10,800 research outputs found
The Dawn of Multi-Messenger Astronomy
The recent discoveries of high-energy astrophysical neutrinos and
gravitational waves have opened new windows of exploration to the Universe.
Combining neutrino observations with measurements of electromagnetic radiation
and cosmic rays promises to unveil the sources responsible for the neutrino
emission and to help solve long-standing problems in astrophysics such as the
origin of cosmic rays. Neutrino observations may also help localize
gravitational-wave sources, and enable the study of their astrophysical
progenitors. In this work we review the current status and future plans for
multi-messenger searches of neutrino sources.Comment: To appear in "Neutrino Astronomy- Current status, future prospects",
Eds. T. Gaisser & A. Karle (World Scientific
Uncovering the Temporal Dynamics of Diffusion Networks
Time plays an essential role in the diffusion of information, influence and
disease over networks. In many cases we only observe when a node copies
information, makes a decision or becomes infected -- but the connectivity,
transmission rates between nodes and transmission sources are unknown.
Inferring the underlying dynamics is of outstanding interest since it enables
forecasting, influencing and retarding infections, broadly construed. To this
end, we model diffusion processes as discrete networks of continuous temporal
processes occurring at different rates. Given cascade data -- observed
infection times of nodes -- we infer the edges of the global diffusion network
and estimate the transmission rates of each edge that best explain the observed
data. The optimization problem is convex. The model naturally (without
heuristics) imposes sparse solutions and requires no parameter tuning. The
problem decouples into a collection of independent smaller problems, thus
scaling easily to networks on the order of hundreds of thousands of nodes.
Experiments on real and synthetic data show that our algorithm both recovers
the edges of diffusion networks and accurately estimates their transmission
rates from cascade data.Comment: To appear in the 28th International Conference on Machine Learning
(ICML), 2011. Website: http://www.stanford.edu/~manuelgr/netrate
Collaborative Inference of Coexisting Information Diffusions
Recently, \textit{diffusion history inference} has become an emerging
research topic due to its great benefits for various applications, whose
purpose is to reconstruct the missing histories of information diffusion traces
according to incomplete observations. The existing methods, however, often
focus only on single information diffusion trace, while in a real-world social
network, there often coexist multiple information diffusions over the same
network. In this paper, we propose a novel approach called Collaborative
Inference Model (CIM) for the problem of the inference of coexisting
information diffusions. By exploiting the synergism between the coexisting
information diffusions, CIM holistically models multiple information diffusions
as a sparse 4th-order tensor called Coexisting Diffusions Tensor (CDT) without
any prior assumption of diffusion models, and collaboratively infers the
histories of the coexisting information diffusions via a low-rank approximation
of CDT with a fusion of heterogeneous constraints generated from additional
data sources. To improve the efficiency, we further propose an optimal
algorithm called Time Window based Parallel Decomposition Algorithm (TWPDA),
which can speed up the inference without compromise on the accuracy by
utilizing the temporal locality of information diffusions. The extensive
experiments conducted on real world datasets and synthetic datasets verify the
effectiveness and efficiency of CIM and TWPDA
A data-driven analysis to question epidemic models for citation cascades on the blogosphere
Citation cascades in blog networks are often considered as traces of
information spreading on this social medium. In this work, we question this
point of view using both a structural and semantic analysis of five months
activity of the most representative blogs of the french-speaking
community.Statistical measures reveal that our dataset shares many features
with those that can be found in the literature, suggesting the existence of an
identical underlying process. However, a closer analysis of the post content
indicates that the popular epidemic-like descriptions of cascades are
misleading in this context.A basic model, taking only into account the behavior
of bloggers and their restricted social network, accounts for several important
statistical features of the data.These arguments support the idea that
citations primary goal may not be information spreading on the blogosphere.Comment: 18 pages, 9 figures, to be published in ICWSM-13 proceeding
Quantifying Self-Organization with Optimal Wavelets
The optimal wavelet basis is used to develop quantitative, experimentally
applicable criteria for self-organization. The choice of the optimal wavelet is
based on the model of self-organization in the wavelet tree. The framework of
the model is founded on the wavelet-domain hidden Markov model and the optimal
wavelet basis criterion for self-organization which assumes inherent increase
in statistical complexity, the information content necessary for maximally
accurate prediction of the system's dynamics. At the same time the method,
presented here for the one-dimensional data of any type, performs superior
denoising and may be easily generalized to higher dimensions.Comment: 12 pages, 3 figure
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