wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Methods: HSPs were generated using G3D basis functions and passed through a boundary element forward model to obtain corresponding BSPs . The generated BSPs (input) and HSPs (output) were used to train a neural network , which was then used to predict a variety of synthesized and decomposed real-world HSPs.
Revised:  Methods: HSPs were generated with single Gaussian basis functions at discrete heart surface locations and projected to corresponding BSPs using a volume conductor torso model. Both BSP (inputs) and HSP (outputs) were mapped to regular 2D surface meshes and used to train a neural network , which was then used to predict a variety of synthesized and decomposed real-world HSPs.
Edit intent: meaning-changed
Original:  Methods: HSPs were generated using G3D basis functions and passed through a boundary element forward model to obtain corresponding BSPs . The generated BSPs (input) and HSPs (output) were used to train a neural network , which was then used to predict a variety of synthesized and decomposed real-world HSPs. Results: Fitted G3D basis function parameters can accurately reconstruct the real-world left ventricular paced recording with percent root mean squared error (RMSE) of 1.34%DIFDELCMD < \pm %%% 1.30\%.
Revised:  Methods: HSPs were generated using G3D basis functions and passed through a boundary element forward model to obtain corresponding BSPs . The generated BSPs (input) and HSPs (output) were used to train a neural network . Predictive capabilities of the network were tested with unseen synthetic and experimental data. A dense full connected single hidden layer neural network was trained to map body surface impulses to heart surface Gaussian basis functions for reconstructing HSP. Synthetic pulses moving across the heart surface were predicted from the neural network with root mean squared error (RMSE) of 1.34%DIFDELCMD < \pm %%% 1.30\%.
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Original:  Results: Fitted G3D basis function parameters can accurately reconstruct the real-world left ventricular paced recording with percent root mean squared error (RMSE) of 1.34%DIFDELCMD < \pm %%% 1.30\%.
Revised:  Results: Fitted G3D basis function parameters can accurately reconstruct the real-world left ventricular paced recording with percent root mean squared error %DIFDELCMD < \pm %%% 1.30\%.
Edit intent: clarity
Original:  Results: Fitted G3D basis function parameters can accurately reconstruct the real-world left ventricular paced recording with percent root mean squared error (RMSE) of 1.34%DIFDELCMD < \pm %%% 1.30\%. The basis data trained neural network was able to predict G3D basis function synthesized data with RMSE of 8.46%DIFDELCMD < \pm %%% 1.55\%, and G3D representation of real-world data with RMSE of 18.5%DIFDELCMD < \pm %%% 5.25\%.
Revised:  Results: Fitted G3D basis function parameters can accurately reconstruct the real-world left ventricular paced recording with percent root mean squared error (RMSE) of 1.34%DIFDELCMD < \pm %%% %DIFDELCMD < \pm %%% 1.55\%, and G3D representation of real-world data with RMSE of 18.5%DIFDELCMD < \pm %%% 5.25\%.
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Original:  The basis data trained neural network was able to predict G3D basis function synthesized data with RMSE of 8.46%DIFDELCMD < \pm %%% 1.55\%, and G3D representation of real-world data with RMSE of 18.5%DIFDELCMD < \pm %%% 5.25\%.
Revised:  The basis data trained neural network was able to predict G3D basis function synthesized data with RMSE of 8.46%DIFDELCMD < \pm %%% %DIFDELCMD < \pm %%% 5.25\%.
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Original:  The basis data trained neural network was able to predict G3D basis function synthesized data with RMSE of 8.46%DIFDELCMD < \pm %%% 1.55\%, and G3D representation of real-world data with RMSE of 18.5%DIFDELCMD < \pm %%% 5.25\%. Activation map produced from the predicted time series had a RMSE of 17.0\% and mean absolute difference of 10.3%DIFDELCMD < \pm %%% 10.8ms when compared to that produced from the actual left ventricular paced recording.
Revised:  The basis data trained neural network was able to predict G3D basis function synthesized data with RMSE of 8.46%DIFDELCMD < \pm %%% 1.55\%, and G3D representation of real-world data with RMSE of 18.5%DIFDELCMD < \pm %%% %DIFDELCMD < \pm %%% 10.8ms when compared to that produced from the actual left ventricular paced recording.
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Original:  Activation map produced from the predicted time series had a RMSE of 17.0\% and mean absolute difference of 10.3%DIFDELCMD < \pm %%% 10.8ms when compared to that produced from the actual left ventricular paced recording. Conclusion: A Gaussian basis function based data driven model for re-framing the inverse problem of electrocardiography as a regression problem is successful and produces promising time series and activation map predictions of real-world recordings even when only trained using Guassian data. Significance: The HSPs predicted by the neural network can be used to create activation maps to identify cardiac dysfunctions during clinical assessment.
Revised:  Activation map produced from the predicted time series had a RMSE of 17.0\% and mean absolute difference of 10.3%DIFDELCMD < \pm %%% of 9.1
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Original:  We  explore a number of applications , including a representation of stationary, monotone time preferences, generalizing Fishburn and Rubinstein (1982) to time lotteries  .
Revised:  We provide a complete characterization of such statistics, and explore a number of applications , including a representation of stationary, monotone time preferences, generalizing Fishburn and Rubinstein (1982) to time lotteries .
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Original:  We  explore a number of applications , including a representation of stationary, monotone time preferences, generalizing Fishburn and Rubinstein (1982) to time lotteries  .
Revised:  We explore a number of applications to models of individual and group decision-making. These include a representation of stationary, monotone time preferences, generalizing Fishburn and Rubinstein (1982) to time lotteries .
Edit intent: meaning-changed
Original:  We  explore a number of applications , including a representation of stationary, monotone time preferences, generalizing Fishburn and Rubinstein (1982) to time lotteries  .
Revised:  We explore a number of applications , including a representation of stationary, monotone time preferences, generalizing Fishburn and Rubinstein (1982) to time lotteries , as well as a characterization of risk-averse preferences over monetary gambles that are invariant to mean-zero background risks .
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Original:  The  first-stage is analogue to the least squares case, i.e., a conditional mean regression of the endogenous variables on the instruments, with the difference that for the QR case is a weighted regression.
Revised:  The quantile first-stage is analogue to the least squares case, i.e., a conditional mean regression of the endogenous variables on the instruments, with the difference that for the QR case is a weighted regression.
Edit intent: meaning-changed
Original:  The  first-stage is analogue to the least squares case, i.e., a conditional mean regression of the endogenous variables on the instruments, with the difference that for the QR case is a weighted regression.
Revised:  The first-stage is analogous to the least squares case, i.e., a conditional mean regression of the endogenous variables on the instruments, with the difference that for the QR case is a weighted regression.
Edit intent: fluency
Original:  The  first-stage is analogue to the least squares case, i.e., a conditional mean regression of the endogenous variables on the instruments, with the difference that for the QR case is a weighted regression.
Revised:  The first-stage is analogue to the least squares case, i.e., a conditional mean regression of the endogenous variables on the instruments, with the difference that the QR case is a weighted regression.
Edit intent: clarity
Original:   The first-stage regression is a natural framework to evaluate the validity of instruments . Thus, we are able to use the first-stage result and suggest testing procedures to evaluate the adequacy of instruments in IVQR models by evaluating their statistical significance .
Revised:  The first-stage regression is a natural framework to evaluate the validity of instruments , and in particular, the validity of the Jacobian identification conditions. Hence, we suggest testing procedures to evaluate the adequacy of instruments in IVQR models by evaluating their statistical significance .
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Original:  Thus, we are able to use the first-stage result and suggest testing procedures to evaluate the adequacy of instruments in IVQR models by evaluating their statistical significance . In the QR case, the instruments may be relevant at some quantiles but not at others or at the mean . Monte Carlo experiments provide numerical evidence that the proposed tests work as expected in terms of empirical size and power in finite samples.
Revised:  Thus, we are able to use the first-stage result and suggest testing procedures to evaluate the adequacy of instruments in IVQR models by evaluating their statistical significance using the first-stage result. This procedure may be specially useful in QR since the instruments may be relevant at some quantiles but not at others or at the mean . Monte Carlo experiments provide numerical evidence that the proposed tests work as expected in terms of empirical size and power in finite samples.
Edit intent: coherence
Original:  In the QR case, the instruments may be relevant at some quantiles but not at others or at the mean . Monte Carlo experiments provide numerical evidence that the proposed tests work as expected in terms of empirical size and power in finite samples.
Revised:  In the QR case, the instruments may be relevant at some quantiles but not at others , which indicates the use of weak-identification robust inference . Monte Carlo experiments provide numerical evidence that the proposed tests work as expected in terms of empirical size and power in finite samples.
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Original:  We illustrate the performance of the plug-in efficient estimator in simulations and in an application to Wood et al. (2020a  )'s study of the staggered rollout of a procedural justice training program for police officers.
Revised:  We illustrate the performance of the plug-in efficient estimator in simulations and in an application to Wood et al. (2020a ,b )'s study of the staggered rollout of a procedural justice training program for police officers.
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Original:  The efficient estimator is not feasible in practice because it requires knowledge of the optimal weights to be placed on pre-treatment outcomes. However, the optimal weights can be estimated from the data, and in large datasets the plug-in estimator that uses the estimated weights has similar properties to the "oracle" efficient estimator .
Revised:  The efficient estimator is asymptotically unbiased with efficiency (weakly) dominating that of existing approaches. We provide both t-based and permutation-test based methods for inference .
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Original:  We illustrate the performance of the plug-in efficient estimator in simulations and in an application to Wood et al. (2020a ,b )'s study of the staggered rollout of a procedural justice training program for police officers.
Revised:  We illustrate the performance of the plug-in efficient estimator in simulations and in an application to Wood et al. (2020a )'s study of the staggered rollout of a procedural justice training program for police officers.
Edit intent: coherence
Original:  Our improved baseline model, incorporated with entity representations with type markersand confidence-based classification for enhanced NA instance detection , achieves an F1 of 75.0\% on TACRED, significantly outperforms previous SOTA methods  .
Revised:  Our improved baseline model, incorporated with entity representations with typed markers , achieves an F1 of 75.0\% on TACRED, significantly outperforms previous SOTA methods .
Edit intent: clarity
Original:  Our improved baseline model, incorporated with entity representations with type markersand confidence-based classification for enhanced NA instance detection , achieves an F1 of 75.0\% on TACRED, significantly outperforms previous SOTA methods  .
Revised:  Our improved baseline model, incorporated with entity representations with type markersand confidence-based classification for enhanced NA instance detection , achieves an F1 of 74.6 on TACRED, significantly outperforms previous SOTA methods .
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Original:  Our improved baseline model, incorporated with entity representations with type markersand confidence-based classification for enhanced NA instance detection , achieves an F1 of 75.0\% on TACRED, significantly outperforms previous SOTA methods  .
Revised:  Our improved baseline model, incorporated with entity representations with type markersand confidence-based classification for enhanced NA instance detection , achieves an F1 of 75.0\% on TACRED, significantly outperforms previous SOTA methods . Furthermore, the presented new baseline achieves an F1 of 91.1\% on the refined Re-TACRED dataset, demonstrating that the pre-trained language models achieve unexpectedly high performance on this task. We release our code to the community for future research .
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Original: Based on some analytic structural properties of the Gini and Kolkata indices for social inequality, as obtained from a generic form of the Lorenz function, and some more new observations on the citation statistics of individual authors (including Nobel laureates), we make a conjecture that about 14\\%DIF < of people or papers or social conflicts tend to earn or attract or cause about 86\\% of wealth or citations or deaths in very competitive situations in markets, universities or wars.
Revised: Based on some analytic structural properties of the Gini and Kolkata indices for social inequality, as obtained from a generic form of the Lorenz function, we make a conjecture that the limiting (effective saturation) value of the above-mentioned indices is about 0.865. This, together with some more new observations on the citation statistics of individual authors (including Nobel laureates), we make a conjecture that about 14\\%DIF < of people or papers or social conflicts tend to earn or attract or cause about 86\\% of wealth or citations or deaths in very competitive situations in markets, universities or wars.
Edit intent: meaning-changed
Original: Based on some analytic structural properties of the Gini and Kolkata indices for social inequality, as obtained from a generic form of the Lorenz function, and some more new observations on the citation statistics of individual authors (including Nobel laureates), we make a conjecture that about 14\\%DIF < of people or papers or social conflicts tend to earn or attract or cause about 86\\% of wealth or citations or deaths in very competitive situations in markets, universities or wars.
Revised: Based on some analytic structural properties of the Gini and Kolkata indices for social inequality, as obtained from a generic form of the Lorenz function, and some more new observations on the citation statistics of individual authors (including Nobel laureates), suggests that about 14\\%DIF < of people or papers or social conflicts tend to earn or attract or cause about 86\\% of wealth or citations or deaths in very competitive situations in markets, universities or wars.
Edit intent: clarity
Original: In this paper, we present a new open source, production first  and production ready  end-to-end (E2E) speech recognition toolkit named WeNet .
Revised: In this paper, we present a new open source, production first and production ready speech recognition toolkit called WeNet in which a new two-pass approach is implemented to unify streaming and non-streaming end-to-end (E2E) speech recognition toolkit named WeNet .
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Original:  This paper introduces WeNet from three aspects, including model architecture, framework design and performance metrics.
Revised:  In our toolkit, a new two-pass method is implemented. Our method propose a dynamic chunk-based attention strategy of the the transformer layers to allow arbitrary right context length modifies in hybrid CTC/attention architecture. The inference latency could be easily controlled by only changing the chunk size. The CTC hypotheses are then rescored by the attention decoder to get the final result.
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Original:  Our experiments on  AISHELL-1 using WeNet, not only give a promising character error rate (CER) on a unified streaming and non-streaming two pass (U2) E2E model but also show reasonable RTF and latency, both of these aspects are favored for production adoption.
Revised:  Our experiments on the AISHELL-1 using WeNet, not only give a promising character error rate (CER) on a unified streaming and non-streaming two pass (U2) E2E model but also show reasonable RTF and latency, both of these aspects are favored for production adoption.
Edit intent: fluency
Original:  Our experiments on  AISHELL-1 using WeNet, not only give a promising character error rate (CER) on a unified streaming and non-streaming two pass (U2) E2E model but also show reasonable RTF and latency, both of these aspects are favored for production adoption. The toolkit is publicly available at URL\\%DIF > relative character error rate (CER) reduction in non-streaming ASR compared to a standard non-streaming transformer.
Revised:  Our experiments on AISHELL-1 dataset using WeNet show that, our model achieves 5.03\\%DIF > relative character error rate (CER) reduction in non-streaming ASR compared to a standard non-streaming transformer.
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Original:  By now only little work focuses on the compactness of the provided state, which is a necessary condition to successfully and efficiently train a navigation agent. Our approach works in three stages. First, we propose a novel preprocessing of the distance measurements and compute a local , egocentric, binary grid map based on the current range measurements. We then autoencode the local map using a variational autoencoder, where the latent space serves as state representation . An important key for a compact and , at the same time, meaningful representation is the degree of disentanglement, which describes the correlation between each latent dimension. Therefore, we finally apply state-of-the-art disentangling methods to improve the representation power . Furthermore, we investige the possibilities of incorporating time-dependent information into the latent space. In particular, we incorporate the relation of consecutive scans, especially ego-motion, by applying a memory model.
Revised:  To achieve high reconstruction power of our autoencoding pipeline, we propose an - in the context of autoencoding 2D-lidar scans - novel preprocessing into a local binary occupancy image. This has no additional requirements, neither self-localization nor robust mapping, and therefore can be applied in any setting and easily transferred from simulation in real-world. In a second stage, we show the usage of the compact state representation generated by our autoencoding pipeline in a simplistic navigation task and expose the pitfall that increased reconstruction power will always lead to an improved performance.
Edit intent: coherence
Original:  The experiments show the capability of our approach to highly compress lidar data, maintain a meaningful distribution of the latent space, and even incorporate time-depended information .
Revised:  The experiments show the capability of our approach for 2D-lidar scans w.r.t. the state of the art. However, as we demonstrate in the experiments the impact on reinforcement learning in lidar-based navigation tasks is non-predictable when improving the latent state representation generated by an autoencoding pipeline. This is surprising and needs to be taken into account during the process of optimizing a pretrained autoencoder for reinforcement learning tasks .
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Original:  Meanwhile, a growing body of evidence suggests that some language models can (1) store a large amount of knowledge in their parameters, and (2) perform inference over tasks in unstructured text to solve new tasks at test time.
Revised:  Meanwhile, a growing body of evidence suggests that some language models can (1) store a large amount of knowledge in their parameters, and (2) perform inference over tasks in textual inputs at test time.
Edit intent: clarity
Original:  In order to carefully control important properties of the data and explanations, we introduce a synthetic dataset for experiments, and we also make use of three existing datasets with explanations: e-SNLI, TACRED,  SemEval.
Revised:  In order to carefully control important properties of the data and explanations, we introduce a synthetic dataset for experiments, and we also make use of three existing datasets with explanations: e-SNLI, TACRED, and SemEval.
Edit intent: fluency
Original:  This problem has been considered in several articles in the last years. We focus on its kernelization , which had been almost unexplored so far. We prove that MMVC does not admit polynomial kernels parameterized by the size of a minimum vertex cover, even on bipartite graphs, unless%DIFDELCMD < {\sf %%% NP \subseteq%DIFDELCMD < {\sf %%% coNP /%DIFDELCMD < {\sf %%% poly .
Revised:  Motivated by the kernelization of MMVC with parameter k, our main contribution is to introduce a simple general framework to obtain lower bounds on the degrees of a certain type of polynomial kernels for vertex optimization problems, which we call lop-kernels. Informally, this type of kernels is required to preserve large optimal solutions in the reduced instance, and captures the vast majority of existing kernels in the literature. As a consequence of this framework, we show that the trivial quadratic kernel for MMVC is essentially optimal, answering a minimum vertex cover, even on bipartite graphs, unless%DIFDELCMD < {\sf %%% NP \subseteq%DIFDELCMD < {\sf %%% coNP /%DIFDELCMD < {\sf %%% poly .
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Original:  We prove that MMVC does not admit polynomial kernels parameterized by the size of a minimum vertex cover, even on bipartite graphs, unless%DIFDELCMD < {\sf %%% NP \subseteq%DIFDELCMD < {\sf %%% coNP /%DIFDELCMD < {\sf %%% poly .
Revised:  We prove that MMVC does not admit polynomial kernels parameterized by the size of a %DIFDELCMD < {\sf %%% NP \subseteq%DIFDELCMD < {\sf %%% coNP /%DIFDELCMD < {\sf %%% poly .
Edit intent: clarity
Original:  We prove that MMVC does not admit polynomial kernels parameterized by the size of a minimum vertex cover, even on bipartite graphs, unless%DIFDELCMD < {\sf %%% NP \subseteq%DIFDELCMD < {\sf %%% coNP /%DIFDELCMD < {\sf %%% poly .
Revised:  We prove that MMVC does not admit polynomial kernels parameterized by the size of a minimum vertex cover, even on bipartite graphs, unless%DIFDELCMD < {\sf %%% \subseteq%DIFDELCMD < {\sf %%% coNP /%DIFDELCMD < {\sf %%% poly .
Edit intent: coherence
Original:  We prove that MMVC does not admit polynomial kernels parameterized by the size of a minimum vertex cover, even on bipartite graphs, unless%DIFDELCMD < {\sf %%% NP \subseteq%DIFDELCMD < {\sf %%% coNP /%DIFDELCMD < {\sf %%% poly .
Revised:  We prove that MMVC does not admit polynomial kernels parameterized by the size of a minimum vertex cover, even on bipartite graphs, unless%DIFDELCMD < {\sf %%% NP %DIFDELCMD < {\sf %%% coNP /%DIFDELCMD < {\sf %%% poly .
Edit intent: meaning-changed