Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Tommaso Pacini"'
Publikováno v:
Foods, Vol 13, Iss 19, p 3131 (2024)
The detection and quantification of polar pesticides in liquid chromatography coupled with mass spectrometry present significant analytical challenges. This study compares the performance of three LC columns (Hypercarb™, Raptor Polar X™, and Anio
Externí odkaz:
https://doaj.org/article/dbe3ca3c91c047b7968d4b1eb831a2cd
Publikováno v:
IEEE Access, Vol 11, Pp 32759-32775 (2023)
In recent years, Convolutional Neural Networks (CNNs) have demonstrated outstanding results in several emerging classification tasks. The high-quality predictions are often achieved with computationally intensive workloads that hinder the hardware ac
Externí odkaz:
https://doaj.org/article/b101ecdca8304b22b85d6d16493d809d
Autor:
Stefano Sdogati, Tommaso Pacini, Rita Bibi, Angela Caporali, Emanuela Verdini, Serenella Orsini, Roberta Ortenzi, Ivan Pecorelli
Publikováno v:
Foods, Vol 13, Iss 2, p 313 (2024)
Mycotoxin contamination of feed and feed materials represent a serious health hazard. This study details the occurrence of aflatoxin B1 (AFB1), zearalenone (ZEN) and ochratoxin A (OTA) in 826 feed and 617 feed material samples, collected in two Itali
Externí odkaz:
https://doaj.org/article/9cfc5a9e10f54003a8571cd193adfed4
Autor:
Emilio Rapuano, Gabriele Meoni, Tommaso Pacini, Gianmarco Dinelli, Gianluca Furano, Gianluca Giuffrida, Luca Fanucci
Publikováno v:
Remote Sensing, Vol 13, Iss 8, p 1518 (2021)
In recent years, research in the space community has shown a growing interest in Artificial Intelligence (AI), mostly driven by systems miniaturization and commercial competition. In particular, the application of Deep Learning (DL) techniques on boa
Externí odkaz:
https://doaj.org/article/5452cd2b05874e2c8294f1ef6fe9f828
Publikováno v:
Computational Intelligence and Neuroscience.
Recurrent Neural Networks (RNNs) have become important tools for tasks such as speech recognition, text generation, or natural language processing. However, their inference may involve up to billions of operations and their large number of parameters
Publikováno v:
Lecture Notes in Electrical Engineering ISBN: 9783030954970
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a5ed5d1a6a720ea796e3d59ad961a93d
https://doi.org/10.1007/978-3-030-95498-7_26
https://doi.org/10.1007/978-3-030-95498-7_26
Autor:
Tommaso Pacini
Extremal length is a classical tool in 1-dimensional complex analysis for building conformal invariants. We propose a higher-dimensional generalization for complex manifolds and provide some ideas on how to estimate and calculate it. We also show how
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2280674ce1e4db4e45aa516dadad9e78
http://hdl.handle.net/2318/1743429
http://hdl.handle.net/2318/1743429
In the last years, Convolutional Neural networks (CNNs) found applications in many fields from computer vision to speech recognition, showing outstanding results in terms of accuracy. Field Programmable Gate Arrays (FPGAs) proved to be a promising pl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1dfeabd69692688c3276fb77018b7ecf
http://hdl.handle.net/11568/1066074
http://hdl.handle.net/11568/1066074
Publikováno v:
Electronics
Volume 10
Issue 20
Electronics, Vol 10, Iss 2514, p 2514 (2021)
Volume 10
Issue 20
Electronics, Vol 10, Iss 2514, p 2514 (2021)
In recent years, FPGAs have demonstrated remarkable performance and contained power consumption for the on-the-edge inference of Convolutional Neural Networks. One of the main challenges in implementing this class of algorithms on board an FPGA is re
Autor:
Jason D. Lotay, Tommaso Pacini
Given a minimal Lagrangian submanifold L in a negative Kaehler--Einstein manifold M, we show that any small Kaehler--Einstein perturbation of M induces a deformation of L which is minimal Lagrangian with respect to the new structure. This provides a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b8a41e03424e60bd255575a9f7074889
https://doi.org/10.1007/s40574-018-0183-z
https://doi.org/10.1007/s40574-018-0183-z