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In this paper, we propose a fully automatic system for the transcription alignment of historical documents. We introduce the ‘Statuti del Doge Tiepolo’ data that include images as well as transcription from the 14th century written in Gothic script. Our tr ...
We present novel Markov-type and Nikolskii-type inequalities for multivariate polynomials associated with arbitrary downward closed multi-index sets in any dimension. Moreover, we show how the constant of these inequalities changes, when the polynomial is ...
Objective Several ultrasound-based methods are currently used to assess aortic diameter, circumferential strain and stiffness in mice, but none of them is flawless and a gold standard is lacking. We aimed to assess the validity and sensitivity of these met ...
We present an algorithm for determining the nature of stochastic processes and their parameters based on the analysis of time series of inertial errors. The algorithm is suitable mainly (but not only) for situations where several stochastic processes are s ...
Structures of biomolecular systems are increasingly computed by integrative modeling that relies on varied types of experimental data and theoretical information. We describe here the proceedings and conclusions from the first wwPDB Hybrid/Integrative Meth ...
This project is set in a context of raising awareness concerning the accumulation of plastics in oceans. As opposed to the marine environment, there is a real gap of knowledge on microplastics pollution in freshwaters. In this project, the Lake Geneva envi ...
We propose a state estimator for linear discrete-time systems composed by coupled subsystems affected by bounded disturbances. The architecture is distributed in the sense that each subsystem is equipped with a local state estimator that exploits suitable ...
In this paper, a new algorithm is proposed for the design of a family of controllers to be used within an adaptive switching control scheme. The resulting switching controller is able to attenuate the effects of disturbances having uncertain and possibly t ...
We investigate a vocoder based on artificial neural networks using a phonological speech representation. Speech decomposition is based on the phonological encoders, realised as neural network classifiers, that are trained for a particular language. The spe ...
We propose a recursive algorithm for estimating time-varying signals from a few linear measurements. The signals are assumed sparse, with unknown support, and are described by a dynamical model. In each iteration, the algorithm solves an ℓ1-ℓ1 minimization ...
