wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Additionally, if lockdowns had been instituted one to three weeks sooner , the number of Covid-19 deaths in New York could have been significantly reduced, but surprisingly not in Texas (perhaps due to aversion to lockdown and absence of a decree).
Revised:  Additionally, if lockdowns had been instituted one to three weeks sooner , the number of Covid-19 deaths in New York could have been significantly lower in most states that opened up. Additionally, our model predicts that decreasing contact rate by 10\%, or increasing testing by approximately 15\%, or doubling lockdown compliance (from the current \sim 15\% to \sim 30\%) will eradicate infections in the state of Texas within a year. Extending our fits for all of the US states, we predict about 11 million total infections (including undetected), 8 million cumulative confirmed cases, and 630,000 cumulative deaths by November 1, 2020.
Edit intent: meaning-changed
Original:  the wealth of real-time vehicle information hidden in  CAN packets is uninterpretable-- severely impeding vehicle-related research including CAN cybersecurity, after-market tuning, efficiency and performance monitoring, and fault diagnosis . Guided by the four-part CAN signal definition, we present CAN-D ( CAN Decoder ), a modular, four-step pipeline for identifying each signal's boundaries (start bit and length), endianness (byte ordering ), signedness (bit-to-integer encoding), and meaningful, physical interpretation (label, unit, scaling factors).
Revised:  the wealth of real-time vehicle information hidden in CAN packets is uninterpretable-- severely impeding vehicle-related research . Guided by the four-part CAN signal definition, we present CAN-D ( CAN Decoder ), a modular, four-step pipeline for identifying each signal's boundaries (start bit and length), endianness (byte ordering ), signedness (bit-to-integer encoding), and meaningful, physical interpretation (label, unit, scaling factors).
Edit intent: coherence
Original:  after-market tuning, efficiency and performance monitoring, and fault diagnosis . Guided by the four-part CAN signal definition, we present CAN-D ( CAN Decoder ), a modular, four-step pipeline for identifying each signal's boundaries (start bit and length), endianness (byte ordering ), signedness (bit-to-integer encoding), and meaningful, physical interpretation (label, unit, scaling factors).
Revised:  after-market tuning, efficiency and performance monitoring, and fault diagnosis . Guided by the 4-part CAN signal definition, we present CAN-D ( CAN Decoder ), a modular, four-step pipeline for identifying each signal's boundaries (start bit and length), endianness (byte ordering ), signedness (bit-to-integer encoding), and meaningful, physical interpretation (label, unit, scaling factors).
Edit intent: fluency
Original:  after-market tuning, efficiency and performance monitoring, and fault diagnosis . Guided by the four-part CAN signal definition, we present CAN-D ( CAN Decoder ), a modular, four-step pipeline for identifying each signal's boundaries (start bit and length), endianness (byte ordering ), signedness (bit-to-integer encoding), and meaningful, physical interpretation (label, unit, scaling factors).
Revised:  after-market tuning, efficiency and performance monitoring, and fault diagnosis . Guided by the four-part CAN signal definition, we present CAN-D ( CAN-Decoder ), a modular, four-step pipeline for identifying each signal's boundaries (start bit and length), endianness (byte ordering ), signedness (bit-to-integer encoding), and meaningful, physical interpretation (label, unit, scaling factors).
Edit intent: fluency
Original:  after-market tuning, efficiency and performance monitoring, and fault diagnosis . Guided by the four-part CAN signal definition, we present CAN-D ( CAN Decoder ), a modular, four-step pipeline for identifying each signal's boundaries (start bit and length), endianness (byte ordering ), signedness (bit-to-integer encoding), and meaningful, physical interpretation (label, unit, scaling factors).
Revised:  after-market tuning, efficiency and performance monitoring, and fault diagnosis . Guided by the four-part CAN signal definition, we present CAN-D ( CAN Decoder ), a modular, 4-step pipeline for identifying each signal's boundaries (start bit and length), endianness (byte ordering ), signedness (bit-to-integer encoding), and meaningful, physical interpretation (label, unit, scaling factors).
Edit intent: fluency
Original:  after-market tuning, efficiency and performance monitoring, and fault diagnosis . Guided by the four-part CAN signal definition, we present CAN-D ( CAN Decoder ), a modular, four-step pipeline for identifying each signal's boundaries (start bit and length), endianness (byte ordering ), signedness (bit-to-integer encoding), and meaningful, physical interpretation (label, unit, scaling factors).
Revised:  after-market tuning, efficiency and performance monitoring, and fault diagnosis . Guided by the four-part CAN signal definition, we present CAN-D ( CAN Decoder ), a modular, four-step pipeline for identifying each signal's boundaries (start bit , length), endianness (byte ordering ), signedness (bit-to-integer encoding), and meaningful, physical interpretation (label, unit, scaling factors).
Edit intent: coherence
Original:  after-market tuning, efficiency and performance monitoring, and fault diagnosis . Guided by the four-part CAN signal definition, we present CAN-D ( CAN Decoder ), a modular, four-step pipeline for identifying each signal's boundaries (start bit and length), endianness (byte ordering ), signedness (bit-to-integer encoding), and meaningful, physical interpretation (label, unit, scaling factors).
Revised:  after-market tuning, efficiency and performance monitoring, and fault diagnosis . Guided by the four-part CAN signal definition, we present CAN-D ( CAN Decoder ), a modular, four-step pipeline for identifying each signal's boundaries (start bit and length), endianness (byte order ), signedness (bit-to-integer encoding), and meaningful, physical interpretation (label, unit, scaling factors).
Edit intent: fluency
Original:  after-market tuning, efficiency and performance monitoring, and fault diagnosis . Guided by the four-part CAN signal definition, we present CAN-D ( CAN Decoder ), a modular, four-step pipeline for identifying each signal's boundaries (start bit and length), endianness (byte ordering ), signedness (bit-to-integer encoding), and meaningful, physical interpretation (label, unit, scaling factors). En route to CAN-D, we provide a comprehensive review of the CAN signal reverse engineering research.
Revised:  after-market tuning, efficiency and performance monitoring, and fault diagnosis . Guided by the four-part CAN signal definition, we present CAN-D ( CAN Decoder ), a modular, four-step pipeline for identifying each signal's boundaries (start bit and length), endianness (byte ordering ), signedness (bit-to-integer encoding), and by leveraging diagnostic standards, augmenting a subset of the extracted signals with physical interpretation. We provide a comprehensive review of the CAN signal reverse engineering research.
Edit intent: clarity
Original:  We formulate and provide an efficient solution to an optimization problem, allowing identification of the optimal set of signal boundaries and byte orderings.
Revised:  Incorporating endianness grows the search space from 128 to 4.72E21 signal tokenizations and introduces a web of changing dependencies. We formulate, formally analyze, and provide an efficient solution to an optimization problem, allowing identification of the optimal set of signal boundaries and byte orderings.
Edit intent: meaning-changed
Original:  In addition, we provide two novel, state-of-the-art signal boundary classifiers (both superior to previous approaches in precision and recall ) and the first signedness classification algorithm , which exhibits >97 \% F-score.
Revised:  We provide two novel, state-of-the-art signal boundary classifiers (both superior to previous approaches in precision and recall ) and the first signedness classification algorithm , which exhibits >97 \% F-score.
Edit intent: coherence
Original:  In addition, we provide two novel, state-of-the-art signal boundary classifiers (both superior to previous approaches in precision and recall ) and the first signedness classification algorithm , which exhibits >97 \% F-score.
Revised:  In addition, we provide two novel, state-of-the-art signal boundary classifiers-both superior to previous approaches in precision and recall ) and the first signedness classification algorithm , which exhibits >97 \% F-score.
Edit intent: fluency
Original:  In addition, we provide two novel, state-of-the-art signal boundary classifiers (both superior to previous approaches in precision and recall ) and the first signedness classification algorithm , which exhibits >97 \% F-score.
Revised:  In addition, we provide two novel, state-of-the-art signal boundary classifiers (both superior to previous approaches in precision and recall in three different test scenarios-and the first signedness classification algorithm , which exhibits >97 \% F-score.
Edit intent: meaning-changed
Original:  In addition, we provide two novel, state-of-the-art signal boundary classifiers (both superior to previous approaches in precision and recall ) and the first signedness classification algorithm , which exhibits >97 \% F-score.
Revised:  In addition, we provide two novel, state-of-the-art signal boundary classifiers (both superior to previous approaches in precision and recall ) and the first signedness classification algorithm which exhibits a >97 \% F-score.
Edit intent: fluency
Original:  In addition, we provide two novel, state-of-the-art signal boundary classifiers (both superior to previous approaches in precision and recall ) and the first signedness classification algorithm , which exhibits >97 \% F-score. Overall, CAN-D is the only solution with the potential to extract any CAN signal and is the state of the art. In evaluation on ten vehicles of different makes, CAN-D's average \ell^1 error is 5 times better than all preceding methods and exhibits lower average error even when considering only signals that meet prior methods' assumptions. Finally, CAN-D is implemented in lightweight hardware allowing OBD-II plugin for real-time in-vehicle CAN decoding.
Revised:  In addition, we provide two novel, state-of-the-art signal boundary classifiers (both superior to previous approaches in precision and recall ) and the first signedness classification algorithm , which exhibits >97 
Edit intent: coherence
Original: Ability to solve large sparse linear systems of equations is very important in modern numerical methods. Creating a solver with a user-friendly interface that can work in many specific scenarios is a challenging task .
Revised: Ability to solve large sparse linear systems of equations is very important in computational fluid mechanics. The discontinuous Galerkin finite element methods have become increasingly popular for incompressible flow problems but their application is limited due to high computational cost .
Edit intent: meaning-changed
Original:  Convenience for the user and efficiency is demonstrated on the example of accelerating a large-scale Stokes problemsolution with a Schur pressure correction preconditioner. The user may select algorithmic components of the solver by adjusting template parameters without any change to the codebase.
Revised:  Convenience for the user and efficiency is demonstrated on the example of accelerating an iterative solver of a discontinuous Galerkin finite element method for the Stokes problem. The implementation allows selecting algorithmic components of the solver by adjusting template parameters without any change to the codebase.
Edit intent: meaning-changed
Original:  It is also possible to switch to block values, or use  mixed precision solution, which results in up to 4 times speedup, and reduces the memory footprint of the algorithm by about 50\%.
Revised:  It is also possible to switch to block values, or use a mixed precision solution, which results in up to 4 times speedup, and reduces the memory footprint of the algorithm by about 50\%.
Edit intent: fluency
Original: %DIFDELCMD < \end{abstract} %DIFDELCMD < %%% \\%DIF > . We evaluate both monolithic and composite preconditioning strategies for the 3 benchmark problems.
Revised: %DIFDELCMD < \end{abstract} %DIFDELCMD < %%% 40\\%DIF > . We evaluate both monolithic and composite preconditioning strategies for the 3 benchmark problems.
Edit intent: meaning-changed
Original:  This feature makes it suitable for inexpensive human-machine interaction (HMI). We rely on a spatial attention-based strategy, which employs SaDNet, our proposed Static and Dynamic gestures Network  .
Revised:  This feature makes it suitable for inexpensive human-robot interaction in social or industrial settings. We employ a pose-driven spatial attention strategy, which employs SaDNet, our proposed Static and Dynamic gestures Network .
Edit intent: meaning-changed
Original:  They are utilized to detect 10 static gestures for each hand and to obtain hand image-embeddings from the last Fully Connected layer, which are subsequently fused with the augmented pose vector and then passed to  stacked Long Short-Term Memory blocks.
Revised:  It is utilized to detect 10 static gestures for each hand and to obtain hand image-embeddings from the last Fully Connected layer, which are subsequently fused with the augmented pose vector and then passed to stacked Long Short-Term Memory blocks.
Edit intent: fluency
Original:  They are utilized to detect 10 static gestures for each hand and to obtain hand image-embeddings from the last Fully Connected layer, which are subsequently fused with the augmented pose vector and then passed to  stacked Long Short-Term Memory blocks.
Revised:  They are utilized to detect 10 static gestures for each hand as well as to obtain the hand image-embeddings from the last Fully Connected layer, which are subsequently fused with the augmented pose vector and then passed to stacked Long Short-Term Memory blocks.
Edit intent: clarity
Original:  They are utilized to detect 10 static gestures for each hand and to obtain hand image-embeddings from the last Fully Connected layer, which are subsequently fused with the augmented pose vector and then passed to  stacked Long Short-Term Memory blocks.
Revised:  They are utilized to detect 10 static gestures for each hand and to obtain hand image-embeddings . These are subsequently fused with the augmented pose vector and then passed to stacked Long Short-Term Memory blocks.
Edit intent: coherence
Original:  They are utilized to detect 10 static gestures for each hand and to obtain hand image-embeddings from the last Fully Connected layer, which are subsequently fused with the augmented pose vector and then passed to  stacked Long Short-Term Memory blocks.
Revised:  They are utilized to detect 10 static gestures for each hand and to obtain hand image-embeddings from the last Fully Connected layer, which are subsequently fused with the augmented pose vector and then passed to the stacked Long Short-Term Memory blocks.
Edit intent: fluency
Original:  Computational medicine is a vibrant interdisciplinary field that could help moving from URLan-centered  to a process-oriented or systemic medicine data analysis. The resulting Computational patient may require an international interdisciplinary effort, probably of larger scientific and technological interdisciplinarity than the human genome sequencing .
Revised:  Computational medicine is a vibrant interdisciplinary field that could help moving from URLan-centered to a process-oriented approach. The ideal computational patient would require an international interdisciplinary effort, probably of larger scientific and technological interdisciplinarity than the human genome sequencing .
Edit intent: clarity
Original:  Our aim is twofold: analyse the modularity and composability of the models-building blocks of the Computational patient and to study the dynamical properties of well-being and disease states in a broader functional context.
Revised:  Our aim is twofold: analyse the modularity and composability of the model-building blocks of the Computational patient and to study the dynamical properties of well-being and disease states in a broader functional context.
Edit intent: fluency
Original:  Our aim is twofold: analyse the modularity and composability of the models-building blocks of the Computational patient and to study the dynamical properties of well-being and disease states in a broader functional context.
Revised:  Our aim is twofold: analyse the modularity and composability of the models-building blocks of the computational patient and to study the dynamical properties of well-being and disease states in a broader functional context.
Edit intent: fluency
Original:  Finally, we test the new method for some problems with deficient rank matrices, and compare it with other  popular interior-point methods such as the path-following method (the subroutine pathfollow.m coded by M.~C.
Revised:  Finally, for some deficient rank problems with or without the small noisy data from the NETLIB collection, we compare it with other popular interior-point methods such as the path-following method (the subroutine pathfollow.m coded by M.~C.
Edit intent: meaning-changed
Original:  Finally, we test the new method for some problems with deficient rank matrices, and compare it with other  popular interior-point methods such as the path-following method (the subroutine pathfollow.m coded by M.~C.
Revised:  Finally, we test the new method for some problems with deficient rank matrices, and compare it with other two popular interior-point methods such as the path-following method (the subroutine pathfollow.m coded by M.~C.
Edit intent: meaning-changed
Original:  Finally, we test the new method for some problems with deficient rank matrices, and compare it with other  popular interior-point methods such as the path-following method (the subroutine pathfollow.m coded by M.~C.
Revised:  Finally, we test the new method for some problems with deficient rank matrices, and compare it with other popular interior-point methods , i.e. the subroutine pathfollow.m coded by M.~C.
Edit intent: coherence
Original:  Finally, we test the new method for some problems with deficient rank matrices, and compare it with other  popular interior-point methods such as the path-following method (the subroutine pathfollow.m coded by M.~C. Ferris \mbox{%DIFAUXCMD \cite{Gonzaga1992,FMW2007}\hspace{0pt}%DIFAUXCMD ) and Mehrotra's predictor-corrector algorithm (}  the built-in subroutine linprog.m of the MATLAB environment , which was implemented by S. Mehrotra and Y. Zhang \mbox{%DIFAUXCMD \cite{MATLAB,Mehrotra1992,Zhang1998}\hspace{0pt}%DIFAUXCMD ). }  Numerical results show that the new method is more robust than those methods for the large-scale deficient-rank problems without sacrificing its computational efficiency .
Revised:  Finally, we test the new method for some problems with deficient rank matrices, and compare it with other popular interior-point methods such as the path-following method (the subroutine pathfollow.m }\hspace{0pt}%DIFAUXCMD ) and Mehrotra's predictor-corrector algorithm (} the built-in subroutine linprog.m of the MATLAB environment , which was implemented by S. Mehrotra and Y. Zhang \mbox{%DIFAUXCMD \cite{MATLAB,Mehrotra1992,Zhang1998}\hspace{0pt}%DIFAUXCMD ). } Numerical results show that the new method is more robust than those methods for the large-scale deficient-rank problems without sacrificing its computational efficiency .
Edit intent: meaning-changed
Original:  Ferris \mbox{%DIFAUXCMD \cite{Gonzaga1992,FMW2007}\hspace{0pt}%DIFAUXCMD ) and Mehrotra's predictor-corrector algorithm (}  the built-in subroutine linprog.m of the MATLAB environment , which was implemented by S. Mehrotra and Y. Zhang \mbox{%DIFAUXCMD \cite{MATLAB,Mehrotra1992,Zhang1998}\hspace{0pt}%DIFAUXCMD ). }  Numerical results show that the new method is more robust than those methods for the large-scale deficient-rank problems without sacrificing its computational efficiency .
Revised:  Ferris \mbox{%DIFAUXCMD \cite{Gonzaga1992,FMW2007}\hspace{0pt}%DIFAUXCMD ) and Mehrotra's predictor-corrector algorithm (} and the built-in subroutine linprog.m of the MATLAB environment , which was implemented by S. Mehrotra and Y. Zhang \mbox{%DIFAUXCMD \cite{MATLAB,Mehrotra1992,Zhang1998}\hspace{0pt}%DIFAUXCMD ). } Numerical results show that the new method is more robust than those methods for the large-scale deficient-rank problems without sacrificing its computational efficiency .
Edit intent: fluency
Original:  Ferris \mbox{%DIFAUXCMD \cite{Gonzaga1992,FMW2007}\hspace{0pt}%DIFAUXCMD ) and Mehrotra's predictor-corrector algorithm (}  the built-in subroutine linprog.m of the MATLAB environment , which was implemented by S. Mehrotra and Y. Zhang \mbox{%DIFAUXCMD \cite{MATLAB,Mehrotra1992,Zhang1998}\hspace{0pt}%DIFAUXCMD ). }  Numerical results show that the new method is more robust than those methods for the large-scale deficient-rank problems without sacrificing its computational efficiency .
Revised:  Ferris \mbox{%DIFAUXCMD \cite{Gonzaga1992,FMW2007}\hspace{0pt}%DIFAUXCMD ) and Mehrotra's predictor-corrector algorithm (} the built-in subroutine linprog.m of the MATLAB environment }\hspace{0pt}%DIFAUXCMD ). } Numerical results show that the new method is more robust than those methods for the large-scale deficient-rank problems without sacrificing its computational efficiency .
Edit intent: coherence
Original:  Ferris \mbox{%DIFAUXCMD \cite{Gonzaga1992,FMW2007}\hspace{0pt}%DIFAUXCMD ) and Mehrotra's predictor-corrector algorithm (}  the built-in subroutine linprog.m of the MATLAB environment , which was implemented by S. Mehrotra and Y. Zhang \mbox{%DIFAUXCMD \cite{MATLAB,Mehrotra1992,Zhang1998}\hspace{0pt}%DIFAUXCMD ). }  Numerical results show that the new method is more robust than those methods for the large-scale deficient-rank problems without sacrificing its computational efficiency .
Revised:  Ferris \mbox{%DIFAUXCMD \cite{Gonzaga1992,FMW2007}\hspace{0pt}%DIFAUXCMD ) and Mehrotra's predictor-corrector algorithm (} the built-in subroutine linprog.m of the MATLAB environment , which was implemented by S. Mehrotra and Y. Zhang \mbox{%DIFAUXCMD \cite{MATLAB,Mehrotra1992,Zhang1998}\hspace{0pt}%DIFAUXCMD ). } . Numerical results show that the new method is more robust than those methods for the large-scale deficient-rank problems without sacrificing its computational efficiency .
Edit intent: fluency
Original:  Ferris \mbox{%DIFAUXCMD \cite{Gonzaga1992,FMW2007}\hspace{0pt}%DIFAUXCMD ) and Mehrotra's predictor-corrector algorithm (}  the built-in subroutine linprog.m of the MATLAB environment , which was implemented by S. Mehrotra and Y. Zhang \mbox{%DIFAUXCMD \cite{MATLAB,Mehrotra1992,Zhang1998}\hspace{0pt}%DIFAUXCMD ). }  Numerical results show that the new method is more robust than those methods for the large-scale deficient-rank problems without sacrificing its computational efficiency .
Revised:  Ferris \mbox{%DIFAUXCMD \cite{Gonzaga1992,FMW2007}\hspace{0pt}%DIFAUXCMD ) and Mehrotra's predictor-corrector algorithm (} the built-in subroutine linprog.m of the MATLAB environment , which was implemented by S. Mehrotra and Y. Zhang \mbox{%DIFAUXCMD \cite{MATLAB,Mehrotra1992,Zhang1998}\hspace{0pt}%DIFAUXCMD ). } Numerical results show that the new method is more robust than the other two methods for the large-scale deficient-rank problems without sacrificing its computational efficiency .
Edit intent: clarity