The increasing concern with misinformation has stimulated research efforts on automatic fact checking. The recentlyreleased FEVER dataset introduced a benchmark factverification task in which a system is asked to verify a claim using evidential sentences from Wikipedia documents. In this paper, we present a connected system consisting of three homogeneous neural semantic matching models that conduct document retrieval, sentence selection, and claim verification jointly for fact extraction and verification. For evidence retrieval (document retrieval and sentence selection), unlike traditional vector space IR models in which queries and sources are matched in some pre-designed term vector space, we develop neural models to perform deep semantic matching from raw textual input, assuming no intermediate term representation and no access to structured external knowledge bases. We also show that Pageview frequency can also help improve the performance of evidence retrieval results, that later can be matched by using our neural semantic matching network. For claim verification, unlike previous approaches that simply feed upstream retrieved evidence and the claim to a natural language inference (NLI) model, we further enhance the NLI model by providing it with internal semantic relatedness scores (hence integrating it with the evidence retrieval modules) and ontological WordNet features. Experiments on the FEVER dataset indicate that (1) our neural semantic matching method outperforms popular TF-IDF and encoder models, by significant margins on all evidence retrieval metrics, (2) the additional relatedness score and WordNet features improve the NLI model via better semantic awareness, and (3) by formalizing all three subtasks as a similar semantic matching problem and improving on all three stages, the complete model is able to achieve the state-of-the-art results on the FEVER test set (two times greater than baseline results). 1 2 The task is often termed as natural language inference (NLI).

Background: Since the first case of a coronavirus disease 2019 (COVID-19) infection pneumonia was detected in Wuhan, China, a series of confirmed cases of the COVID-19 were found in Southwest China. The aim of this study was to describe the imaging manifestations of hospitalized patients with confirmed COVID-19 infection in southwest China. Methods: In this retrospective study, data were collected from 131 patients with confirmed coronavirus disease 2019 (COVID-19) from 3 Chinese hospitals. Their common clinical manifestations, as well as characteristics and evolvement features of chest CT images, were analyzed. Results: A total of 100 (76%) patients had a history of close contact with people living in Wuhan , Hubei. The clinical manifestations of COVID-19 included cough, fever. Most of the lesions identified in chest CT images were multiple lesions of bilateral lungs, lesions were more localized in the peripheral lung, 109 (83%) patients had more than two lobes involved, 20 (15%) patients presented with patchy ground glass opacities, patchy ground glass opacities and consolidation of lesions co-existing in 61 (47%) cases. Complications such as pleural thickening, hydrothorax, pericardial effusion, and enlarged mediastinal lymph nodes were detected but only in rare cases. For the follow-up chest CT examinations (91 cases), We found 66 (73%) cases changed very quickly, with an average of 3.5 days, 25 cases (27%) presented absorbed lesions, progression was observed in 41 cases (46%), 25 (27%) cases showed no significant changes. Conclusion: Chest CT plays an important role in diagnosing COVID-19. The imaging pattern of multifocal peripheral ground glass or mixed consolidation is highly suspicious of COVID-19, that can quickly change over a short period of time.

Results:A total of 100 (76%) patients had a history of close contact with people living in Wuhan, Hubei. The clinical manifestations of COVID-19 included cough, fever. Most of the lesions identified in chest CT images were multiple lesions of bilateral lungs, lesions were more localized in the peripheral lung, 109 (83%) patients had more than two lobes involved, 20 (15%) patients presented with patchy ground glass opacities, patchy ground glass opacities and consolidation of lesions co-existing in 61 (47%) cases. Complications such as pleural thickening, hydrothorax, pericardial effusion, and enlarged mediastinal lymph nodes were detected but only in rare cases. For the follow-up chest CT examinations (91 cases), We found 66 (73%) cases changed very quickly, with an average of 3.5 days, 25 cases (27%) presented absorbed lesions, progression was observed in 41 cases (46%), 25 (27%) cases showed no significant changes. Conclusion:Chest CT plays an important role in diagnosing COVID-19. The imaging pattern of multifocal peripheral ground glass or mixed consolidation is highly suspicious of COVID-19, that can quickly change over a short period of time.

The development of porous metal−organic framework (MOF) solids displaying efficient separation and purification of acetylene is of cardinal significance but challenging in the chemical industry. Among the reported MOFs for such a purpose, there usually exists an issue associated with trade-off between the uptake capacity and adsorption selectivity. In this work, we employed an N-oxide-functionalized dicarboxylate ligand to successfully construct under suitable solvothermal conditions a dicopper paddlewheel-based MOF featuring two different types of nanocages and rich open oxygen atoms on the channel surface. These structural features endow the material with the promising potential for C 2 H 2 recovery from CO 2 and CH 4 at ambient conditions with impressive adsorption selectivity of C 2 H 2 over CO 2 and CH 4 as well as considerable C 2 H 2 capture capacity, which have been validated by isotherm measurements, ideal adsorbed solution theory calculations, and breakthrough experiments. Furthermore, molecular modeling studies revealed the vital role that the oxygen atoms coming from both N-oxide moieties and carboxylate groups play in selectively recognizing C 2 H 2 over CO 2 and CH 4 . KEYWORDS: metal−organic frameworks, C 2 H 2 separation and purification, C 2 H 2 /CO 2 separation, gas separation, N-oxide

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Article history: Available online xxxx Keywords: CASA Radar WSR-88DP QPE Urban flooding DFW s u m m a r yThe Dallas-Fort Worth (DFW) urban radar network consists of a combination of high resolution X band radars and a standard National Weather Service (NWS) Next-Generation Radar (NEXRAD) system operating at S band frequency. High spatiotemporal-resolution quantitative precipitation estimation (QPE) is one of the important applications of such a network. This paper presents a real-time QPE system developed by the Collaborative Adaptive Sensing of the Atmosphere (CASA) Engineering Research Center for the DFW urban region using both the high resolution X band radar network and the NWS S band radar observations. The specific dual-polarization radar rainfall algorithms at different frequencies (i.e., Sand X-band) and the fusion methodology combining observations at different temporal resolution are described. Radar and rain gauge observations from four rainfall events in 2013 that are characterized by different meteorological phenomena are used to compare the rainfall estimation products of the CASA DFW QPE system to conventional radar products from the national radar network provided by NWS. This high-resolution QPE system is used for urban flash flood mitigations when coupled with hydrological models.

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The great success of the Tropical Rainfall Measuring Mission (TRMM) and its successor Global Precipitation Measurement (GPM) has accelerated the development of global high-resolution satellite-based precipitation products (SPP). However, the quantitative accuracy of SPPs has to be evaluated before using these datasets in water resource applications. This study evaluates the following GPM-era and TRMM-era SPPs based on two years (2014–2015) of reference daily precipitation data from rain gauge networks in ten mountainous regions: Integrated Multi-SatellitE Retrievals for GPM (IMERG, version 05B and version 06B), National Oceanic and Atmospheric Administration (NOAA)/Climate Prediction Center Morphing Method (CMORPH), Global Satellite Mapping of Precipitation (GSMaP), and Multi-Source Weighted-Ensemble Precipitation (MSWEP), which represents a global precipitation data-blending product. The evaluation is performed at daily and annual temporal scales, and at 0.1 deg grid resolution. It is shown that GSMaPV07 surpass the performance of IMERGV06B Final for almost all regions in terms of systematic and random error metrics. The new orographic rainfall classification in the GSMaPV07 algorithm is able to improve the detection of orographic rainfall, the rainfall amounts, and error metrics. Moreover, IMERGV05B showed significantly better performance, capturing the lighter and heavier precipitation values compared to IMERGV06B for almost all regions due to changes conducted to the morphing, where motion vectors are derived using total column water vapor for IMERGV06B.