Information physics and quantum space technologies for natural hazard sensing, modelling and prediction

Disruptive socio-natural transformations and climatic change, where system invariants and symmetries break down, defy the traditional complexity paradigms such as machine learning and artificial intelligence. In order to overcome this, we introduced non-ergodic Information Physics, bringing physical meaning to inferential metrics, and a coevolving flexibility to the metrics of information transfer, resulting in new methods for causal discovery and attribution. With this in hand, we develop novel dynamic models and analysis algorithms natively built for quantum information technological platforms, expediting complex system computations and rigour. Moreover, we introduce novel quantum sensing technologies in our Meteoceanics satellite constellation, providing unprecedented spatiotemporal coverage, resolution and lead, whilst using exclusively sustainable materials and processes across the value chain. Our technologies bring out novel information physical fingerprints of extreme events, with recently proven records in capturing early warning signs for extreme hydro-meteorologic events and seismic events, and do so with unprecedented quantum-grade resolution, robustness, security, speed and fidelity in sensing, processing and communication. Our advances, from Earth to Space, further provide crucial predictive edge and added value to early warning systems of natural hazards and long-term predictions supporting climatic security and action.

Information Physical Complexity, Causality and Predictability across Coevolutionary Spacetimes: Theory and Hydro-Climatic Applications

<p>Complex System Dynamics, Causality and Predictability pose fundamental challenges even under well-defined structural stochastic-dynamic conditions where the laws of motion and system symmetries are known.</p><p>However, the edifice of complexity can be profoundly transformed by structural-functional coevolution and non-recurrent elusive mechanisms changing the very same invariants of motion that had been taken for granted. This leads to recurrence collapse and memory loss, precluding the ability of traditional stochastic-dynamic, information-theoretic and artificial intelligence approaches to provide reliable information about the non-recurrent emergence of fundamental new properties absent from the a priori kinematic geometric and statistical features.</p><p>Unveiling causal mechanisms and eliciting system dynamic predictability under such challenging conditions is not only a fundamental problem in mathematical and statistical physics, but also one of critical importance to dynamic modelling, risk assessment and decision support e.g. regarding non-recurrent critical transitions and extreme events.</p><p>In order to address these challenges, generalized metrics in non-ergodic information physics are hereby introduced for unveiling elusive dynamics, causality and predictability of complex dynamical systems undergoing far-from-equilibrium structural-functional coevolution, building from Perdigão (2017, 2018, 2020a, 2020b), Perdigão et al. (2020).</p><p>With these methodological developments at hand, hidden dynamic information is hereby brought out and explicitly quantified even beyond post-critical regime collapse, long after statistical information is lost. The added causal insights and operational predictive value are further highlighted by evaluating the new information metrics among statistically independent variables, where traditional techniques therefore find no information links. Notwithstanding the factorability of the distributions associated to the aforementioned independent variables, synergistic and redundant information are found to emerge from microphysical, event-scale codependencies in far-from-equilibrium nonlinear statistical mechanics.</p><p>The findings are illustrated to shed light onto fundamental causal mechanisms and unveil elusive dynamic predictability of non-recurrent critical transitions and extreme events across multiscale hydro-climatic problems.</p><p> </p><p>References:</p><p>Perdigão R.A.P. (2017): Fluid Dynamical Systems: from Quantum Gravitation to Thermodynamic Cosmology. https://doi.org/10.46337/mdsc.5091.</p><p>Perdigão R.A.P. (2018): Polyadic Entropy, Synergy and Redundancy among Statistically Independent Processes in Nonlinear Statistical Physics with Microphysical Codependence. Entropy, 20(1), 26. https://doi.org/10.3390/e20010026.</p><p>Perdigão R.A.P. (2020a): Synergistic Dynamic Causation and Prediction in Coevolutionary Spacetimes. https://doi.org/10.46337/mdsc.5546.</p><p>Perdigão, R.A.P. (2020b): Information Physical Artificial Intelligence in Complex System Dynamics: Breaking Frontiers in Nonlinear Analytics, Model Design and Socio-Environmental Decision Support in a Coevolutionary World. https://doi.org/10.46337/200930.</p><p>Perdigão R.A.P., Ehret U., Knuth K.H. & Wang, J. (2020) Debates: Does information theory provide a new paradigm for Earth science? Emerging concepts and pathways of information physics. Water Resources Research, 56(2), 1-13. https://doi.org/10.1029/2019WR025270.</p><p> </p>

A Task Offloading Method with Edge for 5G-Envisioned Cyber-Physical-Social Systems

Recently, Cyber-Physical-Social Systems (CPSS) have been introduced as a new information physics system, which enables personnel organizations to control physical entities in a reliable, real-time, secure, and collaborative manner through cyberspace. Moreover, with the maturity of edge computing technology, the data generated by physical entities in CPSS are usually sent to edge computing nodes for effective processing. Nevertheless, it remains a challenge to ensure that edge nodes maintain load balance while minimizing the completion time in the event of the edge node outage. Given these problems, a Unique Task Offloading Method (UTOM) for CPSS is designed in this paper. Technically, the system model is constructed firstly and then a multi-objective problem is defined. Afterward, Improving the Strength Pareto Evolutionary Algorithm (SPEA2) is utilized to generate the feasible solutions of the above problem, whose aims are optimizing the propagation time and achieving load balance. Furthermore, the normalization method has been leveraged to produce standard data and select the global optimal solution. Finally, several necessary experiments of UTOM are introduced in detail.