wanyu/IteraTeR_full_sent huggingface dataset:
Original:  For instance, when p is a constant or p=n^{-3/4}, we show that only \Omega(\sqrt{n\log n}) correct seeds suffice for perfect matching, while the previously best-known results demand \Omega(n) and \Omega(n^{3/4}\log n) correct seeds, respectively} . Numerical experiments on both synthetic and real graphs corroborate our theoretical findings and show that our 2-hop algorithm significantly outperforms the 1-hop algorithm when the graphsare relatively sparse .
Revised:  For instance, when p is a constant or p=n^{-3/4}, we show that only \Omega(\sqrt{n\log n}) correct seeds suffice for perfect matching, while the previously best-known results demand \Omega(n) and \Omega(n^{3/4}\log n) correct seeds, respectively} . Numerical experiments on both synthetic and real graphs corroborate our theoretical findings and show that our 2-hop algorithm on a variety of synthetic and real graphs .
Edit intent: meaning-changed
Original:  In this vision paper we propose CovidSens, the concept of social-sensing-based risk alerting systems to notify the general public  about the COVID-19 spread  .
Revised:  In this vision paper we propose CovidSens, a vision of social sensing based risk alert systems to spontaneously obtain and analyze social data to infer COVID-19 propagation. CovidSens can actively help to keep the general public about the COVID-19 spread .
Edit intent: meaning-changed
Original:  The CovidSens concept is motivated by two recent observations: 1) people have been actively sharing their state of health and experience of the COVID-19 via online social media, and 2) official warning channels and news agencies are relatively slower than people reporting their observations and experiences about COVID-19 on social media .
Revised:  The CovidSens concept is motivated by three observations: 1) people have been actively sharing their state of health and experience of the COVID-19 via online social media, and 2) official warning channels and news agencies are relatively slower than people reporting their observations and experiences about COVID-19 on social media .
Edit intent: meaning-changed
Original:  The CovidSens concept is motivated by two recent observations: 1) people have been actively sharing their state of health and experience of the COVID-19 via online social media, and 2) official warning channels and news agencies are relatively slower than people reporting their observations and experiences about COVID-19 on social media .
Revised:  The CovidSens concept is motivated by two recent observations: 1) people actively share their experience of COVID-19 via online social media, and 2) official warning channels and news agencies are relatively slower than people reporting their observations and experiences about COVID-19 on social media .
Edit intent: clarity
Original:  The CovidSens concept is motivated by two recent observations: 1) people have been actively sharing their state of health and experience of the COVID-19 via online social media, and 2) official warning channels and news agencies are relatively slower than people reporting their observations and experiences about COVID-19 on social media .
Revised:  The CovidSens concept is motivated by two recent observations: 1) people have been actively sharing their state of health and experience of the COVID-19 via online social media, 2) official warning channels and news agencies are relatively slower than people reporting their observations and experiences about COVID-19 on social media .
Edit intent: coherence
Original:  The CovidSens concept is motivated by two recent observations: 1) people have been actively sharing their state of health and experience of the COVID-19 via online social media, and 2) official warning channels and news agencies are relatively slower than people reporting their observations and experiences about COVID-19 on social media .
Revised:  The CovidSens concept is motivated by two recent observations: 1) people have been actively sharing their state of health and experience of the COVID-19 via online social media, and 2) official warning channels and news agencies are relatively slower than people reporting on social media .
Edit intent: clarity
Original:  The CovidSens concept is motivated by two recent observations: 1) people have been actively sharing their state of health and experience of the COVID-19 via online social media, and 2) official warning channels and news agencies are relatively slower than people reporting their observations and experiences about COVID-19 on social media . We anticipate an unprecedented opportunity to leverage the posts generated by the social media users to build a real-time  analytic system for gathering and circulating vital information of the COVID-19 propagation  . Specifically, the vision of CovidSens attempts to answer the questions of: how to track the spread of the COVID-19?
Revised:  The CovidSens concept is motivated by two recent observations: 1) people have been actively sharing their state of health and experience of the COVID-19 via online social media, and 2) official warning channels and news agencies are relatively slower than people reporting their observations and experiences about COVID-19 on social media , and 3) online users are frequently equipped with substantially capable mobile devices that can perform data processing and analytics. We envision unprecedented opportunity to leverage the posts generated by the social media users to build a real-time analytic system for gathering and circulating vital information of the COVID-19 propagation . Specifically, the vision of CovidSens attempts to answer the questions of: how to track the spread of the COVID-19?
Edit intent: meaning-changed
Original:  We anticipate an unprecedented opportunity to leverage the posts generated by the social media users to build a real-time  analytic system for gathering and circulating vital information of the COVID-19 propagation  . Specifically, the vision of CovidSens attempts to answer the questions of: how to track the spread of the COVID-19?
Revised:  We anticipate an unprecedented opportunity to leverage posts generated by the social media users to build a real-time analytic system for gathering and circulating vital information of the COVID-19 propagation . Specifically, the vision of CovidSens attempts to answer the questions of: how to track the spread of the COVID-19?
Edit intent: fluency
Original:  We anticipate an unprecedented opportunity to leverage the posts generated by the social media users to build a real-time  analytic system for gathering and circulating vital information of the COVID-19 propagation  . Specifically, the vision of CovidSens attempts to answer the questions of: how to track the spread of the COVID-19?
Revised:  We anticipate an unprecedented opportunity to leverage the posts generated by ordinary people to build a real-time analytic system for gathering and circulating vital information of the COVID-19 propagation . Specifically, the vision of CovidSens attempts to answer the questions of: how to track the spread of the COVID-19?
Edit intent: clarity
Original:  We anticipate an unprecedented opportunity to leverage the posts generated by the social media users to build a real-time  analytic system for gathering and circulating vital information of the COVID-19 propagation  . Specifically, the vision of CovidSens attempts to answer the questions of: how to track the spread of the COVID-19?
Revised:  We anticipate an unprecedented opportunity to leverage the posts generated by the social media users to build a real-time sensing and analytic system for gathering and circulating vital information of the COVID-19 propagation . Specifically, the vision of CovidSens attempts to answer the questions of: how to track the spread of the COVID-19?
Edit intent: meaning-changed
Original:  We anticipate an unprecedented opportunity to leverage the posts generated by the social media users to build a real-time  analytic system for gathering and circulating vital information of the COVID-19 propagation  . Specifically, the vision of CovidSens attempts to answer the questions of: how to track the spread of the COVID-19?
Revised:  We anticipate an unprecedented opportunity to leverage the posts generated by the social media users to build a real-time analytic system for gathering and circulating COVID-19 propagation . Specifically, the vision of CovidSens attempts to answer the questions of: how to track the spread of the COVID-19?
Edit intent: clarity
Original:  We anticipate an unprecedented opportunity to leverage the posts generated by the social media users to build a real-time  analytic system for gathering and circulating vital information of the COVID-19 propagation  . Specifically, the vision of CovidSens attempts to answer the questions of: how to track the spread of the COVID-19?
Revised:  We anticipate an unprecedented opportunity to leverage the posts generated by the social media users to build a real-time analytic system for gathering and circulating vital information of the COVID-19 propagation data . Specifically, the vision of CovidSens attempts to answer the questions of: how to track the spread of the COVID-19?
Edit intent: clarity
Original:  We anticipate an unprecedented opportunity to leverage the posts generated by the social media users to build a real-time  analytic system for gathering and circulating vital information of the COVID-19 propagation  . Specifically, the vision of CovidSens attempts to answer the questions of: how to track the spread of the COVID-19?
Revised:  We anticipate an unprecedented opportunity to leverage the posts generated by the social media users to build a real-time analytic system for gathering and circulating vital information of the COVID-19 propagation . Specifically, the vision of CovidSens attempts to answer the questions :
Edit intent: clarity
Original:  We investigate the effects on lockdown scenarios and exit strategies by introducing heterogeneities in the model. In particular, we consider Italian regions as separate administrative entities  in which social interactions through different age classes occur.
Revised:  We investigate the effects on lockdown scenarios and exit mechanisms by accounting for two fundamental sources of heterogeneity within the model: geography and demography. We consider Italian Regions as separate administrative entities in which social interactions through different age classes occur.
Edit intent: meaning-changed
Original:  In particular, we consider Italian regions as separate administrative entities  in which social interactions through different age classes occur. We find that, due to the sparsity of the mobility matrix, epidemics  develop independently in different regions once the outbreak starts.
Revised:  In particular, we consider Italian regions as separate administrative entities in which social interactions between age cohorts occur. Due to the sparsity of the mobility matrix, epidemics develop independently in different regions once the outbreak starts.
Edit intent: clarity
Original:  We find that, due to the sparsity of the mobility matrix, epidemics  develop independently in different regions once the outbreak starts. Moreover, after the epidemics ha started, the influence of contacts with other regions becomes soon irrelevant. Sparsity might be responsible for the observed delays among different regions. Analogous arguments apply to the world/countries scenario. We also find that disregarding the structure of social contacts could lead to severe underestimation of the post-lockdown effects .
Revised:  We find that, due to the sparsity of the mobility matrix, epidemics develop independently in different regions . Finally, we show how disregarding the specific structure of social contacts could lead to severe underestimation of the post-lockdown effects .
Edit intent: clarity
Original:  We also find that disregarding the structure of social contacts could lead to severe underestimation of the post-lockdown effects . Nevertheless, age class based strategies can help to mitigate rebound effectswith milder strategies. Finally, we point out that these results can be generalized beyond this particular model by providing a description of the effects of key parameters on non-medical epidemic mitigation strategies .
Revised:  We also find that disregarding the structure of social contacts could lead to severe underestimation of the post-lockdown effects , while specific age cohort based measures can sustain the mitigation of rebound effects. Our model is general, and it highlights the effects of key parameters on non-medical epidemic mitigation strategies .
Edit intent: clarity
Original:  Due to the sparsity of the  mobility matrix,  epidemics tend to develop independently in different regions . Finally , we show how disregarding the specific structure of social contacts could lead to severe underestimation of post-lockdown effects, while specific age cohort based measures can sustain the mitigation of rebound effects.
Revised:  Due to the sparsity of the mobility matrix, epidemics tend to develop independently across areas, justifying the adoption of solutions specific to individual regions or to clusters of regions. Second , we show how disregarding the specific structure of social contacts could lead to severe underestimation of post-lockdown effects, while specific age cohort based measures can sustain the mitigation of rebound effects.
Edit intent: meaning-changed
Original:  Finally , we show how disregarding the specific structure of social contacts could lead to severe underestimation of post-lockdown effects, while specific age cohort based measures can sustain the mitigation of rebound effects.
Revised:  Finally , we show that social contacts between age classes play a fundamental role and that measures which take into account the age structure of the population can provide a significant contribution to mitigate the rebound effects.
Edit intent: clarity
Original:  We introduce a baseline algorithm with \mathcal{O}(n^2) running time which identifies all interesting patterns for all colors in the string satisfying certain minimality conditions. When one is interested in patterns related to only one color , we provide an algorithm that identifies patterns in \mathcal{O}(n^2\log n) time , but is faster than the first algorithm in practice , both on simulated and on real-world patterns.
Revised:  We introduce a baseline algorithm with \mathcal{O}(n^2) running time which identifies all interesting patterns for all colors in the string . For the case where one is interested in patterns related to only one color , we provide an algorithm that identifies patterns in \mathcal{O}(n^2\log n) time , but is faster than the first algorithm in practice , both on simulated and on real-world patterns.
Edit intent: clarity
Original:  When one is interested in patterns related to only one color , we provide an algorithm that identifies patterns in \mathcal{O}(n^2\log n) time , but is faster than the first algorithm in practice , both on simulated and on real-world patterns.
Revised:  When one is interested in patterns related to one color only, we also provide a second algorithm which runs in \mathcal{O}(n^2\log n) time , but is faster than the first algorithm in practice , both on simulated and on real-world patterns.
Edit intent: clarity
Original:  When one is interested in patterns related to only one color , we provide an algorithm that identifies patterns in \mathcal{O}(n^2\log n) time , but is faster than the first algorithm in practice , both on simulated and on real-world patterns.
Revised:  When one is interested in patterns related to only one color , we provide an algorithm that identifies patterns in \mathcal{O}(n^2\log n) time in the worst case but is faster than the first algorithm in practice , both on simulated and on real-world patterns.
Edit intent: meaning-changed
Original:  When one is interested in patterns related to only one color , we provide an algorithm that identifies patterns in \mathcal{O}(n^2\log n) time , but is faster than the first algorithm in practice , both on simulated and on real-world patterns.
Revised:  When one is interested in patterns related to only one color , we provide an algorithm that identifies patterns in \mathcal{O}(n^2\log n) time , but is faster than the baseline algorithm in practice , both on simulated and on real-world patterns.
Edit intent: clarity
Original:  When one is interested in patterns related to only one color , we provide an algorithm that identifies patterns in \mathcal{O}(n^2\log n) time , but is faster than the first algorithm in practice , both on simulated and on real-world patterns.
Revised:  When one is interested in patterns related to only one color , we provide an algorithm that identifies patterns in \mathcal{O}(n^2\log n) time , but is faster than the first algorithm in practice . Both solutions use suffix trees, and the second algorithm also uses an appropriately defined priority queue, which allows us to reduce the number of computations. We performed an experimental evaluation of the proposed approaches over both synthetic and real-world patterns.
Edit intent: meaning-changed
Original:  When one is interested in patterns related to only one color , we provide an algorithm that identifies patterns in \mathcal{O}(n^2\log n) time , but is faster than the first algorithm in practice , both on simulated and on real-world patterns.
Revised:  When one is interested in patterns related to only one color , we provide an algorithm that identifies patterns in \mathcal{O}(n^2\log n) time , but is faster than the first algorithm in practice , both on simulated and on real-world datasets, and found that the second algorithm outperforms the first algorithm on all simulated data, while on the real-world data, the performance varies between a slight slowdown (on half of the datasets) and a speedup by a factor of up to 11.
Edit intent: meaning-changed
Original: In March and April 2020, public health authorities in the United States acted to mitigate transmission of COVID-19. These actions were not coordinated at the national level  , which creates an opportunity to use spatial and temporal variation to measure their effect with greater accuracy.
Revised: Governments issue "stay at home" orders to reduce the spread of contagious diseases, but the magnitude of such orders' effectiveness is uncertain. In the United States these orders were not coordinated at the national level , which creates an opportunity to use spatial and temporal variation to measure their effect with greater accuracy.
Edit intent: meaning-changed
Original:  We combine publicly available data sources on the timing of stay-at-home orders and daily confirmed COVID-19 cases  at the county level in the United States (N = 124,027). We then derive from the classic SIR model a two-way fixed-effects model and apply it to the data with controls for unmeasured differences between counties and over time. Mean county-level daily growth in COVID-19 infections peaked at 17.2\% just before stay-at-home  orders were issued.
Revised:  We combine publicly available data sources on the timing of stay-at-home orders and daily confirmed COVID-19 cases at the county level in the United States . We estimate the effect of stay-at-home orders were issued.
Edit intent: clarity
Original:  Mean county-level daily growth in COVID-19 infections peaked at 17.2\% just before stay-at-home  orders were issued.
Revised:  Mean county-level daily growth in COVID-19 infections peaked at 17.2\% just before stay-at-home orders using a difference-in-differences design that accounts for unmeasured local variation in factors like health systems and demographics and for unmeasured temporal variation in factors like national mitigation actions and access to tests. Compared to counties that did not implement stay-at-home orders were issued.
Edit intent: meaning-changed
Original:  Mean county-level daily growth in COVID-19 infections peaked at 17.2\% just before stay-at-home  orders were issued. Two way fixed-effects regression estimates suggest that orders were associated with a 3.9 percentage point (95\% CI 1.2 to 6.6 ) reduction in the growth rate after one week and an 6.9 percentage point (2.4 to 11.5 ) reduction after two weeks .
Revised:  Mean county-level daily growth in COVID-19 infections peaked at 17.2\% just before stay-at-home orders , the results show that the orders are associated with a 3.9 percentage point (95\% CI 1.2 to 6.6 ) reduction in the growth rate after one week and an 6.9 percentage point (2.4 to 11.5 ) reduction after two weeks .
Edit intent: clarity
Original:  Two way fixed-effects regression estimates suggest that orders were associated with a 3.9 percentage point (95\% CI 1.2 to 6.6 ) reduction in the growth rate after one week and an 6.9 percentage point (2.4 to 11.5 ) reduction after two weeks .
Revised:  Two way fixed-effects regression estimates suggest that orders were associated with a 30.2 percent (11.0 to 45.2 ) reduction in the growth rate after one week and an 6.9 percentage point (2.4 to 11.5 ) reduction after two weeks .
Edit intent: meaning-changed
Original:  Two way fixed-effects regression estimates suggest that orders were associated with a 3.9 percentage point (95\% CI 1.2 to 6.6 ) reduction in the growth rate after one week and an 6.9 percentage point (2.4 to 11.5 ) reduction after two weeks .
Revised:  Two way fixed-effects regression estimates suggest that orders were associated with a 3.9 percentage point (95\% CI 1.2 to 6.6 ) reduction in weekly cases after one week and an 6.9 percentage point (2.4 to 11.5 ) reduction after two weeks .
Edit intent: meaning-changed
Original:  Two way fixed-effects regression estimates suggest that orders were associated with a 3.9 percentage point (95\% CI 1.2 to 6.6 ) reduction in the growth rate after one week and an 6.9 percentage point (2.4 to 11.5 ) reduction after two weeks .
Revised:  Two way fixed-effects regression estimates suggest that orders were associated with a 3.9 percentage point (95\% CI 1.2 to 6.6 ) reduction in the growth rate after one week , a 40.0 percent (23.4 to 53.0 ) reduction after two weeks .
Edit intent: meaning-changed
Original:  Two way fixed-effects regression estimates suggest that orders were associated with a 3.9 percentage point (95\% CI 1.2 to 6.6 ) reduction in the growth rate after one week and an 6.9 percentage point (2.4 to 11.5 ) reduction after two weeks . By day 27 the reduction (22.6 percentage points, 14.8 to 30.5) had surpassed the growth at the peak, indicating that growth had turned negative and the number of new daily infections was beginning to decline. A hypothetical national stay-at-home order issued on March 13 , 2020 when a national emergency was declared might have reduced cumulative infections by 63.3\%, and might have helped to reverse exponential growth in the disease by April 10.
Revised:  Two way fixed-effects regression estimates suggest that orders were associated with a 3.9 percentage point (95\% CI 1.2 to 6.6 ) reduction in the growth rate after one week and an 6.9 percentage point (2.4 to 11.5 ) reduction after two weeks , 2020 when a national emergency was declared might have reduced cumulative infections by 63.3\%, and might have helped to reverse exponential growth in the disease by April 10.
Edit intent: meaning-changed
Original:  A hypothetical national stay-at-home order issued on March 13 , 2020 when a national emergency was declared might have reduced cumulative infections by 63.3\%, and might have helped to reverse exponential growth in the disease by April 10. The results here suggest that a coordinated nationwide stay-at-home order may have reduced by hundreds of thousands the current number of infections and by tens of thousands the total number of deaths from COVID-19.
Revised:  A hypothetical national stay-at-home order issued on March 13 , and a 48.6 percent (31.1 to 61.7) reduction after three weeks. Stay-at-home orders are also associated with a 59.8 percent (18.3 to 80.2) reduction in weekly fatalities after three weeks. These results suggest that stay-at-home order may have reduced by hundreds of thousands the current number of infections and by tens of thousands the total number of deaths from COVID-19.
Edit intent: meaning-changed