Non-Hermitian dynamics and nonreciprocity of optically coupled nanoparticles

Non-Hermitian dynamics, as observed in photonic, atomic, electrical, and optomechanical platforms, holds great potential for sensing applications and signal processing. Recently, fully tunable nonreciprocal optical interaction has been demonstrated between levitated nanoparticles. Here, we use this tunability to investigate the collective non-Hermitian dynamics of two nonreciprocally and nonlinearly interacting nanoparticles. We observe parity-time symmetry breaking and, for sufficiently strong coupling, a collective mechanical lasing transition, where the particles move along stable limit cycles. This work opens up a research avenue of nonequilibrium multi-particle collective effects, tailored by the dynamic control of individual sites in a tweezer array

Human Computation and Convergence

Humans are the most effective integrators and producers of information, directly and through the use of information-processing inventions. As these inventions become increasingly sophisticated, the substantive role of humans in processing information will tend toward capabilities that derive from our most complex cognitive processes, e.g., abstraction, creativity, and applied world knowledge. Through the advancement of human computation - methods that leverage the respective strengths of humans and machines in distributed information-processing systems - formerly discrete processes will combine synergistically into increasingly integrated and complex information processing systems. These new, collective systems will exhibit an unprecedented degree of predictive accuracy in modeling physical and techno-social processes, and may ultimately coalesce into a single unified predictive organism, with the capacity to address societies most wicked problems and achieve planetary homeostasis.Comment: Pre-publication draft of chapter. 24 pages, 3 figures; added references to page 1 and 3, and corrected typ

Challenges in Complex Systems Science

FuturICT foundations are social science, complex systems science, and ICT. The main concerns and challenges in the science of complex systems in the context of FuturICT are laid out in this paper with special emphasis on the Complex Systems route to Social Sciences. This include complex systems having: many heterogeneous interacting parts; multiple scales; complicated transition laws; unexpected or unpredicted emergence; sensitive dependence on initial conditions; path-dependent dynamics; networked hierarchical connectivities; interaction of autonomous agents; self-organisation; non-equilibrium dynamics; combinatorial explosion; adaptivity to changing environments; co-evolving subsystems; ill-defined boundaries; and multilevel dynamics. In this context, science is seen as the process of abstracting the dynamics of systems from data. This presents many challenges including: data gathering by large-scale experiment, participatory sensing and social computation, managing huge distributed dynamic and heterogeneous databases; moving from data to dynamical models, going beyond correlations to cause-effect relationships, understanding the relationship between simple and comprehensive models with appropriate choices of variables, ensemble modeling and data assimilation, modeling systems of systems of systems with many levels between micro and macro; and formulating new approaches to prediction, forecasting, and risk, especially in systems that can reflect on and change their behaviour in response to predictions, and systems whose apparently predictable behaviour is disrupted by apparently unpredictable rare or extreme events. These challenges are part of the FuturICT agenda

Crisis Analytics: Big Data Driven Crisis Response

Disasters have long been a scourge for humanity. With the advances in technology (in terms of computing, communications, and the ability to process and analyze big data), our ability to respond to disasters is at an inflection point. There is great optimism that big data tools can be leveraged to process the large amounts of crisis-related data (in the form of user generated data in addition to the traditional humanitarian data) to provide an insight into the fast-changing situation and help drive an effective disaster response. This article introduces the history and the future of big crisis data analytics, along with a discussion on its promise, challenges, and pitfalls

Chemical event tracking using a low-cost wireless chemical sensing network

A recently developed low-cost light emitting diode (LED) chemical sensing technique is integrated with a Mica2Dot wireless communications platform to form a deployable wireless chemical event indicator network. The operation of the colorimetric sensing node has been evaluated to determine its reproducibility and limit of detection for an acidic airborne contaminant. A test-scale network of five similar chemical sensing nodes is deployed in a star communication topology at fixed points within a custom built Environmental Sensing Chamber (ESC). Presented data sets collected from the deployed wireless chemical sensor network (WCSN) show that during an acidic event scenario it is possible to track the plume speed and direction, and estimate the concentration of chemical plume by examining the collective sensor data relative to individual sensor node location within the monitored environment