Antonio Sutera

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In this work, we propose a simple yet effective solution to the problem of connectome inference in calcium imaging data. The proposed algorithm consists of two steps. First, processing the raw signals to detect neural peak activities. Second, inferring the degree of association between neurons from partial correlation statistics. This paper summarises the(More)
Despite growing interest and practical use in various scientific areas, variable importances derived from tree-based ensemble methods are not well understood from a theoretical point of view. In this work we characterize the Mean Decrease Impurity (MDI) variable importances as measured by an ensemble of totally randomized trees in asymptotic sample and(More)
We suppose that we are given a probability space (Ω, E ,P) and consider random variables defined on it taking a finite number of possible values. We use upper case letters to denote such random variables (e.g. X,Y, Z,W . . .) and calligraphic letters (e.g. X ,Y,Z,W . . .) to denote their image sets (of finite cardinality), and lower case letters (e.g. x, y,(More)
This contribution deals with the description of the Italian Radio Occultation experiment on board the Indian OCEANSAT-2 Mission. Details of the Italian Radio Occultation Ground Segment and results obtained within the validation activity will be discussed. Atmospheric profiles (in terms of refractivity, temperature, and electron density) obtained processing(More)
Surface wind is a variable of great importance in forcing marine waves and circulations, modulating surface fluxes, etc. Surface wind defined on numerical grids is currently used in forecast-analysis, as well as in climatology. Gridded fields, however, suffer for systematic errors associated with the numerical procedures adopted in computing them. In this(More)
Dealing with datasets of very high dimension is a major challenge in machine learning. In this paper, we consider the problem of feature selection in applications where the memory is not large enough to contain all features. In this setting, we propose a novel tree-based feature selection approach that builds a sequence of randomized trees on small(More)
In many cases, feature selection is often more complicated than identifying a single subset of input variables that would together explain the output. There may be interactions that depend on contextual information, i.e., variables that reveal to be relevant only in some specific circumstances. In this setting, the contribution of this paper is to extend(More)
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