Automatic discovery of the statistical types of variables in a dataset

A common practice in statistics and machine learning is to assume that the statistical data types (e.g., ordinal, categorical or real-valued) of variables, and usually also the likelihood model, is known. However, as the availability of real- world data increases, this assumption becomes too restrictive. Data are often heterogeneous, complex, and improperly or incompletely documented. Surprisingly, despite their practical importance, there is still a lack of tools to automatically discover the statistical types of, as well as appropriate likelihood (noise) models for, the variables in a dataset. In this paper, we fill this gap by proposing a Bayesian method, which accurately discovers the statistical data types in both synthetic and real data.Humboldt Research Fellowship for Postdoctoral Researchers, which funded this research during her stay at the Max Planck Institute for Software Systems. ATI Grant EP/N510129/1 EPSRC Grant EP/N014162/1 Googl

The Double Strangeness Pentaquark and Other Exotic Hadrons in the Reaction ξb → j/ψφξ

We study the possibility that four Ξ resonances (Ξ(1620), Ξ(1690), Ξ(1820), Ξ(1950)) could correspond to pentaquark states, in the form of a meson-baryon bound systems. We also explore the possible existence of doubly strange pentaquarks with hidden charm (Pcss) and find two candidates structured in a similar form, at energies of 4493 MeV and 4630 MeV. The meson-baryon interaction is built from t-channel meson exchange processes which are evaluated using effective Lagrangians. Moreover we analyse the Ξb → Ξ J/ψ ϕ decay process, which permits exploring the existence of the heavy double strange pentaquark, as well as other exotic hadrons, in the three different two-body invariant mass spectra of the emitted particles. In the J/ψϕ mass spectrum, we analyse the nature of the X(4140) and X(4160) resonances. In the J/ψΞ invariant mass spectrum, we study the signal produced by the doubly strange pentaquark, where we conclude that it has a good chance to be detected in this reaction if its mass is around 4580 − 4680 MeV. Finally, in the ϕΞ spectrum we study the likelihood to detect the Ξ(2500) state

Modeling the Dynamics of Online Learning Activity

People are increasingly relying on the Web and social media to find solutions to their problems in a wide range of domains. In this online setting, closely related problems often lead to the same characteristic learning pattern, in which people sharing these problems visit related pieces of information, perform almost identical queries or, more generally, take a series of similar actions. In this paper, we introduce a novel modeling framework for clustering continuous-time grouped streaming data, the hierarchical Dirichlet Hawkes process (HDHP), which allows us to automatically uncover a wide variety of learning patterns from detailed traces of learning activity. Our model allows for efficient inference, scaling to millions of actions taken by thousands of users. Experiments on real data gathered from Stack Overflow reveal that our framework can recover meaningful learning patterns in terms of both content and temporal dynamics, as well as accurately track users' interests and goals over time

Modeling the Dynamics of Online Learning Activity

People are increasingly relying on the Web and social media to find solutions to their problems in a wide range of domains. In this online setting, closely related problems often lead to the same characteristic learning pattern, in which people sharing these problems visit related pieces of information, perform almost identical queries or, more generally, take a series of similar actions. In this paper, we introduce a novel modeling framework for clustering continuous-time grouped streaming data, the hierarchical Dirichlet Hawkes process (HDHP), which allows us to automatically uncover a wide variety of learning patterns from detailed traces of learning activity. Our model allows for efficient inference, scaling to millions of actions taken by thousands of users. Experiments on real data gathered from Stack Overflow reveal that our framework can recover meaningful learning patterns in terms of both content and temporal dynamics, as well as accurately track users' interests and goals over time

Handling incomplete heterogeneous data using VAEs.

Variational autoencoders (VAEs), as well as other generative models, have been shown to be efficient and accurate for capturing the latent structure of vast amounts of complex high-dimensional data. However, existing VAEs can still not directly handle data that are heterogenous (mixed continuous and discrete) or incomplete (with missing data at random), which is indeed common in real-world applications. In this paper, we propose a general framework to design VAEs suitable for fitting incomplete heterogenous data. The proposed HI-VAE includes likelihood models for real-valued, positive real valued, interval, categorical, ordinal and count data, and allows accurate estimation (and potentially imputation) of missing data. Furthermore, HI-VAE presents competitive predictive performance in supervised tasks, outperforming supervised models when trained on incomplete data

Primera valoración genética para la disciplina de raid en el caballo de pura raza árabe español

El esquema de selección del caballo de Pura Raza Árabe fue aprobado por el Ministerio de Agricullura Pesca y Alimentación en septiembre de 2005. Dentro de él se especifica que se realizará una selección para mejorar los caracteres que potencien el alto rendimiento, que de forma natural, presenta la raza en la disciplina de raid. Se ha realizado la primera valoración genética para la disciplina de raid en el caballo de Pura Raza Árabe para lo cual se ha contado con datos de 249 caballos con un total de 547 participaciones en raids de diferentes categarías. La valoración genética se ha realizado para los caracteres puesto clasificatorio y tiempo de carrera. Previamente ha sido preciso realizar un estudio de los factores que afectan al rendimiento de esta disciplina. Los factores que se han incluido en el modelo de valoración por resultar estadísticamente significativos han sido el año de celebración de la prueba de raid, la zona geográfica donde se realiza la prueba y los kilómetros del recorrido. Además, se han incluido como covariables el número total de participantes en la prueba de raid para el carácter puesto clasificatorio y el tiempo medio de carrera para el carácter tiempo. las heredabilidades obtenidas presentan un valor bajo-medio (0,18 para el puesto clasificatorio y 0,13 para el tiempo). La evolución del valor genético para dichos caracteres nos muestra que el progreso genético ha sido escaso hasta el momento, pero la elevada variabilidad del carácter asegura un progreso genético adecuado si se realiza una apropiada intensidad de selección para dichos caracteres