1,946 research outputs found
Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches
In the past two decades, functional Magnetic Resonance Imaging has been used
to relate neuronal network activity to cognitive processing and behaviour.
Recently this approach has been augmented by algorithms that allow us to infer
causal links between component populations of neuronal networks. Multiple
inference procedures have been proposed to approach this research question but
so far, each method has limitations when it comes to establishing whole-brain
connectivity patterns. In this work, we discuss eight ways to infer causality
in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality,
Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and
Transfer Entropy. We finish with formulating some recommendations for the
future directions in this area
Holistic Statistical Open Data Integration Based On Integer Linear Programming
International audienceIntegrating several Statistical Open Data (SOD) tables is a very promising issue. Various analysis scenarios are hidden behind these statistical data, which makes it important to have a holistic view of them. However, as these data are scattered in several tables, it is a slow and costly process to use existing pairwise schema matching approaches to integrate several schemas of the tables. Hence, we need automatic tools that rapidly converge to a holistic integrated view of data and give a good matching quality. In order to accomplish this objective, we propose a new 0-1 linear program, which automatically resolves the problem of holistic OD integration. It performs global optimal solutions maximizing the profit of similarities between OD graphs. The program encompasses different constraints related to graph structures and matching setup, in particular 1:1 matching. It is solved using a standard solver (CPLEX) and experiments show that it can handle several input graphs and good matching quality compared to existing tools
Early identification of important patents through network centrality
One of the most challenging problems in technological forecasting is to
identify as early as possible those technologies that have the potential to
lead to radical changes in our society. In this paper, we use the US patent
citation network (1926-2010) to test our ability to early identify a list of
historically significant patents through citation network analysis. We show
that in order to effectively uncover these patents shortly after they are
issued, we need to go beyond raw citation counts and take into account both the
citation network topology and temporal information. In particular, an
age-normalized measure of patent centrality, called rescaled PageRank, allows
us to identify the significant patents earlier than citation count and PageRank
score. In addition, we find that while high-impact patents tend to rely on
other high-impact patents in a similar way as scientific papers, the patents'
citation dynamics is significantly slower than that of papers, which makes the
early identification of significant patents more challenging than that of
significant papers.Comment: 14 page
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