An investigation of synchronization robustness considering randomness and asymmetries

Synchronization phenomena are related to several natural systems defining patterns of interactions. This paper deals with a synchronization robustness investigation evaluating pattern formation on a network of pendulum-chart oscillators receiving energy from a base excitation. Random aspects are investigated establishing the sensitivity to parameter changes and initial conditions. System asymmetries are analysed allowing the investigation of different kinds of dynamical patterns. Results show that asynchronous regions can change due to random effects. The asynchronous region reduces with the dissipation increase and the chimera state can occur under parametric asymmetry. Energetic argues are employed to explain the pattern robustness with respect to randomness and asymmetries.

Social discourse and reopening after COVID-19

Although the COVID-19 pandemic has not been quenched yet, many countries lifted nationwide lockdowns to restart their economies, with citizens discussing the facets of reopening over social media. Investigating these online messages can open a window into people’s minds, unveiling their overall perceptions, their fears and hopes about the reopening. This window is opened and explored here for Italy, the first European country to adopt and release lockdown, by extracting key ideas and emotions over time from 400k Italian tweets about #fase2 — the reopening. Cognitive networks highlighted dynamical patterns of positive emotional contagion and inequality denounce invisible to sentiment analysis, in addition to a behavioural tendency for users to retweet either joyous or fearful content. While trust, sadness and anger fluctuated around quarantine-related concepts over time, Italians perceived politics and the government with a polarised emotional perception, strongly dominated by trust but occasionally featuring also anger and fear.

Chemical Ecosystem Selection on Mineral Surfaces Reveals Long-Term Dynamics Consistent with the Spontaneous Emergence of Mutual Catalysis

How did chemicals first become organized into systems capable of self-propagation and adaptive evolution? One possibility is that the first evolvers were chemical ecosystems localized on mineral surfaces and composed of sets of molecular species that could catalyze each other’s formation. We used a bottom-up experimental framework, chemical ecosystem selection (CES), to evaluate this perspective and search for surface-associated and mutually catalytic chemical systems based on the changes in chemistry that they are expected to induce. Here, we report the results of preliminary CES experiments conducted using a synthetic “prebiotic soup” and pyrite grains, which yielded dynamical patterns that are suggestive of the emergence of mutual catalysis. While more research is needed to better understand the specific patterns observed here and determine whether they are reflective of self-propagation, these results illustrate the potential power of CES to test competing hypotheses for the emergence of protobiological chemical systems.

Similarity Learning and Generalization with Limited Data: A Reservoir Computing Approach

We investigate the ways in which a machine learning architecture known as Reservoir Computing learns concepts such as “similar” and “different” and other relationships between image pairs and generalizes these concepts to previously unseen classes of data. We present two Reservoir Computing architectures, which loosely resemble neural dynamics, and show that a Reservoir Computer (RC) trained to identify relationships between image pairs drawn from a subset of training classes generalizes the learned relationships to substantially different classes unseen during training. We demonstrate our results on the simple MNIST handwritten digit database as well as a database of depth maps of visual scenes in videos taken from a moving camera. We consider image pair relationships such as images from the same class; images from the same class with one image superposed with noise, rotated 90°, blurred, or scaled; images from different classes. We observe that the reservoir acts as a nonlinear filter projecting the input into a higher dimensional space in which the relationships are separable; i.e., the reservoir system state trajectories display different dynamical patterns that reflect the corresponding input pair relationships. Thus, as opposed to training in the entire high-dimensional reservoir space, the RC only needs to learns characteristic features of these dynamical patterns, allowing it to perform well with very few training examples compared with conventional machine learning feed-forward techniques such as deep learning. In generalization tasks, we observe that RCs perform significantly better than state-of-the-art, feed-forward, pair-based architectures such as convolutional and deep Siamese Neural Networks (SNNs). We also show that RCs can not only generalize relationships, but also generalize combinations of relationships, providing robust and effective image pair classification. Our work helps bridge the gap between explainable machine learning with small datasets and biologically inspired analogy-based learning, pointing to new directions in the investigation of learning processes.

Revealing new dynamical patterns in a reaction–diffusion model with cyclic competition via a novel computational framework

Understanding how patterns and travelling waves form in chemical and biological reaction–diffusion models is an area which has been widely researched, yet is still experiencing fast development. Surprisingly enough, we still do not have a clear understanding about all possible types of dynamical regimes in classical reaction–diffusion models, such as Lotka–Volterra competition models with spatial dependence. In this study, we demonstrate some new types of wave propagation and pattern formation in a classical three species cyclic competition model with spatial diffusion, which have been so far missed in the literature. These new patterns are characterized by a high regularity in space, but are different from patterns previously known to exist in reaction–diffusion models, and may have important applications in improving our understanding of biological pattern formation and invasion theory. Finding these new patterns is made technically possible by using an automatic adaptive finite element method driven by a novel a posteriori error estimate which is proved to provide a reliable bound for the error of the numerical method. We demonstrate how this numerical framework allows us to easily explore the dynamical patterns in both two and three spatial dimensions.