wanyu/IteraTeR_full_sent huggingface dataset:
Original:  This work shows that current research is consistent with recommendations for using masks at a population level in regions in which there is transmission of COVID-19, and that nonlinear effects and statistical considerations regarding the percentage of exposures for which the mask is worn must be taken into account when designing empirical studies and interpreting their results.
Revised:  This work shows (1) that both theoretical and empirical evidence is consistent with recommendations for using masks at a population level in regions in which there is transmission of COVID-19, and that nonlinear effects and statistical considerations regarding the percentage of exposures for which the mask is worn must be taken into account when designing empirical studies and interpreting their results.
Edit intent: meaning-changed
Original:  This work shows that current research is consistent with recommendations for using masks at a population level in regions in which there is transmission of COVID-19, and that nonlinear effects and statistical considerations regarding the percentage of exposures for which the mask is worn must be taken into account when designing empirical studies and interpreting their results.
Revised:  This work shows that current research is consistent with masks protecting against respiratory infections and (2) that nonlinear effects and statistical considerations regarding the percentage of exposures for which the mask is worn must be taken into account when designing empirical studies and interpreting their results.
Edit intent: clarity
Original:  This work shows that current research is consistent with recommendations for using masks at a population level in regions in which there is transmission of COVID-19, and that nonlinear effects and statistical considerations regarding the percentage of exposures for which the mask is worn must be taken into account when designing empirical studies and interpreting their results.
Revised:  This work shows that current research is consistent with recommendations for using masks at a population level in regions in which there is transmission of COVID-19, and that nonlinear effects and statistical considerations regarding the percentage of exposures for which masks are worn must be taken into account when designing empirical studies and interpreting their results.
Edit intent: fluency
Original:  Making decisions for maximal reward in a stochastic setting requires learning values and constructing policies over a belief space, i.e., probability distribution of the robot-world state. Value learning over belief spaces suffer from computational challenges in high-dimensional spaces, such as large spatial environments and long temporal horizons for exploration.
Revised:  Making decisions for maximal reward in a stochastic setting requires learning values and constructing policies over a belief space, i.e., probability distribution of the robot-world states. However, belief space planning in a large spatial environment over long temporal horizons for exploration.
Edit intent: clarity
Original:  Value learning over belief spaces suffer from computational challenges in high-dimensional spaces, such as large spatial environments and long temporal horizons for exploration. At the same time, it should be adaptive and resilient to disturbances at run time in order to ensure the robot's safety, as required in many real-world applications .
Revised:  Value learning over belief spaces suffer from computational challenges in high-dimensional spaces, such as large spatial environments and long temporal horizons suffers from severe computational challenges. Moreover, constructed policies must safely adapt to unexpected changes in the belief at runtime .
Edit intent: clarity
Original:  Here we complement these approaches by clarifying some of the connections between kirigami patterns and symmetry in terms of the wallpaper groups, the set of seventeen plane symmetry groups that fully characterize the space of periodic tilings of the plane.
Revised:  Here we complement these approaches by directly linking kirigami patterns to the symmetry associated with the set of seventeen plane symmetry groups that fully characterize the space of periodic tilings of the plane.
Edit intent: clarity
Original:  Here we complement these approaches by clarifying some of the connections between kirigami patterns and symmetry in terms of the wallpaper groups, the set of seventeen plane symmetry groups that fully characterize the space of periodic tilings of the plane.
Revised:  Here we complement these approaches by clarifying some of the connections between kirigami patterns and symmetry in terms of the wallpaper groups, the set of seventeen repeating patterns that fully characterize the space of periodic tilings of the plane.
Edit intent: clarity
Original:  We start by showing that deployable kirigami patterns in any of the seventeen wallpaper groupscan be constructed , and then design symmetry-preserving cut patterns that can be effectively applied for achieving arbitrary size change throughout deployment.
Revised:  We start by showing how to construct deployable kirigami patterns in any of the seventeen wallpaper groupscan be constructed , and then design symmetry-preserving cut patterns that can be effectively applied for achieving arbitrary size change throughout deployment.
Edit intent: clarity
Original:  We start by showing that deployable kirigami patterns in any of the seventeen wallpaper groupscan be constructed , and then design symmetry-preserving cut patterns that can be effectively applied for achieving arbitrary size change throughout deployment.
Revised:  We start by showing that deployable kirigami patterns using any of the seventeen wallpaper groupscan be constructed , and then design symmetry-preserving cut patterns that can be effectively applied for achieving arbitrary size change throughout deployment.
Edit intent: fluency
Original:  We start by showing that deployable kirigami patterns in any of the seventeen wallpaper groupscan be constructed , and then design symmetry-preserving cut patterns that can be effectively applied for achieving arbitrary size change throughout deployment.
Revised:  We start by showing that deployable kirigami patterns in any of the wallpaper groups , and then design symmetry-preserving cut patterns that can be effectively applied for achieving arbitrary size change throughout deployment.
Edit intent: clarity
Original:  We start by showing that deployable kirigami patterns in any of the seventeen wallpaper groupscan be constructed , and then design symmetry-preserving cut patterns that can be effectively applied for achieving arbitrary size change throughout deployment.
Revised:  We start by showing that deployable kirigami patterns in any of the seventeen wallpaper groupscan be constructed , and then design symmetry-preserving cut patterns to achieve arbitrary size changes via deployment.
Edit intent: clarity
Original:  All together, our work provides a systematic approach for creating  deployable structures with any prescribed size and symmetry properties , thereby paving the way for the design of a wide range of metamaterials by harnessing kirigami .
Revised:  All together, our work provides a systematic approach for creating a broad range of kirigami-based deployable structures with any prescribed size and symmetry properties , thereby paving the way for the design of a wide range of metamaterials by harnessing kirigami .
Edit intent: meaning-changed
Original: This work deals with error models for trident quaternion framework proposed in the companion paper "A Trident Quaternion Framework for Inertial-based Navigation Part I: Motion Representation and Computation" and further uses them to investigate the static and in-motion  alignment for land vehicles.
Revised: This work deals with error models for trident quaternion framework proposed in the companion paper (Part I) and further uses them to investigate the static and in-motion alignment for land vehicles.
Edit intent: clarity
Original: This work deals with error models for trident quaternion framework proposed in the companion paper "A Trident Quaternion Framework for Inertial-based Navigation Part I: Motion Representation and Computation" and further uses them to investigate the static and in-motion  alignment for land vehicles.
Revised: This work deals with error models for trident quaternion framework proposed in the companion paper "A Trident Quaternion Framework for Inertial-based Navigation Part I: Motion Representation and Computation" and further uses them to investigate the odometer-aided static/ in-motion alignment for land vehicles.
Edit intent: meaning-changed
Original: This work deals with error models for trident quaternion framework proposed in the companion paper "A Trident Quaternion Framework for Inertial-based Navigation Part I: Motion Representation and Computation" and further uses them to investigate the static and in-motion  alignment for land vehicles.
Revised: This work deals with error models for trident quaternion framework proposed in the companion paper "A Trident Quaternion Framework for Inertial-based Navigation Part I: Motion Representation and Computation" and further uses them to investigate the static and in-motion inertial navigation attitude alignment for land vehicles.
Edit intent: clarity
Original:  Then the two models are used to design the corresponding extended Kalman filters (EKF), namely, the left-quaternion EKF (LQEKF) and the right-quaternion EKF (RQEKF).
Revised:  The two error models are used to design the corresponding extended Kalman filters (EKF), namely, the left-quaternion EKF (LQEKF) and the right-quaternion EKF (RQEKF).
Edit intent: meaning-changed
Original:  Then the two models are used to design the corresponding extended Kalman filters (EKF), namely, the left-quaternion EKF (LQEKF) and the right-quaternion EKF (RQEKF).
Revised:  Then the two models are used to design their corresponding extended Kalman filters (EKF), namely, the left-quaternion EKF (LQEKF) and the right-quaternion EKF (RQEKF).
Edit intent: clarity
Original:  For the in-motion alignment, however, the two filters still need the assistance of the analytical / optimization-based in-motion alignment methods at the very start to avoid extremely large attitude errors, although they possess much larger convergence region than the traditional EKF does  .
Revised:  For the in-motion alignment, the L / optimization-based in-motion alignment methods at the very start to avoid extremely large attitude errors, although they possess much larger convergence region than the traditional EKF does .
Edit intent: clarity
Original:  For the in-motion alignment, however, the two filters still need the assistance of the analytical / optimization-based in-motion alignment methods at the very start to avoid extremely large attitude errors, although they possess much larger convergence region than the traditional EKF does  .
Revised:  For the in-motion alignment, however, the two filters still need the assistance of the analytical / RQEKF possess much larger convergence region than the traditional EKF does .
Edit intent: clarity
Original:  For the in-motion alignment, however, the two filters still need the assistance of the analytical / optimization-based in-motion alignment methods at the very start to avoid extremely large attitude errors, although they possess much larger convergence region than the traditional EKF does  .
Revised:  For the in-motion alignment, however, the two filters still need the assistance of the analytical / optimization-based in-motion alignment methods at the very start to avoid extremely large attitude errors, although they possess much larger convergence region than the traditional EKF does , although they still require the aid of attitude initialization so as to avoid large initial attitude errors .
Edit intent: meaning-changed
Original: To balance exploration and exploitation, multi-armed bandit algorithms need to conduct inference on the true mean reward of each arm in every time step using the data collected so far  . However, the history of arms and rewards observed up to that time step is adaptively collected and there are known challenges in conducting inference with non-iid data .
Revised: During online decision making in Multi-Armed Bandits (MAB), one needs to conduct inference on the true mean reward of each arm in every time step using the data collected so far . However, the history of arms and rewards observed up to that time step is adaptively collected and there are known challenges in conducting inference with non-iid data .
Edit intent: clarity
Original: To balance exploration and exploitation, multi-armed bandit algorithms need to conduct inference on the true mean reward of each arm in every time step using the data collected so far  . However, the history of arms and rewards observed up to that time step is adaptively collected and there are known challenges in conducting inference with non-iid data .
Revised: To balance exploration and exploitation, multi-armed bandit algorithms need to conduct inference on the true mean reward of each arm based on data collected so far . However, the history of arms and rewards observed up to that time step is adaptively collected and there are known challenges in conducting inference with non-iid data .
Edit intent: clarity
Original: To balance exploration and exploitation, multi-armed bandit algorithms need to conduct inference on the true mean reward of each arm in every time step using the data collected so far  . However, the history of arms and rewards observed up to that time step is adaptively collected and there are known challenges in conducting inference with non-iid data .
Revised: To balance exploration and exploitation, multi-armed bandit algorithms need to conduct inference on the true mean reward of each arm in every time step using the data collected so far at each step . However, the history of arms and rewards observed up to that time step is adaptively collected and there are known challenges in conducting inference with non-iid data .
Edit intent: clarity
Original: To balance exploration and exploitation, multi-armed bandit algorithms need to conduct inference on the true mean reward of each arm in every time step using the data collected so far  . However, the history of arms and rewards observed up to that time step is adaptively collected and there are known challenges in conducting inference with non-iid data .
Revised: To balance exploration and exploitation, multi-armed bandit algorithms need to conduct inference on the true mean reward of each arm in every time step using the data collected so far . However, since the arms are adaptively selected--thereby yielding non-iid data .
Edit intent: clarity
Original: To balance exploration and exploitation, multi-armed bandit algorithms need to conduct inference on the true mean reward of each arm in every time step using the data collected so far  . However, the history of arms and rewards observed up to that time step is adaptively collected and there are known challenges in conducting inference with non-iid data .
Revised: To balance exploration and exploitation, multi-armed bandit algorithms need to conduct inference on the true mean reward of each arm in every time step using the data collected so far . However, the history of arms and rewards observed up to that time step is adaptively collected and there are known challenges in conducting inference with non-iid data--conducting inference accurately is not straightforward .
Edit intent: meaning-changed
Original:  In particular, sample averages, which play a prominent role in traditional upper confidence bound algorithms and traditional Thompson sampling algorithms, are neither unbiased nor asymptotically normal. We propose a variant of a Thompson sampling based algorithm that leverages recent advances in the causal inference literature and adaptively re-weighs the terms of a doubly robust estimator on the true mean reward of each arm -- hence its name doubly-adaptive Thompson sampling.
Revised:  In particular, sample averaging, which is used in the family of UCB and Thompson sampling (TS) algorithms, does not provide a good choice as it suffers from bias and a lack of good statistical properties (e.g. asymptotic normality). Our thesis in this paper is that more sophisticated inference schemes that take into account the adaptive nature of the sequentially collected data can unlock further performance gains, even though both UCB and TS type algorithms are optimal in the worst case. In particular, we propose a variant of a Thompson sampling based algorithm that leverages recent advances in the causal inference literature and adaptively re-weighs the terms of a doubly robust estimator on the true mean reward of each arm -- hence its name doubly-adaptive Thompson sampling.
Edit intent: coherence
Original:  We propose a variant of a Thompson sampling based algorithm that leverages recent advances in the causal inference literature and adaptively re-weighs the terms of a doubly robust estimator on the true mean reward of each arm -- hence its name doubly-adaptive Thompson sampling.
Revised:  We propose a variant of TS-style algorithms--which we call doubly adaptive TS--that leverages recent advances in the causal inference literature and adaptively re-weighs the terms of a doubly robust estimator on the true mean reward of each arm -- hence its name doubly-adaptive Thompson sampling.
Edit intent: meaning-changed
Original:  We propose a variant of a Thompson sampling based algorithm that leverages recent advances in the causal inference literature and adaptively re-weighs the terms of a doubly robust estimator on the true mean reward of each arm -- hence its name doubly-adaptive Thompson sampling.
Revised:  We propose a variant of a Thompson sampling based algorithm that leverages recent advances in causal inference and adaptively reweights the terms of a doubly robust estimator on the true mean reward of each arm -- hence its name doubly-adaptive Thompson sampling.
Edit intent: clarity
Original:  We propose a variant of a Thompson sampling based algorithm that leverages recent advances in the causal inference literature and adaptively re-weighs the terms of a doubly robust estimator on the true mean reward of each arm -- hence its name doubly-adaptive Thompson sampling. The regret of the proposed algorithm matches the optimal (minimax) regret rate and its empirical evaluation in a semi-synthetic experiment based on data from a randomized control trial of a web service is performed: we see that the proposed doubly-adaptive Thompson sampling has superior empirical performance to existing baselines  in terms of cumulative regret and statistical power in identifying the best arm.
Revised:  We propose a variant of a Thompson sampling based algorithm that leverages recent advances in the causal inference literature and adaptively re-weighs the terms of a doubly robust estimator on the true mean reward of each arm . Through 20 synthetic domain experiments and a semi-synthetic experiment based on data from a randomized control trial of a web service is performed: we see that the proposed doubly-adaptive Thompson sampling has superior empirical performance to existing baselines in terms of cumulative regret and statistical power in identifying the best arm.
Edit intent: meaning-changed
Original:  The regret of the proposed algorithm matches the optimal (minimax) regret rate and its empirical evaluation in a semi-synthetic experiment based on data from a randomized control trial of a web service is performed: we see that the proposed doubly-adaptive Thompson sampling has superior empirical performance to existing baselines  in terms of cumulative regret and statistical power in identifying the best arm.
Revised:  The regret of the proposed algorithm matches the optimal (minimax) regret rate and its empirical evaluation in a semi-synthetic experiment based on data from an A/B test of a web service is performed: we see that the proposed doubly-adaptive Thompson sampling has superior empirical performance to existing baselines in terms of cumulative regret and statistical power in identifying the best arm.
Edit intent: clarity
Original:  The regret of the proposed algorithm matches the optimal (minimax) regret rate and its empirical evaluation in a semi-synthetic experiment based on data from a randomized control trial of a web service is performed: we see that the proposed doubly-adaptive Thompson sampling has superior empirical performance to existing baselines  in terms of cumulative regret and statistical power in identifying the best arm.
Revised:  The regret of the proposed algorithm matches the optimal (minimax) regret rate and its empirical evaluation in a semi-synthetic experiment based on data from a randomized control trial of a web service , we demonstrate that using an adaptive inferential scheme (while still retaining the exploration efficacy of TS) provides clear benefits in online decision making: the proposed DATS algorithm has superior empirical performance to existing baselines in terms of cumulative regret and statistical power in identifying the best arm.
Edit intent: clarity
Original:  The regret of the proposed algorithm matches the optimal (minimax) regret rate and its empirical evaluation in a semi-synthetic experiment based on data from a randomized control trial of a web service is performed: we see that the proposed doubly-adaptive Thompson sampling has superior empirical performance to existing baselines  in terms of cumulative regret and statistical power in identifying the best arm.
Revised:  The regret of the proposed algorithm matches the optimal (minimax) regret rate and its empirical evaluation in a semi-synthetic experiment based on data from a randomized control trial of a web service is performed: we see that the proposed doubly-adaptive Thompson sampling has superior empirical performance to existing baselines (UCB and TS) in terms of cumulative regret and statistical power in identifying the best arm.
Edit intent: meaning-changed
Original:  The regret of the proposed algorithm matches the optimal (minimax) regret rate and its empirical evaluation in a semi-synthetic experiment based on data from a randomized control trial of a web service is performed: we see that the proposed doubly-adaptive Thompson sampling has superior empirical performance to existing baselines  in terms of cumulative regret and statistical power in identifying the best arm.
Revised:  The regret of the proposed algorithm matches the optimal (minimax) regret rate and its empirical evaluation in a semi-synthetic experiment based on data from a randomized control trial of a web service is performed: we see that the proposed doubly-adaptive Thompson sampling has superior empirical performance to existing baselines in terms of regret and sample complexity in identifying the best arm.
Edit intent: clarity
Original:  Further, we extend this approach to contextual bandits, where there are more sources of bias present apart from the adaptive data collection -- such as the mismatch between the true data generating process and the reward model assumptions or the unequal representations of certain regions of the context space in initial stages of learning -- and propose the linear contextual doubly-adaptive Thompson sampling and the non-parametric contextual doubly-adaptive Thompson sampling extensions of our approach .
Revised:  In addition, we also provide a finite-time regret bound of doubly adaptive TS that matches (up to log factors) those of UCB and TS algorithms, thereby establishing that its improved practical benefits do not come at the expense of worst-case suboptimality .
Edit intent: meaning-changed