wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The first, HYPE-Time, measures visual perception under adaptive time constraints to determine the minimum length of time (e.g. , 250ms) that model output such as a generated face needs to be visible for people to distinguish it as real or fake.
Revised:  The first, HYPE-Time, measures visual perception under adaptive time constraints to determine the threshold at which a model's outputs appear real (e.g. , 250ms) that model output such as a generated face needs to be visible for people to distinguish it as real or fake.
Edit intent: meaning-changed
Original:  The first, HYPE-Time, measures visual perception under adaptive time constraints to determine the minimum length of time (e.g. , 250ms) that model output such as a generated face needs to be visible for people to distinguish it as real or fake.
Revised:  The first, HYPE-Time, measures visual perception under adaptive time constraints to determine the minimum length of time (e.g. 250ms) that model output such as a generated face needs to be visible for people to distinguish it as real or fake.
Edit intent: fluency
Original:  The first, HYPE-Time, measures visual perception under adaptive time constraints to determine the minimum length of time (e.g. , 250ms) that model output such as a generated face needs to be visible for people to distinguish it as real or fake. The second, HYPE-Infinity ,  measures human error rate on fake and real images with no time constraints, maintaining stability and drastically reducing time and cost .
Revised:  The first, HYPE-Time, measures visual perception under adaptive time constraints to determine the minimum length of time (e.g. , 250ms) , measures human error rate on fake and real images with no time constraints, maintaining stability and drastically reducing time and cost .
Edit intent: clarity
Original:  We test HYPE across four state-of-the-art generative adversarial networks (GANs) on unconditional image generation using two datasets, the popular CelebAand the newer higher-resolution FFHQ, and two sampling techniques of model outputs.
Revised:  We test HYPE across six state-of-the-art generative adversarial networks (GANs) on unconditional image generation using two datasets, the popular CelebAand the newer higher-resolution FFHQ, and two sampling techniques of model outputs.
Edit intent: meaning-changed
Original:  We test HYPE across four state-of-the-art generative adversarial networks (GANs) on unconditional image generation using two datasets, the popular CelebAand the newer higher-resolution FFHQ, and two sampling techniques of model outputs.
Revised:  We test HYPE across four state-of-the-art generative adversarial networks and two sampling techniques on conditional and unconditional image generation using two datasets, the popular CelebAand the newer higher-resolution FFHQ, and two sampling techniques of model outputs.
Edit intent: meaning-changed
Original:  We test HYPE across four state-of-the-art generative adversarial networks (GANs) on unconditional image generation using two datasets, the popular CelebAand the newer higher-resolution FFHQ, and two sampling techniques of model outputs. By simulating HYPE 's evaluation multiple times, we demonstrate consistent ranking of different models, identifying StyleGAN with truncation trick sampling (27.6\% HYPE-Infinity deception rate, with roughly one quarter of images being misclassified by humans) as superior to StyleGAN without truncation (19.0\%) on FFHQ. See URL for details .
Revised:  We test HYPE across four state-of-the-art generative adversarial networks (GANs) on unconditional image generation using four datasets: CelebA, FFHQ, CIFAR-10, and ImageNet. We find that HYPE can track model improvements across training epochs, and we confirm via bootstrap sampling that HYPE rankings are consistent and replicable .
Edit intent: meaning-changed
Original:  Prior research lacks the investigation of how machine learning can be applied to a big-data platform to ensure an informed  and judicious prescribing of opioids . In this study , we explore the Massachusetts All Payer Claim Data(MA APCD) , a de-identified healthcare claim dataset, and propose a novel framework to examine how na\"ive users develop opioid use disorder.
Revised:  Prior research lacks the investigation of how machine learning can be applied to a big-data platform to ensure an informed and judicious prescribing of opioids , in particular for commercially insured population. This study explores Massachusetts All Payer Claim Data(MA APCD) , a de-identified healthcare claim dataset, and propose a novel framework to examine how na\"ive users develop opioid use disorder.
Edit intent: meaning-changed
Original:  In this study , we explore the Massachusetts All Payer Claim Data(MA APCD) , a de-identified healthcare claim dataset, and propose a novel framework to examine how na\"ive users develop opioid use disorder.
Revised:  In this study , we explore the Massachusetts All Payer Claims Data , a de-identified healthcare claim dataset, and propose a novel framework to examine how na\"ive users develop opioid use disorder.
Edit intent: coherence
Original:  In this study , we explore the Massachusetts All Payer Claim Data(MA APCD) , a de-identified healthcare claim dataset, and propose a novel framework to examine how na\"ive users develop opioid use disorder.
Revised:  In this study , we explore the Massachusetts All Payer Claim Data(MA APCD) , a de-identified healthcare dataset, and propose a novel framework to examine how na\"ive users develop opioid use disorder.
Edit intent: fluency
Original:  In this study , we explore the Massachusetts All Payer Claim Data(MA APCD) , a de-identified healthcare claim dataset, and propose a novel framework to examine how na\"ive users develop opioid use disorder.
Revised:  In this study , we explore the Massachusetts All Payer Claim Data(MA APCD) , a de-identified healthcare claim dataset, and proposes a machine learning framework to examine how na\"ive users develop opioid use disorder.
Edit intent: meaning-changed
Original:  We then use and compare the predictive power of four well-known machine learning algorithms: logistic regression, random forest, decision tree, and gradient boosting, to predict the risk of such dependency. Results showed that the random forest model outperforms the other three algorithms while determining the features, some of which are consistent with prior clinical findings.
Revised:  We then use and compare the predictive power of four well-known machine learning algorithms: logistic regression, random forest, decision tree, and gradient boosting, to predict the risk of opioid use disorder. The study results show that the Random Forest model outperforms the other three algorithms while determining the features, some of which are consistent with prior clinical findings.
Edit intent: clarity
Original:   We anticipate that this research has the potential for healthcare practitioners to improve the current prescribing practice of opioids , thereby curbing the increasing rate of opioid addiction  .
Revised:  Moreover, alongside the higher predictive accuracy, the proposed framework is capable of extracting some risk factors that will add significant knowledge to what is already known in the extant literature. We anticipate that this research has the potential for healthcare practitioners to improve the current prescribing practice of opioids , thereby curbing the increasing rate of opioid addiction .
Edit intent: meaning-changed
Original:  Here, we extend the unified frameworkof Sonon and co-workers , developed originally for generating particulate and foam-like microstructural geometries of Periodic Unit Cells, to non-periodic microstructural representations based on the formalism of Wang tiles.
Revised:  We extend Sonon et al.'s unified framework , developed originally for generating particulate and foam-like microstructural geometries of Periodic Unit Cells, to non-periodic microstructural representations based on the formalism of Wang tiles.
Edit intent: clarity
Original:  The formalism has been recently proposed as a generalization of the Periodic Unit Cell approach, enabling a fast synthesis of arbitrarily large, stochastic microstructural samples from a handful of domains with predefined microstructural compatibility constraints.
Revised:  This formalism has been recently proposed as a generalization of the Periodic Unit Cell approach, enabling a fast synthesis of arbitrarily large, stochastic microstructural samples from a handful of domains with predefined microstructural compatibility constraints.
Edit intent: fluency
Original:  The formalism has been recently proposed as a generalization of the Periodic Unit Cell approach, enabling a fast synthesis of arbitrarily large, stochastic microstructural samples from a handful of domains with predefined microstructural compatibility constraints.
Revised:  The formalism has been recently proposed in order to generalize the Periodic Unit Cell approach, enabling a fast synthesis of arbitrarily large, stochastic microstructural samples from a handful of domains with predefined microstructural compatibility constraints.
Edit intent: coherence
Original:  The formalism has been recently proposed as a generalization of the Periodic Unit Cell approach, enabling a fast synthesis of arbitrarily large, stochastic microstructural samples from a handful of domains with predefined microstructural compatibility constraints.
Revised:  The formalism has been recently proposed as a generalization of the Periodic Unit Cell approach, enabling a fast synthesis of arbitrarily large, stochastic microstructural samples from a handful of domains with predefined compatibility constraints.
Edit intent: fluency
Original:  Since the original  framework builds on a random sequential addition of particles enhanced with a level-set description , we first devise an analysis based on a connectivity graph of a tile set, resolving the question where should a particle be copied when it intersects a tile boundary.
Revised:  Since the original Sonon et al.'s framework builds on a random sequential addition of particles enhanced with a level-set description , we first devise an analysis based on a connectivity graph of a tile set, resolving the question where should a particle be copied when it intersects a tile boundary.
Edit intent: meaning-changed
Original:  Since the original  framework builds on a random sequential addition of particles enhanced with a level-set description , we first devise an analysis based on a connectivity graph of a tile set, resolving the question where should a particle be copied when it intersects a tile boundary.
Revised:  Since the original framework builds on a random sequential addition of particles enhanced with an implicit representation of particle boundaries by the level-set description , we first devise an analysis based on a connectivity graph of a tile set, resolving the question where should a particle be copied when it intersects a tile boundary.
Edit intent: meaning-changed
Original:  Since the original  framework builds on a random sequential addition of particles enhanced with a level-set description , we first devise an analysis based on a connectivity graph of a tile set, resolving the question where should a particle be copied when it intersects a tile boundary.
Revised:  Since the original framework builds on a random sequential addition of particles enhanced with a level-set field , we first devise an analysis based on a connectivity graph of a tile set, resolving the question where should a particle be copied when it intersects a tile boundary.
Edit intent: clarity
Original:  Since the original  framework builds on a random sequential addition of particles enhanced with a level-set description , we first devise an analysis based on a connectivity graph of a tile set, resolving the question where should a particle be copied when it intersects a tile boundary.
Revised:  Since the original framework builds on a random sequential addition of particles enhanced with a level-set description , we first devise an analysis based on a connectivity graph of a tile set, resolving the question where a particle should be copied when it intersects a tile boundary.
Edit intent: fluency
Original:  Having  a universal procedure for generating tile morphologies at hand , we compare strictly aperiodic and stochastic sets of the same cardinality in terms of reducing the artificial periodicity in reconstructed microstructural samples , and demonstrate the superiority of the vertex-defined tile sets for two-dimensional problems .
Revised:  Having established a universal procedure for generating tile morphologies at hand , we compare strictly aperiodic and stochastic sets of the same cardinality in terms of reducing the artificial periodicity in reconstructed microstructural samples , and demonstrate the superiority of the vertex-defined tile sets for two-dimensional problems .
Edit intent: clarity
Original:  Having  a universal procedure for generating tile morphologies at hand , we compare strictly aperiodic and stochastic sets of the same cardinality in terms of reducing the artificial periodicity in reconstructed microstructural samples , and demonstrate the superiority of the vertex-defined tile sets for two-dimensional problems .
Revised:  Having a universal procedure for generating tile morphologies , we compare strictly aperiodic and stochastic sets of the same cardinality in terms of reducing the artificial periodicity in reconstructed microstructural samples , and demonstrate the superiority of the vertex-defined tile sets for two-dimensional problems .
Edit intent: clarity
Original:  Having  a universal procedure for generating tile morphologies at hand , we compare strictly aperiodic and stochastic sets of the same cardinality in terms of reducing the artificial periodicity in reconstructed microstructural samples , and demonstrate the superiority of the vertex-defined tile sets for two-dimensional problems .
Revised:  Having a universal procedure for generating tile morphologies at hand , we compare strictly aperiodic and stochastic sets with the same cardinality in terms of reducing the artificial periodicity in reconstructed microstructural samples , and demonstrate the superiority of the vertex-defined tile sets for two-dimensional problems .
Edit intent: clarity
Original:  Having  a universal procedure for generating tile morphologies at hand , we compare strictly aperiodic and stochastic sets of the same cardinality in terms of reducing the artificial periodicity in reconstructed microstructural samples , and demonstrate the superiority of the vertex-defined tile sets for two-dimensional problems .
Revised:  Having a universal procedure for generating tile morphologies at hand , we compare strictly aperiodic and stochastic sets of the same cardinality in terms of reducing the artificial periodicity in reconstructed microstructural samples . We demonstrate the superiority of the vertex-defined tile sets for two-dimensional problems .
Edit intent: coherence
Original:  Having  a universal procedure for generating tile morphologies at hand , we compare strictly aperiodic and stochastic sets of the same cardinality in terms of reducing the artificial periodicity in reconstructed microstructural samples , and demonstrate the superiority of the vertex-defined tile sets for two-dimensional problems . Finally, we illustrate the capabilities of the algorithm with two- and three-dimensional examples.
Revised:  Having a universal procedure for generating tile morphologies at hand , we compare strictly aperiodic and stochastic sets of the same cardinality in terms of reducing the artificial periodicity in reconstructed microstructural samples , and demonstrate the superiority of the vertex-defined tile sets for two-dimensional problems and illustrate the capabilities of the algorithm with two- and three-dimensional examples.
Edit intent: coherence
Original:  The problem falls into the entropy-regularized  relaxed stochastic control framework recently introduced in Wang et al. (2019). We derive the feedback exploration policy as the Gaussiandistribution , with time-decaying variance.
Revised:  The problem falls into the entropy-regularized relaxed stochastic control problem. We prove that the optimal feedback policy for this problem must be Gaussian , with time-decaying variance.
Edit intent: clarity
Original:  The PIT leads to an implementable RL algorithm for the continuous-time MV problem. Our algorithm outperforms an adaptive control based method that estimates the underlying parameters in real-time and a state-of-the-art RL method that uses deep neural networks for continuous control problems by a large margin in nearly all simulations.
Revised:  The PIT leads to an implementable RL algorithm . We find that our algorithm outperforms both an adaptive control based method that estimates the underlying parameters in real-time and a state-of-the-art RL method that uses deep neural networks for continuous control problems by a large margin in nearly all simulations.
Edit intent: coherence
Original:  Our algorithm outperforms an adaptive control based method that estimates the underlying parameters in real-time and a state-of-the-art RL method that uses deep neural networks for continuous control problems by a large margin in nearly all simulations.
Revised:  Our algorithm outperforms an adaptive control based method that estimates the underlying parameters in real-time and a state-of-the-art RL method that uses deep neural networks based algorithm by a large margin in nearly all simulations.
Edit intent: clarity
Original:  Our algorithm outperforms an adaptive control based method that estimates the underlying parameters in real-time and a state-of-the-art RL method that uses deep neural networks for continuous control problems by a large margin in nearly all simulations.
Revised:  Our algorithm outperforms an adaptive control based method that estimates the underlying parameters in real-time and a state-of-the-art RL method that uses deep neural networks for continuous control problems by a large margin in our simulations.
Edit intent: clarity
Original:  Conventional financial conditions and empirical evidence related to the implied volatility are incorporated into the neural network architecture design and model training including no static arbitrage,  boundaries, asymptotic slope and volatility smile.
Revised:  Conventional financial conditions and empirical evidence related to the implied volatility are incorporated into the neural network architecture design and model training including no static arbitrage, limiting boundaries, asymptotic slope and volatility smile.
Edit intent: meaning-changed
Original:  The developed neural network model and its simplified variations outperform the widely used surface stochastic volatility inspired (SSVI) model  on the mean average percentage error in both in-sample and out-of-sample datasets.
Revised:  The developed neural network model outperforms the widely used surface stochastic volatility inspired (SSVI) model on the mean average percentage error in both in-sample and out-of-sample datasets.
Edit intent: meaning-changed
Original:  The developed neural network model and its simplified variations outperform the widely used surface stochastic volatility inspired (SSVI) model  on the mean average percentage error in both in-sample and out-of-sample datasets.
Revised:  The developed neural network model and its simplified variations outperform the widely used surface stochastic volatility inspired (SSVI) model and other benchmarked neural network models on the mean average percentage error in both in-sample and out-of-sample datasets.
Edit intent: meaning-changed
Original: OT (Operational Transformation) was invented for supporting real-time co-editors in the late 1980s and has evolved to become a core technique used in today's working co-editors and adopted in major industrial products.
Revised: OT (Operational Transformation) was invented for supporting real-time co-editors in the late 1980s and has evolved to become core techniques widely used in today's working co-editors and adopted in major industrial products.
Edit intent: clarity
Original: OT (Operational Transformation) was invented for supporting real-time co-editors in the late 1980s and has evolved to become a core technique used in today's working co-editors and adopted in major industrial products.
Revised: OT (Operational Transformation) was invented for supporting real-time co-editors in the late 1980s and has evolved to become a core technique used in today's working co-editors and adopted in industrial products.
Edit intent: clarity
Original:  Follow-up CRDT variations are commonly labeled as "post-OT" techniques and have made broad claims of superiority over OT solutions, in terms of correctness, time and space complexity, simplicity, etc .
Revised:  Follow-up CRDT variations are commonly labeled as "post-OT" techniques capable of making concurrent operations natively commutative in co-editors. On top of that, CRDT solutions have made broad claims of superiority over OT solutions, in terms of correctness, time and space complexity, simplicity, etc .
Edit intent: meaning-changed
Original:  Follow-up CRDT variations are commonly labeled as "post-OT" techniques and have made broad claims of superiority over OT solutions, in terms of correctness, time and space complexity, simplicity, etc .
Revised:  Follow-up CRDT variations are commonly labeled as "post-OT" techniques and have made broad claims of superiority over OT solutions, and often portrayed OT as an incorrect and inefficient technique .
Edit intent: meaning-changed
Original:  Over one decade later, however,  OT remains the choice for building the vast majority of  co-editors , whereas CRDTis rarely found in working co-editors. Why?
Revised:  Over one decade later, however, CRDT is rarely found in working co-editors; OT remains the choice for building the vast majority of co-editors , whereas CRDTis rarely found in working co-editors. Why?
Edit intent: fluency
Original:  Over one decade later, however,  OT remains the choice for building the vast majority of  co-editors , whereas CRDTis rarely found in working co-editors. Why? Based on comprehensive review and comparison on representative OT and CRDT solutions and co-editors based on them , we present our discoveries in relation to this question and beyond in a series of three articles.
Revised:  Over one decade later, however, OT remains the choice for building the vast majority of co-editors . Contradictions between the reality and CRDT's purported advantages have been the source of much confusion and debate among co-editing researcher sand developers. To seek truth from facts, we set out to conduct a comprehensive and critical review on representative OT and CRDT solutions and co-editors based on them , we present our discoveries in relation to this question and beyond in a series of three articles.
Edit intent: meaning-changed
Original:  Based on comprehensive review and comparison on representative OT and CRDT solutions and co-editors based on them , we present our discoveries in relation to this question and beyond in a series of three articles. In prior work, we have revealed that CRDT is like OT in following the same general transformation approach and CRDT is not natively commutative for concurrent operations in co-editors, which helps to clarify what CRDT really is and is not for co-editors.
Revised:  Based on comprehensive review and comparison on representative OT and CRDT solutions and co-editors based on them . From this work, we have revealed that CRDT is like OT in following the same general transformation approach and CRDT is not natively commutative for concurrent operations in co-editors, which helps to clarify what CRDT really is and is not for co-editors.
Edit intent: coherence