This paper analyses herding in cryptocurrency markets in the time of the COVID-19
pandemic. We employ a combination of quantitative methods to hourly prices of the four …

In this paper, a novel data-driven model-free adaptive predictive control method based on
lazy learning technique is proposed for a class of discrete-time single-input and single …

Self-attention performs well in long context but has quadratic complexity. Existing RNN
layers have linear complexity, but their performance in long context is limited by the …

This paper considers a new nonparametric estimation of conditional value-at-risk and
expected shortfall functions. Conditional value-at-risk is estimated by inverting the weighted …

We introduce a new class of spatially stochastic physics and data informed deep latent
models for parametric partial differential equations (PDEs) which operate through scalable …

We reformulate the problem of supervised learning as learning to learn with two nested
loops (ie learning problems). The inner loop learns on each individual instance with self …

Transient stability assessment (TSA) is of great importance in power systems. For a given
contingency, one of the most widely-used transient stability indices is the critical clearing …

With the aim of mitigating the possible problem of negativity in the estimation of the
conditional density function, we introduce a so‐called re‐weighted Nadaraya‐Watson …

The real-life signals captured by different measurement systems (such as modern maritime
transport characterized by challenging and varying operating conditions) are often subject to …

Multi-layer perceptron artificial neural networks are used extensively in hydrological and
water resources modelling. However, a significant limitation with their application is that it is …