wanyu/IteraTeR_full_sent huggingface dataset:
Original:  After removing the effects of  environmental inputs, we find that different countries have reliably and substantially different  starting points in average HAZ scores even before considering environmental inputs (a range of 1.7 SD).
Revised:  After removing the effects of environmental inputs, we find that different countries have reliably and substantially different basal starting points in average HAZ scores even before considering environmental inputs (a range of 1.7 SD).
Edit intent: meaning-changed
Original:  As expected from a two-component model, these country-specific starting points (basal HAZ) are not associated with key indicators of undernutrition (e.g., infant mortality and average calorie deficit).
Revised:  As expected from a two-component model, these country-specific starting points , which we define as basal HAZ, are not associated with key indicators of undernutrition (e.g., infant mortality and average calorie deficit).
Edit intent: meaning-changed
Original:  By contrast,  increases in HAZ above a country's basal estimate (accrued HAZ) show strong correlations with these same variables, suggesting that  accrued HAZ captures standard definitions of stunting as impaired growth due to undernutrition.
Revised:  By contrast, average increases in HAZ above a country's basal estimate (accrued HAZ) show strong correlations with these same variables, suggesting that accrued HAZ captures standard definitions of stunting as impaired growth due to undernutrition.
Edit intent: meaning-changed
Original:  By contrast,  increases in HAZ above a country's basal estimate (accrued HAZ) show strong correlations with these same variables, suggesting that  accrued HAZ captures standard definitions of stunting as impaired growth due to undernutrition.
Revised:  By contrast, increases in HAZ above a country's starting point, which we define as accrued HAZ, show strong correlations with these same variables, suggesting that accrued HAZ captures standard definitions of stunting as impaired growth due to undernutrition.
Edit intent: clarity
Original:  By contrast,  increases in HAZ above a country's basal estimate (accrued HAZ) show strong correlations with these same variables, suggesting that  accrued HAZ captures standard definitions of stunting as impaired growth due to undernutrition.
Revised:  By contrast, increases in HAZ above a country's basal estimate (accrued HAZ) show strong correlations with these same variables, suggesting that low accrued HAZ captures standard definitions of stunting as impaired growth due to undernutrition.
Edit intent: meaning-changed
Original:  Using these two components, we show how universal cutoffs can underestimate stunting in specific world regions (e.g., sub-Saharan Africa and the Caribbean), where children on average start off taller.
Revised:  Using these two components, we show how universal cutoffs can underestimate stunting in specific world regions (e.g., sub-Saharan Africa and the Caribbean), where children in even very deprived situations start off taller.
Edit intent: meaning-changed
Original:  The current benchmark for classifying stunting assumes a universal model of growth with height-for-age z-score (HAZ) cutoffs set by the WHO . However, this universal model may hide hotspots of stunting if populations differ in HAZ in ways that are independent of undernutrition.
Revised:  The current benchmark for classifying stunting assumes a universal model of growth with height-for-age z-score (HAZ) cutoffs set by the World Health Organization . However, this universal model may hide hotspots of stunting if populations differ in HAZ in ways that are independent of undernutrition.
Edit intent: clarity
Original:  The current benchmark for classifying stunting assumes a universal model of growth with height-for-age z-score (HAZ) cutoffs set by the WHO . However, this universal model may hide hotspots of stunting if populations differ in HAZ in ways that are independent of undernutrition.
Revised:  The current benchmark for classifying stunting assumes a universal model of growth with height-for-age z-score (HAZ) cutoffs set by the WHO . However, this model may hide hotspots of stunting if populations differ in HAZ in ways that are independent of undernutrition.
Edit intent: fluency
Original:  We assess the potential magnitude of this bias by decomposing variation in HAZ from 1, 406,609 children from 63 low- and middle-income countries into two components ;
Revised:  We examine how universal cutoffs can underestimate stunting in specific regions around the world. Methods: Using a non-linear multi-level model, we decompose variation in height-for-age z-scores (HAZ) from 1, 406,609 children from 63 low- and middle-income countries into two components ;
Edit intent: meaning-changed
Original:  1) a component shaped by environmental inputs, poverty, infectious disease, inadequate sanitation, and healthcare access, and 2) a country-specific basal starting point that is independent of such inputs.
Revised:  1) "accrued HAZ" shaped by environmental inputs, poverty, infectious disease, inadequate sanitation, and healthcare access, and 2) a country-specific basal starting point that is independent of such inputs.
Edit intent: meaning-changed
Original:  After removing the effects of numerous environmental inputs, we find that different countries have reliably and substantially different basal starting points in average HAZ scores (a range of 1.7 SD) . These country-specific starting points, which we define as basal HAZ, are not associated with key indicators of undernutrition (e. g., infant mortality and average calorie deficit).
Revised:  We validate these components by assessing their associations with key indicators of undernutrition. We then re-estimate stunting prevalence with population-sensitive cutoffs based on the estimates of these two components. Results: Basal HAZ differs reliably across countries (range of 1.6 SD) and is independent of measures of undernutrition.
Edit intent: meaning-changed
Original:  By contrast, average increases in HAZ above a country's starting point, which we define as accrued HAZ , show strong correlations with these same variables, suggesting that low accrued HAZ captures standard definitions of stunting as impaired growth due to undernutrition. Using these two components, we show how universal cutoffs can underestimate stunting in specific world regions (e.g., sub-Saharan Africa and the Caribbean), where children in even very deprived situations start off taller .
Revised:  By contrast, average increases in HAZ above a country's starting point, which we define as accrued HAZ , show strong correlations with these same variables, suggesting that low accrued HAZ captures standard definitions of stunting as impaired growth due to deprivation. Population-sensitive estimates suggest that universal cutoffs can underestimate stunting in specific world regions (e.g., sub-Saharan Africa and the Caribbean), where children in even very deprived situations start off taller .
Edit intent: clarity
Original:  Using these two components, we show how universal cutoffs can underestimate stunting in specific world regions (e.g., sub-Saharan Africa and the Caribbean), where children in even very deprived situations start off taller .
Revised:  Using these two components, we show how universal cutoffs can miss 1 in 5 children in specific world regions (e.g., sub-Saharan Africa and the Caribbean), where children in even very deprived situations start off taller .
Edit intent: clarity
Original:  Using these two components, we show how universal cutoffs can underestimate stunting in specific world regions (e.g., sub-Saharan Africa and the Caribbean), where children in even very deprived situations start off taller .
Revised:  Using these two components, we show how universal cutoffs can underestimate stunting in specific world regions , where children have greater basal heights. Conclusion: Current universal cutoffs may miss stunting in populations with taller basal HAZ .
Edit intent: clarity
Original: Background: Stunting-or impaired child growth due to undernutrition-has multiple negative health effects, making it a top global health priority. The current benchmark for classifying stunting assumes a universal model of growth with height-for-age z-score (HAZ) cutoffs set by the World Health Organization.
Revised: Objectives: Current standards for comparing stunting across human populations assume a universal model of growth with height-for-age z-score (HAZ) cutoffs set by the World Health Organization.
Edit intent: clarity
Original:  The current benchmark for classifying stunting assumes a universal model of growth with height-for-age z-score (HAZ) cutoffs set by the World Health Organization. However, this model may hide hotspots of stunting if populations differ in HAZ in ways that are independent of undernutrition.
Revised:  The current benchmark for classifying stunting assumes a universal model of child growth. Such comparisons ignore population differences that are independent of undernutrition.
Edit intent: clarity
Original:  However, this model may hide hotspots of stunting if populations differ in HAZ in ways that are independent of undernutrition. We examine how universal cutoffs can underestimate stunting in specific regions around the world.
Revised:  However, this model may hide hotspots of stunting if populations differ in HAZ in ways that are independent of deprivation and health outcomes. This paper partitions variation in height-for-age that is specifically associated with deprivation and health outcomes to provide a basis for cross-population comparisons. Materials
Edit intent: meaning-changed
Original:  Methods: Using a non-linear multi-level model , we decompose variation in height-for-age z-scores (HAZ) from 1, 430,986 children across 63 countries into two components: 1) "accrued HAZ" shaped by environmental inputs-undernutrition, infectious disease, inadequate sanitation,  and 2) a country-specific "basal HAZ" independent of such inputs.
Revised:  Methods: Using a multi-level model , we decompose variation in height-for-age z-scores (HAZ) from 1, 430,986 children across 63 countries into two components: 1) "accrued HAZ" shaped by environmental inputs-undernutrition, infectious disease, inadequate sanitation, and 2) a country-specific "basal HAZ" independent of such inputs.
Edit intent: clarity
Original:  Methods: Using a non-linear multi-level model , we decompose variation in height-for-age z-scores (HAZ) from 1, 430,986 children across 63 countries into two components: 1) "accrued HAZ" shaped by environmental inputs-undernutrition, infectious disease, inadequate sanitation,  and 2) a country-specific "basal HAZ" independent of such inputs.
Revised:  Methods: Using a non-linear multi-level model with a sigmoid relationship of resources and growth, we partition variation in height-for-age z-scores (HAZ) from 1, 430,986 children across 63 countries into two components: 1) "accrued HAZ" shaped by environmental inputs-undernutrition, infectious disease, inadequate sanitation, and 2) a country-specific "basal HAZ" independent of such inputs.
Edit intent: meaning-changed
Original:  Methods: Using a non-linear multi-level model , we decompose variation in height-for-age z-scores (HAZ) from 1, 430,986 children across 63 countries into two components: 1) "accrued HAZ" shaped by environmental inputs-undernutrition, infectious disease, inadequate sanitation,  and 2) a country-specific "basal HAZ" independent of such inputs.
Revised:  Methods: Using a non-linear multi-level model , we decompose variation in height-for-age z-scores (HAZ) from 1, 522,564 children across 70 countries into two components: 1) "accrued HAZ" shaped by environmental inputs-undernutrition, infectious disease, inadequate sanitation, and 2) a country-specific "basal HAZ" independent of such inputs.
Edit intent: meaning-changed
Original:  Methods: Using a non-linear multi-level model , we decompose variation in height-for-age z-scores (HAZ) from 1, 430,986 children across 63 countries into two components: 1) "accrued HAZ" shaped by environmental inputs-undernutrition, infectious disease, inadequate sanitation,  and 2) a country-specific "basal HAZ" independent of such inputs.
Revised:  Methods: Using a non-linear multi-level model , we decompose variation in height-for-age z-scores (HAZ) from 1, 430,986 children across 63 countries into two components: 1) "accrued HAZ" shaped by environmental inputs (e.g., undernutrition, infectious disease, inadequate sanitation, and 2) a country-specific "basal HAZ" independent of such inputs.
Edit intent: clarity
Original:  Methods: Using a non-linear multi-level model , we decompose variation in height-for-age z-scores (HAZ) from 1, 430,986 children across 63 countries into two components: 1) "accrued HAZ" shaped by environmental inputs-undernutrition, infectious disease, inadequate sanitation,  and 2) a country-specific "basal HAZ" independent of such inputs.
Revised:  Methods: Using a non-linear multi-level model , we decompose variation in height-for-age z-scores (HAZ) from 1, 430,986 children across 63 countries into two components: 1) "accrued HAZ" shaped by environmental inputs-undernutrition, infectious disease, inadequate sanitation, poverty), and 2) a country-specific "basal HAZ" independent of such inputs.
Edit intent: clarity
Original:  We validate these components by assessing their associations with key indicators of undernutrition. We then re-estimate stuntingprevalence with population-sensitive cutoffs based on the estimates of these two components . Results: Basal HAZ differs reliably across countries (range of 1.6 SD) and is independent of measures of undernutrition .
Revised:  We validate these components against population-level infant mortality rates, and assess how these basal differences may affect cross-population comparisons of stunting . Results: Basal HAZ differs reliably across countries (range of 1.6 SD) and is independent of measures of undernutrition .
Edit intent: meaning-changed
Original:  We then re-estimate stuntingprevalence with population-sensitive cutoffs based on the estimates of these two components . Results: Basal HAZ differs reliably across countries (range of 1.6 SD) and is independent of measures of undernutrition .
Revised:  We then re-estimate stuntingprevalence with population-sensitive cutoffs based on the estimates of these two components . Results: Basal HAZ differs reliably across countries (range of 1.5 SD) and is independent of measures of undernutrition .
Edit intent: meaning-changed
Original:  We then re-estimate stuntingprevalence with population-sensitive cutoffs based on the estimates of these two components . Results: Basal HAZ differs reliably across countries (range of 1.6 SD) and is independent of measures of undernutrition .
Revised:  We then re-estimate stuntingprevalence with population-sensitive cutoffs based on the estimates of these two components . Results: Basal HAZ differs reliably across countries (range of 1.6 SD) and is independent of measures of infant mortality .
Edit intent: clarity
Original:  By contrast, accrued HAZ captures stunting as impaired growth due to deprivation . Population-sensitive estimates suggest that universal cutoffs can miss 1 in 5 children in specific world regions, where children have greater basal heights. Conclusion : Current universal cutoffs may miss stunting in populations with taller basal HAZ.
Revised:  By contrast, accrued HAZ captures stunting as impaired growth due to deprivation and is more closely associated with infant mortality than observed HAZ. Ranking populations by accrued HAZ suggest that populations in West Africa and the Caribbean suffer much greater levels of stunting than suggested by observed HAZ. Discussion : Current universal cutoffs may miss stunting in populations with taller basal HAZ.
Edit intent: meaning-changed
Original:  The function , which can be described as an "isodense" network of "moving" regression curves of an order greater than or equal to 1, can be conveniently represented graphically .
Revised:  The function , which can be described as an "isodense" network of "moving" regression curves of an order greater than or equal to 1. When inputting a time series of any security into the function, new mathematical objects emerge spontaneously .
Edit intent: meaning-changed
Original:  The graphical representation , called a "topological network" , reveals the existence of new mathematical objects , which emerge spontaneously (they are not mathematically drawn). What is remarkable is that these objects, called "characteristic figures" , mainly "cords" , have the unique property of attracting and repelling the price, so that the price bounces from one cord to another.
Revised:  The graphical representation , called a "topological network" and the emergent objects are called "characteristic figures" , mainly "cords" , have the unique property of attracting and repelling the price, so that the price bounces from one cord to another.
Edit intent: coherence
Original:  What is remarkable is that these objects, called "characteristic figures" , mainly "cords" , have the unique property of attracting and repelling the price, so that the price bounces from one cord to another.
Revised:  What is remarkable is that these objects, called "characteristic figures" ( mainly "cords" , have the unique property of attracting and repelling the price, so that the price bounces from one cord to another.
Edit intent: fluency
Original:  What is remarkable is that these objects, called "characteristic figures" , mainly "cords" , have the unique property of attracting and repelling the price, so that the price bounces from one cord to another.
Revised:  What is remarkable is that these objects, called "characteristic figures" , mainly "cords" ). What is central to the function is that these cords have the unique property of attracting and repelling the price, so that the price bounces from one cord to another.
Edit intent: meaning-changed
Original: Almost all EEG-based brain-computer interfaces (BCIs) need some labeled subject-specific data to calibrate a new subject, as neural responses are different across subjects to even the same stimulus.
Revised: Objective: This paper targets a major challenge in developing practical EEG-based brain-computer interfaces (BCIs) need some labeled subject-specific data to calibrate a new subject, as neural responses are different across subjects to even the same stimulus.
Edit intent: meaning-changed
Original: Almost all EEG-based brain-computer interfaces (BCIs) need some labeled subject-specific data to calibrate a new subject, as neural responses are different across subjects to even the same stimulus. So, a major challenge in developing high-performance and user-friendly BCIs is to cope with such individual differences so that the calibration can be reduced or even completely eliminated.
Revised: Almost all EEG-based brain-computer interfaces (BCIs) : how to cope with individual differences so that the calibration can be reduced or even completely eliminated.
Edit intent: coherence
Original:  So, a major challenge in developing high-performance and user-friendly BCIs is to cope with such individual differences so that the calibration can be reduced or even completely eliminated. This paper focuses on the latter. More specifically, we consider an offline application scenario, in which we have unlabeled EEG trials from a new subject, and would like to accurately label them by leveraging auxiliary labeled EEG trials from other subjects in the same task.
Revised:  So, a major challenge in developing high-performance and user-friendly BCIs is to cope with such individual differences so that better learning performance can be obtained for a new subject, and would like to accurately label them by leveraging auxiliary labeled EEG trials from other subjects in the same task.
Edit intent: clarity
Original:  More specifically, we consider an offline application scenario, in which we have unlabeled EEG trials from a new subject, and would like to accurately label them by leveraging auxiliary labeled EEG trials from other subjects in the same task. To accommodate the individual differences, we propose a novel unsupervised approach to align the EEG trials from different subjects in the Euclidean space to make them more consistent.
Revised:  More specifically, we consider an offline application scenario, in which we have unlabeled EEG trials from a new subject, with minimum or even no subject-specific data? Methods: We propose a novel unsupervised approach to align the EEG trials from different subjects in the Euclidean space to make them more consistent.
Edit intent: meaning-changed
Original:  To accommodate the individual differences, we propose a novel unsupervised approach to align the EEG trials from different subjects in the Euclidean space to make them more consistent. It has three desirable properties: 1) the aligned trial lie in the Euclidean space, which can be used by any Euclidean space signal processing and machine learning approach ;
Revised:  To accommodate the individual differences, we propose a novel unsupervised approach to align the EEG trials from different subjects in the Euclidean space to make them more similar, and hence improve the learning performance for a new subject. Our approach has three desirable properties: 1) the aligned trial lie in the Euclidean space, which can be used by any Euclidean space signal processing and machine learning approach ;
Edit intent: meaning-changed
Original:  It has three desirable properties: 1) the aligned trial lie in the Euclidean space, which can be used by any Euclidean space signal processing and machine learning approach ;
Revised:  It has three desirable properties: 1) the aligned trial lie in the Euclidean space, which can be used by any Euclidean space signal processing and machine learning algorithms can then be applied to the aligned trials ;
Edit intent: clarity
Original:  Experiments on motor imagery  and event-related potentials demonstrated the effectiveness and efficiency of our approach .
Revised:  Experiments on motor imagery and event-related potential classification verified that our proposed approach outperformed a state-of-the-art Riemannian space data alignment approach, and several approaches without data alignment. Conclusion: The proposed Euclidean space EEG data alignment approach can greatly facilitate transfer learning in BCIs. Significance: Our proposed approach is effective, efficient, and easy to implement. It could be an essential pre-processing step for EEG-based BCIs .
Edit intent: meaning-changed
Original: Jeandel and Rao proved that 11 is the size of the smallest set of Wang tiles, i.e., unit squares with colored edges, that admit valid  (contiguous edges of adjacent tiles have the same color) tilings of the plane  none of them being invariant under a nontrivial translation.
Revised: Jeandel and Rao proved that 11 is the size of the smallest set of Wang tiles, i.e., unit squares with colored edges, that admit valid tilings (contiguous edges of adjacent tiles have the same color) tilings of the plane none of them being invariant under a nontrivial translation.
Edit intent: fluency
Original: Jeandel and Rao proved that 11 is the size of the smallest set of Wang tiles, i.e., unit squares with colored edges, that admit valid  (contiguous edges of adjacent tiles have the same color) tilings of the plane  none of them being invariant under a nontrivial translation.
Revised: Jeandel and Rao proved that 11 is the size of the smallest set of Wang tiles, i.e., unit squares with colored edges, that admit valid (contiguous edges of adjacent tiles have the same color) of the plane none of them being invariant under a nontrivial translation.
Edit intent: fluency