9,884 research outputs found
Lineamenti processuali della nuova azione di classe
No full textIl presente lavoro delinea le caratteristiche della nuova azione di classe, introdotta con l. 31/2019.
Muovendo da una definizione delle situazioni sostanziali che possono accedere alla tutela collettiva,
il saggio individua il campo applicativo dell’istituto e ne sviluppa le dinamiche processuali. Gli autori,
più nel dettaglio, esaminano la fase introduttiva, deputata alla valutazione di ammissibilità dell’azione,
la fase di trattazione, la fase di accertamento dei diritti individuali omogenei e la fase esecutiva.
Fa da sfondo alla riflessione un rapido excursus normativo che dagli esordi del rimedio nel codice
del consumo conduce alle più recenti istanze di uniformazione processuale provenienti dall’Unione
Europe
Metadata Representations for Queryable ML Model Zoos
No full textMachine learning (ML) practitioners and organizations are building model zoos of pre-trained models, containing metadata describing properties of the ML models and datasets that are useful for reporting, auditing, reproducibility, and interpretability purposes. The metatada is currently not standardised; its expressivity is limited; and there is no interoperable way to store and query it. Consequently, model search, reuse, comparison, and composition are hindered. In this paper, we advocate for standardized ML model metadata representation and management, proposing a toolkit supported to help practitioners manage and query that metadata.Web Information SystemsHuman-Centred Artificial Intelligenc
A Manifesto of Nodalism
No full textThis paper proposes the notion of Nodalism as a means describing contemporary culture and of understanding my own creative practice in electronic music composition. It draws on theories and ideas from Kirby, Bauman, Bourriaud, Deleuze, Guatarri, and Gochenour, to demonstrate how networks of ideas or connectionist neural models of cognitive behaviour can be used to contextualize, understand and become a creative tool for the creation of contemporary electronic music
ML-Based Fringe-Frequency Estimation for InSAR
No full textThis letter focuses on estimating the local fringe frequency of the interferometric phase, under the hypothesis of superficial scattering. Starting from the formulation of the maximum-likelihood estimator, a new simplified estimator is derived. Due to computational efficiency and robustness versus model errors, the resulting estimator is suited for large data processing in the presence of model uncertainty. Furthermore, such an estimator can be straightforwardly extended to the multi-baseline case, resulting in the possibility to estimate the terrain slope with great accuracy. An application to real data is presented, based on a multi-baseline ENVISAT data set
Optimizing ML Inference Queries Under Constraints
No full textThe proliferation of pre-trained ML models in public Web-based model zoos facilitates the engineering of ML pipelines to address complex inference queries over datasets and streams of unstructured content. Constructing optimal plan for a query is hard, especially when constraints (e.g. accuracy or execution time) must be taken into consideration, and the complexity of the inference query increases. To address this issue, we propose a method for optimizing ML inference queries that selects the most suitable ML models to use, as well as the order in which those models are executed. We formally define the constraint-based ML inference query optimization problem, formulate it as a Mixed Integer Programming (MIP) problem, and develop an optimizer that maximizes accuracy given constraints. This optimizer is capable of navigating a large search space to identify optimal query plans on various model zoos.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Web Information SystemsHuman-Centred Artificial Intelligenc
Accurate optimal doppler centroid estimation for SAR data
No full textThe paper addresses the problem of finding an optimal estimation of the Doppler centroid for Synthetic Aperture Radar (SAR) data. The original idea is to exploit a bandwidth much wider than the PRF, say 3-5 times, by selecting non-aliased point targets. Non-aliased band of natural and isolated targets with close-to-ideal features is evidenced by spotlight processing of strip map acquisitions and accurate Doppler centroid is estimated by means of a joint Maximum Likelihood (ML) estimator. Lower bound of the estimate is determined and results on both simulated and real X-band SAR data are shown
Channel phase estimate in time variant SIMO systems
No full textThis paper introduces a novel ML based approach to channel identification for time variant SIMO (single input multiple output) systems fed by a stochastic process. We focus on the particular case where the unknowns are represented by the channels phases, that find applications in radar interferometry. Starting from the rigorous formulation of the ML estimator, we derive an approximation that makes use of mixers and FIR filters only. The computational efficiency and the robustness versus model errors of the resulting estimator make it suitable for its implementation is an adaptive framework. An application in topography reconstruction from real SAR (synthetic aperture radar) data is presente
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