wanyu/IteraTeR_full_sent huggingface dataset:
Original: Standard measures such as quarantining suspects and contact tracing could not contain the ongoing COVID-19 pandemic. Unlike for other recent epidemics where such instruments were successful, in the present case a large fraction of the infected have only mild unspecific symptoms. By employing network models we here show that even for near perfect contact tracing and unlimited suspect testing , containment starts to fail when more than approximately half of the carriers fall into the weak-symptom category. The situation becomes considerably more severe if the number of daily available tests is limited. In this case the epidemic transition becomes discontinuous and a much larger percentage of the population becomes infected. While moderate levels of social distancing can bring the situation back under control, the limited number of daily tests introduces a finite time horizon: If social distancing is implemented after the cut off date, containment catastrophically breaks down resulting in an exponential disease spread .
Revised: Standard measures such as quarantining suspects and contact tracing could not contain the ongoing COVID-19 epidemic spreads around the globe, countries rely on non-pharmaceutical interventions to slow the outbreak. An overarching aim is to flatten the epidemic curve so as to not exceed health care capacity limits. Inherent to this strategy is the assumption that with increasing mitigation strength the peak of the epidemic curve continuously decreases. We show that in any setting, where intervention measures include suspect testing and contact tracing, this assumption is incorrect and the process is discontinuous. This discontinuity is linked to a super-exponential growth phase and it considerably raises the stakes in epidemic control: The decisiveness of mitigation measures is not merely a question of 'flattening the curve' anymore but it sets the dividing line between effective suppression and a catastrophic outbreak .
Edit intent: meaning-changed
Original: As the COVID-19 epidemic spreads around the globe, countries rely on non-pharmaceutical interventions to slow the outbreak. An overarching aim is to flatten the epidemic curve so as to not exceed health care capacity limits. Inherent to this strategy is the assumption that with increasing mitigation strength the peak of the epidemic curve continuously decreases.
Revised: High impact epidemics constitute one of the largest threats humanity is facing in the 21st century. Testing, contact tracing and quarantining are critical in slowing down epidemic dynamics, but may prove insufficient for highly contagious diseases. In the absence of pharmaceutical interventions, physical distancing measures remain as the last resort to avoid a widespread outbreak. Here we show that such combined countermeasures drastically change the rules of the epidemic curve continuously decreases.
Edit intent: coherence
Original:  Inherent to this strategy is the assumption that with increasing mitigation strength the peak of the epidemic curve continuously decreases. We show that in any setting, where intervention measures include suspect testing and contact tracing, this assumption is incorrect and the process is discontinuous. This discontinuity is linked to a super-exponential growth phase and it considerably raises the stakes in epidemic control: The decisiveness of mitigation measures is not merely a question of 'flattening the curve' anymore but it sets the dividing line between effective suppression and a catastrophic outbreak .
Revised:  Inherent to this strategy is the assumption that with increasing mitigation strength the peak of the epidemic transition if testing capacities are limited: Instead of continuous the response to countermeasures becomes discontinuous and rather than following the conventional exponential growth, the outbreak accelerates and scales super-exponentially during an intermediate period. As a consequence, containment measures either suffice to stop the outbreak at low total case numbers or fail catastrophically if marginally too weak, thus implying large uncertainties in reliably estimating overall epidemic dynamics, both during initial phases and during second wave scenarios .
Edit intent: meaning-changed
Original:  Our findings suggest that these cost of undisturbed landscapes are considerable, particularly when PV is not entirely rolled out as utility scale, open space installations. The opportunity cost is likely high enough to allow for significant compensation of the ones affected by local  wind turbine externalities.
Revised:  Our findings suggest that these cost of undisturbed landscapes are considerable, particularly when solar PV is mainly realized on roof-tops. Under a wide range of assumptions, the opportunity cost of wind power is high enough to allow for significant compensation of the ones affected by local wind turbine externalities.
Edit intent: meaning-changed
Original:  In particular, we (i) formalize the data-driven design problem as a non-zero-sum game, (ii) leverage this formalism to develop a strategy for retraining the regression model as the design algorithm proceeds---what we refer to as autofocusing the model , and (iii) demonstrate the promise of autofocusing empirically.
Revised:  In particular, we (i) formalize the data-driven design problem as a non-zero-sum game, (ii) develop a principled strategy for retraining the regression model as the design algorithm proceeds---what we refer to as autofocusing the model , and (iii) demonstrate the promise of autofocusing empirically.
Edit intent: clarity
Original:  In particular, we (i) formalize the data-driven design problem as a non-zero-sum game, (ii) leverage this formalism to develop a strategy for retraining the regression model as the design algorithm proceeds---what we refer to as autofocusing the model , and (iii) demonstrate the promise of autofocusing empirically.
Revised:  In particular, we (i) formalize the data-driven design problem as a non-zero-sum game, (ii) leverage this formalism to develop a strategy for retraining the regression model as the design algorithm proceeds---what we refer to as autofocusing , and (iii) demonstrate the promise of autofocusing empirically.
Edit intent: clarity
Original:  When each buyer observes an unboundedly informative private signal about the seller's current-period action, the speed of learning is uniformly bounded  below and a patient seller can secure high returns from building reputations.
Revised:  When each buyer observes an unboundedly informative private signal about the seller's current-period action, the speed of learning is uniformly bounded from below and a patient seller can secure high returns from building reputations.
Edit intent: clarity
Original:  This asymptotic approach ensures a great deal of robustness to statistical uncertainty and temporal fluctuations , and we give various applications. A natural characterization of systems resilient to fire sales emerges, and we provide explicit criteria that regulators may exploit in order to assess the stability of any system.
Revised:  This asymptotic approach ensures a great deal of robustness to statistical uncertainty and temporal fluctuations . A characterization of those systems that are resilient to small shocks emerges, and we provide explicit criteria that regulators may exploit in order to assess the stability of any system.
Edit intent: clarity
Original:  It is shown that the average of the solutions reconstructed at a face by Van Leer's kappa-scheme recovers a cubic solution exactly with kappa = 1/3, the same is true for the average of the nonlinear fluxes evaluated by the reconstructed solutions, and a dissipation term does not contribute to the leading third-order  error.
Revised:  It is shown that the average of the solutions reconstructed at a face by Van Leer's kappa-scheme recovers a cubic solution exactly with kappa = 1/3, the same is true for the average of the nonlinear fluxes evaluated by the reconstructed solutions, and a dissipation term is already sufficiently small with a third-order error.
Edit intent: meaning-changed
Original:  It is shown that the average of the solutions reconstructed at a face by Van Leer's kappa-scheme recovers a cubic solution exactly with kappa = 1/3, the same is true for the average of the nonlinear fluxes evaluated by the reconstructed solutions, and a dissipation term does not contribute to the leading third-order  error.
Revised:  It is shown that the average of the solutions reconstructed at a face by Van Leer's kappa-scheme recovers a cubic solution exactly with kappa = 1/3, the same is true for the average of the nonlinear fluxes evaluated by the reconstructed solutions, and a dissipation term does not contribute to the leading third-order truncation error.
Edit intent: meaning-changed
Original:  In this paper, we present EPIC , a large-scale epidemic corpus that contains 20 millions micro-blog posts, i.e., tweets crawled from Twitter, from year 2006 to 2020.
Revised:  In this paper, we present EPIC30M , a large-scale epidemic corpus that contains 20 millions micro-blog posts, i.e., tweets crawled from Twitter, from year 2006 to 2020.
Edit intent: meaning-changed
Original:  In this paper, we present EPIC , a large-scale epidemic corpus that contains 20 millions micro-blog posts, i.e., tweets crawled from Twitter, from year 2006 to 2020.
Revised:  In this paper, we present EPIC , a large-scale epidemic corpus that contains 30 millions micro-blog posts, i.e., tweets crawled from Twitter, from year 2006 to 2020.
Edit intent: meaning-changed
Original:  EPIC contains a subset of 17.8 millions tweets related to three general diseases, namely Ebola, Cholera and Swine Flu, and another subset of 3.5 millions tweets of six global epidemic outbreaks, including 2009 H1N1 Swine Flu, 2010 Haiti Cholera, 2012 Middle-East Respiratory Syndrome (MERS), 2013 West African Ebola, 2016 Yemen Cholera and 2018 Kivu Ebola.
Revised:  EPIC30M contains a subset of 17.8 millions tweets related to three general diseases, namely Ebola, Cholera and Swine Flu, and another subset of 3.5 millions tweets of six global epidemic outbreaks, including 2009 H1N1 Swine Flu, 2010 Haiti Cholera, 2012 Middle-East Respiratory Syndrome (MERS), 2013 West African Ebola, 2016 Yemen Cholera and 2018 Kivu Ebola.
Edit intent: meaning-changed
Original:  EPIC contains a subset of 17.8 millions tweets related to three general diseases, namely Ebola, Cholera and Swine Flu, and another subset of 3.5 millions tweets of six global epidemic outbreaks, including 2009 H1N1 Swine Flu, 2010 Haiti Cholera, 2012 Middle-East Respiratory Syndrome (MERS), 2013 West African Ebola, 2016 Yemen Cholera and 2018 Kivu Ebola.
Revised:  EPIC contains a subset of 26.2 millions tweets related to three general diseases, namely Ebola, Cholera and Swine Flu, and another subset of 3.5 millions tweets of six global epidemic outbreaks, including 2009 H1N1 Swine Flu, 2010 Haiti Cholera, 2012 Middle-East Respiratory Syndrome (MERS), 2013 West African Ebola, 2016 Yemen Cholera and 2018 Kivu Ebola.
Edit intent: meaning-changed
Original:  EPIC contains a subset of 17.8 millions tweets related to three general diseases, namely Ebola, Cholera and Swine Flu, and another subset of 3.5 millions tweets of six global epidemic outbreaks, including 2009 H1N1 Swine Flu, 2010 Haiti Cholera, 2012 Middle-East Respiratory Syndrome (MERS), 2013 West African Ebola, 2016 Yemen Cholera and 2018 Kivu Ebola.
Revised:  EPIC contains a subset of 17.8 millions tweets related to three general diseases, namely Ebola, Cholera and Swine Flu, and another subset of 4.7 millions tweets of six global epidemic outbreaks, including 2009 H1N1 Swine Flu, 2010 Haiti Cholera, 2012 Middle-East Respiratory Syndrome (MERS), 2013 West African Ebola, 2016 Yemen Cholera and 2018 Kivu Ebola.
Edit intent: meaning-changed
Original: We propose a novel model for describing the spreading processes, in particular, epidemics. Our model is an extension of the SIQR ( susceptible-infected-quarantine-recovered ) and SIRP (susceptible-infected-recovered-pathogen) models used earlier to describe various scenarios of epidemic spread .
Revised: We propose a novel model for describing the spread of epidemics. This model is an extension of the SIQR ( susceptible-infected-quarantine-recovered ) and SIRP (susceptible-infected-recovered-pathogen) models used earlier to describe various scenarios of epidemic spread .
Edit intent: clarity
Original:  Our model is an extension of the SIQR ( susceptible-infected-quarantine-recovered ) and SIRP (susceptible-infected-recovered-pathogen) models used earlier to describe various scenarios of epidemic spread .
Revised:  Our model is an extension of the SIQR ( susceptible-infected-quarantined-recovered ) and SIRP (susceptible-infected-recovered-pathogen) models used earlier to describe various scenarios of epidemic spread .
Edit intent: fluency
Original:  Our model is an extension of the SIQR ( susceptible-infected-quarantine-recovered ) and SIRP (susceptible-infected-recovered-pathogen) models used earlier to describe various scenarios of epidemic spread .
Revised:  Our model is an extension of the SIQR ( susceptible-infected-quarantine-recovered ) and SIRP (susceptible-infected-recovered-pathogen) models used earlier to describe various scenarios of epidemic spreading .
Edit intent: fluency
Original:  As compared to the basic SIR model, our model takes into account two possible routes of virus transmission: direct from the infected compartment to the susceptible compartment and indirect via some intermediate medium or fomites.
Revised:  As compared to the basic SIR model, our model takes into account two possible routes of contagion transmission: direct from the infected compartment to the susceptible compartment and indirect via some intermediate medium or fomites.
Edit intent: clarity
Original:  We also introduce a resource activation function that reflects the load of the epidemics on economics and the limited capacity of the medical infrastructure .
Revised:  We also introduce a collective economic resource associated with the average amount of money or income per individual to describe the socioeconomic interplay between the spreading process and the resource available to infected individuals. The epidemic-resource coupling is supposed to be of activation type, with the recovery rate governed by the Arrhenius-like law .
Edit intent: meaning-changed
Original: We investigate the effect of introducing phone, syllable, or word boundaries on the performance of a Model of Visually Grounded Speech and compare the results with a model that does not use any boundary informationand with a model that uses random boundaries.
Revised: The language acquisition literature shows that children do not build their lexicon by segmenting the spoken input into phonemes and then building up words from them, but rather adopt a top-down approach and start by segmenting word-like units and then break them down into smaller units. This suggests that the ideal way of learning a language is by starting from full semantic units. In this paper, we investigate if this is also the case for a neural model of Visually Grounded Speech and compare the results with a model that does not use any boundary informationand with a model that uses random boundaries.
Edit intent: meaning-changed
Original: We investigate the effect of introducing phone, syllable, or word boundaries on the performance of a Model of Visually Grounded Speech and compare the results with a model that does not use any boundary informationand with a model that uses random boundaries. We introduce a simple way to introduce such information in an RNN-based model and investigate which type of boundary enables a better mapping between an image and its spoken description .
Revised: We investigate the effect of introducing phone, syllable, or word boundaries on the performance of a Model of Visually Grounded Speech trained on a speech-image retrieval task. We evaluated how well such a network is able to learn a reliable speech-to-image mapping when provided with phone, syllable, or word boundary information. We present a simple way to introduce such information in an RNN-based model and investigate which type of boundary enables a better mapping between an image and its spoken description .
Edit intent: clarity
Original:  We also explore where, that is, at which level of the network's architecture such information should be introduced . We show that using a segmentation that results in syllable-like or word-like segments and that respects word boundaries are the most efficient. Also, we show that a linguistically informed subsampling is more efficient than a random subsampling.
Revised:  We also explore where, that is, at which level of the network's architecture such information should be introduced so as to maximise its performances.
Edit intent: clarity
Original:  Finally, we show that using a hierarchical segmentation, by first using a phone segmentation and recomposing words from the phone units yields better results than either using a phone or word segmentation in isolation.
Revised:  Finally, we show that using multiple boundary types at once in a hierarchical structure, by which low-level segments are used to recompose high-level segments, is beneficial and yields better results than either using a phone or word segmentation in isolation.
Edit intent: clarity
Original:  Finally, we show that using a hierarchical segmentation, by first using a phone segmentation and recomposing words from the phone units yields better results than either using a phone or word segmentation in isolation.
Revised:  Finally, we show that using a hierarchical segmentation, by first using a phone segmentation and recomposing words from the phone units yields better results than using low-level or high-level segments in isolation.
Edit intent: clarity
Original:  However, captions of training images may contain redundant information (they can be repetitive or semantically similar to each other), resulting in information deficiency while learning a mapping from  image domain to  language domain.
Revised:  However, captions of training images may contain redundant information (they can be repetitive or semantically similar to each other), resulting in information deficiency while learning a mapping from the image domain to language domain.
Edit intent: fluency
Original:  However, captions of training images may contain redundant information (they can be repetitive or semantically similar to each other), resulting in information deficiency while learning a mapping from  image domain to  language domain.
Revised:  However, captions of training images may contain redundant information (they can be repetitive or semantically similar to each other), resulting in information deficiency while learning a mapping from image domain to the language domain.
Edit intent: fluency
Original:  Experimental results obtained on the RSCID, UCM-Captions and Sydney-Captions datasets show the effectiveness of the proposed approach compared to the state-of-the-art RS image captioning approaches. 
Revised:  Experimental results obtained on the RSCID, UCM-Captions and Sydney-Captions datasets show the effectiveness of the proposed approach compared to the state-of-the-art RS image captioning approaches. The code of the proposed approach is publicly available at URL
Edit intent: meaning-changed
Original: We quantified the probability of developing symptoms (respiratory or fever >= 37.5 {\deg}C) and critical disease (requiring intensive care or resulting in death) of SARS-CoV-2 positive subjects. 4,326 contacts of SARS-CoV-2 index cases detected in Lombardy, Italy were analyzed, and positive subjects were ascertained via nasal swabs and serological assays.
Revised: We quantified the probability of developing symptoms (respiratory or fever \geq 37.5 {\deg}C) and critical disease (requiring intensive care or resulting in death) of SARS-CoV-2 positive subjects. 4,326 contacts of SARS-CoV-2 index cases detected in Lombardy, Italy were analyzed, and positive subjects were ascertained via nasal swabs and serological assays.
Edit intent: clarity
Original: We quantified the probability of developing symptoms (respiratory or fever >= 37.5 {\deg}C) and critical disease (requiring intensive care or resulting in death) of SARS-CoV-2 positive subjects. 4,326 contacts of SARS-CoV-2 index cases detected in Lombardy, Italy were analyzed, and positive subjects were ascertained via nasal swabs and serological assays.
Revised: We quantified the probability of developing symptoms (respiratory or fever >= 37.5 {\deg}C) and critical disease (requiring intensive care or resulting in death) of SARS-CoV-2 positive subjects. 5,484 contacts of SARS-CoV-2 index cases detected in Lombardy, Italy were analyzed, and positive subjects were ascertained via nasal swabs and serological assays.
Edit intent: meaning-changed
Original:  Tracing techniques aim at reconstructing past history of contacts, but can face practical limits in an exponential growth of either testing or overly conservative quarantining. We show how this application of graph theory can be conveniently used to efficiently intercept new virus outbreaks, when they are still in their early stage.
Revised:  Contact tracing apps give the unprecedented possibility to reconstruct graphs of daily contacts, so the question is who should be tested? As human contact networks are known to exhibit community structure, in this paper we show that the Kemeny constant of a graph can be used to identify and analyze bridges between communities in a graph. Our "Kemeny indicator" is the change in Kemeny constant when a node or edge is removed from the graph. We show that testing individuals who are associated with large values of the Kemeny indicator can help in efficiently intercepting new virus outbreaks, when they are still in their early stage.
Edit intent: meaning-changed
Original: The global COVID-19 pandemic has led to a startling rise in social-media fueled misinformation and conspiracies, leading to dangerous outcomes for our society and health. We quantify the reach and belief in COVID-19 related misinformation , revealing a troubling breadth and depth of misinformation that is highly partisan .
Revised: The global COVID-19 pandemic has led to the online proliferation of health-, political-, and conspiratorial-based misinformation. Understanding the reach and belief in COVID-19 related misinformation , revealing a troubling breadth and depth of misinformation that is highly partisan .
Edit intent: clarity
Original:  We quantify the reach and belief in COVID-19 related misinformation , revealing a troubling breadth and depth of misinformation that is highly partisan .
Revised:  We quantify the reach and belief in this misinformation is vital to managing this crisis, as well as future crises. The results from our global survey finds a troubling reach of and belief in COVID-related misinformation, as well as a correlation with those that primarily consume news from social media, and, in the United States, a strong correlation with political leaning .
Edit intent: meaning-changed
Original:  To quantify the impact of government actions and support existing theories as well as policy makers, a data-driven model is developed . Data for two nations, Italy and Taiwan, is gathered and used to train several neural networks.
Revised:  This study seeks to quantify the impact of government actions and support existing theories as well as policy makers, a data-driven model is developed . Data for two nations, Italy and Taiwan, is gathered and used to train several neural networks.
Edit intent: clarity