Magnitude estimation for early warning applications using the initial part of P waves: A case study on the 2008 Wenchuan sequence

A period parameter τ_c and an amplitude parameter Pd determined from the very beginning of P wave are important for earthquake early warning (EEW), yet their dependence on source mechanism, focal depth and epicentral distance has not been fully studied. After the devastating Mw7.9 Wenchuan earthquake, hundreds of M4-6 earthquakes occurred with diverse focal mechanisms and depth range of 2–20 km. We calculate τ_c and Pd of these aftershocks and examine their dependence on magnitude, τ_c, distance, and depth. We find that τ_c correlates well with magnitude, but joint regression including distance and depth does not significantly improve the correlation. The effect of focal mechanism on the τ_c-magnitude correlation is not obvious. When P wave is nodal, τ_c measurement becomes inaccurate. Also, τ_c is systematically greater for slow earthquakes, leading to a possible false alarm. Thus, more studies are required to discriminate slow earthquakes for robust early warning

Highly tunable spin-dependent electron transport through carbon atomic chains connecting two zigzag graphene nanoribbons

Motivated by recent experiments of successfully carving out stable carbon atomic chains from graphene, we investigate a device structure of a carbon chain connecting two zigzag graphene nanoribbons with highly tunable spin-dependent transport properties. Our calculation based on the non-equilibrium Green's function approach combined with the density functional theory shows that the transport behavior is sensitive to the spin configuration of the leads and the bridge position in the gap. A bridge in the middle gives an overall good coupling except for around the Fermi energy where the leads with anti-parallel spins create a small transport gap while the leads with parallel spins give a finite density of states and induce an even-odd oscillation in conductance in terms of the number of atoms in the carbon chain. On the other hand, a bridge at the edge shows a transport behavior associated with the spin-polarized edge states, presenting sharp pure $\alpha$-spin and $\beta$-spin peaks beside the Fermi energy in the transmission function. This makes it possible to realize on-chip interconnects or spintronic devices by tuning the spin state of the leads and the bridge position.Comment: 7 pages, 9 figure

Evolving Multi-Resolution Pooling CNN for Monaural Singing Voice Separation

Monaural Singing Voice Separation (MSVS) is a challenging task and has been studied for decades. Deep neural networks (DNNs) are the current state-of-the-art methods for MSVS. However, the existing DNNs are often designed manually, which is time-consuming and error-prone. In addition, the network architectures are usually pre-defined, and not adapted to the training data. To address these issues, we introduce a Neural Architecture Search (NAS) method to the structure design of DNNs for MSVS. Specifically, we propose a new multi-resolution Convolutional Neural Network (CNN) framework for MSVS namely Multi-Resolution Pooling CNN (MRP-CNN), which uses various-size pooling operators to extract multi-resolution features. Based on the NAS, we then develop an evolving framework namely Evolving MRP-CNN (E-MRP-CNN), by automatically searching the effective MRP-CNN structures using genetic algorithms, optimized in terms of a single-objective considering only separation performance, or multi-objective considering both the separation performance and the model complexity. The multi-objective E-MRP-CNN gives a set of Pareto-optimal solutions, each providing a trade-off between separation performance and model complexity. Quantitative and qualitative evaluations on the MIR-1K and DSD100 datasets are used to demonstrate the advantages of the proposed framework over several recent baselines

Learning Deep Robotic Skills on Riemannian manifolds

In this paper, we propose RiemannianFlow, a deep generative model that allows robots to learn complex and stable skills evolving on Riemannian manifolds. Examples of Riemannian data in robotics include stiffness (symmetric and positive definite matrix (SPD)) and orientation (unit quaternion (UQ)) trajectories. For Riemannian data, unlike Euclidean ones, different dimensions are interconnected by geometric constraints which have to be properly considered during the learning process. Using distance preserving mappings, our approach transfers the data between their original manifold and the tangent space, realizing the removing and re-fulfilling of the geometric constraints. This allows to extend existing frameworks to learn stable skills from Riemannian data while guaranteeing the stability of the learning results. The ability of RiemannianFlow to learn various data patterns and the stability of the learned models are experimentally shown on a dataset of manifold motions. Further, we analyze from different perspectives the robustness of the model with different hyperparameter combinations. It turns out that the model's stability is not affected by different hyperparameters, a proper combination of the hyperparameters leads to a significant improvement (up to 27.6%) of the model accuracy. Last, we show the effectiveness of RiemannianFlow in a real peg-in-hole (PiH) task where we need to generate stable and consistent position and orientation trajectories for the robot starting from different initial poses

A multiscale hybrid mathematical model of epidermal-dermal interactions during skin wound healing.

Following injury, skin activates a complex wound healing programme. While cellular and signalling mechanisms of wound repair have been extensively studied, the principles of epidermal-dermal interactions and their effects on wound healing outcomes are only partially understood. To gain new insight into the effects of epidermal-dermal interactions, we developed a multiscale, hybrid mathematical model of skin wound healing. The model takes into consideration interactions between epidermis and dermis across the basement membrane via diffusible signals, defined as activator and inhibitor. Simulations revealed that epidermal-dermal interactions are critical for proper extracellular matrix deposition in the dermis, suggesting these signals may influence how wound scars form. Our model makes several theoretical predictions. First, basal levels of epidermal activator and inhibitor help to maintain dermis in a steady state, whereas their absence results in a raised, scar-like dermal phenotype. Second, wound-triggered increase in activator and inhibitor production by basal epidermal cells, coupled with fast re-epithelialization kinetics, reduces dermal scar size. Third, high-density fibrin clot leads to a raised, hypertrophic scar phenotype, whereas low-density fibrin clot leads to a hypotrophic phenotype. Fourth, shallow wounds, compared to deep wounds, result in overall reduced scarring. Taken together, our model predicts the important role of signalling across dermal-epidermal interface and the effect of fibrin clot density and wound geometry on scar formation. This hybrid modelling approach may be also applicable to other complex tissue systems, enabling the simulation of dynamic processes, otherwise computationally prohibitive with fully discrete models due to a large number of variables

Extension rates across the northern Shanxi Grabens, China, from Quaternary geology, seismicity and geodesy

Discrepancies between geological, seismic and geodetic rates of strain can indicate that rates of crustal deformation, and hence seismic hazard, are varying through time. Previous studies in the northern Shanxi Grabens, at the northeastern corner of the Ordos Plateau in northern China, have found extension rates of anywhere between 0 and 6 mm a−1 at an azimuth of between 95° and 180°. In this paper we determine extension rates across the northern Shanxi Grabens from offset geomorphological features and a variety of Quaternary dating techniques (including new IRSL and Ar-Ar ages), a Kostrov summation using a 700 yr catalogue of historical earthquakes, and recent campaign GPS measurements. We observe good agreement between Quaternary, seismic and geodetic rates of strain, and we find that the northern Shanxi Grabens are extending at around 1–2 mm a−1 at an azimuth of ≈151°. The azimuth of extension is particularly well constrained and can be reliably inferred from catalogues of small earthquakes. We do not find evidence for any substantial variations in extension rate through time, though there is a notable seismic moment rate deficit since 1750. This deficit could indicate complex fault interactions across large regions, aseismic accommodation of deformation, or that we are quite late in the earthquake cycle with the potential for larger earthquakes in the relatively near future