Zobrazeno 1 - 10
of 11 588
pro vyhledávání: '"A. Zaccaria"'
Real-world applications may be affected by outlying values. In the model-based clustering literature, several methodologies have been proposed to detect units that deviate from the majority of the data (rowwise outliers) and trim them from the parame
Externí odkaz:
http://arxiv.org/abs/2409.07881
Predicting high-growth firms has attracted increasing interest from the technological forecasting and machine learning communities. Most existing studies primarily utilize financial data for these predictions. However, research suggests that a firm's
Externí odkaz:
http://arxiv.org/abs/2408.09149
We consider estimation and inference in a linear model with endogenous regressors where the parameters of interest change across two samples. If the first-stage is common, we show how to use this information to obtain more efficient two-sample GMM es
Externí odkaz:
http://arxiv.org/abs/2406.17056
This letter presents a novel approach in the field of Active Fault Detection (AFD), by explicitly separating the task into two parts: Passive Fault Detection (PFD) and control input design. This formulation is very general, and most existing AFD lite
Externí odkaz:
http://arxiv.org/abs/2405.04938
While Machine Learning has become crucial for Industry 4.0, its opaque nature hinders trust and impedes the transformation of valuable insights into actionable decision, a challenge exacerbated in the evolving Industry 5.0 with its human-centric focu
Externí odkaz:
http://arxiv.org/abs/2404.18525
Defining and finalizing Mergers and Acquisitions (M&A) requires complex human skills, which makes it very hard to automatically find the best partner or predict which firms will make a deal. In this work, we propose the MASS algorithm, a specifically
Externí odkaz:
http://arxiv.org/abs/2404.07179
Pursuing fast and robust interpretability in Anomaly Detection is crucial, especially due to its significance in practical applications. Traditional Anomaly Detection methods excel in outlier identification but are often black-boxes, providing scant
Externí odkaz:
http://arxiv.org/abs/2403.01245
Autor:
Koya, Alemayehu Nana, Zhu, Xiangchao, Ohannesian, Nareg, Yanik, A. Ali, Alabastri, Alessandro, Zaccaria, Remo Proietti, Krahne, Roman, Shih, Wei-Chuan, Garoli, Denis
The field of plasmonics is capable of enabling interesting applications in the different wavelength ranges, spanning from the ultraviolet up to the infrared. The choice of plasmonic material and how the material is nanostructured have significant imp
Externí odkaz:
http://arxiv.org/abs/2312.02609
In this study, we address the challenge of analyzing electrophysiological measurements in neuronal networks. Our computational model, based on the Reservoir Computing Network (RCN) architecture, deciphers spatio-temporal data obtained from electrophy
Externí odkaz:
http://arxiv.org/abs/2311.03131
Publikováno v:
Chaos Solit. Fractals 181 (114630) (2024)
Production networks arise from supply and customer relations among firms. These systems are gaining growing attention as a consequence of disruptions due to natural or man-made disasters that happened in the last years, such as the Covid-19 pandemic
Externí odkaz:
http://arxiv.org/abs/2310.10363