wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We demonstrate the effectiveness of the proposed data-driven hierarchical control framework in  simulations and experiments on a 1/10 scale autonomous car platform where the strategy-guided approach outperforms a model predictive control baseline in both cases.
Revised:  We demonstrate the effectiveness of the proposed data-driven hierarchical control framework in a two-car collision avoidance scenario through simulations and experiments on a 1/10 scale autonomous car platform where the strategy-guided approach outperforms a model predictive control baseline in both cases.
Edit intent: meaning-changed
Original:  However,  existing works on path planning used traditional search-based or sample-based methods which did not consider the kinodynamic characteristics of quadruped robots . And paths generated by these methods contain kinodynamically infeasible parts, which are difficult to track.
Revised:  However, existing works on path planning used traditional search-based or sample-based methods which did not consider the kinodynamic characteristics of quadruped robots , generating kinodynamically infeasible parts, which are difficult to track.
Edit intent: coherence
Original:  DAT breaks an original global problem into many parallel tree-structured small local problems, which can be effectively solved. All such local solutions are then linked together to form a global solution by solving small Dirac assisted linking problemswith an inherent tree structure.
Revised:  DAT breaks an original global problem into many parallel tree-structured small local problems, which are linked together to form a global solution by solving small Dirac assisted linking problemswith an inherent tree structure.
Edit intent: clarity
Original:  All such local solutions are then linked together to form a global solution by solving small Dirac assisted linking problemswith an inherent tree structure. DAT is embedded with the following attractive features: domain decomposition for reducing the problem size, tree structure and tridiagonal matrices for computational efficiency, and adaptivity for further improved performance. In order to solve the local problems in DAT, we shall propose a compact finite difference scheme with arbitrarily high accuracy order and low numerical dispersion for piecewise smooth coefficients and variable wave numbers.
Revised:  All such local solutions are then linked together to form a global solution by solving small linking problems. To solve the local problems in DAT, we shall propose a compact finite difference scheme with arbitrarily high accuracy order and low numerical dispersion for piecewise smooth coefficients and variable wave numbers.
Edit intent: clarity
Original:  In order to solve the local problems in DAT, we shall propose a compact finite difference scheme with arbitrarily high accuracy order and low numerical dispersion for piecewise smooth coefficients and variable wave numbers.
Revised:  In order to solve the local problems in DAT, we shall propose a compact finite difference method (FDM) with arbitrarily high accuracy order and low numerical dispersion for piecewise smooth coefficients and variable wave numbers.
Edit intent: clarity
Original:  With the aid of such high-order compact finite difference schemes, DAT can solve heterogeneous Helmholtz equations with arbitrarily large variable wave numbers accurately by solving small linear systems - 4 by 4 matrices in the extreme case - with tridiagonal coefficient matrices in a parallel fashion.
Revised:  DAT with such compact FDMs can solve heterogeneous Helmholtz equations with arbitrarily large variable wave numbers accurately by solving small linear systems - 4 by 4 matrices in the extreme case - with tridiagonal coefficient matrices in a parallel fashion.
Edit intent: clarity
Original:  With the aid of such high-order compact finite difference schemes, DAT can solve heterogeneous Helmholtz equations with arbitrarily large variable wave numbers accurately by solving small linear systems - 4 by 4 matrices in the extreme case - with tridiagonal coefficient matrices in a parallel fashion.
Revised:  With the aid of such high-order compact finite difference schemes, DAT can solve heterogeneous Helmholtz equations with arbitrarily large variable wave numbers accurately by solving small linear systems - 4 \times 4 matrices in the extreme case - with tridiagonal coefficient matrices in a parallel fashion.
Edit intent: clarity
Original:  We shall also discuss how to solve some special two-dimensional Helmholtz equations using DAT developed for one-dimensional problems. As demonstrated in all our numerical experiments, the convergence rates of DAT measured in relative L_2, L_\infty and H^1 energy norms as a result of using our M-th order compact finite difference scheme are of order M .
Revised:  We shall also discuss how to solve some special two-dimensional Helmholtz equations using DAT .
Edit intent: coherence
Original: Several seroprevalence studies have been or are currently conducted in order to estimate the infection fatality rate (IFR) of the Coronavirus Disease 2019 (COVID-19) . The IFR is one of the most discussed figures in the context of this pandemic.
Revised: Several seroprevalence studies have been or are currently conducted in order to estimate the infection fatality rate (IFR) of the Coronavirus Disease 2019 (COVID-19) is one of the most discussed figures in the context of this pandemic.
Edit intent: coherence
Original:  As the mortality of COVID-19 largely increases with age, it is important to take the age distribution of infected individuals into account. Based on the example of Germany, this study illustrates the time-dependent evolution of effective IFR over the course of the pandemic , by combining age-group specific IFR estimates from multiple recent studies worldwide with publicly available German surveillance data .
Revised:  As the mortality of COVID-19 surveillance data and age-group specific IFR estimates from multiple international studies, this work investigates time-dependent evolution of effective IFR over the course of the pandemic , by combining age-group specific IFR estimates from multiple recent studies worldwide with publicly available German surveillance data .
Edit intent: clarity
Original:  Based on the example of Germany, this study illustrates the time-dependent evolution of effective IFR over the course of the pandemic , by combining age-group specific IFR estimates from multiple recent studies worldwide with publicly available German surveillance data .
Revised:  Based on the example of Germany, this study illustrates the time-dependent variations in effective IFR over the course of the pandemic , by combining age-group specific IFR estimates from multiple recent studies worldwide with publicly available German surveillance data .
Edit intent: clarity
Original:  Results for Germany show that effective IFRs  are estimated to vary largely over time.
Revised:  Results for Germany show that effective IFRs are estimated to vary over time, as the age distributions of confirmed cases and estimated infections are changing during the course of the pandemic. In particular during the first and second waves of infections in spring and autumn/winter 2020, there has been a pronounced shift in the age distribution of confirmed cases towards older age groups, resulting in larger effective IFR estimates. The temporary increase in effective IFR during the first wave is estimated to be smaller but still remains when adjusting for age- and time-dependent dark figures.
Edit intent: meaning-changed
Original: In the recent literature, significant and substantial efforts have been dedicated to the important area of multi-agent decision-making problems. Particularly here, the model predictive control (MPC) methodology has demonstrated its effectiveness in various applications, such as mobile robots, unmanned vehicles, and drones. Nevertheless, in many specific scenarios involving the MPC methodology , accurate and effective system identification is a commonly encountered challenge  .
Revised: In many specific scenarios involving the MPC methodology , accurate and effective system identification is a commonly encountered challenge .
Edit intent: coherence
Original:  Utilizing an innovative modified methodology with suitable closed-loop input/output measurements that comply with the appropriate persistency of excitation condition, a non-parametric predictive model is suitably constructed .
Revised:  Utilizing an innovative modified methodology with suitable closed-loop input/output measurements of the unknown system, the behavior of the system is learned based on the collected dataset, and thus the constructed non-parametric predictive model is suitably constructed .
Edit intent: meaning-changed
Original:  This non-parametric predictive model approach in the work here attains the key advantage of alleviating the rather heavy computational burden  encountered in the optimization procedures typical in alternative methodologies requiring open-loop input/output measurement data collection and parametric system identification.
Revised:  This non-parametric predictive control framework attains the noteworthy key advantage of alleviating the rather heavy computational burden encountered in the optimization procedures typical in alternative methodologies requiring open-loop input/output measurement data collection and parametric system identification.
Edit intent: clarity
Original:  This non-parametric predictive model approach in the work here attains the key advantage of alleviating the rather heavy computational burden  encountered in the optimization procedures typical in alternative methodologies requiring open-loop input/output measurement data collection and parametric system identification.
Revised:  This non-parametric predictive model approach in the work here attains the key advantage of alleviating the heavy computational burden encountered in the optimization procedures typical in alternative methodologies requiring open-loop input/output measurement data collection and parametric system identification.
Edit intent: clarity
Original:  This non-parametric predictive model approach in the work here attains the key advantage of alleviating the rather heavy computational burden  encountered in the optimization procedures typical in alternative methodologies requiring open-loop input/output measurement data collection and parametric system identification.
Revised:  This non-parametric predictive model approach in the work here attains the key advantage of alleviating the rather heavy computational burden commonly encountered in the optimization procedures typical in alternative methodologies requiring open-loop input/output measurement data collection and parametric system identification.
Edit intent: clarity
Original:  This non-parametric predictive model approach in the work here attains the key advantage of alleviating the rather heavy computational burden  encountered in the optimization procedures typical in alternative methodologies requiring open-loop input/output measurement data collection and parametric system identification.
Revised:  This non-parametric predictive model approach in the work here attains the key advantage of alleviating the rather heavy computational burden encountered in the optimization procedures otherwise involved. Such requisite optimization procedures are typical in existing methodologies requiring open-loop input/output measurement data collection and parametric system identification.
Edit intent: meaning-changed
Original:  Then with a conservative approximation of probabilistic chance constraints for the MPC problem, a resulting deterministic optimization problem is formulated and solved efficiently and effectively. In the work here, this intuitive data-driven approach is also shown to preserve good robustness properties  .
Revised:  Then with a conservative approximation of probabilistic chance constraints for the MPC problem, a resulting deterministic optimization problem is formulated and solved effectively. This intuitive data-driven approach is also shown to preserve good robustness properties .
Edit intent: clarity
Original: We provide algorithms with improved pass and space complexities for approximately solving linear programs, optimal transport, bipartite matching, and more in the semi-streaming model. For instance, we provide a (randomized)algorithm computing a maximum cardinality matching in an unweighted bipartite graph in O(\log^2 n \cdot \epsilon ^{-1} ) passes, using O(n \log^2 n \cdot \epsilon^{-1)auxiliary memory.
Revised: We provide (\epsilon ^{-1} ) passes, using O(n \log^2 n \cdot \epsilon^{-1)auxiliary memory.
Edit intent: clarity
Original:  For instance, we provide a (randomized)algorithm computing a maximum cardinality matching in an unweighted bipartite graph in O(\log^2 n \cdot \epsilon ^{-1} ) passes, using O(n \log^2 n \cdot \epsilon^{-1)auxiliary memory.
Revised:  For instance, we provide a (randomized)algorithm computing a maximum cardinality matching in an unweighted bipartite graph in O(\log^2 n \cdot \epsilon ^{-1} )-pass semi-streaming algorithms for computing (1-\epsilon)-approximate maximum cardinality matchings in bipartite graphs. Our most efficient methods are deterministic and use optimal, O(n \log^2 n \cdot \epsilon^{-1)auxiliary memory.
Edit intent: meaning-changed
Original:  For instance, we provide a (randomized)algorithm computing a maximum cardinality matching in an unweighted bipartite graph in O(\log^2 n \cdot \epsilon ^{-1} ) passes, using O(n \log^2 n \cdot \epsilon^{-1)auxiliary memory.
Revised:  For instance, we provide a (randomized)algorithm computing a maximum cardinality matching in an unweighted bipartite graph in O(\log^2 n \cdot \epsilon ^{-1} ) passes, using O(n )auxiliary memory.
Edit intent: fluency
Original:  This marks the first improvements to the O(\log\log\epsilon^{-1}\cdot \epsilon^{-2})pass, O(n \log\log\epsilon}   ^{-1} \cdot \epsilon^{-2 ) -space algorithms of AG13%DIFDELCMD < ] %%% when \epsilon is moderately small .
Revised:  This marks the first improvements to the O(\log\log\epsilon^{-1}\cdot \epsilon^{-2})pass, O(n \log\log\epsilon} ), space, improving upon the space complexity of the previous state-of-the-art ^{-1} \cdot \epsilon^{-2 ) -space algorithms of AG13%DIFDELCMD < ] %%% when \epsilon is moderately small .
Edit intent: meaning-changed
Original:  This marks the first improvements to the O(\log\log\epsilon^{-1}\cdot \epsilon^{-2})pass, O(n \log\log\epsilon}   ^{-1} \cdot \epsilon^{-2 ) -space algorithms of AG13%DIFDELCMD < ] %%% when \epsilon is moderately small .
Revised:  This marks the first improvements to the O(\log\log\epsilon^{-1}\cdot \epsilon^{-2})pass, O(n \log\log\epsilon} (\epsilon ^{-1} \cdot \epsilon^{-2 ) -space algorithms of AG13%DIFDELCMD < ] %%% when \epsilon is moderately small .
Edit intent: meaning-changed
Original:  This marks the first improvements to the O(\log\log\epsilon^{-1}\cdot \epsilon^{-2})pass, O(n \log\log\epsilon}   ^{-1} \cdot \epsilon^{-2 ) -space algorithms of AG13%DIFDELCMD < ] %%% when \epsilon is moderately small .
Revised:  This marks the first improvements to the O(\log\log\epsilon^{-1}\cdot \epsilon^{-2})pass, O(n \log\log\epsilon} ^{-1} ) -space algorithms of AG13%DIFDELCMD < ] %%% when \epsilon is moderately small .
Edit intent: fluency
Original:  This marks the first improvements to the O(\log\log\epsilon^{-1}\cdot \epsilon^{-2})pass, O(n \log\log\epsilon}   ^{-1} \cdot \epsilon^{-2 ) -space algorithms of AG13%DIFDELCMD < ] %%% when \epsilon is moderately small .
Revised:  This marks the first improvements to the O(\log\log\epsilon^{-1}\cdot \epsilon^{-2})pass, O(n \log\log\epsilon} ^{-1} \cdot \epsilon^{-2 ) AG13%DIFDELCMD < ] %%% when \epsilon is moderately small .
Edit intent: clarity
Original:  This marks the first improvements to the O(\log\log\epsilon^{-1}\cdot \epsilon^{-2})pass, O(n \log\log\epsilon}   ^{-1} \cdot \epsilon^{-2 ) -space algorithms of AG13%DIFDELCMD < ] %%% when \epsilon is moderately small .
Revised:  This marks the first improvements to the O(\log\log\epsilon^{-1}\cdot \epsilon^{-2})pass, O(n \log\log\epsilon} ^{-1} \cdot \epsilon^{-2 ) -space algorithms of %DIFDELCMD < ] %%% when \epsilon is moderately small .
Edit intent: meaning-changed
Original:  This marks the first improvements to the O(\log\log\epsilon^{-1}\cdot \epsilon^{-2})pass, O(n \log\log\epsilon}   ^{-1} \cdot \epsilon^{-2 ) -space algorithms of AG13%DIFDELCMD < ] %%% when \epsilon is moderately small .
Revised:  This marks the first improvements to the O(\log\log\epsilon^{-1}\cdot \epsilon^{-2})pass, O(n \log\log\epsilon} ^{-1} \cdot \epsilon^{-2 ) -space algorithms of AG13%DIFDELCMD < ] %%% -pass algorithm of Ahn and Gupta .
Edit intent: meaning-changed
Original:  To obtain our results , we give an O(n) space deterministic semi-streaming algorithm for approximating the value of linear programs (in the form of box-simplex games), based on low-space implementations of She17 , JST19 . We further give a general sampling procedure for explicitly forming a fractional solution in low space, yielding improved semi-streaming guarantees for optimal transport and, in some regimes, maximum weighted matching.
Revised:  To obtain our results we provide semi-streaming algorithm for approximating the value of linear programs (in the form of box-simplex games), based on low-space implementations of She17 , JST19 . We further give a general sampling procedure for explicitly forming a fractional solution in low space, yielding improved semi-streaming guarantees for optimal transport and, in some regimes, maximum weighted matching.
Edit intent: clarity
Original:  To obtain our results , we give an O(n) space deterministic semi-streaming algorithm for approximating the value of linear programs (in the form of box-simplex games), based on low-space implementations of She17 , JST19 . We further give a general sampling procedure for explicitly forming a fractional solution in low space, yielding improved semi-streaming guarantees for optimal transport and, in some regimes, maximum weighted matching.
Revised:  To obtain our results , we give an O(n) space deterministic semi-streaming She17 , JST19 . We further give a general sampling procedure for explicitly forming a fractional solution in low space, yielding improved semi-streaming guarantees for optimal transport and, in some regimes, maximum weighted matching.
Edit intent: clarity
Original:  To obtain our results , we give an O(n) space deterministic semi-streaming algorithm for approximating the value of linear programs (in the form of box-simplex games), based on low-space implementations of She17 , JST19 . We further give a general sampling procedure for explicitly forming a fractional solution in low space, yielding improved semi-streaming guarantees for optimal transport and, in some regimes, maximum weighted matching.
Revised:  To obtain our results , we give an O(n) space deterministic semi-streaming algorithm for approximating the value of linear programs (in the form of box-simplex games), based on low-space implementations of adaptations of more general continuous optimization tools. Further, we leverage these techniques to obtain improvements for streaming variants of approximate linear programming, optimal transport , JST19 . We further give a general sampling procedure for explicitly forming a fractional solution in low space, yielding improved semi-streaming guarantees for optimal transport and, in some regimes, maximum weighted matching.
Edit intent: meaning-changed
Original:  To obtain our results , we give an O(n) space deterministic semi-streaming algorithm for approximating the value of linear programs (in the form of box-simplex games), based on low-space implementations of She17 , JST19 . We further give a general sampling procedure for explicitly forming a fractional solution in low space, yielding improved semi-streaming guarantees for optimal transport and, in some regimes, maximum weighted matching.
Revised:  To obtain our results , we give an O(n) space deterministic semi-streaming algorithm for approximating the value of linear programs (in the form of box-simplex games), based on low-space implementations of She17 , . We further give a general sampling procedure for explicitly forming a fractional solution in low space, yielding improved semi-streaming guarantees for optimal transport and, in some regimes, maximum weighted matching.
Edit intent: clarity
Original:  To obtain our results , we give an O(n) space deterministic semi-streaming algorithm for approximating the value of linear programs (in the form of box-simplex games), based on low-space implementations of She17 , JST19 . We further give a general sampling procedure for explicitly forming a fractional solution in low space, yielding improved semi-streaming guarantees for optimal transport and, in some regimes, maximum weighted matching. Finally, we improve the space complexity of our maximum cardinality matching method using an implicit implementation of the random walk rounding of%DIFDELCMD < [%%% GKK10 via custom turnstile samplers .
Revised:  To obtain our results , we give an O(n) space deterministic semi-streaming algorithm for approximating the value of linear programs (in the form of box-simplex games), based on low-space implementations of She17 , JST19 %DIFDELCMD < [%%% GKK10 via custom turnstile samplers .
Edit intent: meaning-changed
Original:  Finally, we improve the space complexity of our maximum cardinality matching method using an implicit implementation of the random walk rounding of%DIFDELCMD < [%%% GKK10 via custom turnstile samplers .
Revised:  Finally, we improve the space complexity of our maximum cardinality matching method using an implicit implementation of the random walk rounding of%DIFDELCMD < [%%% via custom turnstile samplers .
Edit intent: meaning-changed
Original:  Finally, we improve the space complexity of our maximum cardinality matching method using an implicit implementation of the random walk rounding of%DIFDELCMD < [%%% GKK10 via custom turnstile samplers .
Revised:  Finally, we improve the space complexity of our maximum cardinality matching method using an implicit implementation of the random walk rounding of%DIFDELCMD < [%%% GKK10 and exact matching .
Edit intent: meaning-changed
Original:  We use a reinforcement learning-based navigation scheme that uses a proximity graph of traffic agents and computes a safe trajectory for the ego-vehicle that accounts for aggressive driver maneuvers such as overtaking, over-speeding, weaving, and sudden lane changes.
Revised:  We use our reinforcement learning-based navigation scheme that uses a proximity graph of traffic agents and computes a safe trajectory for the ego-vehicle that accounts for aggressive driver maneuvers such as overtaking, over-speeding, weaving, and sudden lane changes.
Edit intent: fluency
Original:  We use a reinforcement learning-based navigation scheme that uses a proximity graph of traffic agents and computes a safe trajectory for the ego-vehicle that accounts for aggressive driver maneuvers such as overtaking, over-speeding, weaving, and sudden lane changes.
Revised:  We use a reinforcement learning-based navigation scheme to compute safe trajectories for the ego-vehicle that accounts for aggressive driver maneuvers such as overtaking, over-speeding, weaving, and sudden lane changes.
Edit intent: coherence
Original:  We use a reinforcement learning-based navigation scheme that uses a proximity graph of traffic agents and computes a safe trajectory for the ego-vehicle that accounts for aggressive driver maneuvers such as overtaking, over-speeding, weaving, and sudden lane changes.
Revised:  We use a reinforcement learning-based navigation scheme that uses a proximity graph of traffic agents and computes a safe trajectory for the ego-vehicle accounting for aggressive driver maneuvers such as overtaking, over-speeding, weaving, and sudden lane changes.
Edit intent: fluency