GFF: Gated Fully Fusion for Semantic Segmentation

Semantic segmentation generates comprehensive understanding of scenes through densely predicting the category for each pixel. High-level features from Deep Convolutional Neural Networks already demonstrate their effectiveness in semantic segmentation tasks, however the coarse resolution of high-level features often leads to inferior results for small/thin objects where detailed information is important. It is natural to consider importing low level features to compensate for the lost detailed information in high-level features.Unfortunately, simply combining multi-level features suffers from the semantic gap among them. In this paper, we propose a new architecture, named Gated Fully Fusion (GFF), to selectively fuse features from multiple levels using gates in a fully connected way. Specifically, features at each level are enhanced by higher-level features with stronger semantics and lower-level features with more details, and gates are used to control the propagation of useful information which significantly reduces the noises during fusion. We achieve the state of the art results on four challenging scene parsing datasets including Cityscapes, Pascal Context, COCO-stuff and ADE20K.Comment: accepted by AAAI-2020(oral

Long-term in situ observations on typhoon-triggered turbidity currents in the deep sea

This work is supported by the National Science Foundation of China (grants 91528304, 41576005, and 41530964). We thank J. Li, X. Lyu, P. Li, K. Duan, J. Ronan, Y. Wang, P. Ma, and Y. Li for cruise assistance; G. de Lange and J. Hinojosa for editing an early version of manuscript; and E. Pope and two anonymous reviewers for their reviews.Peer reviewedPublisher PD

Rethinking Masked Language Modeling for Chinese Spelling Correction

In this paper, we study Chinese Spelling Correction (CSC) as a joint decision made by two separate models: a language model and an error model. Through empirical analysis, we find that fine-tuning BERT tends to over-fit the error model while under-fit the language model, resulting in poor generalization to out-of-distribution error patterns. Given that BERT is the backbone of most CSC models, this phenomenon has a significant negative impact. To address this issue, we are releasing a multi-domain benchmark LEMON, with higher quality and diversity than existing benchmarks, to allow a comprehensive assessment of the open domain generalization of CSC models. Then, we demonstrate that a very simple strategy, randomly masking 20\% non-error tokens from the input sequence during fine-tuning is sufficient for learning a much better language model without sacrificing the error model. This technique can be applied to any model architecture and achieves new state-of-the-art results on SIGHAN, ECSpell, and LEMON.Comment: Accepted by ACL'202

Tuning the Dzyaloshinskii-Moriya Interaction in Pt/Co/MgO heterostructures through MgO thickness

The interfacial Dzyaloshinskii-Moriya interaction (DMI) in the ferromagnetic/heavy metal ultra-thin film structures , has attracted a lot of attention thanks to its capability to stabilize Neel-type domain walls (DWs) and magnetic skyrmions for the realization of non-volatile memory and logic devices. In this study, we demonstrate that magnetic properties in perpendicularly magnetized Ta/Pt/Co/MgO/Pt heterostructures, such as magnetization and DMI, can be significantly influenced through both the MgO and the Co ultrathin film thickness. By using a field-driven creep regime domain expansion technique, we find that non-monotonic tendencies of DMI field appear when changing the thickness of MgO and the MgO thickness corresponding to the largest DMI field varies as a function of the Co thicknesses. We interpret this efficient control of DMI as subtle changes of both Pt/Co and Co/MgO interfaces, which provide a method to investigate ultra-thin structures design to achieve skyrmion electronics.Comment: 18 pages, 11 figure

Identification and characterization of bovine regulator of telomere length elongation helicase gene (RTEL): molecular cloning, expression distribution, splice variants and DNA methylation profile

BACKGROUND: The genetic basis of telomere length heterogeneity among mammalian species is still not well understood. Recently, a gene named regulator of telomere length elongation helicase (RTEL) was identified and predicted to be an essential participant in species-specific telomere length regulation in two murine species. To obtain broader insights into its structure and biological functions and to ascertain whether RTEL is also a candidate gene in the regulation of telomere length diversity in other mammalian species, data from other mammals may be helpful. RESULTS: Here we report the cDNA cloning, genomic structure, chromosomal location, alternative splicing pattern, expression distribution and DNA methylation profile of the bovine homolog of RTEL. The longest transcript of bovine RTEL is 4440 nt, encompassing 24.8 kb of genomic sequence that was mapped to chromosome 13q2.2. It encodes a conserved helicase-like protein containing seven characterized helicase motifs in the first 750 aa and a PIP box in the C-terminus. Four splice variants were identified within the transcripts in both the coding and 5'-untranslated regions; Western blot revealed that the most abundant splice variant SV-1 was translated to a truncated isoform of RTEL. The different 5'UTRs imply alternative transcription start sites in the promoter; Bovine RTEL was transcribed at the blastocyst stage, and expression levels were highest in adult testis, liver and ovary. DNA methylation analysis of tissues that differed significantly in expression level indicated that relatively low DNA methylation is associated with higher expression. CONCLUSION: In this study, we have identified and characterized a bovine RTEL homolog and obtained basic information about it, including gene structure, expression distribution, splice variants and profile of DNA methylation around two putative transcription start sites. These data may be helpful for further comparative and functional analysis of RTEL in mammals