wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In addition to learning approaches, we also compare to an inverse-kinematics (IK) based footstep planner , and show that our fully-learned framework is competitive in performance with IK under normal conditions, and outperforms it in adverse settings . Our hardwareexperiments show the Daisy hexapod achieving multiple locomotion tasks, such as goal reaching, trajectory and velocity tracking in an unstructured outdoor setting, with only 2000 hardware samples  .
Revised:  In addition to learning approaches, we also compare to an inverse-kinematics (IK) based footstep planner , and show that our fully-learned framework is competitive in performance with IK under normal conditions, and outperforms it in adverse settings on both A1 and Daisy simulations. On hardware, we show the Daisy hexapod achieving multiple locomotion tasks, such as goal reaching, trajectory and velocity tracking in an unstructured outdoor setting, with only 2000 hardware samples .
Edit intent: meaning-changed
Original:  Our hardwareexperiments show the Daisy hexapod achieving multiple locomotion tasks, such as goal reaching, trajectory and velocity tracking in an unstructured outdoor setting, with only 2000 hardware samples  .
Revised:  Our hardwareexperiments show the Daisy hexapod achieving multiple locomotion tasks, such as goal reaching, trajectory and velocity tracking in an unstructured outdoor setting, with only 2000 hardware samples , reinforcing the robustness and sample-efficiency of our approach .
Edit intent: clarity
Original: Recently, the predicate detection problem was shown to be in the parallel complexity class NC . In this paper, we give some more work efficient parallel algorithms to solve the predicate detection problem on a distributed computation with n processes and at most m states per process.
Revised: Recently, the predicate detection problem was shown to be in the parallel complexity class NC . In this paper, we give some more work efficient parallel algorithms to solve the predicate detection problem on a distributed computation with n processes and at most m states per process.
Edit intent: fluency
Original: Recently, the predicate detection problem was shown to be in the parallel complexity class NC . In this paper, we give some more work efficient parallel algorithms to solve the predicate detection problem on a distributed computation with n processes and at most m states per process.
Revised: Recently, the predicate detection problem was shown to be in the parallel complexity class NC . In this paper, we give the first work-optimal parallel algorithm to solve the predicate detection problem on a distributed computation with n processes and at most m states per process.
Edit intent: meaning-changed
Original:  We give two algorithms, a deterministic algorithm with  work complexity O( m^2n^2) and a randomized algorithm with  work complexity O( m^2n^2).
Revised:  We give two algorithms, a deterministic algorithm with work complexity O( m^2n^2) and a randomized algorithm with time complexity (mn)^{1/2 + o(1) work complexity O( m^2n^2).
Edit intent: meaning-changed
Original:  We give two algorithms, a deterministic algorithm with  work complexity O( m^2n^2) and a randomized algorithm with  work complexity O( m^2n^2).
Revised:  We give two algorithms, a deterministic algorithm with work complexity O( m^2n^2) and a randomized algorithm with work complexity O( mn^2). \% and two offline algorithms. The online deterministic algorithm has.
Edit intent: meaning-changed
Original:  Furthermore, our algorithms have a space complexity of \textit{  O(mn^2) whereas ParallelCut had a space complexity of \textit{  O(m^2n^2).
Revised:  Furthermore, our algorithms improve upon the space complexity of \textit{ O(mn^2) whereas ParallelCut had a space complexity of \textit{ O(m^2n^2).
Edit intent: clarity
Original:  Furthermore, our algorithms have a space complexity of \textit{  O(mn^2) whereas ParallelCut had a space complexity of \textit{  O(m^2n^2).
Revised:  Furthermore, our algorithms have a space complexity of \textit{ParallelCut O(mn^2) whereas ParallelCut had a space complexity of \textit{ O(m^2n^2).
Edit intent: meaning-changed
Original:  Furthermore, our algorithms have a space complexity of \textit{  O(mn^2) whereas ParallelCut had a space complexity of \textit{  O(m^2n^2).
Revised:  Furthermore, our algorithms have a space complexity of \textit{ . Both of our algorithms have space complexity O(mn^2) whereas ParallelCut had a space complexity of \textit{ O(m^2n^2).
Edit intent: meaning-changed
Original:  Furthermore, our algorithms have a space complexity of \textit{  O(mn^2) whereas ParallelCut had a space complexity of \textit{  O(m^2n^2).
Revised:  Furthermore, our algorithms have a space complexity of \textit{ O(mn^2) whereas ParallelCut had a space complexity of \textit{ParallelCut O(m^2n^2).
Edit intent: meaning-changed
Original:  Furthermore, our algorithms have a space complexity of \textit{  O(mn^2) whereas ParallelCut had a space complexity of \textit{  O(m^2n^2).
Revised:  Furthermore, our algorithms have a space complexity of \textit{ O(mn^2) whereas ParallelCut had a space complexity of \textit{ has space complexity O(m^2n^2).
Edit intent: meaning-changed
Original:  The popular method, Monte Carlo Localization (MCL) addresses the problem by sampling uniformly ] over the state space , which is unfortunately inefficient when the environment is large .
Revised:  The popular method, Monte Carlo Localization (MCL) addresses the problem by iteratively updating a set of particles with a "sampling-weighting" loop. Sampling is decisive to the performance of MCL ] over the state space , which is unfortunately inefficient when the environment is large .
Edit intent: meaning-changed
Original:  The popular method, Monte Carlo Localization (MCL) addresses the problem by sampling uniformly ] over the state space , which is unfortunately inefficient when the environment is large .
Revised:  The popular method, Monte Carlo Localization (MCL) addresses the problem by sampling uniformly ]. However, traditional MCL can only sample from a uniform distribution over the state space , which is unfortunately inefficient when the environment is large .
Edit intent: meaning-changed
Original:  The popular method, Monte Carlo Localization (MCL) addresses the problem by sampling uniformly ] over the state space , which is unfortunately inefficient when the environment is large .
Revised:  The popular method, Monte Carlo Localization (MCL) addresses the problem by sampling uniformly ] over the state space . Although variants of MCL propose different sampling models, they fail to provide an accurate distribution or generalize across scenes .
Edit intent: meaning-changed
Original:  To better deal with the the problems, we present a  proposal model, named Deep Multimodal Observation Model ( DMOM). DMOM takes a map and a 2D laser scan as inputs and outputs a conditional multimodal probability distribution of the pose, making the samples more focusing on the regions with higher likelihood.
Revised:  To better deal with these problems, we present a proposal model, named Deep Multimodal Observation Model ( DMOM). DMOM takes a map and a 2D laser scan as inputs and outputs a conditional multimodal probability distribution of the pose, making the samples more focusing on the regions with higher likelihood.
Edit intent: fluency
Original:  To better deal with the the problems, we present a  proposal model, named Deep Multimodal Observation Model ( DMOM). DMOM takes a map and a 2D laser scan as inputs and outputs a conditional multimodal probability distribution of the pose, making the samples more focusing on the regions with higher likelihood.
Revised:  To better deal with the the problems, we present a distribution proposal model, named Deep Multimodal Observation Model ( DMOM). DMOM takes a map and a 2D laser scan as inputs and outputs a conditional multimodal probability distribution of the pose, making the samples more focusing on the regions with higher likelihood.
Edit intent: meaning-changed
Original:  To better deal with the the problems, we present a  proposal model, named Deep Multimodal Observation Model ( DMOM). DMOM takes a map and a 2D laser scan as inputs and outputs a conditional multimodal probability distribution of the pose, making the samples more focusing on the regions with higher likelihood.
Revised:  To better deal with the the problems, we present a proposal model, named Deep Samplable Observation Model ( DMOM). DMOM takes a map and a 2D laser scan as inputs and outputs a conditional multimodal probability distribution of the pose, making the samples more focusing on the regions with higher likelihood.
Edit intent: meaning-changed
Original:  To better deal with the the problems, we present a  proposal model, named Deep Multimodal Observation Model ( DMOM). DMOM takes a map and a 2D laser scan as inputs and outputs a conditional multimodal probability distribution of the pose, making the samples more focusing on the regions with higher likelihood.
Revised:  To better deal with the the problems, we present a proposal model, named Deep Multimodal Observation Model ( DSOM). DSOM takes a map and a 2D laser scan as inputs and outputs a conditional multimodal probability distribution of the pose, making the samples more focusing on the regions with higher likelihood.
Edit intent: fluency
Original:  Considering that learning based Samplable Observation Model may fail to capture the true pose sometimes, we furthermore propose the Adaptive Mixture MCL , which adaptively selects updating mode for each particle to tolerate this situation.
Revised:  Considering that the learning-based sampling model may fail to capture the true pose sometimes, we furthermore propose the Adaptive Mixture MCL , which adaptively selects updating mode for each particle to tolerate this situation.
Edit intent: coherence
Original:  Considering that learning based Samplable Observation Model may fail to capture the true pose sometimes, we furthermore propose the Adaptive Mixture MCL , which adaptively selects updating mode for each particle to tolerate this situation.
Revised:  Considering that learning based Samplable Observation Model may fail to capture the true pose sometimes, we furthermore propose the Adaptive Mixture MCL (AdaM MCL), which deploys a trusty mechanism to adaptively select updating mode for each particle to tolerate this situation.
Edit intent: meaning-changed
Original:  Even in real environment with long-term changing, Adaptive Mixture MCL is able to localize the robot using DMON trained only on simulated observations from a SLAM map , or even a blueprint map.
Revised:  Even in real environments with long-term changing, Adaptive Mixture MCL is able to localize the robot using DMON trained only on simulated observations from a SLAM map , or even a blueprint map.
Edit intent: fluency
Original:  Even in real environment with long-term changing, Adaptive Mixture MCL is able to localize the robot using DMON trained only on simulated observations from a SLAM map , or even a blueprint map.
Revised:  Even in real environment with long-term changing, AdaM MCL is able to localize the robot using DMON trained only on simulated observations from a SLAM map , or even a blueprint map.
Edit intent: clarity
Original:  Even in real environment with long-term changing, Adaptive Mixture MCL is able to localize the robot using DMON trained only on simulated observations from a SLAM map , or even a blueprint map.
Revised:  Even in real environment with long-term changing, Adaptive Mixture MCL is able to localize the robot using DSOM trained only by simulation observations from a SLAM map , or even a blueprint map.
Edit intent: clarity
Original:  Even in real environment with long-term changing, Adaptive Mixture MCL is able to localize the robot using DMON trained only on simulated observations from a SLAM map , or even a blueprint map.
Revised:  Even in real environment with long-term changing, Adaptive Mixture MCL is able to localize the robot using DMON trained only on simulated observations from a SLAM map or a blueprint map.
Edit intent: clarity
Original:  In particular, using the Linear Matrix Inequality framework developed in Fazlyab et. al. (2018), we derive analytically a (discrete) Lyapunov function for a two-parameter family of Nesterov optimization methods, which allows for a complete characterization of their convergence rate.
Revised:  In particular, using the Linear Matrix Inequality LMI framework developed by Fazlyab et. al. (2018), we derive analytically a (discrete) Lyapunov function for a two-parameter family of Nesterov optimization methods, which allows for a complete characterization of their convergence rate.
Edit intent: meaning-changed
Original:  In particular, using the Linear Matrix Inequality framework developed in Fazlyab et. al. (2018), we derive analytically a (discrete) Lyapunov function for a two-parameter family of Nesterov optimization methods, which allows for a complete characterization of their convergence rate.
Revised:  In particular, using the Linear Matrix Inequality framework developed in Fazlyab et. al. (2018), we derive analytically a (discrete) Lyapunov function for a two-parameter family of Nesterov optimization methods, which allows for the complete characterization of their convergence rate.
Edit intent: fluency
Original:  We then show that in the appropriate limit  this family of methods may be seen as a discretization of a family of second order ordinary differential equations , which properties can be also understood by a (continuous) Lyapunov function, which can also be obtained by studying the limiting behaviour of the discrete Lyapunov function .
Revised:  In the appropriate limit this family of methods may be seen as a discretization of a family of second order ordinary differential equations , which properties can be also understood by a (continuous) Lyapunov function, which can also be obtained by studying the limiting behaviour of the discrete Lyapunov function .
Edit intent: coherence
Original:  We then show that in the appropriate limit  this family of methods may be seen as a discretization of a family of second order ordinary differential equations , which properties can be also understood by a (continuous) Lyapunov function, which can also be obtained by studying the limiting behaviour of the discrete Lyapunov function .
Revised:  We then show that in the appropriate limit , this family of methods may be seen as a discretization of a family of second order ordinary differential equations , which properties can be also understood by a (continuous) Lyapunov function, which can also be obtained by studying the limiting behaviour of the discrete Lyapunov function .
Edit intent: fluency
Original:  We then show that in the appropriate limit  this family of methods may be seen as a discretization of a family of second order ordinary differential equations , which properties can be also understood by a (continuous) Lyapunov function, which can also be obtained by studying the limiting behaviour of the discrete Lyapunov function .
Revised:  We then show that in the appropriate limit this family of methods may be seen as a discretization of a family of second-order ordinary differential equations , which properties can be also understood by a (continuous) Lyapunov function, which can also be obtained by studying the limiting behaviour of the discrete Lyapunov function .
Edit intent: fluency
Original:  We then show that in the appropriate limit  this family of methods may be seen as a discretization of a family of second order ordinary differential equations , which properties can be also understood by a (continuous) Lyapunov function, which can also be obtained by studying the limiting behaviour of the discrete Lyapunov function .
Revised:  We then show that in the appropriate limit this family of methods may be seen as a discretization of a family of second order ordinary differential equations for which we construct (continuous) Lyapunov function, which can also be obtained by studying the limiting behaviour of the discrete Lyapunov function .
Edit intent: meaning-changed
Original:  We then show that in the appropriate limit  this family of methods may be seen as a discretization of a family of second order ordinary differential equations , which properties can be also understood by a (continuous) Lyapunov function, which can also be obtained by studying the limiting behaviour of the discrete Lyapunov function .
Revised:  We then show that in the appropriate limit this family of methods may be seen as a discretization of a family of second order ordinary differential equations , which properties can be also understood by a (continuous) Lyapunov functions by means of the LMI framework. The continuous Lyapunov functions may alternatively, be obtained by studying the limiting behaviour of the discrete Lyapunov function .
Edit intent: meaning-changed
Original:  We then show that in the appropriate limit  this family of methods may be seen as a discretization of a family of second order ordinary differential equations , which properties can be also understood by a (continuous) Lyapunov function, which can also be obtained by studying the limiting behaviour of the discrete Lyapunov function .
Revised:  We then show that in the appropriate limit this family of methods may be seen as a discretization of a family of second order ordinary differential equations , which properties can be also understood by a (continuous) Lyapunov function, which can also be obtained by studying the limiting behaviour of their discrete counterparts .
Edit intent: clarity
Original:  Finally, we show that the majority of typical discretizations of this ODE , such as the Heavy ball method, do not possess suitable discrete Lyapunov functions , and hence fail to reproduce the desired limiting behaviour of this ODE, which in turn implies that their converge rates when seen as optimization methods cannot behave in an "accerelated" manner .
Revised:  Finally, we show that the majority of typical discretizations of the family of ODEs , such as the Heavy ball method, do not possess suitable discrete Lyapunov functions , and hence fail to reproduce the desired limiting behaviour of this ODE, which in turn implies that their converge rates when seen as optimization methods cannot behave in an "accerelated" manner .
Edit intent: clarity
Original:  Finally, we show that the majority of typical discretizations of this ODE , such as the Heavy ball method, do not possess suitable discrete Lyapunov functions , and hence fail to reproduce the desired limiting behaviour of this ODE, which in turn implies that their converge rates when seen as optimization methods cannot behave in an "accerelated" manner .
Revised:  Finally, we show that the majority of typical discretizations of this ODE , such as the Heavy ball method, do not possess Lyapunov functions with properties similar to those of the Lyapunov function constructed here for the Nesterov method .
Edit intent: meaning-changed
Original:  We propose here an extension of these approaches,  using non-linear slope tomography (rather than standard tomographic migration velocity analysis as in previous publications).
Revised:  We propose here an extension of these approaches, sampling an equi-probable contour of the tomography posterior probability density function (pdf) rather than the full pdf, and using non-linear slope tomography (rather than standard tomographic migration velocity analysis as in previous publications).
Edit intent: meaning-changed
Original:  In addition to the advantages in terms of accuracy and efficiency of the velocity model building (compared to standard tomography) it can be used to assess the quality of standard uncertainty-related assumptions (linearity and Gaussian hypothesis within the Bayesian theory) and estimate volumetric migration positioning uncertainties (a generalization of horizon uncertainties)  .
Revised:  Our approach allows to assess the quality of standard uncertainty-related assumptions (linearity and Gaussian hypothesis within the Bayesian theory) and estimate volumetric migration positioning uncertainties (a generalization of horizon uncertainties) .
Edit intent: clarity
Original:  In addition to the advantages in terms of accuracy and efficiency of the velocity model building (compared to standard tomography) it can be used to assess the quality of standard uncertainty-related assumptions (linearity and Gaussian hypothesis within the Bayesian theory) and estimate volumetric migration positioning uncertainties (a generalization of horizon uncertainties)  .
Revised:  In addition to the advantages in terms of accuracy and efficiency of the velocity model building (compared to standard tomography) it can be used to assess the quality of uncertainty-related assumptions (linearity and Gaussian hypothesis within the Bayesian theory) and estimate volumetric migration positioning uncertainties (a generalization of horizon uncertainties) .
Edit intent: fluency
Original:  In addition to the advantages in terms of accuracy and efficiency of the velocity model building (compared to standard tomography) it can be used to assess the quality of standard uncertainty-related assumptions (linearity and Gaussian hypothesis within the Bayesian theory) and estimate volumetric migration positioning uncertainties (a generalization of horizon uncertainties)  .
Revised:  In addition to the advantages in terms of accuracy and efficiency of the velocity model building (compared to standard tomography) it can be used to assess the quality of standard uncertainty-related assumptions (linearity and Gaussian hypothesis within the Bayesian theory) and estimate volumetric migration positioning uncertainties (a generalization of horizon uncertainties) , in addition to the advantages in terms of efficiency .
Edit intent: coherence
Original:  A main advantage is that we work directly in the full model space rather than in a preconditioned model space, (1) avoiding biased uncertainty analysis and (2) splitting the analysis into the resolved and unresolved tomography spaces.
Revised:  As the method works in the full model space rather than in a preconditioned model space, (1) avoiding biased uncertainty analysis and (2) splitting the analysis into the resolved and unresolved tomography spaces.
Edit intent: clarity
Original:  A main advantage is that we work directly in the full model space rather than in a preconditioned model space, (1) avoiding biased uncertainty analysis and (2) splitting the analysis into the resolved and unresolved tomography spaces.
Revised:  A main advantage is that we work directly in the full model space rather than in a preconditioned model space, we split the analysis into the resolved and unresolved tomography spaces.
Edit intent: coherence
Original:  These advantages provide robustness to the approach .
Revised:  These advantages provide robustness to the resolved space uncertainties are to be used in further steps leading to decision-making and can be related to the output of methods that work in a preconditioned model space. The unresolved space uncertainties represent a qualitative byproduct specific to our method, strongly highlighting the most uncertain gross areas, thus useful for QCs .
Edit intent: meaning-changed
Original:  However, open questions about URLanization mechanisms in animal groups remain: Evolutionary adaptation towards  group-level optima (group-level selection), often implicitly assumed in the criticality hypothesis, is in general not a reasonable mechanism in fission-fusion groups composed of non-related individuals.
Revised:  However, open questions about URLanization mechanisms in animal groups remain: Evolutionary adaptation towards a group-level optima (group-level selection), often implicitly assumed in the criticality hypothesis, is in general not a reasonable mechanism in fission-fusion groups composed of non-related individuals.
Edit intent: fluency