wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Results suggest that the robot using the learned apex point can achieve success rates of 81.7\% in vaulting, 65.0\% in knocking, and 60.0\% in weaving, while a baseline with a fixed apex across the three tasks achieves respective success rates of 51.7\%, 36.7\%, and 15.0\%, and a baseline with human-specified  task-specific apex points achieves 66.7\%, 56.7\%, and 15.0\% success rate respectively  . Code, data, and supplementary materials are available at URL //
Revised:  Results suggest that the robot using the learned apex point can achieve success rates of 81.7\% in vaulting, 65.0\% in knocking, and 60.0\% in weaving, while a baseline with a fixed apex across the three tasks achieves respective success rates of 51.7\%, 36.7\%, and 15.0\%, and a baseline with human-specified , task-specific apex points achieves 66.7\%, 56.7\%, and 15.0\% success rate respectively . Code, data, and supplementary materials are available at URL //
Edit intent: fluency
Original:  Results suggest that the robot using the learned apex point can achieve success rates of 81.7\% in vaulting, 65.0\% in knocking, and 60.0\% in weaving, while a baseline with a fixed apex across the three tasks achieves respective success rates of 51.7\%, 36.7\%, and 15.0\%, and a baseline with human-specified  task-specific apex points achieves 66.7\%, 56.7\%, and 15.0\% success rate respectively  . Code, data, and supplementary materials are available at URL //
Revised:  Results suggest that the robot using the learned apex point can achieve success rates of 81.7\% in vaulting, 65.0\% in knocking, and 60.0\% in weaving, while a baseline with a fixed apex across the three tasks achieves respective success rates of 51.7\%, 36.7\%, and 15.0\%, and a baseline with human-specified task-specific apex points achieves 66.7\%, 56.7\%, and 15.0\% success rate respectively , while the robot using the learned apex point can achieve success rates of 81.7\% in vaulting, 65.0\% in knocking, and 60.0\% in weaving . Code, data, and supplementary materials are available at URL //
Edit intent: fluency
Original:  In this paper , confirmed cases of healthcare workers are considered as accurately small samples to analyze the impact of under-reporting on the spatiotemporal distribution of COVID-19 from three perspectives , namely : parameter estimation, spatiotemporal correlation, and spatiotemporal lag.
Revised:  In this study , confirmed cases of healthcare workers are considered as accurately small samples to analyze the impact of under-reporting on the spatiotemporal distribution of COVID-19 from three perspectives , namely : parameter estimation, spatiotemporal correlation, and spatiotemporal lag.
Edit intent: clarity
Original:  In this paper , confirmed cases of healthcare workers are considered as accurately small samples to analyze the impact of under-reporting on the spatiotemporal distribution of COVID-19 from three perspectives , namely : parameter estimation, spatiotemporal correlation, and spatiotemporal lag.
Revised:  In this paper , confirmed cases of healthcare workers are considered as accurate small samples to analyze the impact of under-reporting on the spatiotemporal distribution of COVID-19 from three perspectives , namely : parameter estimation, spatiotemporal correlation, and spatiotemporal lag.
Edit intent: fluency
Original:  In this paper , confirmed cases of healthcare workers are considered as accurately small samples to analyze the impact of under-reporting on the spatiotemporal distribution of COVID-19 from three perspectives , namely : parameter estimation, spatiotemporal correlation, and spatiotemporal lag.
Revised:  In this paper , confirmed cases of healthcare workers are considered as accurately small samples to analyze the impact of under-reporting on the spatiotemporal distribution of COVID-19 from three perspectives : parameter estimation, spatiotemporal correlation, and spatiotemporal lag.
Edit intent: coherence
Original:  The results show that : (1) the lognormal distribution was the most suitable to describe the evolution of epidemic with time . (2) The estimated peak infection time of the reported cases lags behind the peak infection time of the healthcare worker cases, and the estimated infection time interval of the reported cases is smaller than the infection time interval of the healthcare worker cases.
Revised:  The results show that (1) the lognormal distribution was the most suitable to describe the evolution of epidemic with time . (2) The estimated peak infection time of the reported cases lags behind the peak infection time of the healthcare worker cases, and the estimated infection time interval of the reported cases is smaller than the infection time interval of the healthcare worker cases.
Edit intent: fluency
Original:  The results show that : (1) the lognormal distribution was the most suitable to describe the evolution of epidemic with time . (2) The estimated peak infection time of the reported cases lags behind the peak infection time of the healthcare worker cases, and the estimated infection time interval of the reported cases is smaller than the infection time interval of the healthcare worker cases.
Revised:  The results show that : (1) the lognormal distribution was the most suitable to describe the evolution of epidemic with time ; (2) The estimated peak infection time of the reported cases lags behind the peak infection time of the healthcare worker cases, and the estimated infection time interval of the reported cases is smaller than the infection time interval of the healthcare worker cases.
Edit intent: fluency
Original:  The results show that : (1) the lognormal distribution was the most suitable to describe the evolution of epidemic with time . (2) The estimated peak infection time of the reported cases lags behind the peak infection time of the healthcare worker cases, and the estimated infection time interval of the reported cases is smaller than the infection time interval of the healthcare worker cases.
Revised:  The results show that : (1) the lognormal distribution was the most suitable to describe the evolution of epidemic with time . (2) the estimated peak infection time of the reported cases lags behind the peak infection time of the healthcare worker cases, and the estimated infection time interval of the reported cases is smaller than the infection time interval of the healthcare worker cases.
Edit intent: fluency
Original:  The results show that : (1) the lognormal distribution was the most suitable to describe the evolution of epidemic with time . (2) The estimated peak infection time of the reported cases lags behind the peak infection time of the healthcare worker cases, and the estimated infection time interval of the reported cases is smaller than the infection time interval of the healthcare worker cases.
Revised:  The results show that : (1) the lognormal distribution was the most suitable to describe the evolution of epidemic with time . (2) The estimated peak infection time of the reported cases lagged the peak infection time of the healthcare worker cases, and the estimated infection time interval of the reported cases is smaller than the infection time interval of the healthcare worker cases.
Edit intent: clarity
Original:  The results show that : (1) the lognormal distribution was the most suitable to describe the evolution of epidemic with time . (2) The estimated peak infection time of the reported cases lags behind the peak infection time of the healthcare worker cases, and the estimated infection time interval of the reported cases is smaller than the infection time interval of the healthcare worker cases.
Revised:  The results show that : (1) the lognormal distribution was the most suitable to describe the evolution of epidemic with time . (2) The estimated peak infection time of the reported cases lags behind the peak infection time of the healthcare worker cases, and the estimated infection time interval of the reported cases was smaller than that of the healthcare worker cases.
Edit intent: clarity
Original:  (3) The impact of under-reporting cases on the early stages of the pandemic is greater than that on its later stages, and the impact on the early onset area is greater than that on the late onset area.
Revised:  (3) The impact of under-reporting cases on the early stages of the pandemic was greater than that on its later stages, and the impact on the early onset area is greater than that on the late onset area.
Edit intent: fluency
Original:  (3) The impact of under-reporting cases on the early stages of the pandemic is greater than that on its later stages, and the impact on the early onset area is greater than that on the late onset area.
Revised:  (3) The impact of under-reporting cases on the early stages of the pandemic is greater than that on its later stages, and the impact on the early onset area was greater than that on the late onset area.
Edit intent: fluency
Original: Robust and safe self-driving in complex and dynamic environments is quite challenging due to the requirement of scalable driving policies against the wide variety of traffic scenarios (e,g., road topologies, traffic rules and interaction with road agents).
Revised: Traditional modular self-driving in complex and dynamic environments is quite challenging due to the requirement of scalable driving policies against the wide variety of traffic scenarios (e,g., road topologies, traffic rules and interaction with road agents).
Edit intent: meaning-changed
Original: Robust and safe self-driving in complex and dynamic environments is quite challenging due to the requirement of scalable driving policies against the wide variety of traffic scenarios (e,g., road topologies, traffic rules and interaction with road agents). In this area, traditional modular frameworks scale poorly in new environments, and require tremendous and iterative hand-tuning of rules and parameters to maintain  performance in all foreseeable scenarios.
Revised: Robust and safe self-driving frameworks scale poorly in new environments, and require tremendous and iterative hand-tuning of rules and parameters to maintain performance in all foreseeable scenarios.
Edit intent: coherence
Original:  In this area, traditional modular frameworks scale poorly in new environments, and require tremendous and iterative hand-tuning of rules and parameters to maintain  performance in all foreseeable scenarios.
Revised:  In this area, traditional modular frameworks scale poorly in new environments, and require tremendous and iterative hand-tuning of rules and parameters to maintain performance in all foreseeable occasions. Therefore, robust and safe self-driving using traditional frameworks is still challenging, especially in complex and dynamic environments.
Edit intent: meaning-changed
Original:  In this paper, we propose a graph-based deep network to achieve unified and scalable self-driving in diverse dynamic environments. The extensive evaluation results show that our model can safely navigate the vehicle in a large variety of urban, rural, and highway areas with dense traffic while obeying traffic rules . Specifically, more than 7, 500 km of closed-loop driving evaluation is conducted in dynamic simulation environments , in which our method can handle complex driving situations, and achieve higher success rates (73.5\%-83.2\%) and driving scores than the baselines .
Revised:  In this paper, we propose a graph-based deep network to achieve unified and scalable self-driving that can handle massive traffic scenarios . Specifically, more than 7, 500 km of closed-loop driving evaluation is conducted in dynamic simulation environments , in which our method can handle complex driving situations, and achieve higher success rates (73.5\%-83.2\%) and driving scores than the baselines .
Edit intent: clarity
Original:  The extensive evaluation results show that our model can safely navigate the vehicle in a large variety of urban, rural, and highway areas with dense traffic while obeying traffic rules . Specifically, more than 7, 500 km of closed-loop driving evaluation is conducted in dynamic simulation environments , in which our method can handle complex driving situations, and achieve higher success rates (73.5\%-83.2\%) and driving scores than the baselines .
Revised:  The extensive evaluation results show that our model can safely navigate the vehicle in a large variety of urban, rural, and highway areas with dense traffic while obeying traffic rules . Specifically, more than 7, 000 km of evaluation is conducted in dynamic simulation environments , in which our method can handle complex driving situations, and achieve higher success rates (73.5\%-83.2\%) and driving scores than the baselines .
Edit intent: clarity
Original:  The extensive evaluation results show that our model can safely navigate the vehicle in a large variety of urban, rural, and highway areas with dense traffic while obeying traffic rules . Specifically, more than 7, 500 km of closed-loop driving evaluation is conducted in dynamic simulation environments , in which our method can handle complex driving situations, and achieve higher success rates (73.5\%-83.2\%) and driving scores than the baselines .
Revised:  The extensive evaluation results show that our model can safely navigate the vehicle in a large variety of urban, rural, and highway areas with dense traffic while obeying traffic rules . Specifically, more than 7, 500 km of closed-loop driving evaluation is conducted in a high-fidelity driving simulator , in which our method can handle complex driving situations, and achieve higher success rates (73.5\%-83.2\%) and driving scores than the baselines .
Edit intent: clarity
Original:  The extensive evaluation results show that our model can safely navigate the vehicle in a large variety of urban, rural, and highway areas with dense traffic while obeying traffic rules . Specifically, more than 7, 500 km of closed-loop driving evaluation is conducted in dynamic simulation environments , in which our method can handle complex driving situations, and achieve higher success rates (73.5\%-83.2\%) and driving scores than the baselines .
Revised:  The extensive evaluation results show that our model can safely navigate the vehicle in a large variety of urban, rural, and highway areas with dense traffic while obeying traffic rules . Specifically, more than 7, 500 km of closed-loop driving evaluation is conducted in dynamic simulation environments , in which our method can obey the traffic rules and safely navigate the vehicle in a large variety of urban, rural, and highway environments, including unprotected left turns, narrow roads, roundabouts, and pedestrian-rich intersections. The results also show that our method achieves better performance over the baselines in terms of success rate. This work is accompanied with some demonstration videos which are available at URL
Edit intent: meaning-changed
Original: We introduce difference estimators for data stream computation, which provide approximations to F(v)-F(u) for frequency vectors v\succeq u and a given function F. We show how to use such estimators to carefully trade error for memory in an iterative manner. The function F is generally non-linear, and we give the first difference estimators for the frequency moments F_p for p\in[0,2], as well as for integers p>2. Using these, we resolve a number of central open questions in adversarial robust streaming and sliding window models. For adversarially robust streams, we obtain a (1+\epsilon)-approximation to F_p using%DIFDELCMD < \mathcal{O}\left(%%% \frac{\log n{\epsilon^2}}%DIFDELCMD < \right) %%% bits of space for p\in[0,2] and using%DIFDELCMD < \mathcal{O}\left(%%% \frac{1{\epsilon^2}n^{1-2/p}}%DIFDELCMD < \right) %%% bits of space for integers p>2.
Revised: %DIFDELCMD < \mathcal{O}\left(%%% \frac{\log n{\epsilon^2}}%DIFDELCMD < \right) %%% bits of space for p\in[0,2] and using%DIFDELCMD < \mathcal{O}\left(%%% \frac{1{\epsilon^2}n^{1-2/p}}%DIFDELCMD < \right) %%% bits of space for integers p>2.
Edit intent: meaning-changed
Original:  For adversarially robust streams, we obtain a (1+\epsilon)-approximation to F_p using%DIFDELCMD < \mathcal{O}\left(%%% \frac{\log n{\epsilon^2}}%DIFDELCMD < \right) %%% bits of space for p\in[0,2] and using%DIFDELCMD < \mathcal{O}\left(%%% \frac{1{\epsilon^2}n^{1-2/p}}%DIFDELCMD < \right) %%% bits of space for integers p>2.
Revised:  For adversarially robust streams, we obtain a (1+\epsilon)-approximation to F_p using%DIFDELCMD < \mathcal{O}\left(%%% {\epsilon^2}}%DIFDELCMD < \right) %%% bits of space for p\in[0,2] and using%DIFDELCMD < \mathcal{O}\left(%%% \frac{1{\epsilon^2}n^{1-2/p}}%DIFDELCMD < \right) %%% bits of space for integers p>2.
Edit intent: meaning-changed
Original:  For adversarially robust streams, we obtain a (1+\epsilon)-approximation to F_p using%DIFDELCMD < \mathcal{O}\left(%%% \frac{\log n{\epsilon^2}}%DIFDELCMD < \right) %%% bits of space for p\in[0,2] and using%DIFDELCMD < \mathcal{O}\left(%%% \frac{1{\epsilon^2}n^{1-2/p}}%DIFDELCMD < \right) %%% bits of space for integers p>2.
Revised:  For adversarially robust streams, we obtain a (1+\epsilon)-approximation to F_p using%DIFDELCMD < \mathcal{O}\left(%%% \frac{\log n{\epsilon^2}}%DIFDELCMD < \right) %%% %DIFDELCMD < \mathcal{O}\left(%%% \frac{1{\epsilon^2}n^{1-2/p}}%DIFDELCMD < \right) %%% bits of space for integers p>2.
Edit intent: coherence
Original:  For adversarially robust streams, we obtain a (1+\epsilon)-approximation to F_p using%DIFDELCMD < \mathcal{O}\left(%%% \frac{\log n{\epsilon^2}}%DIFDELCMD < \right) %%% bits of space for p\in[0,2] and using%DIFDELCMD < \mathcal{O}\left(%%% \frac{1{\epsilon^2}n^{1-2/p}}%DIFDELCMD < \right) %%% bits of space for integers p>2.
Revised:  For adversarially robust streams, we obtain a (1+\epsilon)-approximation to F_p using%DIFDELCMD < \mathcal{O}\left(%%% \frac{\log n{\epsilon^2}}%DIFDELCMD < \right) %%% bits of space for p\in[0,2] and using%DIFDELCMD < \mathcal{O}\left(%%% {\epsilon^2}n^{1-2/p}}%DIFDELCMD < \right) %%% bits of space for integers p>2.
Edit intent: fluency
Original:  For adversarially robust streams, we obtain a (1+\epsilon)-approximation to F_p using%DIFDELCMD < \mathcal{O}\left(%%% \frac{\log n{\epsilon^2}}%DIFDELCMD < \right) %%% bits of space for p\in[0,2] and using%DIFDELCMD < \mathcal{O}\left(%%% \frac{1{\epsilon^2}n^{1-2/p}}%DIFDELCMD < \right) %%% bits of space for integers p>2. We also obtain an adversarially robust algorithm for the L_2-heavy hitters problem using%DIFDELCMD < \mathcal{O}\left(%%% \frac{\log n{\epsilon^2}}%DIFDELCMD < \right) %%% bits of space.
Revised:  For adversarially robust streams, we obtain a (1+\epsilon)-approximation to F_p using%DIFDELCMD < \mathcal{O}\left(%%% \frac{\log n{\epsilon^2}}%DIFDELCMD < \right) %%% bits of space for p\in[0,2] and using%DIFDELCMD < \mathcal{O}\left(%%% \frac{1{\epsilon^2}n^{1-2/p}}%DIFDELCMD < \right) %%% %DIFDELCMD < \mathcal{O}\left(%%% \frac{\log n{\epsilon^2}}%DIFDELCMD < \right) %%% bits of space.
Edit intent: meaning-changed
Original:  We also obtain an adversarially robust algorithm for the L_2-heavy hitters problem using%DIFDELCMD < \mathcal{O}\left(%%% \frac{\log n{\epsilon^2}}%DIFDELCMD < \right) %%% bits of space.
Revised:  We also obtain an adversarially robust algorithm for the L_2-heavy hitters problem using%DIFDELCMD < \mathcal{O}\left(%%% {\epsilon^2}}%DIFDELCMD < \right) %%% bits of space.
Edit intent: meaning-changed
Original:  We also obtain an adversarially robust algorithm for the L_2-heavy hitters problem using%DIFDELCMD < \mathcal{O}\left(%%% \frac{\log n{\epsilon^2}}%DIFDELCMD < \right) %%% bits of space. Our bounds are optimal up to \text{poly(\log\log n+\log(1/\epsilon)) factors, and improve the 1{\epsilon^3} dependence of }  Ben-Eliezer et  al.
Revised:  We also obtain an adversarially robust algorithm for the L_2-heavy hitters problem using%DIFDELCMD < \mathcal{O}\left(%%% \frac{\log n{\epsilon^2}}%DIFDELCMD < \right) %%% (\log\log n+\log(1/\epsilon)) factors, and improve the 1{\epsilon^3} dependence of } Ben-Eliezer et al.
Edit intent: meaning-changed
Original:  Our bounds are optimal up to \text{poly(\log\log n+\log(1/\epsilon)) factors, and improve the 1{\epsilon^3} dependence of }  Ben-Eliezer et  al.
Revised:  Our bounds are optimal up to \text{poly(\log\log n+\log(1/\epsilon)) factors, and improve the 1{\epsilon^3} dependence of } In the adversarially robust streaming model, a stream of elements is presented to an algorithm and is allowed to depend on the output of the algorithm at earlier times during the stream. In the classic insertion-only model of data streams, Ben-Eliezer et al.
Edit intent: meaning-changed
Original:  Our bounds are optimal up to \text{poly(\log\log n+\log(1/\epsilon)) factors, and improve the 1{\epsilon^3} dependence of }  Ben-Eliezer et  al.
Revised:  Our bounds are optimal up to \text{poly(\log\log n+\log(1/\epsilon)) factors, and improve the 1{\epsilon^3} dependence of } Ben-Eliezer et . al.
Edit intent: fluency
Original: 5}} dependence of Hassidim et}  al. (NeurIPS 2020, oral presentation) .
Revised: 5}} dependence of Hassidim et} show how to convert a non-robust algorithm into a robust one with a roughly 1/\varepsilon factor overhead. This was subsequently improved to a 1/\sqrt{\varepsilon al. (NeurIPS 2020, oral presentation) .
Edit intent: meaning-changed
Original: 5}} dependence of Hassidim et}  al. (NeurIPS 2020, oral presentation) . For sliding windows, we obtain a (1+\epsilon)-approximation to F_p for p\in(0,2 , resolving a longstanding question of Braverman and Ostrovsky (FOCS 2007).
Revised: 5}} dependence of Hassidim et} al. (NeurIPS 2020, oral presentation) , resolving a longstanding question of Braverman and Ostrovsky (FOCS 2007).
Edit intent: coherence
Original:  For sliding windows, we obtain a (1+\epsilon)-approximation to F_p for p\in(0,2 , resolving a longstanding question of Braverman and Ostrovsky (FOCS 2007). For example, for p = 2 we improve the dependence on \epsilon from \frac{1{\epsilon^4} to an optimal \frac{1}{\epsilon^2}.
Revised:  For sliding windows, we obtain a (1+\epsilon)-approximation to F_p for p\in(0,2 , suppressing logarithmic factors. For general functions the latter is known to be best-possible, by a result of Kaplan et. al. (CRYPTO 2021). We show how to bypass this impossibility result by developing data stream algorithms for a large class of streaming problems, with no overhead in the approximation factor. Our class of streaming problems includes the most well-studied problems such as the L_2-heavy hitters problem, F_p-moment estimation, as well as empirical entropy estimation. We substantially improve upon all prior work on these problems, giving the first optimal dependence on the approximation factor. As in previous work, we obtain a general transformation that applies to any non-robust streaming algorithm and depends on the dependence on \epsilon from \frac{1{\epsilon^4} to an optimal \frac{1}{\epsilon^2}.
Edit intent: meaning-changed
Original:  For both models, our dependence on \epsilon shows, up to \log\frac{1}{\epsilon} factors, that there is no overhead over the standard insertion-only data stream model for any of these problems. } 
Revised:  For both models, our dependence on \epsilon shows, up to \log\frac{1}{\epsilon} factors, that there is no overhead over the standard insertion-only data stream model for any of these problems. } so-called flip number. However, the key technical innovation is that we apply the transformation to what we call a difference estimator for the streaming problem, rather than an estimator for the streaming problem itself. We then develop the first difference estimators for a wide range of problems. Our difference estimator methodology is not only applicable to the adversarially robust model, but to other streaming models where temporal properties of the data play a central role. (Abstract shortened to meet arXiv limit.)
Edit intent: meaning-changed