The transport literature is dense regarding short-term traffic predictions, up to the scale of 1 hour, yet less for long-term predictions. also sparse when it comes city-scale mainly because low data availability. In this work, we report an effort investigate whether deep learning models can be useful large-scale prediction task, while focusing on scalability models. We a dataset with 14 weeks ...

let $g$ be a group and $mathcal{n}$ be the class of all nilpotent groups. a subset $a$ of $g$ is said to be nonnilpotent if for any two distinct elements $a$ and $b$ in $a$, $langle a, brangle notin mathcal{n}$. if, for any other nonnilpotent subset $b$ in $g$, $|a|geq |b|$, then $a$ is said to be a maximal nonnilpotent subset and the cardinality of this subset (if it exists) is denoted by $ome...

If A is a weak C∗-Hopf algebra then the category of finite dimensional unitary representations of A is a monoidal C∗-category with monoidal unit being the GNS representation Dε associated to the counit ε. This category has isomorphic left dual and right dual objects which leads, as usual, to the notion of dimension function. However, if ε is not pure the dimension function is matrix valued with...

A probabilistic method that takes account of the ordinal character of the MSK Scale has been adapted to generate a 1755 seismic intensity map for the Iberian Peninsula. Statistical models are employed to estimate isoseismals and to compute standard error uncertainties. The output includes intensity maps, estimates of the effects associated with each intensity level, and estimates of the distrib...