The Zwicky Transient Facility (ZTF), a public–private enterprise, is a new time-domain survey employing a dedicated camera on the Palomar 48-inch Schmidt telescope with a 47 deg2 field of view and an 8 second readout time. It is well positioned in the development of time-domain astronomy, offering operations at 10% of the scale and style of the Large Synoptic Survey Telescope (LSST) with a single 1-m class survey telescope. The public surveys will cover the observable northern sky every three nights in g and r filters and the visible Galactic plane every night in g and r. Alerts generated by these surveys are sent in real time to brokers. A consortium of universities that provided funding (“partnership”) are undertaking several boutique surveys. The combination of these surveys producing one million alerts per night allows for exploration of transient and variable astrophysical phenomena brighter than r ∼ 20.5 on timescales of minutes to years. We describe the primary science objectives driving ZTF, including the physics of supernovae and relativistic explosions, multi-messenger astrophysics, supernova cosmology, active galactic nuclei, and tidal disruption events, stellar variability, and solar system objects.
In the age of social media, faced with a huge amount of knowledge and information, accurate and effective keyphrase extraction methods are needed to be applied in information retrieval and natural language processing. It is difficult for traditional keyphrase extraction models to contain a large amount of external knowledge information, but with the rise of pre-trained language models, there is a new way to solve this problem. Based on the above background, we propose a new baseline for unsupervised keyphrase extraction based on pre-trained language model called SIFRank. SIFRank combines sentence embedding model SIF and autoregressive pre-trained language model ELMo, and it has the best performance in keyphrase extraction for short documents. We speed up SIFRank while maintaining its accuracy by document segmentation and contextual word embeddings alignment. For long documents, we upgrade SIFRank to SIFRank+ by position-biased weight, greatly improve its performance on long documents. Compared to other baseline models, our model achieves state-of-the-art level on three widely used datasets. INDEX TERMS Keyphrase extraction, pre-trained language model, sentence embeddings, position-biased weight, SIFRank. I. INTRODUCTION Keyphrase extraction is the task of selecting a set of words or phrases from a document that could summarize the main topics discussed in the document [1]. Keyphrase extraction can greatly accelerate the speed of information retrieval, help people get the first-hand information from a long text quickly and accurately. A. MOTIVATION Keyphrase Extraction can be divided into two main kinds of approaches: supervised and unsupervised. Supervised methods perform better on specific domain tasks, but it takes a lot of labor to annotate the corpus, and the model after training may overfit and do not work well on other datasets. The main traditional unsupervised methods are mainly divided into the models based on statistics and the models based on The associate editor coordinating the review of this manuscript and approving it for publication was Shuai Han .
Inhibition of the PDGF signal pathway results in loss of pericytes and a reduction in vessel density in advanced stage mouse corneal neovascularization. Furthermore, pericyte attenuation caused by blockade PDGF signaling can enhance the anti-angiogenesis efficacy of VEGF receptor inhibitor. Combined treatment against both endothelial cells and pericytes is required for advanced stage corneal new vessels.
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