We introduce scientific claim verification, a new task to select abstracts from the research literature containing evidence that SUP-PORTS or REFUTES a given scientific claim, and to identify rationales justifying each decision. To study this task, we construct SCI-FACT, a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts annotated with labels and rationales. We develop baseline models for SCIFACT, and demonstrate that simple domain adaptation techniques substantially improve performance compared to models trained on Wikipedia or political news. We show that our system is able to verify claims related to COVID-19 by identifying evidence from the CORD-19 corpus. Our experiments indicate that SCIFACT will provide a challenging testbed for the development of new systems designed to retrieve and reason over corpora containing specialized domain knowledge. Data and code for this new task are publicly available at https:// github.com/allenai/scifact. A leaderboard and COVID-19 fact-checking demo are available at https://scifact.apps. allenai.org. * Work performed during internship with the Allen Institute for Artificial Intelligence.More severe COVID-19 infection is associated with higher mean troponin (SMD 0.53, 95% CI 0.30 to 0.75, p < 0.001) Decision: SUPPORTS Claim Fact-checker Rationale CorpusCardiac injury is common in critical cases of COVID-19.Claim 1: Lopinavir / ritonavir have exhibited favorable clinical responses when used as a treatment for coronavirus. Supports: . . . Interestingly, after lopinavir/ritonavir (Kaletra, AbbVie) was administered, β-coronavirus viral loads significantly decreased and no or little coronavirus titers were observed. Refutes:The focused drug repurposing of known approved drugs (such as lopinavir/ritonavir) has been reported failed for curing SARS-CoV-2 infected patients. It is urgent to generate new chemical entities against this virus . . . Claim 2:The coronavirus cannot thrive in warmer climates. Supports: ...most outbreaks display a pattern of clustering in relatively cool and dry areas...This is because the environment can mediate human-to-human transmission of SARS-CoV-2, and unsuitable climates can cause the virus to destabilize quickly... Refutes: ...significant cases in the coming months are likely to occur in more humid (warmer) climates, irrespective of the climate-dependence of transmission and that summer temperatures will not substrantially limit pandemic growth.

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We introduce a large-scale dataset of math word problems and an interpretable neural math problem solver that learns to map problems to operation programs. Due to annotation challenges, current datasets in this domain have been either relatively small in scale or did not offer precise operational annotations over diverse problem types. We introduce a new representation language to model precise operation programs corresponding to each math problem that aim to improve both the performance and the interpretability of the learned models. Using this representation language, our new dataset, MathQA, significantly enhances the AQuA dataset with fully-specified operational programs. We additionally introduce a neural sequence-to-program model enhanced with automatic problem categorization. Our experiments show improvements over competitive baselines in our MathQA as well as the AQuA datasets. The results are still significantly lower than human performance indicating that the dataset poses new challenges for future research. Our dataset is available at: https: //math-qa.github.io/math-QA/.

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Zoom input and background shotZoom with new background Our Zoom plugin with new background Figure 1: Current video conferencing tools like Zoom can take an input feed (left) and replace the background, often introducing artifacts, as shown in the center result with close-ups of hair and glasses that still have the residual of the original background. Leveraging a frame of video without the subject (far left inset), our method produces real-time, high-resolution background matting without those common artifacts. The image on the right is our result with the corresponding close-ups, screenshot from our Zoom plugin implementation.

Our method produces robust and coherent results on all videos without requirements for trimaps or pre-captured backgrounds.

ABIN1, an important immune regulator, has been shown to be involved in various cellular functions, such as immunity, development, tissue homeostasis, and tumor progression. It inhibits TNF- and TLR-induced NF-κB signaling activation and the consequent gene expression. Despite its functional significance, the mechanism of ABIN1 in the regulation of various cellular functions remains unclear. In this study, we identified HDAC1, a key regulator of eukaryotic gene expression and many important cellular events, including cell proliferation, differentiation, cancer and immunity, as an interacting partner of ABIN1. The results showed that ABIN1 acted as a modulator to down-regulate HDAC1 ubiquitination via three different linkages, thereby stabilizing HDAC1 by inhibiting its lysosomal and proteasomal degradation. Interestingly, the inhibitory function of ABIN1 required direct binding with HDAC1. Moreover, the level of p53, which was a tumor suppressor and a well-studied substrate of HDAC1, was under the regulation of ABIN1 via the modulation of HDAC1 levels, suggesting that ABIN1 was physiologically significant in tumor progression. This study has revealed a new function of ABIN1 in mediating HDAC1 modification and stability.