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Volcanic eruptions provide broad spectral forcing to the atmosphere and understanding the primary mechanisms that are relevant to explain the variety in waveform characteristics in the Ionosphere‐Thermosphere (IT) is still an important open question for the community. In this study, Global Navigation Satellite System (GNSS) Total Electron Content (TEC) data are analyzed and compared to simulations performed by the Global Ionosphere‐Thermosphere Model with Local Mesh Refinement (GITM‐R) for the first phase of the 2015 Calbuco eruption that occurred on 22 April. A simplified source representation and spectral acoustic‐gravity wave (AGW) propagation model are used to specify the perturbation at the lower boundary of GITM‐R at 100 km altitude. Two assumptions on the propagation structure, Direct Spherical (DS) and Ground Coupled (GC), are compared to the GNSS data and these modeling specifications show good agreement with different aspects of the observations for some waveform characteristics. Most notably, GITM‐R is able to reproduce the relative wave amplitude of AGWs as a function of radial distance from the vent, showing acoustic dominant forcing in the near field (<500 km) and gravity dominant forcing in the far‐field (>500 km). The estimated apparent phase speeds from GITM‐R simulations are consistent with observations with ∼10% difference from observation for both acoustic wave packets and a trailing gravity mode. The relevance of the simplifications made in the lower atmosphere to the simulated IT response is then discussed.

 

Geomagnetic storms transfer massive amounts of energy from the sun to geospace. Some of that energy is dissipated in the ionosphere as energetic particles precipitate and transfer their energy to the atmosphere, creating the aurora. We used the Time History of Events and Macroscale Interactions during Substorms (THEMIS) mosaic of all‐sky‐imagers across Canada and Alaska to measure the amount of energy flux deposited into the ionosphere via auroral precipitation during the 2013 March 17 storm. We determined the time‐dependent percent of the total energy flux that is contributed by meso‐scale (<500 km wide) auroral features, discovering they contribute up to 80% during the sudden storm commencement (SSC) and >∼40% throughout the main phase, indicating meso‐scale dynamics are important aspects of a geomagnetic storm. We found that average conductance was higher north of 65° until SYM‐H reached −40 nT. We also found that the median conductance was higher in the post‐midnight sector during the SSC, though localized conductance peaks (sometimes >75 mho) were much higher in the pre‐midnight sector throughout. We related the post‐midnight/pre‐dawn conductance to other recent findings regarding meso‐scale dynamics in the literature. We found sharp conductance peaks and gradients in both time and space related to meso‐scale aurora. Data processing included a new moonlight removal algorithm and cross‐instrument calibration with a meridian scanning photometer and a standard photometer. We compared ASI results to Poker Flat Incoherent Scatter Radar (PFISR) observations, finding energy flux, mean energy, and Hall conductance were highly correlated, moderately correlated, and highly correlated, respectively.

 

We report the results of thermodynamic measurements in external magnetic field of the cubic Ce-based cage compounds CeT₂Cd₂₀(T= Ni,Pd). Our analysis of the heat-capacity data shows that the Γ₇doublet is the ground state multiplet of the Ce³⁺ions. Consequently, for the Γ₇doublet it can be theoretically shown that the Ruderman–Kittel–Kasuya–Yosida interaction between the localized Ce moments mediated by the conduction electrons, must vanish at temperatures much lower than the energy separating the ground state doublet from the first excited Γ₈quartet. Our findings provide an insight as to why no long range order has been observed in these compounds down to temperatures in the milliKelvin range.

 

In this paper we prove that Local (S)GD (or FedAvg) can optimize deep neural networks with Rectified Linear Unit (ReLU) activation function in polynomial time. Despite the established convergence theory of Local SGD on optimizing general smooth functions in communication-efficient distributed optimization, its convergence on non-smooth ReLU networks still eludes full theoretical understanding. The key property used in many Local SGD analysis on smooth function is gradient Lipschitzness, so that the gradient on local models will not drift far away from that on averaged model. However, this decent property does not hold in networks with non-smooth ReLU activation function. We show that, even though ReLU network does not admit gradient Lipschitzness property, the difference between gradients on local models and average model will not change too much, under the dynamics of Local SGD. We validate our theoretical results via extensive experiments. This work is the first to show the convergence of Local SGD on non-smooth functions, and will shed lights on the optimization theory of federated training of deep neural networks. 

An approach for creating continental‐scale, multi‐scale plasma convection maps in the nightside high‐latitude ionosphere using the spherical elementary current systems technique has been developed and evaluated. The capability to reconstruct meso‐scale flow channels improved dramatically, and the velocity errors were reduced by ∼30% compared to the spherical harmonic fitting method. Uncertainties of velocity vectors estimated by varying the model setup was also low. Convection maps for a substorm event revealed multiple flow channels in the polar cap, dominating the convection in the quiet time and early growth phase. The meso‐scale flows extended toward the nightside auroral oval and had continuous flow channels over >20° of latitude, and the flow channels dynamically merged and bifurcated. The substorm onset occurred along one of the flow channels, and the azimuthal extent of the enhanced flows coincided with the initial width of the auroral breakup. During the expansion phase, the meso‐scale flows repetitively crossed the oval poleward boundary, and some of them contributed to subauroral polarization streams enhancements. Increased flows extended duskward, along with the westward traveling surge. Then, flows near midnight weakened and evolved to the Harang flow shear. The meso‐scale flow channels had significant (∼10%–40% on average) contributions to the total plasma transport. The meso‐scale flows were highly variable on ∼10 min time scales and their individual maximum contributions reached upto 73%. These results demonstrate the capability of specifying realistic convection patterns, quantifying the contribution of meso‐scale transport, and evaluating the relationship between meso‐scale flows and localized auroral forms.

 

Despite the established convergence theory of Optimistic Gradient Descent Ascent (OGDA) and Extragradient (EG) methods for the convex-concave minimax problems, little is known about the theoretical guarantees of these methods in nonconvex settings. To bridge this gap, for the first time, this paper establishes the convergence of OGDA and EG methods under the nonconvex-strongly-concave (NC-SC) and nonconvex-concave (NC-C) settings by providing a unified analysis through the lens of single-call extra-gradient methods. We further establish lower bounds on the convergence of GDA/OGDA/EG, shedding light on the tightness of our analysis. We also conduct experiments supporting our theoretical results. We believe our results will advance the theoretical understanding of OGDA and EG methods for solving complicated nonconvex minimax real-world problems, e.g., Generative Adversarial Networks (GANs) or robust neural networks training. 

Gaussian process (GP) is a popular machine learning technique that is widely used in many application domains, especially in robotics. However, GP is very computation intensive and time consuming during the inference phase, thereby bringing severe challenges for its large-scale deployment in real-time applications. In this paper, we propose two efficient hardware architecture for GP accelerator. One architecture targets for general GP inference, and the other architecture is specifically optimized for the scenario when the data point is gradually observed. Evaluation results show that the proposed hardware accelerator provides significant hardware performance improvement than the general-purpose computing platform. 

Binary granular soil mixtures, as common heterogeneous soils, are ubiquitous in nature and man-made deposits. Fines content and particle size ratio are two important gradation parameters for a binary mixture, which have potential influences on mechanical behaviours. However, experimental studies on drained shear behaviour considering the whole range of fines content and different particle size ratios are scarce in the literature. For this purpose, we performed a series of drained triaxial compression tests on dense binary silica sand mixtures with 4 different particle size ratios to systematically investigate the effects of fines content and particle size ratio on the drained shear behaviours. Based on these tests, the strength-dilation behaviour and critical state behaviour were examined. It was observed that both fines content and particle size ratio have significant influence on the stress-strain response, the critical state void ratio, the critical state friction angle, the maximum dilation angle, the peak friction angle, and the strength–dilatancy relation. The underlying mechanism for the effects of fines content and particle size ratio was discussed from the perspective of the kinematic movements at particle level.