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SARS-CoV-2 was first detected at a seafood market in Wuhan, China, where exotic live animals such as bat, snake, pangolins, birds were sold. Reports indicated that initially 41 human subjects were infected who were someway connected to this market place. Reportedly, Dr. Ai Fen, the head of the Emergency division of Wuhan Central Hospital, first detected and realized the severity of the SARS-CoV-2 infection and indicated that this virus could transmit from human-to-human. However, the hospital administration disbelieved Dr. Ai Fen’s assertion and accused and disciplined her of spreading panic and rumors. Documents revealed that on December 10, 2019, a 57-year old woman was first diagnosed with COVID-19, and on March 6, 2020, The Wall Street Journal reported that, probably this woman was the Patient Zero. Since the hospital was located near the seafood market, the hospital’s emergency room was flooded with virus infected patients and the hospital was treating over 1,500 patients per day, which was three times more than what the hospital usually handled. The Chinese authority eventually closed down the market. While the origin of SARS-CoV-2 could not be confirmed, there were a couple of assumptions and one suspicion. The assumptions were: (1) a virus infected person could be the source of infection, as a lot of tourists were visiting Wuhan at that time due to the new year celebration, (2) there could be a virus infected animal, such as pangolin or horseshoe bat in the market which was the source of infection. The suspicion was, the virus could have spread from a laboratory.

USA Wrestling is pleased to announce that United World Wrestling has awarded both the 2022 and 2023 Men’s and Women’s Freestyle World Cup events to Xtream Arena in Coralville, Iowa. The 2022 competition will be held December 10-11, and the 2023 competition is set for December 9-10. LIVE: WRESTLING STREAMING ONLINE Version 143 of the dataset. MAJOR CHANGE NOTE: The dataset files: full_dataset.tsv.gz and full_dataset_clean.tsv.gz have been split in 1 GB parts using the Linux utility called Split. So make sure to join the parts before unzipping. We had to make this change as we had huge issues uploading files larger than 2GB's (hence the delay in the dataset releases). The peer-reviewed publication for this dataset has now been published in Epidemiologia an MDPI journal, and can be accessed here: https://doi.org/10.3390/epidemiologia2030024. Please cite this when using the dataset.rtyrt The World Cup is the annual international dual meet championships. This will be the first time in history that the Men’s Freestyle World Cup and the Women’s Freestyle World Cup events will be held side-by-side. The top five countries who have qualified for the 2022 Men’s Freestyle World Cup are the United States, Iran, Japan, Georgia, and Mongolia. The top five countries who have qualified for the 2022 Women’s Freestyle World Cup are Japan, United States, China, Mongolia, and Ukraine. Each side also has an All-World Team represented by top athletes whose countries did not qualify. Qualifying countries are determined based upon the overall team results from the Senior World Championships events held earlier each year. The 2022 Senior World Championships were held in Belgrade, Serbia in September. The 2023 Senior World Championships will be hosted in Russia. 2021-09-09: Version 6.0.0 was created. Now includes data for the North Sea Link (NSL) interconnector from Great Britain to Norway (https://www.northsealink.com). The previous version (5.0.4) should not be used - as there was an error with interconnector data having a static value over the summer 2021.tryruj 2021-05-05: Version 5.0.0 was created. Datetimes now in ISO 8601 format (with capital letter 'T' between the date and time) rather than previously with a space (to RFC 3339 format) and with an offset to identify both UTC and localtime. MW values now all saved as integers rather than floats. Elexon data as always from www.elexonportal.co.uk/fuelhh, National Grid data from https://data.nationalgrideso.com/demand/historic-demand-data Raw data now added again for comparison of pre and post cleaning - to allow for training of additional cleaning methods. If using Microsoft Excel, the T between the date and time can be removed using the =SUBSTITUTE() command - and substitute "T" for a space " "eetrtuj 2021-03-02: Version 4.0.0 was created. Due to a new interconnecter (IFA2 - https://en.wikipedia.org/wiki/IFA-2) being commissioned in Q1 2021, there is an additional column with data from National Grid - this is called 'POWER_NGEM_IFA2_FLOW_MW' in the espeni dataset. In addition, National Grid has dropped the column name 'FRENCH_FLOW' that used to provide the value for the column 'POWER_NGEM_FRENCH_FLOW_MW' in previous espeni versions. However, this has been changed to 'IFA_FLOW' in National Grid's original data, which is now called 'POWER_NGEM_IFA_FLOW_MW' in the espeni dataset. Lastly, the IO14 columns have all been dropped by National Grid - and potentially unlikely to appear again in future.ytit 2020-12-02: Version 3.0.0 was created. There was a problem with earlier versions local time format - where the +01:00 value was not carried through into the data properly. Now addressed - therefore - local time now has the format e.g. 2020-03-31 20:00:00+01:00 when in British Summer Time.rtyrtuj This dataset contains impact metrics and indicators for a set of publications that are related to the COVID-19 infectious disease and the coronavirus that causes it. It is based on:yu Τhe CORD-19 dataset released by the team of Semantic Scholar1 and Τhe curated data provided by the LitCovid hub2. 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 501,088 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: 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/PaperRanking) library4.tyu 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. 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. 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.tyt 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, PMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).tyu The work is based on the following publications:tuy COVID-19 Open Research Dataset (CORD-19). 2020. Version 2022-11-25 Retrieved from https://pages.semanticscholar.org/coronavirus-research. Accessed 2022-11-25. doi:10.5281/zenodo.3715506 Chen Q, Allot A, & Lu Z. (2020) Keep up with the latest coronavirus research, Nature 579:193 (version 2022-11-25) R. Motwani L. Page, S. Brin and T. Winograd. 1999. The PageRank Citation Ranking: Bringing Order to the Web. Technical Report. Stanford InfoLab. I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. Vassiliou: Impact-Based Ranking of Scientific Publications: A Survey and Experimental Evaluation. TKDE 2019 I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. Vassiliou: Ranking Papers by their Short-Term Scientific Impact. CoRR abs/2006.00951 (2020) Rumi Ghosh, Tsung-Ting Kuo, Chun-Nan Hsu, Shou-De Lin, and Kristina Lerman. 2011. Time-Aware Ranking in Dynamic Citation Networks. In Data Mining Workshops (ICDMW). 373–380 A Web user interface that uses these data to facilitate the COVID-19 literature exploration, can be found here. More details in our peer-reviewed publication here (also here there is an outdated preprint version).tuyt Funding: We acknowledge support of this work by the project "Moving from Big Data Management to Data Science" (MIS 5002437/3) which is implemented under the Action "Reinforcement of the Research and Innovation Infrastructure", funded by the Operational Programme "Competitiveness, Entrepreneurship and Innovation" (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund).tuyt 2020-10-03: Version 2.0.0 was created as it looks like National Grid has had a significant change to the methodology underpinning the embedded wind calculations. The wind profile seems similar to previous values, but with an increasing value in comparison to the value published in earlier the greater the embedded value is. The 'new' values are from https://data.nationalgrideso.com/demand/daily-demand-update from 2013.truy Previously: raw and cleaned datasets for Great Britain's publicly available electrical data from Elexon (www.elexonportal.co.uk) and National Gridtuyt (https://demandforecast.nationalgrid.com/efs_demand_forecast/faces/DataExplorer). Updated versions with more recent data will be uploaded with a differing version number and doi All data is released in accordance with Elexon's disclaimer and reservation of rights. This disclaimer is also felt to cover the data from National Grid, and the parsed data from the Energy Informatics Group at the University of Birmingham.tujty Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. Since our first release we have received additional data from our new collaborators, allowing this resource to grow to its current size. Dedicated data gathering started from March 11th yielding over 4 million tweets a day. We have added additional data provided by our new collaborators from January 27th to March 27th, to provide extra longitudinal coverage. Version 10 added ~1.5 million tweets in the Russian language collected between January 1st and May 8th, gracefully provided to us by: Katya Artemova (NRU HSE) and Elena Tutubalina (KFU). From version 12 we have included daily hashtags, mentions and emoijis and their frequencies the respective zip files. From version 14 we have included the tweet identifiers and their respective language for the clean version of the dataset. Since version 20 we have included language and place location for all tweets.tuyti The data collected from the stream captures all languages, but the higher prevalence are: English, Spanish, and French. We release all tweets and retweets on the full_dataset.tsv file (1,373,244,490 unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (356,005,294 unique tweets). There are several practical reasons for us to leave the retweets, tracing important tweets and their dissemination is one of them. For NLP tasks we provide the top 1000 frequent terms in frequent_terms.csv, the top 1000 bigrams in frequent_bigrams.csv, and the top 1000 trigrams in frequent_trigrams.csv. Some general statistics per day are included for both datasets in the full_dataset-statistics.tsv and full_dataset-clean-statistics.tsv files. For more statistics and some visualizations visit: http://www.panacealab.org/covid19/tuyt Wolf, Thomas; Debut, Lysandre; Sanh, Victor; Chaumond, Julien; Delangue, Clement; Moi, Anthony; Cistac, Perric; Ma, Clara; Jernite, Yacine; Plu, Julien; Xu, Canwen; Le Scao, Teven; Gugger, Sylvain; Drame, Mariama; Lhoest, Quentin; Rush, Alexander M.tut PyTorch 2.0 stack support We are very excited by the newly announced PyTorch 2.0 stack. You can enable torch.compile on any of our models, and get support with the Trainer (and in all our PyTorch examples) by using the torchdynamo training argument. For instance, just add --torchdynamo inductor when launching those examples from the command line. This API is still experimental and may be subject to changes as the PyTorch 2.0 stack matures. Note that to get the best performance, we recommend:yht using an Ampere GPU (or more recent) sticking to fixed shaped for now (so use --pad_to_max_length in our examples) Repurpose torchdynamo training args towards torch._dynamo by @sgugger in #20498 Audio Spectrogram Transformer The Audio Spectrogram Transformer model was proposed in AST: Audio Spectrogram Transformer by Yuan Gong, Yu-An Chung, James Glass. The Audio Spectrogram Transformer applies a Vision Transformer to audio, by turning audio into an image (spectrogram). The model obtains state-of-the-art results for audio classification.tyuity Add Audio Spectogram Transformer by @NielsRogge in #19981 Jukebox The Jukebox model was proposed in Jukebox: A generative model for music by Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, Ilya Sutskever. It introduces a generative music model which can produce minute long samples that can be conditionned on an artist, genres and lyrics.tyuti Add Jukebox model (replaces #16875) by @ArthurZucker in #17826 Switch Transformers The SwitchTransformers model was proposed in Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity by William Fedus, Barret Zoph, Noam Shazeer. It is the first MoE model supported in transformers, with the largest checkpoint currently available currently containing 1T parameters.ytrtuj Add Switch transformers by @younesbelkada and @ArthurZucker in #19323 RocBert The RoCBert model was proposed in RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining by HuiSu, WeiweiShi, XiaoyuShen, XiaoZhou, TuoJi, JiaruiFang, JieZhou. It's a pretrained Chinese language model that is robust under various forms of adversarial attacks.tyut Add RocBert by @sww9370 in #20013 CLIPSeg The CLIPSeg model was proposed in Image Segmentation Using Text and Image Prompts by Timo Lüddecke and Alexander Ecker. CLIPSeg adds a minimal decoder on top of a frozen CLIP model for zero- and one-shot image segmentation.rytru NAT was proposed in Neighborhood Attention Transformer by Ali Hassani, Steven Walton, Jiachen Li, Shen Li, and Humphrey Shi.tyht It is a hierarchical vision transformer based on Neighborhood Attention, a sliding-window self attention pattern. DiNAT DiNAT was proposed in Dilated Neighborhood Attention Transformer by Ali Hassani and Humphrey Shi. It extends NAT by adding a Dilated Neighborhood Attention pattern to capture global context, and shows significant performance improvements over it.rytu Add Neighborhood Attention Transformer (NAT) and Dilated NAT (DiNAT) models by @alihassanijr in #20219 MobileNetV2 The MobileNet model was proposed in MobileNetV2: Inverted Residuals and Linear Bottlenecks by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen.tryrtuj add MobileNetV2 model by @hollance in #17845 MobileNetV1 The MobileNet model was proposed in MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications by Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam.tyhu add MobileNetV1 model by @hollance in #17799 Image processors Image processors replace feature extractors as the processing class for computer vision models.rtyhtu Important changes: size parameter is now a dictionary of {"height": h, "width": w}, {"shortest_edge": s}, {"shortest_egde": s, "longest_edge": l} instead of int or tuple. Addition of data_format flag. You can now specify if you want your images to be returned in "channels_first" - NCHW - or "channels_last" - NHWC - format. Processing flags e.g. do_resize can be passed directly to the preprocess method instead of modifying the class attribute: image_processor([image_1, image_2], do_resize=False, return_tensors="pt", data_format="channels_last") Leaving return_tensors unset will return a list of numpy arrays. The classes are backwards compatible and can be created using existing feature extractor configurations - with the size parameter converted.tyr Add Image Processors by @amyeroberts in #19796 Add Donut image processor by @amyeroberts #20425 Add segmentation + object detection image processors by @amyeroberts in #20160 AutoImageProcessor by @amyeroberts in #20111 Backbone for computer vision models We're adding support for a general AutoBackbone class, which turns any vision model (like ConvNeXt, Swin Transformer) into a backbone to be used with frameworks like DETR and Mask R-CNN. The design is in early stages and we welcome feedback.tyu Add AutoBackbone + ResNetBackbone by @NielsRogge in #20229 Improve backbone by @NielsRogge in #20380 [AutoBackbone] Improve API by @NielsRogge in #20407 Support for safetensors offloading If the model you are using has a safetensors checkpoint and you have the library installed, offload to disk will take advantage of this to be more memory efficient and roughly 33% faster.dyhrtju Safetensors offload by @sgugger in #20321 Contrastive search in the generate method Generate: TF contrastive search with XLA support by @gante in #20050 Generate: contrastive search with full optional outputs by @gante in #19963 Breaking changes 🚨 🚨 🚨 Fix Issue 15003: SentencePiece Tokenizers Not Adding Special Tokens in convert_tokens_to_string by @beneyal in #15775 Bugfixes and improvements add dataset by @stevhliu in #20005 Add BERT resources by @stevhliu in #19852 Add LayoutLMv3 resource by @stevhliu in #19932 fix typo by @stevhliu in #20006 Update object detection pipeline to use post_process_object_detection methods by @alaradirik in #20004 clean up vision/text config dict arguments by @ydshieh in #19954 make sentencepiece import conditional in bertjapanesetokenizer by @ripose-jp in #20012 Fix gradient checkpoint test in encoder-decoder by @ydshieh in #20017 Quality by @sgugger in #20002 Update auto processor to check image processor created by @amyeroberts in #20021 [Doctest] Add configuration_deberta_v2.py by @Saad135 in #19995 Improve model tester by @ydshieh in #19984 Fix doctest by @ydshieh in #20023 Show installed libraries and their versions in CI jobs by @ydshieh in #20026 reorganize glossary by @stevhliu in #20010 Now supporting pathlike in pipelines too. by @Narsil in #20030 Add **kwargs by @amyeroberts in #20037 Fix some doctests after PR 15775 by @ydshieh in #20036 [Doctest] Add configuration_camembert.py by @Saad135 in #20039 [Whisper Tokenizer] Make more user-friendly by @sanchit-gandhi in #19921 [FuturWarning] Add futur warning for LEDForSequenceClassification by @ArthurZucker in #19066 fix jit trace error for model forward sequence is not aligned with jit.trace tuple input sequence, update related doc by @sywangyi in #19891 Update esmfold conversion script by @Rocketknight1 in #20028 Fixed torch.finfo issue with torch.fx by @michaelbenayoun in #20040 Only resize embeddings when nec

Saturday's Croke Park double header will be the first time headquarters has hosted ladies club football finals. The earlier Intermediate decider sees Longford Slashers up against Tipperary side Mullinahone. Kilkerrin-Clonberne beat Donaghmoyne in last season's semi-final. Nicola Ward's two first-half goals proved the difference as the Galway outfit clinched a 2-8 to 0-8 win before going out to beat Cork club Mourneabbey in the decider. LIVE: GAA FOOTBALL 2022 STREAMING ONLINE Version 143 of the dataset. MAJOR CHANGE NOTE: The dataset files: full_dataset.tsv.gz and full_dataset_clean.tsv.gz have been split in 1 GB parts using the Linux utility called Split. So make sure to join the parts before unzipping. We had to make this change as we had huge issues uploading files larger than 2GB's (hence the delay in the dataset releases). The peer-reviewed publication for this dataset has now been published in Epidemiologia an MDPI journal, and can be accessed here: https://doi.org/10.3390/epidemiologia2030024. Please cite this when using the dataset.rtyrt After their 2019 final defeat by the Cork side, it was a triumph Kilkerrin-Clonberne craved and their impressive form this season - as they clinched a fifth successive Connacht title and then outclassed Waterford side Ballymacarbry in the All-Ireland semi-final - marks them out as favourites heading into Croke Park. Galway star Ailish Morrissey hit 1-3 in the semi-final and from All Star nominated goalkeeper Lisa Murphy onwards, Kilkerrin-Clonberne have talented performers all over the pitch. But facing them is a group of Donaghmoyne players with a never-say-die attitude. Donaghmoyne captured a 20th Monaghan crown before landing a fourth Ulster title on the trot - and a 14th in total - as they saw off St Ergnat's Moneyglass despite playing much of the game with 14 players. 2021-09-09: Version 6.0.0 was created. Now includes data for the North Sea Link (NSL) interconnector from Great Britain to Norway (https://www.northsealink.com). The previous version (5.0.4) should not be used - as there was an error with interconnector data having a static value over the summer 2021.tryruj 2021-05-05: Version 5.0.0 was created. Datetimes now in ISO 8601 format (with capital letter 'T' between the date and time) rather than previously with a space (to RFC 3339 format) and with an offset to identify both UTC and localtime. MW values now all saved as integers rather than floats. Elexon data as always from www.elexonportal.co.uk/fuelhh, National Grid data from https://data.nationalgrideso.com/demand/historic-demand-data Raw data now added again for comparison of pre and post cleaning - to allow for training of additional cleaning methods. If using Microsoft Excel, the T between the date and time can be removed using the =SUBSTITUTE() command - and substitute "T" for a space " "eetrtuj 2021-03-02: Version 4.0.0 was created. Due to a new interconnecter (IFA2 - https://en.wikipedia.org/wiki/IFA-2) being commissioned in Q1 2021, there is an additional column with data from National Grid - this is called 'POWER_NGEM_IFA2_FLOW_MW' in the espeni dataset. In addition, National Grid has dropped the column name 'FRENCH_FLOW' that used to provide the value for the column 'POWER_NGEM_FRENCH_FLOW_MW' in previous espeni versions. However, this has been changed to 'IFA_FLOW' in National Grid's original data, which is now called 'POWER_NGEM_IFA_FLOW_MW' in the espeni dataset. Lastly, the IO14 columns have all been dropped by National Grid - and potentially unlikely to appear again in future.ytit 2020-12-02: Version 3.0.0 was created. There was a problem with earlier versions local time format - where the +01:00 value was not carried through into the data properly. Now addressed - therefore - local time now has the format e.g. 2020-03-31 20:00:00+01:00 when in British Summer Time.rtyrtuj This dataset contains impact metrics and indicators for a set of publications that are related to the COVID-19 infectious disease and the coronavirus that causes it. It is based on:yu Τhe CORD-19 dataset released by the team of Semantic Scholar1 and Τhe curated data provided by the LitCovid hub2. 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 501,088 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: 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/PaperRanking) library4.tyu 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. 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. 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.tyt 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, PMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).tyu The work is based on the following publications:tuy COVID-19 Open Research Dataset (CORD-19). 2020. Version 2022-11-25 Retrieved from https://pages.semanticscholar.org/coronavirus-research. Accessed 2022-11-25. doi:10.5281/zenodo.3715506 Chen Q, Allot A, & Lu Z. (2020) Keep up with the latest coronavirus research, Nature 579:193 (version 2022-11-25) R. Motwani L. Page, S. Brin and T. Winograd. 1999. The PageRank Citation Ranking: Bringing Order to the Web. Technical Report. Stanford InfoLab. I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. Vassiliou: Impact-Based Ranking of Scientific Publications: A Survey and Experimental Evaluation. TKDE 2019 I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. Vassiliou: Ranking Papers by their Short-Term Scientific Impact. CoRR abs/2006.00951 (2020) Rumi Ghosh, Tsung-Ting Kuo, Chun-Nan Hsu, Shou-De Lin, and Kristina Lerman. 2011. Time-Aware Ranking in Dynamic Citation Networks. In Data Mining Workshops (ICDMW). 373–380 A Web user interface that uses these data to facilitate the COVID-19 literature exploration, can be found here. More details in our peer-reviewed publication here (also here there is an outdated preprint version).tuyt Funding: We acknowledge support of this work by the project "Moving from Big Data Management to Data Science" (MIS 5002437/3) which is implemented under the Action "Reinforcement of the Research and Innovation Infrastructure", funded by the Operational Programme "Competitiveness, Entrepreneurship and Innovation" (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund).tuyt 2020-10-03: Version 2.0.0 was created as it looks like National Grid has had a significant change to the methodology underpinning the embedded wind calculations. The wind profile seems similar to previous values, but with an increasing value in comparison to the value published in earlier the greater the embedded value is. The 'new' values are from https://data.nationalgrideso.com/demand/daily-demand-update from 2013.truy Previously: raw and cleaned datasets for Great Britain's publicly available electrical data from Elexon (www.elexonportal.co.uk) and National Gridtuyt (https://demandforecast.nationalgrid.com/efs_demand_forecast/faces/DataExplorer). Updated versions with more recent data will be uploaded with a differing version number and doi All data is released in accordance with Elexon's disclaimer and reservation of rights. This disclaimer is also felt to cover the data from National Grid, and the parsed data from the Energy Informatics Group at the University of Birmingham.tujty Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. Since our first release we have received additional data from our new collaborators, allowing this resource to grow to its current size. Dedicated data gathering started from March 11th yielding over 4 million tweets a day. We have added additional data provided by our new collaborators from January 27th to March 27th, to provide extra longitudinal coverage. Version 10 added ~1.5 million tweets in the Russian language collected between January 1st and May 8th, gracefully provided to us by: Katya Artemova (NRU HSE) and Elena Tutubalina (KFU). From version 12 we have included daily hashtags, mentions and emoijis and their frequencies the respective zip files. From version 14 we have included the tweet identifiers and their respective language for the clean version of the dataset. Since version 20 we have included language and place location for all tweets.tuyti The data collected from the stream captures all languages, but the higher prevalence are: English, Spanish, and French. We release all tweets and retweets on the full_dataset.tsv file (1,373,244,490 unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (356,005,294 unique tweets). There are several practical reasons for us to leave the retweets, tracing important tweets and their dissemination is one of them. For NLP tasks we provide the top 1000 frequent terms in frequent_terms.csv, the top 1000 bigrams in frequent_bigrams.csv, and the top 1000 trigrams in frequent_trigrams.csv. Some general statistics per day are included for both datasets in the full_dataset-statistics.tsv and full_dataset-clean-statistics.tsv files. For more statistics and some visualizations visit: http://www.panacealab.org/covid19/tuyt Wolf, Thomas; Debut, Lysandre; Sanh, Victor; Chaumond, Julien; Delangue, Clement; Moi, Anthony; Cistac, Perric; Ma, Clara; Jernite, Yacine; Plu, Julien; Xu, Canwen; Le Scao, Teven; Gugger, Sylvain; Drame, Mariama; Lhoest, Quentin; Rush, Alexander M.tut PyTorch 2.0 stack support We are very excited by the newly announced PyTorch 2.0 stack. You can enable torch.compile on any of our models, and get support with the Trainer (and in all our PyTorch examples) by using the torchdynamo training argument. For instance, just add --torchdynamo inductor when launching those examples from the command line. This API is still experimental and may be subject to changes as the PyTorch 2.0 stack matures. Note that to get the best performance, we recommend:yht using an Ampere GPU (or more recent) sticking to fixed shaped for now (so use --pad_to_max_length in our examples) Repurpose torchdynamo training args towards torch._dynamo by @sgugger in #20498 Audio Spectrogram Transformer The Audio Spectrogram Transformer model was proposed in AST: Audio Spectrogram Transformer by Yuan Gong, Yu-An Chung, James Glass. The Audio Spectrogram Transformer applies a Vision Transformer to audio, by turning audio into an image (spectrogram). The model obtains state-of-the-art results for audio classification.tyuity Add Audio Spectogram Transformer by @NielsRogge in #19981 Jukebox The Jukebox model was proposed in Jukebox: A generative model for music by Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, Ilya Sutskever. It introduces a generative music model which can produce minute long samples that can be conditionned on an artist, genres and lyrics.tyuti Add Jukebox model (replaces #16875) by @ArthurZucker in #17826 Switch Transformers The SwitchTransformers model was proposed in Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity by William Fedus, Barret Zoph, Noam Shazeer. It is the first MoE model supported in transformers, with the largest checkpoint currently available currently containing 1T parameters.ytrtuj Add Switch transformers by @younesbelkada and @ArthurZucker in #19323 RocBert The RoCBert model was proposed in RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining by HuiSu, WeiweiShi, XiaoyuShen, XiaoZhou, TuoJi, JiaruiFang, JieZhou. It's a pretrained Chinese language model that is robust under various forms of adversarial attacks.tyut Add RocBert by @sww9370 in #20013 CLIPSeg The CLIPSeg model was proposed in Image Segmentation Using Text and Image Prompts by Timo Lüddecke and Alexander Ecker. CLIPSeg adds a minimal decoder on top of a frozen CLIP model for zero- and one-shot image segmentation.rytru NAT was proposed in Neighborhood Attention Transformer by Ali Hassani, Steven Walton, Jiachen Li, Shen Li, and Humphrey Shi.tyht It is a hierarchical vision transformer based on Neighborhood Attention, a sliding-window self attention pattern. DiNAT DiNAT was proposed in Dilated Neighborhood Attention Transformer by Ali Hassani and Humphrey Shi. It extends NAT by adding a Dilated Neighborhood Attention pattern to capture global context, and shows significant performance improvements over it.rytu Add Neighborhood Attention Transformer (NAT) and Dilated NAT (DiNAT) models by @alihassanijr in #20219 MobileNetV2 The MobileNet model was proposed in MobileNetV2: Inverted Residuals and Linear Bottlenecks by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen.tryrtuj add MobileNetV2 model by @hollance in #17845 MobileNetV1 The MobileNet model was proposed in MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications by Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam.tyhu add MobileNetV1 model by @hollance in #17799 Image processors Image processors replace feature extractors as the processing class for computer vision models.rtyhtu Important changes: size parameter is now a dictionary of {"height": h, "width": w}, {"shortest_edge": s}, {"shortest_egde": s, "longest_edge": l} instead of int or tuple. Addition of data_format flag. You can now specify if you want your images to be returned in "channels_first" - NCHW - or "channels_last" - NHWC - format. Processing flags e.g. do_resize can be passed directly to the preprocess method instead of modifying the class attribute: image_processor([image_1, image_2], do_resize=False, return_tensors="pt", data_format="channels_last") Leaving return_tensors unset will return a list of numpy arrays. The classes are backwards compatible and can be created using existing feature extractor configurations - with the size parameter converted.tyr Add Image Processors by @amyeroberts in #19796 Add Donut image processor by @amyeroberts #20425 Add segmentation + object detection image processors by @amyeroberts in #20160 AutoImageProcessor by @amyeroberts in #20111 Backbone for computer vision models We're adding support for a general AutoBackbone class, which turns any vision model (like ConvNeXt, Swin Transformer) into a backbone to be used with frameworks like DETR and Mask R-CNN. The design is in early stages and we welcome feedback.tyu Add AutoBackbone + ResNetBackbone by @NielsRogge in #20229 Improve backbone by @NielsRogge in #20380 [AutoBackbone] Improve API by @NielsRogge in #20407 Support for safetensors offloading If the model you are using has a safetensors checkpoint and you have the library installed, offload to disk will take advantage of this to be more memory efficient and roughly 33% faster.dyhrtju Safetensors offload by @sgugger in #20321 Contrastive search in the generate method Generate: TF contrastive search with XLA support by @gante in #20050 Generate: contrastive search with full optional outputs by @gante in #19963 Breaking changes 🚨 🚨 🚨 Fix Issue 15003: SentencePiece Tokenizers Not Adding Special Tokens in convert_tokens_to_string by @beneyal in #15775 Bugfixes and improvements add dataset by @stevhliu in #20005 Add BERT resources by @stevhliu in #19852 Add LayoutLMv3 resource by @stevhliu in #19932 fix typo by @stevhliu in #20006 Update object detection pipeline to use post_process_object_detection methods by @alaradirik in #20004 clean up vision/text config dict arguments by @ydshieh in #19954 make sentencepiece import conditional in bertjapanesetokenizer by @ripose-jp in #20012 Fix gradient checkpoint test in encoder-decoder by @ydshieh in #20017 Quality by @sgugger in #20002 Update auto processor to check image processor created by @amyeroberts in #20021 [Doctest] Add configuration_deberta_v2.py by @Saad135 in #19995 Improve model tester by @ydshieh in #19984 Fix doctest by @ydshieh in #20023 Show installed libraries and their versions in CI jobs by @ydshieh in #20026 reorganize glossary by @stevhliu in #20010 Now supporting pathlike in pipelines too. by @Narsil in #20030 Add **kwargs by @amyeroberts in #20037 Fix some doctests after PR 15775 by @ydshieh in #20036 [Doctest] Add configuration_camembert.py by @Saad135 in #20039 [Whisper Tokenizer] Make more user-friendly by @sanchit-gandhi in #19921 [FuturWarning] Add futur warning for LEDForSequenceClassification by @ArthurZucker in #19066 fix jit trace error for model forward sequence is not aligned with jit.trace tuple input sequence, update related doc by @sywangyi in #19891 Update esmfold conversion script by @Rocketknight1 in #20028 Fixed torch.finfo issue with torch.fx by @michaelbenayoun in #20040

Week 14 in the NFL kicks off with the Las Vegas Raiders heading to Los Angeles to play the Rams on Thursday Night Football (TNF). Both teams are under .500, but the teams are headed in opposite directions. The Raiders have not lost a game in almost a month, while the Rams have not won a game since October. WATCH LIVE STREAMS HERE Recent studies show a correlation between the content of vitamin D3 in the human body and the severity of COVID19. Part of the world’s population is deficient in vitamin D3. How to watch the Raiders vs. Rams on Thursday Night Football If you want to watch the Raiders take on the Rams, the event will air live on Amazon Prime Video at 8:15 p.m. ET tonight. Coverage starts at 7:00 p.m. ET. The announcers for tonight’s game are Al Michaels, Kirk Herbstreit, and Kaylee Hartung. The game can be seen on Amazon through the Prime Video app, which is available on connected TVs, smartphones, tablets, laptops, and game consoles. Prime Video costs $15 per month or $139 per year. If you are not interested in receiving the perks of Amazon Prime and only want to access movies and TV shows, a Prime Video membership is $9 per month. Students can receive a discounted Amazon Prime membership for less than $8 per month or $69 per year. In addition to Prime Video, the game will be available on broadcast affiliates in Las Vegas and Los Angeles. Plus, you can follow the game on the Prime Video Twitch Channel. The solution to this problem is possible by the development and inclusion of foodstuffs fortified with vitamin D in diets. The aim of this study was to develop a D3 -fortified sour cream dessert using an emulsion system as a vitamin D delivery system. Commercially available raw materials: vitamin D3 powder, sodium carboxymethylcellulose, skimmed milk powder, and sunflower oil were used to create a vitamin D-fortified emulsion.yuiysdg The latter is used in the technology of sour cream dessert production. The emulsion microstructure and stability were investigated using rheology and dynamic light scattering methods. The content of vitamin D3 was determined by coulometric titration and spectroscopy. Experimentally determined data on the viscosity of emulsions indicate the pseudoplastic behavior of the f low. The use of a structural approach (Casson model) made it possible to determine the emulsion viscosity parameters, which can be used as a quantitative criterion for emulsion stability.ujtyk This conclusion was confirmed by microstructural data on distribution size of droplets volume of emulsion. Amount of vitamin D in the emulsion and dessert was 1.96 ± 0.22 µg/g (97.8 % of the added amount) and 0.019±0,005 µg/g, respectively. Using the developed stable emulsion as a vitamin D delivery system, a technology for the production of a dessert based on sour cream fortified with vitamin D3 was proposed.ytujty The Worldwide Soundscapes project is a global, open inventory of spatio-temporally replicated soundscape datasets. This Zenodo entry comprises the data tables that constitute its (meta-)database, as well as their description.yuy The overview of all sampling sites can be found on the corresponding project on ecoSound-web, as well as a demonstration collection containing selected recordings. More information on the project can be found here and on ResearchGate.yuji The audio recording criteria justifying inclusion into the meta-database are: Stationary (no transects, towed sensors or microphones mounted on cars) Passive (unattended, no human disturbance by the recordist) Ambient (no spatial or temporal focus on a particular species or direction) Spatially and/or temporally replicated (multiple sites sampled at least at one common daytime or multiple days sampled at least in one common site)tyuyt The individual columns of the provided data tables are described in the following. Data tables are linked through primary keys; joining them will result in a database.ytuj datasets dataset_id: incremental integer, primary key name: name of the dataset. if it is repeated, incremental integers should be used in the "subset" column to differentiate them. subset: incremental integer that can be used to distinguish datasets with identical names collaborators: full names of people deemed responsible for the dataset, separated by commas contributors: full names of people who are not the main collaborators but who have significantly contributed to the dataset, and who could be contacted for in-depth analyses, separated by commas. date_added: when the datased was added (DD/MM/YYYY) URL_open_recordings: if recordings (even only some) from this dataset are openly available, indicate the internet link where they can be found. URL_project: internet link for further information about the corresponding project DOI_publication: DOI of corresponding publications, separated by comma core_realm_IUCN: The core realm of the dataset. Datasets may have multiple realms, but the main one should be listed. Datasets may contain sampling sites from different realms in the "sites" sheet. IUCN Global Ecosystem Typology (v2.0): https://global-ecosystems.org/ medium: the physical medium the microphone is situated in protected_area: Whether the sampling sites were situated in protected areas or not, or only some. GADM0: For datasets on land or in territorial waters, Global Administrative Database level0 https://gadm.org/ GADM1: For datasets on land or in territorial waters, Global Administrative Database level1 https://gadm.org/ GADM2: For datasets on land or in territorial waters, Global Administrative Database level2 https://gadm.org/ IHO: For marine locations, the sea area that encompassess all the sampling locations according to the International Hydrographic Organisation. Map here: https://www.arcgis.com/home/item.html?id=44e04407fbaf4d93afcb63018fbca9e2 locality: optional free text about the locality latitude_numeric_region: study region approximate centroid latitude in WGS84 decimal degrees longitude_numeric_region: study region approximate centroid longitude in WGS84 decimal degrees sites_number: number of sites sampled year_start: starting year of the sampling year_end: ending year of the sampling deployment_schedule: description of the sampling schedule, provisional temporal_recording_selection: list environmental exclusion criteria that were used to determine which recording days or times to discard high_pass_filter_Hz: frequency of the high-pass filter of the recorder, in Hz variable_sampling_frequency: Does the sampling frequency vary? If it does, write "NA" in the sampling_frequency_kHz column and indicate it in the sampling_frequency_kHz column inside the deployments sheet sampling_frequency_kHz: frequency the microphone was sampled at (sounds of half that frequency will be recorded) variable_recorder: recorder: recorder model used microphone: microphone used freshwater_recordist_position: position of the recordist relative to the microphone during sampling (only for freshwater) collaborator_comments: free-text field for comments by the collaborators validated: This cell is checked if the contents of all sheets are complete and have been found to be coherent and consistent with our requirements. validator_name: name of person doing the validation validation_comments: validators: please insert the date when someone was contacted cross-check: this cell is checked if the collaborators confirm the spatial and temporal data after checking the corresponding site maps, deployment and operation time graphs found at https://drive.google.com/drive/folders/1qfwXH_7dpFCqyls-c6b8RZ_fbcn9kXbp?usp=share_linktuy datasets-sites dataset_ID: primary key of datasets table dataset_name: lookup field site_ID: primary key of sites table site_name: lookup field sites site_ID: unique site IDs, larger than 1000 for compatibility with ecoSound-web site_name: name or code of sampling site as used in respective projects latitude_numeric: exact numeric degrees coordinates of latitude longitude_numeric: exact numeric degrees coordinates of longitude topography_m: for sites on land: elevation. For marine sites: depth (negative). in meters freshwater_depth_m realm: Ecosystem type according to IUCN GET https://global-ecosystems.org/ biome: Ecosystem type according to IUCN GET https://global-ecosystems.org/ functional_group: Ecosystem type according to IUCN GET https://global-ecosystems.org/ commentstuyt deployments dataset_ID: primary key of datasets table dataset_name: lookup field deployment: use identical subscript letters to denote rows that belong to the same deployment. For instance, you may use different operation times and schedules for different target taxa within one deployment. start_date_min: earliest date of deployment start, double-click cell to get date-picker start_date_max: latest date of deployment start, if applicable (only used when recorders were deployed over several days), double-click cell to get date-picker start_time_mixed: deployment start local time, either in HH:MM format or a choice of solar daytimes (sunrise, sunset, noon, midnight). Corresponds to the recording start time for continuous recording deployments. If multiple start times were used, you should mention the latest start time (corresponds to the earliest daytime from which all recorders are active). If applicable, positive or negative offsets from solar times can be mentioned (For example: if data are collected one hour before sunrise, this will be "sunrise-60") permanent: is the deployment permanent (in which case it would be ongoing and the end date or duration would be unknown)? variable_duration_days: is the duration of the deployment variable? in days duration_days: deployment duration per recorder (use the minimum if variable) end_date_min: earliest date of deployment end, only needed if duration is variable, double-click cell to get date-picker end_date_max: latest date of deployment end, only needed if duration is variable, double-click cell to get date-pickertuy end_time_mixed: deployment end local time, either in HH:MM format or a choice of solar daytimes (sunrise, sunset, noon, midnight). Corresponds to the recording end time for continuous recording deployments. recording_time: does the recording last from the deployment start time to the end time (continuous) or at scheduled daily intervals (scheduled)? Note: we consider recordings with duty cycles to be continuous. operation_start_time_mixed: scheduled recording start local time, either in HH:MM format or a choice of solar daytimes (sunrise, sunset, noon, midnight). If applicable, positive or negative offsets from solar times can be mentioned (For example: if data are collected one hour before sunrise, this will be "sunrise-60") operation_duration_minutes: duration of operation in minutes, if constant operation_end_time_mixed: scheduled recording end local time, either in HH:MM format or a choice of solar daytimes (sunrise, sunset, noon, midnight). If applicable, positive or negative offsets from solar times can be mentioned (For example: if data are collected one hour before sunrise, this will be "sunrise-60") duty_cycle_minutes: duty cycle of the recording (i.e. the fraction of minutes when it is recording), written as "recording(minutes)/period(minutes)". For example: "1/6" if the recorder is active for 1 minute and standing by for 5 minutes. sampling_frequency_kHz: only indicate the sampling frequency if it is variable within a particular dataset so that we need to code different frequencies for different deployments recorder subset_sites: If the deployment was not done in all the sites of the corresponding datasest, site IDs can be indicated here, separated by commas comments We investigated the influence of wormwood-wild rue mixture with high anthelmintic effect on the diuretic process in sheep and on the physical and chemical properties of the urinary excretion of the sheep fed with (6 g / kg), three and fivefold increased therapeutic dose (18 and 30 g / kg) of the mixture. No pain was observed during urination in the experimental animals. The urine of the experimental animals was clear, light yellowish in color, there was no smell. The density of urine in animals fed with the mixture at a dose of 30 g / kg was 1.029, pH was 8.48, which is the norm. Proteins, sugars, ketone bodies, bilirubin were not found in the urine of animals undergoing experiments. In the tested urine, individual blood vessels appeared, and a small amount of indican and urobilins was found. The findings show that wormwood does not have a toxic effect on the physical and chemical properties of urine in sheep.tu is an open-source package which allows to focus on a network-oriented approach to identify regulatory mechanisms linked to a disease, identify genes of interest, simulate and score the effect of a drug at transcriptional level, and perform drug repurposing with adaptive testing.tu The article informs about the ecological evaluation of soils in the Kangarli administrative Region. For the ecological evaluation of soils, physico-geographical condition of this area (relief, climate, hydrological and hydrogeological, plant and animal world, anthropogenic influence, etc.) degradation processes (salinity, erosion, waterlogging, rockiness, overgrown areas, etc) morphological, physical and chemical characteristics in the region were studied. At the same time, soils under cultivated and natural plants were assessed. The highest points received mountain chestnut (brown) (100 points), chestnut (brown) (96 points), alluvial (92 points) soils. The lowest points received sandy marshy-meadow (32 points), stony-gravelly river bed (18 points) and stony river bed (10 points) soils. Some recommendations and suggestions for the rational use of the soils for the cultural and natural plants of the Kengirlinsky administrative region were made.thtyj This dataset contains a selection of bias-corrected data from the preoperational MiKlip system for decadal climate predictions (Mueller et al., 2018) used within the Italian research project PNRA18_00199-IPSODES. The adopted method for bias correction is described in the file bias_correction.pdf. Also data from the assimilation run are provided. Nomenclature of variables follows that of the original MiKlip output.tyuht Mueller, W., et al. A Higher‐resolution Version of the Max Planck Institute Earth System Model (MPI‐ESM1.2‐HR). J. Adv. Model. Earth Syst. 10, 1383-1413 (2018)tru

NFL 2022 How to Stream Sunday Night Football Live online free week 13 [LIVE+Streams]* Cowboys vs Colts live stream free: How to watch NFL Week 13 [Whereto-watch ]* Dallas Cowboys vs. Indianapolis Colts 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).

বর্তমানে করোনাভাইরাস COVID-19 সারা বিশ্বে ২১২টি দেশকে প্রভাবিত করেছে। এই পরিস্থিতিতে হোম কেয়ার বিশেষত গুরুত্বপূর্ণ কারণ মহামারী পরিস্থিতি চলাকালীন হাসপাতালগুলি আপাতদৃষ্টিতে নিরাপদ নয়। এছাড়াও লকডাউন পিরিয়ডের সময় বাড়ি থেকে বেরিয়ে আসার সুযোগও সীমিত। টেলিমেডিসিন এবং টেলিহেলথ প্রযুক্তিগুলি মহামারী প্রাদুর্ভাবের সময় বিশেষত কার্যকর হয়, বিশেষ করে যখন স্বাস্থ্য আধিকারিকরা সামাজিক দূরত্ব-ব্যবস্থা বাস্তবায়নের পরামর্শ দেয়। টেলিফোন-ভিত্তিক এই পদক্ষেপ উপযুক্ত তথ্য এবং প্রতিক্রিয়া সংযুক্ত করে কার্যকারিতার উন্নতি করছে। স্বাস্থ্যসেবাতে অধিগমন বাড়ানোর পাশাপাশি, টেলিমেডিসিন স্বাস্থ্যসেবা রোগীদের বিভিন্ন সুবিধা প্রদান, জটিল অবস্থায় উপনীত ও দীর্ঘস্থায়ী স্বাস্থ্যের পরিস্থিতি নির্ণয় এবং পর্যবেক্ষণ, স্বাস্থ্যসেবার মান উন্নত করা এবং ব্যয় হ্রাস করার ফলপ্রসূ ও সক্রিয় একটি উপায়। (Currently, coronavirus COVID-19 has affected 212 countries around the world. Home-care is especially important in these situations because hospitals are not seemingly safe during pandemic outbreaks. Also, the chance to get out of the home during the lockdown period is limited. Telemedicine and telehealth technologies are especially effective during epidemic outbreaks, when health authorities recommend implementing social distance systems. In addition to increasing access to healthcare, telemedicine is a fruitful and proactive way to provide a variety of benefits to patients seeking healthcare; diagnose and monitor critical and chronic health conditions; improve healthcare quality and reduce costs). Newspaper article (Views) Link: https://www.ajsarabela.com/2020/05/15/মহামারী-পরিস্থিতিতে-টেলি.html

Document elaborat en el context de confinament davant l’emergència provocada per la COVID-19. El Servei d'Orientació i Acompanyament per a Persones Immigrades (SOAPI) edita un document que recull els dubtes que puguin sorgir sobre les mesures que s’han aplicat en matèria d’estrangeria davant la covid-19. Document prepared in the context of confinement to the emergency caused by COVID-19. Documento elaborado en el contexto de confinamiento ante la emergencia provocada por la COVID-19.

Document elaborat en el context de confinament davant l’emergència provocada per la COVID-19. El Servei d'Orientació i Acompanyament per a Persones Immigrades (SOAPI) edita un document que recull els dubtes que puguin sorgir sobre les mesures que s’han aplicat en matèria d’estrangeria davant la covid-19. Document prepared in the context of confinement to the emergency caused by COVID-19. Documento elaborado en el contexto de confinamiento ante la emergencia provocada por la COVID-19.

Document elaborat en el context de confinament davant l’emergència provocada per la COVID-19. Document prepared in the context of confinement to the emergency caused by COVID-19. Documento elaborado en el contexto de confinamiento ante la emergencia provocada por la COVID-19.