wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Then, the proposed score is applied to assess and compare density forecasts of macroeconomic relevant datasets ( unemployment rate ) and of commodity prices (oil and electricity prices) with a particular focus on the recent COVID crisis period.
Revised:  Then, the proposed score is applied to assess and compare density forecasts of macroeconomic relevant datasets ( unemployment rate ) and of commodity prices (oil and electricity prices) with particular focus on the recent COVID crisis period.
Edit intent: fluency
Original:  Then, the proposed score is applied to assess and compare density forecasts of macroeconomic relevant datasets ( unemployment rate ) and of commodity prices (oil and electricity prices) with a particular focus on the recent COVID crisis period.
Revised:  Then, the proposed score is applied to assess and compare density forecasts of macroeconomic relevant datasets ( unemployment rate ) and of commodity prices (oil and electricity prices) with a particular focus on the recent COVID-19 crisis period.
Edit intent: meaning-changed
Original:  Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 5.7 / 10.1 WER on the noisy/clean test sets of Librispeech.
Revised:  Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 5.2 / 10.1 WER on the noisy/clean test sets of Librispeech.
Edit intent: meaning-changed
Original:  Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 5.7 / 10.1 WER on the noisy/clean test sets of Librispeech.
Revised:  Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 5.7 / 8.6 WER on the noisy/clean test sets of Librispeech.
Edit intent: meaning-changed
Original:  Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 5.2 / 8.6 WERon the noisy/clean test sets of Librispeech .
Revised:  Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8 / 8.6 WERon the noisy/clean test sets of Librispeech .
Edit intent: meaning-changed
Original:  Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 5.2 / 8.6 WERon the noisy/clean test sets of Librispeech .
Revised:  Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 5.2 / 8.2 WER .
Edit intent: meaning-changed
Original:  Very recently, this computational overhead was mitigated by the works of [ZMG19,CGH+19 , yielding an O( Mn ^2)-time second-order algorithm for training  overparametrized neural networks with M parameters .
Revised:  Very recently, this computational overhead was mitigated by the works of [ZMG19,CGH+19 , yielding an O( mn ^2)-time second-order algorithm for training overparametrized neural networks with M parameters .
Edit intent: fluency
Original:  Very recently, this computational overhead was mitigated by the works of [ZMG19,CGH+19 , yielding an O( Mn ^2)-time second-order algorithm for training  overparametrized neural networks with M parameters .
Revised:  Very recently, this computational overhead was mitigated by the works of [ZMG19,CGH+19 , yielding an O( Mn ^2)-time second-order algorithm for training two-layer overparametrized neural networks with M parameters .
Edit intent: meaning-changed
Original:  Very recently, this computational overhead was mitigated by the works of [ZMG19,CGH+19 , yielding an O( Mn ^2)-time second-order algorithm for training  overparametrized neural networks with M parameters .
Revised:  Very recently, this computational overhead was mitigated by the works of [ZMG19,CGH+19 , yielding an O( Mn ^2)-time second-order algorithm for training overparametrized neural networks of polynomial width m .
Edit intent: meaning-changed
Original:  We show how to speed up the algorithm of [CGH+19], achieving an O( Mn )-time backpropagation algorithm for training (mildly overparametrized) ReLU networks, which is near-linear in the dimension ( Mn ) of the full gradient (Jacobian) matrix.
Revised:  We show how to speed up the algorithm of [CGH+19], achieving an O( mn )-time backpropagation algorithm for training (mildly overparametrized) ReLU networks, which is near-linear in the dimension ( Mn ) of the full gradient (Jacobian) matrix.
Edit intent: fluency
Original:  We show how to speed up the algorithm of [CGH+19], achieving an O( Mn )-time backpropagation algorithm for training (mildly overparametrized) ReLU networks, which is near-linear in the dimension ( Mn ) of the full gradient (Jacobian) matrix.
Revised:  We show how to speed up the algorithm of [CGH+19], achieving an O( Mn )-time backpropagation algorithm for training (mildly overparametrized) ReLU networks, which is near-linear in the dimension ( mn ) of the full gradient (Jacobian) matrix.
Edit intent: fluency
Original:  Our result provides a proof-of-concept that advanced machinery from randomized linear algebra-which led to recent breakthroughs in \mathit{convex} \mathit{optimization} (ERM, LPs, Regression) -can be carried over to the realm of deep learning as well.
Revised:  Our result provides a proof-of-concept that advanced machinery from randomized linear algebra -- which led to recent breakthroughs in \mathit{convex} \mathit{optimization} (ERM, LPs, Regression) -can be carried over to the realm of deep learning as well.
Edit intent: fluency
Original:  Our result provides a proof-of-concept that advanced machinery from randomized linear algebra-which led to recent breakthroughs in \mathit{convex} \mathit{optimization} (ERM, LPs, Regression) -can be carried over to the realm of deep learning as well.
Revised:  Our result provides a proof-of-concept that advanced machinery from randomized linear algebra-which led to recent breakthroughs in \mathit{convex} \mathit{optimization} (ERM, LPs, Regression) -- can be carried over to the realm of deep learning as well.
Edit intent: fluency
Original:  To address this lack of comprehensive survey of existing MRC tasks, evaluation metrics  and datasets, herein, (1) we analyzed 57 MRC tasks and datasets ; proposed a more precise classification method of MRC tasks with 4 different attributes  (2) we summarized 9 evaluation metrics of MRC tasks and (3) 7 attributes and 10 characteristics of MRC datasets;
Revised:  To address this lack of comprehensive survey of existing MRC tasks, evaluation metrics and datasets, herein, (1) we analyzed 57 MRC tasks and datasets and propose a more precise classification method of MRC tasks with 4 different attributes (2) we summarized 9 evaluation metrics of MRC tasks and (3) 7 attributes and 10 characteristics of MRC datasets;
Edit intent: coherence
Original:  proposed a more precise classification method of MRC tasks with 4 different attributes  (2) we summarized 9 evaluation metrics of MRC tasks and (3) 7 attributes and 10 characteristics of MRC datasets;
Revised:  proposed a more precise classification method of MRC tasks with 4 different attributes ; (2) we summarized 9 evaluation metrics of MRC tasks and (3) 7 attributes and 10 characteristics of MRC datasets;
Edit intent: fluency
Original:  proposed a more precise classification method of MRC tasks with 4 different attributes  (2) we summarized 9 evaluation metrics of MRC tasks and (3) 7 attributes and 10 characteristics of MRC datasets;
Revised:  proposed a more precise classification method of MRC tasks with 4 different attributes (2) we summarized 9 evaluation metrics of MRC tasks , 7 attributes and 10 characteristics of MRC datasets;
Edit intent: coherence
Original:  In addition, to help the community, we have URLanized, and published our data on a companion URL where MRC researchers could directly access each MRC dataset, papers, baseline projects and browse the leaderboard.
Revised:  In addition, to help the community, we have URLanized, and published our data on the companion URL where MRC researchers could directly access each MRC dataset, papers, baseline projects and browse the leaderboard.
Edit intent: fluency
Original:  In addition, to help the community, we have URLanized, and published our data on a companion URL where MRC researchers could directly access each MRC dataset, papers, baseline projects and browse the leaderboard.
Revised:  In addition, to help the community, we have URLanized, and published our data on a companion URL where MRC researchers could directly access each MRC dataset, papers, baseline projects , and the leaderboard.
Edit intent: coherence
Original:  We demonstrate this feasibility of our approach using a suite of seven models to describe a spread of infectious diseases under different scenarios: wearing masks, infection via fomites and prevention by hand-washing, the concept of vector-borne diseases, testing , contact and tracing interventions, disease propagation within motif-structured populations with shared environments such as schools, and superspreading events. schools, and superspreading events.
Revised:  We demonstrate this feasibility of our approach using a suite of seven models to describe a spread of infectious diseases under different scenarios: wearing masks, infection via fomites and prevention by hand-washing, the concept of vector-borne diseases, testing and contact tracing interventions, disease propagation within motif-structured populations with shared environments such as schools, and superspreading events. schools, and superspreading events.
Edit intent: fluency
Original:  We demonstrate this feasibility of our approach using a suite of seven models to describe a spread of infectious diseases under different scenarios: wearing masks, infection via fomites and prevention by hand-washing, the concept of vector-borne diseases, testing , contact and tracing interventions, disease propagation within motif-structured populations with shared environments such as schools, and superspreading events. schools, and superspreading events.
Revised:  We demonstrate this feasibility of our approach using a suite of seven models to describe a spread of infectious diseases under different scenarios: wearing masks, infection via fomites and prevention by hand-washing, the concept of vector-borne diseases, testing , contact and tracing interventions, disease propagation within motif-structured populations with shared environments such as schools, and superspreading events.
Edit intent: fluency
Original: R^d \rightarrow R ^d . We then show how to design several stochastic second order optimization methods by re-writing the optimization problem of interest as a system of nonlinear equations and applying SNR. For instance, by applying SNR to find a stationary point of a generalized linear model (GLM), we derive completely new and scalable stochastic second order methods.
Revised: R^d \rightarrow R ^n . We then show how to design several stochastic second order optimization methods by re-writing the optimization problem of interest as a system of nonlinear equations and applying SNR. For instance, by applying SNR to find a stationary point of a generalized linear model (GLM), we derive completely new and scalable stochastic second order methods.
Edit intent: meaning-changed
Original:  is a special case of SNR, and use this connection to establish the first global convergence theory of SNM. Indeed, by showing that SNR can be interpreted as a variant of the stochastic gradient descent (SGD)
Revised:  is a special case of SNR, and use this connection to establish the first global convergence theory of SNM. We establish the global convergence of SNR by showing that SNR can be interpreted as a variant of the stochastic gradient descent (SGD)
Edit intent: coherence
Original:  is a special case of SNR, and use this connection to establish the first global convergence theory of SNM. Indeed, by showing that SNR can be interpreted as a variant of the stochastic gradient descent (SGD)
Revised:  is a special case of SNR, and use this connection to establish the first global convergence theory of SNM. Indeed, by showing that it is a variant of the stochastic gradient descent (SGD)
Edit intent: clarity
Original:  method we are able to leverage proof techniques of SGD and establish a global convergence theory and rates of convergence for SNR . As a special case, our theory also provides a new global convergence theory for the original Newton-Raphson method under strictly weaker assumptions as compared to what is commonly used for global convergence .
Revised:  method , and then leveraging proof techniques of SGD and establish a global convergence theory and rates of convergence for SNR . As a special case, our theory also provides a new global convergence theory for the original Newton-Raphson method under strictly weaker assumptions as compared to what is commonly used for global convergence .
Edit intent: coherence
Original:  method we are able to leverage proof techniques of SGD and establish a global convergence theory and rates of convergence for SNR . As a special case, our theory also provides a new global convergence theory for the original Newton-Raphson method under strictly weaker assumptions as compared to what is commonly used for global convergence .
Revised:  method we are able to leverage proof techniques of SGD . As a special case, our theory also provides a new global convergence theory for the original Newton-Raphson method under strictly weaker assumptions as compared to what is commonly used for global convergence .
Edit intent: clarity
Original:  method we are able to leverage proof techniques of SGD and establish a global convergence theory and rates of convergence for SNR . As a special case, our theory also provides a new global convergence theory for the original Newton-Raphson method under strictly weaker assumptions as compared to what is commonly used for global convergence . There are many ways to re-write an optimization problem as nonlinear equations. Each re-write would lead to a distinct method when using SNR. As such, we believe that SNR and its global convergence theorywill open the way to designing and analysing a host of new stochastic second order methods .
Revised:  method we are able to leverage proof techniques of SGD and establish a global convergence theory and rates of convergence for SNR . As a special case, our theory also provides a new global convergence theory for the original Newton-Raphson method under strictly weaker assumptions as compared to the classic monotone convergence theory .
Edit intent: meaning-changed
Original: Monitoring the evolving impacts of non-pharmaceutical intervention measures requires fine-grained estimation of transmission dynamics. We propose a framework to estimate instantaneous reproduction number R_t using Bayesian inference upon a renewal process, disentangling the R_t reduction into mitigation and suppression factors for quantifying their impacts at a finer granularity.
Revised: Epidemic models play a key role in understanding and responding to the emerging COVID-19 pandemic. Widely used compartmental models are static and are of limited use to evaluate intervention strategies with the emerging pandemic. Applying the technology of data assimilation, we propose a Bayesian updating approach for estimating epidemiological parameters using observable information for the purpose of assessing the impacts of different intervention strategies. We adopt a concise renewal model and propose new parameters by disentangling the reduction of instantaneous reproduction number Rt into mitigation and suppression factors for quantifying their impacts at a finer granularity.
Edit intent: meaning-changed
Original:  We propose a framework to estimate instantaneous reproduction number R_t using Bayesian inference upon a renewal process, disentangling the R_t reduction into mitigation and suppression factors for quantifying their impacts at a finer granularity.
Revised:  We propose a framework to estimate instantaneous reproduction number R_t using Bayesian inference upon a renewal process, disentangling the R_t reduction into mitigation and suppression factors for quantifying intervention impacts at a finer granularity.
Edit intent: clarity
Original:   Investigating the impacts of intervention measures of European countries, the United States and Wuhan with the framework, we reveal the effects of interventions in Europe and alert that 30 states in the United States are facing resurgence risks .
Revised:  Then we developed a data assimilation framework for estimating these parameters including constructing an observation function and developing a Bayesian updating scheme. A statistical analysis framework is then built to quantify the impact of intervention strategies by monitoring the evolution of these estimated parameters. By Investigating the impacts of intervention measures of European countries, the United States and Wuhan with the framework, we reveal the effects of interventions in Europe and alert that 30 states in the United States are facing resurgence risks .
Edit intent: meaning-changed
Original:   Investigating the impacts of intervention measures of European countries, the United States and Wuhan with the framework, we reveal the effects of interventions in Europe and alert that 30 states in the United States are facing resurgence risks .
Revised:  Investigating the impacts of intervention measures of European countries, the United States and Wuhan with the framework, we reveal the effects of interventions in these countries and the resurgence risk in the USA .
Edit intent: clarity
Original:  This study also analysed the sentimental dynamics delivered by the hot topics in Twitter such as government policies (e.g. the Australia's JobKeeper program, lock-down , social-distancing) as well as the focused social events (e.g. the Ruby Princess Cruise).
Revised:  This study also analysed the sentimental dynamics delivered by the hot topics in Twitter such as government policies (e.g. the Australia's JobKeeper program, lockdown , social-distancing) as well as the focused social events (e.g. the Ruby Princess Cruise).
Edit intent: fluency
Original:  Three growth regimes (quadratic, power-law and exponential) emerge naturally from our simulations , which suggest that the  containment of the disease within China was close to the quadratic limit and could not have been significantly improved upon.
Revised:  Three growth regimes (quadratic, power-law and exponential) emerge naturally from our simulations . These results suggest that the containment of the disease within China was close to the quadratic limit and could not have been significantly improved upon.
Edit intent: clarity
Original:  Three growth regimes (quadratic, power-law and exponential) emerge naturally from our simulations , which suggest that the  containment of the disease within China was close to the quadratic limit and could not have been significantly improved upon.
Revised:  Three growth regimes (quadratic, power-law and exponential) emerge naturally from our simulations , which suggest that the early containment of the disease within China was close to the quadratic limit and could not have been significantly improved upon.
Edit intent: clarity
Original:  Three growth regimes (quadratic, power-law and exponential) emerge naturally from our simulations , which suggest that the  containment of the disease within China was close to the quadratic limit and could not have been significantly improved upon.
Revised:  Three growth regimes (quadratic, power-law and exponential) emerge naturally from our simulations , which suggest that the containment of the disease within China was close to optimal and could not have been significantly improved upon.
Edit intent: clarity
Original: Recent analysis of  COVID-19 data from China showed that the number of confirmed cases followed a subexponential power-law increase, with a growth exponent of around 2.2 {\it } {\bf   .
Revised: Recent analysis of early COVID-19 data from China showed that the number of confirmed cases followed a subexponential power-law increase, with a growth exponent of around 2.2 {\it } {\bf .
Edit intent: meaning-changed
Original: Recent analysis of  COVID-19 data from China showed that the number of confirmed cases followed a subexponential power-law increase, with a growth exponent of around 2.2 {\it } {\bf   .
Revised: Recent analysis of COVID-19 data from China showed that the number of confirmed cases followed a subexponential power-law increase, with a growth exponent of around 2.2 B.\,F.~Maier, D.~Brockmann,{\it } {\bf .
Edit intent: meaning-changed
Original: Recent analysis of  COVID-19 data from China showed that the number of confirmed cases followed a subexponential power-law increase, with a growth exponent of around 2.2 {\it } {\bf   .
Revised: Recent analysis of COVID-19 data from China showed that the number of confirmed cases followed a subexponential power-law increase, with a growth exponent of around 2.2 {\it Science} {\bf .
Edit intent: meaning-changed
Original: Recent analysis of  COVID-19 data from China showed that the number of confirmed cases followed a subexponential power-law increase, with a growth exponent of around 2.2 {\it } {\bf   .
Revised: Recent analysis of COVID-19 data from China showed that the number of confirmed cases followed a subexponential power-law increase, with a growth exponent of around 2.2 {\it } {\bf 368 .
Edit intent: meaning-changed
Original: Recent analysis of  COVID-19 data from China showed that the number of confirmed cases followed a subexponential power-law increase, with a growth exponent of around 2.2 {\it } {\bf   .
Revised: Recent analysis of COVID-19 data from China showed that the number of confirmed cases followed a subexponential power-law increase, with a growth exponent of around 2.2 {\it } {\bf , 742 (2020) .
Edit intent: meaning-changed
Original:  This network is built with Floor (\lfloor x\rfloor) or ReLU (\max\{0,x\}) activation function in each neuron and hence we call such networks as Floor-ReLU networks.
Revised:  This network is built with Floor (\lfloor x\rfloor) or ReLU (\max\{0,x\}) activation function in each neuron and hence we call such networks Floor-ReLU networks.
Edit intent: fluency
Original:  {For any hyper-parameters N\inN^+ and L\inN^+,} it is shown that Floor-ReLU networks with  width \max\{d,\, 5N+13\} and  depth 64dL+3 can {uniformly approximate a H{\"o}lder function f on [0,1]^d with an approximation rate 3\lambda d^{\alpha/2}N^{-\alphaL}, where \alpha \in(0,1] and \lambda are the H{\"o}lder order and constant, respectively.
Revised:  {For any hyper-parameters N\inN^+ and L\inN^+,} it is shown that Floor-ReLU networks with a width \max\{d,\, 5N+13\} and depth 64dL+3 can {uniformly approximate a H{\"o}lder function f on [0,1]^d with an approximation rate 3\lambda d^{\alpha/2}N^{-\alphaL}, where \alpha \in(0,1] and \lambda are the H{\"o}lder order and constant, respectively.
Edit intent: fluency
Original:  {For any hyper-parameters N\inN^+ and L\inN^+,} it is shown that Floor-ReLU networks with  width \max\{d,\, 5N+13\} and  depth 64dL+3 can {uniformly approximate a H{\"o}lder function f on [0,1]^d with an approximation rate 3\lambda d^{\alpha/2}N^{-\alphaL}, where \alpha \in(0,1] and \lambda are the H{\"o}lder order and constant, respectively.
Revised:  {For any hyper-parameters N\inN^+ and L\inN^+,} it is shown that Floor-ReLU networks with width \max\{d,\, 5N+13\} and a depth 64dL+3 can {uniformly approximate a H{\"o}lder function f on [0,1]^d with an approximation rate 3\lambda d^{\alpha/2}N^{-\alphaL}, where \alpha \in(0,1] and \lambda are the H{\"o}lder order and constant, respectively.
Edit intent: fluency