The fact that the computational complexity of wavefield simulation is proportional to the size of the discretized model and acquisition geometry, and not to the complexity of the simulated wavefield, is a major impediment within seismic imaging. By turning simulation into a compressive sensing problem-where simulated data is recovered from a relatively small number of independent simultaneous sources-we remove this impediment by showing that compressively sampling a simulation is equivalent to compressively sampling the sources, followed by solving a reduced system. As in compressive sensing, this allows for a reduction in sampling rate and hence in simulation costs. We demonstrate this principle for the time-harmonic Helmholtz solver. The solution is computed by inverting the reduced system, followed by a recovery of the full wavefield with a sparsity promoting program.Depending on the wavefield's sparsity, this approach can lead to significant cost reductions, in particular when combined with the implicit preconditioned Helmholtz solver, which is known to converge even for decreasing mesh sizes and increasing angular frequencies. These properties make our scheme a viable alternative to explicit time-domain finite-differences.
An explicit algorithm for the extrapolation of one-way wavefields is proposed which combines recent developments in information theory and theoretical signal processing with the physics of wave propagation. Because of excessive memory requirements, explicit formulations for wave propagation have proven to be a challenge in 3-D. By using ideas from "compressed sensing", we are able to formulate the (inverse) wavefield extrapolation problem on small subsets of the data volume ,thereby reducing the size of the operators. Compressed sensing entails a new paradigm for signal recovery that provides conditions under which signals can be recovered from incomplete samplings by nonlinear recovery methods that promote sparsity of the to-be-recovered signal. According to this theory, signals can successfully be recovered when the measurement basis is incoherent with the representation in which the wavefield is sparse. In this new approach, the eigenfunctions of the Helmholtz operator are recognized as a basis that is incoherent with curvelets that are known to compress seismic
We present the incorporation of periodic gold nanoparticle arrays into graphene-based photodetectors to enhance and tune light absorption of graphene. By the use of electromagnetic simulations, we show that light absorption in graphene can be manipulated by tuning plasmonic resonance. A maximum absorption of 30.3% with a full width of 135 nm at half maximum is achieved through systematic optimization of nanostructures.
A recently proposed method called estimation of primaries by sparse inversion (EPSI) avoids the need for adaptive subtraction of approximate multiple predictions by directly inverting for the multiple-free subsurface impulse response as a collection of band-limited spikes. Although it can be shown that the correct primary impulse response is obtained through the sparsest possible solution, the original EPSI algorithm was not designed to take advantage of this result, and instead it relies on a multitude of inversion parameters, such as the level of sparsity per gradient update. We proposed and tested a new algorithm, named robust EPSI, in which we make obtaining the sparsest solution an explicit goal. Our approach remains a gradient-based approach like the original algorithm, but it is derived from a new biconvex optimization framework based on an extended basis-pursuit denoising formulation. Furthermore, because it is based on a general framework, robust EPSI can recover the impulse response in transform domains, such as sparsifying curvelet-based representations, without changing the underlying algorithm. We discovered that the sparsity-minimizing objective of our formulation enabled it to operate successfully on a variety of synthetic and field marine data sets without excessive tweaking of inversion parameters. We also found that recovering the solution in alternate sparsity domains can significantly improve the quality of the directly estimated primaries, especially for weaker late-arrival events. In addition, we found that robust EPSI produces a more artifact-free impulse response compared to the original algorithm.
Rett syndrome is a rare, genetic neurodevelopmental disorder. Trofinetide is a synthetic analog of glycine–proline–glutamate, the N-terminal tripeptide of the insulin-like growth factor 1 protein, and has demonstrated clinical benefit in phase 2 studies in Rett syndrome. In this phase 3 study (https://clinicaltrials.gov identifier NCT04181723), females with Rett syndrome received twice-daily oral trofinetide (n = 93) or placebo (n = 94) for 12 weeks. For the coprimary efficacy endpoints, least squares mean (LSM) change from baseline to week 12 in the Rett Syndrome Behaviour Questionnaire for trofinetide versus placebo was −4.9 versus −1.7 (P = 0.0175; Cohen’s d effect size, 0.37), and LSM Clinical Global Impression–Improvement at week 12 was 3.5 versus 3.8 (P = 0.0030; effect size, 0.47). For the key secondary efficacy endpoint, LSM change from baseline to week 12 in the Communication and Symbolic Behavior Scales Developmental Profile Infant–Toddler Checklist Social Composite score was −0.1 versus −1.1 (P = 0.0064; effect size, 0.43). Common treatment-emergent adverse events included diarrhea (80.6% for trofinetide versus 19.1% for placebo), which was mostly mild to moderate in severity. Significant improvement for trofinetide compared with placebo was observed for the coprimary efficacy endpoints, suggesting that trofinetide provides benefit in treating the core symptoms of Rett syndrome.
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