Zobrazeno 1 - 10
of 883
pro vyhledávání: '"Vespignani Alessandro"'
Autor:
Pedreschi, Dino, Pappalardo, Luca, Ferragina, Emanuele, Baeza-Yates, Ricardo, Barabasi, Albert-Laszlo, Dignum, Frank, Dignum, Virginia, Eliassi-Rad, Tina, Giannotti, Fosca, Kertesz, Janos, Knott, Alistair, Ioannidis, Yannis, Lukowicz, Paul, Passarella, Andrea, Pentland, Alex Sandy, Shawe-Taylor, John, Vespignani, Alessandro
Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature. Recommender syst
Externí odkaz:
http://arxiv.org/abs/2306.13723
Autor:
Klein, Brennan, Hartle, Harrison, Shrestha, Munik, Zenteno, Ana Cecilia, Cordera, David Barros Sierra, Nicolas-Carlock, José R., Bento, Ana I., Althouse, Benjamin M., Gutierrez, Bernardo, Escalera-Zamudio, Marina, Reyes-Sandoval, Arturo, Pybus, Oliver G., Vespignani, Alessandro, Diaz-Quiñonez, Jose Alberto, Scarpino, Samuel V., Kraemer, Moritz U. G.
During outbreaks of emerging infectious diseases, internationally connected cities often experience large and early outbreaks, while rural regions follow after some delay. This hierarchical structure of disease spread is influenced primarily by the m
Externí odkaz:
http://arxiv.org/abs/2301.13256
Autor:
Klein, Brennan, LaRock, Timothy, McCabe, Stefan, Torres, Leo, Friedland, Lisa, Kos, Maciej, Privitera, Filippo, Lake, Brennan, Kraemer, Moritz U. G., Brownstein, John S., Gonzalez, Richard, Lazer, David, Eliassi-Rad, Tina, Scarpino, Samuel V., Vespignani, Alessandro, Chinazzi, Matteo
The COVID-19 pandemic offers an unprecedented natural experiment providing insights into the emergence of collective behavioral changes of both exogenous (government mandated) and endogenous (spontaneous reaction to infection risks) origin. Here, we
Externí odkaz:
http://arxiv.org/abs/2212.08873
Autor:
Pangallo, Marco, Aleta, Alberto, Chanona, R. Maria del Rio, Pichler, Anton, Martín-Corral, David, Chinazzi, Matteo, Lafond, François, Ajelli, Marco, Moro, Esteban, Moreno, Yamir, Vespignani, Alessandro, Farmer, J. Doyne
The potential tradeoff between health outcomes and economic impact has been a major challenge in the policy making process during the COVID-19 pandemic. Epidemic-economic models designed to address this issue are either too aggregate to consider hete
Externí odkaz:
http://arxiv.org/abs/2212.03567
Science and engineering fields use computer simulation extensively. These simulations are often run at multiple levels of sophistication to balance accuracy and efficiency. Multi-fidelity surrogate modeling reduces the computational cost by fusing di
Externí odkaz:
http://arxiv.org/abs/2206.04872
Autor:
Gozzi, Nicolò, Chinazzi, Matteo, Davis, Jessica T., Mu, Kunpeng, Pastore y Piontti, Ana, Ajelli, Marco, Vespignani, Alessandro, Perra, Nicola
Publikováno v:
In Epidemics December 2024 49
Autor:
Pedreschi, Dino, Pappalardo, Luca, Ferragina, Emanuele, Baeza-Yates, Ricardo, Barabási, Albert-László, Dignum, Frank, Dignum, Virginia, Eliassi-Rad, Tina, Giannotti, Fosca, Kertész, János, Knott, Alistair, Ioannidis, Yannis, Lukowicz, Paul, Passarella, Andrea, Pentland, Alex Sandy, Shawe-Taylor, John, Vespignani, Alessandro
Publikováno v:
In Artificial Intelligence February 2025 339
Autor:
Chinazzi, Matteo, Davis, Jessica T., y Piontti, Ana Pastore, Mu, Kunpeng, Gozzi, Nicolò, Ajelli, Marco, Perra, Nicola, Vespignani, Alessandro
Publikováno v:
In Epidemics June 2024 47
Stochastic simulations such as large-scale, spatiotemporal, age-structured epidemic models are computationally expensive at fine-grained resolution. While deep surrogate models can speed up the simulations, doing so for stochastic simulations and wit
Externí odkaz:
http://arxiv.org/abs/2106.02770
Autor:
Wu, Dongxia, Gao, Liyao, Xiong, Xinyue, Chinazzi, Matteo, Vespignani, Alessandro, Ma, Yi-An, Yu, Rose
Deep learning is gaining increasing popularity for spatiotemporal forecasting. However, prior works have mostly focused on point estimates without quantifying the uncertainty of the predictions. In high stakes domains, being able to generate probabil
Externí odkaz:
http://arxiv.org/abs/2105.11982