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++WEEK+13] LA Chargers Vs. LV Raiders Live Streams Games Live Free
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++WEEK+13] LA Chargers vs. LV Raiders Live streams Games Live free [LIVE+Streams]* Raiders vs Chargers live stream free: How to watch NFL Week 13 [Whereto-watch ]* Los Angeles Chargers vs. Las Vegas Raiders Live How to Watch NFL Week 13 Games Live free WATCH NFL GAME 2022 LIVE Which NFL teams are playing this week? And what channels are airing the games?
Here’s this week’s lineup. (The home team is listed second.) Sunday, Dec. 4
Green Bay Packers vs Chicago Bears, 1:00 p.m. ET on Fox Pittsburgh Steelers vs. Atlanta (...)
++WEEK+13] LA Chargers vs. LV Raiders Live streams Games Live free [LIVE+Streams]* Raiders vs Chargers live stream free: How to watch NFL Week 13 [Whereto-watch ]* Los Angeles Chargers vs. Las Vegas Raiders Live How to Watch NFL Week 13 Games Live free WATCH NFL GAME 2022 LIVE Which NFL teams are playing this week? And what channels are airing the games?
Here’s this week’s lineup. (The home team is listed second.) Sunday, Dec. 4
Green Bay Packers vs Chicago Bears, 1:00 p.m. ET on Fox Pittsburgh Steelers vs. Atlanta Falcons, 1:00 p.m. ET on CBS New York Jets vs. Minnesota Vikings, 1:00 p.m. ET on CBS Jacksonville Jaguars vs. Detroit Lions, 1:00 p.m. ET on Fox Tennessee Titans vs. Philadelphia Eagles, 1:00 p.m. ET on Fox Cleveland Browns vs. Houston Texans, 1:00 p.m. ET on CBS Washington Commanders vs. New York Giants, 1:00 p.m. ET on Fox Denver Broncos vs. Baltimore Ravens, 1:00 p.m. ET on CBS Miami Dolphins vs. San Francisco 49ers, 4:05 p.m. ET on Fox Seattle Seahawks vs. Los Angeles Rams, 4:05 p.m. ET on Fox Los Angeles Chargers vs. Las Vegas Raiders, 4:25 p.m. ET on CBS Kansas City Chiefs vs. Cincinnati Bengals, 4:25 p.m. ET on CBS Indianapolis Colts vs. Dallas Cowboys, 8:20 p.m. ET on NBC Not since 2006 have the Socceroos made the knockout stage while Belgium have never played a last-16 game at the World Cup, and with a ferocious backing in Qatar they will be under pressure to grab a vital win today.dfg Τhe CORD-19 dataset released by the team of Semantic Scholar1 anddg
Τhe curated data provided by the LitCovid hub2.gdgdf These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:dgf Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/zdhPaperRanking) library4.dgfd These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:sdgfdfggh Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwifss/PaperRanking) library4.sddfggd
Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset.sddggf safs Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article.asdsgdg sf Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sfbsdf
Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset. We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PdfMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).
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