Crystal calorimeters have a long history of pushing the frontier on high-resolution electromagnetic (EM) calorimetry for photons and electrons. We explore in this paper major innovations in collider detector performance that can be achieved with crystal calorimetry when longitudinal segmentation and dual-readout capabilities are combined with a new high EM resolution approach to Particle Flow in multi-jet events, such as 𝑒 + 𝑒 + → 𝐻𝑍 events in all-hadronic final-states at Higgs factories. We demonstrate a new technique for pre-processing 𝜋 0 momenta through combinatoric di-photon pairing in advance of applying jet algorithms. This procedure significantly reduces 𝜋 0 photon splitting across jets in multi-jet events. The correct photon-to-jet assignment efficiency improves by a factor of about 3 when the EM resolution is improved from 15 to 3%/

Non-Pharmaceutical Interventions (NPIs), aimed at reducing the diffusion of the COVID-19 pandemic, have dramatically influenced our everyday behaviour. In this work, we study how individuals adapted their daily movements and person-to-person contact patterns over time in response to the NPIs. We leverage longitudinal GPS mobility data of hundreds of thousands of anonymous individuals to empirically show and quantify the dramatic disruption in people’s mobility habits and social behaviour. We find that local interventions did not just impact the number of visits to different venues but also how people experience them. Individuals spend less time in venues, preferring simpler and more predictable routines, also reducing person-to-person contacts. Moreover, we find that the individual patterns of visits are influenced by the strength of the NPIs policies, the local severity of the pandemic and a risk adaptation factor, which increases the people’s mobility regardless of the stringency of interventions. Finally, despite the gradual recovery in visit patterns, we find that individuals continue to keep person-to-person contacts low. This apparent conflict hints that the evolution of policy adherence should be carefully addressed by policymakers, epidemiologists and mobility experts.

The availability of massive data-sets describing human mobility offers the possibility to design simulation tools to monitor and improve the resilience of transport systems in response to traumatic events such as natural and man-made disasters (e.g., floods, terrorist attacks, etc. . . ). In this perspective we propose ACHILLES, an application to models people's movements in a given transport mode through a multiplex network representation based on mobility data. ACHILLES is a web-based application which provides an easy-to-use interface to explore the mobility fluxes and the connectivity of every urban zone in a city, as well as to visualize changes in the transport system resulting from the addition or removal of transport modes, urban zones and single stops. Notably, our application allows the user to assess the overall resilience of the transport network by identifying its weakest node, i.e. Urban Achilles Heel, with reference to the ancient Greek mythology. To demonstrate the impact of ACHILLES for humanitarian aid we consider its application to a real-world scenario by exploring human mobility in Singapore in response to flood prevention.

“Code is law” is the founding principle of cryptocurrencies. The security, transferability, availability, and other properties of crypto-assets are determined by the code through which they are created. If code is open source, as is customary for cryptocurrencies, this would prevent manipulations and grant transparency to users and traders. However, this approach considers cryptocurrencies as isolated entities, neglecting possible connections between them. Here, we show that 4% of developers contribute to the code of more than one cryptocurrency and that the market reflects these cross-asset dependencies. In particular, we reveal that the first coding event linking two cryptocurrencies through a common developer leads to the synchronization of their returns. Our results identify a clear link between the collaborative development of cryptocurrencies and their market behavior. More broadly, they reveal a so-far overlooked systemic dimension for the transparency of code-based ecosystems that will be of interest for researchers, investors, and regulators.