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$^{-1}$ 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