Resonant waves drive long-range correlations in fMRI signals

The fundamental principles driving spontaneous long-range correlations between distant brain areas - known as intrinsic functional connectivity - remain unclear. To investigate this, we develop an ultrafast functional Magnetic Resonance Imaging (fMRI) approach with unprecedented temporal resolution (38 milliseconds) in the rat brain. We detect a repertoire of principal components exhibiting standing wave properties, i.e., with phase relationships varying gradually across space and oscillating in time, driving in- and anti-phase synchronization across distinct cortical and subcortical structures. The spatial configuration, stability and peak frequency of these standing waves is found to depend on the sedation/anaesthesia state, with medetomidine sedation revealing the most stable (i.e., less damped) standing waves, resonating at frequencies extending up to 0.25 Hz. Our findings show that the complex activity patterns observed in resting-state fMRI signals result from the superposition of standing waves, supporting the hypothesis that intrinsic functional connectivity is inherently associated to resonance phenomena.

Approximating the Steady-state Temperature of 3D Electronic Systems with Convolutional Neural Networks

Thermal simulations are an important part in the design of electronic systems, especially as systems with high power density become common. In simulation-based design approaches, a considerable amount of time is spent by repeated simulations. In this work, we present a proof-of-concept study of the application of convolutional neural networks to accelerate those thermal simulations. The goal is not to replace standard simulation tools but to provide a method to quickly select promising samples for more detailed investigations. Based on a training set of randomly generated circuits with corresponding Finite Element solutions, the full 3D steady-state temperature field is estimated using a fully convolutional neural network. A custom network architecture is proposed which captures the long-range correlations present in heat conduction problems. We test the network on a separate dataset and find that the mean relative error is around 2 % and the typical evaluation time is 35 ms per sample ( 2 ms for evaluation, 33 ms for data transfer). The benefit of this neural-network-based approach is that, once training is completed, the network can be applied to any system within the design space spanned by the randomised training dataset (which includes different components, material properties, different positioning of components on a PCB, etc.).

Tightly coupled inhibitory and excitatory functional networks in the developing primary visual cortex

Intracortical inhibition plays a critical role in shaping activity patterns in the mature cortex. However, little is known about the structure of inhibition in early development prior to the onset of sensory experience, a time when spontaneous activity exhibits long-range correlations predictive of mature functional networks. Here, using calcium imaging of GABAergic neurons in the ferret visual cortex, we show that spontaneous activity in inhibitory neurons is already highly organized into distributed modular networks before visual experience. Inhibitory neurons exhibit spatially modular activity with long-range correlations and precise local organization that is in quantitative agreement with excitatory networks. Furthermore, excitatory and inhibitory networks are strongly co-aligned at both millimeter and cellular scales. These results demonstrate a remarkable degree of organization in inhibitory networks early in the developing cortex, providing support for computational models of self-organizing networks and suggesting a mechanism for the emergence of distributed functional networks during development.

Long-Range Correlations and Natural Time Series Analyses from Acoustic Emission Signals

This work focuses on analyzing acoustic emission (AE) signals as a means to predict failure in structures. Two main approaches are considered: (i) long-range correlation analysis using both the Hurst (H) and the Detrended Fluctuation Analysis (DFA) exponents, and (ii) natural time domain (NT) analysis. These methodologies are applied to the data collected from two application examples: a glass fiber reinforced polymeric plate and a spaghetti bridge model, where both structures were subjected to increasing loads until collapse. A traditional (AE) signal analysis is also performed to reference the study of the other methods. Results indicate that the proposed methods yield a reliable indication of failure in the studied structures.

System scan of the multiplicity correlation between forward and backward rapidities in the relativistic heavy-ion collisions by using a multi-phase transport model

A systematic study on forward-backward (FB) multiplicity correlations from large systems to small ones through a multi-phase transport model (AMPT) has been performed and the phenomenon that correlation strength increases with the centrality can be explained by taking the distribution of events as the superposition of a series of Gaussian distributions. It is also found that correlations in the η - Φ plane can imply the shape of event. Furthermore, long range correlations originate from the fluctuations associated with the source information. FB correlations allow us to decouple long range correlations from short range correlations, and may provide a chance to investigate the $\alpha$-clustering structure in initial colliding light nuclei as well. It seems the tetrahedron $^{16}$O + $^{16}$O collision gives a more uniform and symmetrical fireball, after that emits the final particles more isotropically or independently in the longitudinal direction, indicating that the forward-backward multiplicity correlation could be used to identify the pattern of $\alpha$-clustered $^{16}$O in future experiments.

Statistical approaches to the problem of homogeneous melting of solids in the microcanonical ensemble

Melting is a common phenomenon in our daily life, and although it is understood in thermodynamic (macroscopic) terms, the transition itself has eluded a description from the point of view of microscopic dynamics. While there are studies of metastable states in classical spin Hamiltonians, cellular automata, glassy systems and other models, the statistical mechanical description of the microcanonical superheated solid state is lacking. Our work is oriented to the study of the melting process of superheated solids, which is believed to be caused by thermal vacancies in the crystal or by the occupation of interstitial sites. When the crystal reaches a critical temperature, it becomes unstable and a collective self-diffusion process is triggered. These studies are often observed in a microcanonical environment, revealing long-range correlations due to collective effects, and from theoretical models using random walks over periodic lattices. Our results suggest that the cooperative motion made possible by the presence of vacancy-interstitial pairs (Frenkel pairs) above the melting temperature can induce long-range effective interatomic forces even beyond the neighboring fourth layer. From microcanonical simulations it is also known that an ideal crystal needs a random waiting time until the solid phase collapses. Regarding this, our results also point towards a description of these waiting times using a statistical model in which there is a positive quantity X that accumulates from zero in incremental steps, until it exceeds a threshold value.