wanyu/IteraTeR_v2 huggingface dataset:
Original:  We demonstrate the benefits of the proposed setupby performing a thoroughquantitative investigation on the CNN/Daily Mail dataset , which in the meantime reveals useful insights of state-of-the-art summarization methods. %DIFDELCMD < \footnote{%%% Data can be found at URL
Revised:  We demonstrate the benefits of the proposed setupby performing a thorough investigation on the CNN/Daily Mail dataset , which in the meantime reveals useful insights of state-of-the-art summarization methods. %DIFDELCMD < \footnote{%%% Data can be found at URL
Edit intent: clarity
Original:  We demonstrate the benefits of the proposed setupby performing a thoroughquantitative investigation on the CNN/Daily Mail dataset , which in the meantime reveals useful insights of state-of-the-art summarization methods. %DIFDELCMD < \footnote{%%% Data can be found at URL
Revised:  We demonstrate the benefits of the proposed setupby performing a thoroughquantitative CNN/Daily Mail dataset , which in the meantime reveals useful insights of state-of-the-art summarization methods. %DIFDELCMD < \footnote{%%% Data can be found at URL
Edit intent: clarity
Original: For the well-known problemof balls dropped uniformly at random into bins , the number of singletons --- those bins with a single ball --- is important to the analysis of backoff algorithms.
Revised: For the well-known problemof balls dropped uniformly at random into bins , the number of singletons---those bins with a single ball --- is important to the analysis of backoff algorithms.
Edit intent: fluency
Original: For the well-known problemof balls dropped uniformly at random into bins , the number of singletons --- those bins with a single ball --- is important to the analysis of backoff algorithms.
Revised: For the well-known problemof balls dropped uniformly at random into bins , the number of singletons --- those bins with a single ball---is important to the analysis of backoff algorithms.
Edit intent: fluency
Original:  Here  we show that standard Chernoff bounds can be used instead, and the simplicity of this approach is illustrated by re-analyzing several fundamental backoff algorithms.
Revised:  Here , we show that standard Chernoff bounds can be used instead, and the simplicity of this approach is illustrated by re-analyzing several fundamental backoff algorithms.
Edit intent: fluency
Original:  Here  we show that standard Chernoff bounds can be used instead, and the simplicity of this approach is illustrated by re-analyzing several fundamental backoff algorithms.
Revised:  Here we show that standard Chernoff bounds can be used instead, and the simplicity of this approach is illustrated by re-analyzing some well-known backoff algorithms.
Edit intent: clarity
Original:  Multi-articulate prosthetic hands are capable of performing dexterous hand movements.
Revised: Background: Multi-articulate prosthetic hands are capable of performing dexterous hand movements.
Edit intent: clarity
Original:  Multi-articulate prosthetic hands are capable of performing dexterous hand movements.
Revised:  Multi-articulate prostheses are capable of performing dexterous hand movements.
Edit intent: clarity
Original:  Ad-hoc modifications include thresholds and non-unity gains on the output of the Kalman filter.
Revised:  Ad-hoc modifications include thresholds and non-unity gains on the output of a Kalman filter.
Edit intent: fluency
Original:   We demonstrate that both neural and EMG data can be combined effectively to improve control .
Revised:  Results: We demonstrate that both neural and EMG data can be combined effectively to improve control .
Edit intent: coherence
Original:   We demonstrate that both neural and EMG data can be combined effectively to improve control .
Revised:  We demonstrate that both neural and EMG data can be combined effectively .
Edit intent: clarity
Original:   In contrast to traditional pattern recognitioncontrol schemes, the modified Kalman filter allows users to continuously modulate their force output, which is critical for fine dexterity.
Revised:  In contrast to pattern recognition, the MKF allows users to continuously modulate their force output, which is critical for fine dexterity.
Edit intent: clarity
Original:  Secondly , DMPs are a framework for `one-shot learning', meaning that they are limited to learn from a unique demonstration.
Revised:  Lastly , DMPs are a framework for `one-shot learning', meaning that they are limited to learn from a unique demonstration.
Edit intent: coherence
Original:  Secondly , DMPs are a framework for `one-shot learning', meaning that they are limited to learn from a unique demonstration.
Revised:  Secondly , DMPs are a framework for `one-shot learning', meaning that they are constrained to learn from a unique demonstration.
Edit intent: clarity
Original:  In this work, we propose a technique to make the generalization of the trajectory robust against  the choice of hyperparameters and new goal positions.
Revised:  In this work, we propose a technique to make the generalization of the trajectory robust against both the choice of hyperparameters and new goal positions.
Edit intent: clarity
Original:  Secondly,  adaptation of the trajectory generated by the DMP heavily depends on the choice of hyperparameters and on the new desired goal position.
Revised:  Secondly, the adaptation of the trajectory generated by the DMP heavily depends on the choice of hyperparameters and on the new desired goal position.
Edit intent: fluency
Original:  Secondly,  adaptation of the trajectory generated by the DMP heavily depends on the choice of hyperparameters and on the new desired goal position.
Revised:  Secondly, adaptation of the trajectory generated by the DMP heavily depends on the choice of hyperparameters and the new desired goal position.
Edit intent: fluency
Original:  Then, we show how to use the invariance of DMPs w.r.t. affine transformations in order to make the generalization of the trajectory robust against both the choice of hyperparameters and new goal position, performing both synthetic tests and experiments with real robots to show this increased robustness.
Revised:  Then, we show how to use the invariance of DMPs w.r.t. affine transformations to make the generalization of the trajectory robust against both the choice of hyperparameters and new goal position, performing both synthetic tests and experiments with real robots to show this increased robustness.
Edit intent: coherence
Original:  This method is useful for problems where periodicity must be preserved and the precise anisotropy is not known a priori.
Revised:  This method targets applications where periodicity must be preserved and the precise anisotropy is not known a priori.
Edit intent: clarity
Original:  We present several numerical examples of our algorithm where the adaptives procedure successfully recovers the theoretical "best" convergence rate, including an application to a periodic PES approximation.
Revised:  We present several numerical examples of our algorithm where the adaptive procedure successfully recovers the theoretical "best" convergence rate, including an application to a periodic PES approximation.
Edit intent: fluency
Original:  They usually take a program and make some prediction about it, e.g., bug prediction.
Revised:  They usually take a program and make some predictions about it, e.g., bug prediction.
Edit intent: fluency
Original:  In this paper, we describe our ongoing efforts in developing effective techniques to test neural programs.
Revised:  In this paper, we describe our ongoing efforts to develop effective techniques for testing neural programs.
Edit intent: fluency
Original:  We discuss the challenges  in developing such tools and our future plans.
Revised:  We discuss the challenges involved in developing such tools and our future plans.
Edit intent: clarity
Original:  In our preliminary experiment on a recent neural model proposed in the literature, we found that the model is very brittle  and simple perturbations in the input can cause the model to make a mistake in its prediction.
Revised:  In our preliminary experiment on a neural model recently proposed in the literature, we found that the model is very brittle and simple perturbations in the input can cause the model to make a mistake in its prediction.
Edit intent: fluency
Original:  In our preliminary experiment on a recent neural model proposed in the literature, we found that the model is very brittle  and simple perturbations in the input can cause the model to make a mistake in its prediction.
Revised:  In our preliminary experiment on a recent neural model proposed in the literature, we found that the model is very brittle , and simple perturbations in the input can cause the model to make a mistake in its prediction.
Edit intent: fluency
Original:  In our preliminary experiment on a recent neural model proposed in the literature, we found that the model is very brittle  and simple perturbations in the input can cause the model to make a mistake in its prediction.
Revised:  In our preliminary experiment on a recent neural model proposed in the literature, we found that the model is very brittle and simple perturbations in the input can cause the model to make mistakes in its prediction.
Edit intent: fluency
Original: Recently, progressive learning has shown its capacity of improving speech quality and speech intelligibility when it is combined with deep neural network (DNN) and long short-term memory (LSTM) based monaural speech enhancement algorithms, especially in low signal-to-noise ratio (SNR) conditions.
Revised: Recently, progressive learning has shown its capacity to improve speech quality and speech intelligibility when it is combined with deep neural network (DNN) and long short-term memory (LSTM) based monaural speech enhancement algorithms, especially in low signal-to-noise ratio (SNR) conditions.
Edit intent: clarity
Original:  Nevertheless, due to a large number of parameters and highly computational complexity, it is hard to implement in current resource-limited micro-controllers and thus, it is important to significantly reduce both the amount of parameters and the computational load for practical applications.
Revised:  Nevertheless, due to a large number of parameters and high computational complexity, it is hard to implement in current resource-limited micro-controllers and thus, it is important to significantly reduce both the amount of parameters and the computational load for practical applications.
Edit intent: fluency
Original:  Nevertheless, due to a large number of parameters and highly computational complexity, it is hard to implement in current resource-limited micro-controllers and thus, it is important to significantly reduce both the amount of parameters and the computational load for practical applications.
Revised:  Nevertheless, due to a large number of parameters and highly computational complexity, it is hard to implement in current resource-limited micro-controllers and thus, it is essential to significantly reduce both the amount of parameters and the computational load for practical applications.
Edit intent: coherence
Original:  Nevertheless, due to a large number of parameters and highly computational complexity, it is hard to implement in current resource-limited micro-controllers and thus, it is important to significantly reduce both the amount of parameters and the computational load for practical applications.
Revised:  Nevertheless, due to a large number of parameters and highly computational complexity, it is hard to implement in current resource-limited micro-controllers and thus, it is important to significantly reduce both the number of parameters and the computational load for practical applications.
Edit intent: clarity
Original:  For this purpose, we propose a novel progressive learning framework with  convolutional recurrent neural networks called PL-CRNN, which takes advantages of both convolutional neural networks and recurrent neural networks to drastically reduce the amount of parameters and simultaneously improve speech quality and speech intelligibility.
Revised:  For this purpose, we propose a novel progressive learning framework with convolutional recurrent neural networks called PL-CRNN, which takes advantage of both convolutional neural networks and recurrent neural networks to drastically reduce the amount of parameters and simultaneously improve speech quality and speech intelligibility.
Edit intent: fluency
Original:  For this purpose, we propose a novel progressive learning framework with  convolutional recurrent neural networks called PL-CRNN, which takes advantages of both convolutional neural networks and recurrent neural networks to drastically reduce the amount of parameters and simultaneously improve speech quality and speech intelligibility.
Revised:  For this purpose, we propose a novel progressive learning framework with convolutional recurrent neural networks called PL-CRNN, which takes advantages of both convolutional neural networks and recurrent neural networks to drastically reduce the number of parameters and simultaneously improve speech quality and speech intelligibility.
Edit intent: clarity
Original:  Numerous experiments verify the effectiveness of  proposed PL-CRNN model and indicate that it yields consistent better performance than the PL-DNN and PL-LSTM algorithms and also it gets results close even better than the CRNN in terms of various evaluation metrics . Compared with PL-DNN, PL-LSTM and state-of-the-art CRNNmodels , the proposed PL-CRNN algorithm can reduce the amount of parameters up to 77%DIFDELCMD < \\%%% %DIF < , 93\\% and 93 %DIFDELCMD < \\%%% %DIF < , respectively.
Revised:  Numerous experiments verify the effectiveness of the proposed PL-CRNN model and indicate that it yields consistent better performance than the PL-DNN and PL-LSTM algorithms and also it gets results close even better than the CRNN in terms of various evaluation metrics . Compared with PL-DNN, PL-LSTM and state-of-the-art CRNNmodels , the proposed PL-CRNN algorithm can reduce the amount of parameters up to 77%DIFDELCMD < \\%%% %DIF < , 93\\% and 93 %DIFDELCMD < \\%%% %DIF < , respectively.
Edit intent: fluency
Original:  Numerous experiments verify the effectiveness of  proposed PL-CRNN model and indicate that it yields consistent better performance than the PL-DNN and PL-LSTM algorithms and also it gets results close even better than the CRNN in terms of various evaluation metrics . Compared with PL-DNN, PL-LSTM and state-of-the-art CRNNmodels , the proposed PL-CRNN algorithm can reduce the amount of parameters up to 77%DIFDELCMD < \\%%% %DIF < , 93\\% and 93 %DIFDELCMD < \\%%% %DIF < , respectively.
Revised:  Numerous experiments verify the effectiveness of proposed PL-CRNN model and indicate that it yields consistent better performance than the PL-DNN and PL-LSTM algorithms and also it gets results close even better than the CRNN in terms of objective measurements . Compared with PL-DNN, PL-LSTM and state-of-the-art CRNNmodels , the proposed PL-CRNN algorithm can reduce the amount of parameters up to 77%DIFDELCMD < \\%%% %DIF < , 93\\% and 93 %DIFDELCMD < \\%%% %DIF < , respectively.
Edit intent: clarity
Original:  Numerous experiments verify the effectiveness of  proposed PL-CRNN model and indicate that it yields consistent better performance than the PL-DNN and PL-LSTM algorithms and also it gets results close even better than the CRNN in terms of various evaluation metrics . Compared with PL-DNN, PL-LSTM and state-of-the-art CRNNmodels , the proposed PL-CRNN algorithm can reduce the amount of parameters up to 77%DIFDELCMD < \\%%% %DIF < , 93\\% and 93 %DIFDELCMD < \\%%% %DIF < , respectively.
Revised:  Numerous experiments verify the effectiveness of proposed PL-CRNN model and indicate that it yields consistent better performance than the PL-DNN and PL-LSTM algorithms and also it gets results close even better than the CRNN in terms of various evaluation metrics . Compared with PL-DNN, PL-LSTM , and CRNN , the proposed PL-CRNN algorithm can reduce the amount of parameters up to 77%DIFDELCMD < \\%%% %DIF < , 93\\% and 93 %DIFDELCMD < \\%%% %DIF < , respectively.
Edit intent: clarity
Original:  Numerous experiments verify the effectiveness of  proposed PL-CRNN model and indicate that it yields consistent better performance than the PL-DNN and PL-LSTM algorithms and also it gets results close even better than the CRNN in terms of various evaluation metrics . Compared with PL-DNN, PL-LSTM and state-of-the-art CRNNmodels , the proposed PL-CRNN algorithm can reduce the amount of parameters up to 77%DIFDELCMD < \\%%% %DIF < , 93\\% and 93 %DIFDELCMD < \\%%% %DIF < , respectively.
Revised:  Numerous experiments verify the effectiveness of proposed PL-CRNN model and indicate that it yields consistent better performance than the PL-DNN and PL-LSTM algorithms and also it gets results close even better than the CRNN in terms of various evaluation metrics . Compared with PL-DNN, PL-LSTM and state-of-the-art CRNNmodels , the proposed PL-CRNN algorithm can reduce the number of parameters up to 77%DIFDELCMD < \\%%% %DIF < , 93\\% and 93 %DIFDELCMD < \\%%% %DIF < , respectively.
Edit intent: clarity
Original:  I discuss such findings both as they directly relate  local concerns in CSCW and in the context of longstanding theoretical discussions around genre theory and how socio-technical structures and affordances impact participation in distributed, computer-mediated organizations and user-generated content platforms.
Revised:  I discuss such findings both as they directly relate to local concerns in CSCW and in the context of longstanding theoretical discussions around genre theory and how socio-technical structures and affordances impact participation in distributed, computer-mediated organizations and user-generated content platforms.
Edit intent: fluency
Original:  For general strongly convex potentials with \alpha-th order smoothness, we prove that the mixing time scales as O \left( \frac{d^{1/3}{\varepsilon^{2/3}} } }{\varepsilon^{1/2}} } + d^{1/2}{\varepsilon^{1/(\alpha - 1)}} \right).
Revised:  For general strongly convex potentials with \alpha-th order smoothness, we prove that the mixing time scales as O \left( }{\varepsilon^{2/3}} } }{\varepsilon^{1/2}} } + d^{1/2}{\varepsilon^{1/(\alpha - 1)}} \right).
Edit intent: clarity
Original:  The potential due to source charges embedded in layered media is decomposed into a free space and reaction component(s).
Revised:  The potential due to charges embedded in layered media is decomposed into a free space and reaction component(s).
Edit intent: clarity
Original:  The free space component will be handled by the classic FMM while new multipole expansions (MEs)  , as well as the multipole to local (M2L) translation operators , will be developed for the reaction components, for which ME-based FMMs can be then developed.
Revised:  The free space component will be handled by the classic FMM while new multipole expansions (MEs) , as well as the multipole to local (M2L) translation operators are derived for the reaction components, for which ME-based FMMs can be then developed.
Edit intent: clarity
Original:  It is found that the FMMs for the reaction field components are much faster than the  FMM for the free space component due to the fact that the polarization charges are separated from the original source charges by a material interface.
Revised:  It is found that the FMMs for the reaction components are found to be much faster than the FMM for the free space component due to the fact that the polarization charges are separated from the original source charges by a material interface.
Edit intent: clarity
Original:  It is found that the FMMs for the reaction field components are much faster than the  FMM for the free space component due to the fact that the polarization charges are separated from the original source charges by a material interface.
Revised:  It is found that the FMMs for the reaction field components are much faster than the FMM for the free space components due to the fact that the polarization charges are separated from the original source charges by a material interface.
Edit intent: fluency
Original:  It is found that the FMMs for the reaction field components are much faster than the  FMM for the free space component due to the fact that the polarization charges are separated from the original source charges by a material interface.
Revised:  It is found that the FMMs for the reaction field components are much faster than the FMM for the free space component due to the separation of equivalent polarization charges and the associated target charges by a material interface.
Edit intent: clarity
Original:  As a result, the FMM for charges in layered media costs  the same as the classic FMM in the free space case.
Revised:  As a result, the FMM for potential in layered media costs the same as the classic FMM in the free space case.
Edit intent: clarity
Original:  Numerical results validate the fast convergence of the MEs for the reaction field components, and the O(N) complexity of the FMM for charges in 3-D layered media.
Revised:  Numerical results validate the fast convergence of the MEs for the reaction components, and the O(N) complexity of the FMM for charges in 3-D layered media.
Edit intent: clarity
Original: The standard algorithm to eliminate indirect left recursion takes a preventative approach, reorganising a grammar in such a way that indirect left recursion is no longer possible, rather than eliminating it only as and when it occurs.
Revised: The standard algorithm to eliminate indirect left recursion takes a preventative approach, reorganising a grammar 's rules so that that indirect left recursion is no longer possible, rather than eliminating it only as and when it occurs.
Edit intent: clarity
Original:  This approach results in many of the grammar's rules being lost, however, so that the resulting parse trees are often devoid of the detail that the grammar was supposed to pick out in the first place.
Revised:  This approach results in many of the rules being lost, however, so that the resulting parse trees are often devoid of the detail that the grammar was supposed to pick out in the first place.
Edit intent: fluency
Original:  This approach results in many of the grammar's rules being lost, however, so that the resulting parse trees are often devoid of the detail that the grammar was supposed to pick out in the first place.
Revised:  This approach results in many of the grammar's rules being lost, so that the resulting parse trees are often devoid of the detail that the grammar was supposed to pick out in the first place.
Edit intent: coherence
Original:  This approach results in many of the grammar's rules being lost, however, so that the resulting parse trees are often devoid of the detail that the grammar was supposed to pick out in the first place.
Revised:  This approach results in many of the grammar's rules being lost, however, so that the parse trees are often devoid of the detail that the grammar was supposed to pick out in the first place.
Edit intent: clarity
Original:  This approach results in many of the grammar's rules being lost, however, so that the resulting parse trees are often devoid of the detail that the grammar was supposed to pick out in the first place.
Revised:  This approach results in many of the grammar's rules being lost, however, so that the resulting parse trees are often devoid of the detail that the grammar was supposed to capture in the first place.
Edit intent: clarity
Original:  To avoid this pitfall, we extend a revised version of the standard algorithm to eliminate immediate left recursion to a recursive algorithm to explicitly eliminate indirect left recursion.
Revised:  To avoid this pitfall, we revise the standard algorithm to eliminate immediate left recursion to a recursive algorithm to explicitly eliminate indirect left recursion.
Edit intent: clarity