Zobrazeno 1 - 10
of 308
pro vyhledávání: '"Parziale, P."'
Publikováno v:
International Journal of Interactive Multimedia and Artificial Intelligence, Volume 7, number 4, Pages 171-184, 2022
The order in which the trajectory is executed is a powerful source of information for recognizers. However, there is still no general approach for recovering the trajectory of complex and long handwriting from static images. Complex specimens can res
Externí odkaz:
http://arxiv.org/abs/2406.03194
Publikováno v:
Pattern Recognition Letters, Volume: 121, Pages 113-122 (2019)
Building upon findings in computational model of handwriting learning and execution, we introduce the concept of stability to explain the difference between the actual movements performed during multiple execution of the subject's signature, and conj
Externí odkaz:
http://arxiv.org/abs/2405.11978
Autor:
Patel, Meet, Rubio, Juan Sebastian, Shekhtman, David, Parziale, Nick, Rabinovitch, Jason, Ni, Rui, Capecelatro, Jesse
Publikováno v:
J. Fluid Mech. 1000 (2024) A60
Experiments and numerical simulations of inertial particles in underexpanded jets are performed. The structure of the jet is controlled by varying the nozzle pressure ratio, while the influence of particles on emerging shocks and rarefaction patterns
Externí odkaz:
http://arxiv.org/abs/2404.07329
Publikováno v:
Pattern Recognition 68, p.p 233 - 244 (2017)
New methods for generating synthetic handwriting images for biometric applications have recently been developed. The temporal evolution of handwriting from childhood to adulthood is usually left unexplored in these works. This paper proposes a novel
Externí odkaz:
http://arxiv.org/abs/2401.15472
Autor:
Bensalah, Asma, Parziale, Antonio, De Gregorio, Giuseppe, Marcelli, Angelo, Fornés, Alicia, Lladós
During recent years, there here has been a boom in terms of deep learning use for handwriting analysis and recognition. One main application for handwriting analysis is early detection and diagnosis in the health field. Unfortunately, most real case
Externí odkaz:
http://arxiv.org/abs/2312.05086
Autor:
Hegselmann, Stefan, Parziale, Antonio, Shanmugam, Divya, Tang, Shengpu, Asiedu, Mercy Nyamewaa, Chang, Serina, Hartvigsen, Thomas, Singh, Harvineet
A collection of the accepted Findings papers that were presented at the 3rd Machine Learning for Health symposium (ML4H 2023), which was held on December 10, 2023, in New Orleans, Louisiana, USA. ML4H 2023 invited high-quality submissions on relevant
Externí odkaz:
http://arxiv.org/abs/2312.00655
Autor:
Parziale, Antonio, Agrawal, Monica, Joshi, Shalmali, Chen, Irene Y., Tang, Shengpu, Oala, Luis, Subbaswamy, Adarsh
A collection of the extended abstracts that were presented at the 2nd Machine Learning for Health symposium (ML4H 2022), which was held both virtually and in person on November 28, 2022, in New Orleans, Louisiana, USA. Machine Learning for Health (ML
Externí odkaz:
http://arxiv.org/abs/2211.15564
Publikováno v:
Heliyon, Vol 10, Iss 14, Pp e34504- (2024)
This article aims to provide a systematic review of the literature on animal biomass and biogas plants through an analysis of externalities and benefits in economic, social, and environmental terms. In recent years, the spread of biogas plants has pl
Externí odkaz:
https://doaj.org/article/2b2df002a46a4257ad6fb83255a0f25b
Autor:
Mengxin Zhang, Xihui Chen, Hongming Xie, Luca Esposito, Anna Parziale, Shilpa Taneja, Ahsan Siraj
Publikováno v:
Heliyon, Vol 10, Iss 10, Pp e31579- (2024)
In the swiftly evolving business landscape, digital transformation (DT) has emerged as a crucial strategy for firms to gain a competitive edge. Despite the abundance of literature on DT in firms, there remains a dearth of empirical research that defi
Externí odkaz:
https://doaj.org/article/734b4dbcf14b4107a9fa4dfef637a2c9
Autor:
Nagesh, Srikanth T., Banik, Indranil, Thies, Ingo, Kroupa, Pavel, Famaey, Benoit, Wittenburg, Nils, Parziale, Rachel, Haslbauer, Moritz
Publikováno v:
Canadian Journal of Physics, 99, 607 - 613 ( 2021)
This document describes the general process of setting up, running, and analysing disc galaxy simulations using the freely available program Phantom of RAMSES (PoR). This implements Milgromian Dynamics (MOND) with a patch to the RAMSES grid-based $N$
Externí odkaz:
http://arxiv.org/abs/2101.11011