Coupled ocean-atmosphere dynamics of the 2017 extreme coastal El Niño.

In March 2017, sea surface temperatures off Peru rose above 28 °C, causing torrential rains that affected the lives of millions of people. This coastal warming is highly unusual in that it took place with a weak La Niña state. Observations and ocean model experiments show that the downwelling Kelvin waves caused by strong westerly wind events over the equatorial Pacific, together with anomalous northerly coastal winds, are important. Atmospheric model experiments further show the anomalous coastal winds are forced by the coastal warming. Taken together, these results indicate a positive feedback off Peru between the coastal warming, atmospheric deep convection, and the coastal winds. These coupled processes provide predictability. Indeed, initialized on as early as 1 February 2017, seasonal prediction models captured the extreme rainfall event. Climate model projections indicate that the frequency of extreme coastal El Niño will increase under global warming

Transition between wurtzite and zinc-blende GaN: An effect of deposition condition of molecular-beam epitaxy

GaN exists in both wurtzite and zinc-blende phases and the growths of the two on its (0001) or (111) surfaces are achieved by choosing proper deposition conditions of molecular-beam epitaxy (MBE). At low substrate temperatures but high gallium fluxes, metastable zinc-blende GaN films are obtained, whereas at high temperatures and/or using high nitrogen fluxes, equilibrium wurtzite phase GaN epilayers resulted. This dependence of crystal structure on substrate temperature and source flux is not affected by deposition rate. Rather, the initial stage nucleation kinetics plays a primary role in determining the crystallographic structures of epitaxial GaN by MBE. © 2006 American Institute of Physics.published_or_final_versio

Growth mode and strain evolution during InN growth on GaN(0001) by molecular-beam epitaxy

The plasma-assisted molecular-beam epitaxy technique was used to study the epitaxial growth of InN on GaN. A relationship between film growth mode and the deposition condition was established by combining reflection high-energy electron diffraction (RHEED) and scanning tunneling microscopy (STM). The sustained RHEED intensity oscillations were recorded for 2D growth while 2D nucleation islands were revealed by STM. Results showed less than three oscillation periods for 3 D growth, indicating the Strnski-Krastanov (SK) growth mode of the film.published_or_final_versio

Semantics-Consistent Feature Search for Self-Supervised Visual Representation Learning

In contrastive self-supervised learning, the common way to learn discriminative representation is to pull different augmented "views" of the same image closer while pushing all other images further apart, which has been proven to be effective. However, it is unavoidable to construct undesirable views containing different semantic concepts during the augmentation procedure. It would damage the semantic consistency of representation to pull these augmentations closer in the feature space indiscriminately. In this study, we introduce feature-level augmentation and propose a novel semantics-consistent feature search (SCFS) method to mitigate this negative effect. The main idea of SCFS is to adaptively search semantics-consistent features to enhance the contrast between semantics-consistent regions in different augmentations. Thus, the trained model can learn to focus on meaningful object regions, improving the semantic representation ability. Extensive experiments conducted on different datasets and tasks demonstrate that SCFS effectively improves the performance of self-supervised learning and achieves state-of-the-art performance on different downstream tasks

Online Corrupted User Detection and Regret Minimization

In real-world online web systems, multiple users usually arrive sequentially into the system. For applications like click fraud and fake reviews, some users can maliciously perform corrupted (disrupted) behaviors to trick the system. Therefore, it is crucial to design efficient online learning algorithms to robustly learn from potentially corrupted user behaviors and accurately identify the corrupted users in an online manner. Existing works propose bandit algorithms robust to adversarial corruption. However, these algorithms are designed for a single user, and cannot leverage the implicit social relations among multiple users for more efficient learning. Moreover, none of them consider how to detect corrupted users online in the multiple-user scenario. In this paper, we present an important online learning problem named LOCUD to learn and utilize unknown user relations from disrupted behaviors to speed up learning, and identify the corrupted users in an online setting. To robustly learn and utilize the unknown relations among potentially corrupted users, we propose a novel bandit algorithm RCLUB-WCU. To detect the corrupted users, we devise a novel online detection algorithm OCCUD based on RCLUB-WCU's inferred user relations. We prove a regret upper bound for RCLUB-WCU, which asymptotically matches the lower bound with respect to $T$ up to logarithmic factors, and matches the state-of-the-art results in degenerate cases. We also give a theoretical guarantee for the detection accuracy of OCCUD. With extensive experiments, our methods achieve superior performance over previous bandit algorithms and high corrupted user detection accuracy