Dynamical Systems

Complex systems are pervasive in many areas of science integrated in our daily lives. Examples include financial markets, highway transportation networks, telecommunication networks, world and country economies, social networks, immunological systems, living organisms, computational systems and electrical and mechanical structures. Complex systems are often composed of a large number of interconnected and interacting entities, exhibiting much richer global scale dynamics than the properties and behavior of individual entities. Complex systems are studied in many areas of natural sciences, social sciences, engineering and mathematical sciences. This special issue therefore intends to contribute towards the dissemination of the multifaceted concepts in accepted use by the scientific community. We hope readers enjoy this pertinent selection of papers which represents relevant examples of the state of the art in present day research. [...

Noise, coherent activity and network structure in neuronal cultures

In this thesis we apply a multidisciplinary approach, based on statistical physics and complex systems, to the study of neuronal dynamics. We focus on understanding, using theoretical and computational tools, how collective neuronal activity emerges in a controlled system, a neuronal culture. We show how the interplay between noise and network structure defines the emergent collective behavior of the system. We build, using theory and simulation, a framework that takes carefully describes spontaneous activity in neuronal cultures by taking into account the underlying network structure of neuronal cultures and use an accurate, yet simple, model for the individual neuronal dynamics. We show that the collective behavior of young cultures is dominated by the nucleation and propagations of activity fronts (bursts) throughout the system. These bursts nucleate at specific sites of the culture, called nucleation points, which result in a highly heterogeneous probability distribution of nucleation. We are able to explain the nucleation mechanism theoretically as a mechanism of noise propagation and amplification called noise focusing. We also explore the internal structure of activity avalanches by using well--defined regular networks, in which all the neurons have the same connectivity rules (motifs). Within these networks, we are able to associate to the avalanches an effective velocity and topological size and relate it to specific motifs. We also devise a continuum description of a neuronal culture at the mesoscale, i.e., we move away from the single neuron dynamics into a coarse--grained description that is able to capture most of the characteristic observables presented in previous chapters. This thesis also studies the spontaneous activity of neuronal cultures within the framework of quorum percolation. We study the effect of network structure within quorum percolation and propose a new model, called stochastic quorum percolation, that includes dynamics and the effect of internal noise. Finally, we use tools from information theory, namely transfer entropy, to show how to reliably infer the connectivity of a neuronal network from its activity, and how to distinguish between different excitatory and inhibitory connections purely from the activity, with no prior knowledge of the different neuronal types. The technique works directly on the fluorescence traces obtained in calcium imaging experiments, without the need to infer the underlying spike trains

Population Dynamics In A Model Closed Ecosystem

For almost any species in any environment, it is nearly impossible to predict its fitness from molecular knowledge. If fitness is not to be a mere tautology, reproducible measurements of the survival and reproduction of populations are needed over many generations. Laboratory microbial ecosystems afford the short time and length scales required for such measurements. Their conventional implementations, batch cultures with period refreshment of growth medium or chemostats with continuous refreshment, have a number of disadvantages, such as the introduction of additional frequencies, selection for surface growth and the distortion of chemical interactions. In closed ecosystems free energy is instead supplied as light, allowing for simpler, replicable protocols and a consistent interpretation of interactions, independent of their mode or timescale. Here, I describe a model closed ecosystem consisting of three singlecelled microbes, Escherichia coli, Chlamydomonas reinhardtii and Tetrahymena thermophila and show that these species can coexist for hundreds of days under closure. Using a custom built in situ fluorescence microscopy set up, the densities of these three species can be measured automatically and noninvasively over months with low classification error and large dynamical range. When kept under identical boundary conditions, these ecosystems reproducibly diverge in composition, with characteristic divergence times of ~20 days for T. thermophila, ~40 days for the other two species, and an approximately linear increase of an aggregate divergence measure over the first ~60 days. For two ecosystems, densities were measured continuously under constant conditions and their dynamics shown to be nonstationary for all three species \u3e100 days after closure. As a consequence, conventional time series methods assuming stationarity are inadequate and wavelet analysis is proposed as an alternative. Species-species interactions are further investigated using oscillations in illumination intensity. Densities of C. reinhardtii and, surprisingly, E. coli respond to modest perturbations of light intensity. Variation of the modulation frequency strongly implicates the circadian clock of C. reinhardtii in its response. The nonlinearity of the E. coli response suggests that it depends on C. reinhardtii density or spatial distribution rather than directly responds to the modulation of illumination. Further improvements in the detection of interactions are proposed

On Controllability of Artificial Intelligence

Invention of artificial general intelligence is predicted to cause a shift in the trajectory of human civilization. In order to reap the benefits and avoid pitfalls of such powerful technology it is important to be able to control it. However, possibility of controlling artificial general intelligence and its more advanced version, superintelligence, has not been formally established. In this paper, we present arguments as well as supporting evidence from multiple domains indicating that advanced AI can’t be fully controlled. Consequences of uncontrollability of AI are discussed with respect to future of humanity and research on AI, and AI safety and security. This paper can serve as a comprehensive reference for the topic of uncontrollability

Understanding Quantum Technologies 2022

Understanding Quantum Technologies 2022 is a creative-commons ebook that provides a unique 360 degrees overview of quantum technologies from science and technology to geopolitical and societal issues. It covers quantum physics history, quantum physics 101, gate-based quantum computing, quantum computing engineering (including quantum error corrections and quantum computing energetics), quantum computing hardware (all qubit types, including quantum annealing and quantum simulation paradigms, history, science, research, implementation and vendors), quantum enabling technologies (cryogenics, control electronics, photonics, components fabs, raw materials), quantum computing algorithms, software development tools and use cases, unconventional computing (potential alternatives to quantum and classical computing), quantum telecommunications and cryptography, quantum sensing, quantum technologies around the world, quantum technologies societal impact and even quantum fake sciences. The main audience are computer science engineers, developers and IT specialists as well as quantum scientists and students who want to acquire a global view of how quantum technologies work, and particularly quantum computing. This version is an extensive update to the 2021 edition published in October 2021.Comment: 1132 pages, 920 figures, Letter forma