The paper by Dan Andrews and Marion Kohler provides an interesting and very useful contribution to the empirical literature that seeks to explain the factors infl uencing bilateral output correlations. While there are a number of papers that have developed empirical models of the cross-section distribution of bilateral (de-trended) output correlations (Baxter and Kouparitsas 2005, Imbs 2004 and...

Recent developments in neural networks have shown the potential of estimating drag on irregular rough surfaces. Nevertheless, difficulty obtaining a large high-fidelity dataset to train is deterring their use practical applications. In this study, we propose transfer learning framework model surfaces even with limited amount direct numerical simulations. We show that empirical correlations, rep...

Empirical correlations between various key parameters have been extensively explored ever since the discovery of gamma-ray bursts (GRBs) and widely used as standard candles to probe Universe. The Amati relation Yonetoku are two good examples that enjoyed special attention. former reflects connection peak photon energy (Ep) isotropic $\gamma$-ray release (Eiso), while latter links Ep with lumino...

Proper estimation of droplet growth rate plays a crucial role on appropriate prediction of supersonic separators performance for separation of fine droplets from a gas stream. Up to now, all available researches employ empirical or semi-empirical correlations to define the relationship between droplet growth rate (dr/dt) and other operating variables such as temperatures (T and TL), Pressure (P...

The Empirical Mode Decomposition (EMD) provides a tool to characterize time series in terms of its implicit components oscillating at different time-scales. We apply this decomposition to intraday time series of the following three financial indices: the S&P 500 (USA), the IPC (Mexico) and the VIX (volatility index USA), obtaining time-varying multidimensional cross-correlations at different ti...