Search

Magnetic reconnection and turbulence are the two most important energy dissipation processes in plasma. These two processes intertwine with each other and play important roles in their respective dynamic evolution. Here, we present the first evidence that secondary reconnections occur in the turbulent outflow driven by a primary reconnection in the Earth's magnetotail. We have identified 14 secondary reconnections in a large number of current filaments in the turbulent outflow, which persisted for about one and half an hour. Most of these secondary reconnections were electron-only reconnection that has recently been discovered in the magnetosheath. These secondary reconnections entangled the magnetic field lines and dissipated the magnetic energy in the outflow region far away from the primary X line.

We investigate whistler-mode waves and broadband electrostatic waves (EWs) within an ion diffusion region (IDR) at the magnetopause. The quasi-parallel whistlers are observed in the separatrix regions associated with the electron anisotropy or loss cone, while the oblique whistlers, the Buneman-type waves, and the oblique EWs are observed in the center of the current sheet associated with the accelerated electron or ion beams. The whistlers are linked with Buneman-type waves by the electron Pacman distribution rather than the wave-wave process. The accelerated cold electron beams excite the Buneman-type instabilities and make the anisotropy or loss cone of hot electrons less apparent, which led to the conversion of the whistlers from quasi-parallel to oblique. Additionally, the oblique EWs are associated with the ion beams produced by the multiple X-line reconnection. These results provide a further understanding of the relation between plasma waves and plasma kinetics in the IDR.

Two-dimensional Hall reconnection model can explain the fast release of magnetic energy and most of its predictions have been demonstrated by in situ satellite observations. However, the three-dimensional effect to reconnection remains poorly known. Recent numerical simulations have shown that three-dimensional evolution is more complex than two-dimensional in that reconnection occurs in multiple sites that are not necessarily in the primary neutral current sheet. Here, we present the first observational evidence for localized secondary reconnection at the separatrix surface of a magnetic flux rope (MFR). This secondary reconnection occurs between the axial magnetic field of the MFR, which points out-of-plane of the Earth's magnetopause reconnection, and the magnetospheric field. This three-dimensional electron-scale reconnection in the exhaust facilitates the cross-scale energy conversion from the macro-scale down to electron-scale.

This work considers the problem of unsupervised domain adaptation in person re-identification (re-ID), which aims to transfer knowledge from the source domain to the target domain. Existing methods are primary to reduce the inter-domain shift between the domains, which however usually overlook the relations among target samples. This paper investigates into the intra-domain variations of the target domain and proposes a novel adaptation framework w.r.t three types of underlying invariance, i.e., Exemplar-Invariance, Camera-Invariance, and Neighborhood-Invariance. Specifically, an exemplar memory is introduced to store features of samples, which can effectively and efficiently enforce the invariance constraints over the global dataset. We further present the Graph-based Positive Prediction (GPP) method to explore reliable neighbors for the target domain, which is built upon the memory and is trained on the source samples. Experiments demonstrate that 1) the three invariance properties are complementary and indispensable for effective domain adaptation, 2) the memory plays a key role in implementing invariance learning and improves the performance with limited extra computation cost, 3) GPP can facilitate the invariance learning and thus significantly improves the results, and 4) our approach produces new state-of-the-art adaptation accuracy on three re-ID large-scale benchmarks.

In this paper, we tackle the problem of discovering new classes in unlabeled visual data given labeled data from disjoint classes. Existing methods typically first pre-train a model with labeled data, and then identify new classes in unlabeled data via unsupervised clustering. However, the labeled data that provide essential knowledge are often underexplored in the second step. The challenge is that the labeled and unlabeled examples are from non-overlapping classes, which makes it difficult to build a learning relationship between them. In this work, we introduce Open-Mix to mix the unlabeled examples from an open set and the labeled examples from known classes, where their non-overlapping labels and pseudo-labels are simultaneously mixed into a joint label distribution. OpenMix dynamically compounds examples in two ways. First, we produce mixed training images by incorporating labeled examples with unlabeled examples. With the benefit of unique prior knowledge in novel class discovery, the generated pseudo-labels will be more credible than the original unlabeled predictions. As a result, OpenMix helps preventing the model from overfitting on unlabeled samples that may be assigned with wrong pseudo-labels. Second, the first way encourages the unlabeled examples with high class-probabilities to have considerable accuracy. We introduce these examples as reliable anchors and further integrate them with un-labeled samples. This enables us to generate more combinations in unlabeled examples and exploit finer object relations among the new classes. Experiments on three classification datasets demonstrate the effectiveness of the proposed OpenMix, which is superior to state-of-the-art methods in novel class discovery.