wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We then propose an objective personal epidemic risk scoring model named HiRES-p based on HiRES risk maps, and use it to develop statistical inference based method and machine learning based method for detecting suspected epidemic-infected individuals.
Revised:  We then propose an objective personal epidemic risk scoring model named HiRES-p based on HiRES risk maps, and use it to develop statistical inference and machine learning based method for detecting suspected epidemic-infected individuals.
Edit intent: fluency
Original:  We then propose an objective personal epidemic risk scoring model named HiRES-p based on HiRES risk maps, and use it to develop statistical inference based method and machine learning based method for detecting suspected epidemic-infected individuals.
Revised:  We then propose an objective personal epidemic risk scoring model named HiRES-p based on HiRES risk maps, and use it to develop statistical inference based method and machine learning methods for detecting suspected epidemic-infected individuals.
Edit intent: fluency
Original:  Results show that the HiRES risk map has strong ability in capturing global trend and local variability of the epidemic risk, thus can be applied to monitor epidemic risk at country, province, city and community levels, as well as at specific high-risk locations such as at hospital and station.
Revised:  Results show that the HiRES risk map has strong ability in capturing global trend and local variability of the epidemic risk, thus can be applied to monitor epidemic risk at country, province, city and community levels, as well as at specific high-risk locations such as hospital and station.
Edit intent: fluency
Original:  Concretely, for the relation sparsity and noises problems , a GCN-based autoencoder is developed to augment the relation data by encoding high-order and complex connectivity patterns, and meanwhile is optimized subject to the constraint of reconstructing the original social profile to guarantee the validity of new identified neighborhood.
Revised:  Concretely, for (1) and (2) , a GCN-based autoencoder is developed to augment the relation data by encoding high-order and complex connectivity patterns, and meanwhile is optimized subject to the constraint of reconstructing the original social profile to guarantee the validity of new identified neighborhood.
Edit intent: meaning-changed
Original:  Concretely, for the relation sparsity and noises problems , a GCN-based autoencoder is developed to augment the relation data by encoding high-order and complex connectivity patterns, and meanwhile is optimized subject to the constraint of reconstructing the original social profile to guarantee the validity of new identified neighborhood.
Revised:  Concretely, for the relation sparsity and noises problems , a GCN-based autoencoder is developed to augment the relation data by encoding high-order and complex connectivity patterns, and meanwhile is optimized subject to the constraint of reconstructing the social profile to guarantee the validity of new identified neighborhood.
Edit intent: clarity
Original:  Concretely, for the relation sparsity and noises problems , a GCN-based autoencoder is developed to augment the relation data by encoding high-order and complex connectivity patterns, and meanwhile is optimized subject to the constraint of reconstructing the original social profile to guarantee the validity of new identified neighborhood.
Revised:  Concretely, for the relation sparsity and noises problems , a GCN-based autoencoder is developed to augment the relation data by encoding high-order and complex connectivity patterns, and meanwhile is optimized subject to the constraint of reconstructing the original social profile to guarantee the validity of the identified neighborhood.
Edit intent: clarity
Original:  Finally, we adopt adversarial training to unify and intensify all components by playing a minimax game and ensure a coordinated effort to enhance social recommendation . Experimental results on multiple open datasets demonstrate the superiority of our framework and the ablation study confirms the importance and effectiveness of each component.
Revised:  Finally, we adopt adversarial training to unify all the components by playing a minimax game and ensure a coordinated effort to enhance social recommendation . Experimental results on multiple open datasets demonstrate the superiority of our framework and the ablation study confirms the importance and effectiveness of each component.
Edit intent: clarity
Original:  Finally, we adopt adversarial training to unify and intensify all components by playing a minimax game and ensure a coordinated effort to enhance social recommendation . Experimental results on multiple open datasets demonstrate the superiority of our framework and the ablation study confirms the importance and effectiveness of each component.
Revised:  Finally, we adopt adversarial training to unify and intensify all components by playing a Minimax game and ensure a coordinated effort to enhance social recommendation . Experimental results on multiple open datasets demonstrate the superiority of our framework and the ablation study confirms the importance and effectiveness of each component.
Edit intent: fluency
Original:  Finally, we adopt adversarial training to unify and intensify all components by playing a minimax game and ensure a coordinated effort to enhance social recommendation . Experimental results on multiple open datasets demonstrate the superiority of our framework and the ablation study confirms the importance and effectiveness of each component.
Revised:  Finally, we adopt adversarial training to unify and intensify all components by playing a minimax game and ensure a coordinated effort to enhance recommendation performance. Extensive experiments on multiple open datasets demonstrate the superiority of our framework and the ablation study confirms the importance and effectiveness of each component.
Edit intent: clarity
Original: We present a novel methodology in order to perform the epidemic risk assessment in terms of different factors which are useful to understand the different impact of an epidemic in different areas of a country. In particular we discuss the case of COVID-19 outbreak in Italy.
Revised: We propose a novel data-driven framework for assessing the a-priori epidemic risk of a geographical area, and for identifying high-risk areas within a country. Our risk index is evaluated as a function of three different components: the hazard of the disease, the exposure of the area and its vulnerability. As an application, we discuss the case of COVID-19 outbreak in Italy.
Edit intent: coherence
Original:  Our epidemic risk index shows strong correlations with the available official data of the COVID-19 outbreak in Italy and explain in particular why regions like Lombardia, Emilia-Romagna, Piemonte and Veneto are suffering much more than the rest of the country in terms of infected cases, intensive care units and deceased patients .
Revised:  Our epidemic risk index shows strong correlations with the available official data on the number of infected individuals, patients in intensive care and deceased patients, and can explain why regions such as Lombardia, Emilia-Romagna, Piemonte and Veneto are suffering much more than the rest of the country in terms of infected cases, intensive care units and deceased patients .
Edit intent: meaning-changed
Original:  Our epidemic risk index shows strong correlations with the available official data of the COVID-19 outbreak in Italy and explain in particular why regions like Lombardia, Emilia-Romagna, Piemonte and Veneto are suffering much more than the rest of the country in terms of infected cases, intensive care units and deceased patients .
Revised:  Our epidemic risk index shows strong correlations with the available official data of the COVID-19 outbreak in Italy and explain in particular why regions like Lombardia, Emilia-Romagna, Piemonte and Veneto are suffering much more than the rest of the country .
Edit intent: coherence
Original:  Although the COVID-19 outbreak started almost in the same period, at the beginning of 2020, in both north (Lombardia and Veneto) and central part of Italy (Lazio)  , when the first infected were officially certified , the outbreak has been more diffuse and lethal only in those regions with higher epidemic risk.
Revised:  Although the COVID-19 outbreak started in both North (Lombardia and Veneto) and central part of Italy (Lazio) , when the first infected were officially certified , the outbreak has been more diffuse and lethal only in those regions with higher epidemic risk.
Edit intent: coherence
Original:  Although the COVID-19 outbreak started almost in the same period, at the beginning of 2020, in both north (Lombardia and Veneto) and central part of Italy (Lazio)  , when the first infected were officially certified , the outbreak has been more diffuse and lethal only in those regions with higher epidemic risk.
Revised:  Although the COVID-19 outbreak started almost in the same period, at the beginning of 2020, in both north (Lombardia and Veneto) and Central Italy (Lazio) , when the first infected were officially certified , the outbreak has been more diffuse and lethal only in those regions with higher epidemic risk.
Edit intent: clarity
Original:  Although the COVID-19 outbreak started almost in the same period, at the beginning of 2020, in both north (Lombardia and Veneto) and central part of Italy (Lazio)  , when the first infected were officially certified , the outbreak has been more diffuse and lethal only in those regions with higher epidemic risk.
Revised:  Although the COVID-19 outbreak started almost in the same period, at the beginning of 2020, in both north (Lombardia and Veneto) and central part of Italy (Lazio) almost at the same time , when the first infected were officially certified , the outbreak has been more diffuse and lethal only in those regions with higher epidemic risk.
Edit intent: meaning-changed
Original:  Although the COVID-19 outbreak started almost in the same period, at the beginning of 2020, in both north (Lombardia and Veneto) and central part of Italy (Lazio)  , when the first infected were officially certified , the outbreak has been more diffuse and lethal only in those regions with higher epidemic risk.
Revised:  Although the COVID-19 outbreak started almost in the same period, at the beginning of 2020, in both north (Lombardia and Veneto) and central part of Italy (Lazio) , when the first infected were officially certified at the beginning of 2020, the disease has spread faster and with heavier consequences in regions with higher epidemic risk.
Edit intent: clarity
Original:  Due to the fact that a large part of infected people do not show evident symptoms of the disease, we claim that in the regions with a lower epidemic risk, i.e. the central and south part of Italy, the epidemic , although probably already very diffused, should not be as lethal as in the northern part of Italy due to a different a-priori risk exposure .
Revised:  Our framework can be extended and tested on other epidemic data, such as those on seasonal flu .
Edit intent: coherence
Original:  To derive future ICU load from reported infections, we suggest a simple statistical model that (1) accounts for time lags and (2) allows for making predictions depending on different future growth rates. We evaluate our model for public data from Berlin, Germany, by first estimating the model parameters (i.e., time lag and average stay in ICU)for March 2020 and then using an exponential model to predict the future ICU load for April and May 2020.
Revised:  To derive future ICU load from reported infections, we suggest a simple statistical model that (1) accounts for time lags and (2) allows for making predictions depending on different future growth of infections. We have evaluated our model for public data from Berlin, Germany, by first estimating the model parameters (i.e., time lag and average stay in ICU)for March 2020 and then using an exponential model to predict the future ICU load for April and May 2020.
Edit intent: clarity
Original:  We evaluate our model for public data from Berlin, Germany, by first estimating the model parameters (i.e., time lag and average stay in ICU)for March 2020 and then using an exponential model to predict the future ICU load for April and May 2020. Assuming an ICU rate of 5 \%, a time lag of 5 days and an average stay of 14 days in ICU provide the best fit of the data  and is in accord with independent estimates.
Revised:  We evaluate our model for three regions, namely Berlin (Germany), Lombardy (Italy), and Madrid (Spain). Before extensive containment measures made an impact, we first estimate the region-specific model parameters. Whereas for Berlin, an ICU rate of 5 \%, a time lag of 5 days and an average stay of 14 days in ICU provide the best fit of the data and is in accord with independent estimates.
Edit intent: meaning-changed
Original:  Assuming an ICU rate of 5 \%, a time lag of 5 days and an average stay of 14 days in ICU provide the best fit of the data  and is in accord with independent estimates.
Revised:  Assuming an ICU rate of 6 \%, a time lag of 5 days and an average stay of 14 days in ICU provide the best fit of the data and is in accord with independent estimates.
Edit intent: meaning-changed
Original:  Assuming an ICU rate of 5 \%, a time lag of 5 days and an average stay of 14 days in ICU provide the best fit of the data  and is in accord with independent estimates.
Revised:  Assuming an ICU rate of 5 \%, a time lag of 6 days, and an average stay of 14 days in ICU provide the best fit of the data and is in accord with independent estimates.
Edit intent: meaning-changed
Original:  Assuming an ICU rate of 5 \%, a time lag of 5 days and an average stay of 14 days in ICU provide the best fit of the data  and is in accord with independent estimates.
Revised:  Assuming an ICU rate of 5 \%, a time lag of 5 days and an average stay of 12 days in ICU provide the best fit of the data and is in accord with independent estimates.
Edit intent: meaning-changed
Original:  Assuming an ICU rate of 5 \%, a time lag of 5 days and an average stay of 14 days in ICU provide the best fit of the data  and is in accord with independent estimates.
Revised:  Assuming an ICU rate of 5 \%, a time lag of 5 days and an average stay of 14 days in ICU provide the best fit of the data , for Lombardy and Madrid the ICU rate was higher (18\% and 15\%) and the time lag (0 and is in accord with independent estimates.
Edit intent: meaning-changed
Original:  Assuming an ICU rate of 5 \%, a time lag of 5 days and an average stay of 14 days in ICU provide the best fit of the data  and is in accord with independent estimates. Our model is then used to predict future ICU load assuming  a continued exponential phase with varying growth rates (0-15\%) .
Revised:  Assuming an ICU rate of 5 \%, a time lag of 5 days and an average stay of 14 days in ICU provide the best fit of the data and 3 days) and the average stay (4 and 8 days) in ICU shorter. The region-specific models are then used to predict future ICU load assuming a continued exponential phase with varying growth rates (0-15\%) .
Edit intent: meaning-changed
Original:  Our model is then used to predict future ICU load assuming  a continued exponential phase with varying growth rates (0-15\%) . For example, based on our parameters the model predicts that the number of ICU patients at the end of May would be 246 if the exponential growth were to continue at a rate of 3\%, 1,056 if the growth rate were 5\% and 3,758 if the growth rate were 7\%. The model can be adjusted as estimates of parameters develop and can thus also help to predict a potential exceedance of ICU capacity.
Revised:  Our model is then used to predict future ICU load assuming a continued exponential phase with varying growth rates (0-15\%) or linear growth. Thus, the model can help to predict a potential exceedance of ICU capacity.
Edit intent: meaning-changed
Original:  Although our predictions are based on a small data set, disregard non-stationary dynamics, and have a number of assumptions, especially an exponential development of cases, our model is simple, robust, adaptable and can be up-dated when further data become available .
Revised:  Although our predictions are based on small data sets and disregard non-stationary dynamics, and have a number of assumptions, especially an exponential development of cases, our model is simple, robust, adaptable and can be up-dated when further data become available .
Edit intent: fluency
Original:  Although our predictions are based on a small data set, disregard non-stationary dynamics, and have a number of assumptions, especially an exponential development of cases, our model is simple, robust, adaptable and can be up-dated when further data become available .
Revised:  Although our predictions are based on a small data set, disregard non-stationary dynamics, our model is simple, robust, adaptable and can be up-dated when further data become available .
Edit intent: coherence
Original:  Although our predictions are based on a small data set, disregard non-stationary dynamics, and have a number of assumptions, especially an exponential development of cases, our model is simple, robust, adaptable and can be up-dated when further data become available .
Revised:  Although our predictions are based on a small data set, disregard non-stationary dynamics, and have a number of assumptions, especially an exponential development of cases, our model is simple, robust, and can be up-dated when further data become available .
Edit intent: clarity
Original:  Although our predictions are based on a small data set, disregard non-stationary dynamics, and have a number of assumptions, especially an exponential development of cases, our model is simple, robust, adaptable and can be up-dated when further data become available .
Revised:  Although our predictions are based on a small data set, disregard non-stationary dynamics, and have a number of assumptions, especially an exponential development of cases, our model is simple, robust, adaptable and can be used in early phases of the disease when data are scarce .
Edit intent: clarity
Original: We argue that random testing (polling the fraction of infected people in the population) is central to managing the COVID-19 pandemic because it both measures  the key variable controlled by restrictive measures, and anticipates the load on the healthcare system via the progression of the disease.
Revised: We argue that frequent sampling of the fraction of infected people in the population) is central to managing the COVID-19 pandemic because it both measures the key variable controlled by restrictive measures, and anticipates the load on the healthcare system via the progression of the disease.
Edit intent: clarity
Original: We argue that random testing (polling the fraction of infected people in the population) is central to managing the COVID-19 pandemic because it both measures  the key variable controlled by restrictive measures, and anticipates the load on the healthcare system via the progression of the disease.
Revised: We argue that random testing (polling the fraction of infected people (either by random testing or by analysis of sewage water), is central to managing the COVID-19 pandemic because it both measures the key variable controlled by restrictive measures, and anticipates the load on the healthcare system via the progression of the disease.
Edit intent: meaning-changed
Original: We argue that random testing (polling the fraction of infected people in the population) is central to managing the COVID-19 pandemic because it both measures  the key variable controlled by restrictive measures, and anticipates the load on the healthcare system via the progression of the disease.
Revised: We argue that random testing (polling the fraction of infected people in the population) is central to managing the COVID-19 pandemic because it both measures in real time the key variable controlled by restrictive measures, and anticipates the load on the healthcare system via the progression of the disease.
Edit intent: meaning-changed
Original: We argue that random testing (polling the fraction of infected people in the population) is central to managing the COVID-19 pandemic because it both measures  the key variable controlled by restrictive measures, and anticipates the load on the healthcare system via the progression of the disease.
Revised: We argue that random testing (polling the fraction of infected people in the population) is central to managing the COVID-19 pandemic because it both measures the key variable controlled by restrictive measures, and anticipates the load on the healthcare system due to progression of the disease.
Edit intent: clarity
Original:  Knowledge of random testing outcomes will therefore (i) significantly improve the predictability of the course of the pandemic, (ii) allow informed and optimized decisions on how to modify restrictive measures, with much shorter delay times than the present ones, and (iii) enable the real-time assessment of the efficiency of new means to reduce transmission rates (such as new tracing strategies based on the mobile telephone network, wearing face masks, etc. ).
Revised:  Knowledge of random testing outcomes will (i) significantly improve the predictability of the course of the pandemic, (ii) allow informed and optimized decisions on how to modify restrictive measures, with much shorter delay times than the present ones, and (iii) enable the real-time assessment of the efficiency of new means to reduce transmission rates (such as new tracing strategies based on the mobile telephone network, wearing face masks, etc. ).
Edit intent: coherence
Original:  Knowledge of random testing outcomes will therefore (i) significantly improve the predictability of the course of the pandemic, (ii) allow informed and optimized decisions on how to modify restrictive measures, with much shorter delay times than the present ones, and (iii) enable the real-time assessment of the efficiency of new means to reduce transmission rates (such as new tracing strategies based on the mobile telephone network, wearing face masks, etc. ).
Revised:  Knowledge of random testing outcomes will therefore (i) significantly improve the predictability of the pandemic, (ii) allow informed and optimized decisions on how to modify restrictive measures, with much shorter delay times than the present ones, and (iii) enable the real-time assessment of the efficiency of new means to reduce transmission rates (such as new tracing strategies based on the mobile telephone network, wearing face masks, etc. ).
Edit intent: clarity