For recurrent neural networks trained on time series with target and exogenous variables, in
addition to accurate prediction, it is also desired to provide interpretable insights into the …

Finding an optimal subset of nodes in a network that is able to efficiently disrupt the
functioning of a corrupt or criminal organization or contain an epidemic or the spread of …

The efficient control of complex dynamical systems has many applications in the natural and
applied sciences. In most real-world control problems, both control energy and cost …

Detection of patient zero can give new insights to epidemiologists about the nature of first
transmissions into a population. In this Letter, we study the statistical inference problem of …

A recent theoretical analysis shows the equivalence between non-negative matrix factorization
(NMF) and spectral clustering based approach to subspace clustering. As NMF and many …

In this paper we analyze the predictability of the bankruptcy of 7795 Italian municipalities in
the period 2009–2016. The prediction task is extremely hard due to the small number of …

Bitcoin is one of the most prominent decentralized digital cryptocurrencies. Ability to
understand which factors drive the fluctuations of the Bitcoin price and to what extent they are …

The current global systemic crisis reveals how globalised societies are unprepared to face a
pandemic. Beyond the dramatic loss of human life, the COVID-19 pandemic has triggered …

Bitcoin is the first decentralized digital cryptocurrency, which has showed significant market
capitalization growth in last few years. It is important to understand what drives the …

In the study of disease spreading on empirical complex networks in SIR model, initially infected
nodes can be ranked according to some measure of their epidemic impact. The highest …