Non-commutative Geometry Modified Non-Gaussianities of Cosmological Perturbation

We investigate the noncommutative effect on the non-Gaussianities of primordial cosmological perturbation. In the lowest order of string length and slow-roll parameter, we find that in the models with small speed of sound the noncommutative modifications could be observable if assuming a relatively low string scale. In particular, the dominant modification of non-Gaussianity estimator f_{NL} could reach O(1) in DBI inflation and K-inflation. The corrections are sensitive to the speed of sound and the choice of string length scale. Moreover the shapes of the corrected non-Gaussianities are distinct from that of ordinary ones.Comment: 26 pages, 3 figures Added references, changed conten

Automated GNSS and Teleseismic Earthquake Inversion (AutoQuake Inversion) for Tsunami Early Warning: Retrospective and Real-Time Results

Rapid finite fault source determination is critical for reliable and robust tsunami early warnings. Near-field Global Navigation Satellite System (GNSS) observations have shown value to constrain the source inversion, but real-time GNSS stations are sparse along most of the active faults. Here we propose an automatic earthquake finite source inversion (AutoQuake Inversion) algorithm jointly using near-field (epicentral distance < 1000 km) GNSS data and mid-range (epicentral distance from 30° to 45°) teleseismic P displacement waveforms. Neither the near-field GNSS nor the mid-range teleseismic data clip or saturate during large earthquakes, while the fast-traveling P-waves are still essential to constrain the source in regions where very few or no GNSS stations are available. Real-time determination of the fault geometry remains to be the main challenge for rapid finite source inversion. We adopt a strategy to use the pre-defined geometry Slab2 for earthquakes within it or to forecast a focal mechanism based on near-by historical events for earthquakes without Slab2 prior. The algorithm has been implemented successfully in the prototype of JPL’s GPS-Aided Tsunami Early-Detection system and tested for many real events recently. This article provides the framework of the algorithm, documents the retrospective and real-time results, and discusses remaining challenges for future improvements

Advanced Wireless Communications and Mobile Computing Technologies for the Internet of Things

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Automated GNSS and Teleseismic Earthquake Inversion (AutoQuake Inversion) for Tsunami Early Warning: Retrospective and Real-Time Results

Rapid finite fault source determination is critical for reliable and robust tsunami early warnings. Near-field Global Navigation Satellite System (GNSS) observations have shown value to constrain the source inversion, but real-time GNSS stations are sparse along most of the active faults. Here we propose an automatic earthquake finite source inversion (AutoQuake Inversion) algorithm jointly using near-field (epicentral distance < 1000 km) GNSS data and mid-range (epicentral distance from 30° to 45°) teleseismic P displacement waveforms. Neither the near-field GNSS nor the mid-range teleseismic data clip or saturate during large earthquakes, while the fast-traveling P-waves are still essential to constrain the source in regions where very few or no GNSS stations are available. Real-time determination of the fault geometry remains to be the main challenge for rapid finite source inversion. We adopt a strategy to use the pre-defined geometry Slab2 for earthquakes within it or to forecast a focal mechanism based on near-by historical events for earthquakes without Slab2 prior. The algorithm has been implemented successfully in the prototype of JPL’s GPS-Aided Tsunami Early-Detection system and tested for many real events recently. This article provides the framework of the algorithm, documents the retrospective and real-time results, and discusses remaining challenges for future improvements

Optimal Computational Power Allocation in Multi-Access Mobile Edge Computing for Blockchain

Blockchain has emerged as a decentralized and trustable ledger for recording and storing digital transactions. The mining process of Blockchain, however, incurs a heavy computational workload for miners to solve the proof-of-work puzzle (i.e., a series of the hashing computation), which is prohibitive from the perspective of the mobile terminals (MTs). The advanced multi-access mobile edge computing (MEC), which enables the MTs to offload part of the computational workloads (for solving the proof-of-work) to the nearby edge-servers (ESs), provides a promising approach to address this issue. By offloading the computational workloads via multi-access MEC, the MTs can effectively increase their successful probabilities when participating in the mining game and gain the consequent reward (i.e., winning the bitcoin). However, as a compensation to the ESs which provide the computational resources to the MTs, the MTs need to pay the ESs for the corresponding resource-acquisition costs. Thus, to investigate the trade-off between obtaining the computational resources from the ESs (for solving the proof-of-work) and paying for the consequent cost, we formulate an optimization problem in which the MTs determine their acquired computational resources from different ESs, with the objective of maximizing the MTs’ social net-reward in the mining process while keeping the fairness among the MTs. In spite of the non-convexity of the formulated problem, we exploit its layered structure and propose efficient distributed algorithms for the MTs to individually determine their optimal computational resources acquired from different ESs. Numerical results are provided to validate the effectiveness of our proposed algorithms and the performance of our proposed multi-access MEC for Blockchain

Optimal Computational Power Allocation in Multi-Access Mobile Edge Computing for Blockchain

Blockchain has emerged as a decentralized and trustable ledger for recording and storing digital transactions. The mining process of Blockchain, however, incurs a heavy computational workload for miners to solve the proof-of-work puzzle (i.e., a series of the hashing computation), which is prohibitive from the perspective of the mobile terminals (MTs). The advanced multi-access mobile edge computing (MEC), which enables the MTs to offload part of the computational workloads (for solving the proof-of-work) to the nearby edge-servers (ESs), provides a promising approach to address this issue. By offloading the computational workloads via multi-access MEC, the MTs can effectively increase their successful probabilities when participating in the mining game and gain the consequent reward (i.e., winning the bitcoin). However, as a compensation to the ESs which provide the computational resources to the MTs, the MTs need to pay the ESs for the corresponding resource-acquisition costs. Thus, to investigate the trade-off between obtaining the computational resources from the ESs (for solving the proof-of-work) and paying for the consequent cost, we formulate an optimization problem in which the MTs determine their acquired computational resources from different ESs, with the objective of maximizing the MTs’ social net-reward in the mining process while keeping the fairness among the MTs. In spite of the non-convexity of the formulated problem, we exploit its layered structure and propose efficient distributed algorithms for the MTs to individually determine their optimal computational resources acquired from different ESs. Numerical results are provided to validate the effectiveness of our proposed algorithms and the performance of our proposed multi-access MEC for Blockchain

Obesity Paradox in Lung Diseases: What Explains It?

Background: Obesity is a globally increasing health problem that impacts multiple organ systems and a potentially modifiable risk factor for many diseases. Obesity has a significant impact on lung function and is strongly linked to the pathophysiology that contributes to lung diseases. On the other hand, reports have emerged that obesity is associated with a better prognosis than for normal weight individuals in some lung diseases, including pneumonia, acute lung injury/acute respiratory distress syndrome, chronic obstructive pulmonary disease, and lung cancer. The lesser mortality and better prognosis in patients with obesity is known as obesity paradox. While obesity paradox is both recognized and disputed in epidemiological studies, recent research has suggested possible mechanisms. Summary: In this review, we attempted to explain and summarize these factors and mechanisms, including immune response, pulmonary fibrosis, lung function, microbiota, fat and muscle reserves, which are significantly altered by obesity and may contribute to the obesity paradox in lung diseases. We also discuss contrary literature that attributes the “obesity paradox” to confounding. Key Messages: The review will illustrate the possible role of obesity in the prognosis or course of lung diseases, leading to a better understanding of the obesity paradox and provide hints for further basic and clinical research in lung diseases