wanyu/IteraTeR_full_sent huggingface dataset:
Original: To address the need for refined information in COVID-19 pandemic, we propose a deep learning-based system that uses state-of-the-art natural language processing (NLP) question answering (QA) techniques combined with summarization  for mining the available scientific literature.
Revised: To address the need for refined information in COVID-19 pandemic, we propose a deep learning-based system that uses state-of-the-art natural language processing (NLP) question answering (QA) techniques combined with summarization techniques for mining the available scientific literature.
Edit intent: meaning-changed
Original: The outbreak of COVID-19 raises attention from the researchers from various communities. While many scientific articles have been published , a system that can provide reliable information to COVID-19 related questions from the latest academic resources is crucial, especially for the medical community in the current time-critical race to treat patients and to find a cure for the virus.
Revised: The outbreak of COVID-19 Open Research Dataset Challenge, judged by medical experts. Our system aims to tackle the recent challenge of mining the numerous scientific articles being published on COVID-19 related questions from the latest academic resources is crucial, especially for the medical community in the current time-critical race to treat patients and to find a cure for the virus.
Edit intent: meaning-changed
Original:  While many scientific articles have been published , a system that can provide reliable information to COVID-19 related questions from the latest academic resources is crucial, especially for the medical community in the current time-critical race to treat patients and to find a cure for the virus.
Revised:  While many scientific articles have been published , a system that can provide reliable information to COVID-19 by answering high priority questions from the latest academic resources is crucial, especially for the medical community in the current time-critical race to treat patients and to find a cure for the virus.
Edit intent: meaning-changed
Original:  While many scientific articles have been published , a system that can provide reliable information to COVID-19 related questions from the latest academic resources is crucial, especially for the medical community in the current time-critical race to treat patients and to find a cure for the virus. To address the requests, we propose our CAiRE-COVID, a neural-based system that uses open-domain question answering (QA) techniques combined with summarization techniques for mining the available scientific literature. It leverages the Information Retrieval (IR) system and QA models to extract relevant snippets from existing literature given a query.
Revised:  While many scientific articles have been published , a system that can provide reliable information to COVID-19 related questions from the community and summarizing salient question-related information. It combines information extraction with state-of-the-art QA and query-focused multi-document summarization techniques, selecting and highlighting evidence snippets from existing literature given a query.
Edit intent: clarity
Original:  Fluent summaries are also provided to help understand the content in a more efficient way. Our system has been awarded as winner for one of the tasks in CORD-19 Kaggle Challenge. We also launched our CAiRE-COVID website for broader use .
Revised:  We also propose query-focused abstractive and extractive multi-document summarization methods, to provide more relevant information related to the question. We further conduct quantitative experiments that show consistent improvements on various metrics for each module. We have launched our website CAiRE-COVID for broader use .
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Original:  While official vaccination guidelines largely focus on the question to whom vaccines should be provided first (e.g. to risk groups), here we propose a generic strategy for their distribution in time and space, which sequentially prioritizes regions with a high local infection growth rate.
Revised:  While official vaccination guidelines largely focus on the question to whom vaccines should be provided first (e.g. to risk groups), here we propose a strategy for their distribution in time and space, which sequentially prioritizes regions with a high local infection growth rate.
Edit intent: clarity
Original:  To demonstrate this strategy, we develop an agent-based model describing the time-evolution of infection patterns and their response to vaccination . For heterogeneous infection patterns,  the proposed strategy at least halves the number of deaths in our simulations compared to the standard practice of distributing vaccines proportionally to the population density.
Revised:  To demonstrate this strategy, we develop an agent-based model describing the time-evolution of infection patterns and their response to vaccination , for infectious diseases like Covid-19. For inhomogeneous infection patterns, the proposed strategy at least halves the number of deaths in our simulations compared to the standard practice of distributing vaccines proportionally to the population density.
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Original:  For heterogeneous infection patterns,  the proposed strategy at least halves the number of deaths in our simulations compared to the standard practice of distributing vaccines proportionally to the population density.
Revised:  For heterogeneous infection patterns, locally well-mixed populations and basic reproduction numbers R_0\sim 1.5-4 the proposed strategy at least halves the number of deaths in our simulations compared to the standard practice of distributing vaccines proportionally to the population density.
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Original:  This applies for a wide range of reproduction numbers and vaccine production rates and might stimulate discussions on the importance of the spatiotemporal distribution of vaccines for official guidelines.
Revised:  For R_0\sim 1 we still find a significant increase of the survival rate. The proposed vaccine distribution strategy can be further tested in detailed modelling works and could excite discussions on the importance of the spatiotemporal distribution of vaccines for official guidelines.
Edit intent: meaning-changed
Original:  We conduct a grounded theory analysis and develop a substantive theory that (1) interprets the extent to which latent data abstractions exist, (2) reveals the far-reaching effects that the interventionist pursuit of such abstractions can have on data workers, (3) predicts why and when data workers may resist such explorations, and (4) suggests how to take advantage of opportunities and mitigate risks through transparency about visualization research perspectives and agendas  .
Revised:  We conduct a Grounded Theory analysis that (1) interprets the extent to which latent data abstractions exist, (2) reveals the far-reaching effects that the interventionist pursuit of such abstractions can have on data workers, (3) predicts why and when data workers may resist such explorations, and (4) suggests how to take advantage of opportunities and mitigate risks through transparency about visualization research perspectives and agendas .
Edit intent: coherence
Original:  We conduct a grounded theory analysis and develop a substantive theory that (1) interprets the extent to which latent data abstractions exist, (2) reveals the far-reaching effects that the interventionist pursuit of such abstractions can have on data workers, (3) predicts why and when data workers may resist such explorations, and (4) suggests how to take advantage of opportunities and mitigate risks through transparency about visualization research perspectives and agendas  .
Revised:  We conduct a grounded theory analysis and develop a substantive theory that (1) interprets the extent to which latent data abstractions exist, (2) reveals the far-reaching effects that the interventionist pursuit of such abstractions can have on data workers, (3) describes why and when data workers may resist such explorations, and (4) suggests how to take advantage of opportunities and mitigate risks through transparency about visualization research perspectives and agendas .
Edit intent: clarity
Original:  We conduct a grounded theory analysis and develop a substantive theory that (1) interprets the extent to which latent data abstractions exist, (2) reveals the far-reaching effects that the interventionist pursuit of such abstractions can have on data workers, (3) predicts why and when data workers may resist such explorations, and (4) suggests how to take advantage of opportunities and mitigate risks through transparency about visualization research perspectives and agendas  .
Revised:  We conduct a grounded theory analysis and develop a substantive theory that (1) interprets the extent to which latent data abstractions exist, (2) reveals the far-reaching effects that the interventionist pursuit of such abstractions can have on data workers, (3) predicts why and when data workers may resist such explorations, and (4) suggests how to take advantage of opportunities and mitigate risks through transparency about visualization research perspectives and agendas . We then use the themes and codes discovered in the Grounded Theory analysis to develop guidelines for data abstraction in visualization projects .
Edit intent: meaning-changed
Original:  This paradigm is evolving . Recent developments on Graph Convolutional Networks have shown that explicitly using information not directly present in the features to represent a type of relationship between samplescan improve the classification performance by a significant margin. However, graphs are not often immediately present in data sets, thus limiting the applicability of Graph Convolutional Networks. In this paper, we explore if graphs extracted from the features themselves can aid classification performance. First, we show that constructing optimal geometric graphs directly from data features can aid classification tasks on both synthetic and real-world data sets from different domains.
Revised:  This paradigm is evolving to include other sources of information, such as known relations between samples. Here we show that, even if additional relational information is not available in the data set, one can improve classification by constructing geometric graphs from the features themselves, and using them within a Graph Convolutional Network. The improvement in classification accuracy is maximized by graphs that capture sample similarity with relatively low edge density. We show that such feature-derived graphs increase the alignment of the data to the ground truth while improving class separation. We also demonstrate that the graphs can be made more efficient using spectral sparsification, which reduces the number of edges while still improving classification performance. We illustrate our findings using synthetic and real-world data sets from different domains.
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Original:  First, we show that constructing optimal geometric graphs directly from data features can aid classification tasks on both synthetic and real-world data sets from different domains. Second, we introduce two metrics to characterize optimal graphs: i) by measuring the alignment between the subspaces spanned by the features convolved with the graph and the ground truth ; and ii) ratio of class separationin the output activations of Graph Convolutional Networks: this shows that the optimal graph maximally separates classes. Finally, we find that sparsifying the optimal graph can potentially improve classification performance .
Revised:  First, we show that constructing optimal geometric graphs directly from data features can aid classification tasks on both synthetic and real-world data sets from various scientific domains .
Edit intent: clarity
Original: Living systems are hierarchically structured, whereby replicating components are grouped into reproducing collectives; URLanelles are grouped into cells; cells, into URLanisms; and URLanisms, into colonies. We consider how such hierarchically-structured replicator systems evolve along a continuum -- between cooperation and cheating. Cooperation is selected through competition among collectives, whereas cheating is selected through competition among replicating components within collectives .
Revised: The evolution of altruism is a key step in major evolutionary transitions. Previous theoretical studies often assume that individuals adopt one of two possible strategies, altruism or selfishness. In reality, however, cooperative strategies are likely to constitute a continuum between selfishness and altruism. Here, we investigate how such continuous variation affects the evolution of altruism through multilevel selection. In our model, altruism is favoured by selection among groups, whereas selfishness is favoured by selection within groups .
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Original:  We determine how the evolution depends on the number of replicating components per collective (N) and the mutation rate of replicating components (m)  such that evolution becomes increasingly independent of m as s decreases (i.e., \alpha}\downarrow0 \downarrow0 .
Revised:  We determine how evolution depends on the number of replicating components per collective (N) and the mutation rate of replicating components (m) such that evolution becomes increasingly independent of m as s decreases (i.e., \alpha}\downarrow0 \downarrow0 .
Edit intent: fluency
Original:  We determine how the evolution depends on the number of replicating components per collective (N) and the mutation rate of replicating components (m)  such that evolution becomes increasingly independent of m as s decreases (i.e., \alpha}\downarrow0 \downarrow0 .
Revised:  We determine how the evolution depends on the number of replicators per group (N) and the mutation rate of replicating components (m) such that evolution becomes increasingly independent of m as s decreases (i.e., \alpha}\downarrow0 \downarrow0 .
Edit intent: clarity
Original:  We determine how the evolution depends on the number of replicating components per collective (N) and the mutation rate of replicating components (m)  such that evolution becomes increasingly independent of m as s decreases (i.e., \alpha}\downarrow0 \downarrow0 .
Revised:  We determine how the evolution depends on the number of replicating components per collective (N) , mutation rate (m) such that evolution becomes increasingly independent of m as s decreases (i.e., \alpha}\downarrow0 \downarrow0 .
Edit intent: coherence
Original:  We determine how the evolution depends on the number of replicating components per collective (N) and the mutation rate of replicating components (m)  such that evolution becomes increasingly independent of m as s decreases (i.e., \alpha}\downarrow0 \downarrow0 .
Revised:  We determine how the evolution depends on the number of replicating components per collective (N) and the mutation rate of replicating components (m) , and selection strength (s). We find that decreasing N or m promotes evolution toward altruism, and if this evolution is exactly balanced out by evolution towards selfishness, N and m satisfy a scaling relation N\,\propto\, m^{-\alpha such that evolution becomes increasingly independent of m as s decreases (i.e., \alpha}\downarrow0 \downarrow0 .
Edit intent: meaning-changed
Original:  We determine how the evolution depends on the number of replicating components per collective (N) and the mutation rate of replicating components (m)  such that evolution becomes increasingly independent of m as s decreases (i.e., \alpha}\downarrow0 \downarrow0 .
Revised:  We determine how the evolution depends on the number of replicating components per collective (N) and the mutation rate of replicating components (m) such that evolution becomes increasingly independent of m as s decreases (i.e., \alpha}\downarrow0 as s\downarrow0 .
Edit intent: clarity
Original:  We determine how the evolution depends on the number of replicating components per collective (N) and the mutation rate of replicating components (m)  such that evolution becomes increasingly independent of m as s decreases (i.e., \alpha}\downarrow0 \downarrow0 .
Revised:  We determine how the evolution depends on the number of replicating components per collective (N) and the mutation rate of replicating components (m) such that evolution becomes increasingly independent of m as s decreases (i.e., \alpha}\downarrow0 \downarrow0) .
Edit intent: fluency
Original:  We develop a simple theory that can account for this scaling relation using moment evolution equations and clarify the limit of applicability of the theory using time-backward observation}  .
Revised:  We develop a simple theory that can account for this scaling relation using moment evolution equations and clarify the limit of applicability of the theory using time-backward observation} This relation markedly differs from that predicted by a binary-trait model, where evolution becomes increasingly dependent on m as s decreases. We show that this difference is caused by the assumption implicit in binary-trait models that mutation causes convergent evolution even in the absence of selection. Our study illustrates how considering quantitative variation can be important for understanding the evolution of altruism during major evolutionary transitions .
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Original: A modified SEIR model is used in the paper to evaluate the effect of inter-state migrant worker transportation during the lockdown in India . The model considers different levels of COVID-19 transmission during travel, daily arrival rate and total migrant workers.
Revised: Nationwide lockdown for COVID-19 created an urgent demand for public transportation among migrant workers stranded at different parts of India to return to their native places. Arranging transportation could spike the number of COVID-19 transmission during travel, daily arrival rate and total migrant workers.
Edit intent: clarity
Original:  Indian states with significant migrant workers are considered to estimate the rise in confirmed and active COVID-19 cases . It is observed that reduced daily arrival rate of migrant workers can help Uttar Pradesh and Bihar to lower the rise in confirmed and active cases .
Revised:  Indian states with significant migrant workers are considered to estimate the rise in confirmed and active cases of COVID-19 infection and assesses the train and bus fleet size required for the repatriating migrant workers. The expected to repatriate migrant worker population was obtained by forecasting the 2011 census data and comparing it with the information reported in the news media. A modified susceptible-exposed-infected-removed (SEIR) model was proposed to estimate the surge in confirmed and active cases .
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Original:  It is observed that reduced daily arrival rate of migrant workers can help Uttar Pradesh and Bihar to lower the rise in confirmed and active cases . By the end of May, 2020, the daily arrival rate of migrant workers will have similar effect on Rajasthan.
Revised:  It is observed that reduced daily arrival rate of migrant workers can help Uttar Pradesh and Bihar to lower the rise in confirmed and active cases of COVID-19 patients in India's selected states with high outflux of migrants. The developed model considered combinations of different levels of the daily arrival rate of migrant workers will have similar effect on Rajasthan.
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Original:  By the end of May, 2020, the daily arrival rate of migrant workers will have similar effect on Rajasthan. For the daily arrival rate of migrant workers considered, Madhya Pradesh may experience higher rise in confirmed and active cases.
Revised:  By the end of May, 2020, the daily arrival rate of migrant workers , total migrant workers in need of transportation, and the origin of the trip dependent symptomatic cases on arrival. Reducing the daily arrival rate of migrant workers considered, Madhya Pradesh may experience higher rise in confirmed and active cases.
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Original:  Maharashtra, on the other hand, may experience marginal rise in the confirmed and active cases. A stringent policy to screen migrant workers for possible trace COVID-19 virus prior to the travel and stricter isolation norm after reaching the destination state can be helpful in reducing the rise in confirmed and actives cases attributed to the migrant worker transportation .
Revised:  Nevertheless, it could create a disparity in the number of days needed to transport all repatriating migrant workers to the home states. Hence, travel arrangements for about 100,000 migrant workers per day to Uttar Pradesh and Bihar, about 50,000 per day to Rajasthan and Madhya Pradesh, 20,000 per day to Maharashtra and less than 20,000 per day to other states of India was recommended .
Edit intent: meaning-changed
Original:  We start with an ODE version of a SEIHARD model that we optimize to obtain relevant parameters for these regions . Subsequently, spatially distributed versions of the corresponding models in the form of convection-reaction-diffusion equations are considered.
Revised:  We start with a 0D compartmental epidemiological model consisting of Susceptible, Exposed, Asymptomatic, (symptomatically) Infected, Hospitalized, Recovered, and deceased populations. We emphasize the importance of the viral latent period and the key role of an asymptomatic population. We optimize model parameters for both regions by comparing predictions to the cumulative number of infected and total number of deaths via minimizing the \ell^2 norm of the difference between predictions and observed data. We consider the sensitivity of model predictions on reasonable variations of model parameters and initial conditions, addressing issues of parameter identifiability. We model both pre-quarantine and post-quarantine evolution of the epidemic by a time-dependent change of the viral transmission rates that arises in response to containment measures. Subsequently, a spatially distributed version of the 0D model in the form of convection-reaction-diffusion equations are considered.
Edit intent: meaning-changed
Original:  We start with an ODE version of a SEIHARD model that we optimize to obtain relevant parameters for these regions . Subsequently, spatially distributed versions of the corresponding models in the form of convection-reaction-diffusion equations are considered. We show that these models bear significant potential towards capturing the spatial expansion of the pandemic and veins of future work are considered to that effect .
Revised:  We start with an ODE version of a SEIHARD model that we optimize to obtain relevant parameters for these regions . Subsequently, spatially distributed versions of the corresponding models in the form of reaction-diffusion equations is developed. We consider that, after an initial localized seeding of the infection, its spread is governed by the diffusion (and 0D model "reactions") of the asymptomatic and symptomatically infected populations, which decrease with the imposed restrictive measures. We inserted the maps of the two regions, and we imported population-density data into COMSOL, which was subsequently used to solve numerically the model PDEs. Upon discussing how to adapt the 0D model to this spatial setting, we show that these models bear significant potential towards capturing the spatial expansion of the pandemic and veins of future work are considered to that effect .
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