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The classification of sleep stages plays a crucial role in understanding and diagnosing sleep pathophysiology. Sleep stage scoring relies heavily on visual inspection by an expert that is time consuming and subjective procedure. Recently, deep learning neural network approaches have been leveraged to develop a generalized automated sleep staging and account for shifts in distributions that may be caused by inherent inter/intra-subject variability, heterogeneity across datasets, and different recording environments. However, these networks ignore the connections among brain regions, and disregard the sequential connections between temporally adjacent sleep epochs. To address these issues, this work proposes an adaptive product graph learning-based graph convolutional network, named ProductGraphSleepNet, for learning joint spatio-temporal graphs along with a bidirectional gated recurrent unit and a modified graph attention network to capture the attentive dynamics of sleep stage transitions. Evaluation on two public databases: the Montreal Archive of Sleep Studies (MASS) SS3; and the SleepEDF, which contain full night polysomnography recordings of 62 and 20 healthy subjects, respectively, demonstrates performance comparable to the state-of-the-art (Accuracy: 0.867;0.838, F1-score: 0.818;0.774 and Kappa: 0.802;0.775, on each database respectively). More importantly, the proposed network makes it possible for clinicians to comprehend and interpret the learned connectivity graphs for sleep stages., Comment: 9 pages, 6 figures

Multiwalled carbon nanotubes (MWCNTs) are decorated with Pt nanoparticles by a "layer-by-layer" approach using poly (sodium 4-styrene sulfonate) (PSS) and poly (diallyl dimethylammonium chloride) (PDDA). Transmission electron microscopy (TEM) images and Energy Dispersive X-Ray (EDX) analysis of the samples confirm Pt deposition on surfaces of CNTs. Dispersibility and dispersion stability of MWCNTs in the solvents are enhanced when MWCNTs are coated with Pt nanoparticles. Mg AZ91 composites reinforced with MWCNTs are then produced by a melt stirring process. Compression tests of the composites show that adding 0.05\% wt Pt-coated MWCNTs in AZ91 improves the composite's mechanical properties compared to the pure AZ91 and pristine MWCNT/AZ91. Fracture surface analysis of the composite using a scanning electron microscope (SEM) shows individuals pulled out MWCNTs in the case of the Pt-coated MWCNT/AZ91 composites. We attribute this finding to the uniform dispersion of Pt-coated MWCNTs in Mg due to the improved wettability of Pt-coated MWCNTs in Mg melts. Molecular dynamics (MD) simulations of the interaction between Pt-coated MWCNTs and Mg support this interpretation., Comment: 20 pages,7 figures, one table

This paper describes atomistic simulations of deformation and fracture of Al reinforced with carbon nanotubes (CNTs). We use density functional theory (DFT) to understand the energetics of Al-graphene interfaces and gain reference data for the parameterization of Al-C empirical potentials. We then investigate the load transfer between CNTs and Al and its effect on composite strengthening. To this end, we perform uniaxial tensile simulations of an Al crystal reinforced with CNTs of various volume fractions. We also study the interaction of the embedded CNTs with a crack. We show that the interaction between CNTs and Al is weak such that, under tensile loading, CNTs can easily slide inside the Al matrix and get pulled out from the cracked surface. This effect is almost independent of CNT length and volume fraction. Little load transfer and consequently no crack bridging are observed during the simulation of pristine CNTs threading the crack surfaces. CNTs that are geometrically fixated inside Al, on the other hand, can increase the fracture stress and enhance plastic dissipation in the matrix. CNTs located in front of a growing crack blunt the crack and induce plastic deformation of the Al matrix. Depending on the CNT orientation, these processes can either increase or decrease the failure stress of the composite., Comment: 19 pages, 9 figures, two tables

In recent years there has been renewed interest in the behavior of dislocations in crystals that exhibit strong atomic scale disorder, as typical of compositionally complex single phase alloys. The behavior of dislocations in such crystals has been often studied in the framework of elastic manifold pinning in disordered systems. Here we discuss modifications of this framework that may need to be adapted when dealing with extended dislocations that split into widely separated partials. We demonstrate that the presence of a stacking fault gives rise to an additional stress scale that needs to be compared with the pinning stress of elastic manifold theory to decide whether the partials are pinned individually or the dislocation is pinned as a whole. For the case of weakly interacting partial dislocations, we demonstrate the existence of multiple metastable states at stresses below the depinning threshold and analyze the stress evolution of the stacking fault width during loading. In addition we investigate how geometrical constraints can modulate the dislocation-solute interaction and enhance the pinning stress. We compare our theoretical arguments with results of atomistic and discrete (partial) dislocation dynamics (D(P)DD) simulations.

The interaction between edge dislocations and carbon nanotubes (CNTs) embedded into an Al matrix is investigated. Both pristine and Ni-coated CNTs are considered. It is demonstrated that the embedded CNTs lead to a flow stress contribution equivalent to that of an array of non-shearable inclusions that are by-passed via the Orowan mechanism. The strain hardening mechanism associated with multiple dislocation passes shows, however, characteristic differences from the Orowan picture that indicate that the embedded CNTs behave analogous to embedded voids., Comment: 18 pages, 10 figures, 1 table

The brain must extract behaviorally relevant latent variables from the signals streamed by the sensory organs. Such latent variables are often encoded in the dynamics that generated the signal rather than in the specific realization of the waveform. Therefore, one problem faced by the brain is to segment time series based on underlying dynamics. We present two algorithms for performing this segmentation task that are biologically plausible, which we define as acting in a streaming setting and all learning rules being local. One algorithm is model-based and can be derived from an optimization problem involving a mixture of autoregressive processes. This algorithm relies on feedback in the form of a prediction error, and can also be used for forecasting future samples. In some brain regions, such as the retina, the feedback connections necessary to use the prediction error for learning are absent. For this case, we propose a second, model-free algorithm that uses a running estimate of the autocorrelation structure of the signal to perform the segmentation. We show that both algorithms do well when tasked with segmenting signals drawn from autoregressive models with piecewise-constant parameters. In particular, the segmentation accuracy is similar to that obtained from oracle-like methods in which the ground-truth parameters of the autoregressive models are known. We also test our methods on datasets generated by alternating snippets of voice recordings. We provide implementations of our algorithms at https://github.com/ttesileanu/bio-time-series., Comment: v2.1; 34 pages, 14 figures

We report results of a multiscale simulation study of multilayer structures consisting of graphene sheets with embedded Pt nanoparticles. Density functional theory is used to understand the energetics of Pt-graphene interfaces and provide reference data for the parameterization of a Pt-graphene interaction potential. Molecular dynamics simulations then provide the conformation and energetics of graphene sheets with embedded Pt nanoparticles of varying density, form and size. These results are interpreted using a continuum mechanical model of sheet deformation, and serve to parameterize a meso-scale Monte Carlo model to investigate the question under which conditions the free volume around the Pt nanoparticles forms a percolating cluster, such that the structures can be used in catalytic applications. We conclude with a discussion of potential applications of such multilayer structures., Comment: 21 pages, 10 figures, 2 tables, one supplementary information file

In this work we use density functional theory (DFT) calculations to benchmark empirical potentials for the interaction between nickel and sp$^2$ bonded carbon nanoparticles. These potentials are then used in order to investigate how Ni decorated or coated carbon nanotubes (CNT) affect the mechanical properties of Al/CNT composites. In particular we look at the pull-out behaviour of pristine as well as Ni-decorated and Ni-coated CNT from an Al matrix. Our result shows that Ni coating may produce an extended interface (interphase) where a significant amount of energy is dissipated during CNT pull-out, leading to a high pull-out force. We also demonstrate that surface decorated CNT may act as efficient nano-crystallization agents and thus provide a novel strengthening mechanism not previously discussed in the literature. We discuss our results in view of promising approaches for engineering CNT-metal interfaces such as to achieve high strength metal-CNT composite., Comment: 17 pages, 9 figures, 3 tables