2,105 research outputs found
A multi-modal representation of El Ni\~no Southern Oscillation Diversity
The El Ni\~no-Southern Oscillation (ENSO) is characterized by alternating
periods of warm (El Ni\~no) and cold (La Ni\~na) sea surface temperature
anomalies (SSTA) in the equatorial Pacific. Although El Ni\~no and La Ni\~na
are well-defined climate patterns, no two events are alike. To date, ENSO
diversity has been described primarily in terms of the longitudinal location of
peak SSTA, used to define a bimodal classification of events in Eastern Pacific
(EP) and Central Pacific (CP) types. Here, we use low-dimensional
representations of Pacific SSTAs to argue that binary categorical memberships
are unsuitable to describe ENSO events. Using fuzzy unsupervised clustering, we
recover the four known ENSO categories, along with a fifth category: an Extreme
El Ni\~no. We show that Extreme El Ni\~nos differ both in their intensity and
temporal evolution from canonical EP El Ni\~nos. We also find that CP La
Ni\~nas, EP El Ni\~nos, and Extreme El Ni\~nos contribute the most to
interdecadal ENSO variability
Topological mappings of video and audio data
We review a new form of self-organizing map which is based on a nonlinear projection of latent points into data space, identical to that performed in the Generative Topographic Mapping (GTM).1 But whereas the GTM is an extension of a mixture of experts, this model is an extension of a product of experts.2 We show visualisation and clustering results on a data set composed of video data of lips uttering 5 Korean vowels. Finally we note that we may dispense with the probabilistic underpinnings of the product of experts and derive the same algorithm as a minimisation of mean squared error between the prototypes and the data. This leads us to suggest a new algorithm which incorporates local and global information in the clustering. Both ot the new algorithms achieve better results than the standard Self-Organizing Map
Approximate Inference for Constructing Astronomical Catalogs from Images
We present a new, fully generative model for constructing astronomical
catalogs from optical telescope image sets. Each pixel intensity is treated as
a random variable with parameters that depend on the latent properties of stars
and galaxies. These latent properties are themselves modeled as random. We
compare two procedures for posterior inference. One procedure is based on
Markov chain Monte Carlo (MCMC) while the other is based on variational
inference (VI). The MCMC procedure excels at quantifying uncertainty, while the
VI procedure is 1000 times faster. On a supercomputer, the VI procedure
efficiently uses 665,000 CPU cores to construct an astronomical catalog from 50
terabytes of images in 14.6 minutes, demonstrating the scaling characteristics
necessary to construct catalogs for upcoming astronomical surveys.Comment: accepted to the Annals of Applied Statistic
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