wanyu/IteraTeR_v2 huggingface dataset:
Original: In the  COVID-19 pandemic, a  range of epidemiological models have been used to predict the number of new daily infections, I, daily rate of exponential growth, r , and effective reproduction number, R (t).
Revised: In the recent COVID-19 pandemic, a range of epidemiological models have been used to predict the number of new daily infections, I, daily rate of exponential growth, r , and effective reproduction number, R (t).
Edit intent: clarity
Original: In the  COVID-19 pandemic, a  range of epidemiological models have been used to predict the number of new daily infections, I, daily rate of exponential growth, r , and effective reproduction number, R (t).
Revised: In the COVID-19 pandemic, a wide range of epidemiological models have been used to predict the number of new daily infections, I, daily rate of exponential growth, r , and effective reproduction number, R (t).
Edit intent: clarity
Original: In the  COVID-19 pandemic, a  range of epidemiological models have been used to predict the number of new daily infections, I, daily rate of exponential growth, r , and effective reproduction number, R (t).
Revised: In the COVID-19 pandemic, a range of epidemiological modelling approaches have been used to predict the number of new daily infections, I, daily rate of exponential growth, r , and effective reproduction number, R (t).
Edit intent: clarity
Original: In the  COVID-19 pandemic, a  range of epidemiological models have been used to predict the number of new daily infections, I, daily rate of exponential growth, r , and effective reproduction number, R (t).
Revised: In the COVID-19 pandemic, a range of epidemiological models have been used to predict the number of new daily infections, I, daily rate of exponential growth, r (t).
Edit intent: clarity
Original:  These models differ in their approaches (e.g. mechanistic or empirical) and/ or  assumptions about spatial or age mixing, and some capture  uncertainty in scientific understanding of disease dynamics , and/or have different simplifying assumptions . Combining estimates from multiple models to better understand the variation of these outcome measures is important to help inform decision making.
Revised:  These candidate models use different modelling approaches or assumptions about spatial or age mixing, and some capture uncertainty in scientific understanding of disease dynamics , and/or have different simplifying assumptions . Combining estimates from multiple models to better understand the variation of these outcome measures is important to help inform decision making.
Edit intent: clarity
Original:  These models differ in their approaches (e.g. mechanistic or empirical) and/ or  assumptions about spatial or age mixing, and some capture  uncertainty in scientific understanding of disease dynamics , and/or have different simplifying assumptions . Combining estimates from multiple models to better understand the variation of these outcome measures is important to help inform decision making.
Revised:  These models differ in their approaches (e.g. mechanistic or empirical) and/ or differing assumptions about spatial or age mixing, and some capture uncertainty in scientific understanding of disease dynamics , and/or have different simplifying assumptions . Combining estimates from multiple models to better understand the variation of these outcome measures is important to help inform decision making.
Edit intent: clarity
Original:  These models differ in their approaches (e.g. mechanistic or empirical) and/ or  assumptions about spatial or age mixing, and some capture  uncertainty in scientific understanding of disease dynamics , and/or have different simplifying assumptions . Combining estimates from multiple models to better understand the variation of these outcome measures is important to help inform decision making.
Revised:  These models differ in their approaches (e.g. mechanistic or empirical) and/ or assumptions about spatial or age mixing, and some capture uncertainty in scientific understanding of disease dynamics . Combining estimates from multiple models to better understand the variation of these outcome measures is important to help inform decision making.
Edit intent: coherence
Original:  These models differ in their approaches (e.g. mechanistic or empirical) and/ or  assumptions about spatial or age mixing, and some capture  uncertainty in scientific understanding of disease dynamics , and/or have different simplifying assumptions . Combining estimates from multiple models to better understand the variation of these outcome measures is important to help inform decision making.
Revised:  These models differ in their approaches (e.g. mechanistic or empirical) and/ or assumptions about spatial or age mixing, and some capture uncertainty in scientific understanding of disease dynamics , and/or have different simplifying assumptions . Combining estimates from multiple models to better understand the variation of these outcome measures to help inform decision making.
Edit intent: coherence
Original:  We incorporate estimates of these outcome measures from a number of candidate models for specific UK nations /regions using meta analysis.
Revised:  In this paper, we combine these estimates for specific UK nations /regions using meta analysis.
Edit intent: clarity
Original:  Restricted maximum likelihood (REML) is used to estimate the heterogeneity variance parameter, with two approaches to calculate the confidence interval for the combined effect: standard Wald-type intervals  and the Knapp and Hartung (KNHA) method.
Revised:  Restricted maximum likelihood (REML) method to estimate the heterogeneity variance parameter, with two approaches to calculate the confidence interval for the combined effect: standard Wald-type intervals and the Knapp and Hartung (KNHA) method.
Edit intent: clarity
Original:  Restricted maximum likelihood (REML) is used to estimate the heterogeneity variance parameter, with two approaches to calculate the confidence interval for the combined effect: standard Wald-type intervals  and the Knapp and Hartung (KNHA) method.
Revised:  Restricted maximum likelihood (REML) is used to estimate the heterogeneity variance parameter, and two approaches to calculate the confidence interval for the combined effect: standard Wald-type intervals and the Knapp and Hartung (KNHA) method.
Edit intent: coherence
Original:  Restricted maximum likelihood (REML) is used to estimate the heterogeneity variance parameter, with two approaches to calculate the confidence interval for the combined effect: standard Wald-type intervals  and the Knapp and Hartung (KNHA) method.
Revised:  Restricted maximum likelihood (REML) is used to estimate the heterogeneity variance parameter, with two approaches to calculate the confidence interval for the combined estimate: the standard Wald-type intervals and the Knapp and Hartung (KNHA) method.
Edit intent: clarity
Original:  Restricted maximum likelihood (REML) is used to estimate the heterogeneity variance parameter, with two approaches to calculate the confidence interval for the combined effect: standard Wald-type intervals  and the Knapp and Hartung (KNHA) method.
Revised:  Restricted maximum likelihood (REML) is used to estimate the heterogeneity variance parameter, with two approaches to calculate the confidence interval for the combined effect: standard Wald-type intervals ; and the Knapp and Hartung (KNHA) method.
Edit intent: fluency
Original:  Utilising these meta-analysis techniques has allowed for statistically robust combined estimates to be calculated for key COVID-19 outcome measures , allowing an overall assessment of the current response measures with associated uncertainty .
Revised:  Utilising these meta-analysis techniques has allowed for statistically robust combined estimates to be calculated for key COVID-19 outcome measures .
Edit intent: coherence
Original:  This in turn allows timely and informed decision making based on all  available information.
Revised:  This in turn allows timely and informed decision making based on all of the available information.
Edit intent: fluency
Original: Measuring preferential attachment in growing networks is an important topic in network science, since the experimental confirmation of assumptions about the generative processes that give rise to heavy-tail degree distributions characteristic of real-world networks depends on it.
Revised: Measuring preferential attachment in growing networks has received considerable attention, not only for understanding how heavy-tail degree distributions characteristic of real-world networks depends on it.
Edit intent: clarity
Original:  We propose a novel nonparametric method, called PAFit-oneshot, for estimating the preferential attachment function for a growing network from one snapshot.
Revised:  We propose a nonparametric method, called PAFit-oneshot, for estimating the preferential attachment function for a growing network from one snapshot.
Edit intent: clarity
Original:  We propose a novel nonparametric method, called PAFit-oneshot, for estimating the preferential attachment function for a growing network from one snapshot.
Revised:  We propose a novel nonparametric method, called PAFit-oneshot, for estimating preferential attachment in a growing network from one snapshot.
Edit intent: clarity
Original:  The PAFit-oneshot method corrects for a  bias that arises when estimating preferential attachment values only for degrees observed in the single snapshot.
Revised:  PAFit-oneshot method corrects for a bias that arises when estimating preferential attachment values only for degrees observed in the single snapshot.
Edit intent: coherence
Original:  The PAFit-oneshot method corrects for a  bias that arises when estimating preferential attachment values only for degrees observed in the single snapshot.
Revised:  The PAFit-oneshot corrects for a bias that arises when estimating preferential attachment values only for degrees observed in the single snapshot.
Edit intent: clarity
Original:  The PAFit-oneshot method corrects for a  bias that arises when estimating preferential attachment values only for degrees observed in the single snapshot.
Revised:  The PAFit-oneshot method corrects for a previously unnoticed bias that arises when estimating preferential attachment values only for degrees observed in the single snapshot.
Edit intent: clarity
Original:  Our work opens up a new path for network scientists to measure preferential attachment in a large number of  one-snapshot networks that have been out-of-reach until now .
Revised:  Our work opens up a new path for network scientists to measure preferential attachment in a large number of one-snapshot networks .
Edit intent: clarity
Original:  As a demonstration, we nonparametrically estimated the preferential attachment function in three such networks and found all are sub-linear.
Revised:  As a demonstration, we estimated preferential attachment in three such networks and found all are sub-linear.
Edit intent: clarity
Original:  Numerous real-world datasets are, however, available only as single snapshots, rendering most conventional methods inapplicable of estimating a general preferential attachment function .
Revised:  Numerous real-world datasets are, however, only available as single snapshots, rendering most conventional methods inapplicable of estimating a general preferential attachment function .
Edit intent: fluency
Original:  Our work provides scientists with a means of measuring preferential attachment in a large number of publicly available one-snapshot networks.
Revised:  Our work provides a means of measuring preferential attachment in a large number of publicly available one-snapshot networks.
Edit intent: clarity
Original: Preferential attachment is commonly invoked to explain the emergence of those heavy-tailed degree distributions characteristic of growing network representations of divers real-world phenomena.
Revised: Preferential attachment is commonly invoked to explain the emergence of those heavy-tailed degree distributions characteristic of growing network representations of diverse real-world phenomena.
Edit intent: fluency
Original:  Unlike most existing approaches, the positions of the arrays in space are assumed to be unknown.
Edit intent: coherence
Original:  The DOAs are then fed to a fusion center where they are concatenated and used to perform the localization based on two proposed methods, which require only few labeled source locations for calibration .
Revised:  The DOAs are fed to a fusion center where they are concatenated and used to perform the localization based on two proposed methods, which require only few labeled source locations for calibration .
Edit intent: coherence
Original:  The DOAs are then fed to a fusion center where they are concatenated and used to perform the localization based on two proposed methods, which require only few labeled source locations for calibration .
Revised:  The DOAs are then fed to a fusion center , concatenated, and used to perform the localization based on two proposed methods, which require only few labeled source locations for calibration .
Edit intent: clarity
Original:  The PCA approach has fewer requirements on the training sequence, but is less robust to missing DOAs from one of the arrays.
Revised:  The PCA has fewer requirements on the training sequence, but is less robust to missing DOAs from one of the arrays.
Edit intent: clarity
Original:  The approach is demonstrated with a set of five 8-microphone circular arrays, placed at unknown fixed locations in an office.
Revised:  The methods are demonstrated with five IoT 8-microphone circular arrays, placed at unknown fixed locations in an office.
Edit intent: clarity
Original:  The approach is demonstrated with a set of five 8-microphone circular arrays, placed at unknown fixed locations in an office.
Revised:  The approach is demonstrated with a set of five 8-microphone circular arrays, placed at unspecified fixed locations in an office.
Edit intent: clarity
Original:  Both the PCA approach and the direct approach can easily map out a rectangle based on a few calibration points with similar accuracy as calibration points.
Revised:  Both the PCA approach and the direct approach can easily map out a rectangle based on a few anchor points with similar accuracy as calibration points.
Edit intent: clarity
Original:  The study concludes with a set of take-home-messages on different aspects of the correlation between the verification behavior of human and machine.
Revised:  The study concludes with a set of take-home messages on different aspects of the correlation between the verification behavior of human and machine.
Edit intent: fluency
Original:  In the majority of environments, we find that compared to off-policy baselines, MAPPO achieves better or comparable sample complexity as well as substantially faster running time .
Revised:  In the majority of environments, we find that compared to off-policy baselines, MAPPO achieves strong results while exhibiting comparable sample efficiency .
Edit intent: clarity
Original:  Finally, we present 5 factors most influential to MAPPO's practical performance with ablation studies .
Revised:  Finally, we present 5 factors most influential to MAPPO's practical performance .
Edit intent: clarity
Original: The recent advances in cancer immunotherapy paved the way for the development of tumor-immune system mathematical models aiming to provide the missing mechanistic understanding and indicate more efficient treatment regimes.
Revised: The recent advances in cancer immunotherapy boosted the development of tumor-immune system mathematical models aiming to provide the missing mechanistic understanding and indicate more efficient treatment regimes.
Edit intent: clarity
Original: The recent advances in cancer immunotherapy paved the way for the development of tumor-immune system mathematical models aiming to provide the missing mechanistic understanding and indicate more efficient treatment regimes.
Revised: The recent advances in cancer immunotherapy paved the way for the development of tumor-immune system models aiming to provide the missing mechanistic understanding and indicate more efficient treatment regimes.
Edit intent: clarity
Original: The recent advances in cancer immunotherapy paved the way for the development of tumor-immune system mathematical models aiming to provide the missing mechanistic understanding and indicate more efficient treatment regimes.
Revised: The recent advances in cancer immunotherapy paved the way for the development of tumor-immune system mathematical models aiming to provide mechanistic understanding and indicate more efficient treatment regimes.
Edit intent: clarity
Original:  Here, a fundamental and overparameterized model formulating the interactions of tumor cells with natural killer cells, CD8 + T cells and circulating lymphocytes is adopted .
Revised:  Here, a fundamental and overparameterized model formulating the interactions of tumor cells with natural killer cells, CD8 ^+ T cells and circulating lymphocytes is adopted .
Edit intent: fluency
Original:  Here, a fundamental and overparameterized model formulating the interactions of tumor cells with natural killer cells, CD8 + T cells and circulating lymphocytes is adopted .
Revised:  Here, a fundamental and overparameterized model formulating the interactions of tumor cells with natural killer cells, CD8 + T cells and circulating lymphocytes is examined .
Edit intent: clarity
Original:  It is first shown that the long-term evolution of the system towards the high-tumor or the tumor-free equilibrium is determined by the dynamics of an initial explosive stage of tumor progression.
Revised:  It is first shown that the long-term evolution of the system towards high-tumor or the tumor-free equilibrium is determined by the dynamics of an initial explosive stage of tumor progression.
Edit intent: fluency
Original:  It is first shown that the long-term evolution of the system towards the high-tumor or the tumor-free equilibrium is determined by the dynamics of an initial explosive stage of tumor progression.
Revised:  It is first shown that the long-term evolution of the system towards the high-tumor or tumor-free equilibrium is determined by the dynamics of an initial explosive stage of tumor progression.
Edit intent: fluency
Original:  It is first shown that the long-term evolution of the system towards the high-tumor or the tumor-free equilibrium is determined by the dynamics of an initial explosive stage of tumor progression.
Revised:  It is first shown that the long-term evolution of the system towards the high-tumor or the tumor-free equilibria is determined by the dynamics of an initial explosive stage of tumor progression.
Edit intent: clarity
Original:  It is first shown that the long-term evolution of the system towards the high-tumor or the tumor-free equilibrium is determined by the dynamics of an initial explosive stage of tumor progression.
Revised:  It is first shown that the long-term evolution of the system towards the high-tumor or the tumor-free equilibrium is determined by the dynamics of an initial explosive stage of tumor progression.
Edit intent: fluency
Original:  Focusing on this stage, the algorithmic Computational Singular Perturbation methodology is employed to identify the underlying mechanisms confining the system to evolve towards the high-tumor equilibrium and the governing slow dynamics along them.
Revised:  Focusing on this stage, the algorithmic Computational Singular Perturbation methodology is employed to identify the underlying mechanisms confining the system 's evolution towards the equilibrium and the governing slow dynamics along them.
Edit intent: clarity
Original:  This preliminary analysis demonstrates the potential of algorithmic asymptotic analysis to simplify the complex, overeparameterized and multi-scale  cancer immunology models and to indicate the interactions and cell types to target for more effective treatment development.
Revised:  This preliminary analysis demonstrates the potential of algorithmic asymptotic analysis to simplify the complex, overeparameterized and multi-scale nature of cancer immunology models and to indicate the interactions and cell types to target for more effective treatment development.
Edit intent: clarity
Original:  We also introduce the \textit{stochastic k-secretary} -- effectively reducing online blackbox  attacks to a k-secretary problem under noise -- and prove theoretical bounds on the competitive ratios of any online algorithms adapted to this setting.
Revised:  We also introduce the \textit{stochastic k-secretary} -- effectively reducing online blackbox attacks to a k-secretary problem under noise -- and prove theoretical bounds on the performance of any online algorithms adapted to this setting.
Edit intent: clarity
Original:  The policies and scenarios are modelled after the state of the public discussion and government guidelines in early March 2021 in England before the planned nationwide reopening of schools on the 8th of March.
Revised:  The policies and scenarios we model reflect the public discussion and government guidelines in early March 2021 in England before the planned nationwide reopening of schools on the 8th of March.
Edit intent: clarity
Original: We propose a game-theoretic framework to study the outcomes of packetized payments - a cross-ledger transaction protocol - with strategic and possibly malicious agents.
Revised: We propose a game-theoretic framework to study the outcomes of packetized payments , a cross-ledger transaction protocol - with strategic and possibly malicious agents.
Edit intent: fluency
Original: We propose a game-theoretic framework to study the outcomes of packetized payments - a cross-ledger transaction protocol - with strategic and possibly malicious agents.
Revised: We propose a game-theoretic framework to study the outcomes of packetized payments - a cross-ledger transaction protocol , with strategic and possibly malicious agents.
Edit intent: fluency
Original:  We derive the transaction failure rate and demonstrate that  packetized payments are likely to be incomplete , at least without disciplinary mechanisms .
Revised:  We derive the transaction failure rate and demonstrate that without disciplinary mechanisms, packetized payments are likely to be incomplete , at least without disciplinary mechanisms .
Edit intent: fluency
Original:  We derive the transaction failure rate and demonstrate that  packetized payments are likely to be incomplete , at least without disciplinary mechanisms .
Revised:  We derive the transaction failure rate and demonstrate that packetized payments are likely to be incomplete .
Edit intent: clarity
Original:  Our analysis suggests that collateral deposits can prevent malicious agents to take advantage of the protocols, and we infer that the deposit amount should depend on the underlying asset price volatility or that it should be dynamically adjusted as the prices changes.
Revised:  Our analysis suggests that collateral deposits can prevent malicious agents from taking advantage of the protocols, and we infer that the deposit amount should depend on the underlying asset price volatility or that it should be dynamically adjusted as the prices changes.
Edit intent: fluency
Original:  Our analysis suggests that collateral deposits can prevent malicious agents to take advantage of the protocols, and we infer that the deposit amount should depend on the underlying asset price volatility or that it should be dynamically adjusted as the prices changes.
Revised:  Our analysis suggests that collateral deposits can prevent malicious agents to take advantage of the protocol. We further infer that the deposit amount should depend on the underlying asset price volatility or that it should be dynamically adjusted as the prices changes.
Edit intent: clarity