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Register a new userModeling spatio-temporal nonlocality in mean-field dynamos
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When scale separation in space and time is poor, the alpha effect and turbulent diffusivity have to be replaced by integral kernels. Earlier work in computing these kernels using the test-field method is now generalized to the case in which both spatial and temporal scale separations are poor. The approximate form of the kernel is such that it can be treated in a straightforward manner by solving a partial differential equation for the mean electromotive force. The resulting mean-field equations are solved for oscillatory alpha-shear dynamos as well as alpha^2 dynamos in which alpha is antisymmetric about the equator, making this dynamo also oscillatory. In both cases, the critical values of the dynamo number is lowered by the fact that the dynamo is oscillatory.
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We study activity waves of the kind that determine cyclic magnetic activity of various stars, including the Sun, as a more general physical rather than a purely astronomical problem. We try to identify resonances which are expected to occur when a mean-field dynamo excites waves of quasi-stationary magnetic field in two distinct spherical layers. We isolate some features that can be associated with resonances in the profiles of energy or frequency plotted versus a dynamo governing parameter. Rather unexpectedly however the resonances in spherical dynamos take a much less spectacular form than resonances in many more familiar branches of physics. In particular, we find that the magnitudes of resonant phenomena are much smaller than seem detectable by astronomical observations, and plausibly any related effects in laboratory dynamo experiments (which of course are not in gravitating spheres!) are also small. We discuss specific features relevant to resonant phenomena in spherical dynamos, and find parametric resonance to be the most pronounced type of resonance phenomena. Resonance conditions for these dynamo wave resonances are rather different from those for more conventional branches of physics. We suggest that the relative insignificance of the phenomenon in this case is because the phenomena of excitation and propagation of the activity waves are not well-separated from each other and this, together with the nonlinear nature of more-or-less realistic dynamos, suppress the resonances and makes them much less pronounced than resonant effects, for example in optics.
This paper presents our solution to the AVA-Kinetics Crossover Challenge of ActivityNet workshop at CVPR 2021. Our solution utilizes multiple types of relation modeling methods for spatio-temporal action detection and adopts a training strategy to integrate multiple relation modeling in end-to-end training over the two large-scale video datasets. Learning with memory bank and finetuning for long-tailed distribution are also investigated to further improve the performance. In this paper, we detail the implementations of our solution and provide experiments results and corresponding discussions. We finally achieve 40.67 mAP on the test set of AVA-Kinetics.
Magnetic field diagnostics of the transition region from the chromosphere to the corona faces us with the problem that one has to apply extreme UV spectro-polarimetry. While for coronal diagnostic techniques already exist through infrared coronagraphy above the limb and radio observations on the disk, for the transition region one has to investigate extreme UV observations. However, so far the success of such observations has been limited, but there are various projects to get spectro-polarimetric data in the extreme UV in the near future. Therefore it is timely to study the polarimetric signals we can expect for such observations through realistic forward modeling. We employ a 3D MHD forward model of the solar corona and synthesize the Stokes I and Stokes V profiles of C IV 1548 A. A signal well above 0.001 in Stokes V can be expected, even when integrating for several minutes in order to reach the required signal-to-noise ratio, despite the fact that the intensity in the model is rapidly changing (just as in observations). Often this variability of the intensity is used as an argument against transition region magnetic diagnostics which requires exposure times of minutes. However, the magnetic field is evolving much slower than the intensity, and thus when integrating in time the degree of (circular) polarization remains rather constant. Our study shows the feasibility to measure the transition region magnetic field, if a polarimetric accuracy on the order of 0.001 can be reached, which we can expect from planned instrumentation.
Lip-reading aims to recognize speech content from videos via visual analysis of speakers lip movements. This is a challenging task due to the existence of homophemes-words which involve identical or highly similar lip movements, as well as diverse lip appearances and motion patterns among the speakers. To address these challenges, we propose a novel lip-reading model which captures not only the nuance between words but also styles of different speakers, by a multi-grained spatio-temporal modeling of the speaking process. Specifically, we first extract both frame-level fine-grained features and short-term medium-grained features by the visual front-end, which are then combined to obtain discriminative representations for words with similar phonemes. Next, a bidirectional ConvLSTM augmented with temporal attention aggregates spatio-temporal information in the entire input sequence, which is expected to be able to capture the coarse-gained patterns of each word and robust to various conditions in speaker identity, lighting conditions, and so on. By making full use of the information from different levels in a unified framework, the model is not only able to distinguish words with similar pronunciations, but also becomes robust to appearance changes. We evaluate our method on two challenging word-level lip-reading benchmarks and show the effectiveness of the proposed method, which also demonstrate the above claims.
The vision for smart city imperiously appeals to the implementation of Internet-of-Things (IoT), some features of which, such as massive access and bursty short packet transmissions, require new methods to enable the cellular system to seamlessly support its integration. Rigorous theoretical analysis is indispensable to obtain constructive insight for the networking design of massive access. In this paper, we propose and define the notion of massive and sporadic access (MSA) to quantitatively describe the massive access of IoT devices. We evaluate the temporal correlation of interference and successful transmission events, and verify that such correlation is negligible in the scenario of MSA. In view of this, in order to resolve the difficulty in any precise spatio-temporal analysis where complex interactions persist among the queues, we propose an approximation that all nodes are moving so fast that their locations are independent at different time slots. Furthermore, we compare the original static network and the equivalent network with high mobility to demonstrate the effectiveness of the proposed approximation approach. The proposed approach is promising for providing a convenient and general solution to evaluate and design the IoT network with massive and sporadic access.
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