wanyu/IteraTeR_full_sent huggingface dataset:
Original:  This paper introduces a novel analytical approach that poses the problem of safe trajectory planning under occlusions as a hybrid zero-sum dynamic game between the autonomous vehicle (evader) , and an initially hidden traffic participant (pursuer).
Revised:  This paper introduces a novel analytical approach that poses the problem of safe trajectory planning under occlusions as a hybrid zero-sum dynamic game between the autonomous vehicle (evader) and an initially hidden traffic participant (pursuer).
Edit intent: fluency
Original:  Tested on fMRI data from 59 participants adminstrated with either oxytocin or placebo, we demonstrate that oxytocin changes the topology of brain communities in DMN and FPN, leading to higher synchronization in the DMN and lower synchronization in the FPN, as well as  higher variance of the coupling strength within the DMN and more flexible coupling patterns across time.
Revised:  Tested on fMRI data from 59 participants administrated with either oxytocin or placebo, we demonstrate that oxytocin changes the topology of brain communities in DMN and FPN, leading to higher synchronization in the DMN and lower synchronization in the FPN, as well as higher variance of the coupling strength within the DMN and more flexible coupling patterns across time.
Edit intent: fluency
Original:  Tested on fMRI data from 59 participants adminstrated with either oxytocin or placebo, we demonstrate that oxytocin changes the topology of brain communities in DMN and FPN, leading to higher synchronization in the DMN and lower synchronization in the FPN, as well as  higher variance of the coupling strength within the DMN and more flexible coupling patterns across time.
Revised:  Tested on fMRI data from 59 participants adminstrated with either oxytocin or placebo, we demonstrate that oxytocin changes the topology of brain communities in DMN and FPN, leading to higher synchronization in the DMN and lower synchronization in the FPN, as well as a higher variance of the coupling strength within the DMN and more flexible coupling patterns across time.
Edit intent: fluency
Original:  These results together imply that oxytocin may increase  capability to overcome the dispersion of corresponding intrinsic oscillations and yield flexibility in neural synchrony under various social contexts, providing new evidence to account for oxytocin modulated social behaviors.
Revised:  These results together imply that oxytocin may increase the capability to overcome the dispersion of corresponding intrinsic oscillations and yield flexibility in neural synchrony under various social contexts, providing new evidence to account for oxytocin modulated social behaviors.
Edit intent: fluency
Original:  By modifying the acquisition function's optimization, we are reliably able to present a puzzle with a bespoke difficulty for players with different skill levels in less than five iterations (for Sudoku ) and fifteen iterations ( for the simple Roguelike ), significantly outperforming simpler heuristics for difficulty adjustment in said domains, with the added benefit of maintaining a model of the user.
Revised:  By modifying the acquisition function's optimization, we are reliably able to present a content with a bespoke difficulty for players with different skill levels in less than five iterations (for Sudoku ) and fifteen iterations ( for the simple Roguelike ), significantly outperforming simpler heuristics for difficulty adjustment in said domains, with the added benefit of maintaining a model of the user.
Edit intent: clarity
Original:  By modifying the acquisition function's optimization, we are reliably able to present a puzzle with a bespoke difficulty for players with different skill levels in less than five iterations (for Sudoku ) and fifteen iterations ( for the simple Roguelike ), significantly outperforming simpler heuristics for difficulty adjustment in said domains, with the added benefit of maintaining a model of the user.
Revised:  By modifying the acquisition function's optimization, we are reliably able to present a puzzle with a bespoke difficulty for players with different skill levels in less than five iterations for Sudoku and fifteen iterations ( for the simple Roguelike ), significantly outperforming simpler heuristics for difficulty adjustment in said domains, with the added benefit of maintaining a model of the user.
Edit intent: fluency
Original:  By modifying the acquisition function's optimization, we are reliably able to present a puzzle with a bespoke difficulty for players with different skill levels in less than five iterations (for Sudoku ) and fifteen iterations ( for the simple Roguelike ), significantly outperforming simpler heuristics for difficulty adjustment in said domains, with the added benefit of maintaining a model of the user.
Revised:  By modifying the acquisition function's optimization, we are reliably able to present a puzzle with a bespoke difficulty for players with different skill levels in less than five iterations (for Sudoku ) and fifteen iterations for the simple Roguelike ), significantly outperforming simpler heuristics for difficulty adjustment in said domains, with the added benefit of maintaining a model of the user.
Edit intent: fluency
Original:  By modifying the acquisition function's optimization, we are reliably able to present a puzzle with a bespoke difficulty for players with different skill levels in less than five iterations (for Sudoku ) and fifteen iterations ( for the simple Roguelike ), significantly outperforming simpler heuristics for difficulty adjustment in said domains, with the added benefit of maintaining a model of the user.
Revised:  By modifying the acquisition function's optimization, we are reliably able to present a puzzle with a bespoke difficulty for players with different skill levels in less than five iterations (for Sudoku ) and fifteen iterations ( for the simple Roguelike . Our method significantly outperforms simpler DDA heuristics with the added benefit of maintaining a model of the user.
Edit intent: clarity
Original:  Among those efforts, cleaning and disinfecting public areas has become an important task. This task is not restricted to disinfection, but it is also applied to painting or precision agriculture. Current state of the art planners determine a route, but they do not consider that the plan will be executed in closed areas or they do not model the sprinkler coverage. In this paper, we propose a coverage path planning algorithm for area disinfection, our method considers the scene restrictions as well as the sprinkler coverage. We have tested the method in several simulation scenes .
Revised:  Among those efforts, cleaning and disinfecting public areas have become important tasks. In order to contribute in this direction, this paper proposes a coverage path planning algorithm for a spraying drone, a micro aerial vehicle that has mounted a sprayer/sprinkler system, to disinfect areas. In contrast with planners in the state-of-the-art, this proposal presents i) a new sprayer/sprinkler model that fits a more realistic coverage volume to the drop dispersion and ii) a planning algorithm that efficiently restricts the flight to the region of interest avoiding potential collisions in bounded scenes. The drone with the algorithm has been tested in several simulation scenes .
Edit intent: coherence
Original:  This latter condition is often met in differences-in-differences designs, where the CATEs are estimators of the effect of having been treated for a certain number of time periods. In that case, the optimal estimator is no longer feasible , as it depends on unknown quantities, but a feasible estimator can easily be constructed by replacing those unknown quantities by estimators .
Revised:  This latter condition is often met in differences-in-differences designs, if errors are homoscedastic and uncorrelated. Under those assumptions, I show that the minimax estimator is still feasible and can easily be constructed by replacing those unknown quantities by estimators .
Edit intent: meaning-changed
Original:  Tree-boosting shows excellent predictive accuracy on many data sets, but potential drawbacks are that it assumes conditional independence of samples, produces discontinuous predictions for,  and it can have difficulty with high-cardinality categorical variables.
Revised:  Tree-boosting shows excellent predictive accuracy on many data sets, but potential drawbacks are that it assumes conditional independence of samples, produces discontinuous predictions for, e.g., spatial data, and it can have difficulty with high-cardinality categorical variables.
Edit intent: meaning-changed
Original:  Instead of segmenting the point cloud semantically, i.e., predicting the semantic classes such as vehicles, pedestrians, buildings, roads, etc., our approach accurately segments the scene into moving and static objects, i.e.,  distinguishing between moving cars vs. parked cars.
Revised:  Instead of segmenting the point cloud semantically, i.e., predicting the semantic classes such as vehicles, pedestrians, roads, etc., our approach accurately segments the scene into moving and static objects, i.e., distinguishing between moving cars vs. parked cars.
Edit intent: clarity
Original:  Instead of segmenting the point cloud semantically, i.e., predicting the semantic classes such as vehicles, pedestrians, buildings, roads, etc., our approach accurately segments the scene into moving and static objects, i.e.,  distinguishing between moving cars vs. parked cars.
Revised:  Instead of segmenting the point cloud semantically, i.e., predicting the semantic classes such as vehicles, pedestrians, buildings, roads, etc., our approach accurately segments the scene into moving and static objects, i.e., also distinguishing between moving cars vs. parked cars.
Edit intent: coherence
Original:  (Recently, Wager and Xu (arXiv:2101.09855) independently proposed this regime and developed similar SDE and random ODE approximations  .) Our weak convergence theory covers both the classical multi-armed and linear bandit settings,  and can be used, for instance, to obtain insight about the characteristics of the regret distribution when there is information sharing among arms, as well as the effects of variance estimation, model mis-specification and batched updates in bandit learning.
Revised:  (Recently, Wager and Xu (arXiv:2101.09855) independently proposed this regime and developed similar SDE and random ODE approximations for Thompson sampling in the multi-armed bandit setting .) Our weak convergence theory covers both the classical multi-armed and linear bandit settings, and can be used, for instance, to obtain insight about the characteristics of the regret distribution when there is information sharing among arms, as well as the effects of variance estimation, model mis-specification and batched updates in bandit learning.
Edit intent: meaning-changed
Original:  (Recently, Wager and Xu (arXiv:2101.09855) independently proposed this regime and developed similar SDE and random ODE approximations  .) Our weak convergence theory covers both the classical multi-armed and linear bandit settings,  and can be used, for instance, to obtain insight about the characteristics of the regret distribution when there is information sharing among arms, as well as the effects of variance estimation, model mis-specification and batched updates in bandit learning.
Revised:  (Recently, Wager and Xu (arXiv:2101.09855) independently proposed this regime and developed similar SDE and random ODE approximations .) Our weak convergence theory , which covers both multi-armed and linear bandit settings, and can be used, for instance, to obtain insight about the characteristics of the regret distribution when there is information sharing among arms, as well as the effects of variance estimation, model mis-specification and batched updates in bandit learning.
Edit intent: coherence
Original:  (Recently, Wager and Xu (arXiv:2101.09855) independently proposed this regime and developed similar SDE and random ODE approximations  .) Our weak convergence theory covers both the classical multi-armed and linear bandit settings,  and can be used, for instance, to obtain insight about the characteristics of the regret distribution when there is information sharing among arms, as well as the effects of variance estimation, model mis-specification and batched updates in bandit learning.
Revised:  (Recently, Wager and Xu (arXiv:2101.09855) independently proposed this regime and developed similar SDE and random ODE approximations .) Our weak convergence theory covers both the classical multi-armed and linear bandit settings, is developed from first principles using the Continuous Mapping Theorem and can be used, for instance, to obtain insight about the characteristics of the regret distribution when there is information sharing among arms, as well as the effects of variance estimation, model mis-specification and batched updates in bandit learning.
Edit intent: meaning-changed
Original:  (Recently, Wager and Xu (arXiv:2101.09855) independently proposed this regime and developed similar SDE and random ODE approximations  .) Our weak convergence theory covers both the classical multi-armed and linear bandit settings,  and can be used, for instance, to obtain insight about the characteristics of the regret distribution when there is information sharing among arms, as well as the effects of variance estimation, model mis-specification and batched updates in bandit learning. Our theory is developed from first-principles and can also be adapted to analyze other sampling-based bandit algorithms .
Revised:  (Recently, Wager and Xu (arXiv:2101.09855) independently proposed this regime and developed similar SDE and random ODE approximations .) Our weak convergence theory covers both the classical multi-armed and linear bandit settings, and can be directly adapted to analyze other sampling-based bandit algorithms .
Edit intent: clarity
Original:  We try to answer these questions considering the law in force in both US and EU and the future alternatives, trying to define a set of  guidelines for artists and developers working on deep learning generated art.
Revised:  We try to answer these questions considering the law in force in both the United States of America and the European Union, and potential future alternatives. Finally, we also formulate a set of guidelines for artists and developers working on deep learning generated art.
Edit intent: clarity
Original: Recently, plenty of work has tried to introduce transformers into computer vision tasks, with good results. Unlike classic convolution networks, which extract features within a local receptive field, transformers can adaptively aggregate similar features from a global view using self-attention mechanism. For object detection, Feature Pyramid Network (FPN) proposes feature interaction across layers and proves its extremely importance .
Revised: As one of the prevalent components, Feature Pyramid Network (FPN) proposes feature interaction across layers and proves its extremely importance .
Edit intent: clarity
Original:  In this paper, we utilize a linearized attention function to overcome above problems and build a novel architecture , named Content-Augmented Feature Pyramid Network (CA-FPN) , which proposes a global content extraction module and deeply combines with FPN through light linear transformers.
Revised:  In this paper, to simulate a global view of human vision in object detection and address the inherent defects of interaction mode in FPN, we construct a novel architecture , named Content-Augmented Feature Pyramid Network (CA-FPN) , which proposes a global content extraction module and deeply combines with FPN through light linear transformers.
Edit intent: meaning-changed
Original:  In this paper, we utilize a linearized attention function to overcome above problems and build a novel architecture , named Content-Augmented Feature Pyramid Network (CA-FPN) , which proposes a global content extraction module and deeply combines with FPN through light linear transformers.
Revised:  In this paper, we utilize a linearized attention function to overcome above problems and build a novel architecture termed Content-Augmented Feature Pyramid Network (CA-FPN) , which proposes a global content extraction module and deeply combines with FPN through light linear transformers.
Edit intent: clarity
Original:  In this paper, we utilize a linearized attention function to overcome above problems and build a novel architecture , named Content-Augmented Feature Pyramid Network (CA-FPN) , which proposes a global content extraction module and deeply combines with FPN through light linear transformers.
Revised:  In this paper, we utilize a linearized attention function to overcome above problems and build a novel architecture , named Content-Augmented Feature Pyramid Network (CA-FPN) . Unlike the vanilla FPN, which fuses features within a local receptive field, CA-FPN can adaptively aggregate similar features from a global view. It is equipped with a global content extraction module and deeply combines with FPN through light linear transformers.
Edit intent: meaning-changed
Original:  In this paper, we utilize a linearized attention function to overcome above problems and build a novel architecture , named Content-Augmented Feature Pyramid Network (CA-FPN) , which proposes a global content extraction module and deeply combines with FPN through light linear transformers. What's more, light transformers can further make the application of multi-head attention mechanism easier. Most importantly, our CA-FPN can be readily plugged into existing FPN-based models.
Revised:  In this paper, we utilize a linearized attention function to overcome above problems and build a novel architecture , named Content-Augmented Feature Pyramid Network (CA-FPN) , which proposes a global content extraction module and light linear spatial transformers. The former allows to extract multi-scale context information and the latter can deeply combine the global content extraction module with the vanilla FPN using the linearized attention function, which is designed to reduce model complexity. Furthermore, CA-FPN can be readily plugged into existing FPN-based models.
Edit intent: meaning-changed
Original:  To this end, we propose %DIFDELCMD < \method%%%  , a training method to obtain a single unified multilingual translation model. mCOLT is empowered by two techniques: (i ) a contrastive learning scheme to close the gap among representations of different languages, and (ii ) data augmentation on both multiple parallel and monolingual data to further align token representations.
Revised:  To this end, we propose %DIFDELCMD < \method%%% mRASP2 , a training method to obtain a single unified multilingual translation model. mCOLT is empowered by two techniques: (i ) a contrastive learning scheme to close the gap among representations of different languages, and (ii ) data augmentation on both multiple parallel and monolingual data to further align token representations.
Edit intent: meaning-changed
Original:  To this end, we propose %DIFDELCMD < \method%%%  , a training method to obtain a single unified multilingual translation model. mCOLT is empowered by two techniques: (i ) a contrastive learning scheme to close the gap among representations of different languages, and (ii ) data augmentation on both multiple parallel and monolingual data to further align token representations.
Revised:  To this end, we propose %DIFDELCMD < \method%%% , a training method to obtain a single unified multilingual translation model. mRASP2 is empowered by two techniques: (i ) a contrastive learning scheme to close the gap among representations of different languages, and (ii ) data augmentation on both multiple parallel and monolingual data to further align token representations.
Edit intent: meaning-changed
Original:  To this end, we propose %DIFDELCMD < \method%%%  , a training method to obtain a single unified multilingual translation model. mCOLT is empowered by two techniques: (i ) a contrastive learning scheme to close the gap among representations of different languages, and (ii ) data augmentation on both multiple parallel and monolingual data to further align token representations.
Revised:  To this end, we propose %DIFDELCMD < \method%%% , a training method to obtain a single unified multilingual translation model. mCOLT is empowered by two techniques: a ) a contrastive learning scheme to close the gap among representations of different languages, and (ii ) data augmentation on both multiple parallel and monolingual data to further align token representations.
Edit intent: fluency
Original:  To this end, we propose %DIFDELCMD < \method%%%  , a training method to obtain a single unified multilingual translation model. mCOLT is empowered by two techniques: (i ) a contrastive learning scheme to close the gap among representations of different languages, and (ii ) data augmentation on both multiple parallel and monolingual data to further align token representations.
Revised:  To this end, we propose %DIFDELCMD < \method%%% , a training method to obtain a single unified multilingual translation model. mCOLT is empowered by two techniques: (i ) a contrastive learning scheme to close the gap among representations of different languages, and b ) data augmentation on both multiple parallel and monolingual data to further align token representations.
Edit intent: coherence
Original:  For English-centric directions, mCOLT achieves competitive or even better performance than a strong pre-trained  model mBART on tens of WMT benchmarks .
Revised:  For English-centric directions, mRASP2 outperforms existing best unified model and achieves competitive or even better performance than a strong pre-trained model mBART on tens of WMT benchmarks .
Edit intent: meaning-changed
Original:  KLUE is a collection of 8 Korean natural language understanding (NLU) tasks, including Topic Classification, Semantic Textual Similarity, Natural Language Inference, Named Entity Recognition, Relation Extraction, Dependency Parsing, Machine Reading Comprehension, and Dialogue State Tracking.
Revised:  KLUE is a collection of 8 Korean natural language understanding (NLU) tasks, including Topic Classification, SemanticTextual Similarity, Natural Language Inference, Named Entity Recognition, Relation Extraction, Dependency Parsing, Machine Reading Comprehension, and Dialogue State Tracking.
Edit intent: fluency
Original:  We furthermore release the pretrained language models (PLM), KLUE-BERT and KLUE-RoBERTa, to help reproduce baseline models on KLUE and thereby facilitate future research.
Revised:  We furthermore release the pretrained language models (PLM), KLUE-BERT and KLUE-RoBERTa, to help reproducing baseline models on KLUE and thereby facilitate future research.
Edit intent: fluency
Original:  Unlike prior work in more restricted settings, our benchmark measures the ability of models to take an arbitrary natural language specification and generate Python code fulfilling this specification . Similar to how companies assess candidate software developers, we then evaluate models by checking their generated code on test cases.
Revised:  Unlike prior work in more restricted settings, our benchmark measures the ability of models to take an arbitrary natural language specification and generate satisfactory Python code . Similar to how companies assess candidate software developers, we then evaluate models by checking their generated code on test cases.
Edit intent: clarity
Original:  We find through a novel tail regression model that a higher maximum tax rate is associated with a higher wealth Pareto exponent .
Revised:  We find through a novel tail regression method, which accommodates the unique feature that the data are truncated from below at the 400th order statistic. Applying this tail regression to the "natural experiment" (Feldstein, 1995) of the event that tax rates rapidly declined in the 1980s, we find that higher maximum marginal income tax rates induce higher Pareto exponents of the wealth distribution .
Edit intent: meaning-changed