wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We  derive a growth law that accounts for RNA-polymerase auto-catalytic cycle, which we use to explain the dependence of growth rate on an inducible expression of  rpoB and rpoC genes that code for RpoB and RpoC protein subunits of RNA-polymerase, and to explain how the concentration of rifampicin -- a drug that targets RNA-polymerase , affects growth rate without changing the RNA to protein ratio.
Revised:  We also derive a growth law that accounts for RNA-polymerase auto-catalytic cycle, which we use to explain the dependence of growth rate on an inducible expression of rpoB and rpoC genes that code for RpoB and RpoC protein subunits of RNA-polymerase, and to explain how the concentration of rifampicin -- a drug that targets RNA-polymerase , affects growth rate without changing the RNA to protein ratio.
Edit intent: coherence
Original:  We  derive a growth law that accounts for RNA-polymerase auto-catalytic cycle, which we use to explain the dependence of growth rate on an inducible expression of  rpoB and rpoC genes that code for RpoB and RpoC protein subunits of RNA-polymerase, and to explain how the concentration of rifampicin -- a drug that targets RNA-polymerase , affects growth rate without changing the RNA to protein ratio.
Revised:  We derive a growth law that accounts for the RNA polymerase autocatalytic cycle, which we use to explain the dependence of growth rate on an inducible expression of rpoB and rpoC genes that code for RpoB and RpoC protein subunits of RNA-polymerase, and to explain how the concentration of rifampicin -- a drug that targets RNA-polymerase , affects growth rate without changing the RNA to protein ratio.
Edit intent: fluency
Original:  We  derive a growth law that accounts for RNA-polymerase auto-catalytic cycle, which we use to explain the dependence of growth rate on an inducible expression of  rpoB and rpoC genes that code for RpoB and RpoC protein subunits of RNA-polymerase, and to explain how the concentration of rifampicin -- a drug that targets RNA-polymerase , affects growth rate without changing the RNA to protein ratio.
Revised:  We derive a growth law that accounts for RNA-polymerase auto-catalytic cycle, which we use to explain how growth rate depends on the inducible expression of rpoB and rpoC genes that code for RpoB and RpoC protein subunits of RNA-polymerase, and to explain how the concentration of rifampicin -- a drug that targets RNA-polymerase , affects growth rate without changing the RNA to protein ratio.
Edit intent: clarity
Original:  We  derive a growth law that accounts for RNA-polymerase auto-catalytic cycle, which we use to explain the dependence of growth rate on an inducible expression of  rpoB and rpoC genes that code for RpoB and RpoC protein subunits of RNA-polymerase, and to explain how the concentration of rifampicin -- a drug that targets RNA-polymerase , affects growth rate without changing the RNA to protein ratio.
Revised:  We derive a growth law that accounts for RNA-polymerase auto-catalytic cycle, which we use to explain the dependence of growth rate on an inducible expression of the rpoB and rpoC genes that code for RpoB and RpoC protein subunits of RNA-polymerase, and to explain how the concentration of rifampicin -- a drug that targets RNA-polymerase , affects growth rate without changing the RNA to protein ratio.
Edit intent: fluency
Original:  We  derive a growth law that accounts for RNA-polymerase auto-catalytic cycle, which we use to explain the dependence of growth rate on an inducible expression of  rpoB and rpoC genes that code for RpoB and RpoC protein subunits of RNA-polymerase, and to explain how the concentration of rifampicin -- a drug that targets RNA-polymerase , affects growth rate without changing the RNA to protein ratio.
Revised:  We derive a growth law that accounts for RNA-polymerase auto-catalytic cycle, which we use to explain the dependence of growth rate on an inducible expression of rpoB and rpoC genes , which code for the RpoB and RpoC protein subunits of RNA-polymerase, and to explain how the concentration of rifampicin -- a drug that targets RNA-polymerase , affects growth rate without changing the RNA to protein ratio.
Edit intent: fluency
Original:  We  derive a growth law that accounts for RNA-polymerase auto-catalytic cycle, which we use to explain the dependence of growth rate on an inducible expression of  rpoB and rpoC genes that code for RpoB and RpoC protein subunits of RNA-polymerase, and to explain how the concentration of rifampicin -- a drug that targets RNA-polymerase , affects growth rate without changing the RNA to protein ratio.
Revised:  We derive a growth law that accounts for RNA-polymerase auto-catalytic cycle, which we use to explain the dependence of growth rate on an inducible expression of rpoB and rpoC genes that code for RpoB and RpoC protein subunits of RNA polymerase, and how the concentration of rifampicin -- a drug that targets RNA-polymerase , affects growth rate without changing the RNA to protein ratio.
Edit intent: fluency
Original:  We  derive a growth law that accounts for RNA-polymerase auto-catalytic cycle, which we use to explain the dependence of growth rate on an inducible expression of  rpoB and rpoC genes that code for RpoB and RpoC protein subunits of RNA-polymerase, and to explain how the concentration of rifampicin -- a drug that targets RNA-polymerase , affects growth rate without changing the RNA to protein ratio.
Revised:  We derive a growth law that accounts for RNA-polymerase auto-catalytic cycle, which we use to explain the dependence of growth rate on an inducible expression of rpoB and rpoC genes that code for RpoB and RpoC protein subunits of RNA-polymerase, and to explain how the concentration of rifampicin , which targets RNA polymerase , affects growth rate without changing the RNA to protein ratio.
Edit intent: clarity
Original:  We  derive a growth law that accounts for RNA-polymerase auto-catalytic cycle, which we use to explain the dependence of growth rate on an inducible expression of  rpoB and rpoC genes that code for RpoB and RpoC protein subunits of RNA-polymerase, and to explain how the concentration of rifampicin -- a drug that targets RNA-polymerase , affects growth rate without changing the RNA to protein ratio.
Revised:  We derive a growth law that accounts for RNA-polymerase auto-catalytic cycle, which we use to explain the dependence of growth rate on an inducible expression of rpoB and rpoC genes that code for RpoB and RpoC protein subunits of RNA-polymerase, and to explain how the concentration of rifampicin -- a drug that targets RNA-polymerase , affects growth rate without changing the RNA-to-protein ratio.
Edit intent: fluency
Original:  We first propose a 4-approximation algorithm in O(m^{7k}n\log k) time for this problem , where k is a positive integer  .
Revised:  We first propose a 4-approximation algorithm in O(m^{7k}n\log k) time for this problem and then show that the running time can be improved by a multiplicative factor of m^k, where a positive integer .
Edit intent: meaning-changed
Original:  We then generalize our approach to yield a family of \rho\lceil{\tau}}\rceil -approximation algorithms in O(m^{\alpha kn\log k) time, where (\rho,\alpha)\in \{(4, 7), (6,5), (7, 5), (9,4)\}.
Revised:  We then generalize our approach to exhibit a O((1+\lceil{\tau}}\rceil -approximation algorithms in O(m^{\alpha kn\log k) time, where (\rho,\alpha)\in \{(4, 7), (6,5), (7, 5), (9,4)\}.
Edit intent: meaning-changed
Original:  We then generalize our approach to yield a family of \rho\lceil{\tau}}\rceil -approximation algorithms in O(m^{\alpha kn\log k) time, where (\rho,\alpha)\in \{(4, 7), (6,5), (7, 5), (9,4)\}.
Revised:  We then generalize our approach to yield a family of \rho\lceil\frac{2{\tau}}\rceil -approximation algorithms in O(m^{\alpha kn\log k) time, where (\rho,\alpha)\in \{(4, 7), (6,5), (7, 5), (9,4)\}.
Edit intent: meaning-changed
Original:  We then generalize our approach to yield a family of \rho\lceil{\tau}}\rceil -approximation algorithms in O(m^{\alpha kn\log k) time, where (\rho,\alpha)\in \{(4, 7), (6,5), (7, 5), (9,4)\}.
Revised:  We then generalize our approach to yield a family of \rho\lceil{\tau}}\rceil)^2) -approximation algorithms in O(m^{\alpha kn\log k) time, where (\rho,\alpha)\in \{(4, 7), (6,5), (7, 5), (9,4)\}.
Edit intent: meaning-changed
Original:  We then generalize our approach to yield a family of \rho\lceil{\tau}}\rceil -approximation algorithms in O(m^{\alpha kn\log k) time, where (\rho,\alpha)\in \{(4, 7), (6,5), (7, 5), (9,4)\}.
Revised:  We then generalize our approach to yield a family of \rho\lceil{\tau}}\rceil -approximation n\log k) time, where (\rho,\alpha)\in \{(4, 7), (6,5), (7, 5), (9,4)\}.
Edit intent: clarity
Original:  We further generalize this to exhibit a O(1{\tau})-approximation } algorithm in O(m^{ \alpha\lfloor{\sqrt{12}}}\rfloor k}n\log k) time for a given grid width 1 \leq \tau \leq 2, where \alpha=O(\tau^2).
Revised:  We further generalize this to exhibit a O(1{\tau})-approximation } algorithm in O(m^{ (\lfloor{\sqrt{12}}}\rfloor k}n\log k) time for a given grid width 1 \leq \tau \leq 2, where \alpha=O(\tau^2).
Edit intent: meaning-changed
Original:  We further generalize this to exhibit a O(1{\tau})-approximation } algorithm in O(m^{ \alpha\lfloor{\sqrt{12}}}\rfloor k}n\log k) time for a given grid width 1 \leq \tau \leq 2, where \alpha=O(\tau^2).
Revised:  We further generalize this to exhibit a O(1{\tau})-approximation } algorithm in O(m^{ \alpha\lfloor\frac{4\pi+8\tau+\tau^2{\sqrt{12}}}\rfloor k}n\log k) time for a given grid width 1 \leq \tau \leq 2, where \alpha=O(\tau^2).
Edit intent: meaning-changed
Original:  We further generalize this to exhibit a O(1{\tau})-approximation } algorithm in O(m^{ \alpha\lfloor{\sqrt{12}}}\rfloor k}n\log k) time for a given grid width 1 \leq \tau \leq 2, where \alpha=O(\tau^2).
Revised:  We further generalize this to exhibit a O(1{\tau})-approximation } algorithm in O(m^{ \alpha\lfloor{\sqrt{12}}}\rfloor) k}n\log k) time for a given grid width 1 \leq \tau \leq 2, where \alpha=O(\tau^2).
Edit intent: fluency
Original:  We further generalize this to exhibit a O(1{\tau})-approximation } algorithm in O(m^{ \alpha\lfloor{\sqrt{12}}}\rfloor k}n\log k) time for a given grid width 1 \leq \tau \leq 2, where \alpha=O(\tau^2).
Revised:  We further generalize this to exhibit a O(1{\tau})-approximation } algorithm in O(m^{ \alpha\lfloor{\sqrt{12}}}\rfloor k}n\log k) time for a given 1 \leq \tau \leq 2, where \alpha=O(\tau^2).
Edit intent: clarity
Original:  We further generalize this to exhibit a O(1{\tau})-approximation } algorithm in O(m^{ \alpha\lfloor{\sqrt{12}}}\rfloor k}n\log k) time for a given grid width 1 \leq \tau \leq 2, where \alpha=O(\tau^2).
Revised:  We further generalize this to exhibit a O(1{\tau})-approximation } algorithm in O(m^{ \alpha\lfloor{\sqrt{12}}}\rfloor k}n\log k) time for a given grid width 1 \leq \tau \leq 2.
Edit intent: meaning-changed
Original: In this paper, we investigate projection-based intrusive and data-driven non-intrusive model order reduction (MOR) methods in the numerical simulation of the rotating thermal shallow water equation (RTSWE) in parametric and non-parametric form.
Revised: In this paper, we investigate projection-based intrusive and data-driven non-intrusive model order reduction methods in numerical simulation of the rotating thermal shallow water equation (RTSWE) in parametric and non-parametric form.
Edit intent: clarity
Original: In this paper, we investigate projection-based intrusive and data-driven non-intrusive model order reduction (MOR) methods in the numerical simulation of the rotating thermal shallow water equation (RTSWE) in parametric and non-parametric form.
Revised: In this paper, we investigate projection-based intrusive and data-driven non-intrusive model order reduction (MOR) methods in the numerical simulation of rotating thermal shallow water equation (RTSWE) in parametric and non-parametric form.
Edit intent: fluency
Original:  The intrusive reduced-order model (ROM) is constructed with the proper orthogonal decomposition (POD) with the Galerkin projection (POD-G)  . We apply the operator inference (OpInf) with re-projection for non-intrusive reduced-order modeling.
Revised:  The full-order model (FOM) is obtained by applying linearly implicit Kahan's method in time. Applying proper orthogonal decomposition (POD) with the Galerkin projection (POD-G) . We apply the operator inference (OpInf) with re-projection for non-intrusive reduced-order modeling.
Edit intent: meaning-changed
Original:  The intrusive reduced-order model (ROM) is constructed with the proper orthogonal decomposition (POD) with the Galerkin projection (POD-G)  . We apply the operator inference (OpInf) with re-projection for non-intrusive reduced-order modeling.
Revised:  The intrusive reduced-order model (ROM) is constructed with the proper orthogonal decomposition with Galerkin projection (POD-G) . We apply the operator inference (OpInf) with re-projection for non-intrusive reduced-order modeling.
Edit intent: fluency
Original:  The intrusive reduced-order model (ROM) is constructed with the proper orthogonal decomposition (POD) with the Galerkin projection (POD-G)  . We apply the operator inference (OpInf) with re-projection for non-intrusive reduced-order modeling.
Revised:  The intrusive reduced-order model (ROM) is constructed with the proper orthogonal decomposition (POD) with the Galerkin projection (POD-G) , we construct the intrusive reduced-order model (ROM) . We apply the operator inference (OpInf) with re-projection for non-intrusive reduced-order modeling.
Edit intent: coherence
Original:  The intrusive reduced-order model (ROM) is constructed with the proper orthogonal decomposition (POD) with the Galerkin projection (POD-G)  . We apply the operator inference (OpInf) with re-projection for non-intrusive reduced-order modeling.
Revised:  The intrusive reduced-order model (ROM) is constructed with the proper orthogonal decomposition (POD) with the Galerkin projection (POD-G) . We apply operator inference (OpInf) with re-projection for non-intrusive reduced-order modeling.
Edit intent: coherence
Original:  Both reduced models are able to accurately re-predict the training data and capture much of the overall system behavior in the prediction period. Due to re-projection, the OpInf is more costly than the POG-G, nevertheless, speed-up factors of order two are achieved for both ROMs. Numerical results demonstrate that the conserved quantities of the RTSWE are preserved for both ROMs over time and the long-term stability of the reduced solutions is achieved .
Revised:  Both reduced models are able to accurately re-predict the training data and capture much of the overall system behavior in the prediction phase with several orders of computational speedup over the FOM. The preservation of system physics such as the conserved quantities of the RTSWE are preserved for both ROMs over time and the long-term stability of the reduced solutions is achieved .
Edit intent: clarity
Original:  Due to re-projection, the OpInf is more costly than the POG-G, nevertheless, speed-up factors of order two are achieved for both ROMs. Numerical results demonstrate that the conserved quantities of the RTSWE are preserved for both ROMs over time and the long-term stability of the reduced solutions is achieved .
Revised:  Due to re-projection, the OpInf is more costly than the POG-G, nevertheless, speed-up factors of order two are achieved for both ROMs. Numerical results demonstrate that the conserved quantities of the RTSWE by both ROMs enables that the models fit better to data and stable solutions are obtained in long-term stability of the reduced solutions is achieved .
Edit intent: clarity
Original:  Due to re-projection, the OpInf is more costly than the POG-G, nevertheless, speed-up factors of order two are achieved for both ROMs. Numerical results demonstrate that the conserved quantities of the RTSWE are preserved for both ROMs over time and the long-term stability of the reduced solutions is achieved .
Revised:  Due to re-projection, the OpInf is more costly than the POG-G, nevertheless, speed-up factors of order two are achieved for both ROMs. Numerical results demonstrate that the conserved quantities of the RTSWE are preserved for both ROMs over time and the long-term predictions, which are robust to parameter changes .
Edit intent: clarity
Original:  COVID-19 vaccination drives across the globe are accompanied by a wide variety of expressed opinions , often colored by emotions . We extracted a corpus of Twitter posts related to COVID-19 vaccination and created two classes of lexical categories- Emotions and Influencing factors. Using unsupervised word embeddings, we tracked the longitudinal change in the latent space of the lexical categories in five countries with strong vaccine roll-out programs, i.e. India, USA , Brazil, UK , and Australia.
Revised:  COVID-19 vaccination drives across the globe are accompanied by a wide variety of expressed opinions , often colored by emotions , which change along with rising cases, approval of vaccines, and multiple factors discussed online. This study aims at analyzing the temporal evolution of different Emotion categories: Hesitation, Rage, Sorrow, Anticipation, Faith, and Contentment with Influencing Factors: Vaccine Rollout, Misinformation, Health Effects, and Inequities as lexical categories created from Tweets belonging to five countries with strong vaccine roll-out programs, i.e. India, USA , Brazil, UK , and Australia.
Edit intent: meaning-changed
Original:  Using unsupervised word embeddings, we tracked the longitudinal change in the latent space of the lexical categories in five countries with strong vaccine roll-out programs, i.e. India, USA , Brazil, UK , and Australia.
Revised:  Using unsupervised word embeddings, we tracked the longitudinal change in the latent space of the lexical categories in five countries with vital vaccine roll-out programs, i.e. India, USA , Brazil, UK , and Australia.
Edit intent: clarity
Original:  Using unsupervised word embeddings, we tracked the longitudinal change in the latent space of the lexical categories in five countries with strong vaccine roll-out programs, i.e. India, USA , Brazil, UK , and Australia.
Revised:  Using unsupervised word embeddings, we tracked the longitudinal change in the latent space of the lexical categories in five countries with strong vaccine roll-out programs, namely, India, United States of America , Brazil, UK , and Australia.
Edit intent: clarity
Original:  Using unsupervised word embeddings, we tracked the longitudinal change in the latent space of the lexical categories in five countries with strong vaccine roll-out programs, i.e. India, USA , Brazil, UK , and Australia.
Revised:  Using unsupervised word embeddings, we tracked the longitudinal change in the latent space of the lexical categories in five countries with strong vaccine roll-out programs, i.e. India, USA , Brazil, United Kingdom , and Australia.
Edit intent: clarity
Original:  Nearly 600 thousand vaccine-related tweets from the United States and India were analyzed for an overall understanding of the situation around the world for the time period of 8 months from June 2020 to January 2021.
Revised:  We extracted a corpus of nearly 1.8 million Twitter posts related to COVID-19 vaccination. Using cosine distance from selected seed words, we expanded the vocabulary of each category and tracked the longitudinal change in their strength from June 2020 to January 2021.
Edit intent: meaning-changed
Original:  Nearly 600 thousand vaccine-related tweets from the United States and India were analyzed for an overall understanding of the situation around the world for the time period of 8 months from June 2020 to January 2021.
Revised:  Nearly 600 thousand vaccine-related tweets from the United States and India were analyzed for an overall understanding of the situation around the world for the time period of 8 months from June 2020 to April 2021.
Edit intent: meaning-changed
Original:  Cosine distance between lexical categories was used to create similarity networks and modules using community detection algorithms . We demonstrate that negative emotions like hesitancy towards vaccines have a high correlation with health-related effects and misinformation.
Revised:  Cosine distance between lexical categories was used to create similarity networks and modules using community detection algorithms to find modules in positive correlation networks. Our findings suggest that tweets expressing hesitancy towards vaccines have a high correlation with health-related effects and misinformation.
Edit intent: meaning-changed
Original:  We demonstrate that negative emotions like hesitancy towards vaccines have a high correlation with health-related effects and misinformation. These associations formed a major module with the highest importance in the network formed for January 2021, when millions of vaccines were administered .
Revised:  We demonstrate that negative emotions like hesitancy towards vaccines have a high correlation with health-related effects in all countries. Our results indicated that the patterns of hesitancy were variable across geographies and can help us learn targeted interventions. We also observed a significant change in the linear trends of categories like hesitation and contentment before and after approval of vaccines. Negative emotions like rage and sorrow gained the highest importance in the network formed for January 2021, when millions of vaccines were administered .
Edit intent: meaning-changed
Original:  To thisend, we propose in this article  a novel framework of Blockchain-enabled intElligent Safe-driving assessmenT (BEST) to offer a smart and reliable approach for conducting safe driving supervision while protecting vehicular information.
Revised:  Motivated by this, in this article a novel framework of Blockchain-enabled intElligent Safe-driving assessmenT (BEST) to offer a smart and reliable approach for conducting safe driving supervision while protecting vehicular information.
Edit intent: coherence
Original:  To thisend, we propose in this article  a novel framework of Blockchain-enabled intElligent Safe-driving assessmenT (BEST) to offer a smart and reliable approach for conducting safe driving supervision while protecting vehicular information.
Revised:  To thisend, we propose in this article , we propose a novel framework of Blockchain-enabled intElligent Safe-driving assessmenT (BEST) to offer a smart and reliable approach for conducting safe driving supervision while protecting vehicular information.
Edit intent: fluency
Original:  To thisend, we propose in this article  a novel framework of Blockchain-enabled intElligent Safe-driving assessmenT (BEST) to offer a smart and reliable approach for conducting safe driving supervision while protecting vehicular information.
Revised:  To thisend, we propose in this article a novel framework , namely Blockchain-enabled intElligent Safe-driving assessmenT (BEST) to offer a smart and reliable approach for conducting safe driving supervision while protecting vehicular information.
Edit intent: clarity
Original:  To thisend, we propose in this article  a novel framework of Blockchain-enabled intElligent Safe-driving assessmenT (BEST) to offer a smart and reliable approach for conducting safe driving supervision while protecting vehicular information.
Revised:  To thisend, we propose in this article a novel framework of Blockchain-enabled intElligent Safe-driving assessmenT (BEST) , that offers a smart and reliable approach for conducting safe driving supervision while protecting vehicular information.
Edit intent: coherence