wanyu/IteraTeR_full_sent huggingface dataset:
Original:  However we show that measures of ` lived population density', which capture density as perceived by a randomly chosen person, do a better job of explaining variations in the rate of spread , achieving R^2 = 0.45 in Europe.
Revised:  However we show that measures of ` population-weighted density', which capture density as perceived by a randomly chosen person, do a better job of explaining variations in the rate of spread , achieving R^2 = 0.45 in Europe.
Edit intent: meaning-changed
Original:  However we show that measures of ` lived population density', which capture density as perceived by a randomly chosen person, do a better job of explaining variations in the rate of spread , achieving R^2 = 0.45 in Europe.
Revised:  However we show that measures of ` lived population density', which capture density as perceived by a randomly chosen individual. These measures of population density can significantly explain variation in the initial rate of spread , achieving R^2 = 0.45 in Europe.
Edit intent: meaning-changed
Original:  However we show that measures of ` lived population density', which capture density as perceived by a randomly chosen person, do a better job of explaining variations in the rate of spread , achieving R^2 = 0.45 in Europe. We show that adding further measures based on the timing of the outbreak into the regression can increase this to R^2 = 0.58 .
Revised:  However we show that measures of ` lived population density', which capture density as perceived by a randomly chosen person, do a better job of explaining variations in the rate of spread of COVID-19 between countries within Europe. However, such measures do not explain differences on a global scale, particularly when considering countries in East Asia, or looking later into the epidemics. Therefore, to control for country-level differences in response to COVID-19 we consider the cross-cultural measure of individualism proposed by Hofstede. This score can significantly explain variation in the size of epidemics across Europe, North America, and East Asia. Using both our measure of population-weighted density and the Hofstede score we can significantly explain half the variation in the current size of epidemics across Europe and North America. By controlling for country-level responses to the virus and population density, our analysis of the global incidence of COVID-19 can help focus attention on epidemic control measures that are effective for individual countries .
Edit intent: meaning-changed
Original:  We study four metrics: Interdaily stability (IS), intradaily variability (IV), Hurst parameter estimation via detrended fluctuation analysis (DFA), and a novel nonparametric estimator which we call the proportion of variance (PoV), which calculates the strength of the circadian rhythm using spectral density estimation.
Revised:  We study four metrics: Interdaily stability (IS), intradaily variability (IV), the scaling exponent from detrended fluctuation analysis (DFA), and a novel nonparametric estimator which we call the proportion of variance (PoV), which calculates the strength of the circadian rhythm using spectral density estimation.
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Original: The foldability of rigid origami has been widely studied and applied to many engineering fields. In this article we introduce the statics of rigid origami, find the form of stress and load, and prove the equivalence of static rigidity and  first-order rigidity.
Revised: Rigid origami is examined from the perspective of rigidity theory. First and second order rigidity are defined from local differential analysis of the consistency constraint; while the static rigidity and first-order rigidity.
Edit intent: meaning-changed
Original:  In this article we introduce the statics of rigid origami, find the form of stress and load, and prove the equivalence of static rigidity and  first-order rigidity.
Revised:  In this article we introduce the statics of rigid origami, find the form of stress and load, and prove the equivalence of static rigidity and prestress stability are defined after finding the form of internal force and load. This article will show that first-order rigidity.
Edit intent: meaning-changed
Original:  In this article we introduce the statics of rigid origami, find the form of stress and load, and prove the equivalence of static rigidity and  first-order rigidity. Another concept introduced here is the pre-stress stability . We concern what is the stability of a creased paper when it is rigid but not statically rigid and how would the pre-stress contribute to the stability. The answer to these questions is also linked with the second-order rigidity. A brief discussion on the numerical methods to determine these rigidity concludes the article .
Revised:  In this article we introduce the statics of rigid origami, find the form of stress and load, and prove the equivalence of static rigidity and first-order or static rigidity implies prestress stability, which implies second-order rigidity, which implies rigidity, but none of these is reversible. Examples are given of rigid origami structures with these different kinds of rigidity. Examining the different aspects of the rigidity of origami might give a novel perspective for the development of new folding patterns, or for the design of origami structures where some rigidity is required .
Edit intent: coherence
Original: For a graph parameter \pi, the Contraction(\pi) problem consists in, given a graph G and two positive integers k,d, deciding whether one can contract at most k edges of G to obtain a graph in which \pi has dropped by at least d. Galby et al. [ISAAC 2019, MFCS 2019] recently studied the case where \pi is the size of a minimum dominating set.
Revised: For a graph invariant \pi, the Contraction(\pi) problem consists in, given a graph G and two positive integers k,d, deciding whether one can contract at most k edges of G to obtain a graph in which \pi has dropped by at least d. Galby et al. [ISAAC 2019, MFCS 2019] recently studied the case where \pi is the size of a minimum dominating set.
Edit intent: clarity
Original:  This challenge stems from the enormous space of available solutions, with different sets of data instances and features that could be used for training, several algorithms to choose from, and instantiations of these algorithms  (i.e., models) that perform differently according to diverse metrics.
Revised:  This challenge stems from the enormous space of available solutions, with different sets of data instances and features that could be used for training, several algorithms to choose from, and instantiations of these algorithms using diverse parameters (i.e., models) that perform differently according to diverse metrics.
Edit intent: clarity
Original:  This challenge stems from the enormous space of available solutions, with different sets of data instances and features that could be used for training, several algorithms to choose from, and instantiations of these algorithms  (i.e., models) that perform differently according to diverse metrics.
Revised:  This challenge stems from the enormous space of available solutions, with different sets of data instances and features that could be used for training, several algorithms to choose from, and instantiations of these algorithms (i.e., models) that perform differently according to various metrics.
Edit intent: clarity
Original:  Our system, StackGenVis, assists users in dynamically  managing data instances, selecting the most important features for a given data set, and choosing a set of top-performant and diverse algorithms  .
Revised:  Our system, StackGenVis, assists users in dynamically adapting performance metrics, managing data instances, selecting the most important features for a given data set, and choosing a set of top-performant and diverse algorithms .
Edit intent: meaning-changed
Original:  Our system, StackGenVis, assists users in dynamically  managing data instances, selecting the most important features for a given data set, and choosing a set of top-performant and diverse algorithms  .
Revised:  Our system, StackGenVis, assists users in dynamically managing data instances, selecting the most important features for a given data set, and choosing a set of top-performant and diverse algorithms , and measuring the predictive performance .
Edit intent: meaning-changed
Original:  Different from prior arts in transfer learning, we show that transferability of off-the-shelf representation heavily depends on specific pathology image sets, and may obtain classification performance close to random guess .
Revised:  Different from prior arts in transfer learning, we show that transferability of off-the-shelf representation heavily depends on specific pathology image sets, the general representation generated by early layers does convey transferred knowledge in various image classification applications .
Edit intent: meaning-changed
Original:  We  also present the SoftNER model that combines contextual information with domain specific knowledge using an attention network.
Revised:  We trained in-domain BERT representations (BERTOverflow) on 152 million sentences from StackOverflow, which lead to an absolute increase of +10 F-1 score over off-the-shelf BERT. We also present the SoftNER model that combines contextual information with domain specific knowledge using an attention network.
Edit intent: meaning-changed
Original:  We  also present the SoftNER model that combines contextual information with domain specific knowledge using an attention network. The code token recognizer combined with an entity segmentation model we proposed, consistently improves the performance of the named entity tagger. Our proposed SoftNER tagger outperforms the BiLSTM-CRF model with an absolute increase of +9.73 F-1 score on StackOverflow data.
Revised:  We also present the SoftNER model which achieves an overall 79.10 F_1 score for code and named entity recognition on StackOverflow data. Our SoftNER model incorporates a context-independent code token classifier with corpus-level features to improve the BERT-based tagging model. Our code and data are available at: URL
Edit intent: meaning-changed
Original: Over sixty percent of employees at a large South African financial services company select the minimum rate of 7.5 percent for their monthly retirement contributions , far below the recommended rate of 15 percent  .
Revised: Over sixty percent of employees at a large South African financial services company select the minimum rate of 7.5 percent for their monthly retirement contributions , far below the recommended rate of 15 percent recommended by financial advisers .
Edit intent: meaning-changed
Original:  In this paper, we present the first study to evaluate complete pipelines for leveraging these transcripts to train machine learning model to  generate these notes . We first describe a unique dataset of patient visit records, consisting of transcripts , paired SOAP notes, and annotations marking noteworthy utterances that support each summary sentence.
Revised:  In this paper, we present the first study to evaluate complete pipelines for leveraging these transcripts to train machine learning model to generate these notes from conversations between physicians and patients. We benefit from a dataset that, along with transcripts and paired SOAP notes, and annotations marking noteworthy utterances that support each summary sentence.
Edit intent: clarity
Original:   Our best performing method first (i) extracts noteworthy utterances via multi-label classification assigns them to summary section(s);
Revised:  We observe that the performance improves constantly as the extractive subtask is made more complex - an observation that we also replicate on the well-known AMI meeting summarization dataset. Our best performing method first (i) extracts noteworthy utterances via multi-label classification assigns them to summary section(s);
Edit intent: meaning-changed
Original:  Compared to an end-to-end approach that generates the full SOAP note from the full conversation, our approach improves by 7 ROUGE-1 points . Oracle experiments indicate that fixing our generative capabilities, improvements in extraction alone could provide (up to) a further 9 ROUGE point gain .
Revised:  Compared to an end-to-end approach that generates the full SOAP note from the full conversation, our approach improves by 7 ROUGE-1 points .
Edit intent: coherence
Original: Following each patient visit, physicians must draft a detailed clinical summary called a SOAP note. Moreover, with electronic health records, these notes must be digitized. Despite the benefits of this documentation, their creation remains an onerous process, contributing to increasing physician burnout.
Revised: Following each patient visit, physicians draft long semi-structured clinical summaries called SOAP notes. While invaluable to clinicians and researchers, creating digital SOAP notes is burdensome, contributing to physician burnout.
Edit intent: clarity
Original:  We decompose the problem into extractive and abstractive subtasks, exploring a spectrum of approaches according to how much they demand from each component. We observe that the performance improves constantly as the extractive subtask is made more complex - an observation that we also replicate on the well-known AMI meeting summarization dataset. Our best performing method first (i) extracts noteworthy utterances via multi-label classification, assigning each to summary section(s) ;
Revised:  We decompose the problem into extractive and abstractive subtasks, exploring a spectrum of methods across the extractive-abstractive spectrum, we propose Cluster2Sent, an algorithm that (i) extracts noteworthy utterances via multi-label classification, assigning each to summary section(s) ;
Edit intent: meaning-changed
Original:  and (iii) generates the summary sentences by conditioning on the corresponding cluster and the subsection of the SOAP sentence to be generated. Compared to an end-to-end approach that generates the full SOAP note from the full conversation, our approach improves by around 8 ROUGE-1 points .
Revised:  and (iii) generates one summary sentence per cluster. Cluster2Sent outperforms its purely abstractive counterpart by 8 ROUGE-1 points .
Edit intent: meaning-changed
Original:  Compared to an end-to-end approach that generates the full SOAP note from the full conversation, our approach improves by around 8 ROUGE-1 points .
Revised:  Compared to an end-to-end approach that generates the full SOAP note from the full conversation, our approach improves by around 8 ROUGE-1 points, and produces significantly more factual and coherent sentences as assessed by expert human evaluators. For reproducibility, we demonstrate similar benefits on the publicly available AMI dataset. Our results speak to the benefits of structuring summaries into sections and annotating supporting evidence when constructing summarization corpora .
Edit intent: meaning-changed
Original: We present a simple sublinear-time algorithm for sampling an arbitrary subgraph H exactly uniformly from a graph G  , to which the algorithm has access by performing the following types of queries: (1) uniform vertex queries, (2) degree queries, (3) neighbor queries, (4) pair queries and ( 5 ) edge sampling queries.
Revised: We present a simple sublinear-time algorithm for sampling an arbitrary subgraph H exactly uniformly from a graph G with m edges , to which the algorithm has access by performing the following types of queries: (1) uniform vertex queries, (2) degree queries, (3) neighbor queries, (4) pair queries and ( 5 ) edge sampling queries.
Edit intent: meaning-changed
Original: We present a simple sublinear-time algorithm for sampling an arbitrary subgraph H exactly uniformly from a graph G  , to which the algorithm has access by performing the following types of queries: (1) uniform vertex queries, (2) degree queries, (3) neighbor queries, (4) pair queries and ( 5 ) edge sampling queries.
Revised: We present a simple sublinear-time algorithm for sampling an arbitrary subgraph H exactly uniformly from a graph G , to which the algorithm has access by performing the following types of queries: (1) degree queries, (2) degree queries, (3) neighbor queries, (4) pair queries and ( 5 ) edge sampling queries.
Edit intent: clarity
Original: We present a simple sublinear-time algorithm for sampling an arbitrary subgraph H exactly uniformly from a graph G  , to which the algorithm has access by performing the following types of queries: (1) uniform vertex queries, (2) degree queries, (3) neighbor queries, (4) pair queries and ( 5 ) edge sampling queries.
Revised: We present a simple sublinear-time algorithm for sampling an arbitrary subgraph H exactly uniformly from a graph G , to which the algorithm has access by performing the following types of queries: (1) uniform vertex queries, (2) neighbor queries, (3) neighbor queries, (4) pair queries and ( 5 ) edge sampling queries.
Edit intent: coherence
Original: We present a simple sublinear-time algorithm for sampling an arbitrary subgraph H exactly uniformly from a graph G  , to which the algorithm has access by performing the following types of queries: (1) uniform vertex queries, (2) degree queries, (3) neighbor queries, (4) pair queries and ( 5 ) edge sampling queries.
Revised: We present a simple sublinear-time algorithm for sampling an arbitrary subgraph H exactly uniformly from a graph G , to which the algorithm has access by performing the following types of queries: (1) uniform vertex queries, (2) degree queries, (3) pair queries and ( 5 ) edge sampling queries.
Edit intent: clarity
Original: We present a simple sublinear-time algorithm for sampling an arbitrary subgraph H exactly uniformly from a graph G  , to which the algorithm has access by performing the following types of queries: (1) uniform vertex queries, (2) degree queries, (3) neighbor queries, (4) pair queries and ( 5 ) edge sampling queries.
Revised: We present a simple sublinear-time algorithm for sampling an arbitrary subgraph H exactly uniformly from a graph G , to which the algorithm has access by performing the following types of queries: (1) uniform vertex queries, (2) degree queries, (3) neighbor queries, (4) pair queries and ( 4 ) edge sampling queries.
Edit intent: coherence
Original:  and the lower bound by Eden and Rosenbaum [APPROX 2018] for approximately counting cliques, our results suggest that in our query model, approximately counting cliques is ``equivalent to'' exactly uniformly sampling cliques, in the sense that the query and time complexities of exactly uniform sampling and randomized approximate counting are within a polylogarithmic factor of each other.
Revised:  and the lower bound by Eden and Rosenbaum [APPROX 2018] for approximately counting cliques, our results suggest that in our query model, approximately counting cliques is "equivalent to" exactly uniformly sampling cliques, in the sense that the query and time complexities of exactly uniform sampling and randomized approximate counting are within a polylogarithmic factor of each other.
Edit intent: fluency
Original:  This includes a frequency-weighted SpecAugment policy and a frequency-divided CNN feature extractor for better capturing the high frequency structures of whispered speech, and a layer-wise transfer learning approach to pre-train a model with normal speech then fine-tuning it with whispered speech to bridge the gap between whispered and normal speech.
Revised:  This includes a frequency-weighted SpecAugment policy and a frequency-divided CNN feature extractor for better capturing the high-frequency structures of whispered speech, and a layer-wise transfer learning approach to pre-train a model with normal speech then fine-tuning it with whispered speech to bridge the gap between whispered and normal speech.
Edit intent: fluency
Original:  This includes a frequency-weighted SpecAugment policy and a frequency-divided CNN feature extractor for better capturing the high frequency structures of whispered speech, and a layer-wise transfer learning approach to pre-train a model with normal speech then fine-tuning it with whispered speech to bridge the gap between whispered and normal speech.
Revised:  This includes a frequency-weighted SpecAugment policy and a frequency-divided CNN feature extractor for better capturing the high frequency structures of whispered speech, and a layer-wise transfer learning approach to pre-train a model with normal or normal-to-whispered converted speech then fine-tune it with whispered speech to bridge the gap between whispered and normal speech.
Edit intent: meaning-changed
Original:  The results indicate as long as we have a good E2E model pre-trained on normal  speech, a relatively small set of whispered speech may suffice to obtain a reasonably good E2E whispered speech recognizer.
Revised:  The results indicate as long as we have a good E2E model pre-trained on normal or pseudo-whispered speech, a relatively small set of whispered speech may suffice to obtain a reasonably good E2E whispered speech recognizer.
Edit intent: meaning-changed
Original:  We propose a new soft robot skin that can detect human contact through thermal features. An electrically conductive pipe is interposed between graphite sheet that has high heat conductivity and softmaterial for cover compliance. Then the temperature of the water pipe is controlled by a heat source and a small water pump .  Besides, an observer for estimating heat flow is implemented;
Revised:  We propose a new robotic cover prototype that achieves thermal display while also being soft . Besides, an observer for estimating heat flow is implemented;
Edit intent: clarity
Original:  Then the temperature of the water pipe is controlled by a heat source and a small water pump .  Besides, an observer for estimating heat flow is implemented;
Revised:  Then the temperature of the water pipe is controlled by a heat source and a small water pump . We focus on the thermal cue because previous human studies have identified it as part of the touch pleasantness. The robotic cover surface can be regulated to the desired temperature by circulating water through a thermally conductive pipe embedded in the cover, of which temperature is controlled. Besides, an observer for estimating heat flow is implemented;
Edit intent: meaning-changed
Original:  These restrictions generate moment inequalities and therefore set identification , which can lead to wide confidence sets for the structural parameters. The paper shows that employing aggregated survey data that are informative about the extensive margin of adjustment i.e. the proportion of agents whose behavior is distorted over time due to financial frictions  can provide additional information that can tighten the corresponding bounds.
Revised:  These restrictions generate moment inequalities and therefore set identification . Employing aggregated survey data that are informative about the extensive margin of adjustment i.e. the proportion of agents whose behavior is distorted over time due to financial frictions can provide additional information that can tighten the corresponding bounds.
Edit intent: coherence
Original:  The paper shows that employing aggregated survey data that are informative about the extensive margin of adjustment i.e. the proportion of agents whose behavior is distorted over time due to financial frictions  can provide additional information that can tighten the corresponding bounds.
Revised:  The paper shows that employing aggregated survey data that are informative about the extensive margin of adjustment i.e. the proportion of agents whose behavior is distorted over time due to financial frictions , can provide additional information that can tighten the corresponding bounds.
Edit intent: fluency