wanyu/IteraTeR_full_sent huggingface dataset:
Original: In this paper we consider a  velocity jump process with excluded-volume interactions in the context of cell chemotaxis, where  the size of each particle affects the motion . Starting with a system of N individual hard rod particles in one dimension, we derive a nonlinear kinetic model using two different approaches.
Revised: In this paper we consider a velocity jump process with excluded-volume interactions in the context of cell chemotaxis, where the size of each particle . Starting with a system of N individual hard rod particles in one dimension, we derive a nonlinear kinetic model using two different approaches.
Edit intent: clarity
Original:  The inferential theory for the imputed values allows us to test for individual treatment effects at any time . We apply our method to portfolio investment strategies and find that around 14\% of their average returns are significantly reduced by the academic publication of these strategies .
Revised:  The inferential theory for the imputed values allows us to test for individual treatment effects at any time under general adoption patterns where the units can be affected by unobserved factors .
Edit intent: meaning-changed
Original:  Our study is based on formal asymptotic analysis and numerical simulations of a system of non-local parabolic equations that describes the phenotypic evolution of tumour cells and their nonlinear dynamic interactions with  oxygen, which is released from the intratumoural vascular network.
Revised:  Our study is based on formal asymptotic analysis and numerical simulations of a system of non-local parabolic equations that describes the phenotypic evolution of tumour cells and their nonlinear dynamic interactions with the oxygen, which is released from the intratumoural vascular network.
Edit intent: fluency
Original:  Numerical simulations are carried out both in the case of arbitrary distributions of intratumour blood vessels and in the case where the intra-tumoural vascular network is reconstructed from clinical images obtained using dynamic optical coherence tomography.
Revised:  Numerical simulations are carried out both in the case of arbitrary distributions of intratumour blood vessels and in the case where the intratumoural vascular network is reconstructed from clinical images obtained using dynamic optical coherence tomography.
Edit intent: fluency
Original: We design a high efficient exponential time differencing method (ETD) for simulating pulse-coupled Hodgkin-Huxley (HH) neuronal networks. The ETD method is attractive because it can solve the stiff problem when a HH neuron fires a spike and allows a large time step to raise efficiency .
Revised: We design a high efficient exponential time differencing method (ETD) method allows using a large time step to raise efficiency .
Edit intent: coherence
Original:  The ETD method is attractive because it can solve the stiff problem when a HH neuron fires a spike and allows a large time step to raise efficiency .
Revised:  The ETD method is attractive because it can solve the stiff problem when a HH neuron fires a spike and allows a large time step to efficiently evolve the stiff system such as Hodgkin-Huxley (HH) neural networks .
Edit intent: meaning-changed
Original:  For pulse-coupled HH networks, the synaptic spike times are unknown before evolving. We have to evolve the network without considering them and carefully assign them at the end of time step to achieve a second-order accuracy. We find that the second-order ETD method with Runge-Kutta time stepping (ETD2RK) is less accurate than the second-order Runge-Kutta method (RK2) using a common time step. Besides, we observe that the HH equations are quite flat outside the stiff period where RK2 method also allows a large time step.
Revised:  For pulse-coupled HH networks, the synaptic spike times cannot be predetermined and are convoluted with neuron's trajectory itself. This presents a challenging issue for the design of an efficient numerical simulation algorithm. The stiffness in the HH equations are quite flat outside the stiff period where RK2 method also allows a large time step.
Edit intent: clarity
Original:  Besides, we observe that the HH equations are quite flat outside the stiff period where RK2 method also allows a large time step. Thus, we adaptively assign the ETD2RK and RK2 schemes depending on the stiff and flat regimes to achieve optimal accuracy and efficiency . Compared with the regular RK2 method , our ETD2RK method can enlarge time steps and raise efficiency more than one order of magnitude while capturing excellent accurate traces of membrane potentials and spike times of HH neurons.
Revised:  Besides, we observe that the HH equations are quite different between the spike and non-spike regions. Here, we design a second-order adaptive exponential time differencing algorithm (AETD2) for the numerical evolution of HH neural networks . Compared with the regular RK2 method , our ETD2RK method can enlarge time steps and raise efficiency more than one order of magnitude while capturing excellent accurate traces of membrane potentials and spike times of HH neurons.
Edit intent: clarity
Original:  Thus, we adaptively assign the ETD2RK and RK2 schemes depending on the stiff and flat regimes to achieve optimal accuracy and efficiency . Compared with the regular RK2 method , our ETD2RK method can enlarge time steps and raise efficiency more than one order of magnitude while capturing excellent accurate traces of membrane potentials and spike times of HH neurons.
Revised:  Thus, we adaptively assign the ETD2RK and RK2 schemes depending on the stiff and flat regimes to achieve optimal accuracy and efficiency . Compared with the regular second-order Runge-Kutta method (RK2), our AETD2 method can use time steps one order of magnitude while capturing excellent accurate traces of membrane potentials and spike times of HH neurons.
Edit intent: clarity
Original:  Thus, we adaptively assign the ETD2RK and RK2 schemes depending on the stiff and flat regimes to achieve optimal accuracy and efficiency . Compared with the regular RK2 method , our ETD2RK method can enlarge time steps and raise efficiency more than one order of magnitude while capturing excellent accurate traces of membrane potentials and spike times of HH neurons.
Revised:  Thus, we adaptively assign the ETD2RK and RK2 schemes depending on the stiff and flat regimes to achieve optimal accuracy and efficiency . Compared with the regular RK2 method , our ETD2RK method can enlarge time steps and raise efficiency more than one order of magnitude larger and improve computational efficiency more than ten times while excellently capturing accurate traces of membrane potentials and spike times of HH neurons.
Edit intent: meaning-changed
Original:  Thus, we adaptively assign the ETD2RK and RK2 schemes depending on the stiff and flat regimes to achieve optimal accuracy and efficiency . Compared with the regular RK2 method , our ETD2RK method can enlarge time steps and raise efficiency more than one order of magnitude while capturing excellent accurate traces of membrane potentials and spike times of HH neurons.
Revised:  Thus, we adaptively assign the ETD2RK and RK2 schemes depending on the stiff and flat regimes to achieve optimal accuracy and efficiency . Compared with the regular RK2 method , our ETD2RK method can enlarge time steps and raise efficiency more than one order of magnitude while capturing excellent accurate traces of membrane potentials of HH neurons.
Edit intent: clarity
Original:  By estimating the large error covariance matrix consistently, the proposed feasible GLS (FGLS) estimator is robust to heteroskedasticity, serial correlation, and cross-sectional correlation.
Revised:  By estimating the large error covariance matrix consistently, the proposed feasible GLS (FGLS) estimator is more efficient than the ordinary least squares (OLS) in the presence of heteroskedasticity, serial correlation, and cross-sectional correlation.
Edit intent: meaning-changed
Original:  By estimating the large error covariance matrix consistently, the proposed feasible GLS (FGLS) estimator is robust to heteroskedasticity, serial correlation, and cross-sectional correlation.
Revised:  By estimating the large error covariance matrix consistently, the proposed feasible GLS (FGLS) estimator is robust to heteroskedasticity, serial and cross-sectional correlation.
Edit intent: fluency
Original:  By estimating the large error covariance matrix consistently, the proposed feasible GLS (FGLS) estimator is robust to heteroskedasticity, serial correlation, and cross-sectional correlation. It is more efficient than the OLS estimator. To control serial correlation , we employ the banding method.
Revised:  By estimating the large error covariance matrix consistently, the proposed feasible GLS (FGLS) estimator is robust to heteroskedasticity, serial correlation, and cross-sectional correlations. To take into account the serial correlations , we employ the banding method.
Edit intent: coherence
Original:  By estimating the large error covariance matrix consistently, the proposed feasible GLS (FGLS) estimator is more efficient than the ordinary least squares (OLS) in the presence of heteroskedasticity, serial  and cross-sectional correlations.
Revised:  By estimating the large error covariance matrix consistently, the proposed feasible GLS (FGLS) estimator is more efficient than the ordinary least squares (OLS) in the presence of heteroskedasticity, serial , and cross-sectional correlations.
Edit intent: fluency
Original:  We demonstrated that these feature scores are significantly prognostic for time to event endpoints (biochemical recurrence and cancer-specific survival) and have simultaneously molecular biologic associations to relevant genomic alterations and molecular subtypes using already trained DL models that are not exposed to the datasets of the current study.
Revised:  We demonstrated that these feature scores were significantly prognostic for time to event endpoints (biochemical recurrence and cancer-specific survival) and have simultaneously molecular biologic associations to relevant genomic alterations and molecular subtypes using already trained DL models that are not exposed to the datasets of the current study.
Edit intent: fluency
Original:  We demonstrated that these feature scores are significantly prognostic for time to event endpoints (biochemical recurrence and cancer-specific survival) and have simultaneously molecular biologic associations to relevant genomic alterations and molecular subtypes using already trained DL models that are not exposed to the datasets of the current study.
Revised:  We demonstrated that these feature scores are significantly prognostic for time to event endpoints (biochemical recurrence and cancer-specific survival) and had simultaneously molecular biologic associations to relevant genomic alterations and molecular subtypes using already trained DL models that are not exposed to the datasets of the current study.
Edit intent: fluency
Original:  We demonstrated that these feature scores are significantly prognostic for time to event endpoints (biochemical recurrence and cancer-specific survival) and have simultaneously molecular biologic associations to relevant genomic alterations and molecular subtypes using already trained DL models that are not exposed to the datasets of the current study.
Revised:  We demonstrated that these feature scores are significantly prognostic for time to event endpoints (biochemical recurrence and cancer-specific survival) and have simultaneously molecular biologic associations to relevant genomic alterations and molecular subtypes using already trained DL models that were not previously exposed to the datasets of the current study.
Edit intent: clarity
Original:  Further, we will discuss the potential of such feature scores to improve the current tumor grading system and the challenges that are associated with tumor heterogeneity and the development of prognostic models from histology images.
Revised:  Further, we discussed the potential of such feature scores to improve the current tumor grading system and the challenges that are associated with tumor heterogeneity and the development of prognostic models from histology images.
Edit intent: coherence
Original:  We calculate the potential of mean force between poly(lysine) and poly(glutamate) oligomers using three different forcfields , an atomistic forcefield and two coarse-grained forcefields .
Revised:  We calculate the potential of mean force between poly(lysine) and poly(glutamate) oligomers using three different forcefields , an atomistic forcefield and two coarse-grained forcefields .
Edit intent: fluency
Original:  We calculate the potential of mean force between poly(lysine) and poly(glutamate) oligomers using three different forcfields , an atomistic forcefield and two coarse-grained forcefields .
Revised:  We calculate the potential of mean force between poly(lysine) and poly(glutamate) oligomers using three different forcfields , an atomistic force field and two coarse-grained forcefields .
Edit intent: fluency
Original:  We calculate the potential of mean force between poly(lysine) and poly(glutamate) oligomers using three different forcfields , an atomistic forcefield and two coarse-grained forcefields .
Revised:  We calculate the potential of mean force between poly(lysine) and poly(glutamate) oligomers using three different forcfields , an atomistic forcefield and two coarse-grained force fields .
Edit intent: fluency
Original:  The approach is model agnostic - neither the interpretable model nor the reference model are restricted to be from a certain class of models - and the optimization problem can be solved using standard tools in the chosen model family .
Revised:  The approach is model agnostic -- neither the interpretable model nor the reference model are restricted to be from a certain class of models - and the optimization problem can be solved using standard tools in the chosen model family .
Edit intent: fluency
Original:  The approach is model agnostic - neither the interpretable model nor the reference model are restricted to be from a certain class of models - and the optimization problem can be solved using standard tools in the chosen model family .
Revised:  The approach is model agnostic - neither the interpretable model nor the reference model are restricted to be from a certain class of models -- and the optimization problem can be solved using standard tools in the chosen model family .
Edit intent: fluency
Original:  Through experiments on real-word data sets  using decision trees as interpretable models and Bayesian additive regression models as reference models, we show that for the same level of interpretability, our approach generates more accurate models than the earlier alternative of restricting the prior.
Revised:  Through experiments on real-word data sets , using decision trees as interpretable models and Bayesian additive regression models as reference models, we show that for the same level of interpretability, our approach generates more accurate models than the earlier alternative of restricting the prior.
Edit intent: fluency
Original:  Through experiments on real-word data sets  using decision trees as interpretable models and Bayesian additive regression models as reference models, we show that for the same level of interpretability, our approach generates more accurate models than the earlier alternative of restricting the prior.
Revised:  Through experiments on real-word data sets using decision trees as interpretable models and Bayesian additive regression models as reference models, we show that for the same level of interpretability, our approach generates more accurate models than the alternative of restricting the prior.
Edit intent: clarity
Original:  However, designing  an evaluation is always complex and time-consuming and may end in a lack of statistical evidence . The raising of modern efficient computer vision techniques  may help visualization researchers to  adjust their evaluation hypothesis and thus reduces the risk of failure.
Revised:  However, designing an evaluation is always complex and time-consuming and may end in a lack of statistical evidence and reproducibility. We believe that modern and efficient computer vision techniques may help visualization researchers to adjust their evaluation hypothesis and thus reduces the risk of failure.
Edit intent: meaning-changed
Original:  Our assumption is that the performance of each model allows to compare the efficiencies of the corresponding visualization techniques ; as current machine learning models are not capable enough to reflect human capabilities, including their imperfections, such results should be interpreted with caution.
Revised:  Our assumption is that the performance of each model allows to compare the efficiency of visualization techniques based on the performance of their corresponding model. As current machine learning models are not capable enough to reflect human capabilities, including their imperfections, such results should be interpreted with caution.
Edit intent: clarity
Original:  However, we argue that using machine learning-based evaluation as a pre-process of standard user evaluations  should help researchers to perform a more exhaustive study of the design spaceand thus should improve the final user evaluation by providing  better test cases.
Revised:  However, we think that using machine learning-based evaluation as a pre-process of standard user evaluations should help researchers to perform a more exhaustive study of the design spaceand thus should improve the final user evaluation by providing better test cases.
Edit intent: clarity
Original:  However, we argue that using machine learning-based evaluation as a pre-process of standard user evaluations  should help researchers to perform a more exhaustive study of the design spaceand thus should improve the final user evaluation by providing  better test cases.
Revised:  However, we argue that using machine learning-based pre-evaluation, as a pre-process of standard user evaluations should help researchers to perform a more exhaustive study of the design spaceand thus should improve the final user evaluation by providing better test cases.
Edit intent: coherence
Original:  However, we argue that using machine learning-based evaluation as a pre-process of standard user evaluations  should help researchers to perform a more exhaustive study of the design spaceand thus should improve the final user evaluation by providing  better test cases.
Revised:  However, we argue that using machine learning-based evaluation as a pre-process of standard user evaluations , should help researchers to perform a more exhaustive study of the design spaceand thus should improve the final user evaluation by providing better test cases.
Edit intent: fluency
Original:  However, we argue that using machine learning-based evaluation as a pre-process of standard user evaluations  should help researchers to perform a more exhaustive study of the design spaceand thus should improve the final user evaluation by providing  better test cases.
Revised:  However, we argue that using machine learning-based evaluation as a pre-process of standard user evaluations should help researchers to perform a more exhaustive study of their design space. Thus, it should improve their final user evaluation by providing better test cases.
Edit intent: clarity
Original:  However, we argue that using machine learning-based evaluation as a pre-process of standard user evaluations  should help researchers to perform a more exhaustive study of the design spaceand thus should improve the final user evaluation by providing  better test cases.
Revised:  However, we argue that using machine learning-based evaluation as a pre-process of standard user evaluations should help researchers to perform a more exhaustive study of the design spaceand thus should improve the final user evaluation by providing it better test cases.
Edit intent: clarity
Original:  To show that our methodology can reproduce, up to a certain level, results of user evaluations, we applied it to compare two mainstream graph visualization techniques: node-link ( NL \NL ) and adjacency-matrix ( MD \MD ) diagrams.
Revised:  In this paper, we present the results of two experiments we have conducted to assess how correlated the performance of users and computer vision techniques can be. That study compares two mainstream graph visualization techniques: node-link ( NL \NL ) and adjacency-matrix ( MD \MD ) diagrams.
Edit intent: clarity
Original:  To show that our methodology can reproduce, up to a certain level, results of user evaluations, we applied it to compare two mainstream graph visualization techniques: node-link ( NL \NL ) and adjacency-matrix ( MD \MD ) diagrams.
Revised:  To show that our methodology can reproduce, up to a certain level, results of user evaluations, we applied it to compare two mainstream graph visualization techniques: node-link ( \NL ) and adjacency-matrix ( MD \MD ) diagrams.
Edit intent: fluency
Original:  To show that our methodology can reproduce, up to a certain level, results of user evaluations, we applied it to compare two mainstream graph visualization techniques: node-link ( NL \NL ) and adjacency-matrix ( MD \MD ) diagrams.
Revised:  To show that our methodology can reproduce, up to a certain level, results of user evaluations, we applied it to compare two mainstream graph visualization techniques: node-link ( NL \NL ) and adjacency-matrix ( \MD ) diagrams.
Edit intent: fluency
Original:  We partially reproduced a user evaluation from Ghoniem et al. using two well-known deep convolutional neural networks as machine learning-based systems. Our results show up that Ghoniem et al.\textit{ \textit{  results can be reproduced automatically at a larger scale with our system .
Revised:  We partially reproduced a user evaluation from Ghoniem et al. using two well-known deep convolutional neural networks , we partially reproduced user evaluations from Ghoniem\textit{ \textit{ results can be reproduced automatically at a larger scale with our system .
Edit intent: coherence
Original:  This approach reaches state-of-the-art performance with  SNNs: test accuracy of 97.4\% for the MNIST dataset, and of 99.2\% for the Caltech Face/Motorbike dataset.
Revised:  This approach reaches state-of-the-art performance with supervised multi fully-connected layer SNNs: test accuracy of 97.4\% for the MNIST dataset, and of 99.2\% for the Caltech Face/Motorbike dataset.
Edit intent: meaning-changed
Original:  This approach reaches state-of-the-art performance with  SNNs: test accuracy of 97.4\% for the MNIST dataset, and of 99.2\% for the Caltech Face/Motorbike dataset.
Revised:  This approach reaches state-of-the-art performance with SNNs: test accuracy of 97.4\% for the MNIST dataset, and 99.2\% for the Caltech Face/Motorbike dataset.
Edit intent: fluency
Original:  Yet  the neuron model  we use, non-leaky integrate-and-fire, are simpler and more hardware friendly than the one used in all previous similar proposals.
Revised:  Yet the neuron model we use, non-leaky integrate-and-fire, are simpler and more hardware friendly than the one used in all previous works. The source codes of the proposed S4NN are publicly available at URL
Edit intent: meaning-changed
Original: Partial identification approaches have seen a sharp increase in interest in econometrics due to improved flexibility and robustness compared to point-identification approaches . However, formidable computational requirements of existing approaches often offset these undeniable advantages---particularly in general instrumental variable models with continuous variables .
Revised: Partial identification approaches have seen a sharp increase in interest in econometrics due to improved flexibility and robustness compared to point-identification approaches in general instrumental variable models with continuous variables .
Edit intent: clarity