Archivio della ricerca - Fondazione Bruno Kessler
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Transformer-based Spatio-temporal Change Detection Network Using Satellite Image Time Series: A Case Study of Forest Disturbance in Trentino, Italy Following the Vaia Storm
No full textSmart Irrigation with Fuzzy Decision Support Systems in Trentino Vineyards
No full textEfficient water management is a critical challenge for modern agriculture, particularly in the context of increasing climate variability and limited freshwater resources. This study presents a comparative field-based evaluation of two fuzzy-logic-based irrigation decision support systems for vineyard management: a Mamdani-type controller with expert-defined rules and a Takagi–Sugeno system designed to enable automated learning from ultra-local historical field data. Both systems integrate soil moisture sensing, short-term forecasting, and weather predictions to provide optimized irrigation recommendations. The evaluation combines counterfactual simulations with a bootstrap-based statistical analysis to assess water use efficiency, soil moisture control, and robustness to environmental variability. The comparison highlights distinct strengths of the two approaches, revealing trade-offs between water conservation and crop stress mitigation, and offering practical insights for the design and deployment of intelligent irrigation management solutions
Splitformer: An improved early-exit architecture for automatic speech recognition on edge devices
No full textThe ability to dynamically adjust the computational load of neural models during inference in a resource aware manner is crucial for on-device processing scenarios, characterised by limited and time-varying computational resources. Early-exit architectures represent an elegant and effective solution, since they can process the input with a subset of their layers, exiting at intermediate branches (the upmost layers are hence removed from the model). From a different perspective, for automatic speech recognition applications there are memory-efficient neural architectures that apply variable frame rate analysis, through downsampling/upsampling operations in the middle layers, reducing the overall number of operations and improving significantly the performance on well established benchmarks. One example is the Zipformer. However, these architectures lack the modularity necessary to inject early-exit branches. With the aim of improving the performance in early-exit models, we propose introducing parallel layers in the architecture that process downsampled versions of their inputs. % in conjunction with standard processing layers. We show that in this way the speech recognition performance on standard benchmarks significantly improve, at the cost of a small increase in the overall number of model parameters but without affecting the inference time
Pratiche e tendenze nella selezione del personale
No full textIl processo di selezione del personale riveste un ruolo fondamentale per il successo di un’impresa. Identificare i candidati più idonei consente di garantire un migliore allineamento tra competenze richieste e disponibili, garantendo maggiore efficienza operativa e ottimizzazione delle risorse, elementi cruciali per la produttività e la competitività di un’impresa.
Nel processo di selezione, oltre alle dinamiche interne alle imprese, anche le specificità del mercato del lavoro e del sistema economico in cui operano, giocano un ruolo fondamentale. Questo articolo, basato sui risultati di una tesi di Master in Organizzazione e Gestione delle Risorse Umane analizza come le imprese adattino le loro strategie di selezione in base alle peculiarità del territorio e alla relativa offerta di lavoro. Il focus di questo lavoro ricade sulle due province autonome di Bolzano e di Trento, due province simili per assetto istituzionale, caratteristiche demografiche, morfologia del territorio ma con alcune differenze in termini di crescita e composizione del tessuto economico locale
Towards Optically-Transparent Electromagnetic Metasurfaces via Microfabricated Square Wafer Modules
No full textOptically transparent electromagnetic metasurfaces (EMS) are essential for enabling the integration of 5G/6G communication infrastructure into modern urban environments. In this work, we present a prototype developed on square transparent wafer substrates, designed to serve as modular building blocks for assembling large-area, puzzle-like EMS
Platform-Aware Mission Planning
No full textPlanning for autonomous systems typically requires reasoning with models at different levels of abstraction, and the harmonization of two competing sets of objectives: high-level mission goals that refer to an interaction of the system with the external environment, and low-level platform constraints that aim to preserve the integrity and the correct interaction of the subsystems. The complicated interplay between these two models makes it very hard to reason on the system as a whole, especially when the objective is to find plans with robustness guarantees, considering the non-deterministic behavior of the lower layers of the system. In this paper, we introduce the problem of Platform-Aware Mission Planning (PAMP), addressing it in the setting of temporal durative actions. The PAMP problem differs from standard temporal planning for its exists-forall nature: the high-level plan dealing with mission goals is required to satisfy safety and executability constraints, for all the possible non-deterministic executions of the low-level model of the platform and the environment. We propose two approaches for solving PAMP. The first baseline approach amalgamates the mission and platform levels, while the second is based on an abstraction-refinement loop that leverages the combination of a planner and a verification engine. We prove the soundness and completeness of the proposed approaches and validate them experimentally, demonstrating the importance of heterogeneous modeling and the superiority of the technique based on abstraction-refinement
Measurements of the production cross-sections of a Higgs boson in association with a vector boson and decaying into WW* with the ATLAS detector at √s= 13 TeV
No full textElectroweak, QCD and flavour physics studies with ATLAS data from Run 2 of the LHC
No full textA summary of precision measurements sensitive to electroweak, QCD and quark-flavour effects performed by the ATLAS Collaboration at the Large Hadron Collider is reported. The measurements are predominantly performed on proton–proton (
) collision data recorded at a centre-of-mass energy of 13 TeV taken from 2015 to 2018, with an integrated luminosity of up to 140 fb−1, with some results based on
and Pb+Pb data recorded at lower nucleon centre-of-mass energies. The results cover a wide range of topics, from strong production of particles at low energies and the spectroscopy of hadrons to perturbative QCD with hadronic jets and electroweak and strong production of single and multiple vector bosons. They provide precise measurements of fundamental constants and stringent tests of the Standard Model with unprecedented precision and in energy ranges never explored before. They are also used to explore the proton structure and to perform model-independent searches for new physics