wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The introduced tick size regime impacted the microstructure in accordance with existing literature on tick size changes. Remarkably, the modification of the microstructure also impacted volatility and transacted volume .
Revised:  The introduced tick size regime , both intraday and in the closing auction. An increase (decrease) in tick size is associated with a decrease (increase) in intraday liquidity, but a more (less) stable market. In the closing auction an increase in tick size has a positive effect on liquidity. Moreover, we report a positive relationship between tick size and transacted volume .
Edit intent: meaning-changed
Original:  Remarkably, the modification of the microstructure also impacted volatility and transacted volume . Furthermore, it is shown that closing auction volumes increased heavily since MiFID II , leading to higher absolute returns in the auctions.
Revised:  Remarkably, the modification of the microstructure also impacted volatility and transacted volume , in particular in the closing auction. Finally, closing auction volumes increased heavily since MiFID II , leading to higher absolute returns in the auctions.
Edit intent: coherence
Original: Lack of interactions and engagement among different  URLanizations, donors, and the general public severely limits opportunities that URLanizations create for street childrenin third-world countries . Developing a digital hub can eliminate barriers by integrating people across all groups .
Revised: Computing for the development of marginalized communities is a big deal of challenges for researchers. Different URLanizations are working to develop the conditions of a specialized marginalized community namely Street Children, one of the most underprivileged communities in Bangladesh. However, lack of proper engagement among different URLanizations, donors, and the general public severely limits opportunities that URLanizations create for street childrenin third-world countries . Developing a digital hub can eliminate barriers by integrating people across all groups .
Edit intent: meaning-changed
Original: Lack of interactions and engagement among different  URLanizations, donors, and the general public severely limits opportunities that URLanizations create for street childrenin third-world countries . Developing a digital hub can eliminate barriers by integrating people across all groups .
Revised: Lack of interactions and engagement among different social URLanizations, donors, and the general public severely limits opportunities that URLanizations create for street childrenin third-world countries . Developing a digital hub can eliminate barriers by integrating people across all groups .
Edit intent: meaning-changed
Original: Lack of interactions and engagement among different  URLanizations, donors, and the general public severely limits opportunities that URLanizations create for street childrenin third-world countries . Developing a digital hub can eliminate barriers by integrating people across all groups .
Revised: Lack of interactions and engagement among different URLanizations, donors, and the mass community limits the goal of the development of street children . Developing a digital hub can eliminate barriers by integrating people across all groups .
Edit intent: clarity
Original: Lack of interactions and engagement among different  URLanizations, donors, and the general public severely limits opportunities that URLanizations create for street childrenin third-world countries . Developing a digital hub can eliminate barriers by integrating people across all groups .
Revised: Lack of interactions and engagement among different URLanizations, donors, and the general public severely limits opportunities that URLanizations create for street childrenin third-world countries . Developing a URLanization hub can eliminate barriers by integrating people across all groups .
Edit intent: clarity
Original: Lack of interactions and engagement among different  URLanizations, donors, and the general public severely limits opportunities that URLanizations create for street childrenin third-world countries . Developing a digital hub can eliminate barriers by integrating people across all groups .
Revised: Lack of interactions and engagement among different URLanizations, donors, and the general public severely limits opportunities that URLanizations create for street childrenin third-world countries . Developing a digital hub can eliminate communication gap as well as the information gap by involving people of all communities .
Edit intent: clarity
Original:  However, in doing so, critical human factor challenges need to be overcome. Accordingly, in this study conducted over a couple of years, we design and develop a digital hub deployed to serve children living on the streets in Dhaka, the capital of Bangladesh (an excellent candidate to represent life in highly populated cities in a third-world country). Here, we - a ) conduct face-to-face interviews with members of representative URLanizations to identify fundamental human-centric challenges; b) design a digital hub that provides an effective interface for pairing URLanizations, donors, and the general public; c) enable URLanizations to use our system 24%DIFDELCMD < \times7%%% , and report results;
Revised:  However, %DIFDELCMD < \times7%%% , and report results;
Edit intent: coherence
Original:  c) enable URLanizations to use our system 24%DIFDELCMD < \times7%%% , and report results; and d) present critical human-computing issues, more challenges, theoretical advancement, and potential scopes for future work in this realm with broad applicability .
Revised:  c) enable URLanizations to use our system 24%DIFDELCMD < \times7%%% some human imposed stigmas may often limit the rate of success of potential virtual computing solutions intended URLanizations working with the marginalized communities, which we also face in our case. After a partial successful deployment, the design itself needs to be self comprehensive and trustworthy in order to overcome the stigmas that demand a reasonable amount of time. Moreover, after a wide scalable deployment, it is yet to be investigated whether the design of our computational solution can attain the goal for the facilitation of URLanizations so that URLanizations can become more effective for the development of street children than before .
Edit intent: meaning-changed
Original:  It predicts that the disease will run its full course over six to seven months, starting from the date on which the world URLanisation provided the first ` Situation report' (21 January 2020 - day 1).
Revised:  It predicts that the disease will run its full course over six to seven months, starting from the date on which the world URLanisation provided the first ' Situation report' (21 January 2020 - day 1).
Edit intent: fluency
Original:  The disease may be expected to infect approximately 25%DIFDELCMD < \\%%% %DIF < of the global population, i.e. about 1.95 billion people, taking approximately 85 million lives.
Revised:  The disease may be expected to infect approximately %DIFDELCMD < \\%%% %DIF < of the global population, i.e. about 1.95 billion people, taking approximately 85 million lives.
Edit intent: clarity
Original:  As such, the efficacy of the measures that are currently being taken to contain the coronavirus should be carefully weighed against their impact on the world economy.abstract 
Revised:  As such, the efficacy of the measures that are currently being taken to contain the coronavirus should be carefully weighed against their impact on the world economy.abstract 24\% of the global population, i.e. about 1.83 billion people, taking approximately 86 million lives. Unless drastic new interventions become possible, the global number of new cases is predicted to peak on day 129 (about the middle of May 2020), with an estimated 65 million new cases per day. Since our simple model fits the available global data so well, we suggest that the measures being taken so far to contain the pandemic may be ineffective in curbing the global spread of the virus. As such, the efficacy of these measures should be more carefully weighed against their impact on the world economy.
Edit intent: meaning-changed
Original:  In contrast  we show that the situation with two additive inequalities is quite different, by showing that it is NP-hard to evaluate simple aggregation queries, with two additive inequalities, with any bounded relative error .
Revised:  In contrast , we show that the situation with two additive inequalities is quite different, by showing that it is NP-hard to evaluate simple aggregation queries, with two additive inequalities, with any bounded relative error .
Edit intent: fluency
Original:  In contrast  we show that the situation with two additive inequalities is quite different, by showing that it is NP-hard to evaluate simple aggregation queries, with two additive inequalities, with any bounded relative error . We end by considering the problem of computing the gradient of the objective function in the Support Vector Machines (SVM) problem, a canonical machine learning problem. While computing the gradient for SVM can be reduced to the problem of computing an aggregation query with one additive inequality, our algorithm is not applicable due to what we call the ``subtraction problem''. However, we show how to circumvent this subtraction problem within the context of SVM to obtain a gradient-descent algorithm that will result in an approximately correct optimal solution, using an alternative notion of approximate correctness, which may be of independent interest .
Revised:  In contrast we show that the situation with two additive inequalities is quite different, by showing that it is NP-hard to evaluate simple aggregation queries, with two additive inequalities, with any bounded relative error .
Edit intent: coherence
Original:  The actual number of infected cases in France is probably much higher than the observations: we find here a factor x 15 (95\%-CI: 1.5-11.7 ), which leads to a 5.2/1000 mortality rate (95\%-CI: 1.5 / 1000-11.7/ 1000) at the end of the observation period.
Revised:  The actual number of infected cases in France is probably much higher than the observations: we find here a factor x 15 (95\%-CI: 4-33 ), which leads to a 5.2/1000 mortality rate (95\%-CI: 1.5 / 1000-11.7/ 1000) at the end of the observation period.
Edit intent: meaning-changed
Original: The emerging area of computational pathology (CPath) is ripe ground for the application of deep learning (DL) methods to healthcare due to the sheer volume of raw pixel data in whole-slide images (WSIs) of cancerous tissue slides , generally of the order of 100K \times%DIFDELCMD < }%%% 80K pixels .
Revised: The emerging area of computational pathology (CPath) is ripe ground for the application of deep learning (DL) methods to healthcare due to the sheer volume of raw pixel data in whole-slide images (WSIs) of cancerous tissue slides \times%DIFDELCMD < }%%% 80K pixels .
Edit intent: coherence
Original: The emerging area of computational pathology (CPath) is ripe ground for the application of deep learning (DL) methods to healthcare due to the sheer volume of raw pixel data in whole-slide images (WSIs) of cancerous tissue slides , generally of the order of 100K \times%DIFDELCMD < }%%% 80K pixels .
Revised: The emerging area of computational pathology (CPath) is ripe ground for the application of deep learning (DL) methods to healthcare due to the sheer volume of raw pixel data in whole-slide images (WSIs) of cancerous tissue slides , generally of the order of 100K %DIFDELCMD < }%%% 80K pixels .
Edit intent: fluency
Original: The emerging area of computational pathology (CPath) is ripe ground for the application of deep learning (DL) methods to healthcare due to the sheer volume of raw pixel data in whole-slide images (WSIs) of cancerous tissue slides , generally of the order of 100K \times%DIFDELCMD < }%%% 80K pixels .
Revised: The emerging area of computational pathology (CPath) is ripe ground for the application of deep learning (DL) methods to healthcare due to the sheer volume of raw pixel data in whole-slide images (WSIs) of cancerous tissue slides , generally of the order of 100K \times%DIFDELCMD < }%%% .
Edit intent: meaning-changed
Original:  We study, and extend recently released PanNuke dataset consisting of more than 200 \sim200 ,000 nuclei categorized into 5 clinically important classes for the challenging tasks of detecting, segmenting and classifying nuclei in WSIs %DIFDELCMD < \footnote{%%% Download dataset here URL gamper_pannuke:_2019.
Revised:  We study, and extend recently released PanNuke dataset consisting of \sim200 ,000 nuclei categorized into 5 clinically important classes for the challenging tasks of detecting, segmenting and classifying nuclei in WSIs %DIFDELCMD < \footnote{%%% Download dataset here URL gamper_pannuke:_2019.
Edit intent: clarity
Original:  We study, and extend recently released PanNuke dataset consisting of more than 200 \sim200 ,000 nuclei categorized into 5 clinically important classes for the challenging tasks of detecting, segmenting and classifying nuclei in WSIs %DIFDELCMD < \footnote{%%% Download dataset here URL gamper_pannuke:_2019.
Revised:  We study, and extend recently released PanNuke dataset consisting of more than 200 \sim200 ,000 nuclei categorized into 5 clinically important classes for the challenging tasks of segmenting and classifying nuclei in WSIs %DIFDELCMD < \footnote{%%% Download dataset here URL gamper_pannuke:_2019.
Edit intent: clarity
Original:  We study, and extend recently released PanNuke dataset consisting of more than 200 \sim200 ,000 nuclei categorized into 5 clinically important classes for the challenging tasks of detecting, segmenting and classifying nuclei in WSIs %DIFDELCMD < \footnote{%%% Download dataset here URL gamper_pannuke:_2019.
Revised:  We study, and extend recently released PanNuke dataset consisting of more than 200 \sim200 ,000 nuclei categorized into 5 clinically important classes for the challenging tasks of detecting, segmenting and classifying nuclei in WSIs %DIFDELCMD < \footnote{%%% gamper_pannuke:_2019.
Edit intent: coherence
Original:  Previous pan-cancer datasets consisted of only up to 9 different tissues and up to 21,000 unlabeled nuclei kumar2019multi and just over 24,000 labeled nuclei with segmentation masks \mbox{%DIFAUXCMD graham_hover-net:_2019 . PanNuke consists of 19 different tissue types from over 20,000 WSIs that have been semi-automatically annotated and quality controlled by clinical pathologists, leading to a dataset with statistics similar to `the clinical wild' and with minimal selection bias.
Revised:  Previous pan-cancer datasets consisted of only up to 9 different tissues and up to 21,000 unlabeled nuclei kumar2019multi and just over 24,000 labeled nuclei with segmentation masks \mbox{%DIFAUXCMD graham2019hover . PanNuke consists of 19 different tissue types from over 20,000 WSIs that have been semi-automatically annotated and quality controlled by clinical pathologists, leading to a dataset with statistics similar to `the clinical wild' and with minimal selection bias.
Edit intent: clarity
Original:  Previous pan-cancer datasets consisted of only up to 9 different tissues and up to 21,000 unlabeled nuclei kumar2019multi and just over 24,000 labeled nuclei with segmentation masks \mbox{%DIFAUXCMD graham_hover-net:_2019 . PanNuke consists of 19 different tissue types from over 20,000 WSIs that have been semi-automatically annotated and quality controlled by clinical pathologists, leading to a dataset with statistics similar to `the clinical wild' and with minimal selection bias.
Revised:  Previous pan-cancer datasets consisted of only up to 9 different tissues and up to 21,000 unlabeled nuclei kumar2019multi and just over 24,000 labeled nuclei with segmentation masks \mbox{%DIFAUXCMD graham_hover-net:_2019 . PanNuke consists of 19 different tissue types that have been semi-automatically annotated and quality controlled by clinical pathologists, leading to a dataset with statistics similar to `the clinical wild' and with minimal selection bias.
Edit intent: meaning-changed
Original:  We study, and extend recently released PanNuke dataset consisting of %DIFDELCMD < \sim200%%%  ,000 nuclei categorized into 5 clinically important classes for the challenging tasks of segmenting and classifying nuclei in WSIs \mbox{%DIFAUXCMD gamper_pannuke:
Revised:  We study, and extend recently released PanNuke dataset consisting of %DIFDELCMD < \sim200%%% ~200 ,000 nuclei categorized into 5 clinically important classes for the challenging tasks of segmenting and classifying nuclei in WSIs \mbox{%DIFAUXCMD gamper_pannuke:
Edit intent: meaning-changed
Original:  We study, and extend recently released PanNuke dataset consisting of %DIFDELCMD < \sim200%%%  ,000 nuclei categorized into 5 clinically important classes for the challenging tasks of segmenting and classifying nuclei in WSIs \mbox{%DIFAUXCMD gamper_pannuke:_2019 . Previous pan-cancer datasets consisted of only up to 9 different tissues and up to 21,000 unlabeled nuclei \mbox{%DIFAUXCMD kumar2019multi and just over 24,000 labeled nuclei with segmentation masks \mbox{%DIFAUXCMD graham2019hover . PanNuke consists of 19 different tissue types that have been semi-automatically annotated and quality controlled by clinical pathologists, leading to a dataset with statistics similar to ` the clinical wild ' and with minimal selection bias.
Revised:  We study, and extend recently released PanNuke dataset consisting of %DIFDELCMD < \sim200%%% ,000 nuclei categorized into 5 clinically important classes for the challenging tasks of segmenting and classifying nuclei in WSIs . Previous pan-cancer datasets consisted of only up to 9 different tissues and up to 21,000 unlabeled nuclei \mbox{%DIFAUXCMD kumar2019multi and just over 24,000 labeled nuclei with segmentation masks \mbox{%DIFAUXCMD graham2019hover . PanNuke consists of 19 different tissue types that have been semi-automatically annotated and quality controlled by clinical pathologists, leading to a dataset with statistics similar to ` the clinical wild ' and with minimal selection bias.
Edit intent: clarity
Original: _2019 . Previous pan-cancer datasets consisted of only up to 9 different tissues and up to 21,000 unlabeled nuclei \mbox{%DIFAUXCMD kumar2019multi and just over 24,000 labeled nuclei with segmentation masks \mbox{%DIFAUXCMD graham2019hover . PanNuke consists of 19 different tissue types that have been semi-automatically annotated and quality controlled by clinical pathologists, leading to a dataset with statistics similar to ` the clinical wild ' and with minimal selection bias.
Revised: _2019 . Previous pan-cancer datasets consisted of only up to 9 different tissues and up to 21,000 unlabeled nuclei and just over 24,000 labeled nuclei with segmentation masks \mbox{%DIFAUXCMD graham2019hover . PanNuke consists of 19 different tissue types that have been semi-automatically annotated and quality controlled by clinical pathologists, leading to a dataset with statistics similar to ` the clinical wild ' and with minimal selection bias.
Edit intent: clarity
Original: _2019 . Previous pan-cancer datasets consisted of only up to 9 different tissues and up to 21,000 unlabeled nuclei \mbox{%DIFAUXCMD kumar2019multi and just over 24,000 labeled nuclei with segmentation masks \mbox{%DIFAUXCMD graham2019hover . PanNuke consists of 19 different tissue types that have been semi-automatically annotated and quality controlled by clinical pathologists, leading to a dataset with statistics similar to ` the clinical wild ' and with minimal selection bias.
Revised: _2019 . Previous pan-cancer datasets consisted of only up to 9 different tissues and up to 21,000 unlabeled nuclei \mbox{%DIFAUXCMD kumar2019multi and just over 24,000 labeled nuclei with segmentation masks . PanNuke consists of 19 different tissue types that have been semi-automatically annotated and quality controlled by clinical pathologists, leading to a dataset with statistics similar to ` the clinical wild ' and with minimal selection bias.
Edit intent: coherence
Original: _2019 . Previous pan-cancer datasets consisted of only up to 9 different tissues and up to 21,000 unlabeled nuclei \mbox{%DIFAUXCMD kumar2019multi and just over 24,000 labeled nuclei with segmentation masks \mbox{%DIFAUXCMD graham2019hover . PanNuke consists of 19 different tissue types that have been semi-automatically annotated and quality controlled by clinical pathologists, leading to a dataset with statistics similar to ` the clinical wild ' and with minimal selection bias.
Revised: _2019 . Previous pan-cancer datasets consisted of only up to 9 different tissues and up to 21,000 unlabeled nuclei \mbox{%DIFAUXCMD kumar2019multi and just over 24,000 labeled nuclei with segmentation masks \mbox{%DIFAUXCMD graham2019hover . PanNuke consists of 19 different tissue types that have been semi-automatically annotated and quality controlled by clinical pathologists, leading to a dataset with statistics similar to the clinical wild ' and with minimal selection bias.
Edit intent: fluency
Original: _2019 . Previous pan-cancer datasets consisted of only up to 9 different tissues and up to 21,000 unlabeled nuclei \mbox{%DIFAUXCMD kumar2019multi and just over 24,000 labeled nuclei with segmentation masks \mbox{%DIFAUXCMD graham2019hover . PanNuke consists of 19 different tissue types that have been semi-automatically annotated and quality controlled by clinical pathologists, leading to a dataset with statistics similar to ` the clinical wild ' and with minimal selection bias.
Revised: _2019 . Previous pan-cancer datasets consisted of only up to 9 different tissues and up to 21,000 unlabeled nuclei \mbox{%DIFAUXCMD kumar2019multi and just over 24,000 labeled nuclei with segmentation masks \mbox{%DIFAUXCMD graham2019hover . PanNuke consists of 19 different tissue types that have been semi-automatically annotated and quality controlled by clinical pathologists, leading to a dataset with statistics similar to ` the clinical wild and with minimal selection bias.
Edit intent: fluency
Original:  Los Angeles has been one of the first cities in the United States to declare the state of emergency on March 4th, progressively issuing stronger policies involving (among the others) social distancing, the prohibition of crowded private and public gatherings and closure of leisure premises.
Revised:  Los Angeles has been one of the first cities in the United States to declare the state of emergency on March 4th, progressively issuing stronger policies involving--among the others--social distancing, the prohibition of crowded private and public gatherings and closure of leisure premises.
Edit intent: fluency
Original:  Exploiting public data on crime in Los Angeles, and relying on routine activity and pattern theories of crime, this work investigates whether and how new coronavirus containment policies have an impact on crime trends in a metropolis  .
Revised:  Exploiting public data on crime in Los Angeles, and relying on routine activity and pattern theories of crime, this work investigates whether and how new coronavirus containment policies have an impact on crime trends in a metropolis using Bayesian structural time-series (BSTS) models .
Edit intent: meaning-changed
Original:  The analyses will be updated bi-weekly to th  dynamically assess the short- and medium-term effects of these interventions to shed light on how crime adapts to such structural modification of the environment.
Revised:  The analyses will be updated bi-weekly (up to March 16 th dynamically assess the short- and medium-term effects of these interventions to shed light on how crime adapts to such structural modification of the environment.
Edit intent: meaning-changed