Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images

© 1982-2012 IEEE. Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. However, segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. Further, collecting a large amount of data is impractical within a short time period, inhibiting the training of a deep model. To address these challenges, a novel COVID-19 Lung Infection Segmentation Deep Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices. In our Inf-Net, a parallel partial decoder is used to aggregate the high-level features and generate a global map. Then, the implicit reverse attention and explicit edge-attention are utilized to model the boundaries and enhance the representations. Moreover, to alleviate the shortage of labeled data, we present a semi-supervised segmentation framework based on a randomly selected propagation strategy, which only requires a few labeled images and leverages primarily unlabeled data. Our semi-supervised framework can improve the learning ability and achieve a higher performance. Extensive experiments on our COVID-SemiSeg and real CT volumes demonstrate that the proposed Inf-Net outperforms most cutting-edge segmentation models and advances the state-of-the-art performance

Polarized electron-beam acceleration driven by vortex laser pulses

We propose a new approach based on an all-optical set-up for generating relativistic polarized electron beams via vortex Laguerre-Gaussian (LG) laser-driven wakefield acceleration. Using a pre-polarized gas target, we find that the topology of the vortex wakefield resolves the depolarization issue of the injected electrons. In full three-dimensional particle-in-cell simulations, incorporating the spin dynamics via the Thomas-Bargmann Michel Telegdi equation, the LG laser preserves the electron spin polarization by more than 80% at high beam charge and flux. The method releases the limit on beam flux for polarized electron acceleration and promises more than an order of magnitude boost in peak flux, as compared to Gaussian beams. These results suggest a promising table-top method to produce energetic polarized electron beams.Comment: We replace some results and revise some description

Can Programming Languages Boost Each Other via Instruction Tuning?

When human programmers have mastered a programming language, it would be easier when they learn a new programming language. In this report, we focus on exploring whether programming languages can boost each other during the instruction fine-tuning phase of code large language models. We conduct extensive experiments of 8 popular programming languages (Python, JavaScript, TypeScript, C, C++, Java, Go, HTML) on StarCoder. Results demonstrate that programming languages can significantly improve each other. For example, CodeM-Python 15B trained on Python is able to increase Java by an absolute 17.95% pass@1 on HumanEval-X. More surprisingly, we found that CodeM-HTML 7B trained on the HTML corpus can improve Java by an absolute 15.24% pass@1. Our training data is released at https://github.com/NL2Code/CodeM.Comment: Work in progres

Poly(ADP-ribosyl)ation of heterogeneous nuclear ribonucleoproteins modulates splicing

The biological functions of poly(ADP-ribosyl)ation of heterogeneous nuclear ribonucleoproteins (hnRNPs) are not well understood. However, it is known that hnRNPs are involved in the regulation of alternative splicing for many genes, including the Ddc gene in Drosophila. Therefore, we first confirmed that poly(ADP-ribose) (pADPr) interacts with two Drosophila hnRNPs, Squid/hrp40 and Hrb98DE/hrp38, and that this function is regulated by Poly(ADP-ribose) Polymerase 1 (PARP1) and Poly(ADP-ribose) Glycohydrolase (PARG) in vivo. These findings then provided a basis for analyzing the role of pADPr binding to these two hnRNPs in terms of alternative splicing regulation. Our results showed that Parg null mutation does cause poly(ADP-ribosyl)ation of Squid and hrp38 protein, as well as their dissociation from active chromatin. Our data also indicated that pADPr binding to hnRNPs inhibits the RNA-binding ability of hnRNPs. Following that, we demonstrated that poly(ADP-ribosyl)ation of Squid and hrp38 proteins inhibits splicing of the intron in the Hsrω-RC transcript, but enhances splicing of the intron in the Ddc pre-mRNA. Taken together, these findings suggest that poly(ADP-ribosyl)ation regulates the interaction between hnRNPs and RNA and thus modulates the splicing pathways