wanyu/IteraTeR_full_sent huggingface dataset:
Original:  However, in disaster situations, such as epidemics, data in the form of location traces of a community of people can potentially be helpful to proactively locate susceptible people from the community and enforce quarantine on them as early as possible. Since procuring such data for the purpose of restricted use is difficult (time-consuming) due to the sensitive nature of the data, a strong case needs to be made that how could such data be useful in disaster situations. The aim of this article is to to demonstrate a proof-of-the-concept that with the availability of massive amounts of real check-in data , it is feasible to develop a scalable system that is both effective (in terms of identifying the susceptible people) and efficient (in terms of the time taken to do so). We believe that this proof-of-the-concept will encourage sharing (with restricted use) of such sensitive data in order to help mitigate disaster situations. In this article, we describe a software resource to efficiently (consuming a small run-time) locate a set of susceptible persons given a global database of user check-ins and a set of infected people. Specifically, we describe a system, named InfecTracer, that seeks to find out cases of close proximity of a person with another infected person .
Revised:  However, in disaster situations, such as epidemics, data in the given list of infected persons as queries, we investigate the feasibility of applying approximate nearest neighbour (ANN) based indexing and retrieval approaches to obtain a list of top-k suspected users in real-time. Since leveraging information from true user location data can lead to security and privacy concerns, we also investigate what effects does distance-preserving encoding methods have on the effectiveness of the ANN methods. Experiments conducted on real and synthetic datasets demonstrate that the top-k retrieved lists of susceptible users retrieved with existing ANN approaches (KD-tree and HNSW) yield satisfactory precision and recall values, thus indicating that ANN approaches can potentially be applied in practice to facilitate real-time contact tracing even under the presence of imposed privacy constraints .
Edit intent: meaning-changed
Original:  We illustrate, however, that a similar benefit arises by simple linear regressions that incorporate the leverage effect . Finally, we show how a faulty training/test data split, possibly along with an additional 'tagging' of data, leads to a significant overestimation of the outperformance of neural networks .
Revised:  We illustrate, however, that a similar benefit arises by simple linear regressions that incorporate the leverage effect .
Edit intent: coherence
Original: In their article, "Egalitarianism under Severe Uncertainty", ( Philosophy and Public Affairs,  2018 ) , Thomas Rowe and Alex Voorhoeve elegantly develop a theory of distributive justice , called "pluralist egalitarianism  " , for cases under maximal uncertainty .
Revised: In their article, "Egalitarianism under Severe Uncertainty", Philosophy and Public Affairs, 2018 ) , Thomas Rowe and Alex Voorhoeve elegantly develop a theory of distributive justice , called "pluralist egalitarianism " , for cases under maximal uncertainty .
Edit intent: fluency
Original: In their article, "Egalitarianism under Severe Uncertainty", ( Philosophy and Public Affairs,  2018 ) , Thomas Rowe and Alex Voorhoeve elegantly develop a theory of distributive justice , called "pluralist egalitarianism  " , for cases under maximal uncertainty .
Revised: In their article, "Egalitarianism under Severe Uncertainty", ( Philosophy and Public Affairs, 46:3, 2018 ) , Thomas Rowe and Alex Voorhoeve elegantly develop a theory of distributive justice , called "pluralist egalitarianism " , for cases under maximal uncertainty .
Edit intent: meaning-changed
Original: In their article, "Egalitarianism under Severe Uncertainty", ( Philosophy and Public Affairs,  2018 ) , Thomas Rowe and Alex Voorhoeve elegantly develop a theory of distributive justice , called "pluralist egalitarianism  " , for cases under maximal uncertainty .
Revised: In their article, "Egalitarianism under Severe Uncertainty", ( Philosophy and Public Affairs, 2018 , Thomas Rowe and Alex Voorhoeve elegantly develop a theory of distributive justice , called "pluralist egalitarianism " , for cases under maximal uncertainty .
Edit intent: fluency
Original: In their article, "Egalitarianism under Severe Uncertainty", ( Philosophy and Public Affairs,  2018 ) , Thomas Rowe and Alex Voorhoeve elegantly develop a theory of distributive justice , called "pluralist egalitarianism  " , for cases under maximal uncertainty .
Revised: In their article, "Egalitarianism under Severe Uncertainty", ( Philosophy and Public Affairs, 2018 ) , Thomas Rowe and Alex Voorhoeve develop an original moral decision theory for cases under uncertainty , called "pluralist egalitarianism " , for cases under maximal uncertainty .
Edit intent: clarity
Original: In their article, "Egalitarianism under Severe Uncertainty", ( Philosophy and Public Affairs,  2018 ) , Thomas Rowe and Alex Voorhoeve elegantly develop a theory of distributive justice , called "pluralist egalitarianism  " , for cases under maximal uncertainty .
Revised: In their article, "Egalitarianism under Severe Uncertainty", ( Philosophy and Public Affairs, 2018 ) , Thomas Rowe and Alex Voorhoeve elegantly develop a theory of distributive justice , called "pluralist egalitarianism under uncertainty " , for cases under maximal uncertainty .
Edit intent: fluency
Original: In their article, "Egalitarianism under Severe Uncertainty", ( Philosophy and Public Affairs,  2018 ) , Thomas Rowe and Alex Voorhoeve elegantly develop a theory of distributive justice , called "pluralist egalitarianism  " , for cases under maximal uncertainty .
Revised: In their article, "Egalitarianism under Severe Uncertainty", ( Philosophy and Public Affairs, 2018 ) , Thomas Rowe and Alex Voorhoeve elegantly develop a theory of distributive justice , called "pluralist egalitarianism " .
Edit intent: clarity
Original:  Finally, I suggest several objections against their view, proposing a two-stage ambiguity thought experiment .
Revised:  Finally, I suggest several objections against their view, proposing a two-stage Ellsberg thought experiment challenging the core of the principle of their theory. In such an experiment pluralist egalitarianism seems to suggest the wrong, morally and rationally speaking, course of action -- no matter whether I consider my thought experiment in a simultaneous or a sequential setting .
Edit intent: meaning-changed
Original:  We present DeepPurpose, a deep learning toolkit for simple and efficient drug repurposing.
Revised: Accurate prediction of drug-target interactions (DTI) enables drug discovery tasks, including virtual screening and drug repurposing, which can shorten the time to identify promising drug candidates and provide cures to patients. Recently, there is a growing number of research that developed deep learning (DL) models for DTI. Despite their superior performance, these research models are difficult to use in real drug discovery practice due to the complexity of deploying the research code as well as the restricted data formatting, model capacity, and evaluation setting. We present DeepPurpose, a deep learning toolkit for simple and efficient drug repurposing.
Edit intent: coherence
Original:  We present DeepPurpose, a deep learning toolkit for simple and efficient drug repurposing. With a few lines of code, DeepPurpose generates drug candidates based on aggregating five pretrained state-of-the-art models while offering flexibility for users to train their own models with 15 drug / target encodings and 50+ novel architectures.
Revised:  We present DeepPurpose, a comprehensive and easy-to-use software toolkit for DL based drug-target interaction (DTI) prediction with applications to drug screening and repurposing. The unique feature of DeepPurpose is that it enables non-computational drug development scientists to identify drug candidates based on aggregating five pretrained state-of-the-art models while offering flexibility for users to train their own models with 15 drug / target encodings and 50+ novel architectures.
Edit intent: meaning-changed
Original:  With a few lines of code, DeepPurpose generates drug candidates based on aggregating five pretrained state-of-the-art models while offering flexibility for users to train their own models with 15 drug / target encodings and 50+ novel architectures.
Revised:  With a few lines of code, DeepPurpose generates drug candidates based on five pre-trained DL models with only a few lines of codes. Further, computer scientists can use DeepPurpose to train customized DTI prediction models with 15 drug / target encodings and 50+ novel architectures.
Edit intent: meaning-changed
Original:  With a few lines of code, DeepPurpose generates drug candidates based on aggregating five pretrained state-of-the-art models while offering flexibility for users to train their own models with 15 drug / target encodings and 50+ novel architectures.
Revised:  With a few lines of code, DeepPurpose generates drug candidates based on aggregating five pretrained state-of-the-art models while offering flexibility for users to train their own models with 15 drug and target encodings and 50+ novel architectures.
Edit intent: fluency
Original:  With a few lines of code, DeepPurpose generates drug candidates based on aggregating five pretrained state-of-the-art models while offering flexibility for users to train their own models with 15 drug / target encodings and 50+ novel architectures. We demonstrated DeepPurpose using case studies, including  repurposing for COVID-19 where promising candidates under trials are ranked high in our results .
Revised:  With a few lines of code, DeepPurpose generates drug candidates based on aggregating five pretrained state-of-the-art models while offering flexibility for users to train their own models with 15 drug / target encodings and 50+ novel DL architectures. To tackle method development challenges, DeepPurpose also supports various data split settings and preloads five benchmarking datasets. We demonstrated that DeepPurpose allows users to obtain state-of-the-art prediction performance on several benchmark datasets. We also presented several case studies, including repurposing for COVID-19 where promising candidates under trials are ranked high in our results .
Edit intent: meaning-changed
Original: Accurate prediction of drug-target interactions (DTI) enables drug discoverytasks, including virtual screening and drug repurposing, which can shorten the time to identify promising drug candidates and provide cures to patients . Recently, there is a growing number of research that developed deep learning (DL) models for DTI.
Revised: Accurate prediction of drug-target interactions (DTI) is crucial for drug discovery . Recently, there is a growing number of research that developed deep learning (DL) models for DTI.
Edit intent: clarity
Original: Accurate prediction of drug-target interactions (DTI) enables drug discoverytasks, including virtual screening and drug repurposing, which can shorten the time to identify promising drug candidates and provide cures to patients . Recently, there is a growing number of research that developed deep learning (DL) models for DTI.
Revised: Accurate prediction of drug-target interactions (DTI) enables drug discoverytasks, including virtual screening and drug repurposing, which can shorten the time to identify promising drug candidates and provide cures to patients . Recently, deep learning (DL) models for DTI.
Edit intent: coherence
Original: Accurate prediction of drug-target interactions (DTI) enables drug discoverytasks, including virtual screening and drug repurposing, which can shorten the time to identify promising drug candidates and provide cures to patients . Recently, there is a growing number of research that developed deep learning (DL) models for DTI. Despite their superior performance, these research models are difficult to use in real drug discovery practice due to the complexity of deploying the research code as well as the restricted data formatting, model capacity, and evaluation setting .
Revised: Accurate prediction of drug-target interactions (DTI) enables drug discoverytasks, including virtual screening and drug repurposing, which can shorten the time to identify promising drug candidates and provide cures to patients . Recently, there is a growing number of research that developed deep learning (DL) models for show promising performance for DTI prediction. However, these models can be difficult to use in real drug discovery practice due to the complexity of deploying the research code as well as the restricted data formatting, model capacity, and evaluation setting .
Edit intent: clarity
Original:  We present DeepPurpose, a comprehensive and easy-to-use software toolkit for DL based drug-target interaction (DTI ) predictionwith applications to drug screening and repurposing. The unique feature of DeepPurpose is that it enables non-computational drug development scientists to identify drug candidates based on five pre-trained DL models with only a few lines of codes. Further, computer scientists can use DeepPurpose to train customized DTI prediction models with 15 drug and target encodings and 50 + novel DL architectures.
Revised:  We present DeepPurpose, a comprehensive and easy-to-use deep learning library for DTI prediction. DeepPurpose supports training of customized DTI prediction models with 15 drug and target encodings and 50 + novel DL architectures.
Edit intent: clarity
Original:  Further, computer scientists can use DeepPurpose to train customized DTI prediction models with 15 drug and target encodings and 50 + novel DL architectures. To tackle method development challenges, DeepPurpose also supports various data split settings and preloads five benchmarking datasets. We demonstrated that DeepPurpose allows users to obtain state-of-the-art prediction performance on several benchmark datasets .
Revised:  Further, computer scientists can use DeepPurpose to train customized DTI prediction models with 15 drug and target encodings and 50 neural architectures, along with providing many other useful features. We demonstrate state-of-the-art prediction performance on several benchmark datasets .
Edit intent: meaning-changed
Original:  We demonstrated that DeepPurpose allows users to obtain state-of-the-art prediction performance on several benchmark datasets . We also presented several case studies, including a study on drug repurposing for COVID-19, where promising drug candidates currently investigated in clinical trials are ranked high in DeepPurpose predictions .
Revised:  We demonstrated that DeepPurpose allows users to obtain state-of-the-art prediction performance on several benchmark datasets .
Edit intent: coherence
Original: 2} + n^{0.9})-time algorithm [Ghaffari, Nowicki, Thorup, SODA'20] works only on simple networks (no weights and no parallel edges).
Revised: 2} + n^{0.9})-time algorithm [Ghaffari, Nowicki, Thorup, SODA'20] works only on *simple* networks (no weights and no parallel edges).
Edit intent: fluency
Original:  At the heart of our algorithm are a combination of two kinds of tree-decompositions and a novel structural theorem that generalizes a theorem in Mukhopadhyay-Nanongkai [STOC'20] and , in turn, helps simplify their algorithms .
Revised:  At the heart of our algorithm are a clever routing trick and two structural lemmas regarding the structure of a minimum cut of a graph. These two structural lemmas considerably strengthen and generalize the framework of Mukhopadhyay-Nanongkai [STOC'20] and , in turn, helps simplify their algorithms .
Edit intent: meaning-changed
Original:  At the heart of our algorithm are a combination of two kinds of tree-decompositions and a novel structural theorem that generalizes a theorem in Mukhopadhyay-Nanongkai [STOC'20] and , in turn, helps simplify their algorithms .
Revised:  At the heart of our algorithm are a combination of two kinds of tree-decompositions and a novel structural theorem that generalizes a theorem in Mukhopadhyay-Nanongkai [STOC'20] and can be of independent interest .
Edit intent: meaning-changed
Original:  It has been shown that the pluripotency transcription factor OCT4 is inherited asymmetricallyand that OCT4 is more similar in closely related cells.
Revised:  The pluripotency transcription factors (PTFs) which regulate pluripotency are inherently stochastic (with small fluctuations impacting cell fate), inherited asymmetrically, and more similar in closely related cells.
Edit intent: meaning-changed
Original:  We evaluate the internal self-regulation of OCT4  , quantify the intra-cellular fluctuations and consider the diffusive nature of OCT4 over time for individual  and pairs of related cells.
Revised:  We evaluate the internal self-regulation of OCT4 using the Hurst exponent and autocorrelation analysis , quantify the intra-cellular fluctuations and consider the diffusive nature of OCT4 over time for individual and pairs of related cells.
Edit intent: meaning-changed
Original:  We evaluate the internal self-regulation of OCT4  , quantify the intra-cellular fluctuations and consider the diffusive nature of OCT4 over time for individual  and pairs of related cells.
Revised:  We evaluate the internal self-regulation of OCT4 , quantify the intra-cellular fluctuations and consider the diffusive nature of OCT4 over time for individual cells and pairs of related cells.
Edit intent: fluency
Original:  We evaluate the internal self-regulation of OCT4  , quantify the intra-cellular fluctuations and consider the diffusive nature of OCT4 over time for individual  and pairs of related cells. This quantitative framework provides a basis for comparison to other experiments, and the development of mathematical models of pluripotency.
Revised:  We evaluate the internal self-regulation of OCT4 , quantify the intra-cellular fluctuations and consider the diffusive nature of OCT4 over time for individual and pairs of their descendants. After possible asymmetric splitting, OCT4 abundance fluctuates sub-diffusively, showing anti-persistent self-regulation with a Hurst exponent of 0.38. Auto-correlation analysis shows anti-persistence for five hours or longer is seen in 86
Edit intent: meaning-changed
Original:  The pluripotency transcription factors (PTFs) which regulate pluripotency are inherently stochastic (with small fluctuations impacting cell fate), inherited asymmetrically  , and more similar in closely related  cells.
Revised:  The pluripotency transcription factors (PTFs) which regulate pluripotency are inherently stochastic (with small fluctuations impacting cell fate), inherited asymmetrically upon cell divisions , and more similar in closely related cells.
Edit intent: clarity
Original:  The pluripotency transcription factors (PTFs) which regulate pluripotency are inherently stochastic (with small fluctuations impacting cell fate), inherited asymmetrically  , and more similar in closely related  cells.
Revised:  The pluripotency transcription factors (PTFs) which regulate pluripotency are inherently stochastic (with small fluctuations impacting cell fate), inherited asymmetrically , and more similar in closely related (separated by less division events) cells.
Edit intent: meaning-changed
Original:  We evaluate the internal self-regulation of OCT4 using the Hurst exponent and autocorrelation analysis, quantify the intra-cellular fluctuations and consider the diffusive nature of OCT4 over time for individual cells and pairs of their descendants.
Revised:  We characterise the internal self-regulation of OCT4 using the Hurst exponent and autocorrelation analysis, quantify the intra-cellular fluctuations and consider the diffusive nature of OCT4 over time for individual cells and pairs of their descendants.
Edit intent: clarity
Original:  We evaluate the internal self-regulation of OCT4 using the Hurst exponent and autocorrelation analysis, quantify the intra-cellular fluctuations and consider the diffusive nature of OCT4 over time for individual cells and pairs of their descendants.
Revised:  We evaluate the internal self-regulation of OCT4 using the Hurst exponent and autocorrelation analysis, quantify the intra-cellular fluctuations and consider the diffusive nature of OCT4 evolution for individual cells and pairs of their descendants.
Edit intent: clarity
Original:  After possible asymmetric splitting , OCT4 abundance  fluctuates sub-diffusively, showing anti-persistent self-regulation with a Hurst exponent of 0.38.
Revised:  After possible asymmetric splitting , OCT4 abundance in the daughter cells fluctuates sub-diffusively, showing anti-persistent self-regulation with a Hurst exponent of 0.38.
Edit intent: meaning-changed
Original:  Auto-correlation analysis shows anti-persistence for five hours or longer is seen in 86 %DIFDELCMD < \\%%% %DIF < of cells, on average between three and 12 hours into the cell cycle.
Revised:  Auto-correlation analysis shows anti-persistence for five hours or longer in 86 %DIFDELCMD < \\%%% %DIF < of cells, on average between three and 12 hours into the cell cycle.
Edit intent: clarity
Original:  This quantitative framework provides a basis for comparison to other experiments, and the development of mathematical models of pluripotency.abstract 
Revised:  This quantitative framework provides a basis for comparison to other experiments, and the development of mathematical models of pluripotency.abstract \% of cells, on average between three and 12 hours into the cell cycle. The OCT4 fluctuations in five minute intervals follow a Laplace distribution, with BMP4 addition provoking smaller changes and tighter self-regulation, particularly in the differentiated fate group. This quantitative framework provides a basis for comparison to other experiments, and the development of mathematical models of pluripotency.
Edit intent: meaning-changed
Original: Pluripotency is the defining characteristic of human embryonic stem cells (hESCs) allowing them to differentiate into any somatic cell in the human body. For the promising clinical applications of hESCs, improved regulation of pluripotency and differentiation trajectories of their colonies is required .
Revised: Pluripotency is the defining characteristic of human embryonic stem cell (hESC) pluripotency and differentiation trajectories of their colonies is required .
Edit intent: clarity
Original:  For the promising clinical applications of hESCs, improved regulation of pluripotency and differentiation trajectories of their colonies is required .
Revised:  For the promising clinical applications of hESCs, improved regulation of pluripotency and differentiation trajectories is required for their promising clinical applications. The temporal and spatial quantification of the molecular interactions controlling pluripotency is also necessary for the development of successful mathematical and computational models .
Edit intent: meaning-changed
Original:  Here we use time-lapse experimental data of OCT4 fluorescence intensity to quantify the temporal  dynamics of the PTF OCT4 over a cell lifetime .
Revised:  Here we use time-lapse experimental data of OCT4 fluorescence intensity to quantify the temporal dynamics of the PTF OCT4 in a growing hESC colony in the presence and absence of BMP4 .
Edit intent: meaning-changed