wanyu/IteraTeR_full_sent huggingface dataset:
Original:  New results include a characterization of convergence with respect to transport metrics, the existence of Fr\'echet means  for any distribution of diagrams, and an exhaustive description of continuous linear representations of persistence diagrams.
Revised:  New results include a characterization of convergence with respect to transport metrics, the existence of Fr\'echet means ) for any distribution of diagrams, and an exhaustive description of continuous linear representations of persistence diagrams.
Edit intent: fluency
Original:  As opposed to current deep learning approaches, DKT is interpretable, which allowsus to understand underlying disease mechanisms. Here we demonstrate DKT on Alzheimer's disease (AD) variants and its ability to predict trajectories for multimodal biomarkers in Posterior Cortical Atrophy (PCA), in lack of such data from PCA subjects .
Revised:  Our proposed method allows, for the first time, the estimation of plausible, multimodal biomarker trajectories in Posterior Cortical Atrophy (PCA), in lack of such data from PCA subjects .
Edit intent: clarity
Original:  Here we demonstrate DKT on Alzheimer's disease (AD) variants and its ability to predict trajectories for multimodal biomarkers in Posterior Cortical Atrophy (PCA), in lack of such data from PCA subjects .
Revised:  Here we demonstrate DKT on Alzheimer's disease (AD) variants and its ability to predict trajectories for multimodal biomarkers in Posterior Cortical Atrophy (PCA), a rare neurodegenerative disease where only unimodal MRI data is available .
Edit intent: meaning-changed
Original:  For this we train DKT on a combined dataset containing subjects with two distinct diseases and sizes of data available: 1) a larger, multimodal typical AD (tAD) dataset from the TADPOLE Challenge, and 2) a smaller unimodal Posterior Cortical Atrophy (PCA) dataset from the Dementia Research Centre (DRC) UK , for which only a limited number of Magnetic Resonance Imaging (MRI) scans are available.
Revised:  For this we train DKT on a combined dataset containing subjects with two distinct diseases and sizes of data available: 1) a larger, multimodal typical AD (tAD) dataset from the TADPOLE Challenge, and 2) a smaller unimodal Posterior Cortical Atrophy (PCA) dataset from the Dementia Research Centre (DRC) , for which only a limited number of Magnetic Resonance Imaging (MRI) scans are available.
Edit intent: clarity
Original:  We first show that DKT estimates plausible multimodal trajectories in PCAthat agree with previous literature. We further validate DKT in two situations: (1) on synthetic data , showing that it can accurately estimate the ground truth parameters and (2) on 20 DTI scans from controls and PCA patients, showing that it has favourable predictive performance compared to standard approaches .
Revised:  Although validation is challenging due to lack of data in PCA, we validate DKT on synthetic data , showing that it can accurately estimate the ground truth parameters and (2) on 20 DTI scans from controls and PCA patients, showing that it has favourable predictive performance compared to standard approaches .
Edit intent: coherence
Original:  We further validate DKT in two situations: (1) on synthetic data , showing that it can accurately estimate the ground truth parameters and (2) on 20 DTI scans from controls and PCA patients, showing that it has favourable predictive performance compared to standard approaches .
Revised:  We further validate DKT in two situations: (1) on synthetic data and two patient datasets (TADPOLE and PCA cohorts), showing it can estimate the ground truth parameters and (2) on 20 DTI scans from controls and PCA patients, showing that it has favourable predictive performance compared to standard approaches .
Edit intent: meaning-changed
Original:  We further validate DKT in two situations: (1) on synthetic data , showing that it can accurately estimate the ground truth parameters and (2) on 20 DTI scans from controls and PCA patients, showing that it has favourable predictive performance compared to standard approaches .
Revised:  We further validate DKT in two situations: (1) on synthetic data , showing that it can accurately estimate the ground truth parameters in the simulation and predict unseen biomarkers on the two patient datasets .
Edit intent: meaning-changed
Original: We study the effectiveness of a community-level information intervention aimed at reducing open defecation (OD) and increasing sanitation investments in Nigeria. The results of a cluster-randomized control trial conducted in 247 communities between 2014 and 2018 suggest that average impacts are exiguous .
Revised: We study the effectiveness of a community-level information intervention aimed at improving sanitation using a cluster-randomized control trial conducted in 247 communities between 2014 and 2018 suggest that average impacts are exiguous .
Edit intent: clarity
Original:  The results of a cluster-randomized control trial conducted in 247 communities between 2014 and 2018 suggest that average impacts are exiguous .
Revised:  The results of a cluster-randomized controlled trial (RCT) in Nigerian communities. The intervention, Community-Led Total Sanitation (CLTS), is currently part of national sanitation policy in more than 25 countries. While average impacts are exiguous .
Edit intent: meaning-changed
Original:  The results of a cluster-randomized control trial conducted in 247 communities between 2014 and 2018 suggest that average impacts are exiguous . However, these results hide important community heterogeneity, as the intervention has strong and lasting effects on OD habits in poorer communities.
Revised:  The results of a cluster-randomized control trial conducted in 247 communities between 2014 and 2018 suggest that average impacts are exiguous almost three years after implementation at scale, the results hide important community heterogeneity, as the intervention has strong and lasting effects on OD habits in poorer communities.
Edit intent: meaning-changed
Original:  This result is robust across several measures of community socio-economic characteristics, and is not driven by baseline differences in toilet coverage. In poor communities, OD rates decreased by 9pp from a baseline level of 75%DIFDELCMD < \\%%% %DIF < , while we find no effect in richer communities.
Revised:  %DIFDELCMD < \\%%% %DIF < , while we find no effect in richer communities.
Edit intent: coherence
Original:  Our findings point to community wealth as a widely available and sufficient statistic for effective intervention targeting.abstract 
Revised:  Our findings point to community wealth as a widely available and sufficient statistic for effective intervention targeting.abstract These are realized through increased sanitation investments. We show that community wealth, widely available in secondary data, is a key statistic for effective intervention targeting. Using data from five other similar randomized interventions in various contexts, we find that community-level wealth heterogeneity can rationalize the wide range of impact estimates in the literature. This exercise provides plausible external validity to our findings, with implications for intervention scale-up. JEL Codes: O12, I12, I15, I18.
Edit intent: meaning-changed
Original:  We show that even when the dimension D of the conditioning variable is larger than the sample size n, estimation and inference is feasible as long as the distribution of the conditioning variable has small intrinsic dimension d, as measured by the doubling dimension .
Revised:  We show that even when the dimension D of the conditioning variable is larger than the sample size n, estimation and inference is feasible as long as the distribution of the conditioning variable has small intrinsic dimension d, as measured by locally low doubling measures .
Edit intent: clarity
Original:  We show that if the intrinsic dimension of the co-variate distribution is equal to d, then the finite sample estimation error of our estimator is of order n^{-1/(d+2)} and our estimate is n^{1/(d+2)}-asymptotically normal, irrespective of D  . We discuss extensions and applications to heterogeneous treatment effect estimation.
Revised:  We show that if the intrinsic dimension of the covariate distribution is equal to d, then the finite sample estimation error of our estimator is of order n^{-1/(d+2)} and our estimate is n^{1/(d+2)}-asymptotically normal, irrespective of D . We discuss extensions and applications to heterogeneous treatment effect estimation.
Edit intent: fluency
Original:  We show that if the intrinsic dimension of the co-variate distribution is equal to d, then the finite sample estimation error of our estimator is of order n^{-1/(d+2)} and our estimate is n^{1/(d+2)}-asymptotically normal, irrespective of D  . We discuss extensions and applications to heterogeneous treatment effect estimation.
Revised:  We show that if the intrinsic dimension of the co-variate distribution is equal to d, then the finite sample estimation error of our estimator is of order n^{-1/(d+2)} and our estimate is n^{1/(d+2)}-asymptotically normal, irrespective of D . The sub-sampling size required for achieving these results depends on the unknown intrinsic dimension d. We propose an adaptive data-driven approach for choosing this parameter and prove that it achieves the desired rates . We discuss extensions and applications to heterogeneous treatment effect estimation.
Edit intent: meaning-changed
Original: We propose a risk-sharing framework for bilateral contracts to find the optimal pair, initial price and amount of collateral, with presence of default risks, collateral, and funding spreads. The derived optimal collateral can be used for contracts between financial firms and non-financial firms. For inter-dealers contracts, which are governed by regulations, the optimal collateral can interpret circumstances where the margin requirement is indeed optimal. We will see later that absence of market frictions is an inherent assumption for the margin requirement in Basel III.
Revised: We introduce a two-agent problem which is inspired by price asymmetry arising from funding difference. When two parties have different funding rates, the margin requirement in Basel III.
Edit intent: coherence
Original:  We will see later that absence of market frictions is an inherent assumption for the margin requirement in Basel III. In addition, as we consider entity-specific information in bilateral pricing , law of one price does not hold. Moreover, inclusion of funding spreads causes asymmetry in individual pricing.
Revised:  We will see later that absence of market frictions is an inherent assumption for the two parties deduce different fair prices for derivative contracts even under the same pricing methodology and parameters.
Edit intent: clarity
Original:  Thus, the two parties should enter  derivative contracts with a negotiated price, which is the other part of the solution of the risk-sharing framework. The risk-sharing framework defines the negotiation as a problem that maximizes the sum of utilities of the two parties.
Revised:  Thus, the two parties should enter derivative contracts with a negotiated price, which is the other part of the solution of the risk-sharing problem. This framework defines the negotiation as a problem that maximizes the sum of utilities of the two parties.
Edit intent: clarity
Original:  The optimal pricefrom the risk-sharing framework does not have asymmetry due to different funding spreads of each party .
Revised:  The optimal pricefrom the risk-sharing framework produces an optimal amount of collateral. The derived optimal collateral can be used for contracts between financial firms and non-financial firms. However, inter-dealers markets are governed by regulations. As recommended in Basel III, it is a convention in inter-dealer contracts to pledge the full amount of a close-out price as collateral. In this case, using the optimal collateral, we interpret conditions for the full margin requirement to be indeed optimal .
Edit intent: meaning-changed
Original: Recent advances in quantifying phenotypic heterogeneity in bacteria have shown its broad prevalence in nature. However, evaluating the ultimate impact of URLanism heterogeneity at the population level remains an active area of research in microbiology. We show that the generalized Fibonacci recurrence provides a simple, mathematically rigorous and computationally tractable model for heterogeneous bacterial growth.
Revised:  provides a simple, mathematically rigorous and computationally tractable model for heterogeneous bacterial growth.
Edit intent: coherence
Original:  We show that the generalized Fibonacci recurrence provides a simple, mathematically rigorous and computationally tractable model for heterogeneous bacterial growth. We examine the impact of small-scale heterogeneity during bacterial division, of the sort observed due to the asymmetric partitioning of aged molecules. We find that small consistent differences in growth rates between daughter cells can have ecologically significant population growth rate benefits and that different models of the cost of heterogeneity may lead to an optimal intermediate amount of heterogeneity between daughter cells. The mathematical framework generalizes the previous combinatorial models developed for the division of yeast, inadequate for modeling bacteria due to the finer control required over the amount of heterogeneity encountered in the latter. Furthermore, when k and m are relatively prime, by juxtaposing the point of view of binary heterogeneous division to the original combinatorial perspective on (GF), in terms of diagonals in Pascal's triangle, we obtain new combinatorial insights. In particular, we obtain an explicit binomial identity for the sums of elements lying on the diagonals kx+my=n in Pascal's triangle \mathbb N_0\times \mathbb N_0\ni(x,y)\mapsto {x+y\choose x} .
Revised:  We show that the generalized Fibonacci recurrence The generalized Fibonacci recurrence g_n=g_{n-k an explicit binomial identity for the sums of elements lying on the diagonals kx+my=n in Pascal's triangle \mathbb N_0\times \mathbb N_0\ni(x,y)\mapsto {x+y\choose x} .
Edit intent: meaning-changed
Original:  In particular, we obtain an explicit binomial identity for the sums of elements lying on the diagonals kx+my=n in Pascal's triangle \mathbb N_0\times \mathbb N_0\ni(x,y)\mapsto {x+y\choose x} . This provides a missing piece in the earlier work of Dickinson ~\mbox{%DIFAUXCMD Dickinson19500pt%DIFAUXCMD , Raab \mbox{%DIFAUXCMD Raab1963}0pt%DIFAUXCMD }  ]  , and Green \mbox{%DIFAUXCMD Green1968 .
Revised:  In particular, we obtain an explicit binomial identity for the sums of elements lying on the diagonals kx+my=n in Pascal's triangle \mathbb N_0\times \mathbb N_0\ni(x,y)\mapsto {x+y\choose x} 0pt%DIFAUXCMD , Raab \mbox{%DIFAUXCMD Raab1963}0pt%DIFAUXCMD } ] , and Green \mbox{%DIFAUXCMD Green1968 .
Edit intent: meaning-changed
Original:  This provides a missing piece in the earlier work of Dickinson ~\mbox{%DIFAUXCMD Dickinson19500pt%DIFAUXCMD , Raab \mbox{%DIFAUXCMD Raab1963}0pt%DIFAUXCMD }  ]  , and Green \mbox{%DIFAUXCMD Green1968 .
Revised:  This provides a missing piece in the earlier work of Dickinson ~\mbox{%DIFAUXCMD Dickinson19500pt%DIFAUXCMD , Raab \mbox{%DIFAUXCMD Raab1963}0pt%DIFAUXCMD } , previously sought by Dickinson ] , and Green \mbox{%DIFAUXCMD Green1968 .
Edit intent: meaning-changed
Original:  This provides a missing piece in the earlier work of Dickinson ~\mbox{%DIFAUXCMD Dickinson19500pt%DIFAUXCMD , Raab \mbox{%DIFAUXCMD Raab1963}0pt%DIFAUXCMD }  ]  , and Green \mbox{%DIFAUXCMD Green1968 .
Revised:  This provides a missing piece in the earlier work of Dickinson ~\mbox{%DIFAUXCMD Dickinson19500pt%DIFAUXCMD , Raab \mbox{%DIFAUXCMD Raab1963}0pt%DIFAUXCMD } Dic50] , and Green \mbox{%DIFAUXCMD Green1968 .
Edit intent: meaning-changed
Original:  This provides a missing piece in the earlier work of Dickinson ~\mbox{%DIFAUXCMD Dickinson19500pt%DIFAUXCMD , Raab \mbox{%DIFAUXCMD Raab1963}0pt%DIFAUXCMD }  ]  , and Green \mbox{%DIFAUXCMD Green1968 .
Revised:  This provides a missing piece in the earlier work of Dickinson ~\mbox{%DIFAUXCMD Dickinson19500pt%DIFAUXCMD , Raab \mbox{%DIFAUXCMD Raab1963}0pt%DIFAUXCMD } ], Raab , and Green \mbox{%DIFAUXCMD Green1968 .
Edit intent: fluency
Original:  This provides a missing piece in the earlier work of Dickinson ~\mbox{%DIFAUXCMD Dickinson19500pt%DIFAUXCMD , Raab \mbox{%DIFAUXCMD Raab1963}0pt%DIFAUXCMD }  ]  , and Green \mbox{%DIFAUXCMD Green1968 .
Revised:  This provides a missing piece in the earlier work of Dickinson ~\mbox{%DIFAUXCMD Dickinson19500pt%DIFAUXCMD , Raab \mbox{%DIFAUXCMD Raab1963}0pt%DIFAUXCMD } ] Raa63 , and Green \mbox{%DIFAUXCMD Green1968 .
Edit intent: meaning-changed
Original:  This provides a missing piece in the earlier work of Dickinson ~\mbox{%DIFAUXCMD Dickinson19500pt%DIFAUXCMD , Raab \mbox{%DIFAUXCMD Raab1963}0pt%DIFAUXCMD }  ]  , and Green \mbox{%DIFAUXCMD Green1968 .
Revised:  This provides a missing piece in the earlier work of Dickinson ~\mbox{%DIFAUXCMD Dickinson19500pt%DIFAUXCMD , Raab \mbox{%DIFAUXCMD Raab1963}0pt%DIFAUXCMD } ] , and Green Gre68 .
Edit intent: fluency
Original:  Recent advances in medical imaging and computational algorithms for analyzing said images have made subject-specific modeling of the cardiovascular system a feasible task . This is particularly important in the case of pulmonary hypertension , as the pulmonary circulation transports oxygen to the lungs via a rapidly bifurcating network of highly compliant blood vessels.
Revised:  Recent advances in medical imaging and computational algorithms for analyzing said images have made subject-specific modeling of the cardiovascular system a feasible task , which is particularly important in the case of pulmonary hypertension , as the pulmonary circulation transports oxygen to the lungs via a rapidly bifurcating network of highly compliant blood vessels.
Edit intent: coherence
Original:  This is particularly important in the case of pulmonary hypertension , as the pulmonary circulation transports oxygen to the lungs via a rapidly bifurcating network of highly compliant blood vessels. However, medical image analysis is subject to uncertainty, which can lead to variation in fluid model predictions .
Revised:  This is particularly important in the case of pulmonary hypertension (PH), which requires a combination of invasive and non-invasive procedures for diagnosis. Uncertainty in image segmentation can easily propagate to CFD model predictions, making uncertainty quantification crucial for subject-specific models .
Edit intent: meaning-changed
Original:  This study addresses this issue by developing a computational algorithm to determine pulmonary hemodynamics ( blood pressure and flow ) in a large network of arterial vessels and subsequently quantify the uncertainty of these predictionsto geometric variation associated with image segmentation.
Revised:  This study quantifies the variability of one-dimensional (1D) CFD predictions by propagating the uncertainty of network geometry and connectivity to blood pressure and flow ) in a large network of arterial vessels and subsequently quantify the uncertainty of these predictionsto geometric variation associated with image segmentation.
Edit intent: meaning-changed
Original:  This study addresses this issue by developing a computational algorithm to determine pulmonary hemodynamics ( blood pressure and flow ) in a large network of arterial vessels and subsequently quantify the uncertainty of these predictionsto geometric variation associated with image segmentation. We introduce uncertainty in the blood vessel network by generating multiple segmentations of a pulmonary tree from a single image of an excised mouse lung using randomly selected pre-segmentation parameters.
Revised:  This study addresses this issue by developing a computational algorithm to determine pulmonary hemodynamics ( blood pressure and flow predictions. We analyse multiple segmentations of a pulmonary tree from a single image of an excised mouse lung using randomly selected pre-segmentation parameters.
Edit intent: clarity
Original:  We quantify the uncertainty in geometric features of the network using density estimation techniques to specify probability densities for vessel radius and length . We sample from these distributions , compute model parameters, and propagate uncertainties to pressure and flow predictions using a one-dimensional ( 1D ) fluid dynamics model.
Revised:  We quantify the uncertainty in geometric features of the network using density estimation techniques to specify probability densities for vessel radius and length , and then sample from these distributions , compute model parameters, and propagate uncertainties to pressure and flow predictions using a one-dimensional ( 1D ) fluid dynamics model.
Edit intent: coherence
Original: Conditional risk (co-risk) measures and risk contribution measures are increasingly used in quantitative risk analysis to evaluate the systemic risk that the failure (or loss) of a component spreads to other components or even to the entire system. Co-risk measures are conditional versions of measures usually employed to assess isolated risks, while risk contribution measures evaluate how a stress situation for one component incrementally affects another one. In this article , we introduce the rich classes of conditional distortion (CoD) risk measures and distortion risk contribution (\DeltaCoD) measures , which contain the well-known conditional Value-at-Risk (CoVaR) , conditional Expected Shortfall (CoES) , and risk contribution measures in terms of the VaR and ES (%DIFDELCMD < \DeltaCoVaR %%% and%DIFDELCMD < \DeltaCoES%%% ) as special cases.
Revised: In this paper , we introduce the rich classes of conditional distortion (CoD) risk measures and distortion risk contribution (\DeltaCoD) measures , which contain the well-known conditional Value-at-Risk (CoVaR) , conditional Expected Shortfall (CoES) , and risk contribution measures in terms of the VaR and ES (%DIFDELCMD < \DeltaCoVaR %%% and%DIFDELCMD < \DeltaCoES%%% ) as special cases.
Edit intent: coherence
Original:  In this article , we introduce the rich classes of conditional distortion (CoD) risk measures and distortion risk contribution (\DeltaCoD) measures , which contain the well-known conditional Value-at-Risk (CoVaR) , conditional Expected Shortfall (CoES) , and risk contribution measures in terms of the VaR and ES (%DIFDELCMD < \DeltaCoVaR %%% and%DIFDELCMD < \DeltaCoES%%% ) as special cases.
Revised:  In this article , we introduce the rich classes of conditional distortion (CoD) risk measures and distortion risk contribution (\DeltaCoD) measures as measures of systemic risk and analyze their properties and representations. The classes include the well-known conditional Value-at-Risk (CoVaR) , conditional Expected Shortfall (CoES) , and risk contribution measures in terms of the VaR and ES (%DIFDELCMD < \DeltaCoVaR %%% and%DIFDELCMD < \DeltaCoES%%% ) as special cases.
Edit intent: meaning-changed
Original:  In this article , we introduce the rich classes of conditional distortion (CoD) risk measures and distortion risk contribution (\DeltaCoD) measures , which contain the well-known conditional Value-at-Risk (CoVaR) , conditional Expected Shortfall (CoES) , and risk contribution measures in terms of the VaR and ES (%DIFDELCMD < \DeltaCoVaR %%% and%DIFDELCMD < \DeltaCoES%%% ) as special cases.
Revised:  In this article , we introduce the rich classes of conditional distortion (CoD) risk measures and distortion risk contribution (\DeltaCoD) measures , which contain the well-known conditional Value-at-Risk , conditional Expected Shortfall (CoES) , and risk contribution measures in terms of the VaR and ES (%DIFDELCMD < \DeltaCoVaR %%% and%DIFDELCMD < \DeltaCoES%%% ) as special cases.
Edit intent: fluency