Neural networks for learning macroscopic chemotactic sensitivity from microscopic models
Erban, R
SIAM Journal on Life Sciences
Thu, 05 Feb 2026
16:00 -
17:00
L5
Linking Path-Dependent and Stochastic Volatility Models
Cephas Svosve
((Mathematical Institute University of Oxford))
Abstract
We explore a link between stochastic volatility (SV) and path-dependent volatility (PDV) models. Using assumed density filtering, we map a given SV model into a corresponding PDV representation. The resulting specification is lightweight, improves in-sample fit, and delivers robust out-of-sample forecasts. We also introduce a calibration procedure for both SV and PDV models that produces standard errors for parameter estimates and supports joint calibration of SPX/VIX smile.
Sufficient Condition for Universal Quantum Computation Using Bosonic Circuits
Calcluth, C
Reichel, N
Ferraro, A
Ferrini, G
PRX Quantum
volume 5
issue 2
(17 May 2024)
Efficient simulatability of continuous-variable circuits with large Wigner negativity
García-Álvarez, L
Calcluth, C
Ferraro, A
Ferrini, G
Physical Review Research
volume 2
issue 4
(04 Dec 2020)
Vacuum provides quantum advantage to otherwise simulatable architectures
Calcluth, C
Ferraro, A
Ferrini, G
Physical Review A
volume 107
issue 6
(15 Jun 2023)
Classical Simulation of Circuits with Realistic Odd-Dimensional Gottesman-Kitaev-Preskill States
Calcluth, C
Hahn, O
Bermejo-Vega, J
Ferraro, A
Ferrini, G
Physical Review Letters
volume 135
issue 1
(01 Jul 2025)
Efficient simulation of Gottesman-Kitaev-Preskill states with Gaussian circuits
Calcluth, C
Ferraro, A
Ferrini, G
Quantum
volume 6
867-867
(01 Dec 2022)