wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Second, we study the differences between these two communities. /r/ Coronavirus is recommended as the official community for COVID-19 on Reddit, while /r  /  China _flu adopts a loose moderation practice. As a result, we observe that these two communities are gradually growing apart and more extremism is being found in /r/China _flu.
Revised:  Second, we study the differences between these two communities. /r/ Coronavirus is recommended as the official community for COVID-19 on Reddit, while /r / China flu from January to March were more likely to leave, while those who started in later months tend to remain highly "loyal". To understand this difference, we develop a movement analysis framework to understand membership changes in these two communities are gradually growing apart and more extremism is being found in /r/China _flu.
Edit intent: meaning-changed
Original:  As a result, we observe that these two communities are gradually growing apart and more extremism is being found in /r/China _flu. Finally, we examine the spillover effect of the COVID-19 pandemic on user activity across the entire Reddit platform. Our results show significant changes in user activities outside COVID-related communities . In subreddits related to finance, food, and countries / cities, user activity is recovering to the pre-pandemic level in late April and May as countries reopen, but subreddits related to travel and sports remain highly impacted and show lower activity levels than the pre-pandemic period.
Revised:  As a result, we observe that these two communities are gradually growing apart and more extremism is being found in /r/China flu members (around 50\%) that moved to / cities, user activity is recovering to the pre-pandemic level in late April and May as countries reopen, but subreddits related to travel and sports remain highly impacted and show lower activity levels than the pre-pandemic period.
Edit intent: meaning-changed
Original:  In subreddits related to finance, food, and countries / cities, user activity is recovering to the pre-pandemic level in late April and May as countries reopen, but subreddits related to travel and sports remain highly impacted and show lower activity levels than the pre-pandemic period. Our work highlights the strength of Reddit as a source for understanding public reactions to COVID-19 and the importance of content moderation on the Internet during a pandemic .
Revised:  In subreddits related to finance, food, and countries / r/Coronavirus in February. This movement turns out to be highly predictable based on other subreddits that users were previously active in. Our work demonstrates how two highly-related communities emerge and develop their own identity in a crisis, and highlights the important role of existing communities in understanding such an emergence .
Edit intent: meaning-changed
Original: Although a significant number of compressed indexes for highly repetitive strings have been proposed thus far, developing compressed indexes that support faster queries remains a challenge.
Revised: Indexing highly repetitive strings (i.e., strings with many repetitions) for fast queries has become a central research topic in string processing, because it has a wide variety of applications in bioinformatics and natural language processing. Although a substantial number of indexes for highly repetitive strings have been proposed thus far, developing compressed indexes that support faster queries remains a challenge.
Edit intent: meaning-changed
Original:  Run-length Burrows-Wheeler transform (RLBWT) is a lossless data compression by a reversible permutation of an input string and run-length encoding, and it has become a popular research topic in string processing.
Revised:  The run-length Burrows-Wheeler transform (RLBWT) is a lossless data compression by a reversible permutation of an input string and run-length encoding, and it has become a popular research topic in string processing.
Edit intent: clarity
Original:  Run-length Burrows-Wheeler transform (RLBWT) is a lossless data compression by a reversible permutation of an input string and run-length encoding, and it has become a popular research topic in string processing. R-index Gagie et al., ACM'20%DIFDELCMD < ] %%% is an efficient compressed index on RLBWTwhose space usage depends not on string length but the number of runs in an RLBWT, and it supports locate queries in an optimal time with \omega are two key functions for building indexes on RLBWT, and the best previous result computes LF and \phi^{-1} in O(\log \log n) time with O} (r) words for the number r of runs in the RLBWTof an input string.
Revised:  Run-length Burrows-Wheeler transform (RLBWT) is a lossless data compression by a reversible permutation of an input string and run-length encoding, and it has Gagie et al., ACM'20%DIFDELCMD < ] %%% is an efficient compressed index on RLBWTwhose space usage depends not on string length but the number of runs in an RLBWT, and it supports locate queries in an optimal time with \omega are two key functions for building indexes on RLBWT, and the best previous result computes LF and \phi^{-1} in O(\log \log n) time with O} (r) words for the number r of runs in the RLBWTof an input string.
Edit intent: clarity
Original:  R-index Gagie et al., ACM'20%DIFDELCMD < ] %%% is an efficient compressed index on RLBWTwhose space usage depends not on string length but the number of runs in an RLBWT, and it supports locate queries in an optimal time with \omega are two key functions for building indexes on RLBWT, and the best previous result computes LF and \phi^{-1} in O(\log \log n) time with O} (r) words for the number r of runs in the RLBWTof an input string.
Revised:  R-index %DIFDELCMD < ] %%% is an efficient compressed index on RLBWTwhose space usage depends not on string length but the number of runs in an RLBWT, and it supports locate queries in an optimal time with \omega are two key functions for building indexes on RLBWT, and the best previous result computes LF and \phi^{-1} in O(\log \log n) time with O} (r) words for the number r of runs in the RLBWTof an input string.
Edit intent: clarity
Original:  R-index Gagie et al., ACM'20%DIFDELCMD < ] %%% is an efficient compressed index on RLBWTwhose space usage depends not on string length but the number of runs in an RLBWT, and it supports locate queries in an optimal time with \omega are two key functions for building indexes on RLBWT, and the best previous result computes LF and \phi^{-1} in O(\log \log n) time with O} (r) words for the number r of runs in the RLBWTof an input string.
Revised:  R-index Gagie et al., ACM'20%DIFDELCMD < ] %%% received interest for indexing highly repetitive strings. LF and \phi^{-1 are two key functions for building indexes on RLBWT, and the best previous result computes LF and \phi^{-1} in O(\log \log n) time with O} (r) words for the number r of runs in the RLBWTof an input string.
Edit intent: clarity
Original:  R-index Gagie et al., ACM'20%DIFDELCMD < ] %%% is an efficient compressed index on RLBWTwhose space usage depends not on string length but the number of runs in an RLBWT, and it supports locate queries in an optimal time with \omega are two key functions for building indexes on RLBWT, and the best previous result computes LF and \phi^{-1} in O(\log \log n) time with O} (r) words for the number r of runs in the RLBWTof an input string.
Revised:  R-index Gagie et al., ACM'20%DIFDELCMD < ] %%% is an efficient compressed index on RLBWTwhose space usage depends not on string length but the number of runs in an RLBWT, and it supports locate queries in an optimal time with \omega are two key functions for building indexes on RLBWT, and the best previous result computes LF and \phi^{-1} in O(\log \log n) time with O} (r) words of space for the string length n and the number r of runs in the RLBWTof an input string.
Edit intent: clarity
Original:  R-index Gagie et al., ACM'20%DIFDELCMD < ] %%% is an efficient compressed index on RLBWTwhose space usage depends not on string length but the number of runs in an RLBWT, and it supports locate queries in an optimal time with \omega are two key functions for building indexes on RLBWT, and the best previous result computes LF and \phi^{-1} in O(\log \log n) time with O} (r) words for the number r of runs in the RLBWTof an input string. Following this line of research, we present the first compressed index on RLBWT, which we callr-index-f , that supports various queries including locate, count, extract queries, decompression and prefix search in the optimal time with smaller working space of O(r) words for small alphabets in this paper.
Revised:  R-index Gagie et al., ACM'20%DIFDELCMD < ] %%% is an efficient compressed index on RLBWTwhose space usage depends not on string length but the number of runs in an RLBWT, and it supports locate queries in an optimal time with \omega are two key functions for building indexes on RLBWT, and the best previous result computes LF and \phi^{-1} in O(\log \log n) time with O} (r) words for the number r of runs in r-index-f , that supports various queries including locate, count, extract queries, decompression and prefix search in the optimal time with smaller working space of O(r) words for small alphabets in this paper.
Edit intent: coherence
Original:  Following this line of research, we present the first compressed index on RLBWT, which we callr-index-f , that supports various queries including locate, count, extract queries, decompression and prefix search in the optimal time with smaller working space of O(r) words for small alphabets in this paper.
Revised:  Following this line of research, we present the first compressed index on RLBWT, which we call , that supports various queries including locate, count, extract queries, decompression and prefix search in the optimal time with smaller working space of O(r) words for small alphabets in this paper.
Edit intent: clarity
Original:  Following this line of research, we present the first compressed index on RLBWT, which we callr-index-f , that supports various queries including locate, count, extract queries, decompression and prefix search in the optimal time with smaller working space of O(r) words for small alphabets in this paper. We present efficient data structures for computing two important functions of LF and \phi^{-1} in constant time with O(r) words of space , which is a bit step forward in computation time from the previous best result of O(\log \log n)time for string length n and O(r) words of space.
Revised:  Following this line of research, we present the first compressed index on RLBWT, which we callr-index-f RLBWT. In this paper, we improve LF and \phi^{-1} in constant time with O(r) words of space , which is a bit step forward in computation time from the previous best result of O(\log \log n)time for string length n and O(r) words of space.
Edit intent: clarity
Original:  We present efficient data structures for computing two important functions of LF and \phi^{-1} in constant time with O(r) words of space , which is a bit step forward in computation time from the previous best result of O(\log \log n)time for string length n and O(r) words of space.
Revised:  We present efficient data structures for computing two important functions of LF and \phi^{-1} so that they can be computed in a constant time with O(r) words of space , which is a bit step forward in computation time from the previous best result of O(\log \log n)time for string length n and O(r) words of space.
Edit intent: clarity
Original:  We present efficient data structures for computing two important functions of LF and \phi^{-1} in constant time with O(r) words of space , which is a bit step forward in computation time from the previous best result of O(\log \log n)time for string length n and O(r) words of space. Finally, We present algorithms for computing queries on RLBWT by leveraging those two data structures in optimal time with O(r) words of space.
Revised:  We present efficient data structures for computing two important functions of LF and \phi^{-1} in constant time with O(r) words of space . Subsequently, we present OptBWTR (optimal-time queries on BWT-runs compressed indexes), the first string index that supports various queries including locate, count, extract queries in optimal time with O(r) words of space.
Edit intent: meaning-changed
Original: Powered by artificial intelligence (AI) , particularly deep neural networks, computer aided detection (CAD) tools can be trained to recognize TB-related abnormalities on chest radiographs , thereby screening large numbers of people and reducing the pressure on healthcare professionals.
Revised: Artificial intelligence (AI) , particularly deep neural networks, computer aided detection (CAD) tools can be trained to recognize TB-related abnormalities on chest radiographs , thereby screening large numbers of people and reducing the pressure on healthcare professionals.
Edit intent: clarity
Original: Powered by artificial intelligence (AI) , particularly deep neural networks, computer aided detection (CAD) tools can be trained to recognize TB-related abnormalities on chest radiographs , thereby screening large numbers of people and reducing the pressure on healthcare professionals.
Revised: Powered by artificial intelligence (AI) products can be trained to recognize TB-related abnormalities on chest radiographs , thereby screening large numbers of people and reducing the pressure on healthcare professionals.
Edit intent: clarity
Original: Powered by artificial intelligence (AI) , particularly deep neural networks, computer aided detection (CAD) tools can be trained to recognize TB-related abnormalities on chest radiographs , thereby screening large numbers of people and reducing the pressure on healthcare professionals.
Revised: Powered by artificial intelligence (AI) , particularly deep neural networks, computer aided detection (CAD) tools can be trained to recognize tuberculosis (TB)-related abnormalities on chest radiographs , thereby screening large numbers of people and reducing the pressure on healthcare professionals.
Edit intent: clarity
Original: Powered by artificial intelligence (AI) , particularly deep neural networks, computer aided detection (CAD) tools can be trained to recognize TB-related abnormalities on chest radiographs , thereby screening large numbers of people and reducing the pressure on healthcare professionals. Addressing the lack of studies comparing the performance of different products, we evaluated five AI software platforms specific to TB : CAD4TB ( v6 ), InferReadDR (v2), Lunit INSIGHT for Chest Radiography (v4.9.0), JF CXR-1 (v2) by and qXR (v3) by on an unseen dataset of chest X-rays collected in three TB screening center in Dhaka, Bangladesh.
Revised: Powered by artificial intelligence (AI) , particularly deep neural networks, computer aided detection (CAD) tools can be trained to recognize TB-related abnormalities on chest radiographs . Various AI products are available commercially, yet there is lack of evidence on how their performance compared with each other and with radiologists. We evaluated five AI software platforms specific to TB : CAD4TB ( v6 ), InferReadDR (v2), Lunit INSIGHT for Chest Radiography (v4.9.0), JF CXR-1 (v2) by and qXR (v3) by on an unseen dataset of chest X-rays collected in three TB screening center in Dhaka, Bangladesh.
Edit intent: coherence
Original:  Addressing the lack of studies comparing the performance of different products, we evaluated five AI software platforms specific to TB : CAD4TB ( v6 ), InferReadDR (v2), Lunit INSIGHT for Chest Radiography (v4.9.0), JF CXR-1 (v2) by and qXR (v3) by on an unseen dataset of chest X-rays collected in three TB screening center in Dhaka, Bangladesh.
Revised:  Addressing the lack of studies comparing the performance of different products, we evaluated five AI software products for screening and triaging TB using a large dataset that had not been used to train any commercial AI products. Individuals (>=15 years old) presenting to three TB screening centers in Dhaka, Bangladesh, were recruited consecutively. All CXR were read independently by a group of three Bangladeshi registered radiologists and five commercial AI products : CAD4TB ( v6 ), InferReadDR (v2), Lunit INSIGHT for Chest Radiography (v4.9.0), JF CXR-1 (v2) by and qXR (v3) by on an unseen dataset of chest X-rays collected in three TB screening center in Dhaka, Bangladesh.
Edit intent: meaning-changed
Original:  Addressing the lack of studies comparing the performance of different products, we evaluated five AI software platforms specific to TB : CAD4TB ( v6 ), InferReadDR (v2), Lunit INSIGHT for Chest Radiography (v4.9.0), JF CXR-1 (v2) by and qXR (v3) by on an unseen dataset of chest X-rays collected in three TB screening center in Dhaka, Bangladesh.
Revised:  Addressing the lack of studies comparing the performance of different products, we evaluated five AI software platforms specific to TB : CAD4TB ( v7 ), InferReadDR (v2), Lunit INSIGHT for Chest Radiography (v4.9.0), JF CXR-1 (v2) by and qXR (v3) by on an unseen dataset of chest X-rays collected in three TB screening center in Dhaka, Bangladesh.
Edit intent: meaning-changed
Original:  Addressing the lack of studies comparing the performance of different products, we evaluated five AI software platforms specific to TB : CAD4TB ( v6 ), InferReadDR (v2), Lunit INSIGHT for Chest Radiography (v4.9.0), JF CXR-1 (v2) by and qXR (v3) by on an unseen dataset of chest X-rays collected in three TB screening center in Dhaka, Bangladesh.
Revised:  Addressing the lack of studies comparing the performance of different products, we evaluated five AI software platforms specific to TB : CAD4TB ( v6 ), InferReadDR (v2), Lunit INSIGHT CXR (v4.9.0), JF CXR-1 (v2) by and qXR (v3) by on an unseen dataset of chest X-rays collected in three TB screening center in Dhaka, Bangladesh.
Edit intent: clarity
Original:  Addressing the lack of studies comparing the performance of different products, we evaluated five AI software platforms specific to TB : CAD4TB ( v6 ), InferReadDR (v2), Lunit INSIGHT for Chest Radiography (v4.9.0), JF CXR-1 (v2) by and qXR (v3) by on an unseen dataset of chest X-rays collected in three TB screening center in Dhaka, Bangladesh.
Revised:  Addressing the lack of studies comparing the performance of different products, we evaluated five AI software platforms specific to TB : CAD4TB ( v6 ), InferReadDR (v2), Lunit INSIGHT for Chest Radiography (v4.9.0), JF CXR-1 (v2) , and qXR (v3) by on an unseen dataset of chest X-rays collected in three TB screening center in Dhaka, Bangladesh.
Edit intent: fluency
Original:  Addressing the lack of studies comparing the performance of different products, we evaluated five AI software platforms specific to TB : CAD4TB ( v6 ), InferReadDR (v2), Lunit INSIGHT for Chest Radiography (v4.9.0), JF CXR-1 (v2) by and qXR (v3) by on an unseen dataset of chest X-rays collected in three TB screening center in Dhaka, Bangladesh. The 23,566 individuals included in the study all received a CXR read by a group of three Bangladeshi board-certified radiologists. A sample of CXRs were re-read by US board-certified radiologists. Xpert was used as the reference standard.
Revised:  Addressing the lack of studies comparing the performance of different products, we evaluated five AI software platforms specific to TB : CAD4TB ( v6 ), InferReadDR (v2), Lunit INSIGHT for Chest Radiography (v4.9.0), JF CXR-1 (v2) by and qXR (v3) .
Edit intent: meaning-changed
Original:  The areas under the receiver operating characteristic curves are qXR: 0.91 (95\% CI: 0.90-0.91), Lunit INSIGHT CXR: 0.89 (95\% CI: 0.88-0.89), InferReadDR: 0.85 (95\% CI: 0.84-0.86), JF CXR-1: 0.85 (95\% CI: 0.84-0.85) , CAD4TB: 0.82 (95\% CI: 0.81-0.83).
Revised:  The areas under the receiver operating characteristic curve are qXR: 0.91 (95\% CI: 0.90-0.91), Lunit INSIGHT CXR: 0.89 (95\% CI: 0.88-0.89), InferReadDR: 0.85 (95\% CI: 0.84-0.86), JF CXR-1: 0.85 (95\% CI: 0.84-0.85) , CAD4TB: 0.82 (95\% CI: 0.81-0.83).
Edit intent: clarity
Original:  The areas under the receiver operating characteristic curves are qXR: 0.91 (95\% CI: 0.90-0.91), Lunit INSIGHT CXR: 0.89 (95\% CI: 0.88-0.89), InferReadDR: 0.85 (95\% CI: 0.84-0.86), JF CXR-1: 0.85 (95\% CI: 0.84-0.85) , CAD4TB: 0.82 (95\% CI: 0.81-0.83).
Revised:  The areas under the receiver operating characteristic curves are qXR: 90.81\% (95\% CI: 0.90-0.91), Lunit INSIGHT CXR: 0.89 (95\% CI: 0.88-0.89), InferReadDR: 0.85 (95\% CI: 0.84-0.86), JF CXR-1: 0.85 (95\% CI: 0.84-0.85) , CAD4TB: 0.82 (95\% CI: 0.81-0.83).
Edit intent: meaning-changed
Original:  The areas under the receiver operating characteristic curves are qXR: 0.91 (95\% CI: 0.90-0.91), Lunit INSIGHT CXR: 0.89 (95\% CI: 0.88-0.89), InferReadDR: 0.85 (95\% CI: 0.84-0.86), JF CXR-1: 0.85 (95\% CI: 0.84-0.85) , CAD4TB: 0.82 (95\% CI: 0.81-0.83).
Revised:  The areas under the receiver operating characteristic curves are qXR: 0.91 (95\% CI: 90.33-91.29\%), CAD4TB: 90.34\% (95\% CI: 0.88-0.89), InferReadDR: 0.85 (95\% CI: 0.84-0.86), JF CXR-1: 0.85 (95\% CI: 0.84-0.85) , CAD4TB: 0.82 (95\% CI: 0.81-0.83).
Edit intent: meaning-changed
Original:  The areas under the receiver operating characteristic curves are qXR: 0.91 (95\% CI: 0.90-0.91), Lunit INSIGHT CXR: 0.89 (95\% CI: 0.88-0.89), InferReadDR: 0.85 (95\% CI: 0.84-0.86), JF CXR-1: 0.85 (95\% CI: 0.84-0.85) , CAD4TB: 0.82 (95\% CI: 0.81-0.83).
Revised:  The areas under the receiver operating characteristic curves are qXR: 0.91 (95\% CI: 0.90-0.91), Lunit INSIGHT CXR: 0.89 (95\% CI: 89.81-90.87), Lunit INSIGHT CXR: 88.61\% (95\% CI: 0.84-0.86), JF CXR-1: 0.85 (95\% CI: 0.84-0.85) , CAD4TB: 0.82 (95\% CI: 0.81-0.83).
Edit intent: meaning-changed
Original:  The areas under the receiver operating characteristic curves are qXR: 0.91 (95\% CI: 0.90-0.91), Lunit INSIGHT CXR: 0.89 (95\% CI: 0.88-0.89), InferReadDR: 0.85 (95\% CI: 0.84-0.86), JF CXR-1: 0.85 (95\% CI: 0.84-0.85) , CAD4TB: 0.82 (95\% CI: 0.81-0.83).
Revised:  The areas under the receiver operating characteristic curves are qXR: 0.91 (95\% CI: 0.90-0.91), Lunit INSIGHT CXR: 0.89 (95\% CI: 0.88-0.89), InferReadDR: 0.85 (95\% CI: 88.03\%-89.20\%), InferReadDR: 84.90\% (95\% CI: 0.84-0.85) , CAD4TB: 0.82 (95\% CI: 0.81-0.83).
Edit intent: meaning-changed
Original:  The areas under the receiver operating characteristic curves are qXR: 0.91 (95\% CI: 0.90-0.91), Lunit INSIGHT CXR: 0.89 (95\% CI: 0.88-0.89), InferReadDR: 0.85 (95\% CI: 0.84-0.86), JF CXR-1: 0.85 (95\% CI: 0.84-0.85) , CAD4TB: 0.82 (95\% CI: 0.81-0.83).
Revised:  The areas under the receiver operating characteristic curves are qXR: 0.91 (95\% CI: 0.90-0.91), Lunit INSIGHT CXR: 0.89 (95\% CI: 0.88-0.89), InferReadDR: 0.85 (95\% CI: 0.84-0.86), JF CXR-1: 0.85 (95\% CI: 84.27-85.54\%) and JF CXR-1: 84.89\% (95\% CI: 0.81-0.83).
Edit intent: meaning-changed
Original:  The areas under the receiver operating characteristic curves are qXR: 0.91 (95\% CI: 0.90-0.91), Lunit INSIGHT CXR: 0.89 (95\% CI: 0.88-0.89), InferReadDR: 0.85 (95\% CI: 0.84-0.86), JF CXR-1: 0.85 (95\% CI: 0.84-0.85) , CAD4TB: 0.82 (95\% CI: 0.81-0.83). We also proposed a new analytical framework that evaluates a screening and triage test and informs threshold selection through tradeoff between cost efficiency and ability to triage. Further, we assessed the performance of the five AI algorithms across the subgroups of age , use cases, and prior TB history , and found that the threshold scores performed differently across different subgroups.
Revised:  The areas under the receiver operating characteristic curves are qXR: 0.91 (95\% CI: 0.90-0.91), Lunit INSIGHT CXR: 0.89 (95\% CI: 0.88-0.89), InferReadDR: 0.85 (95\% CI: 0.84-0.86), JF CXR-1: 0.85 (95\% CI: 0.84-0.85) , CAD4TB: 0.82 (95\% CI: 84.26-85.53\%). Only qXR met the TPP with 74.3\% specificity at 90\% sensitivity. Five AI algorithms can reduce the number of Xpert tests required by 50\%, while maintaining a sensitivity above 90\%. All AI algorithms performed worse among the older age and people with prior TB history , and found that the threshold scores performed differently across different subgroups.
Edit intent: meaning-changed