398 research outputs found
Investigating the Transition Region Explosive Events and Their Relationship to Network Jets
Recent imaging observations with the Interface Region Imaging Spectrograp
(IRIS) have revealed prevalent intermittent jets with apparent speeds of
80--250 km~s from the network lanes in the solar transition region (TR).
On the other hand, spectroscopic observations of the TR lines have revealed the
frequent presence of highly non-Gaussian line profiles with enhanced emission
at the line wings, often referred as explosive events (EEs). Using simultaneous
imaging and spectroscopic observations from IRIS, we investigate the
relationship between EEs and network jets. We first identify EEs from the
Si~{\sc{iv}}~1393.755 {\AA} line profiles in our observations, then examine
related features in the 1330 {\AA} slit-jaw images. Our analysis suggests that
EEs with double peaks or enhancements in both wings appear to be located at
either the footpoints of network jets, or transient compact brightenings. These
EEs are most likely produced by magnetic reconnection. We also find that EEs
with enhancements only at the blue wing are mainly located on network jets,
away from the footpoints. These EEs clearly result from the superposition of
the high-speed network jets on the TR background. In addition, EEs showing
enhancement only at the red wing of the line are often located around the jet
footpoints, possibly caused by the superposition of reconnection downflows on
the background emission. Moreover, we find some network jets that are not
associated with any detectable EEs. Our analysis suggests that some EEs are
related to the birth or propagation of network jets, and that others are not
connected to network jets.Comment: 9 figures; to appear in Ap
Distilling Universal and Joint Knowledge for Cross-Domain Model Compression on Time Series Data
For many real-world time series tasks, the computational complexity of
prevalent deep leaning models often hinders the deployment on resource-limited
environments (e.g., smartphones). Moreover, due to the inevitable domain shift
between model training (source) and deploying (target) stages, compressing
those deep models under cross-domain scenarios becomes more challenging.
Although some of existing works have already explored cross-domain knowledge
distillation for model compression, they are either biased to source data or
heavily tangled between source and target data. To this end, we design a novel
end-to-end framework called Universal and joint knowledge distillation (UNI-KD)
for cross-domain model compression. In particular, we propose to transfer both
the universal feature-level knowledge across source and target domains and the
joint logit-level knowledge shared by both domains from the teacher to the
student model via an adversarial learning scheme. More specifically, a
feature-domain discriminator is employed to align teacher's and student's
representations for universal knowledge transfer. A data-domain discriminator
is utilized to prioritize the domain-shared samples for joint knowledge
transfer. Extensive experimental results on four time series datasets
demonstrate the superiority of our proposed method over state-of-the-art (SOTA)
benchmarks.Comment: Accepted by IJCAI 202
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