series using Neural Ordinary Differential Equations (Neural ODEs). The key innovation lies in modeling the temporal dynamics of the contemporaneous structure, drawing inspiration from recent advances in Neural ODEs on constrained manifolds. We reformulate the task of learning causal structures at each time step as solving the solution trajectory of a Neural ODE on the directed acyclic graph (DAG) manifold. To accommodate high-dimensional causal structures, we extend DyCAST by learning the temporal dynamics of the hidden state for contemporaneous causal structure. Experiments on both synthetic and real-world datasets demonstrate that DyCAST achieves superior or comparable performance compared to existing causal discovery models
Optimal data scaling for principal component pursuit: a Lyapunov approach to convergence
No full textIn principle component pursuit (PCP), the essential idea is to replace the original non-convex optimization problem of the matrix rank and the count of non-zero entries by a convex optimization problem of the nuclear and I1 norms. In the PCP literature, it is rigorously proved that the validity of this idea depends on the coherence of the uncontaminated data. Specifically, the lower the coherence is, the equivalence of the convex optimization problem to the original non-convex one will hold by a larger probability. Although the coherence index is fixed for a given data set, it is possible to adjust this index by introducing different scalings to the data. The target of this work is thus to find the optimal scaling of the data such that the coherence index is minimized. Based on the analysis of the PCP problem structure, a non-convex optimization problem with implicit dependence on the scaling parameters is firstly formulated. To solve this problem, a coordinate descent algorithm is proposed. Under mild conditions on the structure of the data matrix, the convergence of the algorithm to a global optimal point is rigorously proved by treating the algorithm as a discrete-time dynamic system and utilizing a Lyapunov-type approach. Monte Carlo simulation experiments are performed to verify the effectiveness of the developed results
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
Exploring aggregate effect with weighted transcoding graphs for efficient cache replacement in transcoding proxies
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