wanyu/IteraTeR_full_sent huggingface dataset:
Original: There are three major contributions and break-through: 1.As the advancement of deep learning technology, there have been tremendous innovations in the NLP field, yet the traditional topic modeling as one of the NLP applications lag behind the tide of deep learning.
Revised: There are three major break-through: 1.As the advancement of deep learning technology, there have been tremendous innovations in the NLP field, yet the traditional topic modeling as one of the NLP applications lag behind the tide of deep learning.
Edit intent: clarity
Original:  In the methodology and technical perspective, we adopt transfer learning to fine-tune a BERT-based multiclassification system to categorize the main topics and then utilize the novel PSHTI model to infer the sub-topics under the predicted main topics .2.
Revised:  In the methodology and technical perspective, we adopt transfer learning to fine-tune a BERT-based multiclassification system to categorize the main topics and then utilize the novel PSHTI model to infer the sub-topics under the predicted main topics . 2.
Edit intent: fluency
Original:  The traditional unsupervised learning-based topic models or clustering methods suffer from the difficulty of automatically generating a meaningful topic label, but our system enables mapping the top words to the self-help issues by utilizing domain knowledge about the product through web-crawling .3.
Revised:  The traditional unsupervised learning-based topic models or clustering methods suffer from the difficulty of automatically generating a meaningful topic label, but our system enables mapping the top words to the self-help issues by utilizing domain knowledge about the product through web-crawling . 3.
Edit intent: fluency
Original:  Using a recent result on inf-convolution of Range-Value-at-Risk, which includes Value-at-Risk and Expected Shortfall as special cases, we establish new analytical bounds  which we call convolution bounds.
Revised:  Using a recent result on inf-convolution of Range-Value-at-Risk, which includes Value-at-Risk and Expected Shortfall as special cases, we establish new analytical bounds for quantile aggregation which we call convolution bounds.
Edit intent: meaning-changed
Original:  To the best of our knowledge, there is no other theoretical result on quantile aggregation which is not covered by the convolution bounds, and thus the convolution bounds are genuinely the best one available. The results can be applied to compute bounds on the distribution of the sum of random variables . Some applications to operations researchare discussed .
Revised:  The results directly lead to bounds on the distribution of the sum of random variables . Some applications to operations researchare discussed .
Edit intent: clarity
Original:  We use two different methods to calculate the expectations E[X_{1}^{2}f(X)] for any measurable function f satisfying some regular conditions. Applying this result, we obtain recursion formulae for product moments of multivariate Gaussian random variablesand multivariate elliptical random variables ^{2}f(X)}]  .
Revised:  We use two different methods to calculate the expectations E[X_{1}^{2}f(X)] for any measurable function f satisfying some regularity conditions. Then, by applying this result, we obtain recursion formulae for product moments of multivariate Gaussian random variablesand multivariate elliptical random variables ^{2}f(X)}] .
Edit intent: coherence
Original:  Applying this result, we obtain recursion formulae for product moments of multivariate Gaussian random variablesand multivariate elliptical random variables ^{2}f(X)}]  .
Revised:  Applying this result, we obtain recursion formulae for product moments of multivariate Gaussian random variablesand multivariate elliptical random variables X_{1^{2}f(X)}] .
Edit intent: meaning-changed
Original:  Applying this result, we obtain recursion formulae for product moments of multivariate Gaussian random variablesand multivariate elliptical random variables ^{2}f(X)}]  .
Revised:  Applying this result, we obtain recursion formulae for product moments of multivariate Gaussian random variablesand multivariate elliptical random variables ^{2}f(X)}] for multivariate Student-t, logistic and Laplace distributions .
Edit intent: meaning-changed
Original:  Scientists may wish to quantify features of the growth and equilibrium periods, as well as the timing of the change point between the two . Specifically, we are motivated by problems in the study of electrical cell-substrate impedance sensing (ECIS) data.
Revised:  Scientists may wish to quantify features of the two periods, as well as the timing of the change point between the two . Specifically, we are motivated by problems in the study of electrical cell-substrate impedance sensing (ECIS) data.
Edit intent: clarity
Original:  Scientists may wish to quantify features of the growth and equilibrium periods, as well as the timing of the change point between the two . Specifically, we are motivated by problems in the study of electrical cell-substrate impedance sensing (ECIS) data.
Revised:  Scientists may wish to quantify features of the growth and equilibrium periods, as well as the timing of the change point . Specifically, we are motivated by problems in the study of electrical cell-substrate impedance sensing (ECIS) data.
Edit intent: clarity
Original:  In this paper, we assume that measurements during the growth period are independent deviations from a smooth nonlinear function of time, and that measurements during the equilibrium period are characterized by a simple long memory model.
Revised:  In this paper, we assume that measurements during the trend period are independent deviations from a smooth nonlinear function of time, and that measurements during the equilibrium period are characterized by a simple long memory model.
Edit intent: clarity
Original: In this paper we consider the following optimal stopping problem V^{\omega}_{ \rm A }(s) = \tau\in\mathcal{T} E_{s}[e^{-\int_0^\tau \omega(S_w) dw} g(S_\tau)], where the process S_t is a jump-diffusion process, T is a family of stopping times and g and \omega are fixed payoff function and discounting function, respectively.
Revised: In this paper we consider the following optimal stopping problem V^{\omega}_{ \rm A }(s) = \tau\in\mathcal{T} E_{s}[e^{-\int_0^\tau \omega(S_w) dw} g(S_\tau)], where the process S_t is a jump-diffusion process, T is a family of stopping times while g and \omega are fixed payoff function and discounting function, respectively.
Edit intent: coherence
Original: In this paper we consider the following optimal stopping problem V^{\omega}_{ \rm A }(s) = \tau\in\mathcal{T} E_{s}[e^{-\int_0^\tau \omega(S_w) dw} g(S_\tau)], where the process S_t is a jump-diffusion process, T is a family of stopping times and g and \omega are fixed payoff function and discounting function, respectively.
Revised: In this paper we consider the following optimal stopping problem V^{\omega}_{ \rm A }(s) = \tau\in\mathcal{T} E_{s}[e^{-\int_0^\tau \omega(S_w) dw} g(S_\tau)], where the process S_t is a jump-diffusion process, T is a family of stopping times and g and \omega are fixed payoff function and discount function, respectively.
Edit intent: fluency
Original:  We conclude their randomized, double-blind, placebo-controlled trial present consistent statistical evidence, at 99\% confidence level, that the treatment of Covid-19 patients with hydroxychloroquine is effective in reducing the appearance of symptoms if used from 0 to 2 days after exposure to virus with estimated relative reduction in symptomatic outcomes of 72\% after 0 days, 48.9\% after 1 day and 29.3\% after 2 days.
Revised:  We conclude their randomized, double-blind, placebo-controlled trial presents statistical evidence, at 99\% confidence level, that the treatment of Covid-19 patients with hydroxychloroquine is effective in reducing the appearance of symptoms if used from 0 to 2 days after exposure to virus with estimated relative reduction in symptomatic outcomes of 72\% after 0 days, 48.9\% after 1 day and 29.3\% after 2 days.
Edit intent: clarity
Original:  We conclude their randomized, double-blind, placebo-controlled trial present consistent statistical evidence, at 99\% confidence level, that the treatment of Covid-19 patients with hydroxychloroquine is effective in reducing the appearance of symptoms if used from 0 to 2 days after exposure to virus with estimated relative reduction in symptomatic outcomes of 72\% after 0 days, 48.9\% after 1 day and 29.3\% after 2 days.
Revised:  We conclude their randomized, double-blind, placebo-controlled trial present consistent statistical evidence, at 99\% confidence level, that the treatment of Covid-19 patients with hydroxychloroquine is effective in reducing the appearance of symptoms if used before or right after exposure to the virus. For 0 to 2 days after exposure to virus with estimated relative reduction in symptomatic outcomes of 72\% after 0 days, 48.9\% after 1 day and 29.3\% after 2 days.
Edit intent: meaning-changed
Original:  We conclude their randomized, double-blind, placebo-controlled trial present consistent statistical evidence, at 99\% confidence level, that the treatment of Covid-19 patients with hydroxychloroquine is effective in reducing the appearance of symptoms if used from 0 to 2 days after exposure to virus with estimated relative reduction in symptomatic outcomes of 72\% after 0 days, 48.9\% after 1 day and 29.3\% after 2 days.
Revised:  We conclude their randomized, double-blind, placebo-controlled trial present consistent statistical evidence, at 99\% confidence level, that the treatment of Covid-19 patients with hydroxychloroquine is effective in reducing the appearance of symptoms if used from 0 to 2 days after exposure to virus , the estimated relative reduction in symptomatic outcomes of 72\% after 0 days, 48.9\% after 1 day and 29.3\% after 2 days.
Edit intent: coherence
Original:  We conclude their randomized, double-blind, placebo-controlled trial present consistent statistical evidence, at 99\% confidence level, that the treatment of Covid-19 patients with hydroxychloroquine is effective in reducing the appearance of symptoms if used from 0 to 2 days after exposure to virus with estimated relative reduction in symptomatic outcomes of 72\% after 0 days, 48.9\% after 1 day and 29.3\% after 2 days.
Revised:  We conclude their randomized, double-blind, placebo-controlled trial present consistent statistical evidence, at 99\% confidence level, that the treatment of Covid-19 patients with hydroxychloroquine is effective in reducing the appearance of symptoms if used from 0 to 2 days after exposure to virus with estimated relative reduction in symptomatic outcomes is 72\% after 0 days, 48.9\% after 1 day and 29.3\% after 2 days.
Edit intent: coherence
Original:  Notably, all DNNs face the issue of overfitting as they learn, which is when performance on one data set increases but the network's performance fails to generalize (often measured by the divergence of performance on training vs  testing data sets).
Revised:  Notably, all DNNs face the issue of overfitting as they learn, which is when performance on one data set increases but the network's performance fails to generalize (often measured by the divergence of performance on training vs . testing data sets).
Edit intent: fluency
Original: We study a special nonconvex optimization problem with a single spherical constraint to find a global minimizer of it. One important application of this problem is the discretized energy functional minimization problem of non-rotating Bose-Einstein condensate (BEC) .
Revised: We study a special nonconvex optimization problem with a single spherical constraint , which includes the discretized energy functional minimization problem of non-rotating Bose-Einstein condensate (BEC) .
Edit intent: clarity
Original:  One important application of this problem is the discretized energy functional minimization problem of non-rotating Bose-Einstein condensate (BEC) . We solve such a problem by exploiting its characterization as a nonlinear eigenvalue problem with eigenvector nonlinearity (NEPv) .
Revised:  One important application of this problem is the discretized energy functional minimization problem of non-rotating Bose-Einstein condensates (BECs) as one of the important applications. Such a problem is studied by exploiting its characterization as a nonlinear eigenvalue problem with eigenvector nonlinearity (NEPv) .
Edit intent: meaning-changed
Original:  We solve such a problem by exploiting its characterization as a nonlinear eigenvalue problem with eigenvector nonlinearity (NEPv) . We show that with the property of NEPv , any algorithm finding the positive stationary point of this optimization problem actually finds its global minimum, such as the regularized Newton (RN) method.
Revised:  We solve such a problem by exploiting its characterization as a nonlinear eigenvalue problem with eigenvector nonlinearity (NEPv) , which admits a unique nonnegative eigenvector, and this eigenvector is exactly the global minimizer to the quartic minimization. With these properties , any algorithm finding the positive stationary point of this optimization problem actually finds its global minimum, such as the regularized Newton (RN) method.
Edit intent: meaning-changed
Original:  We show that with the property of NEPv , any algorithm finding the positive stationary point of this optimization problem actually finds its global minimum, such as the regularized Newton (RN) method.
Revised:  We show that with the property of NEPv , any algorithm converging to the nonnegative stationary point of this optimization problem actually finds its global minimum, such as the regularized Newton (RN) method.
Edit intent: meaning-changed
Original:  In this paper, we formulate the problem of graph adversarial immunization as a bilevel optimization problem, i.e., vaccinating a fraction of node pairs, connected or unconnected, to improve the certifiable robustness of  graph against any admissible adversarial attack.
Revised:  In this paper, we formulate the problem of graph adversarial immunization as a bilevel optimization problem, i.e., vaccinating an affordable fraction of node pairs, connected or unconnected, to improve the certifiable robustness of graph against any admissible adversarial attack.
Edit intent: clarity
Original:  In this paper, we formulate the problem of graph adversarial immunization as a bilevel optimization problem, i.e., vaccinating a fraction of node pairs, connected or unconnected, to improve the certifiable robustness of  graph against any admissible adversarial attack.
Revised:  In this paper, we formulate the problem of graph adversarial immunization as a bilevel optimization problem, i.e., vaccinating a fraction of node pairs, connected or unconnected, to improve the certifiable robustness of the graph against any admissible adversarial attack.
Edit intent: fluency
Original:  Experimental results demonstrate that the proposed adversarial immunization method remarkably improves the fraction of robust nodes by 14\%-50\%, with an affordable immune budget of only 3.2 \% edges.
Revised:  Experimental results demonstrate that the proposed AdvImmune immunization method remarkably improves the fraction of robust nodes by 14\%-50\%, with an affordable immune budget of only 3.2 \% edges.
Edit intent: meaning-changed
Original:  Experimental results demonstrate that the proposed adversarial immunization method remarkably improves the fraction of robust nodes by 14\%-50\%, with an affordable immune budget of only 3.2 \% edges.
Revised:  Experimental results demonstrate that the proposed adversarial immunization method remarkably improves the fraction of robust nodes by 12\%, 42\%, 65\%, with an affordable immune budget of only 3.2 \% edges.
Edit intent: meaning-changed
Original:  Experimental results demonstrate that the proposed adversarial immunization method remarkably improves the fraction of robust nodes by 14\%-50\%, with an affordable immune budget of only 3.2 \% edges.
Revised:  Experimental results demonstrate that the proposed adversarial immunization method remarkably improves the fraction of robust nodes by 14\%-50\%, with an affordable immune budget of only 5 \% edges.
Edit intent: meaning-changed
Original:  In this paper, we formulate the problem of graph adversarial immunization as a bilevel optimization problem, i.e., vaccinating an affordable fraction of node pairs, connected or unconnected, to improve the certifiable robustness of the graph against any admissible adversarial attack.
Revised:  In this paper, we propose and formulate the graph adversarial immunization as a bilevel optimization problem, i.e., vaccinating an affordable fraction of node pairs, connected or unconnected, to improve the certifiable robustness of the graph against any admissible adversarial attack.
Edit intent: clarity
Original:  In this paper, we formulate the problem of graph adversarial immunization as a bilevel optimization problem, i.e., vaccinating an affordable fraction of node pairs, connected or unconnected, to improve the certifiable robustness of the graph against any admissible adversarial attack.
Revised:  In this paper, we formulate the problem of graph adversarial immunization problem, i.e., vaccinating an affordable fraction of node pairs, connected or unconnected, to improve the certifiable robustness of the graph against any admissible adversarial attack.
Edit intent: coherence
Original:  In this paper, we formulate the problem of graph adversarial immunization as a bilevel optimization problem, i.e., vaccinating an affordable fraction of node pairs, connected or unconnected, to improve the certifiable robustness of the graph against any admissible adversarial attack.
Revised:  In this paper, we formulate the problem of graph adversarial immunization as a bilevel optimization problem, i.e., vaccinating an affordable fraction of node pairs, connected or unconnected, to improve the certifiable robustness of graph against any admissible adversarial attack.
Edit intent: fluency
Original:  We further propose an efficient algorithm, called AdvImmune, which optimizes  meta-gradient in a discrete way to circumvent the computationally expensive combinatorial optimization when solving the adversarial immunization problem.
Revised:  We further propose an effective algorithm, called AdvImmune, which optimizes meta-gradient in a discrete way to circumvent the computationally expensive combinatorial optimization when solving the adversarial immunization problem.
Edit intent: clarity
Original:  We further propose an efficient algorithm, called AdvImmune, which optimizes  meta-gradient in a discrete way to circumvent the computationally expensive combinatorial optimization when solving the adversarial immunization problem.
Revised:  We further propose an efficient algorithm, called AdvImmune, which optimizes with meta-gradient in a discrete way to circumvent the computationally expensive combinatorial optimization when solving the adversarial immunization problem.
Edit intent: meaning-changed
Original:  Experimental results demonstrate that the proposed AdvImmune immunization method remarkably improves the fraction of robust nodes by 12\%, 42\%, 65\%, with an affordable immune budget of only 5\% edges.
Revised:  Experimental results demonstrate that the proposed AdvImmune method remarkably improves the fraction of robust nodes by 12\%, 42\%, 65\%, with an affordable immune budget of only 5\% edges.
Edit intent: clarity
Original:  Experimental results demonstrate that the proposed AdvImmune immunization method remarkably improves the fraction of robust nodes by 12\%, 42\%, 65\%, with an affordable immune budget of only 5\% edges.
Revised:  Experimental results demonstrate that the proposed AdvImmune immunization method remarkably improves the ratio of robust nodes by 12\%, 42\%, 65\%, with an affordable immune budget of only 5\% edges.
Edit intent: clarity
Original:  Here, we compare the credibility of tweets about COVID-19  to datasets pertaining to other health issues. We find that the quantity of information about COVID-19 is indeed overwhelming, but that the majority of links shared cannot be rated for its credibility.
Revised:  Here, we compare the credibility of tweets about COVID-19 to datasets pertaining to other health topics. Low-credibility sources were less likely to post about COVID-19 is indeed overwhelming, but that the majority of links shared cannot be rated for its credibility.
Edit intent: meaning-changed
Original:  We find that the quantity of information about COVID-19 is indeed overwhelming, but that the majority of links shared cannot be rated for its credibility. Reasons for this failure to rate include widespread use of social media and news aggregators. The majority of links that could be rated came from credible sources; however, we found a large increase in the proportion of state-sponsored propaganda among non-credible and less credible URLs, suggesting that COVID-19 may be used as a vector to spread misinformation and disinformation for political purposes.
Revised:  We find that the quantity of information about COVID-19 , with the important exception of state-sponsored propaganda among non-credible and less credible URLs, suggesting that COVID-19 may be used as a vector to spread misinformation and disinformation for political purposes.
Edit intent: clarity
Original:  however, we found a large increase in the proportion of state-sponsored propaganda among non-credible and less credible URLs, suggesting that COVID-19 may be used as a vector to spread misinformation and disinformation for political purposes. Overall, results indicate that COVID-19 is unfolding in a highly uncertain information environment that not may amenable to fact-checking as scientific understanding of the disease, and appropriate public health measures, evolve. As a consequence, public service announcements must adequately communicate the uncertainly underlying these recommendations, while still encouraging healthy behaviors .
Revised:  however, we found a large increase in the proportion of state-sponsored propaganda outlets on Twitter. Finally, a known conspiracy theory was more likely to be ``laundered'' through social media platforms than low-credibility news sources. In contrast, misinformation reflecting legitimate scientific uncertainty was primarily discussed by credible news sources. These results suggest that the social media discourse reflects an attempt to come to grips with deep scientific uncertainty during an emerging pandemic, with significant content emanating from unrated; not low credibility, sources. When low-quality content is generated by these unrated sources, they may be amplified by high-credibility sources inadvertently lending them credibility .
Edit intent: clarity
Original: Several high-profile sources have focused worldwide attention on the dangers of misinformation about COVID, with the World Health Organization declaring a COVID-19 social media ``infodemic''. Prior work has associated such misinformation with low-credibility sources that are known to spread conspiracy theories and malicious content.5, 6 Here, we report the results of an analysis of over 500 million social media posts from Twitter and Facebook  between March 8 and May 1, 2020.
Revised: Several high-profile sources have focused worldwide attention on the dangers of misinformation about COVID, with the World Health Organization declaring a COVID-19 social media "infodemic", with special attention to misinformation -- frequently understood as false claims. To understand the infodemic's scope and scale, we analyzed over 500 million social media posts from Twitter and Facebook between March 8 and May 1, 2020.
Edit intent: clarity
Original: 5, 6 Here, we report the results of an analysis of over 500 million social media posts from Twitter and Facebook  between March 8 and May 1, 2020.
Revised: 5, 6 Here, we report the results of an analysis of over 500 million social media posts from Twitter and Facebook about COVID-19 and other health topics, between March 8 and May 1, 2020.
Edit intent: meaning-changed