The outbreak of COVID-19 has brought the world to a standstill. More than half of the world's population was in lockdown at the beginning of April. People were urged not to go out and avoid public transport. A few figures to take stock of the situation: around 4 billion people subject to various movement restrictions worldwide, air traffic is reduced by about 90% and shared mobility platforms activity reduced to 2% to 5% of their regular business. The current crisis, which is impacting both the supply and demand for transport, appears to be a privileged moment to rethink mobility and place it on a more sustainable trajectory. The Covid-19 crisis has a significant repercussion on demand and supply of mobility in Victoria as the Victorian government has put citizens in lockdown to limit the spread of COVID-19. This research aims to investigate the travel containment measures (government restrictions) taken in response to the COVID-19 emergency have on Victorian's mobility as this insight is vital to estimate parameters appropriate for the different stages of the crisis, before and after the implementation of prevention policies.

In the real world, the duality of high-dimensional data is widespread. The coclustering method has been widely used because they can exploit the co-occurring structure between samples and features. In fact, most of the existing coclustering methods cluster the graphs in the original data matrix. However, these methods fail to output an affinity graph with an explicit cluster structure and still call for the postprocessing step to obtain the final clustering results. In addition, these methods are difficult to find a good projection direction to complete the clustering task on high-dimensional data. In this article, we modify the flexible manifold embedding theory and embed it into the bipartite spectral graph partition. Then, we propose a new method called structured optimal graph-based clustering with flexible embedding (SOGFE). The SOGFE method can learn an affinity graph with an optimal and explicit clustering structure and does not require any postprocessing step. Additionally, the SOGFE method can learn a suitable projection direction to map high-dimensional data to a low-dimensional subspace. We perform extensive experiments on two synthetic data sets and seven benchmark data sets. The experimental results verify the superiority, robustness, and good projection direction selection ability of our proposed method.

We present a discrete particle based method capable of creating very realistic animations of bubbles in fluids. It allows for the generation (nucleation) of bubbles from gas dissolved in the fluid, the motion of the discrete bubbles including bubble collisions and drag interactions with the liquid which could be undergoing complex free surface motion, the formation and motion of coupled foams and the final dissipation of bubbles. This allows comprehensive simulations of dynamic bubble behavior. The underlying fluid simulation is based on the mesh-free Smoothed Particle Hydrodynamics method. Each particle representing the liquid contains an amount of dissolved gas. Gas is transferred from the continuum fluid model to the discrete bubble model at nucleation sites on the surface of solid bodies. The rate of gas transport to the nucleation sites controls the rate of bubble generation, producing very natural time variations in bubble numbers. Rising bubbles also grow by gathering more gas from the surrounding liquid as they move. This model contains significant bubble scale physics and allows, in principle, the capturing of many important processes that cannot be directly modeled by traditional methods. The method is used here to realistically animate the pouring of a glass of beer, starting with a stream of fresh beer entering the glass, the formation of a dense cloud of bubbles, which rise to create a good head as the beer reaches the top of the glass.