physics.data-an

arXiv:2412.18041v1 Announce Type: cross Abstract: In recent years generative artificial intelligence has been used to create data to support science analysis. For example, Generative Adversarial Networks (GANs) have been trained using Monte Carlo simulated input and then used to generate data for the same problem. This has the advantage that a GAN creates data in a significantly reduced computing time. N training events for a GAN can result in GN generated events with the gain factor, G, being more than one. This appears to violate the principle that one cannot get information for free. This is not the only way to amplify data so this process will be referred to as data amplification which is studied using information theoretic concepts. It is shown that a gain of greater than one is possible whilst keeping the information content of the data unchanged. This leads to a mathematical bound which only depends on the number of generated and training events. This study determines conditions on both the underlying and reconstructed probability distributions to ensure this bound. In particular, the resolution of variables in amplified data is not improved by the process but the increase in sample size can still improve statistical significance. The bound is confirmed using computer simulation and analysis of GAN generated data from the literature.

arXiv:2412.17342v1 Announce Type: new Abstract: Online social networks are increasingly being utilized for collective sense making and information processing in disasters. However, the underlying mechanisms that shape the dynamics of collective intelligence in online social networks during disasters is not fully understood. To bridge this gap, we examine the mechanisms of collective information processing in human networks during five threat cases including airport power outage, hurricanes, wildfire, and blizzard, considering the temporal and spatial dimensions. Using the 13MM Twitter data generated by 5MM online users during these threats, we examined human activities, communication structures and frequency, social influence, information flow, and medium response time in social networks. The results show that the activities and structures are stable in growing networks, which lead to a stable power-law distribution of the social influence in networks. These temporally invariant patterns are not affected by people's memory and ties' strength. In addition, spatially localized communication spikes and global transmission gaps in the networks. The findings could inform about network intervention strategies to enable a healthy and efficient online environment, with potential long-term impact on risk communication and emergency response.