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Robocrystallographer: Automated crystal structure text descriptions and analysis
Our ability to describe crystal structure features is of crucial importance when attempting to understand structure-property relationships in the solid state. In this paper, the authors introduce robocrystallographer, an open-source toolkit for analyzing crystal structures. This package combines new and existing open-source analysis tools to provide structural information, including the local coordination and polyhedral type, polyhedral connectivity, octahedral tilt angles, component-dimensionality, and molecule-within-crystal and fuzzy prototype identification. Using this information, robocrystallographer can generate text-based descriptions of crystal structures that resemble descriptions written by human crystallographers. The authors use robocrystallographer to investigate the dimensionalities of all compounds in the Materials Project database and highlight its potential in machine learning studies
Pose Embeddings: A Deep Architecture for Learning to Match Human Poses
We present a method for learning an embedding that places images of humans in
similar poses nearby. This embedding can be used as a direct method of
comparing images based on human pose, avoiding potential challenges of
estimating body joint positions. Pose embedding learning is formulated under a
triplet-based distance criterion. A deep architecture is used to allow learning
of a representation capable of making distinctions between different poses.
Experiments on human pose matching and retrieval from video data demonstrate
the potential of the method
Variational approximation for mixtures of linear mixed models
Mixtures of linear mixed models (MLMMs) are useful for clustering grouped
data and can be estimated by likelihood maximization through the EM algorithm.
The conventional approach to determining a suitable number of components is to
compare different mixture models using penalized log-likelihood criteria such
as BIC.We propose fitting MLMMs with variational methods which can perform
parameter estimation and model selection simultaneously. A variational
approximation is described where the variational lower bound and parameter
updates are in closed form, allowing fast evaluation. A new variational greedy
algorithm is developed for model selection and learning of the mixture
components. This approach allows an automatic initialization of the algorithm
and returns a plausible number of mixture components automatically. In cases of
weak identifiability of certain model parameters, we use hierarchical centering
to reparametrize the model and show empirically that there is a gain in
efficiency by variational algorithms similar to that in MCMC algorithms.
Related to this, we prove that the approximate rate of convergence of
variational algorithms by Gaussian approximation is equal to that of the
corresponding Gibbs sampler which suggests that reparametrizations can lead to
improved convergence in variational algorithms as well.Comment: 36 pages, 5 figures, 2 tables, submitted to JCG
The Galactic Halo in Mixed Dark Matter Cosmologies
A possible solution to the small scale problems of the cold dark matter (CDM)
scenario is that the dark matter consists of two components, a cold and a warm
one. We perform a set of high resolution simulations of the Milky Way halo
varying the mass of the WDM particle () and the cosmic dark matter
mass fraction in the WDM component (). The scaling ansatz
introduced in combined analysis of LHC and astroparticle searches postulates
that the relative contribution of each dark matter component is the same
locally as on average in the Universe (e.g. ). Here we find however, that the normalised local WDM fraction ( / ) depends strongly on for 1 keV. Using the scaling ansatz can therefore introduce significant
errors into the interpretation of dark matter searches. To correct this issue a
simple formula that fits the local dark matter densities of each component is
provided.Comment: 19 pages, 10 figures, accepted for publication in JCA
ScannerS: Constraining the phase diagram of a complex scalar singlet at the LHC
We present the first version of a new tool to scan the parameter space of
generic scalar potentials, ScannerS. The main goal of ScannerS is to help
distinguish between different patterns of symmetry breaking for each scalar
potential. In this work we use it to investigate the possibility of excluding
regions of the phase diagram of several versions of a complex singlet extension
of the Standard Model, with future LHC results. We find that if another scalar
is found, one can exclude a phase with a dark matter candidate in definite
regions of the parameter space, while predicting whether a third scalar to be
found must be lighter or heavier. The first version of the code is publicly
available and contains various generic core routines for tree level vacuum
stability analysis, as well as implementations of collider bounds, dark matter
constraints, electroweak precision constraints and tree level unitarity.Comment: 24 pages, 4 figures, 3 tables. Project development webpage -
http://gravitation.web.ua.pt/Scanner
Eye guidance during real-world scene search:The role color plays in central and peripheral vision
The visual system utilizes environmental features to direct gaze efficiently when locating objects. While previous research has isolated various features' contributions to gaze guidance, these studies generally used sparse displays and did not investigate how features facilitated search as a function of their location on the visual field. The current study investigated how features across the visual field-particularly color-facilitate gaze guidance during real-world search. A gaze-contingent window followed participants' eye movements, restricting color information to specified regions. Scene images were presented in full color, with color in the periphery and gray in central vision or gray in the periphery and color in central vision, or in grayscale. Color conditions were crossed with a search cue manipulation, with the target cued either with a word label or an exact picture. Search times increased as color information in the scene decreased. A gaze-data based decomposition of search time revealed color-mediated effects on specific subprocesses of search. Color in peripheral vision facilitated target localization, whereas color in central vision facilitated target verification. Picture cues facilitated search, with the effects of cue specificity and scene color combining additively. When available, the visual system utilizes the environment's color information to facilitate different real-world visual search behaviors based on the location within the visual field
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