A study conducted at the Institut de Physique du Globe de Paris uses images from NASA's Dawn mission and a Bayesian inversion of the Hapke photometric model to analyze avalanches and ejecta deposits ...
Decades ago, Paul Erdős used randomness to illuminate the vast and weird world of networks. Now mathematicians are making his ...
Defining security in quantum key distribution with Carla Ferradini, Martin Sandfuchs, and Renato Renner The security of quantum key distribution (QKD) is quantified by a parameter ε >0, which—under ...
TensorFlow Probability is a library for probabilistic reasoning and statistical analysis in TensorFlow. As part of the TensorFlow ecosystem, TensorFlow Probability provides integration of ...
Objectives Diagnostic reasoning in systemic lupus erythematosus (SLE) is a complex process reflecting the probability of disease at a given timepoint against competing diagnoses. We applied machine ...
When you throw a ball in the air, the equations of classical physics will tell you exactly what path the ball will take as it falls, and when and where it will land. But if you were to squeeze that ...
Most optimization problems in control theory assume you're working in flat space. You have a system, you want to steer it from one state to another, and you minimize some cost along the way. This ...
Abstract: The ever-growing insertion of intermittent energy sources requires to account for this uncertainty by precise models. Probabilistic constraints are an interesting technique to deal with the ...
Predicting how complex stochastic systems respond to small external perturbations is central in physics, climate science, and engineering. We combine the generalized fluctuation–dissipation theorem ...
Quantum computing and artificial intelligence (AI) can be combined with classical computing methods to design and discover small-molecule candidates that target the cancer-driving KRAS protein, ...
The model equations are as follows. $$ \begin{align*} \dfrac{\mathrm dS}{\mathrm dt} &= -\frac{\beta c S I}{N}, \\ \dfrac{\mathrm dI}{\mathrm dt} &= \frac{\beta c S I ...
c) after eliminating the 1’s on the diagonal, derive the pair-wise Pearson correlation between all returns applying Equation (4). The result is shown in the classical correlation matrix in Table 1.
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