107,289 research outputs found
User Experience for Model-Driven Engineering : Challenges and Future Directions
Since its infancy, Model Driven Engineering (MDE) research has primarily focused on technical issues. Although it is becoming increasingly common for MDE research papers to evaluate their theoretical and practical solutions, extensive usability studies are still uncommon. We observe a scarcity of User eXperience (UX)-related research in the MDE community, and posit that many existing tools and languages have room for improvement with respect to UX [26], [44], [37], where UX is a key focus area in the software development industry. We consider this gap a fundamental problem that needs to be addressed by the community if MDE is to gain widespread use. In this vision paper, we explore how and where UX fits into MDE by considering motivating use cases that revolve around different dimensions of integration: model integration, tool integration, and integration between process and tool support. Based on the literature and our collective experience in research and industrial collaborations, we propose future directions for addressing these challenges
Latent tree models
Latent tree models are graphical models defined on trees, in which only a
subset of variables is observed. They were first discussed by Judea Pearl as
tree-decomposable distributions to generalise star-decomposable distributions
such as the latent class model. Latent tree models, or their submodels, are
widely used in: phylogenetic analysis, network tomography, computer vision,
causal modeling, and data clustering. They also contain other well-known
classes of models like hidden Markov models, Brownian motion tree model, the
Ising model on a tree, and many popular models used in phylogenetics. This
article offers a concise introduction to the theory of latent tree models. We
emphasise the role of tree metrics in the structural description of this model
class, in designing learning algorithms, and in understanding fundamental
limits of what and when can be learned
Symbolic regression of generative network models
Networks are a powerful abstraction with applicability to a variety of
scientific fields. Models explaining their morphology and growth processes
permit a wide range of phenomena to be more systematically analysed and
understood. At the same time, creating such models is often challenging and
requires insights that may be counter-intuitive. Yet there currently exists no
general method to arrive at better models. We have developed an approach to
automatically detect realistic decentralised network growth models from
empirical data, employing a machine learning technique inspired by natural
selection and defining a unified formalism to describe such models as computer
programs. As the proposed method is completely general and does not assume any
pre-existing models, it can be applied "out of the box" to any given network.
To validate our approach empirically, we systematically rediscover pre-defined
growth laws underlying several canonical network generation models and credible
laws for diverse real-world networks. We were able to find programs that are
simple enough to lead to an actual understanding of the mechanisms proposed,
namely for a simple brain and a social network
Graphical continuous Lyapunov models
The linear Lyapunov equation of a covariance matrix parametrizes the
equilibrium covariance matrix of a stochastic process. This parametrization can
be interpreted as a new graphical model class, and we show how the model class
behaves under marginalization and introduce a method for structure learning via
-penalized loss minimization. Our proposed method is demonstrated to
outperform alternative structure learning algorithms in a simulation study, and
we illustrate its application for protein phosphorylation network
reconstruction.Comment: 10 pages, 5 figure
Warped Riemannian metrics for location-scale models
The present paper shows that warped Riemannian metrics, a class of Riemannian
metrics which play a prominent role in Riemannian geometry, are also of
fundamental importance in information geometry. Precisely, the paper features a
new theorem, which states that the Rao-Fisher information metric of any
location-scale model, defined on a Riemannian manifold, is a warped Riemannian
metric, whenever this model is invariant under the action of some Lie group.
This theorem is a valuable tool in finding the expression of the Rao-Fisher
information metric of location-scale models defined on high-dimensional
Riemannian manifolds. Indeed, a warped Riemannian metric is fully determined by
only two functions of a single variable, irrespective of the dimension of the
underlying Riemannian manifold. Starting from this theorem, several original
contributions are made. The expression of the Rao-Fisher information metric of
the Riemannian Gaussian model is provided, for the first time in the
literature. A generalised definition of the Mahalanobis distance is introduced,
which is applicable to any location-scale model defined on a Riemannian
manifold. The solution of the geodesic equation is obtained, for any Rao-Fisher
information metric defined in terms of warped Riemannian metrics. Finally,
using a mixture of analytical and numerical computations, it is shown that the
parameter space of the von Mises-Fisher model of -dimensional directional
data, when equipped with its Rao-Fisher information metric, becomes a Hadamard
manifold, a simply-connected complete Riemannian manifold of negative sectional
curvature, for . Hopefully, in upcoming work, this will be
proved for any value of .Comment: first version, before submissio
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